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Bill Massey is President of LITMUS Molecular Design, LLC. He has over 15 years experience in pharmaceuticals, spanning a variety of areas including pre-clinical research, clinical research, regulatory affairs, marketing, and business operations/planning. Bill has held senior management positions at Astra Merck, Quintiles CNS Therapeutics, Scirex Corporation, and AstraZeneca. He has actively managed the preparation and submission of numerous original INDs and product marketing applications, and has leadership experience in international drug development programs in the cardiovascular and CNS therapeutic areas. Bill is also a successful entrepreneur formerly being a Co-Founder and Managing Partner of Scientific Commercialization LLC, a respected consulting firm.
Bill received his Ph.D. from the Department of Pharmacology and Interdisciplinary Toxicology of the University of Arkansas for Medical Sciences. Prior to his industry career, Bill conducted basic pharmacological research in primates at the Pritzker School of Medicine of The University of Chicago. He has numerous publications in the areas of neuropharmacology and drug abuse liability assessment. Bill currently holds two adjunct faculty appointments in pharmacology and psychiatry, where he lectures on the pharmacology of antipsychotics and antidepressants, and on development of drugs for neurological and psychiatric diseases. LITMUS Molecular Design creates new drug candidates with desired characteristics using its proprietary technology Spectral Data Activity Relationship.
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An In Silico Approach to Rational Antipsychotic Drug Design using Spectral Data Activity Relationship
Bill Massey, LITMUS Molecular Design
Spectral Data Activity Relationship (SDAR) modeling is a computational method that uses the steric and electronic characteristics of small molecules (that determine their biological activity), as measured through spectral data, by which accurate models of a molecule’s biological activity can be created. SDAR modeling can be used to screen molecules for a particular activity (e.g., efficacy, toxicity) or to design novel drugs having a set of desired characteristics with a high degree of accuracy. The present program’s purpose was to use spectral modeling to create a series of atypical antipsychotic drug candidates predicted to have targeted characteristics that meet unmet medical needs in the treatment of schizophrenia. Spectral models were created using a random 10% cross-validation process and published human experimental data. Highly accurate spectral models were created for D2 (q210% = 0.96) and 5-HT2A (q210% = 0.99) receptor binding affinities, antipsychotic activity, agranulocytosis activity, agranulocytosis relative risk (q210% = 0.98), and hERG inhibition (q210% = 1.0). Data extracted from the spectral models were used to create 125 new atypical antipsychotic drug candidates. When screened against the spectral models, 83 drug candidates were predicted to have antipsychotic activity (confidence >80%), 21 of those 83 had the desired receptor binding profiles, and after toxicity prediction, 12 were selected for synthesis and further development. The drug development process through the lead selection stage usually takes years and hundreds of millions of dollars. Using spectral modeling and rational drug design principles, the entire process took 3 months. These time and cost savings realized through spectral modeling could have positive ramifications for the economics of the pharmaceutical industry.
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