Excerpted from keynote address at the American Conference on Pharmacometrics, November 2023
In today’s ever-evolving landscape of healthcare, the concept of patient centricity has taken center stage. Patient-centric drug development means putting the patient at the center of all we do. We strive to develop treatments considering the diverse needs and responses of individual patients, ultimately striving for increased diversity within clinical trials and better outcomes for all patients.
Understanding Sources of Variability in Drug Response
At the heart of patient-centric drug development lies a fundamental question: why do patients respond differently to drugs? This question serves as the guiding principle for Clinical Pharmacology and Pharmacometrics, disciplines dedicated to unraveling the complexities of drug exposure and response variability across the drug development life cycle. If we understand the sources of variability in drug exposure and response, we can identify the right drug and treat patients who may need lower or higher doses. Relating patient covariates to the time course of response can also help to identify patient characteristics predictive of response. This remit affords the opportunity to create evidence for more inclusive development strategies through tailored dose recommendations.
Fostering Innovation in a Multidisciplinary Landscape
The field of Clinical Pharmacology encompasses a wide array of disciplines, skills, and methodologies. Recent publications have highlighted the emerging role in driving innovation and advancing diversity and inclusion (D&I) strategies within drug development. By building upon foundational knowledge of pharmacology/physiology and PK/PD and integrating different fields, the discipline can pioneer novel approaches to understanding patient variability in exposure and response to facilitate and promote increased inclusivity in clinical trials.
Innovation thrives in inspiring environments that encourage creativity, risk-taking, and collaboration. Drawing inspiration from both the arts and sciences, we can cultivate a culture of innovation within the realm of drug development. As leaders in the field, it’s crucial to provide researchers with the tools, resources, and freedom to explore unconventional approaches. By fostering a culture of innovation and employing new science and technologies, we can unlock new solutions that cater to the diverse needs of patients worldwide.
Implementing innovative strategies often requires courage and conviction, especially in an industry known for its conservative approach. Real-life examples, such as regulatory decisions made by authorities like the FDA, highlight the transformative power of bold actions. It has been wonderful to have the pull from the FDA’s Model-Informed Drug Development Meeting and Program which advocates and expects quantitative approaches to improve drug development; other regulatory authorities are advocating these approaches, too. By challenging the status quo and embracing courage, we can drive meaningful change in drug development, ultimately benefiting patients and society.
Leveraging Data for Precision Dosing
Precision dosing is central for optimizing therapy and accommodating patient variability. Model-informed drug development (MIDD) frameworks offer a comprehensive approach to leveraging various in vitro, non-clinical, and clinical data sources and informing dosing strategies. By integrating quantitative models and multidisciplinary skill sets, we can tailor treatments to individual patient needs, optimize trial designs, and advance D&I in clinical trials.
Technological advancements, such as biomarker technologies (liquid biopsy, digital pathology and radiomics) and computational modeling, offer new avenues for enhancing D&I in drug development. By leveraging patient-specific data and quantitative tools, we can optimize treatment strategies for distinct populations. Organ-on-chip technology, for example, provides a platform for assessing safety parameters with greater relevance to diverse patient populations. By integrating these tools into our workflows, we can bridge the gap between non-clinical and clinical studies and characterize the key features that may drive variability in human response leading to better translation key safety and efficacy signals.
Enabling Diversity & Inclusion in Clinical Trials
As we move toward more inclusive trials, patient level data together with model-based predictions will help us to anticipate patient variability and guide clinical D&I strategies. Computational modeling of patient heterogeneity is a powerful approach to enhance our understanding of disease complexity and optimize treatment strategies. The FDA’s draft guidance on Diversity Plans underscores the importance of inclusivity in clinical trials, emphasizing the need to incorporate race, ethnicity, and other covariates into study designs. The guidance aims to ensure data generated reflects the population expected to use the product. The plan should describe how race, ethnicity, and other factors with known potential to affect PK, PD, safety, and effectiveness will be assessed. In order to safely enroll a more diverse population, a totality of evidence approach can be used to predict exposure-response and inform the dose regimen(s) to be studied across the intended patient populations. Multiregional trials require careful consideration of intrinsic and extrinsic factors relevant to the disease and/or drug. By assessing disease trajectories and population characteristics, we can design studies that accommodate regional differences and optimize inclusivity.
Conclusion
Patient-centric drug development is not just a vision; it’s a call to action. By embracing innovation, leveraging technology, and promoting diversity and inclusion, we can bring the right solutions to patients, regardless of their background or characteristics. Let’s seize this opportunity to transform healthcare and improve outcomes for everyone.
For additional reading on this topic, check out Diversity in clinical trials: Optimizing drug development strategy, leveraging data and increasing efficiency with modeling and simulation.
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