PK/PD Analyses for QDMRK, A Phase III Study of the Safety & Efficacy of Once Versus Twice Daily Raltegravir in Treatment-Naïve HIV-Infected Patients

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1 PK/PD Analyses for QDMRK, A Phase III Study of the Safety & Efficacy of Once Versus Twice Daily Raltegravir in Treatment-Naïve HIV-Infected Patients Larissa Wenning, Matthew Rizk, Wen-Lin Luo, Yaming Hang, Jing Su, Havilland Campbell, Bach-Yen Nguyen, Peter Sklar Merck Research Laboratories, North Wales, PA, USA 1

2 Background: QDMRK (P071) Study Design Multicenter, double-blind, randomized, active-controlled study; noninferiority with -10% margin Intensive PK profiles collected in subset of ~20 patients per arm at Week 4; non-compartmental analysis to determine AUC, C max, C 12hr /C 24hr Sparse PK samples (1 per visit) collected in all patients at Wks 2, 4, 8, 12, 16, 24, 48 Primary endpoint Week 48 Secondary endpoint Week 96 HIV-1-infected Treatment naive HIV-1 RNA >5000 copies/ml No CD4 cell cut-off No documented resistance to tenofovir or emtricitabine 1:1 Raltegravir 800 mg QD + TDF/FTC FDC n=382 Raltegravir 400 mg BID + TDF/FTC FDC n=388 2

3 Eron et al; CROI 2011; 150LB Background: QDMRK Efficacy Results % of Patients with HIV RNA < 50 copies/ml (NC=F ) 100 BID 88.9% Percent of Patients with HIV RNA <50 Copies/mL QD 83.2% (QD-BID) [95% CI] = -5.7 [-10.7, -0.83] Number of Contributing Patients Study Week RAL 800 mg QD RAL 400 mg BID *All patients received TDF/FTC FDC Non-completer equals failure (NC=F) approach treats all discontinuations as failures 3

4 Background: Raltegravir PK Representative Raltegravir Plasma Concentration Profiles from Phase I Studies with Population Mean Predicted Line Overlaid Relatively large degree of interoccasion variability in absorption Development of a population PK model was problematic Sparse PK data analyzed using 3 summary parameters: C all (geometric mean of all concentrations) C trough (geometric mean of concentrations collected during window around end of dosing interval: hrs BID, hrs QD) C min (minimum of all concentrations) 4

5 QDMRK - Summary of Intense and Sparse PK Parameters RAL 800 mg QD RAL 400 mg BID Ratio RAL QD / RAL BID PK Parameter N LS Mean (% CV ) N LS Mean (% CV ) GMR (90% CI) AUC 0-24hr (µm hr) (70) (99) 1.17 (0.80, 1.72) C max (µm) (69) (135) 3.98 (2.58, 6.16) C trough (nm) (111) (167) 0.15 (0.09, 0.26) C all (nm) (176) (92) 0.43 (0.38, 0.49) GM C trough (nm) (140) (126) 0.22 (0.19, 0.25) C min (nm) (189) (143) 0.43 (0.38, 0.50) Back-transformed from log scale; LS Mean = Geometric Least-Squares Mean. %CV = 100 x sqrt(exp(s 2 ) - 1), where s 2 is the observed variance on the natural log-scale. C trough = C 12hr for BID and C 24hr for QD Raltegravir PK generally consistent with expectations 24-hour exposure similar for both regimens; higher C max and lower C trough for 800 mg QD compared to 400 mg BID 5

6 Methods: Statistical Analysis of PK/PD Relationships All PK/PD analyses used observed failure approach for PD endpoints HIV RNA <50 copies/ml, <400 copies/ml at Week 48 of treatment Virologic failure: defined as HIV RNA >50 copies/ml at week 24 or virologic relapse after initial response defined by HIV RNA >50 copies/ml Logistic regression model including PK parameters and baseline HIV RNA (both on log scale) Odds ratio = fold-change in probability of an event occurring / probability of event not occurring per 10-fold increase in PK parameter Odds ratio =1: no correlation of outcome with concentrations Odds ratio >1: odds of event occurring increase with increasing concentrations (expected direction for vrna<50, <400) Odds ratio <1: odds of event occurring decrease with increasing concentrations (expected direction for virologic failure) 6

7 Sparse PK Parameters as a Predictor for Antiretroviral Responses (400 mg BID) n N Odd Ratio (95% CI) p-value HIV RNA <50 at Week 48 C 12hr (0.2, 1.8) C all (0.4, 3.9) C min (0.5, 2.8) HIV RNA <400 at Week 48 C 12hr (0.2, 7.5) C all (0.1, 4.6) C min (0.2, 2.6) Virologic Failure C 12hr (0.5, 4.1) C all (0.4, 4.0) C min (0.4, 2.0) N=number of patients with both PK and efficacy data; n=number of patients (out of N) with event No significant relationships: consistent with prior analyses of BID naïve data 7

8 Sparse PK Parameters as a Predictor for Antiretroviral Responses (800 mg QD) n N Odd Ratio (95% CI) p-value HIV RNA <50 at Week 48 C 24hr (0.8, 5.5) C all (0.9, 3.3) C min (0.9, 3.2) HIV RNA <400 at Week 48 C 24hr (0.8, 12.3) C all (1.1, 5.8) C min (0.7, 3.2) Virologic Failure C 24hr (0.2, 1.1) C all (0.3, 1.2) C min (0.3, 1.1) N=number of patients with both PK and efficacy data; n=number of patients (out of N) with event Only one significant p-value, but see consistent trends in expected directions 8

9 Sparse PK Parameters as a Predictor for Antiretroviral Responses (Pooled Data) n N Odd Ratio (95% CI) p-value HIV RNA <50 at Week 48 C trough (0.9, 2.8) C all (1.2, 3.3) C min (1.1, 2.8) HIV RNA <400 at Week 48 C trough (1.5, 8.1) C all (1.4, 5.6) C min (0.8, 3.0) Virologic Failure C trough (0.3, 0.9) C all (0.3, 0.9) C min (0.3, 0.9) N=number of patients with both PK and efficacy data; n=number of patients (out of N) with event Lots of significant relationships; everything trends in expected direction 9

10 GM C trough and Probability of HIV RNA <50 copies/ml (QD and BID Pooled Data) Median C12 for BID (373 nm) Median C24 for QD (76.5 nm) 10

11 C trough and Baseline HIV as a Predictor for HIV RNA <50 copies/ml (QD and BID Treatments) 6 Failure to reach 50 copies/ml appears at high baseline values QD no group appears to be clustered at lower C trough values HIV RNA Baseline Value in Log 10 Scale 5 4 Q4 Q3 Q2 Q1 3 HIV RNA < 50 Copies/mL b.i.d. No b.i.d. Yes q.d. No q.d. Yes Geometric Mean of Pooled Trough Conc. (nm) 11

12 Histograms for GM C trough (divided into quartiles) and % with HIV RNA <50 copies/ml GM C12hr (nm) GM C24hr (nm) Range Median Range Median Overall Responses: 400 mg BID 92% 800 mg QD 87% In 800 mg QD dataset, there is a drop-off in efficacy for patients in lowest C trough quartile 12

13 ROC Analysis for QDMRK (QD arm alone; efficacy endpoint = HIV RNA <50) True Positive (S ensitivity) ROC Curver for Different Predictors QD Group Call Cmin Ctrough Log10 Baseline HIV RNA False Positive (1- Specificity) ROC = Receiver Operating Characteristic Statistical analysis to define a threshold parameter, balancing sensitivity and specificity (best balance of true positive and true negative) Threshold that results in largest separation between sensitivity and 1-specificity for each predictor Predictor Threshold Sensitivity Specificity GM Ctrough (nm) Cmin (nm) GM Call (nm) Log10 Baseline HIV RNA (log10 copies/ml) Baseline HIV RNA provides best threshold value No PK parameter results in very sensitive or specific threshold 13

14 QDMRK PK/PD Conclusions Raltegravir pharmacokinetic profile was generally as expected; 24-hour exposure was similar for 800 mg QD versus 400 mg BID In BID arm, no association between PK and virologic efficacy With BID dosing, PK is likely well above the minimum needed for efficacy In QD arm and pooled dataset, there was an apparent association between PK and efficacy However, a useful PK threshold was not identified Patients with low raltegravir PK and high baseline viral load (VL) were at higher risk of virologic failure (VF) Results imply that shape of the PK curve is important for long-term efficacy of raltegravir With current dataset, see correlations between efficacy and trough concentrations, but can t evaluate other possible drivers such as time above a threshold concentration 14

15 Acknowledgments All patients who participated in QDMRK QDMRK Investigators: Australia: Workman C, Bloch M, Hoy J; Brazil: Madruga JV, Souza T, Telles F; Canada: Trottier B, Smith G, Montaner J, Angel J; Columbia: Tamara J, Velez J; Denmark: Gerstoft J, Laursen A; France: Reynes J, Katlama C, Raffi F, Molina JM, Yazdanpanah Y; Germany: Faetkenheuer G, Jäger H, van Lunzen J, Bogner J, Hartl H, Kuhlmann B, Lutz T; Greece: Petrikkos G; India: Shobha V, Dinakar M; Italy: Antinori A, Cauda R, Mezzaroma I, Narciso P; Mexico: Crabtree B, Andrade J; Netherlands: Van Der Ende M; Portugal: Branco T, Marques R, Antunes F; Russian Federation: Pokrovsky V, Rakhmanova A; South Africa: Mohapi L, Ive P, Botes M, Johnson D; Spain: Arribas López J, Berenguer Berenguer J, Aguirrebengoa Ibarguren K, Gatell Artigas J; Switzerland: Hirschel B, Weber R, Cavassini M; Thailand: Sungkanuparph S, Anekthananon T, Supparatpinyo K; United Kingdom: Fisher M, Nelson M, Youle M; United States: Bellos N, Berger D, Campo R, Corales R, DeJesus E, Gathe J Jr., Kinder C, Fish D, Liporace R, Markowitz M, McDonald C, Mills A, Morales-Ramírez J, Waldman L, Myers R, Nahass R, Osiyemi O, Rixinger A, Rhame F, Ruane P, Santiago S, Scarsella A, Shalit P, Stansell J, Torres J, Young B, Tedaldi E QDMRK Data Monitoring Committee The QDMRK and REALMRK Scientific Advisory Committee Merck Research Laboratories QDMRK Study Team: Sklar P, Robertson M, Leavitt R, Nguyen B-Y, Isaacs R, Campbell H, Zhao J, Rodgers A, Miller M, Hazuda D, Wenning L, Rizk M 15