•The pharmacokinetics of antibody drug conjugate (ADC) therapeutics typically show a discrepancy between the PK of total antibody (conjugated and unconjugated antibody) and that of conjugated antibody, carrying one or more payload molecules
•This discrepancy is often attributed to deconjugation, however recent evidence suggests that the underlying mechanisms may be more complex
•Created a computational quantitative systems pharmacology (QSP) approach to understand the impact of drug antibody ratio (DAR) and the resulting changes in molecular properties on overall PK and relative payload disposition as observed in preclinical and clinical studies
•Established the benefit of using computational models to design novel ADCs and to optimize the discovery and development of existing ADCs
•The model described the kinetics of individual DAR species and agrees well with typical ADC PK, individual DAR PK, and average DAR measurements in vivo.
•The model quantitatively describes the trade-off between higher DAR and lower exposure.
•Model simulations show that longer mAb half-life reduces payload delivery after multiple doses. ADC half-life affects the percent of payload delivered through different mechanisms, with deconjugation increasing with longer half-life.
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.
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.
The case studies shown on this page are only a sample of our work. We would be happy to discuss additional examples with you and explore with you how our services can best fit your need. Email us at firstname.lastname@example.org or call 617-914-8800.
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.