Quantitative Systems Pharmacology (QSP) modeling is a mathematical modeling approach that mechanistically integrates biology, pharmacology, and in vivo/clinical data to quantitatively describe effect of various therapies on a disease state and progression. QSP modeling helps to bridge the gap between biology and clinical outcomes, providing a quantitative understanding of drug-target-pathway-disease relationships and supporting better, faster, and more confident decisions.
By running in silico experiments, QSP models help R&D teams evaluate hypotheses, prioritize targets, choose appropriate patient population, and optimize development strategies before costly clinical studies.
Key Questions QSP Modeling Can Answer
- How does a drug influence disease pathways?
- What is the expected dose-response relationship in different patient populations?
- Which biomarkers are most informative for efficacy or safety?
- How do changes in biology or patient characteristics affect treatment outcomes?
- What dosing strategies are most likely to achieve the desired therapeutic effect?
Case Studies
QSP modeling to predict optimal SUD for Talratamab to mitigate CRS
QSP model to guide dose selection for bsAb GEN1046
Application of quantitative systems pharmacology model of Alzheimer’s disease for simulation of efficacy of tyrosine kinase inhibitors
