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| About Anton Hopfinger (University of New Mexico) |
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Anton [Tony] Hopfinger is currently Distinguished Research Professor of Pharmacy at the University of New Mexico, Professor Emeritus of Medicinal Chemistry at the University of Illinois and Founder and Chief Science Officer of The Chem21 Group, Incorporated. He is also the former Director of Medicinal Chemistry at G.D. Searle & Company [now part of Pfizer Pharmaceuticals], and has held the position of Professor of Macromolecular Science at Case Western Reserve University. His field of interest is computer-assisted molecular discovery, CAMD, with a current special interest in predictive ADME and toxicology. He has published over 270 research papers, including three books, has presented over 400 invited lectures throughout the world on CAMD and is a coauthor on ten patents. Dr. Hopfinger has also been active as an entrepreneur being the cofounder of five companies, and serving on the boards of seven other start-up companies. He is, or has been, a consultant to more than 45 biotechnology, chemical and pharmaceutical companies.
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Novel MI-QSAR Descriptors for Use in Modeling Membrane Transport Processes Such as Skin Penetration Enhancement
Anton Hopfinger, Distinguished Research Professor of Pharmacy, University of New Mexico
Membrane-interaction (MI) QSAR analysis is a structure-based design methodology combining unique intermolecular descriptors computed from molecular simulations with classic intramolecular QSAR descriptors to model chemically and structurally diverse compounds interacting with cellular membranes. The most recently developed MI-QSAR descriptor, the difference in the integrated cylindrical distribution functions over a phospholipid monolayer model, in and out of the presence of a monolayer penetrator, ÄÓh(r), greatly reduces the size and complexity of the MI-QSAR models as compared to corresponding classic intramolecular QSAR models. By way of example of demonstrating the utility of MI-QSAR analysis, as well as its descriptors including ÄÓh(r), for studying transport-related ADMET endpoints, two skin penetration enhancer data sets of 61 and 42 compounds, respectively, were investigated. These two data sets involve skin penetration enhancement of hydrocortisone and hydrocortisone acetate and the enhancers are generally similar in structure to lipids and surfactants. MI-QSAR models were constructed and compared to QSAR models constructed using only classic intramolecular QSAR descriptors. The MI-QSAR models are quite simple and compact in form as compared to the classic QSAR models. Good penetration enhancers are seen from the MI-QSAR models to make bigger ‘holes’ in the monolayer and are less aqueous soluble, so as to preferentially enter the monolayer, than are poor penetration enhancers. The skin penetration enhancer thus alters the structure and organization of the monolayer. This space and time alteration in the structure and dynamics of the membrane monolayer is captured by ÄÓh(r) and is simplistically referred to as ‘holes’ in the monolayer. The MI-QSAR models explain 70-80% of the variance in skin penetration enhancement across each of the two training sets, and are stable predictive models using accepted diagnostic measures of robustness and predictivity.
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