Predicting treatment outcomes using rich adherence data & antiretroviral pharmacometrics

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1 Predicting treatment outcomes using rich adherence data & antiretroviral pharmacometrics Daniel Scholes Rosenbloom, Alison L. Hill, Robert F. Siliciano, Martin A. Nowak, Carol Golin, Robert Remien, Ira Wilson, and Honghu Liu for the MACH14 Investigators

2 Regimen & Adherence Pharmacokinetics, Pharmacodynamics, Drug resistance, Replication capacity, Immune effects, Other host factors, Other viral factors... Virologic Outcome 1 1 1

3 Quantifying Pharmacometrics: Efavirenz example E E E E E+ 1 Drug concentration, fraction of C max Time since C max (days) Shen et al. (Nat Med 28), Sampah et al. (PNAS 211) Jilek et al. (Nat Med 212)

4 Quantifying Pharmacometrics: Efavirenz vs. Atazanavir/ritonavir EFV ATV/r.1 1.E E E E E+ 1 Drug concentration, fraction of C max ATV/r EFV Time since C max (days) Shen et al. (Nat Med 28), Sampah et al. (PNAS 211) Jilek et al. (Nat Med 212)

5 Pharmacometrics explain adherence-resistance relationships Prob. of Resist NNRTIs: resistance at lower adherence Unboosted PIs: resistance at higher adherence Boosted PIs: low peak at intermediate adherence Adherence (%) Rosenbloom, Hill, Rabi et al. (Nat Med 212) cf. Maggiolo et al. (HIV Clin Trials 27) & Bangsberg et al. (J Antimicrob Chemother 24)

6 Pharmacometrics predict virologic outcomes Viral fitness Burst rate, Virus decay rate, Infection rate... Viral dynamic model Viral load predictions Infection rate depends on fitness

7 Pharmacometrics predict virologic outcomes Viral fitness Allows comparison across regimens! Burst rate, Virus decay rate, Infection rate... Viral dynamic model Viral load predictions Infection rate depends on fitness

8 Pharmacometrics predict virologic outcomes after treatment changes Training Prediction Viral load (c/ml) NIMROD program, Commenges & Thiébaut Group Prague et al. (Biometrics 212) Prague et al. (Adv Drug Deliv Rev 213) AZT 3TC d4t ddi Time (days)

9 Pharmacometrics + adherence predict virologic outcomes after treatment changes Training Prediction Viral load (c/ml) NIMROD program, Commenges & Thiébaut Group Prague et al. (Biometrics 212) Prague et al. (Adv Drug Deliv Rev 213) AZT 3TC d4t ddi Time (days)

10 Drug Conc. Viral Fitness Pharmacometrics + MEMS Viral fitness over time (illustrative) Time Time Rosenbloom et al. (Nat Med 212)

11 MACH14: 2,835 patients, 16, VL, 678, MEMS * Requirements: 1 12 days btwn. VL observation 3 consecutive days missing data Adherence btwn. 2% and 11% 1 pill taken in 1 days before VL observation 12,985 inter-vl measurement periods..97 starting suppressed ( 2 c/ml) w/o big data gaps* with 3+ drug regimen data 86 / 97 rebound (9%) 14 / 119 rebound (12%)

12 119 cases with 3 drugs Lower fitness 12 Rebounded Stays suppressed Log 1 decrease in time-avg fitness* 8 4 Higher fitness 2% 4% 6% 8% 1% * Over inter-vl period up to 6 days Adherence*

13 119 cases with 3 drugs Lower fitness 12 Rebounded Stays suppressed Inhibitory factor (IF) 8 4 Higher fitness 2% 4% 6% 8% 1% Adherence

14 119 cases with 3 drugs IF is higher in suppressors 12 Rebounded Stays suppressed IF p = % 4% 6% 8% 1% (Mann-Whitney U test) 66% Adherence 78% p =.252

15 119 cases with 3 drugs A protective effect at IF > 8? IF > 8: / 36 rebound 12 IF 8: 14 / 83 rebound (Fisher exact test, p <.1) IF p = % 4% 6% 8% 1% 66% Adherence 78% p =.252

16 IF may signal strong antiviral activity even when adherence is spotty Daily IF day average = EFV 6mg (q.d) / 57 = 6% 8. AZT 3mg + 3TC 15mg (b.i.d.) 8/ /13/2 9/2/2 9/27/2 1/4/2 1/11/2 9/13 9/2 9/27 1/4 1/11 97 / 114 = 85% VL = 121 c/ml VL < 5 c/ml

17 Goals Use fitness from rich adherence data in viral dynamic models Test fitness for real-time guidance?

18 Thank you! MACH14 Team Carol Golin Honghu Liu Bob Remien Yan Wang Ira Wilson Harvard Program for Evolutionary Dynamics Alison Hill Martin Nowak Johns Hopkins Ali Rabi Bob Siliciano Financial support: Harvard Organismic & Evolutionary Biology NIH HHMI

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20 MACH14 regimen overview: 119 cases with 3 drugs Count 3 5 / 31 Rebounded Continued suppression 2 1 / 2 3 / 19 / 14 1 / 1 3 / 9 / 6 2 / 8 / 2 BC + AZT + 3TC FV + TDF + FTC FV + d4t + 3TC FV + AZT + 3TC 4T + TDF + FTC FV + d4t + 3TC Other PI-based r NNRTI-based Mixed

21 A protective effect at IF > 8?... for all 97 with full/partial regimens IF > 8: / 39 rebound 12 IF 8: 86 / 868 rebound (Fisher exact test, p =.4) IF 8 4 2% 4% 6% 8% 1% Adherence

22 IF is common currency that describes many regimens IF % 4% 6% 8% 1% Adherence

23 Above 7% adherence, only IF matters 12 IF p =.43 2% 4% 6% 8% 1% (Mann-Whitney U test) Adherence 93% p =.79

24 IF may signal strong antiviral activity even when adherence is spotty Daily IF day average = EFV 6mg (q.d) 4 18 / 28 = 64% AZT 3mg + 3TC 15mg (b.i.d.) 2 7/18/2 7/25/2 8/1/2 8/8/2 8/15/2 7/18 7/25 8/1 8/8 8/15 39 / 56 = 7% VL < 5 c/ml VL = 121 c/ml