Understanding and predicting dose dependent and idiosyncratic events

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Special thanks to the DILIsim Partners DILIsym ® And Cindy Ashfari <strong>and</strong> Ryan Morgan - Amgen 50
Thanks UNC Institute for Drug Safety Sciences Leaders DILIsim Team <strong>and</strong> SAB Mouse Genetics: Merrie Mosedale Human Genetics: Tom Urban Organotypic liver cultures: Ed LeCluyse Cellular Imagine Core: Joe Trask UNC –Hamner Organ Toxicity Biomarker Core: Rachel Church UNC-Hamner Metabolomics Core- Jeff MacDonald UNC Chapel Hill Kim Brouwer U. of Liverpool Kevin Park Dean Naisbitt Daniel Antoine Munir Pirmohamed Qualyst Ken Brouwer 51
Page 1 and 2: ® Modeling Hepatoxicity: Understan
Page 3 and 4: Outline of Talk 1). Problem of live
Page 5 and 6: Posted: 5:17 p.m. Friday, Nov. 4, 2
Page 7 and 8: UNC Institute for Drug Safety Scien
Page 9 and 10: Approach 1). Build mechanistic “m
Page 11 and 12: Outline of Talk 1). Problem of drug
Page 13 and 14: Drugs Can Inhibit Bile Acid Transpo
Page 15 and 16: AMG 009 No evidence of liver injury
Page 17 and 18: Modeling in vitro transporter inhib
Page 19 and 20: DILIsym ® Predicts Dose-Dependent
Page 21 and 22: AMG 853 • AMG 853 was the backup
Page 23 and 24: Why mechanism of transport inhibiti
Page 25: Posted: 5:17 p.m. Friday, Nov. 4, 2
Page 28 and 29: Two issues with solithromycin: - Im
Page 30 and 31: DILIsym ® Mechanism-Based Modeling
Page 32 and 33: Contribution to Predicted ALT eleva
Page 34 and 35: Contribution to Predicted ALT eleva
Page 36 and 37: FDA panel narrowly backs Cempra ant
Page 38 and 39: Multiple Steps Involved in Idiosync
Page 40 and 41: Multiple Steps Involved in Idiosync
Page 42 and 43: Summary New mechanistic biomarkers
Page 44 and 45: DILIsym ® Mechanism-Based Modeling
Page 46 and 47: Confidence in Predictions Depends o
Page 48 and 49: Conclusion Quantitative Systems Pha
Page 52 and 53: Back up

Understanding and predicting dose dependent and idiosyncratic events

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