Clinical Trials Archives - Techsol Life Sciences

Understanding how Disease Registries can Power Oncology R&D Pipeline and De-Risk Clinical Trials

Techsol Life Sciences — Wed, 24 Jan 2024 07:01:19 +0000

Understanding how Disease Registries can Power Oncology R&D Pipeline and De-Risk Clinical Trials

Developing robust pipelines in the biotech industry presents numerous challenges, including lengthy development timelines, substantial financial investments, and the inherent risks associated with clinical trials. Sponsors can leverage disease registries to effectively navigate these complexities and optimize their scientific research and development (R&D) efforts. The data collected through disease registries can provide valuable insights from real-world patients about the natural history of diseases, treatment outcomes, and patient demographics. By integrating information from these registries into their pipeline development strategies, biotech companies can significantly enhance patient recruitment, refine trial designs, and identify gaps in current therapeutic approaches. Embracing the power of disease registries empowers biotech companies to make data-driven decisions, accelerate R&D timelines, and ultimately bring innovative treatments to patients in need.

Let’s take a closer look at how disease registries can be leveraged by biotech companies to optimize their R&D effort.

Building a Strong Pipeline

1. Target Identification and Validation: Registries offer large datasets on specific cancer types, enabling researchers to identify genetic/molecular patterns associated with disease progression or treatment response. This helps pinpoint promising targets for drug development.
2. Patient Stratification and Recruitment: Registries facilitate identifying specific patient subpopulations for clinical trials, leading to the development of drugs targeted to highly responsive groups. This improves trial efficiency and efficacy.
3. Natural History Data: Studying disease progression and treatment patterns in registries helps understand the baseline course of the disease without intervention. This data guides trial design, endpoint selection, and sample size calculations, increasing trial success rates.

Proven Examples

SWOG Cancer Registry: Used by Bayer to identify a rare genetic mutation in lung cancer patients, leading to the development of the targeted therapy Larotrectinib.
National Cancer Institute’s SEER Program: Helped Novartis design a clinical trial for a PARP inhibitor in ovarian cancer, focusing on patients with specific BRCA mutations identified in SEER data.
Dana-Farber Cancer Institute’s Adult Brain Tumor Registry: Provided critical data on survival rates and treatment patterns for glioma patients, enabling Blueprint Medicines to target a specific subgroup with their ALK inhibitor Lorlatinib.

De-Risking Clinical Trials:

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1. Predictive Biomarkers: Registry data can be used to identify potential biomarkers for treatment response or toxicity, allowing for personalized medicine approaches. This helps tailor therapy and reduce adverse events.
2. Virtual Trials and Real-World Evidence: Registries can support virtual trials by providing existing data for baseline comparisons and post-trial follow-up. This reduces costs and accelerates trials.
3. Safety Monitoring and Risk Mitigation: Continuous data collection from registries allows for real-time safety monitoring of new drugs, enabling early identification and mitigation of potential side effects.

Proven Examples

National Cancer Data Base (NCRN): Used by Pfizer to conduct virtual trials for their PD-1 inhibitor Ibrance, leveraging NCRN data for baseline comparisons and reducing patient recruitment time.
Swedish Childhood Cancer Registry: Helped Genentech identify biomarkers predictive of response to immunotherapy in children with ALL, allowing for personalized treatment and improved trial success rates.
NCI’s Cancer Therapy Evaluation Program (CTEP): Collaborated with multiple drug developers to monitor real-world safety and effectiveness of new cancer drugs after approval, leading to early identification and mitigation of potential adverse events.

Practical Approach on How to Leverage Disease Registries

Let’s take an example of a Biotech company that is aiming to develop bifunctional antibodies and other biologics for solid tumors. This R&D endeavor is really complex and presents unique challenges to identify potential targets, determine suitable biomarker endpoints, identify patient populations, and successfully develop a strong product pipeline. Disease registries can be invaluable assets in this process, offering real-world data and insights to strengthen pipeline development. Here’s a detailed plan on how:

1. Target Identification and Validation:

Tumor-specific registries: Collaborate with registries focused on your target tumor type (e.g., lung, breast, colorectal) to identify genetic alterations, protein expression patterns, and treatment resistance mechanisms. Identify frequent genetic alterations, driver mutations, and tumor microenvironment characteristics associated with specific solid tumor types. Examples: TCGA (The Cancer Genome Atlas), AACR (American Association for Cancer Research) Project GENIE.
Biomarker registries: Partner with registries collecting detailed molecular data (e.g., TCGA, TARGET) to identify potential targets for your bifunctional antibody. Analyze data for correlations between specific biomarkers and clinical outcomes to prioritize promising candidates. Explore potential predictive biomarkers for response to immunotherapy, targeted therapies, or combinations, guiding bifunctional antibody design and patient selection for trials. Examples: NCI TARGET, CPTAC (Clinical Proteomic Tumor Analysis Consortium).
Outcome registries: Analyze long-term disease progression, treatment response, and survival data to define unmet needs and identify promising therapeutic targets. Examples: SEER (Surveillance, Epidemiology, and End Results), NCDR (National Cancer Data Registry).
Survival registries: Utilize data from registries like SEER to understand overall survival rates and identify patient subgroups with unmet medical needs. This helps refine target selection and guide drug development towards high-impact areas.

2. Preclinical and Early Clinical Development:

Patient recruitment: Utilize registries to identify specific subpopulations for preclinical models and early-phase clinical trials, accelerating patient enrollment and ensuring target relevance.
Dose-finding and safety monitoring: Leverage real-world safety data from registries to inform initial dosing ranges and optimize safety protocols for early clinical trials.
Biomarker validation: Use registry data to validate potential biomarkers identified in preclinical models, refining patient selection criteria and enhancing trial efficiency.
Natural history data: Analyze disease progression patterns and treatment response rates in registries to inform clinical trial design. This helps define primary and secondary endpoints, sample size calculations, and patient selection criteria.
Predictive biomarkers: Identify potential biomarkers for response or toxicity from registry data. This allows for personalized medicine approaches in trials, improving efficacy and reducing adverse events.
Virtual trial feasibility: Assess the feasibility of virtual trials using registry data for baseline comparisons and post-trial follow-up. This can significantly reduce costs and accelerate trial timelines.

3. Patient Recruitment and Stratification:

Utilize registry search tools: Leverage registries’ search functionalities to identify eligible patients for your clinical trials based on specific criteria (e.g., tumor type, biomarker status, treatment history).
Patient engagement platforms: Partner with registries to reach out to potential participants through their patient engagement platforms, increasing trial awareness and recruitment rates.
Stratification by tumor subtype or biomarker: Utilize registry data to stratify patients into subgroups for clinical trials based on relevant tumor subtypes or biomarker expression. This enhances trial precision and drug efficacy within specific patient populations.

4. Late-Stage Clinical Trials and Commercialization:

Randomized controlled trials: Partner with registries to conduct large-scale clinical trials, leveraging their infrastructure and patient reach to accelerate study completion.
Real-world evidence generation: Utilize registry data to track real-world effectiveness and safety of approved biologics in diverse patient populations, informing post-marketing surveillance and potential label expansions.
Comparative effectiveness research: Compare the efficacy and safety of your biologics to standard-of-care treatments using registry data, providing valuable insights for healthcare providers and payers.
Longitudinal data collection: Partner with registries to collect real-world data on your drug’s effectiveness and safety in the post-market setting. This provides valuable insights for further development and optimization of your biologics.
Comparative effectiveness studies: Utilize registry data to compare the performance of your drug to existing therapies in real-world settings. This strengthens evidence for your product’s value proposition and informs future treatment guidelines.
Safety monitoring and risk identification: Continuously monitor safety data from registries to identify potential adverse events early and implement necessary mitigation strategies.

Additional Strategies:

Engage with registry stakeholders: Collaborate with researchers and clinicians affiliated with relevant registries to gain deeper insights into specific tumor types and treatment landscapes.
Develop data sharing agreements: Ensure secure and ethical access to registry data while respecting patient privacy and complying with regulations.
Invest in data analytics capabilities: Employ advanced statistical and machine learning techniques to extract meaningful insights from large registry datasets.

Remember: The specific registries and data utilized will depend on the target tumor type, mechanism of action of the biologics, and development stage of the pipeline. Adapting the plan based on evolving research and clinical findings is crucial.

By strategically leveraging disease registries, the biotech company can gain valuable insights, refine target selection, optimize clinical trial design, and ultimately bring safe and effective solid tumor biologics to patients faster.

This plan provides a comprehensive framework, but remember to tailor it to your specific context and adapt it to address emerging challenges and opportunities. By embracing data-driven approaches and fostering collaborations with registry stakeholders, your R&D efforts can lead to breakthrough treatments for patients.

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Leveraging Pre-Clinical Data to Optimize Patient Safety Management During Early Phase Clinical Trials

Vinoth — Mon, 10 Jul 2023 07:20:51 +0000

Leveraging Pre-Clinical Data to Optimize Patient Safety Management During Early Phase Clinical Trials

Pre-clinical data plays a crucial role in managing patient safety during early phase clinical trials. It provides valuable insights into the safety profile of the investigational product and helps sponsors to proactively identify and manage potential safety risks, optimize dose selection, establish robust safety monitoring plans, manage adverse events and implement risk mitigation strategies during early phase clinical trials. This approach promotes patient safety, ensures the reliability of clinical trial data, and contributes to the overall success of the drug development process.

In this blog, we have outlined the key areas of how to effectively leverage pre-clinical data to develop a comprehensive safety management strategy in early phase clinical trials.

Determining the Therapeutic Index: Pre-clinical data helps establish the therapeutic index, which is the ratio between the minimum effective dose and the maximum tolerated dose. By evaluating the dose-response relationship in pre-clinical studies, researchers can identify the range of doses that exhibit therapeutic effects while minimizing toxicity.

Practical Tip: Select a starting dose for the clinical trial that is below the maximum tolerated dose determined from pre-clinical studies. This conservative approach ensures patient safety by minimizing the risk of adverse events while allowing for potential efficacy.

For example, Pre-clinical studies on a new cancer drug revealed that the minimum effective dose required to inhibit tumor growth was 10 mg/kg, while the maximum tolerated dose without significant toxicity was found to be 30 mg/kg. This data allowed researchers to establish a therapeutic index of 3:1, guiding the selection of appropriate doses for further clinical trials.

Assessing Safety Margins: Pre-clinical data enables the assessment of safety margins between effective doses and toxic doses. By identifying the No Observed Adverse Effect Level (NOAEL) or the Maximum Tolerated Dose (MTD), researchers can establish a safe range of doses for human subjects.

Practical Tip: Set the starting dose for the clinical trial below the NOAEL or at a fraction of the MTD determined from pre-clinical studies. This approach ensures that participants are exposed to a dose that is well within the safety margin and minimizes the risk of severe adverse events.

For instance, in pre-clinical studies of a new cardiovascular drug, the No Observable Adverse Effect Level (NOAEL) was determined to be 100 mg/kg, while the Maximum Tolerated Dose (MTD) was found to be 200 mg/kg. To ensure patient safety, the starting dose for the clinical trial was set below the NOAEL, at 50 mg/kg, allowing for a margin of safety and gradual evaluation of the drug’s efficacy.

Evaluating Pharmacokinetics and Pharmacodynamics: Pre-clinical studies provide insights into the pharmacokinetic and pharmacodynamic properties such as bioavailability, half-life, clearance, and volume of distribution for the investigational product. Using this data, researchers can determine the optimal dose frequency, duration, and route of administration.

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Practical Tip: Consider the pharmacokinetic profiles of the investigational product in pre-clinical studies to estimate appropriate dosing intervals in humans. Adjust the starting dose in the clinical trial based on the expected exposure levels and desired pharmacodynamic effects.

To give an example, through pre-clinical studies of an oral antibiotic, it was observed that the investigational product had a half-life of 6 hours and reached peak plasma concentration within 2 hours of administration. Based on this pharmacokinetic profile, it was estimated that dosing intervals of 8 hours would maintain therapeutic levels in humans, ensuring consistent efficacy against target pathogens.

Dosing Regimen Design: Pre-clinical data helps in designing the dosing regimen for the clinical trial. By understanding the drug’s pharmacokinetic parameters, researchers can determine the optimal dosing frequency, duration, and route of administration.

Practical Tip: Consider the pre-clinical pharmacokinetic profiles to establish dosing intervals and administration routes that maintain therapeutic drug levels within the desired range. This ensures consistent exposure to the drug while minimizing the risk of toxicity or subtherapeutic effects. As an example, In pre-clinical studies of a long-acting injectable contraceptive, it was found that the investigational product had a slow-release profile with a half-life of 30 days. Based on this pharmacokinetic profile, dosing intervals were established at once every month to maintain therapeutic drug levels within the desired range, providing effective contraception for an extended duration in human subjects.

Assessing Target Organ Toxicities: Pre-clinical studies enable the identification of potential target organs and toxicities associated with the investigational product. By evaluating organ-specific toxicities, researchers can assess the potential risks and establish safe doses for clinical trials. Through histopathological examinations, imaging techniques, and other assessments, researchers can identify the organs that are most susceptible to the drug’s actions. By understanding the specific target organs, researchers gain insights into the drug’s mechanism of action and potential therapeutic benefits. This knowledge helps guide clinical trial design and focus on relevant patient populations.

Practical Tip: Adjust the starting dose based on pre-clinical data to minimize the risk of target organ toxicities observed in animal models. Use the data to guide the selection of doses that maintain efficacy while minimizing organ-specific adverse effects. For example, in the animal studies, renal toxicity was observed at a dose of 100 mg/kg. To establish a margin of safety and minimize the risk of adverse effects, the starting dose for human clinical trials is set at 10 mg/kg, which is one-tenth of the toxic dose observed in animals.

During the clinical trial phase, the 10 mg/kg dose of Drug X is administered to human subjects. Efficacy is monitored, and regular assessments of renal function and potential adverse effects on the kidneys are conducted. If the starting dose demonstrates efficacy without significant renal toxicity, subsequent cohorts or phases of the trial may gradually increase the dose to find the optimal therapeutic dose.

Considering Species Differences: Pre-clinical data helps identify potential differences in drug response between animal models and humans. These differences may include variances in drug metabolism, elimination, or target engagement. It provide insights into the metabolism of the investigational drug in animal models. This includes understanding how the drug is broken down and processed by various enzymes and pathways in the body. However, it is essential to recognize that there can be species-specific differences in drug metabolism.

By comparing pre-clinical metabolism data with known human metabolic pathways, researchers can identify potential differences that may impact drug response in humans. This information is crucial for understanding how the drug may be transformed and cleared in human subjects during clinical trials.

Practical Tip: Take species differences into account when determining the starting dose for the clinical trial. Consider factors such as body weight, metabolic rates, and the expected sensitivity of the target organ in humans. Adjust the dose accordingly to ensure a safe and effective starting point. By leveraging pre-clinical data in dose selection and starting dose determination, researchers can optimize patient safety by establishing an appropriate dose range for clinical trials. Variations in drug elimination processes between animal models and humans can impact the drug’s pharmacokinetic profile, leading to differences in exposure and efficacy. Pre-clinical data help identify such variances, allowing researchers to account for these differences when designing clinical trials.

Example: Pre-clinical investigations of an experimental anti-inflammatory drug revealed that animal models exhibited greater sensitivity to the drug’s side effects compared to humans. Taking into account this species difference, the starting dose for the clinical trial was determined by considering a lower level, reducing the potential risk of adverse reactions in human subjects.

Identification of Potential Adverse Effects: Pre-clinical data allows for the identification of potential adverse effects or toxicities associated with the investigational product. By analyzing the pre-clinical safety data, researchers can anticipate and monitor specific adverse events during the clinical trial.

Practical Tip: Develop a comprehensive adverse event management plan based on the pre-clinical safety data. Establish specific criteria and procedures for the identification, documentation, and reporting of anticipated adverse events in the clinical trial. For example, based on pre-clinical safety data of a new anti-cancer drug, a comprehensive adverse event management plan was developed for the clinical trial. The plan included regular monitoring of participants for potential adverse events, such as hematological abnormalities, cardiac toxicity, and gastrointestinal disturbances. Specific criteria were established to assess the severity and significance of these events. Depending on the nature and severity of the adverse events, the plan outlined appropriate interventions, including dose adjustments, supportive care measures, patient withdrawal, or trial discontinuation, ensuring participant safety and welfare throughout the trial.

Determination of Dose-Related Adverse Events: Pre-clinical studies provide insights into the dose-response relationship of the investigational product. By evaluating the pre-clinical data, researchers can identify dose-related adverse events and anticipate their occurrence at higher doses in human subjects clinical trial.

Practical Tip: Incorporate the dose-related adverse events observed in pre-clinical studies into the safety monitoring plan for the clinical trial. Set thresholds or criteria for dose adjustments, patient withdrawal, or trial discontinuation based on the occurrence or severity of these adverse events.

Here is an example scenario. Drug Y is a novel investigational drug undergoing clinical trials. Pre-clinical studies have revealed dose-related adverse events, specifically gastrointestinal toxicity, in animal models. To ensure the safety of human subjects in the clinical trial, the following safety monitoring plan is implemented:

Thresholds for Dose Adjustments:

– If mild gastrointestinal adverse events (e.g., mild nausea or transient diarrhea) occur in more than 20% of participants, a dose adjustment may be considered.

– If moderate to severe gastrointestinal adverse events (e.g., persistent vomiting, severe diarrhea) occur in any participant, an immediate dose adjustment is warranted.

Patient Withdrawal Criteria:

– If a participant experiences a severe gastrointestinal adverse event that significantly impacts their daily functioning or requires medical intervention, the participant will be withdrawn from the study.

– If a participant experiences recurrent moderate gastrointestinal adverse events despite dose adjustments, withdrawal may be considered to prioritize patient safety.

Trial Discontinuation Criteria:

– If a significant safety concern arises, such as a severe adverse event affecting multiple participants even after dose adjustments, trial discontinuation will be considered.

– If the occurrence of severe adverse events surpasses predefined thresholds (e.g., more than 5% of participants), trial discontinuation may be necessary to protect participant well-being.

Throughout the clinical trial, regular safety assessments are conducted to monitor adverse events and their severity. The data collected is reviewed by an independent Data Monitoring Committee (DMC) or Safety Monitoring Board (SMB) that oversees the trial’s safety.

Predicting Drug-Drug Interactions: Pre-clinical data helps in predicting potential drug-drug interactions with commonly used medications. By evaluating the drug’s metabolism pathways and interaction potential, researchers can anticipate and manage potential interactions during the clinical trial.

Practical Tip: Review pre-clinical data for indications of drug-drug interactions. Consider incorporating appropriate safety monitoring and assessments in the clinical trial to detect and manage potential adverse events resulting from drug-drug interactions. During the review process, it is important to assess the drug’s metabolism pathways, enzyme induction or inhibition potential, and any known interactions with specific drug classes. This evaluation helps determine the likelihood and severity of potential interactions in human subjects.

As an example, in the review of pre-clinical data for a new psychiatric medication, indications of drug-drug interactions were observed, particularly with medications that targeted the same neurotransmitter receptors. In response, the clinical trial’s safety monitoring plan incorporated assessments of psychiatric symptoms, vital signs, and electrocardiograms (ECGs) to detect and manage potential adverse events resulting from drug-drug interactions. Regular evaluations of medication efficacy and tolerability were also included to ensure appropriate management and adjustment of the treatment regimen.

Organ-Specific Toxicities: Pre-clinical data can help identify potential organ-specific toxicities associated with the investigational product. By analyzing the pre-clinical safety findings, researchers can focus on monitoring specific organs or systems during the clinical trial to detect any related adverse events.

Practical Tip: Include specific organ-specific safety assessments in the clinical trial protocol based on the pre-clinical data. Implement appropriate monitoring measures, such as laboratory tests or imaging, to detect and evaluate potential organ-specific adverse events. Determine the specific biomarkers or indicators associated with organ toxicity identified in pre-clinical studies. Include these tests in the clinical trial protocol and establish appropriate reference ranges or thresholds for monitoring changes in organ function or toxicity.

If the investigational product is expected to impact certain organs or tissues, consider incorporating imaging modalities such as MRI, CT scans, or ultrasound to assess structural and functional changes. These imaging techniques can help detect abnormalities or potential adverse effects in specific organs. Depending on the target organs or systems, consider incorporating specific functional assessments relevant to their physiology. For example, pulmonary function tests, cardiovascular assessments, or neurocognitive evaluations can be implemented to monitor potential adverse events in corresponding organ systems.

For instance, in pre-clinical studies of a new chemotherapy drug, researchers discovered potential organ-specific toxicities in the liver. The data revealed increased levels of liver enzymes and histopathological changes indicative of hepatotoxicity. This information helped identify the need for close monitoring of liver function and the implementation of appropriate safety measures during subsequent clinical trials to mitigate potential liver-related adverse events.

Early Detection of Safety Signals: Pre-clinical data provides a baseline for comparison with the clinical trial safety data. By analyzing the pre-clinical safety findings, researchers can establish expected safety profiles and detect safety signals or unexpected adverse events during the clinical trial.

Practical Tip: Develop robust procedures for the ongoing monitoring and detection of safety signals during the clinical trial. Implement regular safety review meetings or data monitoring committees to analyze and evaluate the clinical trial safety data in comparison to the pre-clinical safety data. Compare the clinical trial safety data with the pre-clinical safety data to assess consistency or deviations. This comparative analysis helps in identifying unexpected safety signals, confirming previously identified risks, or detecting any new safety concerns that were not evident in pre-clinical studies.

As an example, in pre-clinical studies of a new antipsychotic medication, researchers established a baseline safety profile that included minimal adverse events such as sedation and mild extrapyramidal symptoms. During the subsequent clinical trial, the pre-clinical safety data served as a reference point for comparison. Any unexpected or severe adverse events observed in the clinical trial, such as severe akathisia or cardiotoxicity, could be recognized as deviations from the established baseline and thoroughly evaluated.

Prediction of Pharmacological Interactions: Pre-clinical data can provide insights into potential pharmacological interactions with concomitant medications. By evaluating the pre-clinical data, researchers can anticipate drug-drug interactions and monitor for any related adverse events during the clinical trial.

Practical Tip: Review the pre-clinical data for any indications of pharmacological interactions with commonly used medications. Incorporate appropriate safety monitoring and assessments in the clinical trial to detect and manage potential adverse events resulting from drug-drug interactions. Based on the pre-clinical data, identify potential interactions between the investigational product and commonly used medications. Consider both pharmacokinetic interactions (such as changes in metabolism, absorption, or excretion) and pharmacodynamic interactions (such as additive or synergistic effects).

To give an example, in pre-clinical studies of a new anticoagulant drug, researchers discovered that the investigational product had the potential to interact with commonly used blood-thinning medications. The data indicated that the new drug could enhance the anticoagulant effects of the concomitant medications, increasing the risk of bleeding. This pre-clinical insight prompted researchers to consider appropriate dose adjustments or additional monitoring in subsequent clinical trials when the investigational drug was administered alongside these specific concomitant medications.

Identifying Potential Risks: Pre-clinical data provides insights into potential risks associated with the investigational product. By evaluating the pre-clinical safety findings, researchers can identify specific toxicities, organ-specific adverse effects, or other safety concerns. Mapping the pathways involved in the identified risks provides a visual representation of the biological processes affected by the investigational product. This mapping allows for a better assessment of the potential impact on specific organs, systems, or functions.

Practical Tip: Conduct a thorough review of the pre-clinical data to identify potential risks. Consider the severity, frequency, and predictability of adverse events observed in animal models. This helps in defining the scope of potential risks and informs risk assessment strategies in the clinical trial.

For example, pre-clinical studies of a novel anticancer drug demonstrated potential risks of bone marrow suppression and hematological toxicity, as evidenced by decreased red and white blood cell counts and platelet levels in animal models. This information informed the design of subsequent clinical trials, incorporating regular blood cell count monitoring and assessments to detect and manage potential adverse events associated with bone marrow suppression.

Risk Characterization: Pre-clinical data aids in characterizing the identified risks, including their mechanisms of action, target organs, and potential impact on human subjects. By understanding the nature of the risks, researchers can assess their significance and prioritize their management.

Practical Tip: Analyze the pre-clinical data to determine the underlying mechanisms and pathways involved in the identified risks. This helps in formulating appropriate risk characterization and provides insights into potential strategies for risk mitigation. Analyzing the mechanisms involved in the risks can help identify potential biomarkers or surrogate markers that can serve as indicators of safety concerns. This allows for the implementation of specific monitoring measures or laboratory assessments to detect early signs of adverse events related to the identified mechanisms.

Example: Pre-clinical studies of a new cardiotoxic drug revealed its mechanism of action, which involved inhibiting a specific ion channel in cardiac cells. This data helped characterize the identified risk and understand how the drug could potentially affect the heart. Researchers could then focus on monitoring cardiac function and assessing potential adverse events related to cardiotoxicity in subsequent clinical trials.

Risk Minimization Strategies: Pre-clinical data guides the development of risk minimization strategies to mitigate potential risks identified during the pre-clinical evaluation. These strategies aim to reduce the occurrence or severity of adverse events during the clinical trial.

Practical Tip: Based on the pre-clinical data, design risk minimization strategies such as additional safety monitoring measures, specific laboratory assessments, or inclusion/exclusion criteria modifications. These strategies should be implemented to minimize the identified risks while maintaining the trial’s scientific objectives. This may involve dose adjustments, medication discontinuation, participant counseling, or other measures to minimize the risk or impact of these interactions.

For example, Pre-clinical studies of a new immunosuppressant drug revealed potential risks of increased susceptibility to infections due to its mechanism of action. Based on this data, the development team implemented risk minimization strategies during subsequent clinical trials. These strategies included regular monitoring of participants for signs of infection, proactive vaccination against common pathogens, and the use of prophylactic antibiotics to minimize the risk of infections and ensure participant safety.

Post-authorization Safety Studies (PASS): Pre-clinical data can inform the design and implementation of post-authorization safety studies. These studies are conducted after marketing authorization to further evaluate the safety profile of the product.

Practical Tip: Based on the pre-clinical safety data, identify specific areas of concern that warrant post-authorization safety studies. Design appropriate studies, such as long-term safety monitoring, assessment of rare adverse events, or special population evaluations, to address the identified risks. If the pre-clinical data suggests the potential for long-term safety concerns, design post-authorization studies focused on monitoring the product’s safety over extended periods. These studies aim to assess any delayed or cumulative adverse effects that may not have been evident during the initial clinical trials. If the pre-clinical data raises concerns about the product’s safety in specific patient populations (such as pediatrics, elderly individuals, or those with comorbidities), develop post-authorization studies that focus on evaluating the safety profile in these populations. These studies may involve targeted recruitment and specialized assessments to address the unique safety considerations for these patient groups.

As an example, pre-clinical studies of a new medication for a rare disease indicated potential long-term effects on organ function. Based on this data, post-authorization safety studies were designed and implemented to monitor patients for any potential late-onset adverse events, evaluate long-term organ function, and assess the overall safety profile of the medication in a larger patient population.

Risk Communication: Pre-clinical data contributes to effective risk communication with stakeholders, including healthcare professionals, regulatory authorities, and trial participants. By leveraging the pre-clinical safety findings, researchers can provide accurate and transparent information about potential risks.

Practical Tip: Utilize the pre-clinical data to prepare clear and concise risk communication materials. Include information on potential risks, risk mitigation strategies, and steps taken to ensure participant safety. Describe the steps taken to ensure participant safety during the clinical trial. Highlight safety monitoring measures, assessments, and procedures that have been implemented to detect and manage potential risks based on the pre-clinical data. This promotes transparency, informed decision-making, and fosters trust among stakeholders.

For instance, pre-clinical investigations of an experimental vaccine highlighted potential risks of mild injection site reactions and flu-like symptoms. Regulatory authorities were provided with comprehensive pre-clinical data, including safety findings, to support the vaccine’s regulatory approval. Clear and concise risk communication with regulatory authorities, including accurate reporting of potential adverse events and their frequency, contributed to the decision-making process and informed the vaccine’s recommended usage and safety monitoring requirements.

Adverse Event Reporting: Pre-clinical data helps in understanding potential adverse events associated with the investigational product. By analyzing pre-clinical safety findings, researchers can anticipate and identify adverse events that may occur during the clinical trial.

Practical Tip: Incorporate the pre-clinical data into the adverse event reporting process. Develop standardized procedures and forms for documenting and reporting adverse events, ensuring that the information collected aligns with the pre-clinical findings. This facilitates accurate and comprehensive reporting to regulatory authorities and ensures timely communication of safety information.

For example, pre-clinical data of a novel anticancer drug indicated potential adverse events such as bone marrow suppression and gastrointestinal toxicity. This information helped researchers understand the potential risks associated with the investigational product and guided the design of subsequent clinical trials. By analyzing the pre-clinical data, researchers were able to develop appropriate monitoring protocols and safety measures to detect and manage potential adverse events in human subjects.

Signal Detection and Validation: Pre-clinical data provides a baseline for comparison with clinical trial safety data, enabling the detection and validation of safety signals. By evaluating the pre-clinical safety profile, researchers can establish expected safety patterns and identify potential unexpected adverse events during the clinical trial.

Practical Tip: Use pre-clinical data to establish thresholds or criteria for safety signal detection and validation during the clinical trial. Implement robust processes for ongoing safety monitoring, including regular review of safety data, signal detection tools, and statistical analyses, to identify potential safety signals based on the pre-clinical findings.

Example: Pre-clinical studies of a new antipsychotic medication revealed a baseline safety profile with minimal adverse events such as sedation and mild extrapyramidal symptoms. During subsequent clinical trials, the comparison of pre-clinical and clinical trial safety data enabled the detection and validation of safety signals. If unexpected or severe adverse events, such as neuroleptic malignant syndrome or tardive dyskinesia, were observed in the clinical trial, they could be recognized as safety signals and further investigated in relation to the established baseline.

Safety Update Reports: Pre-clinical data contributes to the preparation of safety update reports, such as Periodic Safety Update Reports (PSURs). By analyzing pre-clinical safety findings, researchers can provide a comprehensive and accurate assessment of the product’s safety profile in the reports.

Practical Tip: Integrate the pre-clinical data into the safety update report preparation process. Ensure that the pre-clinical safety findings are appropriately incorporated, including summaries of relevant toxicological data, dose-response relationships, and any other safety-related information. This enhances the completeness and reliability of the safety update reports.

To illustrate, The PSURs incorporated information from pre-clinical studies, including the frequency, severity, and potential mechanisms of arrhythmias observed in animal models. By including this data in the safety update reports, regulatory authorities and stakeholders gained insights into the drug’s cardiac safety profile, helping them monitor and evaluate the occurrence of arrhythmias in clinical settings.

Safety Communication to Stakeholders: Pre-clinical data plays a crucial role in communicating safety information to various stakeholders, including healthcare professionals, regulatory authorities, and trial participants. By leveraging the pre-clinical safety findings, researchers can provide accurate and informative safety communications.

Practical Tip: Utilize the pre-clinical data to prepare clear and concise safety communications for different stakeholders. Include summaries of the pre-clinical safety profile, potential risks, and risk mitigation strategies. Tailor the communication to the target audience, ensuring that the information is accessible and easily understandable.

For example, pre-clinical safety data is communicated to patient advocacy groups and patient forums to ensure that patients have access to accurate and understandable information about potential risks associated with investigational products. By providing clear and comprehensive safety information, stakeholders can engage in meaningful discussions and make informed choices regarding treatment options and participation in clinical trials.

Safety Database Management: Pre-clinical data contributes to the management of safety databases during clinical trials. By integrating pre-clinical safety findings into the database, researchers can ensure comprehensive data collection and analysis.

Practical Tip: Include pre-clinical safety data as a component of the safety database. Develop data management protocols that facilitate the capture and linkage of pre-clinical and clinical safety data. This allows for holistic analysis and reporting of safety information, enhancing the accuracy and reliability of safety databases.

For example, In a clinical trial evaluating a new antipsychotic medication, pre-clinical safety data indicating the potential risk of extrapyramidal symptoms is included as a component of the safety database. The data management protocol ensures that adverse events related to extrapyramidal symptoms are accurately captured and linked to the pre-clinical findings. This allows for a comprehensive analysis of the safety profile and facilitates the identification of any potential correlations between pre-clinical data and clinical safety outcomes.

In conclusion, leveraging pre-clinical data is essential for optimizing patient safety management during early phase clinical trials. Integrating pre-clinical data into the clinical trial design and safety monitoring processes enhances the understanding of the investigational product’s safety profile and aids in early detection and management of adverse events. By proactively addressing safety concerns, researchers can minimize risks and ensure participant safety throughout the trial.

Are you looking to optimize your patient safety management in early phase clinical trials? Leverage Techsol’s PV expertise for comprehensive safety data management and reporting services. Contact us today to learn how we can assist you in effectively leveraging pre-clinical data, implementing robust safety strategies, and ensuring regulatory compliance. Enhance patient safety and drive successful clinical trial outcomes with Techsol as your trusted partner in Pharmacovigilance. Schedule a consultation with our team of professionals now!

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Clinical Trial Design Simulation Through Data Powered Statistical Computing

Techsol Life Sciences — Fri, 24 Mar 2023 13:40:19 +0000

Clinical Trial Design Simulation Through Data Powered Statistical Computing

Sponsors are increasingly looking at novel methods to optimize the design and conduct of Clinical trials for evaluating the safety and effectiveness of new treatments or therapies. The clinical trial design plays a critical role in assessing the validity of safety and efficacy outcomes and ensuring the overall quality of the study. While clinical trials have traditionally been conducted using manual methods and conventional statistical techniques, recent advances in data-powered statistical computing have made it possible to simulate clinical trial design and analysis, providing researchers with more accurate and efficient ways to evaluate treatments. With the unprecedented growth of data, today it is possible to leverage modern technology and advanced statistical computing techniques to simulate clinical trial designs and scenarios to better understand the potential outcomes of the study.

In this blog, we will explore the concept of clinical trial design simulation through data-powered statistical computing and its potential impact on medical research.

What is Clinical Trial Design Simulation?

Clinical trial design simulation involves using statistical computing techniques to model different trial designs and scenarios. This approach enables researchers to explore various factors that could impact the outcome of the study, such as sample size, treatment effect, and dropout rates. By simulating different scenarios, researchers can identify the optimal trial design that will provide the most accurate and reliable results. This helps significantly in estimating the probability of success for a particular treatment, and predicting the safety profile of a drug or therapy.

Simulation techniques can be applied to both the design and analysis of clinical trials. In the design phase, simulations can be used to determine the optimal sample size, study duration, and selection criteria for participants. In the analysis phase, simulations can be used to evaluate the results of the trial, such as determining the treatment effect, identifying potential safety concerns, and assessing the overall risk-benefit profile.

This approach can help to reduce the risk of bias or errors that may occur during the study. By simulating the trial design, researchers can identify potential issues or limitations that may impact the validity of the study, enabling them to adjust the design before it begins.

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Data-Powered Statistical Computing

Data-powered statistical computing can be a valuable tool for simulating the outcomes of a clinical trial. It involves the usage of advanced statistical models on historical data gathered from similar clinical studies, disease registries, electronic health records, scientific journals, Real-world data (RWD) and other relevant public domain datasets to analyze and predict the possible outcomes with selective primary and secondary clinical endpoints. This also helps researchers to identify potential biases or confounding factors that may affect the outcomes of a clinical trial.

Using these advanced mathematical methods, sponsors can come to know the possible positive and negative treatment outcomes before conducting the actual clinical trial. Accordingly, they can draft the study protocol to minimize patient safety risks and take informed decisions across the clinical development timeline.

Methods for Clinical Trial Simulation

Here some of the most common mathematical and statistical algorithms that can be used for clinical trial design simulation:

Algorithm	Purpose
Meta-analysis	Based on the results of meta-analysis, researchers can simulate the potential outcomes of their proposed clinical trial design, including the number of patients needed, the expected treatment effect size, and the power of the study to detect a significant effect.
Monte Carlo simulation	Can be used to model the potential outcomes of the trial based on different factors such as sample size, dose of the drug, duration of the trial, and various other factors that could affect the outcome. This can help to assess the likelihood of different outcomes and estimate the potential impact of different trial design decisions.
Latin hypercube sampling	LHS can be used to evaluate the effects of different design choices on the outcomes of the trial. For example, it can be used to estimate the statistical power of the trial under different sample sizes, to evaluate the performance of different randomization procedures, or to assess the impact of missing data on the trial results.
Bayesian Statistics	Bayesian statistics can be used for clinical trial simulation by incorporating prior knowledge and data into the analysis. In Bayesian analysis, the prior probability distribution is combined with the likelihood function to obtain the posterior probability distribution. This posterior distribution can then be used to make inferences and predictions about the treatment effects in the clinical trial. Simulation studies can be performed to explore the impact of various study design choices and assumptions on the trial results. Bayesian statistics can also be used to perform sample size calculations and determine stopping rules for the trial based on the posterior probabilities of treatment efficacy.
Decision tree analysis	Decision tree analysis can be used to identify the most effective treatment option by modeling the potential outcomes of different treatments and assigning probabilities to each outcome. This can help optimize the design of clinical trials and improve patient outcomes. Secondly, decision tree analysis can help determine the optimal sample size required for a clinical trial by considering factors such as the expected effect size, variability in patient responses, and statistical power. Thirdly, decision tree analysis can be used to evaluate the cost-effectiveness of different treatment options by considering both the potential benefits and costs of each treatment. Finally, decision tree analysis can be used to assess the robustness of a clinical trial design by conducting sensitivity analysis and identifying the key factors that influence the decision.
Hidden Markov Models (HMM)	HMMs can be used to model the progression of diseases and the effects of different treatments over time. By considering the underlying states of the disease and the transitions between them, HMMs can provide insights into the optimal treatment strategy and the likely outcomes for different patient populations. HMMs can also be used to predict patient outcomes and estimate the probability of treatment success. By modeling the potential outcomes of different treatments and the factors that influence them, HMMs can help identify the most effective treatment option for a given patient.
Neural networks	Neural networks can be used to predict patient outcomes based on input variables such as patient demographics, medical history, and treatment regimen. This can help in the design of clinical trials by identifying patient populations that are most likely to benefit from a particular treatment. Neural networks can also be used to simulate the effects of different treatment regimens on patient outcomes, allowing researchers to evaluate the potential benefits and risks of different treatment options before conducting expensive and time-consuming clinical trials.
Gradient boosting	Gradient boosting can also be used to model complex relationships between predictors and outcomes, allowing researchers to evaluate the potential effects of different treatment options on patient outcomes. Additionally, gradient boosting can be used to identify important predictors of treatment response, which can guide the selection of endpoints and inclusion criteria for clinical trials.
Support vector machines	SVMs can identify complex, non-linear relationships between predictors and outcomes, allowing researchers to evaluate the potential effects of different treatment options on patient outcomes. SVMs can also be used to identify subgroups of patients who are likely to benefit most from a particular treatment, which can help in the design of personalized treatment plans. Additionally, SVMs can be used to analyze and interpret data collected during clinical trials, helping researchers to identify patterns and trends that may be missed by traditional statistical methods.
Dynamic programming	Dynamic programming can be used for clinical trial simulation by modeling the decision-making process for treatment assignment and patient selection. The approach involves breaking down the problem into smaller sub-problems and optimizing each sub-problem to find the optimal solution. By simulating the outcomes of various treatment options and patient characteristics, dynamic programming can help to determine the most effective treatment strategies for clinical trials. This approach can also be used to evaluate the expected value of information, allowing researchers to prioritize which data to collect in order to minimize uncertainty and maximize the potential impact of the trial.
Game theory	Game theory can be used for clinical trial simulation by modeling the interactions between different stakeholders involved in the clinical trial process, such as patients, healthcare providers, and drug manufacturers. By simulating the decisions and strategies of each stakeholder, game theory can help to predict the outcomes of different trial designs and interventions, and identify potential conflicts of interest or areas of cooperation. Game theory can also be used to analyze the impact of different incentives or regulations on stakeholder behavior, and identify ways to align their interests and promote cooperation.
Survival analysis	Survival analysis can be used for clinical trial simulation by modeling the time-to-event outcomes, such as time to disease progression or time to death, of patients enrolled in the trial. By analyzing the survival data, researchers can estimate the probability of an event occurring at any given time point, and compare the survival curves of different treatment groups to determine if there is a significant difference in the outcomes. Survival analysis can also be used to identify risk factors that may impact survival outcomes, and to develop predictive models that can help to estimate the probability of survival for individual patients.

Clinical trial design simulation through data-powered statistical computing can offer several benefits, such as:

1. Optimizing trial design: Simulation allows clinical development teams to assess the impact of different trial design factors, such as sample size, randomization, and stratification, on the statistical power of the study. By tweaking these factors, researchers can optimize the trial design to increase the likelihood of obtaining meaningful results. Simulations enable exploration of many more statistical design options which could have been completely unknown through traditional processes.
2. Reducing costs: Clinical trials can be very expensive to run, especially if they fail to produce useful results. Simulation allows sponsors to assess the feasibility of a trial before actually running it, reducing the likelihood of trial failures.
3. Reducing risk: By simulating a trial before it begins, researchers can identify potential clinical safety and operational risks through simulated models. This enables sponsors to take proactive measures to reduce and eliminate quality and compliance risks.
4. Improving accuracy: Simulation can help researchers identify potential sources of bias in their trial design and adjust for them. This can help ensure that the results of the trial are as accurate as possible.

References

1. https://www.ema.europa.eu/en/documents/scientific-guideline/ich-e-8-general-considerations-clinical-trials-step-5_en.pdf
2. Holford, N. H., Kimko, H. C., Monteleone, J. P., & Peck, C. C. (2000). Simulation of clinical trials. Annual review of pharmacology and toxicology, 40, 209–234. https://doi.org/10.1146/annurev.pharmtox.40.1.209

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Practical Applications, Benefits and Risks of Using Artificial Intelligence in Clinical Trials Subjects Screening

Techsol Life Sciences — Mon, 27 Feb 2023 12:01:40 +0000

Current Landscape of Clinical Sites

Clinical trial sites around the globe are rapidly evolving with globalization, consolidation, technology adoption, patient-centricity, and regulatory changes. These trends are likely to continue shaping the industry in the years to come, as clinical trials become increasingly complex and global in scope. However, the fundamental problems faced by Clinical trial sites still remain unaddressed especially for completing subject screening to meet the enrollment targets for the study. This can be particularly difficult for trials that require a specific patient population or have strict inclusion and exclusion criteria.

For therapeutic areas like Oncology, Cardiology, Neurology and Rare diseases, Clinical trial protocols can be complex and may require multiple tests, assessments, or procedures to determine eligibility. With limited resources, including staff, equipment, and funding, clinical sites face difficulties to complete subject screening, especially if the trial has a tight timeline or is competing with other trials for subjects. Completing subject screening can be administratively burdensome for clinical trial sites, particularly when it comes to managing and tracking patient data, obtaining informed consent, and navigating regulatory requirements. The traditional approach to subject screening is time-consuming, expensive, and often results in low enrollment rates.

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How Can Artificial Intelligence (AI) be a Boon To Clinical Sites

Artificial intelligence (AI) has the potential to revolutionize clinical trial screening by facilitating eligibility criteria review, personalizing the informed consent process, identifying suitable trial subjects, and optimizing regulatory compliant study enrollment. In this blog, we will explore the use cases and different types of AI models that can be used for performing clinical trial subject screening, along with their risks, benefits, and implementation methods.

Let us look at five potential Areas where AI can be applied to various aspects of clinical trial subject screening.

Identification of eligible subjects based on inclusion/exclusion criteria
Prediction of subject enrollment and dropout rates
Analysis of subject mood and emotions during pre-screening and actual study screening
Detection of adverse events risks and other safety concerns based on information provided by patients
Optimization of trial design and protocol based on screening outcomes

Each of these use cases requires different types of AI models and technologies, which we will discuss in detail in the following sections.

Artificial Intelligence – A Brief Overview

Artificial Intelligence (AI) is the ability of machines to perform tasks that would typically require human intelligence to complete. In other words, AI enables machines to think, learn and adapt like humans. AI is achieved through the use of algorithms and computer programs that can process large amounts of data, recognize patterns, and make predictions or decisions based on that data. There are several techniques used in Artificial Intelligence (AI). Let us look at some of the common ones that could transform subject screening:

Machine Learning or ML is a broader field of AI that involves building algorithms and models that enable computers to learn from data without being explicitly programmed. ML models can identify patterns in data and make predictions or decisions based on that learning. Think about the enormous amount of Electronic Health Records (EHR), pharmacy and claims data that is generated exponentially today. ML has the ability to crunch large volumes of unstructured data such as medical records, electronic health records (EHRs), and patient-generated data to determine patient populations in different geographies.

Deep Learning or DL is a subfield of ML that involves training artificial neural networks to perform complex tasks. These networks are modeled after the structure of the human brain, with layers of interconnected nodes that can learn to recognize patterns and features in data. DL models can be used for tasks such as image and speech recognition, natural language processing, and recommendation systems. There is a wealth of information that can be mined from medical images (x-rays, CT scans, MRIs, etc. biopsies, etc.) that can be interpreted fast and accurately for taking important medical decisions.

Natural Language Processing or NLP is an application of ML that focuses on the interaction between computers and human language. It involves processing, analyzing, and understanding natural language data, including speech and text, to derive meaning and insights. In simple words, NLP can actively listen to patients narrating or sharing their experiences and extract all useful information for determining clinical trial eligibility.

Computer vision is a field of AI that focuses on enabling machines to interpret and understand visual information from images, videos, and graphical illustrations. It can be used to assess physical examination findings, such as skin lesions or joint swelling, and classify them based on severity. The results obtained from the processed images will have accurate biometric information that will help investigators to take informed decisions on the patient’s trial participation.

Use Cases of Natural Language Processing (NLP), Machine Learning & Deep Learning for Clinical Trial Subject Screening

With the right combination of NLP, DL and ML models, artificial intelligence can revolutionize the complex, effort intensive and time consuming clinical trials subject screening process.
AI can be used to perform mood analysis on clinical trial subjects by analyzing the sentiment and emotion in their responses during interviews or through written surveys. The degree of truth in their responses can be assessed by comparing the sentiment and emotion analysis with the subject’s actual medical condition and history.

There are various AI models that can be used for mood analysis, including:

> Natural Language Processing (NLP) techniques such as sentiment analysis, which uses machine learning algorithms to identify and extract subjective information from text data. It can analyze written or spoken responses from patients during clinical trial screening and identify any red flags or concerns that may need further investigation.

> ML can help to assess the suitability of potential subjects based on their medical history and other relevant data.

> Emotion recognition using facial expression analysis, which uses computer vision technology to detect and classify emotions based on facial expressions.

> Voice analysis, which uses machine learning algorithms to detect emotional states based on changes in pitch, tone, and other vocal characteristics.

> Neural networks, which are complex algorithms that can learn to recognize patterns in data and make predictions based on those patterns.

By leveraging these AI models, clinical trial coordinators and investigators can gain valuable insights into the mood and emotional state of their subjects, which can help them to better understand the impact of the trial on the subject’s well-being, and potentially identify any issues or concerns that may require further investigation.

Let’s look at a practical example.

The traditional Visual Analog Scale (VAS) is a common tool used to assess subjective outcomes in clinical trials, such as pain intensity, quality of life, or mood. It typically involves asking patients to rate their experience on a scale from 0 to 10, where 0 represents no experience of the outcome and 10 represents the worst possible experience. However, this approach has several limitations, such as inter-observer variability, subjective interpretation, and limited granularity.

AI has the potential to transform the traditional VAS approach in several ways, including:

1. Digital Collection of VAS data using mobile devices or web-based applications can improve the accuracy and reliability of the data by eliminating manual entry errors or biases.
2. Automated Analysis of VAS data, such as by using machine learning algorithms can identify patterns or trends in the data over time, or to predict outcomes based on other clinical or demographic variables.
3. Natural Language Processing (NLP) techniques can analyze the written or spoken responses of patients on VAS assessments and provide more detailed and nuanced information about their experience.
4. AI image analysis techniques can analyze facial expressions or other physiological responses of patients on VAS assessments, which can provide additional insights into their experience.
5. AI-based VAS responses provides more indepedent un-biased granualar assessments allowing Principal Investigators to provide personalized recommendations or interventions to improve patient outcomes.

In this manner, AI has the potential to transform the traditional VAS approach by providing more objective, accurate, and personalized assessments of subjective outcomes in clinical trials. However, it’s important to ensure that the AI models used for VAS are carefully validated. This kind of technology requires a lot of model training to increase its accuracy and precision.

Computer Vision – How it can be used in Subject Screening

Computer Vision, a subset of ML and DL has the capability to rapidly identify, analyze, and extract meaningful information from any type of visual content such as images, videos, graphical illustrations, etc. Overall, this technology can help improve the accuracy and efficiency of physical examination assessments and provide objective and consistent measurements for monitoring patient outcomes. Here are some examples of how computer vision technology can be used to assess physical examination findings and classify them based on severity and other medical criteria.

Applications with this AI technology will empower sites to:

> Analyze images of skin lesions and classify them based on severity, extent of disease spread, and other physiological characteristics. For example, a deep learning algorithm could be trained on a dataset of skin lesion images labeled by dermatologists to classify new images as benign or malignant, or to identify specific types of lesions such as melanoma or psoriasis.

> Analyze images or video of joint swelling and classify them based on severity, location, or other criteria. For example, a machine learning algorithm could be trained on a dataset of images or videos of joint swelling labeled by rheumatologists to classify new images or videos as mild, moderate, or severe, or to identify specific joints that are affected by the swelling.

> Analyze retinal images and classify them based on severity or type of abnormality. For example, a deep learning algorithm could be trained on a dataset of retinal images labeled by ophthalmologists to classify new images as normal or abnormal, or to identify specific abnormalities such as diabetic retinopathy or age-related macular degeneration.

However, it’s important to ensure that human oversight is maintained to ensure accurate interpretation of the results.

In clinical trial subject screening, computer vision technology can be used to automatically analyze medical images such as X-rays or CT scans to identify abnormalities or signs of disease. It could also be used to track patient movements or behaviors, such as monitoring gait patterns to detect changes over time. Additionally, computer vision could be used to analyze patient records or other data sources to identify potential candidates for clinical trials or to predict patient outcomes based on a range of factors.

How Does Computer Vision Work?

Computer vision is a field of artificial intelligence that enables computers to interpret and understand the visual world through digital images and videos. Computer vision algorithms generally work in the following way:

Image Acquisition: The first step in computer vision is to acquire, capture or import images or video from various sources, such as cameras, microscopes, or medical imaging equipment.
Preprocessing: It involves preparing the images or video for analysis, such as by adjusting brightness and contrast, reducing noise or artifacts, and applying filters or other enhancements. The acquired image or video is then preprocessed to correct for any distortions or noise that may affect the accuracy of the analysis. This can include techniques such as filtering, color correction, and geometric transformation.
Feature Extraction: Once the image or video is preprocessed, computer vision algorithms identify relevant features of the image, such as edges, corners, and shapes. This process involves using mathematical and statistical techniques to analyze the image and identify patterns.
Object Recognition: Once the relevant features are identified, computer vision algorithms compare them to a database of known objects or patterns to recognize what the image or video is depicting. This can involve techniques such as template matching, classification, and clustering by comparing identified features to known templates or models.
Tracking and Localization: This component involves tracking and localizing objects or regions over time or across frames, such as by using motion estimation algorithms or machine learning models.
Postprocessing: This component involves refining the results of the analysis, such as by combining multiple sources of information, removing false positives or negatives, or improving the accuracy or reliability of the results.
Interpretation: Finally, the computer vision system interprets the results of the analysis and presents them to the user in a way that is meaningful and useful. This can include identifying specific objects or patterns within the image, detecting anomalies or abnormalities, or tracking the movement of objects over time.

Overall, computer vision relies on a combination of mathematical and statistical techniques to analyze images and videos, identify relevant features, and interpret the results of the analysis. The accuracy and effectiveness of computer vision algorithms depend on the quality of the input data, the complexity of the analysis required, and the sophistication of the underlying algorithms.

Some of the benefits of using computer vision for clinical trial subject screening include:

Improved accuracy and consistency of physical examination findings.
Reduction in the time and cost associated with traditional screening methods.
Ability to analyze and interpret visual data that may be difficult for humans to detect.

Risks of Using AI for Subject Screening

There are several risks and benefits associated with using AI models for clinical trial subject screening. Some of the risks include:

Data Privacy: AI models require large amounts of data to be trained effectively. This can raise concerns about data privacy and confidentiality.

Bias: AI models can be biased if they are trained on datasets that are not representative of the population.

Lack of Human Interaction: AI models may lack the human interaction that is necessary for building trust with patients.

Limited Generalizability: AI models may not be generalizable to different populations or therapeutic areas.

Practical Advantages of Using AI for Subject Screening

Despite these risks, there are several benefits to using AI models for clinical trial subject screening. AI can offer several practical advantages to Principal Investigators and Site Personnel for performing subject screening in research studies, including:

Efficiency: AI algorithms can quickly and accurately screen large numbers of potential study participants, reducing the time and resources needed to identify eligible subjects.
Objectivity: AI is not influenced by personal biases or preconceptions, which can lead to more accurate and unbiased screening decisions.
Consistency: AI can consistently apply the same screening criteria to all potential study participants, reducing the risk of human error and ensuring that all subjects are evaluated fairly.
Customization: AI can be trained to recognize and prioritize specific study criteria, enabling researchers to focus on the most important factors in subject selection.
Scalability: AI can be used to screen subjects across multiple studies and research sites simultaneously, enabling researchers to identify eligible subjects more quickly and efficiently.
Reduced Burden: AI can reduce the burden on study staff by automating the screening process, allowing them to focus on other important study tasks.

In the next few years, AI has tremendous potential to streamline and improve the subject screening process, ultimately leading to more efficient and successful research studies.

What the Future Holds

There is a growing interest in the use of artificial intelligence (AI) for clinical trial subject screening, and it is predicted that this trend will continue to grow in the future. One major advantage of AI in subject screening is its ability to analyze large amounts of data quickly and accurately, potentially identifying eligible subjects more efficiently than traditional screening methods. AI algorithms can also be customized to specific study criteria, improving the accuracy of subject selection and reducing the risk of human error. Additionally, the use of AI in subject screening can reduce the burden on study staff, freeing up time for other important study tasks.

As AI technology continues to evolve and improve, it is likely that its use in clinical trial subject screening will become more widespread. However, it is important to note that AI should not be viewed as a replacement for human expertise and judgment. Rather, it should be seen as a tool that can be used in conjunction with traditional screening methods to improve the efficiency and accuracy of subject selection.

Currently, there are no specific regulations that govern the use of artificial intelligence (AI) in clinical trial subject screening and enrollment. However, regulatory bodies such as the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have issued guidance and recommendations related to the use of AI in clinical trials.

In 2019, the FDA released a discussion paper on the use of AI and machine learning in medical devices, which included recommendations on the development and validation of AI algorithms. It emphasizes the need for transparency, interpretability, and reproducibility of AI algorithms, particularly in medical device development.

Similarly, the EMA released a reflection paper in 2020 on the use of AI in drug development, which highlights the importance of ensuring that AI models are validated and fit for purpose, and that the data used to train AI models are of high quality and appropriate for the intended use.

Additionally, ethical considerations surrounding the use of AI in clinical trials have been discussed in the literature, particularly related to issues such as informed consent, privacy, and bias. It is important for researchers and sponsors to consider these ethical considerations when using AI in clinical trial subject screening and enrollment.

Useful References

Agency	Document Name	Summary	Publication Year
FDA	“Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan”	Outlines the FDA’s approach to regulating AI/ML-based SaMD, including how the agency plans to encourage innovation and promote patient safety.	2021
FDA	“Clinical Decision Support Software”	Provides guidance on how the FDA intends to regulate clinical decision support software, including software that uses AI/ML.	2017
EMA	“Reflection paper on development of medicinal products containing a nanomaterial: general considerations”	Offers guidance on the development of medicinal products containing nanomaterials, including those that use AI/ML.	2017
EMA	“Guideline on good pharmacovigilance practices (GVP) – Module VI”	Provides guidance on pharmacovigilance practices, including how to monitor the safety of products that use AI/ML.	2017
MHRA	“Regulatory considerations for software as a medical device (SaMD)”	Offers guidance on regulatory considerations for SaMD, including those that use AI/ML.	2019
Health Canada	“Guidance Document: Software as a Medical Device (SaMD)”	Provides guidance on the regulation of SaMD, including those that use AI/ML.	2019

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The post Practical Applications, Benefits and Risks of Using Artificial Intelligence in Clinical Trials Subjects Screening appeared first on Techsol Life Sciences.

How to Proactively Prevent Clinical Trial Protocol Deviations and Protocol Violations

Techsol Life Sciences — Mon, 04 Jul 2022 14:09:00 +0000

July 4, 2022
Posted by: Techsol Life Sciences
Category: Clinical Trials

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Understanding the Importance of a Clinical Trial Protocol

The fundamental objective of a clinical trial protocol is to provide common scientific guidance on how to conduct the clinical study in a regulatory-compliant manner and gather data that will ultimately provide answers on the safety and efficacy of the investigational medicinal product.

The Guidelines for Good Clinical Practice of the International Council on Harmonization (ICH GCP) define the protocol as “A document that describes the objectives, design, and methodology, statistical considerations, and organization of a clinical trial.”

Therefore, its critical to ensure that all key stakeholders are aware of the clinical study objectives and have a thorough understanding of study procedures as detailed in the clinical trial protocol to avoid risks related to patient safety, regulatory compliance, and overall study completion.

Protocol Deviations & Protocol Violations – What are the key differences

In simple words, both the terms mean that there has been a study conduct mistake and requires immediate attention to safeguard the rights and protection of clinical trial subjects. Many times these terms are interchanged when the differences are not understood. So, here is a closer look to get better clarity on the definition with relevant contextual examples.

Protocol Deviations	Protocol Violations
A protocol deviation is an accidental or unintentional action where a study activity was performed inconsistently or incorrectly against the IRB-approved study protocol. The action itself does not increase or decrease the risk or benefit nor it has a significant effect on the subject’s rights, overall safety, and the integrity of the study data.	A protocol violation is a more serious action which may have been performed accidentally, intentionally, or unintentionally where a study activity was completely not followed or performed incorrectly against the IRB-approved study protocol. The action performed adversely impacts the subject’s rights, overall safety and the study data integrity and results in non-compliance with the actual study procedures.
Examples of protocol deviations may include any of the following actions: Unknowingly performing a study procedure as per the visit schedule with a defective device Conducting a study visit that was marginally outside the study visit schedule timeframe Inconsistencies in following the safety monitoring and reporting plan Did not provide accurate information to the trial participant which led to incorrect study procedure outcomes Inconsistencies in subject’s adherence to study procedures such as untimely study visits, not recording outcomes on patient diaries, etc. Incorrect and delayed reporting of adverse events from subjects to Principal Investigators Inconsistencies observed in the storage condition of the investigational product	Examples of protocol violations may include the any of the following actions: Failing to obtain or document informed consent prior to study enrolment Obtaining informed consent from an unauthorized person Enrolling a subject who did not meet all the inclusion/exclusion criteria as listed in the IRB-approved study protocol Performing a study procedure not described in the study protocol Mistakes in dispensing or dosing of the investigational medicinal product Missing lab tests and mishandled lab samples at sites Subject’s use of prohibited medications and unreported Serious Adverse Events Stakeholders indulge in highly unethical activities, such as intentional fraud or falsification of data.

Protocol Deviations

Protocol Violations

A protocol deviation is an accidental or unintentional action where a study activity was performed inconsistently or incorrectly against the IRB-approved study protocol.

The action itself does not increase or decrease the risk or benefit nor it has a significant effect on the subject’s rights, overall safety, and the integrity of the study data.

A protocol violation is a more serious action which may have been performed accidentally, intentionally, or unintentionally where a study activity was completely not followed or performed incorrectly against the IRB-approved study protocol.

The action performed adversely impacts the subject’s rights, overall safety and the study data integrity and results in non-compliance with the actual study procedures.

Examples of protocol deviations may include any of the following actions:

Unknowingly performing a study procedure as per the visit schedule with a defective device
Conducting a study visit that was marginally outside the study visit schedule timeframe
Inconsistencies in following the safety monitoring and reporting plan
Did not provide accurate information to the trial participant which led to incorrect study procedure outcomes
Inconsistencies in subject’s adherence to study procedures such as untimely study visits, not recording outcomes on patient diaries, etc.
Incorrect and delayed reporting of adverse events from subjects to Principal Investigators
Inconsistencies observed in the storage condition of the investigational product

Examples of protocol violations may include the any of the following actions:

Failing to obtain or document informed consent prior to study enrolment
Obtaining informed consent from an unauthorized person
Enrolling a subject who did not meet all the inclusion/exclusion criteria as listed in the IRB-approved study protocol
Performing a study procedure not described in the study protocol
Mistakes in dispensing or dosing of the investigational medicinal product
Missing lab tests and mishandled lab samples at sites
Subject’s use of prohibited medications and unreported Serious Adverse Events
Stakeholders indulge in highly unethical activities, such as intentional fraud or falsification of data.

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Common Reasons for Protocol Deviations & Protocol Violations

Although pharmaceutical and medical device sponsors, and CROs follow a meticulous risk-based approach, protocol deviations and violations occur almost in every clinical trial due to:

Poor study design with unclear or insufficient explanation of study procedures in the clinical trial protocol and related artifacts
The study visit schedule is too rigid without any window visits to handle random study interventions
Incomplete or incorrect understanding of study procedures where a method or scientific information was misinterpreted and performed in the wrong manner
Deficiencies in Site Personnel Training that leads to mistakes in subject enrolment and randomization
Lack of proper infrastructure and quality measures at the clinical site
Inconsistencies in source data and study data collection (Eg: Mistakes in measurement units, dates, order of procedure completion, etc.)
Lack of trial conduct oversight to monitor trial progress right from screening, subject enrolment, randomization, dosing until trial completion
Failure in educating trial participants on critical study procedure requirements that they need to fulfill during the trial

Consequences of Improper Protocol Deviations & Violations Management

a. Patient Safety-Related Regulatory Non-Compliance

Irrespective of the cause, all protocol deviations and protocol violations require documentation and reporting. If a variance from the protocol is discovered during a regulatory audit and was not previously reported, the FDA would consider it “non-compliant”. Significant or persistent noncompliance may result in penalties, including a warning letter posted on the FDA website, disqualification/restriction/disbarment (also posted on the FDA website), or, in worst-case scenarios, criminal prosecution. Per ICH GCP regulations, sponsors should view non-compliance as a violation of the investigators’ responsibilities.

b. Increased Study Costs & Timelines

Both protocol deviations and violations can significantly increase clinical trial costs because the study duration may get extended to complete new subject enrolments to meet targeted sample size. Additionally, the expenditure to perform data collection, managing site & subject payments, handling trial supply logistics, etc. results in crossing planned study R&D budgets.

c. Poor Study Data Quality

When a clinical study has too many protocol deviations and violations, it has a direct impact on study data quality that ultimately has long-term implications on product approval. The resulting data variations for unique study procedures are the most difficult to address unless the procedures themselves are performed once again which in turn has a cascading effect on study timelines.

Best practices to Reduce and Avoid Protocol Deviations and Violations

Ensure Accurate Clinical Trial Protocol Authoring

Clinical Trial Protocols should be written in a clear and concise manner without any ambiguity so that there is common understanding amongst all stakeholders. It must provide detailed information about the nature of the disease, characteristics of the study population, and the investigational product
Clearly stating study objectives and drafting a risk-based study design accordingly helps a lot to proactively identify opportunities to maximize patient safety, regulatory compliance, and data quality
The clinical trial visit schedule needs to have appropriate ‘window visits’ timeframe that sites and subjects can leverage for missed visits
For long-term clinical studies, consider having a ‘Study Run-in Phase’ prior to enrolment with a placebo to determine potential non-compliant subjects

Provide Site Personnel with Contextual Protocol Training and Engage with Empathetic Feedback

Protocol trainings must be delivered to all stakeholders by explaining the context of study objectives, anticipated outcomes, potential areas for non-compliance and proactive measures to follow for the welfare of study participants
Sponsors and CROs should ensure that Site Personnel receive periodic trainings on how to avoid protocol deviations and violations that were observed in other sites
Unique study procedures and expanded eligibility criteria increase the complexity of protocols. These sections need to be given extra importance to clarify timing, explain detailed methodology and the right process for data collection
Incorporate a positive feedback loop with Principal Investigators and Site Coordinators to engage them with empathetic trial support
Ensure that all stakeholders are timely trained after all mid-study protocol changes
With advancements in technology, Sponsors and CROs can choose provide study procedure instructions through hands-on interactive videos and web-based training

Implement a Risk-based Collaborative Trial Conduct Process with Proactive Oversight

Sponsors and CROs must perform a rigorous risk management exercise and implement study-specific tools such as trackers and checklists that could serve as qualitative check points for ensuring patient safety and compliance
Establish regular open communication between the sponsor, CRO, and investigational sites to relay information on unanticipated outcomes
To ensure patient safety and wellness, site personnel should engage with subjects by providing proper counselling during each subject visit
Sites must be encouraged to send subject reminders and alerts for offsite procedures such as timely medication intake, recording patient diaries, planning next site visit, etc.
Developing a time-bound oversight framework with qualitative and quantitative measures of quality and compliance will certainly help Sponsors and CROs to recognize patterns of protocol adherence across sites
The oversight plan should have periodic assessments of all the protocol deviations and violations that were observed, reported, and documented to timely implement necessary corrective and preventive measures

Conclusion

The successful completion of a clinical study requires tremendous collaboration and communication between various stakeholders starting from Principal Investigators, Site Staff, Clinical Project Managers from CROs, IRB Representatives, Sponsor Personnel and most importantly study subjects. Therefore, its extremely important that all these members have a common understanding of the study objectives and continuously work towards subject safety and regulatory compliance throughout the trial duration.

From our experience, we have seen that long-term clinical studies always have considerable scope for protocol deviations and violations for obvious reasons. Here are some of the approaches that Techsol Life Sciences has implemented across our clinical operations to significantly reduce the occurrence of protocol deviations and violations.

Incorporating risk-based study design while authoring clinical trial protocols and related artifacts
Usage of e-Consent with video support at Sites to ensure that informed consent is obtained from the trial subject after thoroughly going through the study procedures
Use a robust Electronic Data Capture (EDC) and a Clinical Trial Management System (CTMS) to timely report, document and resolve all reported protocol deviations and violations
Conduct risk-based monitoring using pre-defined questionnaires that auto-calculates risk scores for each site
Delivering timely training with follow-ups for clarifying mid-study protocol changes
Providing periodic feedback to sites based on observed study performance and compliance adherence
Continuously observe clinical study data trends to detect anomalies and outliers that indirectly could be a result of protocol deviations
We have a robust clinical trial oversight system has helps us to continuously gain insights on the overall trial progress and discover patterns of protocol deviations and violations across sites

To learn more about our capabilities in conducting clinical trials for Biopharma and Medical Device companies, please visit our website and send us a message with your business needs. We will schedule a meeting to discuss our clinical solutions that can align towards your business goals.

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Food & Nutraceuticals: How Companies can Prove Health Value with Clinical Trials

Techsol Life Sciences — Mon, 20 Dec 2021 13:51:00 +0000

December 20, 2021
Posted by: Techsol Life Sciences
Category: Clinical Trials

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What are Food and Nutraceuticals?

‘Nutraceuticals’ is a composite term of ‘nutrition’ and ‘pharmaceutical’. They are a blend of products that possess both nutritional as well as medicinal value. They are designed to improve physical health, boost immunity, and deal with stress and increase longevity.

Nutraceutical products play a significant role in maintaining normal physiological function in healthy human beings and provide protection against chronic diseases. Changing lifestyle and evolving health trends among the general populace has allowed for the growth of the global nutraceutical market. They significantly help in combating some of the major health problems such as obesity, cardiovascular diseases, cancer, osteoporosis, arthritis, diabetes, cholesterol, etc. Nutraceuticals have led to the new era of medicine, in which the food industry has transformed into a research-oriented sector.

Why Nutraceuticals are Gaining Traction Today?

Most of the nutraceutical products have high antioxidant activity and are considered healthy sources for the prevention of life-threatening diseases. Nutraceutical and food ingredient manufacturers are progressively looking to market their products by demonstrating unique health benefits.
There are many generic, widely used claims – in the EU, Article 13.1 of the European Food Safety Authority’s (EFSA) EC Regulation permits manufacturers to use some 4,637 that pertains to vitamin and mineral content. [1] This proliferation of generic claims and increased competition is driving manufacturers to develop more unique selling propositions (USPs) to use in marketing efforts, which has led to significant growth in demand for nutraceutical trials.

How can we help you?

In the United States, nutraceutical products are regulated as drugs, dietary supplements and food ingredients. The term varies from one country to the other, however, it is usually defined as a “product isolated from foods that are generally sold in medicinal forms”. The demand for nutraceuticals continues to increase due to their potential nutritional, safety, and therapeutic properties. Market research recently proposed that the worldwide market is expanding and would reach the US $250 billion by 2018. [4]

Challenges of Nutraceutical Trial Designs

Nutraceutical trial design demonstrates several unique challenges for researchers. As the market is continuously evolving, many companies in this sector are still unfamiliar with clinical trial designs.

There are several reasons for this:

There is relatively less incentive as broader claims about an ingredient may already exist.
Deriving data and claims from previously completed research are possible if a product’s active ingredients are the same.
Food and ingredients manufacturers often have a very low budget devoted to trials.
The health effects of nutraceuticals are often minimal compared to pharmaceutical products and are more easily affected by the heterogeneity of subjects, environmental and lifestyle factors such as drinking, smoking, physical activity, etc.
Larger sample sizes are required to conduct a viable study compared to a normal drug trial.
It is not easy to finalize on an end-point and establish what claims sponsors want to make in a nutraceutical trial. [2] It is in sponsors’ benefit if they choose their claims for which they want to gather the evidence before commencing a full trial.
To conduct a pilot study to gain insights into desired end results creates an additional cost for sponsors.
Subjects are difficult to recruit for these clinical trials and they have higher drop-out rates than pharmaceutical counterparts as the study group must maintain a healthy lifestyle and record more related information.

Strategies for Effective Nutraceutical Trial Design

The challenges associated with nutraceutical studies are complex, however, they can be managed with a structured and effective trial design.

Apply Surrogate Markers

In order to prove a specific product has preventive qualities, a significant amount of data is required, making the trial run over a longer period. To contradict this, sponsors can use a surrogate marker which must be suitable, reliable and consumers will need to acknowledge it as an appropriate surrogate. For example, instead of claiming that a product ‘lowers the risk of strokes or heart attacks, the surrogate claim is that a product ‘reduces levels of bad cholesterol associated with increased risk of strokes and heart attacks.’ [2]

Uncontrolled Vs. Controlled

Whether a trial will be controlled or uncontrolled is totally dependent on the type of product, the desired end-point, and any logistical or budgetary constraints. Trials should include a reference group where a nutraceutical can ideally be compared against a placebo. People often change their behavior and lifestyle when they are being monitored for a trial, so without a reference (control) group, it is impossible to understand whether changes observed in people taking the nutraceutical are a result of the product or any other factor.

Open-Label Vs. Blinded

Lifestyle choices reasonably influence the end-point in a trial and so it is vital to double-blind, as the knowledge of whether the subjects are taking the active component will change their behavior and so bias the result. Double-blinded trials are the gold standard where placebos are used and should be used where possible.

When it is not possible to design a suitable placebo, an open-label approach would be adopted. This is usually undesirable as compliance to the study protocol is likely to be inferior in placebo groups when participants know they are not taking the active product and sometimes may withdraw from the study as they have no incentive to continue. There are also issues with bias as participants are unlikely to perceive any improvement in their health condition if they know they are taking a placebo, whereas participants who know they are receiving the active ingredient (product) may perceive an improvement whether there is one or not.

Inclusion Criteria

Lifestyle patterns often have a bigger influence on a nutraceutical than a drug and a more significant impact on the outcome of the trial. For this reason, it is essential that the study design acknowledges lifestyle variables and ensures these are effectively recorded and factored in to the final statistical analysis.

Selection of strict participant inclusion and exclusion criteria is vital here as an effect of the nutraceutical in the selected subjects needs to be achievable and evidenced in the trial. [2] As with a pharmaceutical study, subjects need to start off with symptoms severe enough that an improvement can be seen. [3]

Generable Data

The study data needs to be as generable as possible and the results need to show that a nutraceutical could have a substantial effect on a wider population.

A balance needs to be achieved so that the study population is homogeneous enough to see a result. Running the trial from different centers ensures sponsors can capture more diverse demographic and environmental issues and that the subsequent data is more consistent and reliable.

How We Help Clients:

We at Techsol have highly experienced functional teams to conduct nutraceutical trials, right from the study design to commercialization. We have qualified procedures in place for appropriate recruitment, sample and data handling, and skilled statisticians for protocol planning and data analysis. With our expert team we provide the following services:

Study Protocol design
Sample Size Calculation
Study Database / electronic case report form (eCRF) Design
Identification of Primary and secondary data points that are ideal for statistical calculations
End-to-end Clinical Trial Management
Subject Recruitment and Retention strategies
Study Data Management
Clinical Data Statistical Analysis
Preparation and Submission of Final Clinical Study Reports

If you would like support on nutraceutical trials, please reach out to us at info@techsollifesciences.com.

References:

European Food Safety Authority. (2012, August 7). “General function” health claims under Article 13. https://www.efsa.europa.eu/en/topics/topic/general-function-health-claims-under-article-13
Statistical Consultancy Team. (2018). Nutraceutical trials: demand, design and challenges. Quanticate. Retrieved 2021, from https://www.quanticate.com/blog/nutraceutical-trials-design
The trial of nutraceuticals. (2018). The Trial of Nutraceuticals. https://www.nutraceuticalbusinessreview.com/news/article_page/The_trial_of_nutraceuticals/144753
NCBI – WWW Error Blocked Diagnostic. (2014). NCBI. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4336979/

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Enabling eSource in Clinical Studies to Optimize Trial Quality

Techsol Life Sciences — Wed, 17 Nov 2021 11:21:00 +0000

November 17, 2021
Posted by: Techsol Life Sciences
Category: Clinical Trials

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Source data is all information in original records and certified copies of original records of clinical findings, observations, or other activities in a clinical trial necessary for the reconstruction and evaluation of the trial. The electronic data source or eSource is the data that is initially recorded in electronic format in this process.

Biopharma and MedTech companies are gradually introducing eSource using electronic tools to improve standards of clinical trial execution and simplify the process for patients and sites. Read on as we discuss why eSource is advised, how it can be implemented, process barriers sponsors must prepare for and benefits it can offer.

Why sponsors should implement eSource

Adoption of eSource is advised by regulators to optimize the use of electronic data sources and thus efficiently gather data to benefit patients and sites and in turn sponsors. Electronic data sources that are used as primary data sources eliminate/reduce the need to transcribe them manually. This speeds up the data capture and ensures data integrity throughout the trial.

How can we help you?

Data source identification for eSource

Sponsors would have to consider all data sources and install data capture systems in their place to integrate completely enabled eSource. The clinical data sources/ devices/ tools required for enabling such a system can be categorized into four types.

Non-CRF: Data from sponsors and vendors to data repositories and warehouses without going through Case Report Forms.
- Lab reports (verbal)
- Medical images requiring professional interpretation
Devices & Apps: Clinical data gathered from wearable devices and sensors.
- Electronic Clinical Outcome Assessment (eCOA)
- Mobile device data
- Sensors & wearable device data
- Data from vendor device cloud
- eConsent application data
Direct Data Capture: Primary clinical data directly entered into an EDC or a mobile application during the subject’s visit.
- DDC devices
- Site data sources
Electronic Health Record: Patient/subject’s electronic health records shall be collected and reused for clinical research.
- Site-based EHR
- Study data packages
- Patients’ health records
- Public data sources
- Health Information Exchanges (HIE)

Process barriers for eSource implementation

As the scope for eSource grows, some challenges are pertinent. From technological limitations to guiding principles, we present some barriers that sponsors might face during the implementation of eSource and also provide insights on how they can prepare to mitigate them.

Lack of comprehensive regulatory guidelines continues to influence sponsors’ decisions to implement eSource in trials. FDA has started working in this direction however more guidance in terms of devices, apps, and other channels for eSource is yet to be published. Companies can collaborate with regulatory agencies to this expedited process.
Data acquisition from different sites needs to be normalized for metrics. Site-specific requirements could lead investigators to choose different metric systems. Sponsors can implement measures using SOPs to check for metric normalization before the data is used in clinical research.
All data source systems/devices must be validated for compliance with 21 CFR Part 11 and Annex 11 to ensure audit readiness
Patients must be briefed about devices used for automated data collection in advance to address data privacy concerns before recruiting them for the trial to retain
System interoperability is pivotal for eSource and all systems used for data collection must be capable of readily connecting and exchanging information from different sites with appropriate measures for data access.

Benefits of eSource in clinical studies

Implementing eSource in clinical trials offers benefits that improve data quality and compliance of the study by

Eliminating data duplication
Minimizing transcription errors
Promoting real-time entry for eSource
Optimizing data accuracy and completeness

Conclusion

Sponsors should not view eSource as just as electronic enablement for gathering data but as a dynamic system that can be customized to adapt to the needs of individual study. They must work with sites and patients to understand the limitations and scope for designing a suitable system. Finally, quality and compliance requirements must be defined as per local regulations.

Techsol specializes in providing GxP compliant clinical and data solutions to streamline data collection, stakeholder collaboration, and oversee business processes. Please reach out to us by mailing at info@techsollifesciences.com.

References

1. Technology Considerations for Enabling eSource in Clinical Research: Industry Perspective, March 2020, https://pubmed.ncbi.nlm.nih.gov/32865798/
2. Optimizing Use of Electronic Data Sources in Clinical Trials, 2016, https://pubmed.ncbi.nlm.nih.gov/30231687/
3. Wikipedia De-identification, https://en.wikipedia.org/wiki/De-identification
4. Enable eSource Clinical Research Data in Drug Development, 2018, https://www.devdays.com/wp-content/uploads/2019/03/DD18-EU-Michele-Cherry-Enable-eSource-Clinical-Research-Data-in-Drug-Development-2018-11-15.pdf
5. Guidance for Industry – Electronic Source Data in Clinical Investigations, FDA, September 2013, https://www.fda.gov/media/85183/download

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Implementing Electronic Consent for Clinical Trial Participation

Techsol Life Sciences — Tue, 19 Oct 2021 10:44:00 +0000

October 19, 2021
Posted by: Techsol Life Sciences
Category: Clinical Trials

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Industry Background of eConsent

In an attempt to address health authority concerns and improve patient confidence, companies and independent industry bodies have experimented with electronic consent (eConsent) using electronic media such as smartphones, laptops, and tablets. Despite positive outcomes, its implementation remained experimental until FDA officially released a guidance in December 2016. Following in FDA’s footsteps, EMA collaborated with the European CRO Federation (EUCROF) and rolled out its own guidance in March 2021. [1] [2]

With official guidelines available, companies will still require to interpret the guidance properly, understand the study suitability, weigh in the value derivation for patients and be prepared for potential barriers in its implementation. Read on as we discuss all these aspects and also provide Techsol’s expert thought leadership for current and future scope for eConsent.

Value of implementing eConsent in Clinical Studies

Through digitalization, the eConsent process is projected to assist subjects to make informed decisions using multiple multimedia resources, and improve quality of the study by enhancing patient experience, data quality and electronic system. Primarily centered on improving patient experience and their understanding of trial process, implementing eConsent can be advantageous for the sponsors too. In terms of value derivation, implementing eConsent –

How can we help you?

Allows trial investigators to make dynamic changes to consent data and protocol amendments while keeping the patients informed through instant electronic updates.
Helps increase the process efficiency by simplifying the consent communication.
Provides the sponsors regular insights into consent activities.
Ultimately results in a higher patient retention rate and compliance.

How eConsent can be implemented for Clinical Development

a. Phase-wise considerations for implementing eConsent

Executing eConsent is far more complex and critical than simply operating multimedia components. Sponsors/CROs must commence planning the execution by assessing considerations associated with the study to avoid regulatory infractions and financial burden. A pre-assessment detailed phase-wise considerations helps the sponsors, investigators, vendors and other stakeholders to be compliant and diligent throughout.

Pre-implementation Considerations
- Objectives planning – Documenting organizational objectives to implement eConsent and assess/review them against the needs of the study
- Impact analysis – Dividing operational requirements and assessing impact throughout individual phases i.e., planning, development, monitoring, and close-out.
  - Start-up timelines
  - Privacy policies
  - IT diligence activities
  - Organizational and stakeholder impact
  - Critical success factors
Feasibility Considerations
- Assess study considerations – Design, duration, complexity, region, sites, study size, participant population etc.
- Define desired multimedia components – Determine and assess multimedia components required against study requirements and define value derivation
- Review Operational Considerations
  - Device management and technology availability/capability
  - Additional consent considerations – Re-consent and optional consent
- Data privacy and legal considerations
- Define stakeholder roles and responsibilities
Design and Readiness Considerations
- If using vendor – Kickoff meeting followed by discussions on communications and collaboration strategy
- Data access and archiving for vendors, sponsors and IRBs
- Site considerations –
  - Preparing electronic infrastructure for regional disparities
  - Defining SOP documentation/storage
- Define and set-up operational/quality/usage metrics and critical success factors
- Study oversight
- Contingency planning
- Training
Execution and Close-out Considerations
- Monitoring methods
- Examples of oversight /compliance/management reports to support monitoring activities
- Issue management/resolution – Defining escalation matrix and arranging help desk
- Audit/inspection readiness – creating access to eConsent data for review and assessing the adequacy of processes
- Filing and archiving

Acting further based on the considerations assessment outcome allows seamless eConsent implementation. Collectively these considerations can be used as an essential checklist for sponsors/CROs/vendors.

b. Multimedia Requirements for eConsent

Clinical trial information is often extensive and the subject must be allowed to access as much information as possible including research data, number of subjects, trial duration, possible financial impact for the subject, health implications, travel arrangements (if necessary) excluding restricted/ confidential information. In order to present the study information in the eConsent forms compliant with local regulatory requirements, the software and devices used should be capable of supporting various multimedia components in multiple formats. At the least, the eConsent platform should include

Pictures/diagrams –The image files used to provide visual aid of study data such as schedules, statistics, procedures, and precautions must be provided in formats supported by the device or the devices must be enabled to support different image formats.
Audio/video systems – Audio/video files detailing research, purpose & expectations of the study, process overview, study design, etc. using embedded links in different formats must be attached.
Continuous content view – The content must be divided down into sections and precisely categorized using metadata to provide continuous content view facilitating easy navigation for the subject.
Electronic signature – To host electronic signature (eSignature), sponsors/CROs must consider local health authority guidelines. FDA provides eSignature guidance in 21 CFR Part 11 Subpart C and EMA in EU Regulation 910/2014. Time and date stamping, multi-device compatibility, impairment assistance (for subjects with specially challenged), and counterfeit measures must all be included.
Glossary – To enable the subject to understand the meaning and the right interpretation of complex study terminology, a comprehensive dictionary in the form of an indexed glossary must be attached along with the eConsent form.
Knowledge review (Risk and benefits) – Sponsors must include intended benefits and possible risks associated with the study in form of a knowledge review to assist subjects to compare and decide their participation.
Summary boxes – Summarizing individual components of study further simplifies information intake for subjects.
Comment boxes – Comment boxes and flagging options allow subjects to request further clarifications and flag their concerns which will need further addressing from investigators.

The listed components form the minimal structure for eConsent. Additional components could be required depending on the design and requirements of the study. A clinical study expert shall be better equipped to assess and implement any additional components.

Barriers for eConsent Adoption & Measures to Address The Same

Despite offering scope for expediting information delivery and simplifying the consent process, eConsent is still not implemented extensively due to barriers of operational and regulatory in nature. Early adopters and industry experts have identified these challenges.

Regulatory compliance – Regulations pertaining to the eConsent have not been defined up until late 2016. FDA and EMA are the only ones that have an extensive guidance for implementing eConsent. With more countries yet to decide their stand on digitalizing informed consent, many companies are not keen on shifting their approach until most global authorities reach a common ground. Even within the EMA’s member countries, some of the members remain passive on adopting eConsent. FDA’s own guidance is in turn dependent on clauses from Code for Federal Regulations (CFR) 21 Part 11 thus making it complex to interpret.

Gradually, global health authorities are pacing up efforts to draft guidelines for eConsent implementation. ICH has offered overarching guidance that can be referenced to simplify the drafting efforts. Even then, the authorities will have to work towards harmonization as lack of it would still create uncertainty and pose financial setbacks for sponsors in their efforts to explore global markets.

Data privacy – Electronic data capture used for eConsent poses a significant risk to the privacy and security of subject data. A comprehensive set of data privacy practices are yet to be defined in terms of ICH, FDA, and EMA’s GxPs that can be enforced strictly by the regulatory authorities.

21 CFR Part 11, HIPAA, and GDPR laws are currently in place to prevent any systemic leaks and provide the utmost protection. However, concerns of data leak by malware/ ransomware attacks are persistent in the industry.

Infrastructure – Companies are increasingly preferring end-to-end integrated robust software for clinical operations owing to the apparent benefits. Current offerings are often siloed or prove challenging for integrating with CTMS/CDMS, EDC, and other clinical software. The trial sites lack the infrastructure required for a fully operational eConsent process hindering its implementation.

Depending on study needs and organizational objectives, not all studies might require a fully operational eConsent platform meaning sponsors can pick and choose as per the trial parameters. Moreover, until unified eConsent platforms replace the current generation of technology and requisite electronic infrastructure becomes financially viable, study gradually phase-in better equipment depending on availability. Like most technology-dependent processes, an abrupt change is not always possible. In contrast, a phased approach to better the infrastructure provides a cautious roadmap providing scope for continuous improvement.

Patient confidence – Patient confidence and willingness to use eConsent is complex to understand and varies individually. Factors such as lack of familiarity, data security concerns, personal bias play a role in whether a subject is willing to use an electronic format for consenting. Access to necessary devices that support the forms in their entirety is another reason why patients would not be interested in using the same.

Individual bias cannot be controlled. In cases where subjects lack access to necessary infrastructure, the onus lies on sponsors to supplement the same. The industry, health authorities and local laws have their own role in boosting patient confidence towards eConsent adoption by enabling better data security measures and creating awareness about them.

Practical Benefits of eConsent

Although eConsent options were first used almost a decade ago, they did not come to the forefront as an effective alternative. [4] After the COVID-19 outbreak, the industry has quickly adopted eConsent for multiple studies to resume all critical studies. General observations led to the conclusion that compared to the paper-based ICFs that generally lack quality and lead to miscommunication, eConsent has multiple benefits to offer listed here in terms of each beneficiary.

Patients

Simplified presentation of data allowing them to grasp it easily
Proactive communication with a 24/7 active channel
Interactive and engaging explanation
Custom-tailored learning experience

Health Authorities

Improves version control over the forms and study data
Better insights to the patients offering greater protection
Reduced effort in the audit process
Transparency and strong proof of authentication
Effective compliance measures enablement (HIPAA, 21 CFR Part 11, Annex 11, etc.)

Clinical Sites

Sites can have improved focus on addressing patient’s concerns
Improved quality through version control
Decreased administrative burden

Future of eConsent

As more authorities feel inclined to allow eConsent for clinical trials, an end-to-end digitalized consent system with direct data capture capabilities can be envisioned. With decentralized clinical trials gaining momentum, eConsent would be the right addition to the set of tools that are key to the digital transformation of clinical studies. Integrating EMR/EHR to remotely screen and recruitment, eConsent could become more seamless slowly dissolving the healthcare boundaries in clinical research. All of these lead to the possibility of patient portals seamlessly linked to eConsent using EHR data.

As the implementation becomes global, harmonized metadata would have to be developed. We can also foresee greatly increased patient engagement enhanced understanding of their conditions and how the study/research works towards curing/controlling them.

What We Think

Dr. Laxman Jakkala, Ph.D. (Head – Clinical & Data Services, Hinge Clinical-A Techsol Life Sciences Company) and Dr. Aravinda Jagadeesha of Dr. Aravind’s Diabetes Centre have successfully used eConsent forms while conducting an electronic survey study on ‘Diabetes Type II Patient’s Lifestyle Related Behaviour during COVID 19 Pandemic lockdown period.’ Commenting on the resulting benefits, Dr. Laxman said that eConsenting has helped them to successfully authenticate the subjects and conduct the survey without physically interacting with the diabetes patients.

Further expressing his view on eConsent, Dr. Laxman’s quoted, “Currently, active usage (of eConsenting) is majorly seen in survey studies, ePRO studies, RWE studies, HEOR studies, patient-services and food and nutraceutical services, they have the potential for improving the patient experience by helping them gain a better understanding of more complex studies to provide their consent in future.”

Conclusion

In its current form, electronic consent has its own limitations and challenges however it serves as a better option than paper-based consent. Corroborating the same, an NCBI study conclusion also stated that eConsent is ‘a feasible and useful alternative to paper-consent.’ [3]

With configurable options and cloud-hosting ability, eConsent has far-reaching benefits and an accelerating effect on the consent process that allows for better compliance and inspection readiness. In the evolving trial conduct space where decentralization and virtual set-up are gaining momentum, eConsent could prove to be a productive addition.

References:

1. Electronic Informed Consent Implementation Guide, March 2021, https://www.eucrof.eu/images/eConsent_Implementation_Guide_.pdf
2. E6(R2) Good Clinical Practice: Integrated Addendum to ICH E6(R1), March 2018, https://www.fda.gov/files/drugs/published/E6%28R2%29-Good-Clinical-Practice–Integrated-Addendum-to-ICH-E6%28R1%29.pdf
3. Electronic consenting for conducting research remotely: A review of current practice and key recommendations for using e-consenting, September 2020, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7487205/
4. Pfizer Conducts First “Virtual” Clinical Trial Allowing Patients to Participate Regardless Of Geography, June 2011, https://www.pfizer.com/news/press-release/press-release-detail/pfizer_conducts_first_virtual_clinical_trial_allowing_patients_to_participate_regardless_of_geography

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Data Driven Site Selection for Clinical Trials in a Post-COVID-19 World

Techsol Life Sciences — Mon, 20 Sep 2021 07:01:00 +0000

September 20, 2021
Posted by: Techsol Life Sciences
Category: Clinical Trials

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Introduction to clinical trials site selection

Site selection is a comprehensive process that involves analysing sites, patient community, investigator profiles and site infrastructure for conducting a successful clinical trial. Pharma companies often look for alternative site discovery strategies to deal with the constant demand in trial sites globally. A data-driven approach across the clinical trial process offers an opportunity to harness the information for an enhanced clinical trial. Applying analytics with the right criteria and volume of data received from electronic health records (EHRs), drug and medical devices, sales/distribution channels will enable a patient-centric approach for an effective clinical trial.

When choosing a suitable trial site, there are a number of data driven factors that require careful consideration which includes the following:

a. Clinical driven factors
b. Experience driven factors
c. Operational driven factors

How can we help you?

Clinical driven factors

Patient Recruitment Barriers: Sites that specialise in a specific target disease are less likely to experience difficulties recruiting patients or implementing the trial protocol. It is essential to understand the patient’s status in the current disease investigation. It thus becomes imperative to know whether

Does the site currently have patients for the disease under investigation on their lists? If so, how many?
And how many of them do they plan to enrol in your trial?
Does the site have any competing trials?
When will the on-going study finish and will those patients be eligible for inclusion in your study?

The above information is critical to decide whether the site’s involvement in any on-going studies should exclude involvement in your study.

Local demography: Time, cost and inconvenience of travel will impact on subject recruitment and retention. Finding sites close to your target population is key when estimating how quickly you will realise enrolment targets. It is generally viewed that the more visits a study needs, the closer their target population should be to the site. The importance of travel burden on recruitment and retention increases the impact of a disease or condition has on the patient.
Indigenous competition: The presence of only three sites in a particular city will most likely see sites competing for the same patient population. However, In some countries, several centres of excellence are located close together because they are the only centre in that country that treats the condition.

Experience-driven factors

Principal Investigator’s record: Investigators actively involved in research in the proposed field are more likely to early study completion, and may be able to identify other great sites.
Experienced staffing: It is essential to identify experienced staff at the site to monitor the trial and ensure protocol compliance. Their level of experience both with the protocol-defined procedures and study conduct (e.g., maintaining investigator files, completing eCRFs etc.) should be assessed.
History of analogous trials (size and complexity): Review site’s history in conducting trials to ensure it has previously been involved in a study similar to yours. Analyse the previous patient enrolment data to gain insights and estimate the site’s recruitment capacity. Also inquire whether there have been any major changes at site that may affect the proposed study (e.g., changes in key personnel).

Operational driven factors

Site feasibility: The CROs perform the site feasibility assessment as a precursor step to identify and close the decision of study placement. A robust and objective assessment is generally accepted as the best way to identify the feasible sites. There are commercial databases that provide data on patient population densities for key diseases. Some also provide more specific data such as past performance of key sites and country selection (e.g., IMS Health’s Study Optimizer and Pharma intelligence’s Site Trove and Trial. Once the CRO has been selected it is usually followed by a more rigorous feasibility assessment – using lists of possible sites/investigators who are invited to provide information about their capabilities, contractual processes, capacity and budget requirements. This process is often carried out in the form of questionnaires which is sent to sites to identify interest and collect relevant information including an estimate of the number of subjects they might expect to recruit.
Site infrastructure: It is essential to know if the site has appropriate facilities and specialized equipment in order to perform clinical studies effectively. Thoroughly check whether the site has the required infrastructure to fulfil all the activities specified in the protocol. In case of the absence of such equipment, it helps in identifying the costs or logistics of providing that equipment. It in turn helps in analysing the timescales in study initiation or trial conduct if it relies on other departments in the hospital (e.g., pharmacy, imaging etc.)

Digitising the clinical trial site selection process leads to better outcomes

Site selection is a tedious manual process, digitising it will have a profound influence on the study outcomes by evading sub-optimal site choices that result in significant costs and delays.

The volume of data received is humongous, and these data assets are not centralized and are distributed across channels in multiple formats. Big data technologies enable organizations to amalgamate and standardize these assets into a single unified platform to allow uniform processing of data and provide valuable insights from the collected data. By Compiling data from globally acclaimed clinical registries, we have sourced and developed a customizable site selection criteria that enables appropriate site selection according to your preferences/ specific therapeutic areas / geographic location, PI profile / accreditations / certifications etc. Our global investigator database comprises of information about more than 2500 sites using which we can assist you to identify and select the most relevant site for your clinical trial.

Here’s how adopting a data-centric approach shall add value to the industry:

Effective subject enrolment: Through data analytics on patient EHRs, pre-screening is performed for a given set of population. This drastically reduces the overall time for a clinical trial.
Better decision making: Pharmaceutical companies prefer accurate data. Analysing that accurate data can help companies drive better data-driven decisions and clinical trial outcomes
Constant Data monitoring: By constantly monitoring the required trial data, make faster data-driven decisions in site selection.

Conclusion

The paradigm shift in the way clinical research is evolving is anticipated to evolve further. Analytics along with artificial intelligence (AI), NLP and machine learning (ML) will drive the future of clinical research. However, what has been commissioned so far is just the tip of the iceberg seeing the volume of data available. Clinical analytics and emerging technologies will play a key role in driving efficiency and accelerating clinical trial process. There are clinical trial feasibility tools that evolve with ever-growing business needs. Companies need to make sure they find the one that works best for them.

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Clinical Trials Archives - Techsol Life Sciences

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Data Driven Site Selection for Clinical Trials in a Post-COVID-19 World