  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.
