Projecting antibody-drug conjugate (ADC) efficacy and toxicity ahead of the clinic is notoriously difficult. Small changes in antibody, linker, and payload design can shift outcomes in ways preclinical data alone won’t predict. In this poster, we present a quantitative systems pharmacology (QSP) model built and calibrated entirely on preclinical data for trastuzumab emtansine (Kadcyla) in metastatic breast cancer, then translated to simulate clinical pharmacokinetics, efficacy, and dose-limiting thrombocytopenia.
What you’ll learn:
- How cellular and in vivo QSP models can be built from preclinical data alone and translated to human predictions
- The mechanistic drivers linking intracellular payload accumulation to both tumor kill and platelet toxicity
- How virtual clinical trial simulations compare against real Phase 2/3 outcomes across multiple trials
- Why this modeling framework is designed to generalize to other ADC programs
Why it matters:
Better prediction of clinical efficacy and toxicity from preclinical data alone could improve first-in-human dose selection and clinical trial design for ADC programs, reducing risk and guesswork earlier in development.
