Leveraging In Silico and Artificial Intelligence Models to Advance Drug Disposition and Response Predictions Across the Lifespan

Abstract

Incorporating inter-individual differences in drug disposition and responses is essential for ensuring the safe and effective use of drugs in real-world patients. Despite ongoing efforts, lower participation of children, older individuals, pregnant and breastfeeding women, postmenopausal women, and people with disease states and disabilities in drug clinical trials is frequent, and it requires multifaceted strategies and tools to evaluate drug exposure and responses in broad populations. The availability of modeling and simulation tools, such as physiologically based pharmacokinetic (PBPK) and quantitative systems pharmacology/toxicology (QSP/QST) modeling, enables the application of virtual populations that reflect the differences in drug disposition and responses for disease states and different stages of the lifespan. These models integrate clinical trial and real-world data (RWD) to predict drug exposure, efficacy, and safety. Additionally, machine learning (ML) and artificial intelligence (AI) offer powerful tools for analyzing large datasets and identifying key physiological determinants of drug response across the lifespan. This review discusses the application of in silico and AI models to advance the prediction of drug exposure and responses across the lifespan, including examples of virtual populations in PBPK and QSP/QST models. A case study on QST modeling for drug-induced liver injury (DILI) in postmenopausal women is presented, along with opportunities and challenges in applying AI for modeling physiological determinants of drug dosing in individuals ranging in age from 12 to > 80 years old in drug development.

Keywords: AI; ML; clinical pharmacology; drug development; pharmacokinetics; real‐world data.

FIGURE 1

In silico tools and clinical data, including real‐world data, can be used to enhance clinical pharmacology and drug development across lifespan. PBPK/QSP/QST models integrate preclinical and clinical data to predict drug exposure, efficacy, and safety. Clinical data from initial trials in healthy populations can be employed to validate and/or refine PBPK/QSP/QST models (A). Within validated models, virtual populations can be developed based on available population‐specific physiology data to prospectively predict drug exposure, efficacy, and/or safety in patient populations, which can be used to optimize the dosing regimen in planned trials in broader populations (B). Once executed, data from clinical trials can be employed to further validate and/or refine virtual populations (C). Next, validated models and virtual populations can be further expanded to represent patients with characteristics not included in trials to optimize dosing regimens in real‐world patients (d), predictions of which can be validated or further refined leveraging real‐world data (E). If clinical trials in certain disease states or age groups are not feasible, virtual populations initially validated using clinical data from available data can be expanded to represent intended patient groups to inform dosing regiments in real‐world patients (F). Machine learning (ML) and artificial intelligence (AI) offer powerful tools for analyzing large datasets and identifying key physiological determinants of drug exposure and responses, which can be used to predict exposure and drug outcomes in specific patients of interest.

FIGURE 2

Distribution of BMIs in the final postmenopausal women SimPops; the final population includes 124 healthy and 120 NAFLD individuals.

FIGURE 3

(A–C) are schematic representations of the generative adversarial network (GAN), variational autoencoder (VAE), and adversarial autoencoder (AAE) methods, respectively. A GAN is comprised of generator and discriminator neural networks. The generator takes random variables from a latent space and conditioning variables as input and computes generated data via its neural network. The discriminator is a binary classifier that takes training data containing biomarkers and conditioning variables and the generated data from the generator as inputs to compute the generator and discriminator loss functions. The loss functions are used to update the generator and the discriminator neural networks via backpropagation. A VAE consists of an encoder neural network and a decoder neural network. The encoder conducts nonlinear dimensionality reduction to obtain a latent space vector representation of the distributional parameters ($\mu$, the mean vector, $\mathrm{\Sigma }$, the covariance matrix, and $\epsilon$, the noise vector). The encoder and decoder are updated via a variational loss function that takes training data and decoder output. The AAE contains the encoder and decoder features of the VAE, but their neural networks are updated with the aid of an adversarial loss function from a discriminator.

FIGURE 4

(A–C) compare generative adversarial network‐generated data vs. test data for estimated glomerular filtration rate (GFR). The overall probability density histogram is shown in (A), and the age dependence is summarized in (B). The GFR distributions are compared in the groups with and without hepatitis C in (C). The variables GFR and RIAGEYR represent the min‐max transformed values of the logarithms of estimated glomerular filtration rate and age in years.