Clinical interpretation of hepatic safety biomarkers Michael Merz Klinik für Klinische Pharmakologie und Toxikologie UniversitätsSpital Zürich [email protected]

27th AGAH Annual Meeting April 26-27, Munich Outline

• Drug-induced injury (DILI): background and challenges

• DILI assessment: standard approaches and FDA’s eDISH concept

• Suggested improvements/expansions to eDISH

• Case studies

Clinical interpretation of hepatic safety biomarkers – M Merz – AGAH 2018 Page 2 Drug-induced liver injury (DILI) Major threat to patients, substantial burden for drug development

W i t h d r a w a l s

1959 1970 1982 1985 1996 2007 Iproniazid Ibufenac Benoxaprofen Perhexiline Alpidem Lumiracoxib Ticrynafen 1962 1967 1984 Thalidomide Oxyphenisatin Methaqualon 1997 2006 Tolcapone Ximelagatran Reasons for withdrawals Tolrestat 1991 2005 Triazolam 1998 Pemoline Bromfenac Terfenadine 2004 Docetaxel Nefazodone 2000 Rofecoxib Tolcapone Nevirapine Amiodarone Troglitazone Adefovir Naltrexone 2003 Tolvaptan Nefazodone Methotrexate BosentanIdarubicin Alosetron Ketoconazole Felbamate Cisapride 2001 Isoniazid Gemtuzumab Ambrisentan Trovafloxacin Pemoline Epirubicin Cerivastatin Flutamide Dantrolene Drug Info J 2001; 35:293 «Pre-Hy’s Law» «Post-Hy’s Law» • Leading cause of acute liver failure in the US • Substantially reduces treatment options for patients • 3% fatal outcome, 5% need for transplantation • Significantly contributes to attrition in development • Most frequent reason for drug withdrawals • Major challenge: lack of suitable biomarkers

Clinical interpretation of hepatic safety biomarkers – M Merz – AGAH 2018 Page 3 Hy’s law A short introduction Definition 1. The drug causes hepatocellular injury, generally shown by a higher incidence of 3-fold or greater elevations above the ULN of ALT or AST than the (nonhepatotoxic) control drug or placebo 2. Among trial subjects showing such AT elevations, often with ATs much greater than 3xULN, one or more also show elevation of serum TBL to >2xULN, without initial findings of cholestasis (elevated serum ALP) 3. No other reason can be found to explain the combination of increased AT and TBL, such as viral hepatitis A, B, or C; preexisting or acute liver disease; or another drug capable of causing the observed injury

“Finding one Hy’s Law case in the clinical trial database is worrisome; finding two is considered highly predictive that the drug has the potential to cause severe DILI when given to a larger population.”

Clinical interpretation of hepatic safety biomarkers – M Merz – AGAH 2018 Page 4 Tables vs graphics FDA’s eDISH (Evaluation of Drug Induced Serious ) concept

Study 1 Study 2 Pooled data Active Control Active Active

Clinical interpretation of hepatic safety biomarkers – M Merz – AGAH 2018 Page 5 Static vs interactive graphs Drilldown from helicopter to single patient view: from eDISH to...

...time profiles

...narratives

• Straightforward assessment of clinical relevance

• Exclusion of alternative explanations

Clinical interpretation of hepatic safety biomarkers – M Merz – AGAH 2018 Page 6 Improvements (1): Splitting treatments by Trellis panels

eDISH plot by pooled active vs control treatment TBIL [x ULN] [x TBIL

ALT [x ULN] • Potential Hy‘s law cases spotted easily • Only limited assessment of relevance of individual cases feasible

Clinical interpretation of hepatic safety biomarkers – M Merz – AGAH 2018 Page 7 Improvements (2): Adding sequence, time interval, and ALP

Color by peak sequence, size by 1/time interval, shape by R flag TBIL [x ULN] [x TBIL

ALT [x ULN] • Modifications take into account sequence of events, time interval, and pathology • Specifically useful with larger numbers of potential Hy‘s law cases (oncology, hepatitis)

Clinical interpretation of hepatic safety biomarkers – M Merz – AGAH 2018 Page 8 Improvements (3): bivariate NR, multiples of baseline Accounting for correlated variables and baseline differences

• Definition of normal range accounting for bivariate distribution

• mDISH: using change from baseline instead of multiples of ULN

Clinical interpretation of hepatic safety biomarkers – M Merz – AGAH 2018 Page 9 Extension: defining outlier thresholds by patient population GSK example: cancer patients with and without liver metastases Without liver mets With liver mets All patients

Without liver mets

• TBIL 6.5 x bsl, ALT 6.9 x bls irrespective of liver mets • TBIL 7.0 x bsl, ALT 6.2 x bls for patients with liver mets

Clinical interpretation of hepatic safety biomarkers – M Merz – AGAH 2018 Page 10 eDISH vs mDISH in practice

Color coding by gender

]

bsl

TBIL [xTBIL TBIL [x ULN] [xTBIL

ALT [x ULN] ALT [x bsl]

• Using multiples of baseline reduces false positives

• mDISH accounts for different baselines across different patient populations

Clinical interpretation of hepatic safety biomarkers – M Merz – AGAH 2018 Page 11 Change from baseline beyond mDISH Color coding by parameter

• Dose-and time-dependent effect on ALT levels, no apparent effect on bilirubin levels • Easily interpretable integration of dose and time information across variables

Clinical interpretation of hepatic safety biomarkers – M Merz – AGAH 2018 Page 12 Liver test panel profiles over time by patient Treatment end indicated by vertical red line

• Parallel ALT and AST peaks, ALT more pronounced • No apparent effect on bilirubin levels • Signs of adaptation: reversible ALT peaks despite continued treatment

Clinical interpretation of hepatic safety biomarkers – M Merz – AGAH 2018 Page 13 Drill-down to individual patient profiles (ex 1) Synoptic plot of ALT, comeds, and AEs over time

• Acetaminophen intake prior to ALT peak

Clinical interpretation of hepatic safety biomarkers – M Merz – AGAH 2018 Page 14 Drill-down to individual patient profiles (ex 2) Synoptic plot of ALT, comeds, and AEs over time

• Headache prior to ALT peaks: acetaminophen intake?

Clinical interpretation of hepatic safety biomarkers – M Merz – AGAH 2018 Page 15 Expanding dataspace: IMI SAFE-T’s new liver safety biomarkers Supporting early detection, prognosis, and mechanistic understanding

Nine new liver safety biomarkers Marker Application supported by EMA and/or FDA for Total HMGB1 Mechanism (necrosis), prognosis exploratory use in clinical drug Hyperacetylated HMGB1 Mechanism (immune activation), prognosis development: Osteopontin Prognosis Total Keratin 18 Mechanism (necrosis), prognosis Caspase-cleaved keratin 18 Mechanism (apoptosis), prognosis MCSFR1 Mechanism (immune activation), prognosis miR-122 Detection, mechanism (hepatocyte leakage) GLDH Detection, mechanism (mitochondrial injury) SDH Detection

• To account for rich, multivariate data, proper visualization and analysis are needed

Clinical interpretation of hepatic safety biomarkers – M Merz – AGAH 2018 Page 16 Conclusions

• DILI is a major threat to patients, and a substantial burden for drug development o Standard liver safety biomarkers have suboptimal specificity, sensitivity, predictive, and prognostic value

• To make sure as much clinically relevant information as possible is gained from standard markers, application of a systematic data exploration workflow is helpful. o Use of graphics for liver safety assessment has been pioneered since 2008 by FDA’s eDISH concept

• A more comprehensive systematic workflow using e.g. SpotfireTM, including eDISH and modified eDISH (mDISH) plots, ensures optimization of liver safety assessment

• The workflow o Can be easily adapted to specific project needs, o Allows proactive assessment of liver safety profiles, o Mirrors the regulatory review process, and o Facilitates anticipation of regulatory concerns & questions

• A crucial requirement is accounting for baseline abnormalities, and understanding of between- and within-subject variability across key patient populations

Clinical interpretation of hepatic safety biomarkers – M Merz – AGAH 2018 Page 17