INTRODUCTION
There is great interest in harnessing the power and versatility of
artificial intelligence (AI) methods in clinical pharmacology and
pharmacometrics to facilitate modeling and simulation, accelerate drug
development, and improve patient outcomes in drug therapy.
Participants in clinical trials overall are not fully representative of
the population of patients 1-5. In 2011, African
Americans and Hispanics comprised 12% and 16% of the United States
population, respectively, but only 5% and 1% of clinical trial
participants 2. The Food and Drug Administration (FDA)
recently issued a guidance to address representation in clinical trials5 that recommended broadening study eligibility
criteria and addressing other study design and recruitment logistic
factors to improve the participant pools. There is clearly an unmet need
and knowledge gap to enable modeling of treatment effects and safety in
diverse populations and in under-represented groups.
We posit that when successful, the use of AI technologies in the context
of already available “big data” could be a transformative
computational strategy to mitigate the impact of under-representation of
race/ethnicity groups in pharmacometrics and drug development. The
clinical pharmacology and pharmacometrics research community has not
meaningfully leveraged potentially promising AI-enhanced approaches to
address treatment variability, group-level outcome disparities, and real
world clinical applications 6. The increasing
availability of public health databases, de-identified health records
and pharmacogenomics data provides new opportunities for forecasting of
biomarker profiles and drug outcomes in diverse and in under-represented
populations 7.
Generative adversarial networks (GANs) are a powerful, deep learning
(DL)-based, AI technology that enables realistic simulations of complex
patterns 8. GANs utilize two neural networks called
the generator and discriminator that learn from data to generate complex
high-dimensional pattern distributions. The generator neural network
synthesizes instances of plausible samples that conform to the
population distribution of interest. The discriminator network is a
classifier that attempts to categorize whether each instance presented
as input belongs to the training data or has been synthesized by the
generator. The adversarial training process results in a generator that
produces samples that mimic the training data.
We hypothesized that GANs could be an effective approach for generating
realistic biomarker profiles for clinical trial and pharmacometrics
simulations. The objective was to design and assess GANs capable of
generating disease-relevant biomarker profiles by learning from
observations in real-world, population-based studies. An additional goal
was to extend the GAN approach for generating biomarker joint
distributions for under-represented race/ethnicity groups.