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Influence of drug transport proteins on the pharmacokinetics and drug interactions of HIV protease inhibitors.

ABSTRACT: Protease inhibitors, a class of antiretroviral agents frequently used in the treatment of HIV infection, interact with numerous transport proteins resulting in clinically significant drug-drug interactions (DDIs). This review focuses on the proteins that transport protease inhibitors and directly influence the pharmacokinetics of these drugs, as well as the transport proteins that are inhibited or induced by protease inhibitors. Clinically relevant DDIs involving drug transporters and protease inhibitors, either as "victim" drugs or as "perpetrator" drugs, and the pharmacokinetic consequences of such interactions are highlighted. A summary of transporter-mediated processes underlying the toxicity of protease inhibitors is provided. Finally, the effect of HIV infection or co-infection on drug transport proteins, and the implications for protease inhibitor pharmacokinetics is discussed. Transport proteins significantly influence the pharmacokinetics, efficacy and toxicity profiles of this important class of drugs.

SUBMITTER: Griffin L

PROVIDER: S-EPMC3750718 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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Influence of drug transport proteins on the pharmacokinetics and drug interactions of HIV protease inhibitors.

Griffin Latoya L Annaert Pieter P Brouwer Kim L R KL

Journal of pharmaceutical sciences 20110622 9

Protease inhibitors, a class of antiretroviral agents frequently used in the treatment of HIV infection, interact with numerous transport proteins resulting in clinically significant drug-drug interactions (DDIs). This review focuses on the proteins that transport protease inhibitors and directly influence the pharmacokinetics of these drugs, as well as the transport proteins that are inhibited or induced by protease inhibitors. Clinically relevant DDIs involving drug transporters and protease i ...[more]

PMID: 21698598

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Project description:BACKGROUND:HIV-1 can develop resistance to antiretroviral drugs, mainly through mutations within the target regions of the drugs. In HIV-1 protease, a majority of resistance-associated mutations that develop in response to therapy with protease inhibitors are found in the protease's active site that serves also as a binding pocket for the protease inhibitors, thus directly impacting the protease-inhibitor interactions. Some resistance-associated mutations, however, are found in more distant regions, and the exact mechanisms how these mutations affect protease-inhibitor interactions are unclear. Furthermore, some of these mutations, e.g. N88S and L76V, do not only induce resistance to the currently administered drugs, but contrarily induce sensitivity towards other drugs. In this study, mutations N88S and L76V, along with three other resistance-associated mutations, M46I, I50L, and I84V, are analysed by means of molecular dynamics simulations to investigate their role in complexes of the protease with different inhibitors and in different background sequence contexts. RESULTS:Using these simulations for alchemical calculations to estimate the effects of mutations M46I, I50L, I84V, N88S, and L76V on binding free energies shows they are in general in line with the mutations' effect on [Formula: see text] values. For the primary mutation L76V, however, the presence of a background mutation M46I in our analysis influences whether the unfavourable effect of L76V on inhibitor binding is sufficient to outweigh the accompanying reduction in catalytic activity of the protease. Finally, we show that L76V and N88S changes the hydrogen bond stability of these residues with residues D30/K45 and D30/T31/T74, respectively. CONCLUSIONS:We demonstrate that estimating the effect of both binding pocket and distant mutations on inhibitor binding free energy using alchemical calculations can reproduce their effect on the experimentally measured [Formula: see text] values. We show that distant site mutations L76V and N88S affect the hydrogen bond network in the protease's active site, which offers an explanation for the indirect effect of these mutations on inhibitor binding. This work thus provides valuable insights on interplay between primary and background mutations and mechanisms how they affect inhibitor binding.

Project description:BackgroundPeople living with HIV (PLWH) are aging and experience age-related physiological changes and comorbidities. Atorvastatin is a widely prescribed lipid-lowering agent metabolized by cytochrome P450 (CYP) 3A4, whose hepatocyte uptake is facilitated by organic anion transporting polypeptide (OATP) 1B1/1B3. Inhibition or induction of this enzyme and hepatic transporter can increase or decrease atorvastatin exposure, respectively.ObjectiveThis study aimed to describe the pharmacokinetic profile of atorvastatin and its major metabolite, and to evaluate drug-drug interactions (DDIs) with antiretrovirals (ARVs).MethodsThe atorvastatin pharmacokinetic profile was best described by a two-compartment model with first-order absorption and elimination. Metabolite concentrations were described by considering both linear metabolism from atorvastatin and presystemic metabolism. The influence of demographic and clinical covariates on drug and metabolite pharmacokinetics was assessed using NONMEM®. Model-based simulations were performed to evaluate the magnitude of DDIs with ARVs.ResultsFull pharmacokinetic profiles (98 atorvastatin + 62 o-OH-atorvastatin concentrations) and sparse concentrations (78 and 53 for atorvastatin and o-OH-atorvastatin, respectively) were collected in 59 PLWH. Interindividual variability was high. The coadministration of boosted ARVs decreased atorvastatin clearance by 58% and slowed down o-OH-atorvastatin formation by 88%. Atorvastatin clearance increased by 78% when coadministered with CYP3A4 inducers. Simulations revealed a 180% increase and 44% decrease in atorvastatin exposure (area under the curve) in the presence of ARVs with inhibiting and inducing properties, respectively.ConclusionThis study showed an important interindividual variability in atorvastatin pharmacokinetics that remains largely unexplained after the inclusion of covariates. Since boosted ARVs double atorvastatin exposure, the initial dosage might be reduced by half, and titrated based on individual clinical targets.

Project description:The South African national combination antiretroviral therapy (cART) roll-out program started in 2006, with over 4.4 million people accessing treatment since it was first introduced. HIV-1 drug resistance can hamper the success of cART. This study determined the patterns of HIV-1 drug-resistance associated mutations (RAMs) in People Living with HIV-1 (PLHIV-1). Receiving first (for children below 3 years of age) and second-line (for adults) cART regimens in South Africa. During 2017 and 2018, 110 patients plasma samples were selected, 96 samples including those of 17 children and infants were successfully analyzed. All patients were receiving a boosted protease inhibitor (bPI) as part of their cART regimen. The viral sequences were analyzed for RAMs through genotypic resistance testing. We performed genotypic resistance testing (GRT) for Protease inhibitors (PIs), Reverse transcriptase inhibitors (RTIs) and Integrase strand transfer inhibitors (InSTIs). Viral sequences were subtyped using REGAv3 and COMET. Based on the PR/RT sequences, HIV-1 subtypes were classified as 95 (99%) HIV-1 subtype C (HIV-1C) while one sample as 02_AG. Integrase sequencing was successful for 89 sequences, and all the sequences were classified as HIV-1C (99%, 88/89) except one sequence classified CRF02_AG, as observed in PR/RT. Of the 96 PR/RT sequences analyzed, M184V/I (52/96; 54%) had the most frequent RAM nucleoside reverse transcriptase inhibitor (NRTI). The most frequent non-nucleoside reverse transcriptase inhibitor (NNRTI) RAM was K103N/S (40/96, 42%). Protease inhibitor (PI) RAMs M46I and V82A were present in 12 (13%) of the sequences analyzed. Among the InSTI major RAM two (2.2%) sequences have Y143R and T97A mutations while one sample had T66I. The accessory RAM E157Q was identified in two (2.2%). The data indicates that the majority of the patients failed on bPIs didn't have any mutation; therefore adherence could be major issue in these groups of individuals. We propose continued viral load monitoring for better management of infected PLHIV.

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Influence of drug transport proteins on the pharmacokinetics and drug interactions of HIV protease inhibitors.

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Influence of drug transport proteins on the pharmacokinetics and drug interactions of HIV protease inhibitors.

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