Clinical trials, to the surprise of almost no one, and the disappointment of every patient, are rife with diversity problems. The Food and Drug Administration (FDA) revealed 75% of clinical trial participants for approved molecular entities and therapeutic biologics in 2020 were white. Without more robust data that accounts for the constellation of race, body mass index, health conditions, sex, age, and so many other dimensions of diversity we see in our population in clinical trial participants, pharmaceutical companies encounter a key reason most trials end in failure head-on: the vast unknown.
Ultimately, pharmaceutical companies are blind to understanding the mechanisms of how their drug works across all patient populations or even predicting the effectiveness, the side effects and the dosage recommendations of a treatment for patients outside the core clinical trial demographic. But when we look to the next frontier in medicine, powered by deep science and the latest leaps in technology, that’s all changing.
The FDA is increasingly prioritizing the importance of diverse representation in clinical trials. In 2022, the agency issued guidance including recommendations that ensure greater representation in clinical trials and will soon require new diversity plans for clinical trials.
Recruiting diverse patient populations for each clinical trial represents both a financial and time consuming challenge, in an industry where most trials are already delayed due to recruiting challenges for trial patients. There is an elegant and increasingly easy to implement solution that pharmaceutical companies are exploring more and more to accelerate drug development and improve the success rates of clinical trials: clinical trials simulations leveraging next-gen dynamic systems modeling and virtual patient populations.
How will these clinical trial simulations impact diverse representation in traditional clinical trials? Here are just two of the significant impacts in silico trials deliver to increase diverse representation, improve drug development, and accelerate more effective, personalized treatments for patients.
The rise of virtual patients
Virtual patients can play a major role to address the substantial lack of diversity in clinical trial patient populations. The significant breakthrough of digital twins for personalized medicine has been front and center in the media lately, especially as it relates to single-arm trials and regulatory concerns.
Using virtual populations or ‘digital cohorts’ for clinical trials takes this one step further. Digital cohort results deepen our understanding of the impact of a trial design or treatment on clinical outcomes more than any real-life trial could provide. The lack of size and scope limitations in a digital cohort help uncover previously unknown variables that could impact a treatment response, such as different regiments and health states. Instead of capturing one individual digital twin response, you can capture multiple identical siblings, each accounting for all the different potential variables.
Every individual has unique physiological characteristics. Next-gen dynamic systems modeling—a rapidly growing, more advanced approach to modeling encompassing a broader scope of disease-related biological processes and systems for predictions and clinical efficacy outcomes—is a significant step toward more tailored, personalized medicine, where ideal dosages and combinations of treatments can be better identified for the unique biological makeup of different people.
Next-gen dynamic systems modeling and inclusive drug development
In silico trials leveraging next-gen dynamic systems modeling are helping to pave the way toward more inclusive drug development.
Next-gen dynamic systems modeling enables greater representation by considering the diverse characteristics of patient populations, helping to address the lack of diversity in clinical trials and ensuring that therapies are developed and tested for a wide range of individuals.
How does it do this? A given drug’s dose and regimen are often defined using data from two types of studies: pharmacokinetics, or how drugs are absorbed, processed and broken down, and pharmacodynamics, what the drugs do to the body and how they work to achieve the desired outcome.
These studies are often focused on adult populations that do not reflect the broader, general population of varying weight, age from pediatrics to geriatrics, and gender, among other factors. And because most trials are conducted in North America and Europe, clinical trial results often do not accurately reflect treatment effects among different geographies and ethnic groups. These major gaps massively hinders innovation and new discoveries in the pharmaceutical space.
According to a 2022 report from the National Academies of Science, Engineering and Medicine, this gap in representation may also lead to lack of access to effective medical interventions. Approval and indications for new therapeutics are often restricted to the demographics of the populations included in the clinical studies.
Through next-gen dynamic systems modeling, Quantitative Systems Pharmacology (QSP) modelers and clinical development teams are able to simulate the effects of drugs and different dosages on diverse patient populations to enable greater discovery in the overall effectiveness of a particular treatment.
The combination of wider adoption of digital cohorts and next-gen dynamic systems modeling can bridge the diversity gap for clinical trials, in turn accelerating drug development and opening up new avenues for treatment innovation in the pharmaceutical industry.
François-Henri Boissel,
François-Henri Boissel is Founder and CEO of Novadiscovery.
