Abstract
Breakthroughs in high-throughput technologies have provided vast information related to the genome, transcriptome, proteome, and metabolome that enable human physiology to be described mathematically. The collection of biological data at various organizationally complex levels may be translated into a network which describes how these genes, transcripts, proteins, and metabolites collectively function. Quantitative systems pharmacology (QSP) models relate data from the -omics levels to an observable phenotype, associated with a disease and the impact of a drug on the disease, bridging the gap between cell-level biochemical pathways and systemic-level pharmacokinetic–pharmacodynamic (PKPD) models. This systems view promises to decipher the interactions between drug-response pathways and the homeostatic mechanisms of the host to be explored more extensively in healthy and diseased states. These multiscale models transcend the temporal and spatial scales to explain a drug's effect on the network and disease physiology, while also considering the influences of external stimuli such as environmental cues or social structure on systemic function. With appropriate implementation, the “one-gene, one-target, one-mechanism” paradigm is slowly replaced with a more detailed, mechanistic understanding. The concepts and example models presented throughout this chapter highlight the need for QSP in the development of new disease treatments. Although much work remains before human physiology and disease progression are fully understood, the current state of QSP models has successfully explored the link between drug exposure and systemic response.