Screening
build knowledge of toxicity mechanisms. Indeed, data mining of well-annotated bioactivity databas- es for drug associations with adverse effects has been useful for discovering novel mechanisms of toxicity11,15-17. Analysis of phenotypic profiles from a standardised panel of human primary cell- based assays has revealed a novel mechanism involving autophagy in vascular endothelial cells that contributes to thrombosis-related side-effects in humans11, and has also successfully uncovered mechanisms underlying skin hypersensitivity in non-human primates17. As the number of toxicity mechanisms that are identified increases, and either phenotypic or target-based assays for these become available, it will be possible to rely more and more on in vitro tests for predicting safety risks. For drug pharmacokinetics, it is already stan- dard practice to use a combination of in vitro and in silico models to predict human exposures. As the ability to predict human outcomes from these approaches improves, the dependence on animal testing will decrease while programme success rates increase.
For these approaches to be incorporated into reg- ulatory decision-making, given the primary remit of the FDA, EMA and other drug approval agencies to protect patient safety, significant investment in data-driven, performance-based validation studies will be needed. If we are to move the industry for- ward, open shared data on publicly available drugs will be needed, as the quality of predictive models depends on the amount of data that is used to develop the model. There is tremendous potential value from incorporating human-based in vitro data, whether phenotypic or target-based, in regu- latory assessments. These data address human-spe- cific mechanisms and so have the potential for bet- ter predictivity, and their use supports the goal of promoting non-animal-based methods. The addi- tion of phenotypic screening for improving the accuracy of drug safety assessments contributes to the modernisation of regulatory science, bringing more efficacious and safer drugs to market at lower cost. While phenotypic screening today is already making an impact in pharmaceutical R&D, the greatest benefits may be yet to come.
DDW
Dr Ellen L. Berg is Chief Scientific Officer at DiscoverX (now a part of Eurofins Pharma Discovery Services) and leads the scientific direc- tion of the BioMAP® phenotypic profiling plat- form. Dr Berg was co-founder and CSO of BioSeek (acquired by DiscoverX) and prior to that led a research team developing biopharmaceuticals
Drug Discovery World Fall 2017
at Protein Design Labs (now AbbVie). She received her PhD from Northwestern University and was a postdoctoral fellow at Stanford University, where she was a fellow of the American Cancer Society and a Special Fellow of the Leukemia Society of America. Dr Berg has served in various positions with and is a Fellow of the Society of Laboratory Automation and Screening (SLAS). She is a board member of the American Society for Cellular and Computational Toxicology (ASCCT), and a mem- ber of the Society of Toxicology (SOT) and Inflammation Research Association organisations. Her research interests include human-based in vitro systems, chemical biology, drug and toxicity mechanisms of action, phenotypic drug discovery and predictive methods for human efficacy and safety outcomes. Dr Berg holds several patents in the field of inflammation, and has authored more than 80 publications.
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11 Berg, EL. Phenotypic chemical biology for predicting safety and efficacy. Drug Discov Today Technol. 2017. 23: p. 53-60. 12 Edwards, SW, Tan, YM, Villeneuve, DL, Meek, ME and McQueen, CA. Adverse Outcome Pathways-Organizing Toxicological Information to Improve Decision Making. J Pharmacol Exp Ther. 2016. 356(1): p. 170-81. 13 Haasen, D, Schopfer, U, Antczak, C, Guy, C, Fuchs, F and Selzer, P. How Phenotypic Screening Influenced Drug Discovery: Lessons from Five Years of Practice. Assay Drug Dev Technol. 2017. 15(6): p. 239-246. 14 Cheng, T, Hao, M, Takeda, T, Bryant, SH and Wang, Y. Large- Scale Prediction of Drug- Target Interaction: a Data- Centric Review. AAPS J. 2017. In press. 15 Ledford, H. Translational research: 4 ways to fix the clinical trial. Nature. 2011. 28;477(7366): p. 526-8. 16 Maciejewski, M, Lounkine, E, Whitebread, S, Farmer, P, DuMouchel, W, Shoichet, BK and Urban, L. Reverse translation of adverse event reports paves the way for de- risking preclinical off-targets. Elife. 2017. 6. pii: e25818. doi: 10.7554/eLife.25818. 17 Boland, MR, Jacunski, A, Lorberbaum, T, Romano, JD, Moskovitch, R and Tatonetti, NP. Systems biology approaches for identifying adverse drug reactions and elucidating their underlying biological mechanisms. Wiley Interdiscip Rev Syst Biol Med. 2016. 8(2):104-22. 18 Shah, F, Stepan, AF, O’Mahony, A, Velichko, S, Folias, AE, Houle, C, Shaffer, CL, Marcek, J, Whritenour, J, Stanton, R and Berg, EL. Mechanisms of Skin Toxicity Associated with Metabotropic Glutamate Receptor 5 Negative Allosteric Modulators. Cell Chem Biol. 2017. 24(7): p. 858-869.e5.
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