CROI 2015 Program and Abstracts

Abstract Listing

Poster Abstracts

THURSDAY, FEBRUARY 26, 2015 Session P-H5 Poster Session

Poster Hall

2:30 pm– 4:00 pm New Technologies in Assessing Drug Interactions and Systemic and Intracellular Pharmacology 531 Pharmacokinetic Interactions Between Antidiabetics and Efavirenz Using PBPK Modeling Catia Marzolini 1 ; Rajith Rajoli 2 ; Luigia Elzi 1 ; Manuel Battegay 1 ; David Back 2 ; Marco Siccardi 2 1 University Hospital Basel, Basel, Switzerland; 2 Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom

Background: Diabetes has emerged as an important co-morbidity in the aging HIV population. The management of diabetes is complicated by the issue of drug-drug interactions (DDI) between antidiabetics and antiretroviral drugs and the lack of clinical data on how to manage these DDI. The antidiabetics pioglitazone (PIO) and repaglinide (REP) are metabolized by CYP2C8 and CYP3A4 and therefore are subject to DDI with efavirenz (EFV), an inducer of CYP3A4 and inhibitor of CYP2C8. The objective of this study was to simulate the pharmacokinetic (PK) interaction between PIO or REP and EFV using physiologically based pharmacokinetic (PBPK) modeling. Methods: In vitro data describing the physicochemical properties, absorption, distribution, metabolism and elimination of PIO, REP and EFV, as well as the CYP induction and inhibition potential of EFV were obtained from published literature. The experimental data were integrated in a PBPK model developed using Simbiology (Matlab, R2013b), representing molecular, physiological and anatomical processes defining PK. PIO, REP and EFV plasma profiles were simulated in 50 virtual individuals receiving either PIO 15 mg once daily (QD) or REP 2 mg thrice daily (TID) with or without EFV 600 mg QD for 14 days. Dose adjustments of PIO and REP were simulated to overcome the DDI with EFV. Results: Simulated PK parameters were in agreement with observed clinical data. Simulated versus observed mean AUC and Cmax ( ± SD) were: 3699 (1413) vs 5020 (1070) ng.h/ml and 535 (80) vs 597 (115) ng/ml for PIO; 50 (16) vs 69 (78) ng.h/ml and 21 (5) vs 48 (32) ng/ml for REP; 92931 (44533) vs 58089 (23046) ng.h/ml and 6158 (1855) vs 4072 (1168) ng/ ml for EFV. The geometric mean ratios with 90% confidence interval (GMR, 90% CI) of PIO or REP with and without EFV are presented in the table. An increase in PIO and REP dosage to 22.5 mg QD and 4 mg TID, respectively, was predicted to be sufficient to overcome EFV induction.

Conclusions: The prediction of DDI for drugs whose metabolism is concurrently induced and inhibited can be complex. The developed model, integrating both concurrent effects on CYPs and temporal changes in drug concentrations, shows that EFV has mainly an inducing effect on PIO and REP metabolism. PBPK modeling represents a useful tool to predict complex DDI as often encountered in multimorbid elderly HIV-infected patients and to support the design of prospective clinical trials.

532 In Silico Simulation of Interaction Between Rifampicin and Boosted Darunavir Marco Siccardi ; Owain Roberts; Rajith Rajoli; Laura Dickinson; Saye Khoo; Andrew Owen; David Back University of Liverpool, Liverpool, United Kingdom

Background: The optimization of antiretroviral regimens in HIV-infected patients co-administered with anti-TB drugs is challenging since rifampicin (RIF), a principal element of the anti-TB therapy, is a strong inducer of key metabolic enzymes. Physiologically-based pharmacokinetic (PBPK) modelling represents an innovative approach to simulate clinical scenarios in the absence of clinical data, by integrating in vitro data in mathematical models. The aim of this research was to develop a PBPK model for the co-administration of ritonavir-boosted darunavir (DRV/r) and RIF and predict optimal dosing strategies to overcome the drug-drug interaction (DDI). Methods: In vitro data describing physicochemical properties, absorption, distribution, metabolism and elimination (ADME) of DRV, ritonavir (RTV) and RIF, as well as the inhibition and induction potential of RTV and RIF were determined experimentally or obtained from the literature. A PBPK model was developed integrating experimental in vitro data in algorithms representing molecular, physiological and anatomical processes defining ADME. The PK of DRV/r and RIF was simulated in 100 virtual individuals. The impact of RIF (600mg qd ) on DRV/r was determined and DRV and RTV qd and bid dose adjustments were simulated. Results: Simulated DRV/r pharmacokinetic parameters were (mean ± SD) C trough (2.02 ± 1.17 μ g/ml), C max (8.23 ± 1.73 μ g/ml) and AUC (115.6 ± 32.9 μ g/mL.h), which is in agreement with observed PK data for DRV/r 800/100 mg qd in HIV-infected patients: C trough (2.11 ± 1.22 m g/ml), C max (6.75 ± 1.68 μ g/ml) and AUC (75.62 ± 26.44 μ g/mL.h). The simulated effect of RIF on DRV exposure resulted in a decrement of 57.7% for AUC, 79.5% for C trough and 34.6% for C max . The effect of RIF was overcome by increasing the DRV/ r dose to 1600/200 mg qd or 800/100 mg bid (Table 1).

Poster Abstracts

Conclusions: The developed PBPK model predicted the in vivo pharmacokinetics of DRV/r and the interaction with RIF. Based on these findings, a DRV/r regimen of 1600/200 mg qd or 800/100 mg bid could mitigate the effect of RIF on DRV/r PK. Mechanistic evaluation of ADME can inform PBPK models and prediction of interaction between drugs. PBPK may be particularly helpful for the rational design of clinical trials evaluating dose adjustment strategies to overcome DDIs in patients concomitantly receiving antiretrovirals and anti-TB drugs.

346

CROI 2015