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- Berg, E
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| About Katya Tsaioun (Apredica) |
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Dr. Tsaioun has 15 years’ experience in the pharmaceutical industry, the last 6 years building and managing early ADME programs for oncology, infectious disease, metabolic, and cardiovascular disease small molecule and peptide therapeutics programs. Before founding Apredica in 2005 she worked at Enzytech, Mitotix/GPC Biotech, Surface Logix, and NitroMed. Dr. Tsaioun received her doctorate in Biochemistry from Tufts University and post-doctoral training in neurochemistry at Harvard. She currently serves on the Scientific Advisory Boards of the Alzheimer’s Drug Discovery Foundation, the Institute for the Study of Aging, and the Retts Syndrome Foundation. She is a frequent speaker at international conferences on ADME and predictive toxicology.
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In vitro Models that Predict Major Known Mechanisms of Human Toxicity. Validation, Case Studies and Path Forward
Katya Tsaioun, Ph.D. (President and CSO, Apredica)
Idiosyncratic drug reactions (IDRs) are a major reason for withdrawal of drugs from the market. IDRs occur in 1/1000-1/10000 patients, making them impossible to identify in preclinical or even Phase III clinical studies. Thus, drug failures due to IDRs occur after the drugs are on the market, at great expense to the pharmaceutical industry. While determining the underlying causes of IDRs is an active area of investigation, formation of reactive metabolites and mitochondrial toxicity are hypothesized to play an important role in the development of unexpected toxicities of many compounds. A battery of newly developed assays have been developed to detect major mechanisms of toxicity early in drug discovery, before a clinical candidate is nominated. This early toxicity information allows drug-discovery organizations to reduce the risk of IDRs later in the drug development process. Validation of the predictive toxicity battery will be presented. Case studies demonstrating the application of the battery in drug-discovery programs and the relative positioning of these assays in ADMET lead optimization and clinical candidate selection programs will be presented. Clustering of compounds by toxicity mechanism and the application for building predictive in silico models will be discussed.
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