2 nd International Workshop on NASH Biomarkers, Washington DC, May 5-6, 2017
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1 Hepatic Proton Density Fat Fraction Correlates With Histologic Measures of Steatosis and Is Responsive to Changes in Those Measures in a Multi-center Nonalcoholic Steatohepatitis Clinical Trial Michael S. Middleton, 1 Eric J. Lawitz, 2 Saumya Jayakumar, 3 Parvez Mantry, 4 Stephen Caldwell, 5 Anna Mae Diehl, 6 Matt Peach, 7 Ren Xu, 7 Guang Cheng, 7 C. Stephen Djedjos, 7 Robert P. Myers, 7 G. Mani Subramanian, 7 Zachary Goodman, 8 Michael Charlton, 9 Nezam H. Afdhal, 1 Claude B. Sirlin, 1 Rohit Loomba 1 1 UC San Diego, La Jolla, CA; 2 Texas Liver Institute, UT Health, San Antonio, TX; 3 University of Calgary, Alberta, Canada; 4 The Liver Institute at Methodist Dallas, TX; 5 University of Virginia, Charlottesville; 6 Duke Clinical Research Institute, Durham, NC; 7 Gilead Sciences, Inc., Foster City, CA; 8 Inova Fairfax Hospital, Falls Church, VA; 9 Intermountain Medical Center, Salt Lake City, UT; 1 Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 2 nd International Workshop on NASH Biomarkers, Washington DC, May 5-6, 217
2 Disclosures (Dr. M. Middleton) Stockholder: General Electric, Pfizer Consultation or contracted laboratory services through UCSD: Alexion, AstraZeneca, Biomedical Systems, Bristol-Myers Squibb, Celgene, Galmed, Genentech, General Electric, Genzyme, Gilead, Guerbet, Icon, Intercept, Janssen, Keosys, NuSirt, Perspectum, Pfizer, Profil, Sanofi, Shire, Siemens, Synageva, Takeda, VirtualScopics 2
3 Introduction Proton density fat fraction (PDFF) estimated by MRI is a noninvasive biomarker of hepatic steatosis 1 MRI-PDFF correlates with histologic grade of hepatic steatosis 2 3% relative decrease in PDFF has been associated with a histologic response in NASH 2,3 Selonsertib (SEL; formerly GS-4997) is a selective inhibitor of apoptosis signal-regulating kinase 1 (ASK1) being studied for the treatment of NASH (NCT ) 1. Dulai PS, et al. J Hepatol 216; 65: Patel J, et al. Ther Adv Gastroenterol 216; 9: Lawitz, E, et al. EASL 217; GS-9. 3
4 Objectives To examine correlations between hepatic MRI-PDFF and histologic measures of steatosis To assess associations between changes in hepatic MRI-PDFF, liver histology, and biochemical measures 4
5 Study Design 1 Week n=3 n=32 n=1 SEL 6 mg po qd ± SIM 125 mg sc qwk SEL 18 mg po qd ± SIM 125 mg sc qwk SIM 125 mg sc qwk MRI-PDFF, MRE Liver Biopsy Key inclusion criteria 1 Biopsy-proven NASH with NAS 5 ( 1 point for steatosis, lobular inflammation, hepatocellular ballooning) F2-F3 fibrosis Treatment groups combined for this analysis 2 NAS - NAFLD Activity Score; PDFF - proton density fat fraction; MRE - magnetic resonance elastography; SEL - Selonsertib; SIM - Simtuzumab 1. Loomba R, et al. AASLD 216; LB Harrison S, et al. EASL 217; PS-94 5
6 Assessment Methods Imaging MRI-PDFF Hepatic steatosis Colocalized regions of interest (9 Couinaud segments) MRE Liver stiffness 2D, 6 Hz Standardized and centrally read (UC San Diego) NAS Histology Fibrosis according to NASH CRN system Morphometry for hepatic fat and collagen Centrally read (Dr Z. Goodman) 6
7 Study Endpoints Imaging Responses MRI-PDFF 3% reduction in MRI-estimated PDFF 1,2 MRE 15% reduction in MRE-estimated liver-stiffness 3 Histologic Responses NAS Steatosis Lobular inflammation Hepatocellular ballooning Fibrosis 2-point reduction 1-grade reduction 1-stage reduction Serum Markers Cell death CK18 (M3 and M65) Metabolic Glucose, HbA1c, cholesterol PDFF - proton density fat fraction; MRE - magnetic resonance elastoigraphy; NAS - NAFLD Activity Score; CK - cytokeratin 1. Lawitz E, et al. EASL 217; GS-9; 2. Patel J, et al. Ther Adv Gastroenterol 216; 9:692-71; 3. Loomba R, et al. EASL 217; SAT
8 Statistical Analyses Correlations between MRI-PDFF and other variables assessed using Spearman correlation coefficients (r s ) Relative reductions from baseline in MRI-PDFF with histologic variables of steatosis, lobular inflammation, ballooning, and NAS using Kruskal-Wallis tests Changes in morphometric fat and serum markers compared between MRI-PDFF responders and nonresponders using Kruskal-Wallis tests 8
9 Results Demographics and Baseline Characteristics by MRI-PDFF Response Demographics Metabolic Liver Biochemistry Cell Death Markers <3% Reduction in PDFF n=53 3% Reduction in PDFF n=12 Total n=65 Age, y 55 (47, 61) 59 (48, 63) 56 (47, 62) Female, n (%) 37 (7) 9 (75) 46 (71) White, n (%) 47 (89) 12 (1) 59 (91) BMI, kg/m (29.5, 37.3) 31.6 (27., 35.9) 32.9 (29.4, 37.3) Diabetes, n (%) 38 (72) 7 (58) 45 (69) Glucose, mg/dl 115 (97, 149) 112 (99, 154) 115 (97, 149) Cholesterol, mg/dl 188 (166, 21) 191 (168, 21) 188 (166, 21) HbA1c, % 6.6 (5.8, 7.9) 6. (5.6, 7.7) 6.4 (5.8, 7.9) ALT, U/L 58 (47, 88) 54 (34, 89) 58 (47, 88) GGT, U/L 57 (35, 85) 46 (39, 59) 54 (37, 8) CK18 (M3), U/L 547 (311, 85) 428 (23, 897) 511 (267, 863) CK18 (M65), U/L 88 (447, 1465) 618 (222, 1319) 738 (37, 1379) BMI - body mass index; ALT - alanine aminotransferase; GGT - gamma-glutamyltransferase; CK - cytokeratin 9
10 Baseline Histology and Imaging by MRI-PDFF Response <3% Reduction in PDFF n=53 3% Reduction in PDFF n=12 Total n=65 F3 (vs F2) fibrosis, n (%) 35 (66) 9 (75) 44 (68) NAS 7 8, n (%) 9 (17) 3 (25) 12 (19) Histology Steatosis (Grades 2 3), n (%) 17 (32) 4 (33) 21 (32) Lobular inflammation (Grade 3), n (%) Hepatocellular ballooning (Grade 2), n (%) 37 (7) 7 (58) 44 (68) 46 (87) 1 (83) 56 (86) Morphometric hepatic fat, % 17.8 (9.8, 21.3) 18.4 (11.8, 21.4) 17.8 (1., 21.3) Imaging MRI-PDFF, % 16.5 (1.9, 22.) 19.2 (11., 22.4) 16.6 (1.9, 22.) MRE, kpa 3.7 (3.1, 4.6) 4.1 (3., 4.7) 3.7 (3.1, 4.6) NAS - NAFLD activity score; PDFF - proton density fat fraction; MRE - magnetic resonance elastography 1
11 Association Between MRI-PDFF and Percent Morphometric Hepatic Fat MRI-PDFF, % 4 Week 24 R=.87, p < Baseline R=.61, p < Morphometric Hepatic Fat, % MRI-PDFF was significantly correlated with percent morphometric hepatic fat at Baseline and Week 24 11
12 MRI-PDFF (%) Association Between MRI-PDFF, and Steatosis and NAS Steatosis Grade NAS Baseline Week 24 Baseline Week 24 4 r s =.57 p<.1 4 r s =.59 p<.1 4 r s =.22 p=.7 4 r s =.44 p< n= MRI-PDFF associated with histologic grade of steatosis and NAS at Baseline and at Week 24 No associations were observed with lobular inflammation or ballooning 12
13 Relative Change in MRI-PDFF at Week 24, (%) Changes in MRI-PDFF in Histologic Nonresponders vs. Responders Histologic nonresponder Histologic responder* NAS p=.3 Steatosis p=.3 Lobular Inflammation p= n=53 n= n=47 n=18 n=49 n=16 Patients with improvements in NAS, steatosis, and lobular inflammation had greater reductions in MRI-PDFF *NAS response defined as 2-point reduction; steatosis and lobular inflammation responses defined as 1-grade reduction. 13
14 Median % Change in Biomarker at Week 24 Associations Between Changes in Biomarkers and MRI- PDFF Response <3% PDFF reduction 3% PDFF reduction 12 Percent Morphometric Hepatic Fat ALT AST GGT CK18 (M3) CK18 (M65) Glucose Cholesterol p <.1 p <.1 p=.3 p=.2 p=.8 p=.25 p=.2 p=.4 Correlation with % Change in PDFF at Week 24, r s (p-value) (<.1) (<.1) (<.1) (<.1) (<.1) (<.1) (<.1) (.3) PDFF response at Week 24 was associated with reductions in hepatic fat content, liver biochemistry, CK18, glucose, and cholesterol ALT - alanine aminotransferase; AST - aspartate aminotransferase; GGT - gamma-glutamyltransferase; CK - cytokeratin 14
15 Conclusions Hepatic steatosis estimated by MRI-PDFF was correlated with histologic measures of steatosis, including steatosis grade, NAS, and morphometric fat content Relative reductions in MRI-PDFF were associated with improvements in steatosis, NAS, lobular inflammation, and relevant serum markers These data help further confirm and validate MRI-PDFF as a biomarker of hepatic steatosis and support its utility as a noninvasive endpoint for evaluation of NASH treatment 15
16 Acknowledgments We extend our thanks to the patients, their families, and all participating investigators. Canada: M. Elkashab, S. Jayakumar US: M. Abdelmalek, H. Arnold, K. Bambha, J. Behari, S. Caldwell, N. Chalasani, M. Charlton, J. Cooper, B. Freilich, R. Ghalib, B. Hameed, E. Lawitz, K. Kowdley, M. Lai, R. Loomba, P. Mantry, M. Rinella, A. Sanyal, M. Shiffman, P. Thuluvath, D. Torres This study was funded by Gilead Sciences, Inc. 16
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