Immuno-modulation in chronic inflammation
• Customer targeting membrane bound protein using mAb
• Proliferation assay and whole blood biomarker assay appeared to be contradictory
• Competitor molecule 2 years ahead and a tighter binder
• Dosing administration and frequency are major Go/No-Go criteria
• How to interpret the two conflicting assays?
• Should project be terminated, especially given competitor head start?
• Should functional assays be refined?
• Should new lead generation campaign be started to find tighter binder?
• At least 6 months saved, 25% reduction in FTE, over $1m in direct cost savings (estimate by company VP)
• Customer molecule was not discarded, rather accelerated, and now positioned to be best-in-class
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.
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.
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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.