Quantitative systems pharmacology (QSP) is an approach that uses computational and mathematical models to describe dynamic interactions between a drug and the pathophysiology to understand the biological system (body) at the cellular and biochemical network levels. QSP modelling aims to improve the understanding of the body and how it is changed by the disease, facilitates early and more thorough in silico testing of drug candidates, supports rational decision making, and reduces the costs and time of de novo drug development. QSP combines mechanistic modelling of disease pathophysiology, a systemic (whole-body) approach, the pharmacokinetics (PK, which is what the body does with the drug) and pharmacodynamics (PD, which is what the drug does with the body) of a therapeutic agent, and quantitative experimental data. The resulting model can be used to run simulations, beyond of what is currently known, to understand how drugs modify cellular networks, for example in neuronal networks in the brain, and how they are impacted by the pathophysiology, the significant pathways, drug parameters, biological variance, and drug efficacy and safety.
QSP is increasingly used in drug discovery and development to guide research and decision making on areas such as:
Dose optimization: Complex diseases such as cancer, diseases of the central nervous system (e.g. chronic pain), and metabolic diseases typically involve combination therapy. Incorporating disease mechanisms via QSP models leads to important and often counterintuitive insights for deciding optimum dose levels and combination therapy approaches.
Precision medicine: Many diseases but also many medications exhibit heterogeneity, meaning the subpopulations of patients are affected differently. The use of QSP models can incorporate the impact of biological variance on efficacy and safety and lead to rational decisions on which patient subpopulation to treat with which treatment paradigm.
Target feasibility and selection: Designing a therapeutic agent often starts with choosing from a list of potential candidates. Developing QSP models for each potential target leads to establishing affinity and dose requirements and predicting optimal drug parameters early on. This approach helps eliminate less promising drug targets, so one can pursue more promising candidate drugs.
Drug efficacy and safety: Most drugs fail in the clinical trial stage because of low efficacy. High efficacy levels in animal experiments often do not translate to humans. QSP models have the potential to predict this behaviour. In addition to predicting which drugs will be more efficient, QSP can help identify which drugs might fail and for which reasons. Because QSP models can predict drug exposure at the organ and systems biology level, they also provide insights into the mechanism of toxicity and potential side effects.