Anna Kondic | VP, Pharmacometrics; Translational Medicine and Clinical Pharmacology
Bristol Myers Squibb
Dr. Anna Kondic currently heads the Pharmacometrics group at BMS; this group includes mechanistic modeling and clinical pharmacometrics, data science and noncompartmental analysis and has dedicated efforts to create workflow in ML/AI. Anna has worked as a quantitative scientist in various therapeutic areas and companies over the last 25 years and has extensive experience in both mechanistic and empirical model development across the whole spectrum of R&D, as well as reimbursement. She holds a PhD in mathematics from Duke University and MBA from Stern School of Business at New York University. Anna is particularly passionate about the use of quantitative methods to make personalized medicine a reality, especially for cancer patients.
Anna is a competitive master artistic swimmer, participating in both national and international meets. She is particularly proud of having been the captain for the Merck Rhode Island team for Swim Across America in 2018. The charity swim that year raised more than 700 000 for cancer research at the Women and Infants Hospital in Providence, RI.
Appearances:
BioTechX USA Day 1 @ 14:50
Derisking Decision Making by Incorporating Early Economic Evaluation for Pharmaceutical Assets
Effective drug development today requires addressing both clinical efficacy, safety, robust patient characterization but economic viability to ensure successful patient access amid rising healthcare costs and complex regulations. Integrating quantitative approaches such as pharmacometrics, model-based meta analysis with health economics allows pharmaceutical developers to anticipate and mitigate risks related to clinical trial design and reimbursement challenges. In this talk, three different examples will be provided for how this integration can happen. In two of them the integrated analysis led to positive changes in the development program. The third one provides a framework that should be readily implementable in every single biopharmaceutical organization. All examples highlight the 1) critical importance of the cross-functional collaboration, 2) the need to have clearly stated assumptions in the models and 3) the need to conduct these evaluations prior to Phase 3 trials.
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