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| About Tony Hopfinger (University of New Mexico College of Pharmacy) |
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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 265 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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Informatics-Based to Structure-Based ADME/Tox Modeling
Anton J. Hopfinger, College of Pharmacy, University of New Mexico, MSC 09 5360, Albuquerque, NM 87131-0001, USA
The modeling of an ADME/Tox endpoint is highly dependent upon the complexity of the molecular mechanism involved. In cases where the molecular mechanism is complex, and/or pharmacological understanding is quite limited, an empirical informatics approach to develop predictive models is the preferred methodology to apply. We have developed a set of universal descriptors, called 4D-fingerprints, which capture the three-dimensional size, shape, chemical composition, reactive state and molecular flexibility of a molecule for informatics type ADME/Tox modeling. These descriptors have been applied to skin sensitization and eye irritation. For ADME/Tox endpoints where cellular membrane permeation and diffusion are involved, a pseudo structure-based design approach called membrane-interaction (MI-) QSAR analysis can be applied. Here descriptors derived from the simulation of an organic molecule passing through a phospholipid membrane assembly are used with intramolecular descriptors derived from the organic molecule to build MI-QSAR models. MI-QSAR simulation modeling has revealed that some organic compounds pass directly through the membrane and, presumably, into the interior of a cell, while other organic molecules use the membrane bilayer as a two-dimensional ‘sea', hopping from cell membrane to cell membrane in order to cross tissue composed of the cells. We will discuss the MI-QSAR modeling of blood-brain barrier penetration by organic compounds.
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