AI is making significant strides in the rapidly evolving field of clinical trials. Dominique Demolle, CEO of Cognivia, shares her insights on how AI transforms clinical trials. With a focus on reducing data variability and enhancing trial outcomes, Demolle discusses the challenges and opportunities AI presents in managing placebo responses and other critical aspects of clinical research.
Moe: You emphasize reducing data variability by up to 30% in clinical trials. Could you elaborate on the primary sources of this variability and its impact on treatment efficacy?
Dominique Demolle: The primary source of variability we address is the placebo response, which can skew trial results. Patient-specific factors like personality traits and expectation influence this complex phenomenon. We reduce variability by capturing and analyzing this data, explaining up to 30% of the variance. This reduction is crucial for accurately assessing treatment efficacy, as it allows us to isolate the true effects of the treatment from the noise created by placebo responses. For instance, understanding a patient’s optimism can help predict their placebo response, refining the trial’s outcome measures. This approach ensures that we are not misled by placebo effects, which can otherwise mask the true efficacy of a treatment.
By focusing on these factors, we enhance the reliability of our trial results, ultimately leading to more effective treatments and better patient outcomes.
Moe: Traditional methods have struggled to mitigate placebo-related variability. How do AI-driven solutions differ in predicting and managing these responses?
Dominique Demolle: Traditional methods focus on training patients and site personnel to manage expectations and limit placebo responses. These methods, while valuable, only address part of the problem. AI-driven solutions, on the other hand, capture personalized data directly from patients, which traditional methods often overlook. By integrating this data into our analyses, AI reduces variance more effectively. This approach is critical because placebo responses are driven by a complex interplay of psychological and biological factors. For example, AI can model how a patient’s traits, belief in treatment efficacy, etc might trigger biological responses, such as endorphin release, which traditional methods might miss. This allows us to tailor our approaches to individual patients, enhancing the precision and effectiveness of our trials. By leveraging AI, we can better understand and manage the factors that contribute to placebo responses, leading to more accurate and reliable trial outcomes.
Moe: How do multidimensional psychological questionnaires identify and quantify patient traits that contribute to placebo responsiveness?
Dominique Demolle: We leverage extensive scientific research to identify traits that trigger biological responses. For instance, studies have shown that optimistic patients may produce more endorphins, influencing pain perception. By associating these traits with clinical improvements observed after placebo administration, our models quantify the placebo effect. The Placebo effect has been studied using brain imaging and other data to understand the biological underpinnings of these responses. For example, in Parkinson’s disease, the production of dopamine in response to placebo can be linked to specific psychological traits, providing a more comprehensive understanding of patient responses. The collection of traits allows us to tailor our interventions to individual patients, enhancing the precision and effectiveness of our trials. By understanding the psychological and biological factors that contribute to placebo responses, we can better manage these effects, leading to more accurate and reliable trial outcomes.
Moe: Incorporating predictive models into clinical trials presents challenges. What strategies do you employ to ensure seamless integration and compliance with guidelines?
Dominique Demolle: Ensuring model transparency, avoiding overfitting, and maintaining reproducibility are key strategies. Regulatory agencies require that models be pre-existing before analysis, with full data transparency. This means that all patient data must be collected before the first dose is administered, ensuring that the model’s predictions are unbiased. The choice of model, whether deep learning or linear regression, dictates the documentation and validation process. For instance, a linear model might be easier to explain and validate than a complex deep learning model, which requires more detailed documentation to satisfy regulatory scrutiny. By adhering to these guidelines, we ensure that our models are robust and reliable, providing accurate and trustworthy results. This approach not only enhances the credibility of our trials but also ensures that we are in compliance with regulatory requirements, ultimately leading to more effective and efficient clinical trials.
Moe: While focusing on placebo response, what other areas of clinical trials are you targeting with your AI-driven behavioral modeling technologies?
Dominique Demolle: Beyond placebo response, we are evaluating risks of dropout and non-adherence, influenced by factors such as social support and medical history. Behavioral science is crucial for personalizing clinical trials, predicting patient behavior, and improving trial retention and adherence. For example, understanding a patient’s previous experiences with clinical trials can help predict their likelihood of dropping out, allowing us to tailor interventions to keep them engaged. By focusing on these factors, we enhance the reliability of our trial results, ultimately leading to more effective treatments and better patient outcomes. This approach ensures that we are not only addressing the placebo response but also other critical aspects of clinical trials, leading to more comprehensive and effective trial designs.
Moe: Looking ahead five years, how do you envision AI transforming the management of variability in clinical trials?
Dominique Demolle: AI will enhance diagnostics, treatment, and prognostics, particularly in areas like cancer, where biomarkers play a crucial role. Behavioral science will also play a larger role, with digital health enabling real-time, connected data use. This will personalize trials, optimizing patient participation and treatment regimens, ultimately accelerating development and improving patient outcomes. For instance, AI could help identify the best trial for a patient based on their unique profile, ensuring they receive the most appropriate treatment. By leveraging AI, we can better understand and manage the factors that contribute to variability in clinical trials, leading to more accurate and reliable trial outcomes. This approach not only enhances the precision and effectiveness of our trials but also ensures that we are in compliance with regulatory requirements, ultimately leading to more effective and efficient clinical trials.
Moe: Is there anything else you’d like to add?
Dominique Demolle: It’s crucial to understand that reducing variance increases study power, which is directly linked to making informed decisions about compounds. By decreasing variance, we significantly reduce the risk of discarding potentially effective treatments. For example, reducing variance by 30% can increase study power from 80% to 92%, dramatically lowering the risk of making incorrect decisions about a compound’s efficacy. This highlights the importance of our work in variance reduction, as it directly impacts the success of clinical trials. By focusing on these factors, we enhance the reliability of our trial results, ultimately leading to more effective treatments and better patient outcomes. This approach ensures that we are not only addressing the placebo response but also other critical aspects of clinical trials, leading to more comprehensive and effective trial designs.
Moe Alsumidaie is Chief Editor of The Clinical Trial Vanguard. Moe holds decades of experience in the clinical trials industry. Moe also serves as Head of Research at CliniBiz and Chief Data Scientist at Annex Clinical Corporation.
