• Jounce Therapeutics, Biotech company in Immuno-Oncology space
• Thought experiments for PD-1 – Tim3 dual targeting
• 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)
• Fixed dose combinations (FDC)
• Perform risk assessment by performing an in silico differentiation for a bispecific vs. FDC
• Timelines: about 4 months
• Why are the dosing regimens for existing therapeutic antibodies so similar?
Enabled quantitative decision making that impacted high value questions, strategy, and critical thinking, years before going into the clinic:
• 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
Related Case Studies
When designing a biological therapeutic agent, it is critically important to establish the feasibility of achieving a desired target product profile (TPP) as early in the program as possible, typically at the ‘New Target Proposal Stage’ or at the start of Lead Identification (LI). This case studies outlines an example of where systems pharmacology modeling and analysis helped eliminate targets with low developability and helped set up a screening funnel for top candidates.
When selecting a clinical candidate, it is a competitive advantage to have accurate, quantitative predictions that help answer strategic questions such as whether a program should be terminated, if a new lead generation campaign is needed, or what further assays need to be developed. This case studies outlines an example where systems pharmacology modeling helped save a clinical candidate from being terminated and instead accelerated it into the clinical where it is currently slated to be best-in-class.
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Once a drug candidate progresses into the clinic, quantitative systems pharmacology (QSP) modeling can be applied to help with critical issues such as observed variability in clinical data or how the candidate is fairing against competitors. The following case study is an example where QSP modeling was introduced into a project for the first time after Phase 1 for the purpose of explaining observed variability and nonlinearity which in turn saved a molecule from being discarded and helped position it as best-in-class.
With an ever increasing focus on antibody drug conjugates (ADC), computational models are needed to better understand the complexity of the various design parameters and objectives of ADCs. The following case study is a computational exploration of mechanistic determinants of ADC pharmacokinetics using quantitative systems pharmacology (QSP) modeling strategies.