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Boston QSP September 2019 Event Announcement

Boston QSP is excited to announce our September event in the Modeling & Simulation in Drug Development series. This event will feature a talk by Dr. Jaydeep Yadav from Amgen. The talk is titled "Modeling inactivation parameters from time dependent inhibition improves clinical DDI prediction compared to standard practice" and will be followed by a mixer and reception where you can enjoy great company and conversation with fellow community members over chilled craft beers and delicious pizza from a local small business.

The venue is sponsored by CIC.


5:00-5:30 PM: Registration (outside Havana Room)

5:30-6:20 PM: Presentation and Q&A. Presentation title: “Modeling Inactivation Parameters from Time Dependent Inhibition Improved Clinical DDI Prediction Compared to Standard Practice” (Havana Room)

6:20-7:30 PM: Mixer & Reception (Venture Café, a few steps down the hallway from the

Havana room)

Venue: Cambridge Innovation Center, 1 Broadway, Cambridge.

RSVP here Limited seats. * Please do not hesitate to RSVP on the waiting list if the RSVP is full as some guests "un-RSVP" as the event gets closer.

Speaker Detail:

Jaydeep got his Bachelor of Pharmacy (B. Pharm) from Pune University, India, and a Master of Science (Pharmacology) from National institute of Pharmaceutical Education and Research (NIPER). He graduated from Temple University in 2018 with a Ph.D. in Pharmacokinetics and Drug Metabolism where he combined experimental enzyme kinetic and modelling and simulation to improve drug-drug interaction prediction. Jaydeep is currently a Scientist in the Pharmacokinetics and Drug Metabolism at Amgen, Cambridge, where his research focuses on the application in-vitro tools and modeling & simulation to characterize kinetics of drug metabolizing enzymes and transporters with the goal of improving the prediction of human pharmacokinetics and drug-drug interaction at all stages of drug discovery and development.


Time-dependent inactivation (TDI) of CYPs is a leading cause of clinical drug-drug interactions (DDIs). We compared the current practice for TDI analysis (replot method) with a modelling approach incorporating multiple binding kinetics, quasi-irreversible inactivation, sequential metabolism, inhibitor depletion, and membrane partitioning (numerical method). Inactivation parameters (KI and kinact) from both methods were used to predict 77 clinically observed DDIs. The numerical method outperformed the standard replot approach 81% of the time.


About Boston QSP

Boston QSP is a 501(c)(3) non-profit organization whose mission is to foster the sharing of QSP knowledge, challenges, solutions, and opportunities to advance the field​ as an interdisciplinary community in Boston.

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