- Long list of potential multi-targeting pairs for a chronic inflammatory disease
- The marketing requirement for this drug was monthly subcutaneous administration
- It was anticipated that dosing would be a challenge
- It was unclear how to pick compatible pairs of targets among 90 potential molecules with no known efficacy differences
- Developed a systems pharmacology model for each target
- Established affinity and dose requirements for each target
- Benchmarked predictions using data from known monotherapies
- Eliminated targets with low developability
- Set up screening funnel for top candidates based on predicted affinity requirements
- Eliminated dead ends early before significant time and resource investment
- Freed up researchers to pursue other promising business objectives
- Accelerated timelines for promising candidates
Related Case Studies
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.
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.
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.