DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy.

Drug-drug interaction (DDI) may result in clinical toxicity or treatment failure of antiretroviral therapy (ARV) or comedications. Despite the high number of possible drug combinations, only a limited number of clinical DDI studies are conducted. Computational prediction of DDIs could provide key evidence for the rational management of complex therapies. Our study aimed to assess the potential of deep learning approaches to predict DDIs of clinical relevance between ARVs and comedications. DDI severity grading between 30,142 drug pairs was extracted from the Liverpool HIV Drug Interaction database. Two feature construction techniques were employed: 1) drug similarity profiles by comparing Morgan fingerprints, and 2) embeddings from SMILES of each drug via ChemBERTa, a transformer-based model. We developed DeepARV-Sim and DeepARV-ChemBERTa to predict four categories of DDI: i) Red: drugs should not be co-administered, ii) Amber: interaction of potential clinical relevance manageable by monitoring/dose adjustment, iii) Yellow: interaction of weak relevance and iv) Green: no expected interaction. The imbalance in the distribution of DDI severity grades was addressed by undersampling and applying ensemble learning. DeepARV-Sim and DeepARV-ChemBERTa predicted clinically relevant DDI between ARVs and comedications with a weighted mean balanced accuracy of 0.729 ± 0.012 and 0.776 ± 0.011, respectively. DeepARV-Sim and DeepARV-ChemBERTa have the potential to leverage molecular structures associated with DDI risks and reduce DDI class imbalance, effectively increasing the predictive ability on clinically relevant DDIs. This approach could be developed for identifying high-risk pairing of drugs, enhancing the screening process, and targeting DDIs to study in clinical drug development.

Prevalence of Potentially Clinically Significant Drug-Drug Interactions With Antiretrovirals Against HIV Over Three Decades: A Systematic Review of the Literature.

BACKGROUND: Contemporary first-line antiretrovirals have considerably reduced liability for clinically significant drug-drug interactions (DDI). This systematic review evaluates the prevalence of DDI among people receiving antiretrovirals across 3 decades. METHODS: We searched 3 databases for studies reporting the prevalence of clinically significant DDIs in patients receiving antiretrovirals published between January 1987 and July 2022. Clinically significant DDIs were graded by severity. All data extractions were undertaken by 2 independent reviewers, adjudicated by a third. RESULTS: Of 21,665 records returned, 13,474 were duplicates. After screening the remaining 13,596 abstracts against inclusion criteria, 122 articles were included for full-text analysis, from which a final list of 34 articles were included for data synthesis. The proportion of patients experiencing a clinically significant DDI did not change over time (P = 0.072). The most frequently reported classes of antiretrovirals involved in DDIs were protease inhibitors and non-nucleoside reverse transcriptase inhibitors; of note, integrase use in the most recent studies was highly variable and ranged between 0% and 89%. CONCLUSIONS: The absolute risk of DDIs has not decreased over the period covered. This is likely related to continued use of older regimens and an ageing cohort of patients. A greater reduction in DDI prevalence can be anticipated with broader uptake of regimens containing unboosted integrase inhibitors or non-nucleoside reverse transcriptase inhibitors.

Potential drug-drug interactions due to concomitant medicine use among people living with HIV on antiretroviral therapy in Australia.

AIMS: We quantified concomitant medicine use and occurrence of potential drug-drug interactions in people living with HIV in Australia who are treated with antiretroviral therapy (ART). METHODS: In this cohort study using dispensing claims of a 10% random sample of Australians, we identified 2230 people dispensed ART between January 2018 and December 2019 (mean age 49.0 years, standard deviation 12.0 years, 88% male). We examined concomitant medicine use by identifying nontopical medicines dispensed within 90-days of any antiretroviral medicine dispensing during a 12-month follow-up period. For every antiretroviral and nonantiretroviral pair, we identified and classified possible drug-drug interactions using the University of Liverpool HIV drug interactions database. RESULTS: A total of 1728 (78%) people were dispensed at least 1 and 633 (28%) 5 or more unique medicines in addition to ART in a 12-month period; systemic anti-infectives and medicines acting on the nervous system were the most common (68% and 56%, respectively). Among comedicated people, 1637 (95%) had at least 1 medicine combination classified as weak interactions, 558 (32%) interactions requiring close monitoring/dose adjustment and 94 (5%) that should not be coadministered. Contraindication or interactions requiring close monitoring/dose adjustment were more common among people receiving protease inhibitors (50-73% across different antiretrovirals), non-nucleoside reverse transcriptase inhibitors (35-64%), people using single-tablet combinations containing elvitegravir (30-46%) and those using tenofovir disoproxil (26-30%). CONCLUSION: Concomitant medicine use is widespread among people living with HIV in Australia. Despite a relatively low prevalence of contraindicated medicines, almost a third received medicines that require close monitoring or dose adjustment.

Recommendations for the Management of Drug-Drug Interactions Between the COVID-19 Antiviral Nirmatrelvir/Ritonavir (Paxlovid) and Comedications.

The coronavirus disease 2019 (COVID-19) antiviral nirmatrelvir/ritonavir (Paxlovid) has been granted authorization or approval in several countries for the treatment of patients with mild to moderate COVID-19 at high risk of progression to severe disease and with no requirement for supplemental oxygen. Nirmatrelvir/ritonavir will be primarily administered outside the hospital setting as a 5-day course oral treatment. The ritonavir component boosts plasma concentrations of nirmatrelvir through the potent and rapid inhibition of the key drug-metabolizing enzyme cytochrome P450 (CYP) 3A4. Thus nirmatrelvir/ritonavir, even given as a short treatment course, has a high potential to cause harm from drug-drug interactions (DDIs) with other drugs metabolized through this pathway. Options for mitigating risk from DDIs with nirmatrelvir/ritonavir are limited due to the clinical illness, the short window for intervention, and the related difficulty of implementing clinical monitoring or dosage adjustment of the comedication. Pragmatic options are largely confined to preemptive or symptom-driven pausing of the comedication or managing any additional risk through counseling. This review summarizes the effects of ritonavir on drug disposition (i.e., metabolizing enzymes and transporters) and discusses factors determining the likelihood of having a clinically significant DDI. Furthermore, it provides a comprehensive list of comedications likely to be used in COVID-19 patients which are categorized according to their potential DDI risk with nirmatrelvir/ritonavir. It also discusses recommendations for the management of DDIs which balance the risk of harm from DDIs with a short course of ritonavir, against unnecessary denial of nirmatrelvir/ritonavir treatment.

Pharmacokinetics and Drug-Drug Interactions of Long-Acting Intramuscular Cabotegravir and Rilpivirine.

Combined antiretroviral treatments have significantly improved the morbidity and mortality related to HIV infection, thus transforming HIV infection into a chronic disease; however, the efficacy of antiretroviral treatments is highly dependent on the ability of infected individuals to adhere to life-long drug combination therapies. A major milestone in HIV treatment is the marketing of the long-acting intramuscular antiretroviral drugs cabotegravir and rilpivirine, allowing for infrequent drug administration, with the potential to improve adherence to therapy and treatment satisfaction. Intramuscular administration of cabotegravir and rilpivirine leads to differences in pharmacokinetics and drug-drug interaction (DDI) profiles compared with oral administration. A notable difference is the long elimination half-life with intramuscular administration, which reaches 5.6-11.5 weeks for cabotegravir and 13-28 weeks for rilpivirine, compared with 41 and 45 h, respectively, with their oral administration. Cabotegravir and rilpivirine have a low potential to cause DDIs, however these drugs can be victims of DDIs. Cabotegravir is mainly metabolized by UGT1A1, and rilpivirine is mainly metabolized by CYP3A4, therefore these agents are susceptible to DDIs with inhibitors, and particularly inducers of drug-metabolizing enzymes. Intramuscular administration of cabotegravir and rilpivirine has the advantage of eliminating DDIs occurring at the gastrointestinal level, however interactions can still occur at the hepatic level. This review provides insight on the intramuscular administration of drugs and summarizes the pharmacology of long-acting cabotegravir and rilpivirine. Particular emphasis is placed on DDI profiles after oral and intramuscular administration of these antiretroviral drugs.

Prevalence of Potential Drug-Drug Interactions in Patients of the Swiss HIV Cohort Study in the Era of HIV Integrase Inhibitors.

BACKGROUND: Prevalence of potential drug-drug interactions (PDDIs) between antiretroviral drugs (ARVs) and co-medications was high in 2008 in a Swiss HIV Cohort Study (SHCS) survey. We reassessed the prevalence of PDDIs in the era of human immunodeficiency virus (HIV) integrase inhibitors (INIs), characterized by more favorable interaction profiles. METHODS: The prevalence of PDDIs in treated HIV-positive individuals was assessed for the period 01-12/2018 by linkage of the Liverpool HIV drug interactions and SHCS databases. PDDIs were categorized as harmful (red flagged), of potential clinical relevance (amber flagged), or of weak clinical significance (yellow flagged). RESULTS: In 9298 included individuals, median age was 51 years (IQR, 43-58), and 72% were males. Individuals received unboosted INIs (40%), boosted ARVs (30%), and nonnucleoside reverse transcriptase inhibitor (NNRTIs) (32%)-based regimens. In the entire cohort, 68% received ≥1 co-medication, 14% had polypharmacy (≥5 co-medications) and 29% had ≥1 PDDI. Among individuals with co-medication, the prevalence of combined amber and yellow PDDIs was 43% (33% amber-mostly with cardiovascular drugs-and 20% yellow-flagged PDDIs) compared to 59% in 2008. Two percent had red-flagged PDDIs (mostly with corticosteroids), the same as in the 2008 survey. Compared with 2008, fewer individuals received boosted ARVs (-24%) and NNRTIs (-13%) but the use of co-medications was higher. CONCLUSIONS: Prevalence of PDDIs was lower with more widespread use of INIs in 2018 than in 2008. Continued use of boosted regimens and increasing needs for co-medications in this aging population impeded lower rates of PDDIs.

Recommendations for Dosing of Repurposed COVID-19 Medications in Patients with Renal and Hepatic Impairment.

INTRODUCTION: In December 2019, an outbreak of a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began, resulting in a number of antivirals and immune modulators being repurposed to treat the associated coronavirus disease 2019 (COVID-19). Many patients requiring treatment for COVID-19 may have either pre-existing renal or hepatic disease or experience acute renal/hepatic injury as a result of the acute infection. Altered renal or hepatic function can significantly affect drug concentrations of medications due to impaired drug metabolism and excretion, resulting in toxicity or reduced efficacy. The aim of this paper is to review the pharmacokinetics and available study data for the experimental COVID-19 therapies in patients with any degree of renal or hepatic impairment to make recommendations for dosing. METHODS: COVID-19 agents included in these recommendations were listed as primaries on the University of Liverpool COVID-19 drug interaction website ( www.covid19-druginteractions.org ), initially identified from Clinicialtrials.gov and ChicCTR.org.cn. A literature search was performed using PubMed and EMBASE as well as product licences and pharmacokinetic databases. FINDINGS: Remdesivir, dexamethasone, azithromycin, favipiravir, lopinavir/ritonavir, atazanavir, hydroxychloroquine, interferon beta, ribavirin, tocilizumab, anakinra and sarilumab were identified as experimental drugs being used in COVID-19 trials as of November 2020. Limited study data was found for these drugs in patients with renal or hepatic impairment for COVID-19 or other indications. Recommendations were made based on available data, consideration of pharmacokinetic properties (including variability), the dosing and anticipated treatment duration of each regimen in COVID-19 and known toxicities. CONCLUSION: Dosing of drugs used to treat COVID-19 in patients with renal or hepatic impairment is complex. These recommendations were produced to provide guidance to clinicians worldwide who are treating patients with COVID-19, many of whom will have some degree of acute or chronic renal or hepatic impairment.

Drug interactions: a review of the unseen danger of experimental COVID-19 therapies.

As global health services respond to the coronavirus pandemic, many prescribers are turning to experimental drugs. This review aims to assess the risk of drug-drug interactions in the severely ill COVID-19 patient. Experimental therapies were identified by searching ClinicalTrials.gov for 'COVID-19', '2019-nCoV', '2019 novel coronavirus' and 'SARS-CoV-2'. The last search was performed on 30 June 2020. Herbal medicines, blood-derived products and in vitro studies were excluded. We identified comorbidities by searching PubMed for the MeSH terms 'COVID-19', 'Comorbidity' and 'Epidemiological Factors'. Potential drug-drug interactions were evaluated according to known pharmacokinetics, overlapping toxicities and QT risk. Drug-drug interactions were graded GREEN and YELLOW: no clinically significant interaction; AMBER: caution; RED: serious risk. A total of 2378 records were retrieved from ClinicalTrials.gov, which yielded 249 drugs that met inclusion criteria. Thirteen primary compounds were screened against 512 comedications. A full database of these interactions is available at www.covid19-druginteractions.org. Experimental therapies for COVID-19 present a risk of drug-drug interactions, with lopinavir/ritonavir (10% RED, 41% AMBER; mainly a perpetrator of pharmacokinetic interactions but also risk of QT prolongation particularly when given with concomitant drugs that can prolong QT), chloroquine and hydroxychloroquine (both 7% RED and 27% AMBER, victims of some interactions due to metabolic profile but also perpetrators of QT prolongation) posing the greatest risk. With management, these risks can be mitigated. We have published a drug-drug interaction resource to facilitate medication review for the critically ill patient.

Polypharmacy and Drug-Drug Interactions in People Living With Human Immunodeficiency Virus in the Region of Madrid, Spain: A Population-Based Study.

BACKGROUND: Drug-drug interactions (DDIs) that involve antiretrovirals (ARVs) tend to cause harm if unrecognized, especially in the context of comorbidity and polypharmacy. METHODS: A linkage was established between the drug dispensing registry of Madrid and the Liverpool human immunodeficiency virus (HIV) DDI database (January 2017-June 2017). Polypharmacy was defined as the use of ≥5 non-HIV medications, and DDIs were classified by a traffic-light ranking for severity. RESULTS: A total of 22 945 people living with HIV (PLWH) and 6 613 506 individuals without HIV had received medications. ARV regimens were predominantly based on integrase inhibitors (51.96%). Polypharmacy was higher in PLWH (32.94%) than individuals without HIV (22.16%; P < .001); this difference was consistently observed across all age strata except for individuals ≥75 years. Polypharmacy was more common in women than men in both PLWH and individuals without HIV. The prevalence of contraindicated combinations involving ARVs was 3.18%. Comedications containing corticosteroids, quetiapine, or antithrombotic agents were associated with the highest risk for red-flag DDI, and the use of raltegravir- or dolutegravir-based antiretroviral therapy was associated with an adjusted odds ratio of 0.72 (95% confidence interval, .60-.88; P = .001) for red-flag DDI. CONCLUSIONS: Polypharmacy was more frequent among PLWH across all age groups except those aged ≥75 years and was more common in women. The detection of contraindicated medications in PLWH suggests a likely disconnect between hospital and community prescriptions. Switching to alternative unboosted integrase regimens should be considered for patients with risk of harm from DDIs.

Development of an evidence evaluation and synthesis system for drug-drug interactions, and its application to a systematic review of HIV and malaria co-infection.

BACKGROUND: In all settings, there are challenges associated with safely treating patients with multimorbidity and polypharmacy. The need to characterise, understand and limit harms resulting from medication use is therefore increasingly important. Drug-drug interactions (DDIs) are prevalent in patients taking antiretrovirals (ARVs) and if unmanaged, may pose considerable risk to treatment outcome. One of the biggest challenges in preventing DDIs is the substantial gap between theory and clinical practice. There are no robust methods published for formally assessing quality of evidence relating to DDIs, despite the diverse sources of information. We defined a transparent, structured process for developing evidence quality summaries in order to guide therapeutic decision making. This was applied to a systematic review of DDI data with considerable public health significance: HIV and malaria. METHODS AND FINDINGS: This was a systematic review of DDI data between antiretrovirals and drugs used in prophylaxis and treatment of malaria. The data comprised all original research in humans that evaluated pharmacokinetic data and/or related adverse events when antiretroviral agents were combined with antimalarial agents, including healthy volunteers, patients with HIV and/or malaria, observational studies, and case reports. The data synthesis included 36 articles and conference presentations published via PubMed and conference websites/abstract books between 1987-August 2016. There is significant risk of DDIs between HIV protease inhibitors, or NNRTIs and artemesinin-containing antimalarial regimens. For many antiretrovirals, DDI studies with antimalarials were lacking, and the majority were of moderate to very low quality. Quality of evidence and strength of recommendation categories were defined and developed specifically for recommendations concerning DDIs. CONCLUSIONS: There is significant potential for DDIs between antiretrovirals and antimalarials. The application of quality of evidence and strength of recommendation criteria to DDI data is feasible, and allows the assessment of DDIs to be robust, consistent, transparent and evidence-based.

Cobicistat versus ritonavir boosting and differences in the drug-drug interaction profiles with co-medications.

Nearly all HIV PIs and the integrase inhibitor elvitegravir require a pharmacokinetic enhancer in order to achieve therapeutic plasma concentrations at the desired dose and frequency. Whereas ritonavir has been the only available pharmacokinetic enhancer for more than a decade, cobicistat has recently emerged as an alternative boosting agent. Cobicistat and ritonavir are equally strong inhibitors of cytochrome P450 (CYP) 3A4 and consequently were shown to be equivalent pharmacokinetic enhancers for elvitegravir and for the PIs atazanavir and darunavir. Since cobicistat is a more selective CYP inhibitor than ritonavir and is devoid of enzyme-inducing properties, differences are expected in their interaction profiles with some co-medications. Drugs whose exposure might be altered by ritonavir but unaltered by cobicistat are drugs primarily metabolized by CYP1A2, CYP2B6, CYP2C8, CYP2C9 and CYP2C19 or drugs undergoing mainly glucuronidation. Thus, co-medications should be systematically reviewed when switching the pharmacokinetic enhancer to anticipate potential dosage adjustments.

Effects of age on antiretroviral plasma drug concentration in HIV-infected subjects undergoing routine therapeutic drug monitoring.

OBJECTIVES: The pharmacokinetic and pharmacodynamic effects of antiretroviral therapy may differ in older compared with younger subjects with HIV infection. We aimed to assess factors associated with plasma antiretroviral drug exposure, including age, within a large HIV-infected cohort undergoing therapeutic drug monitoring (TDM). METHODS: Data from the Liverpool TDM Registry were linked with the UK Collaborative HIV Cohort (CHIC) Study. All TDM of protease inhibitors (PIs) and non-nucleoside reverse transcriptase inhibitors (NNRTIs) was included and in order to account for different antiretroviral drugs the plasma concentrations were standardized by group measurements according to drug, dosing and timing of TDM. Regression modelling was used to evaluate associations of drug exposure with age and clinical parameters, including hepatic transaminase results and time to antiretroviral treatment modification. RESULTS: Data from 3589 TDM samples were available from 2447 subjects. The greatest numbers of plasma concentrations were assessed for lopinavir (22.4%), efavirenz (18.5%), atazanavir (17.0%) and saquinavir (11.6%). As age increased, median standardized NNRTI concentrations remained constant, whereas PI concentrations increased (correlation coefficient 0.04, P = 0.033). In a regression analysis stratified by antiretroviral drug class, standardized plasma concentrations were significantly associated with age for PIs (0.05 increase in standard deviation of drug concentration with each 10 year increase in age, P = 0.044), but not for NNRTIs or other clinical parameters, including hepatic transaminase results or time to antiretroviral treatment modification. CONCLUSIONS: With increasing age, statistically significant rises in plasma PI exposure, but not NNRTI exposure, were observed. The clinical relevance of this observation merits further investigation.

Prevalence of potential drug-drug interactions involving antiretroviral drugs in a large Kenyan cohort.

BACKGROUND: Clinically significant drug-drug interactions (CSDIs) involving antiretrovirals are frequent and under-recognized in developed countries, but data are lacking for developing countries. METHODOLOGY AND PRINCIPAL FINDINGS: To investigate the prevalence of CSDIs between antiretrovirals and coadministered drugs, we surveyed prescriptions dispensed in a large HIV clinic in Kenya. Of 1040 consecutive patients screened, 996 were eligible for inclusion. CSDIs were defined as 'major' (capable of causing severe or permanent damage, contraindicated, avoid or not recommended by the manufacturer, or requiring dose modification) or 'moderate' (manufacturers advise caution, or close monitoring, or capable of causing clinical deterioration). A total of 334 patients (33.5%) were at risk for a CSDI, potentially lowering antiretroviral drug concentrations in 120 (12%) patients. Major interactions most frequently involved rifampicin (12.4%, mostly with efavirenz) and azoles (2.7%) whereas moderate interactions were frequently azoles (13%), steroids (11%), and antimalarials (3%). Multivariable analyses suggested that patients at risk for CSDIs had lower CD4 counts (P = 0.006) and baseline weight (P = 0.023) and WHO Stage 3 or 4 disease (P≤0.007). Risk for CSDIs was not associated with particular regimens, although only 116 (11.6%) patients were receiving WHO second line regimens. CONCLUSIONS: One in three patients receiving antiretrovirals in our programme were at risk of CSDIs. Strategies need to be urgently developed to avoid important drug interactions, to identify early markers of toxicity and to manage unavoidable interactions safely in order to reduce risk of harm, and to maximize the effectiveness of mass antiretroviral deployment in Africa.

Prevalence of comedications and effect of potential drug-drug interactions in the Swiss HIV Cohort Study.

BACKGROUND: Potential drug-drug interactions (PDDIs) might expand with new combination antiretroviral therapies (ART) and polypharmacy related to increasing age and comorbidities. We investigated the prevalence of comedications and PDDIs within a large HIV cohort, and their effect on ART efficacy and tolerability. METHODS: All medications were prospectively recorded in 1,497 ART-treated patients and screened for PDDIs using a customized version of the Liverpool drug interactions database. RESULTS: Overall, 68% (1,013/1,497) of patients had a comedication and 40% (599/1,497) had > or = 1 PDDI. Among patients with comedication, 2% (21/1,013) had red-flag interactions (contraindicated) and 59% (597/1,013) had orange-flag interactions (potential dose adjustment and/or close monitoring required). The latter involved mainly central nervous system drugs (49%), cardiovascular drugs (34%) and methadone (19%). In the multivariate analysis, factors associated with having a comedication were advanced age, female gender, obesity and HCV infection. Independent risk factors for PDDIs were regimens combining protease inhibitors and non-nucleoside reverse transcriptase inhibitors (odds ratio [OR] 3.06, 95% confidence interval [CI] 1.44-6.48), > or = 2 comedications (OR 1.89, 95% CI 1.32-2.70), current illicit drug use (OR 2.00, 95% CI 1.29-3.10) and patients with HCV infection (OR 1.74, 95% CI 1.19-2.56). Viral response was similar in patients with and without PDDIs (84.5% versus 86.4%; P=0.386). During follow-up, ART was modified in 134 patients with comedication regardless of the presence of PDDIs (P=0.524). CONCLUSIONS: PDDIs increase with complex ART and comorbidities. No adverse effect was noted on ART efficacy or tolerability; however, most PDDIs affected comedication but were manageable through dose adjustment or monitoring.

Recognition of risk for clinically significant drug interactions among HIV-infected patients receiving antiretroviral therapy.

We assessed the risk of clinically significant drug interactions in patients receiving antiretrovirals, and their recognition by physicians. Clinically significant drug interactions were recorded in 27% of 159 patients, with 15% of interactions potentially lowering antiretroviral concentrations. Risk of clinically significant drug interactions was significantly related to receipt of protease inhibitors. Only 36% of clinically significant drug interactions were correctly identified by physicians.

HIV protease inhibitors are substrates for OATP1A2, OATP1B1 and OATP1B3 and lopinavir plasma concentrations are influenced by SLCO1B1 polymorphisms.

OBJECTIVE: OATP1B1 and OATP1B3 are major hepatic drug transporters whilst OATP1A2 is mainly located in the brain but is also located in liver and several other organs. These transporters affect the distribution and clearance of many endobiotics and xenobiotics and have been reported to have functional single nucleotide polymorphisms (SNPs). We have assessed the substrate specificities of these transporters for a panel of antiretrovirals and investigated the effects of SNPs within these transporters on the pharmacokinetics of lopinavir. METHODS: SLCO1A2, SLCO1B1 and SLCO1B3 were cloned, verified and used to generate cRNA for use in the Xenopuslaevis oocyte transport system. Using the oocyte system, antiretrovirals were tested for their substrate specificities. Plasma samples (n=349) from the Liverpool therapeutic drug monitoring registry were genotyped for SNPs in SLCO1A2, SLCO1B1 and SLCO1B3 and associations between SNPs and lopinavir plasma concentrations were analysed. RESULT: Antiretroviral protease inhibitors, but not non-nucleoside reverse transcriptase inhibitors, are substrates for OATP1A2, OATP1B1 and OATP1B3. Furthermore, ritonavir was not an inhibitor of OATP1B1. The 521T>C polymorphism in SLCO1B1 was significantly associated with higher lopinavir plasma concentrations. No associations were observed with functional variants of SLCO1A2 and SLCO1B3. CONCLUSION: These data add to our understanding of the factors that contribute to variability in plasma concentrations of protease inhibitors. Further studies are now required to confirm the association of SLCO1B1 521T>C with lopinavir plasma concentrations and to assess the influence of other polymorphisms in the SLCO family.

Factors influencing lopinavir and atazanavir plasma concentration.

BACKGROUND: The protease inhibitors lopinavir and atazanavir are both recommended for treatment of HIV-infected patients. Considerable inter-individual variability in plasma concentration has been observed for both drugs. The aim of this study was to evaluate which demographic factors and concomitant drugs are associated with lopinavir and atazanavir plasma concentration. METHODS: Data from the Liverpool TDM (therapeutic drug monitoring) Registry were linked with the UK Collaborative HIV Cohort (CHIC) study. For each patient, the first measurement of lopinavir (twice daily) or atazanavir [once daily, ritonavir boosted (/r) or unboosted] plasma concentration was included. Linear regression was used to evaluate the association of dose, gender, age, weight, ethnicity and concomitant antiretroviral drugs or rifabutin with log-transformed drug concentration, adjusted for time since last intake. RESULTS: Data from 439 patients on lopinavir (69% 400 mg/r, 31% 533 mg/r; 3% concomitant rifabutin) and 313 on atazanavir (60% 300 mg/r, 32% 400 mg/r, 8% 400 mg) were included. Multivariable models revealed the following predictors for lopinavir concentration: weight (11% decrease per additional 10 kg; P = 0.001); dose (25% increase for 533 mg/r; P = 0.024); and rifabutin (116% increase; P < 0.001). For atazanavir the predictors were dose (compared with 300 mg/r: 40% increase for 400 mg/r, 67% decrease for 400 mg; overall P < 0.001) and efavirenz (32% decrease; P = 0.016) but not tenofovir (P = 0.54). CONCLUSIONS: This analysis confirms that efavirenz decreases atazanavir concentrations, and there was a negative association of weight and lopinavir concentrations. The strong impact of rifabutin on lopinavir concentration should be studied further.