SystemMatch: optimizing preclinical drug models to human clinical outcomes via generative latent-space matching

TL;DR

SystemMatch is a novel framework that uses generative latent-space matching to optimize preclinical models for better alignment with human clinical outcomes, demonstrated through macrophage modeling.

Contribution

This work introduces SystemMatch, a new method combining single-cell genomics and generative modeling to improve preclinical model relevance to human biology.

Findings

Successfully ranked macrophage subpopulations by similarity to human targets.

Predicted and recommended in vitro and in silico model systems for experimental validation.

Enhanced model development process for more human-like preclinical systems.

Abstract

Translating the relevance of preclinical models ($\textit{in vitro}$, animal models, or organoids) to their relevance in humans presents an important challenge during drug development. The rising abundance of single-cell genomic data from human tumors and tissue offers a new opportunity to optimize model systems by their similarity to targeted human cell types in disease. In this work, we introduce SystemMatch to assess the fit of preclinical model systems to an $\textit{in sapiens}$ target population and to recommend experimental changes to further optimize these systems. We demonstrate this through an application to developing $\textit{in vitro}$ systems to model human tumor-derived suppressive macrophages. We show with held-out $\textit{in vivo}$ controls that our pipeline successfully ranks macrophage subpopulations by their biological similarity to the target population, and apply this analysis to rank a series of 18 $\textit{in vitro}$ macrophage systems perturbed with a variety of cytokine stimulations. We extend this analysis to predict the behavior of 66 $\textit{in silico}$ model systems generated using a perturbational autoencoder and apply a $k$-medoids approach to recommend a subset of these model systems for further experimental development in order to fully explore the space of possible perturbations. Through this use case, we demonstrate a novel approach to model system development to generate a system more similar to human biology.