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| About Heidi Einolf (Novartis) |
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Heidi J. Einolf, Ph.D. received her B.S. in Biology from Allegheny College, Meadville PA in 1991 and in 1996, received her Ph.D. in Biochemistry and Molecular Biology in the Department of Medicinal Chemistry and Pharmacognosy, Purdue University, West Lafayette IN. Her graduate work focused on the roles of cytochrome P450s in the metabolic activation and DNA-binding of chemical carcinogens. Following graduate school she pursued a postdoctoral research fellowship, under the direction of Dr. F. Peter Guengerich, in the Department of Biochemistry and Center in Molecular Toxicology, Vanderbilt University, Nashville, TN. Her postdoctoral work, focused on the catalytic mechanisms of DNA polymerase fidelity at DNA lesions, such as 8 oxo 7,8 dihydroguanosine, by steady state and pre steady state rapid quench kinetic analyses. Since completion of her postdoctoral fellowship in 2000 to the present, she is head of a laboratory in the Drug Metabolism and Pharmacokinetics (DMPK) Department at Novartis Pharmaceuticals Corporation, East Hanover, NJ. In 2006, she became US Head of the Drug-Drug Interactions Group within DMPK. This group is involved primarily in the assessment of drug-drug interaction potential of new molecular entities pertaining to the inhibition and induction of drug metabolizing enzymes and transporters and the in vitro/in vivo extrapolation of drug-drug interaction potential. In addition to her roles at Novartis, she served as the Secretary/Treasurer of the Society of Toxicology Molecular Biology Specialty Section (2005-2007 term), actively participates in PhRMA (Pharmaceutical Research and Manufacturers of America) initiatives sponsored by the Drug Metabolism Technical Group (e.g. Drug Interaction White paper in 2003, P450 Induction and Mechanism-Based Inhibition Work Groups) and is an Adjunct Assistant Professor in the Ernest Mario School of Pharmacy, Rutgers University.
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Predictions of Metabolic Drug-Drug Interactions
Heidi Einolf, Novartis
Drug-drug interactions (DDI) involving cytochrome P450 (CYP) enzymes remain an important factor in pharmaceutical drug development. Increased understanding of the potential clinical drug interaction magnitude caused by compounds deemed as CYP inhibitors (reversible or irreversible) is imperative to avoid compounds with potential dangerous DDI with likely co-medications and to have a competitive safety profile. In later development, this information is important for the strategic design of clinical DDI trials (i.e. rank-ordering of specific CYP inhibition studies and anticipation of actual DDI risk). Many reported prediction approaches for reversible (and irreversible) CYP inhibition focus on predictions of mean changes of the affected drug (or substrate) exposure at steady-state. These mathematical prediction models, expressed with varying levels of complexity, incorporate the relationship of a single in vivo inhibitor concentration [I] and the potency of the CYP inhibition determined from in vitro data. The simplest of the prediction models, although generally over-predictive for CYP reversible inhibition, is the ‘[I]/Ki’ approach. The pragmatic use of this model, using Cmax, total as [I], is currently the recommended approach by the FDA to evaluate whether a clinical drug interaction study might be warranted. However, models that incorporate the fraction of the affected drug metabolized by the inhibited enzyme (fm,CYP), first-pass intestinal availability for CYP3A substrates, and protein binding, in more mechanistic prediction models (herein termed the ‘Mechanistic-Static Model’ or MSM), have proven to be more predictive of actual DDI magnitude than the ‘[I]/Ki’ approach. Both these approaches are, however, limiting as they only consider mean finite inhibitor concentrations in DDI assessment. More physiologically-based drug interaction prediction models account for the time-varying concentration of inhibitors and are currently implemented in specialized platforms such as the Simcyp Population-Based ADME Simulator (Simcyp Ltd, Sheffield UK), herein termed the ‘Mechanistic-Dynamic Model’ or MDM. These types of models have the capability of being more informative for drug interaction assessments as they predict not only the mean, but also a range and frequency distribution of clearance or drug interaction magnitude in a population. The models implemented within Simcyp consider variables such as CYP expression level and genetic polymorphisms, first-pass intestinal metabolism, physiological, and demographic information in the generation of the virtual populations using a Monte Carlo approach. The program can simulate drug concentrations-time profiles of substrates and inhibitors and, therefore, has the potential to be more predictive than less physiologically-based models. In this talk, a comparison of the three approaches (‘[I]/Ki’, MSM, and MDM) to predict actual clinical DDI magnitude will be presented, including specific data required for best predictions using these different approaches. In addition, the importance of predicting the range and frequency of DDI magnitude using the more physiologically-based drug interaction prediction models will be emphasized.
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