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YOUNG INNOVATORS 2009 Exploratory Analysis of Possible Clinical and Pharmacogenetic Patient Covariates in the Exposure and Safety of Sorafenib Treatments in Various Types of Solid Tumors Lokesh Jain, Sukyung Woo, William L Dahut, Elise C Kohn, Shivaani Kummar, Robert Yarchoan, Giuseppe Giaccone, Jürgen Venitz, William D. Figg National Cancer Institute, Bethesda, MD Virginia Commonwealth University, Richmond, VA ABSTRACT • • Purpose: Sorafenib, a multikinase inhibitor, acts by inhibiting Ras/Raf and VEGFR2 kinases. High inter-patient variability is observed in its systemic exposure, clinical efficacy and toxicity. We investigated the factors contributing to variability in sorafenib exposure and treatment-associated toxicities by population PK analysis, exposuretoxicity and genotype-toxicity correlative studies. Methods: A total of 112 patients, enrolled in 5 phase I/II clinical trials, received oral doses of sorafenib 400/200 mg BID in 28-day cycles. 24-hour plasma concentration profiles were measured on Day 1 and (for selected patients) at steady-state. Genetic variation in metabolic enzymes (CYP3A4*1B, CYP3A5*3C, UGT1A9*3, UGT1A9*5) and drug target (VEGFR2 H472Q, VEGFR2 V297I) were assessed. Demographic, liver and kidney function variables were collected at baseline. A population PK model was developed using log-transformed concentrations with NONMEM VI using FOCEI. Model validation was performed by visual predictive check (VPC). Possible associations between sorafenib exposure and toxicity or genetic variation in target (VEGFR2) and toxicity were assessed, using the highest grades of treatment-related toxicities, including the hypertension, hand-foot skin reaction (HFSR), rash/desquamation, diarrhea and fatigue, by Chi-square tests and Kaplan Meier survival analysis. Young Innovators 2009 ABSTRACT • • Results: Sorafenib PK was adequately described by a one-compartmental model with enterohepatic circulation (EHC) and first-order elimination. Square wave function was used for modeling of gall-bladder emptying. Gastrointestinal absorption was described by a transit-compartment model (n=4). PK parameters were estimated as CL/F=8.05 L/h (between-subject variability (BSV) 19%; inter-occasion variability (IOV) 48%), V/F=217 L (BSV 68%) and mean absorption transit time=1.98 h. VPC showed that this structural model appropriately described the data. Frequency of rash for sorafenib single agent therapy and HFSR in patients receiving sorafenib in combination with bevacizumab appeared to be related with sorafenib exposures. Patients carrying the variant allele for VEGFR2 H472Q had higher incidences of grade ≥2 hypertension and HFSR compared to carriers of wild type allele (p<0.02). VEGFR2 V297I polymorphism was not associated with toxicity incidence. Conclusions: Implementation of EHC and transit-compartmental absorption model appropriately described the observed full PK profiles. Sorafenib exposures appear to be associated with treatment-related dermatological toxicities. H472Q polymorphism in VEGFR2 appears to be associated with increased incidence of hypertension and HFSR, independent of sorafenib exposures. Young Innovators 2009 OBJECTIVES • To characterize the pharmacokinetics of sorafenib by population pharmacokinetic modeling and to evaluate the effect of demographic, clinical and pharmacogenetic covariates on sorafenib exposure. • To evaluate the exposure-toxicity relationship. • To study the impact of VEGFR2 SNPs on frequency of treatment-associated toxicities in patients with solid tumors receiving sorafenib. Young Innovators 2009 INTRODUCTION • Sorafenib is an orally administered, cytostatic multi-kinase inhibitor. Mechanism of Action • prevents tumor cell proliferation by targeting the Raf kinase in Raf/MEK/ERK pathway and inhibits angiogenesis by blocking the receptor tyrosine kinases such as vascular endothelial growth factor receptor-2 (VEGFR 2)1. • indicated for the treatment of advanced renal cell carcinoma and unresectable hepatocellular carcinoma1. 1. Nexavar® (Sorafenib) tablet prescribing information (2007) Young Innovators 2009 INTRODUCTION • • • • ADME Metabolized primarily by hepatic CYP3A4 and UGT1A9; both parent drug and metabolites undergo biliary excretion1. Subject to GI solubility-limited absorption as evidenced by less than proportional increase in exposure (AUC) with escalating doses; a plateau is reached at 600 mg BID2. Associated with high between-subject variability in pharmacokinetics as observed in various phase I and phase II clinical studies2. Undergoes enterohepatic circulation (EHC), resulting in typical double peaks in the plasma concentration – time profiles from patients treated with sorafenib. 1. 2. Nexavar® (Sorafenib) tablet prescribing information (2007) Strumberg et al., Oncologist, 2007 Apr;12(4):426-37. Young Innovators 2009 INTRODUCTION • The common sorafenib treatment-associated toxicities are1: Toxicity Incidences Hypertension 30-40% Hand-foot skin reaction 20-30% Rash: desquamation 25-40% Diarrhea 35-45% Fatigue 30-40% 1. Nexavar® (Sorafenib) tablet prescribing information (2007) Young Innovators 2009 MATERIALS AND METHODS • Study Design Cancer Type Phase mCRPC Phase II NSCLC Course No. of patients Sample collection time (hr) ID SS C1D1 46 - 0, 0.25, 0.5, 1, 2, 4, 6, 8, 12 & 24 Phase II C1D1 & C1D15 18 17 0, 0.25, 0.5, 1, 2, 4, 6, 8, 12 & 24 ST Phase I C1D1 & C2D1 28 12 0, 0.25, 0.5, 1, 2, 4, 6, 8, 12 & 24 CR Phase II C1D1 18 -- 0, 1, 2, 4, 8, 12, 16 & 24 KS Phase I C1D7 - 2 0, 1, 2, 4, 8, 12, 16 & 24 ID: Initial doses, SS: Steady-state, mCRPC: metastatic castrate-resistant prostate cancer, C: Cycle, D: Day, NSCLC: Non-small cell lung cancer, ST: refractory solid tumors, CR: Colorectal cancer, KS: Kaposi’s sarcoma Young Innovators 2009 MATERIALS AND METHODS • Pharmacokinetic Analysis • Sample analysis : LC-MS/MS method with an LLOQ and LLOD of 5 and 0.2 ng/mL1 • Software : NONMEM v6.0 (FOCE INTER) • Statistical methods : IIV and IOV – exponential model Residual variability – proportional and additive model • Covariate analysis : Mixed step-wise forward addition (p<0.05) and step-wise backward elimination (p<0.001) • Model evaluation : Visual predictive check & Posterior predictive check 1Jain L et al, J Pharm Biomedical Analysis 2008 Jan; 46(2): 362-367. Young Innovators 2009 MATERIALS AND METHODS • Patient Characteristics (N=111) Variable Value Demographics Median (Range) or N (%) Age, years 63.9 (30-85) BSA, m2 1.9 (1.2-2.5) Weight, kg 81.4 (35-133) Gender (F/M) 34 (31%) / 77 (69%) Race (Caucasian/African-American/Others) Clinical 90(81%) / 12(11%) / 9(8%) Median (Range) Albumin, g/dL 3.6 (2.2-4.4) Total protein, g/dL 6.6 (4.6-8.0) Alakaline phosphatase, U/L 82 (34-414) Bilirubin total, mg/dL 0.6 (0.1-1.7) SGOT, U/L 26 (13-90) SGPT, U/L 21 (8-75) Creatinine clearance, mL/min Young Innovators 2009 95.3 (26-226) MATERIALS AND METHODS • Genetic Variation in Metabolic Enzymes and Drug Target Genetic Variants Genotype Frequencies N (%) N Allele Frequencies (proportion) Wt Het Var p q Metabolic enzymes CYP3A4*1B 108 89 (82.4) 10 (9.3) 9 (8.3) 0.87 0.13 CYP3A5*3C 108 8 (7.4) 17 (15.7) 83 (76.9) 0.15 0.85 UGT1A9*3 107 103 (96.3) 3 (2.8) 1 (0.9) 0.98 0.02 UGT1A9*5 107 107 (100) 0 (0) 0 (0) 1 0 VEGFR2 H472Q 106 66 (62.2) 35 (33.1) 5 (4.7) 0.79 0.21 VEGFR2 V297I 106 78 (73.6) 25 (23.6) 3 (2.8) 0.85 0.15 Drug Target Wt: wild-type, Het: heterozygous, Var: variant genotype p, q are allele frequencies as per Hardy Weinberg Equilibrium nomenclature Young Innovators 2009 MATERIALS AND METHODS • Exposure-Toxicity Relationship Analysis • Patients treated with only sorafenib, sorafenib + bevacizumab and sorafenib + cetuximab combination, were divided into four groups based on distribution quartiles of sorafenib systemic exposure (AUC). • Percent incidences of five common sorafenib treatment-associated toxicities, hypertension, rash/desquamation, hand-foot skin reaction (HFSR), diarrhea and fatigue, were compared among exposure quartiles using Chi-squares test. • Genotype-Toxicity Relationship Analysis • Association of VEGFR2 genotype with sorafenib treatment-associated toxicities was assessed by Fisher’s exact test. RESULTS • Representative Plasma Concentration-Time Profiles for Sorafenib 1st Dose 2nd Dose 1st Dose 2nd Dose Young Innovators 2009 Important characteristics: • Delayed absorption • Enterohepatic circulation RESULTS Key features: • GI transit compartments (N=4) • Enterohepatic circulation • Structural PK Model GI transit compartments Gut Absorption Compartment (A0) ka A1 ka A2 ka An-1 ka An ka Central compartment/ Plasma (Ac.c.) Fent Ehc* kEhc Ka Ke Kb kEhc Fent Ehc A0 An Ac.c. Ag.b. N ke (1-Fent) kb Gall bladder (A ) g.b. : first-order absorption rate constant : first-order elimination rate constant (=CL/V) : first-order rate constant for excretion of drug from central compartment to gallbladder : first-order rate constant for recirculation of drug from gallbladder to absorption compartments : fraction of dose undergoing entero-hepatic recirculation : square-wave function (on-off switch, controls emptying at regular intervals) : amount in absorption compartment : amount in nth G.I. transit compartment : amount in central compartment : amount in gall bladder : Number of G.I. transit compartment Young Innovators 2009 RESULTS • PK Model Equations dA0 k a A0 dt dAn1 k a ( An1 An2 ) where n= 2, 3,….N dt dAn k a ( An An1 ) Ehc k Ehc Ag .b. dt dAc.c. CL k a An Fent k b Ac.c. (1 Fent ) Ac.c. dt V dAg .b. Fent k b Ac.c. Ehc k Ehc Ag .b. dt (t DT ) 40 Ehc (t DT ) 40 (t ) where DT is the dosing time Young Innovators 2009 RESULTS • Goodness-of-fit Plots: model predictions are reasonably consistent with 2 4 6 8 10 8 6 4 2 6 0 2 4 DV 0 0 • • •• •• • •••••• • • • • •• • • •• ••• • •• • • • • •• •• • • •• •• • •• •• • • •• • • •• •• • ••• • • •• • ••••• •• •• • • •• ••• • •• • • • •• •• • • • •• ••••• •••••••• • • •••• ••••••• •••••••• • • •• ••• ••••• • ••• •••••••••••••••••••••••••••••••••••••••••••••••••••• • • • • •••• •••••••••••••••••••••••••••••••••••••••••••••••••••••• •••••••••••• ••• • • ••••••••••••••••••••••••••• ••• ••••• •• ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• •• • • ••••••••••••• • •• •• ••••••••••••••• • ••••••••••••••••••••••••••••••• • • • • • ••• • •• •••••••••••• ••••• • •• ••• •••••• •• ••••••••••• • • • • • • • • • • • ••• • • ••••• • • ••• • • • • •• •••••• • • •• • • ••• ••• • • •••• • •• • • • • • DV 8 10 measured concentrations 10 •• ••••••••••••• • • • • • •••••••••••••••••• •••••• ••••••• •• •••••••••••••••••••••••••••••••••••••••••• •• • • • • • • • • • • • ••••••••••••••••••••• •••• • • • ••••••••••••••••••••••••••••••••••••••••••••••••••••••••• • • • • • • • ••••••••••••••••••••••••• • ••••••••••••••••••••••••••••••••••••••••••••••• •• •••••••••••••••••••••••••••••••••••• •••• • • • ••••••••••••• •••••••••••••••••••••••• •••••••••••• ••• •• • •• ••••••••••• •• ••••• ••••• • • • • ••••• •• •• • • • • ••••• •• •• • • •••• ••••• •• ••• • • •••••• ••• • • • • • • • •••• •••• • •• •• • •• • • 0 2 4 8 10 IPRED 0 -2 IWRES IWRES -1 -3 -4 -2 CWRES CWRES 0 -2 -4 CWRES CWRES 0 2 2 1 4 4 2 PRED 6 0 5 10 TSLD 15 TSLD 20 0 5 10 15 TSLD TSLD 20 25 0 2 4 IPR E 6 IPRED 8 10 RESULTS • Model Evaluation – Visual Predictive Check (VPC) After initial doses At steady-state 0.060 Dose-normalized concentrations (ng/mL/mg) 0.050 0.050 0.040 0.040 0.030 0.030 0.020 0.020 0.010 0.010 0.000 0.000 -0.010 -0.010 0 4 8 12 16 20 24 0 4 8 12 Time (hr) Time (hr) Measured concentrations ( x ). Median and 90% prediction interval for dose-normalized model predicted ( ) and measured ( ) concentrations. Young Innovators 2009 16 20 24 RESULTS • Parameter Estimates from the Final Model Parameter CL/F (L/hr) V/F‡ (L) Mean absorption transit time* (hr) NONMEM estimate 8.05 217 1.98 KEhc (hr-1) Fent t′ Correlation CL/F–V/F Proportional residual error (%CV) Additive residual error (ng/mL) 0.998 0.50 6.66 0.77 51.4% 1 CL / Fi CL / F exp(CL / Fi CL / Fij ) ‡Baseline IIV (%CV) 18.5 68.7 †61.8 IOV (%CV) 47.8 V / Fi V / F (weighti / 81.5)1 exp(V / Fi ) body weight accounted for 4% of IIV in V/F *Mean absorption transit time = (number of transit compartments+1)/ ka = 5/2.53 = 1.98 †IIV estimated for k a t′ : time post-dose administration at which EHC starts RESULTS • Exposure – Toxicity Relationship Analysis Sorafenib single agent Sorafenib and bevacizumab combination 1st Q (AUC <=23.85 mg/L*h) 2nd Q (AUC >23.85 & <=27.72) 3rd Q (AUC >27.72 & <=31.79) 4th Q (AUC >31.79 & <=41.75) % patients with toxicity grade ≥2 1st Q (AUC <=43.15 mg/L*h) 2nd Q (AUC >43.15 & <=48.54) 3rd Q (AUC >48.54 & <=55.33) 4th Q (AUC >55.33 & <=99.26) p=0.004* 80 80 P=0.020* 60 60 40 40 20 20 0 0 Fatigue Rash HFSR Diarrhea HTN Young Innovators 2009 Fatigue Rash HFSR Diarrhea HTN RESULTS % patients with HTN grade ≥2 p=0.021* % patients with HFSR grade ≥2 • Genotype – Toxicity Relationship Analysis wt het+var 40 30 20 10 0 N: 15/71 19/45 H472Q 27/85 40 30 20 10 0 9/31 V297I VEGFR2 *Fisher’s wt het+var p=0.006* 14/71 20/45 24/85 10/31 H472Q V297I VEGFR2 exact test Young Innovators 2009 DISCUSSION & CONCLUSIONS • A mechanism-based population PK model for sorafenib in a diverse oncology population was developed, accounting for known disposition characteristics of sorafenib, such as delayed, solubility-limited absorption and enterohepatic circulation. • Baseline body weight was found to be a statistically significant covariate for volume of distribution, accounting for 4% of IIV. • None of the studied clinical (liver and kidney function) and demographic covariates were found to be clinically important. Young Innovators 2009 DISCUSSION & CONCLUSIONS • The genetic variation in selected metabolic enzymes (CYP3A4*1B, CYP3A5*3, UGT1A9*3, and UGT1A9*5) did not explain the variability in sorafenib disposition, in this population. • Model evaluation by post-hoc visual and posterior predictive checks confirmed that model-predicted concentrations and systemic exposures were consistent with measuredconcentrations and systemic exposures. • Incidences of dermatological toxicities appear to be associated with sorafenib systemic exposures. Young Innovators 2009 DISCUSSION & CONCLUSIONS • Incidences of treatment-related hypertension and HFSR increased to almost double in patients carrying the VEGFR2 H472Q variant allele than wild-type allele. Young Innovators 2009 ACKNOWLEDGMENTS VCU • Jürgen Venitz, M.D., Ph.D NCI • William D. Figg, Pharm.D., M.B.A. • Douglas K. Price, Ph.D. • Jeanny Aragon-Ching, M.D. • PI’s for clinical trials – – – – – – – – William Dahut, M.D. Elise C Kohn, M.D. Giuseppe Giaccone, M.D. Heidi Kong, M.D. Shivaani Kumaar, M.D. Robert Yarchoan, M.D. Martin E. Gutierrez, M.D. Nilofar Azad, M.D. University of Pisa • Romano Danesi, M.D., Ph.D. Dr. Figg’s lab • Su Woo, Ph.D. • Erin R. Gardner, Ph.D. • Tristan Sissung, Ph.D. Projections Research, Inc. • Diane R. Mould, Ph.D. VCU • Pravin Jadhav, Ph.D. (FDA) Young Innovators 2009 BIOS/CONTACT INFO Lokesh Jain, Pre-Doctoral Visiting Research Fellow, National Cancer Institute, NIH – [email protected], [email protected] Lokesh Jain is a pre-doctoral fellow in Clinical Pharmacology Program at National Cancer Institute (NCI). Lokesh received his bachelor of pharmacy (B. Pharm.) degree in 2002 from L. M. College of Science and Technology, Jodhpur affiliated with Rajasthan University, India. He received his master of pharmacy (M. Pharm.) degree in 2005 from Birla Institute of Technology and Science, Pilani, India. In the same year, he joined the Virginia Commonwealth University/NCI joint track clinical pharmacology Ph.D. program and has been continuing his research under supervision of Drs. Jürgen Venitz, M.D., Ph.D. and William D. Figg, Pharm. D., M.B.A. His graduate research is focused on identifying the clinical, laboratory and pharmacogenetic covariates for efficacy and toxicity of anti-cancer drugs. He has worked on drug development aspects related to bioanalysis, pharmacokinetic and pharmacogenetic analysis, exposure-genotyperesponse studies as well as translational studies. His research work has been published in peerreviewed journals. He has received several awards during his graduate career, including the AAPS2009 CPTR graduate symposium award, ACCP-2009 student/trainee abstract award, VCU School of Pharmacy Dean’s award-2009, VCU Department of Pharmaceutics John Wood award-2009 and Thacker award-2007. Young Innovators 2009