Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology

Abstract

Two technologies that have emerged in the last decade offer a new paradigm for modern pharmacology, as well as drug discovery and development. Quantitative systems pharmacology (QSP) is a complementary approach to traditional, target-centric pharmacology and drug discovery and is based on an iterative application of computational and systems biology methods with multiscale experimental methods, both of which include models of ADME-Tox and disease. QSP has emerged as a new approach due to the low efficiency of success in developing therapeutics based on the existing target-centric paradigm. Likewise, human microphysiology systems (MPS) are experimental models complementary to existing animal models and are based on the use of human primary cells, adult stem cells, and/or induced pluripotent stem cells (iPSCs) to mimic human tissues and organ functions/structures involved in disease and ADME-Tox. Human MPS experimental models have been developed to address the relatively low concordance of human disease and ADME-Tox with engineered, experimental animal models of disease. The integration of the QSP paradigm with the use of human MPS has the potential to enhance the process of drug discovery and development.

Keywords: Computational models of ADME-Tox; Computational models of disease; DILI; Drug development; Drug discovery; Drug repurposing; Induced pluripotent stem cells; Microphysiology systems; Omics analyses; PBPK; Personalized medicine; Quantitative systems pharmacology; Toxicology.

Fig. 1

Quantitative systems pharmacology platform for repurposing drugs and developing novel therapeutics

Fig. 2

A unified signaling network generated through the chemogenomic approach (see Fig. 1a’ and Pei et al. 2017, 2019) in investigation of drugs of abuse. Black arrows represent the activation, inhibition, and translocation events during signal transduction. Solid gray arrows represent the known drug-target interactions. Dashed gray arrows represent predicted drug-target interactions. The diagram illustrates the targets of several drugs of abuse belonging to different categories: loperamide, fentanyl, heroin, morphine, and methadone from opioids; midomafetamine, ketamine, dextromethorphan, LSD, and psilocin from hallucinogens; triazolam, diazepam, alprazolam, pentobarbital, eszopiclone, flunitrazepam, and zaleplon from CNS depressants; cannabichromene, 2-AG, cannabinol, and dronabinol from cannabinoids; methamphetamine, cocaine, AMPH, and phendimetrazine from CNS stimulants; and nandrolone from anabolic steroids. mTORC1 emerges as a hub where the effects on several targets of addictive drugs appear to be consolidated to lead to cell death and/or protein synthesis in the CNS and in particular AMPAR/PSD95 synthesis that induces morphological changes in the dendrites. Figure originally published in Pei et al. (2019)

Fig. 3

Human in vitro experimental models span a broad range of experimental throughput and biomimetic structure and function

Fig. 4

Two views of a detailed computational model of immunoreceptor signaling mediated by the high-affinity receptor for IgE (Fc epsilon R1). Panel (a) shows the molecular components (yellow rectangles) and processes (purple circles) that govern the flow of activity in the network. Each process represents either a binding interaction between the components or posttranslational modification of a component (e.g., phosphorylation). Enormous complexity is generated just from the basic interactions that include binding and phosphorylation. Although this complexity does not limit our ability to simulate the dynamics of such systems, it does limit our ability to understand the dynamics. Through a process of static analysis, we can reduce the complexity and interpret the dynamics in terms of simple motifs and mechanisms, such as the positive feedback loop that is illustrated in panel (b) (edges marked with “x”). Modified from Sekar et al. (2017)

Fig. 5

The vascularized liver acinus microphysiology system (vLAMPS). (a) The vLAMPS model is assembled in a three-layer glass microfluidic device from Micronit. The center layer (A2) has an 8 × 16 mm elliptical hole with a porous PET membrane on which matrices and cells are layered. The media flow in the hepatic and vascular chambers, combined with the oxygen consumption by the hepatocytes, creates an oxygen gradient mimicking the in vivo liver acinus, creating Zones 1–3 microenvironments. (b) The three layers are held together in a clamp for robust connections and imaging. (c) The independent flow channels are sealed with elastomer. (d) The proportions of the four human cell types used to construct the model were chosen based on the proportions in the human liver. (e) The organization of the cells and matrices in the assembled model. Adapted from Li et al. (2018)