Predicting optimal drug parameters and doses
In early drug development, quantitative systems pharmacology (QSP) modeling can provide decision support for many critical decisions such as what experiments to proceed with and what parameters have the most impact. It can also lend insight into a drug candidate's competitive landscape. This case study outlines an example where QSP modeling helped assess why the dosing regimens of the two anti-PD-1s are roughly similar and predicted optimal drug parameters for bispecific formats versus fixed dose combination.
- BMS and Merck anti-PD-1s therapeutic antibodies have affinities that differ by two orders of magnitude. Why are dosing regimens so similar?
- Sensitivity analysis for experiment prioritization
- Predict optimal drug properties targeting PD‑1 and TIM3 in oncology for Bispecific biologics (Formats: 2‑2, 2‑1, 1‑2, 1‑1) and Fixed dose combinations (FDC)
- Perform risk assessment by performing an in silico differentiation for a bispecific vs. FDC
- Timelines: about 4 months
- Provided insights as to why the dosing regimens of the two anti-PD-1s are roughly similar
- Identified list of sensitive parameters and aided in experimental design to estimate unknown sensitive parameters (TIM3 Kd and expression, and TMDD)
- Predicted optimal drug parameters for bispecific formats vs. FDC
Enabled quantitative decision making that impacted high value questions, strategy, and critical thinking, years before going into the clinic:
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