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Background: Critical drug-drug interactions (DDI) and hepatotoxicity complicate concurrent use of rifampicin and protease inhibitors. We investigated whether dose escalation of atazanavir/ritonavir could safely overcome the DDI with rifampicin., Methods: DERIVE (NCT04121195, EDCTP) was a dose-escalation trial in people with human immunodeficiency virus (HIV) on atazanavir/ritonavir-based antiretroviral therapy (ART) in Uganda. Four intensive pharmacokinetic (PK) visits were performed: PK1 300/100 mg OD (baseline); PK2 300/100 mg OD with rifampicin 600 mg; PK3 300/100 mg twice a day (BID) with rifampicin 600 mg OD; PK4 300/100 mg BID with rifampicin 1200 mg OD. Dolutegravir 50 mg BID throughout the study period ensured participants remained protected from subtherapeutic atazanavir concentrations. The data were interpreted with noncompartmental analysis. The target minimum concentration was atazanavir's protein-adjusted IC90 (PA-IC90), 0.014 mg/L., Results: We enrolled 26 participants (23 female) with median (range) age 44 (28-61) years and weight 67 (50-75) kg. Compared with PK1, atazanavir Ctau, and AUC were significantly reduced at PK2 by 96% and 85%, respectively. The escalation to BID dosing (PK3) reduced this difference in Ctau, and AUC24 to 18% lower and 8% higher, respectively. Comparable exposures were maintained with double doses of rifampicin. Lowest Ctau during PK1, PK3, and PK4 were 12.7-, 4.8-, and 8.6-fold higher than PA-IC90, respectively, whereas 65% of PK2 Ctau were below the limit of quantification (0.03 mg/L), hence likely below PA-IC90. No participant developed significant elevation of liver enzymes, reported a serious adverse event (SAE) or experienced rebound viraemia., Conclusions: Twice daily atazanavir/ritonavir during rifampicin co-administration was well tolerated and achieved plasma concentrations above the target., Clinical Trials Registration: NCT04121195. Registered on 09 October 2019, https://clinicaltrials.gov/ct2/show/NCT04121195., Competing Interests: Potential conflicts of interest. The authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

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., (© 2024. The Author(s).)

Age-related comorbidities and consequently polypharmacy are highly prevalent in the elderly, resulting in an increased risk for drug-drug interactions (DDIs). The effect of aging on DDI magnitudes is mostly uncertain, leading to missing guidance regarding the clinical DDI management in the elderly. Clinical data obtained in aging people living with HIV ≥ 55 years, who participated in the Swiss HIV Cohort Study, demonstrated unchanged DDI magnitudes with advanced aging for four studied DDI scenarios. These data plus published data for midazolam in the presence of clarithromycin and rifampicin in elderly individuals assessed the predictive potential of the used physiologically-based pharmacokinetic (PBPK) model to simulate DDIs in the elderly. All clinically observed data were generally predicted within the 95% confidence interval of the PBPK simulations. The verified model predicted subsequently the magnitude of 50 DDIs across adulthood (20-99 years) with 42 scenarios being only verified in adults aged 20-50 years in the absence of clinically observed data in the elderly. DDI magnitudes were not impacted by aging regardless of the involved drugs, DDI mechanism, mediators of DDIs, or the sex of the investigated individuals. The prediction of unchanged DDI magnitudes with advanced aging were proofed by 17 published, independent DDIs that were investigated in young and elderly subjects. In conclusion, this study demonstrated by combining clinically observed data with modeling and simulation that aging does not impact DDI magnitudes and thus, clinical management of DDIs can a priori be similar in aging men and women in the absence of severe comorbidities., (© 2020 The Authors Clinical Pharmacology & Therapeutics © 2020 American Society for Clinical Pharmacology and Therapeutics.)

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., (© The Author(s) 2020. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: HIV-positive women receiving efavirenz-based ART and levonorgestrel contraceptive implants are at risk of low levonorgestrel exposure and unintended pregnancy., Objectives: To investigate clinically applicable dose-adjustment strategies to overcome the known drug-drug interaction (DDI) between levonorgestrel and efavirenz, using a physiologically based pharmacokinetic (PBPK) modelling-based approach., Methods: A PBPK model was qualified against clinical data to predict levonorgestrel plasma concentrations when standard-dose (150 mg) levonorgestrel implants were administered alone (control group), as well as when standard-dose or increased-dose (300 mg) levonorgestrel implants were coadministered with either 600 or 400 mg of efavirenz., Results: No difference was seen between in vivo clinical and PBPK-model-simulated levonorgestrel plasma concentrations (P > 0.05). Simulated levonorgestrel plasma concentrations were ∼50% lower at 48 weeks post-implant-placement in virtual individuals receiving standard-dose levonorgestrel with either 600 or 400 mg of efavirenz compared with the control group (efavirenz:control geometric mean ratio = 0.42 and 0.49, respectively). Conversely, increased-dose levonorgestrel in combination with either 600 or 400 mg of efavirenz was sufficient to restore levonorgestrel concentrations to levels similar to those observed in the 150 mg levonorgestrel control group 48 weeks post-implant-placement (efavirenz:control geometric mean ratio = 0.86 and 1.03, respectively)., Conclusions: These results suggest that the clinically significant DDI between efavirenz and levonorgestrel is likely to persist despite efavirenz dose reduction, whereas dose escalation of implantable levonorgestrel may represent a successful clinical strategy to circumvent efavirenz-levonorgestrel DDIs and will be of use to inform clinical trial design to assess coadministration of efavirenz and levonorgestrel implants.

Background: Co-administration of antineoplastics with ART is challenging due to potential drug-drug interactions (DDIs). However, trials specifically assessing such DDIs are lacking. Our objective was to simulate DDIs between the antineoplastics erlotinib and gefitinib with key antiretroviral drugs and to predict dose adjustments using a physiologically based pharmacokinetic (PBPK) model., Methods: In vitro data describing chemical properties and pharmacokinetic processes of each drug and their effect on cytochrome P450 isoforms were obtained from the literature. Plasma drug-concentration profiles were simulated in a virtual population of 50 individuals receiving erlotinib or gefitinib alone or with darunavir/ritonavir, efavirenz or etravirine. Simulated pharmacokinetic parameters and the magnitude of DDIs with probe drugs (midazolam, maraviroc) were compared with literature values. Erlotinib and gefitinib pharmacokinetics with and without antiretrovirals were compared and dose-adjustment strategies were evaluated., Results: Simulated parameters of each drug and the magnitude of DDIs with probe drugs were in agreement with reference values. Darunavir/ritonavir increased erlotinib and gefitinib exposure, while efavirenz and etravirine decreased erlotinib and gefitinib concentrations. Based on our predictions, dose-adjustment strategies may consist of once-daily dosing erlotinib at 25 mg and gefitinib at 125 mg with darunavir/ritonavir; or erlotinib at 200 mg and gefitinib at 375 mg with etravirine. The interaction with efavirenz was not overcome even after doubling erlotinib or gefitinib doses., Conclusions: PBPK models predicted the in vivo pharmacokinetics of erlotinib, gefitinib and the antiretrovirals darunavir/ritonavir, efavirenz and etravirine, and the DDIs between them. The simulated dose-adjustments may represent valuable strategies to optimize antineoplastic therapy in HIV-infected patients., (© The Author 2016. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Objectives: Treatment of HIV/TB coinfection is challenging due to potent drug-drug interactions between antiretrovirals and rifampicin. The effect of rifampicin on darunavir/ritonavir has not been studied. Population pharmacokinetic modelling was applied to investigate the interaction and generate alternative doses to inform clinical trial design., Patients and Methods: Darunavir/ritonavir concentrations were modelled simultaneously, including data from three studies in HIV patients (n = 51, 7 female). The darunavir/ritonavir-rifampicin interaction was assumed to mimic that previously observed with lopinavir/ritonavir. Daily darunavir/ritonavir 800/100 mg was simulated as a reference (n = 1000; -rifampicin). Simulations with apparent oral clearance increased by 71% and 36% and relative bioavailability decreased by 20% and 45% for darunavir and ritonavir, respectively, were performed for +rifampicin, 600 mg once daily (n = 1000). Darunavir/ritonavir 1200/200 mg once daily, 1600/200 mg once daily, 800/100 mg twice daily and 1200/150 mg twice daily +rifampicin were simulated. Darunavir parameters for each dose +rifampicin were compared with -rifampicin by geometric mean ratio (90% CI)., Results: A maximum effect model, with ritonavir inhibiting darunavir clearance, best described the relationship between the drugs. Compared with -rifampicin, simulated darunavir AUC0-24 was 57%, 26%, 1% and 16% lower for 800/100 mg once daily, 1200/200 mg once daily, 1600/200 mg once daily and 800/100 mg twice daily +rifampicin, respectively; but 39% higher with 1200/150 mg twice daily +rifampicin., Conclusions: Darunavir/ritonavir 1600/200 mg once daily, 800/100 mg twice daily and 1200/150 mg twice daily could potentially overcome reduced darunavir concentrations with rifampicin. In the absence of clinical data, modelling and simulation may be useful to predict drug-drug interactions and aid optimal dose selection., (© The Author 2015. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Introduction: Metabolic inducers can expose people with polypharmacy to adverse health outcomes. A limited fraction of potential drug–drug interactions (DDIs) have been or can ethically be studied in clinical trials, leaving the vast majority unexplored. In the present study, an algorithm has been developed to predict the induction DDI magnitude, integrating data related to drug-metabolising enzymes. Methods: The area under the curve ratio (AUCratio) resulting from the DDI with a victim drug in the presence and absence of an inducer (rifampicin, rifabutin, efavirenz, or carbamazepine) was predicted from various in vitro parameters and then correlated with the clinical AUCratio (N = 319). In vitro data including fraction unbound in plasma, substrate specificity and induction potential for cytochrome P450s, phase II enzymes and uptake, and efflux transporters were integrated. To represent the interaction potential, the in vitro metabolic metric (IVMM) was generated by combining the fraction of substrate metabolised by each hepatic enzyme of interest with the corresponding in vitro fold increase in enzyme activity (E) value for the inducer. Results: Two independent variables were deemed significant and included in the algorithm: IVMM and fraction unbound in plasma. The observed and predicted magnitudes of the DDIs were categorised accordingly: no induction, mild, moderate, and strong induction. DDIs were assumed to be well classified if the predictions were in the same category as the observations, or if the ratio between these two was < 1.5-fold. This algorithm correctly classified 70.5% of the DDIs. Conclusion: This research presents a rapid screening tool to identify the magnitude of potential DDIs utilising in vitro data which can be highly advantageous in early drug development. [ABSTRACT FROM AUTHOR]

Despite the advancement of antiretroviral therapy (ART) for the treatment of human immunodeficiency virus (HIV), drug–drug interactions (DDIs) remain a relevant clinical issue for people living with HIV receiving ART. Antiretroviral (ARV) drugs can be victims and perpetrators of DDIs, and a detailed investigation during drug discovery and development is required to determine whether dose adjustments are necessary or coadministrations are contraindicated. Maintaining therapeutic ARV plasma concentrations is essential for successful ART, and changes resulting from potential DDIs could lead to toxicity, treatment failure, or the emergence of ARV‐resistant HIV. The challenges surrounding DDI management are complex in special populations of people living with HIV, and often lack evidence‐based guidance as a result of their underrepresentation in clinical investigations. Specifically, the prevalence of hepatic and renal impairment in people living with HIV are between five and 10 times greater than in people who are HIV‐negative, with each condition constituting approximately 15% of non‐AIDS‐related mortality. Therapeutic strategies tend to revolve around the treatment of risk factors that lead to hepatic and renal impairment, such as hepatitis C, hepatitis B, hypertension, hyperlipidemia, and diabetes. These strategies result in a diverse range of potential DDIs with ART. The purpose of this review was 2‐fold. First, to summarize current pharmacokinetic DDIs and their mechanisms between ARVs and co‐medications used for the prevention and treatment of hepatic and renal impairment in people living with HIV. Second, to identify existing knowledge gaps surrounding DDIs related to these special populations and suggest areas and techniques to focus upon in future research efforts. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to simulate the drug–drug interaction (DDI) between ritonavir-boosted atazanavir (ATV/r) and rifampicin (RIF) using physiologically based pharmacokinetic (PBPK) modelling, and to predict suitable dose adjustments for ATV/r for the treatment of people living with HIV (PLWH) co-infected with tuberculosis. Methods: A whole-body DDI PBPK model was designed using Simbiology 9.6.0 (MATLAB R2019a) and verified against reported clinical data for all drugs administered alone and concomitantly. The model contained the induction mechanisms of RIF and ritonavir (RTV), the inhibition effect of RTV for the enzymes involved in the DDI, and the induction and inhibition mechanisms of RIF and RTV on the uptake and efflux hepatic transporters. The model was considered verified if the observed versus predicted pharmacokinetic values were within twofold. Alternative ATV/r dosing regimens were simulated to achieve the trough concentration (Ctrough) clinical cut-off of 150 ng/mL. Results: The PBPK model was successfully verified according to the criteria. Simulation of different dose adjustments predicted that a change in regimen to twice-daily ATV/r (300/100 or 300/200 mg) may alleviate the induction effect of RIF on ATV Ctrough, with > 95% of individuals predicted to achieve Ctrough above the clinical cut-off. Conclusions: The developed PBPK model characterized the induction-mediated DDI between RIF and ATV/r, accurately predicting the reduction of ATV plasma concentrations in line with observed clinical data. A change in the ATV/r dosing regimen from once-daily to twice-daily was predicted to mitigate the effect of the DDI on the Ctrough of ATV, maintaining plasma concentration levels above the therapeutic threshold for most patients. [ABSTRACT FROM AUTHOR]

Objectives: The World Health Organization recommends that all countries adopt dolutegravir‐based antiretroviral therapy as the preferred regimen for all individuals living with HIV. Levonorgestrel is a commonly used hormonal contraceptive, which undergoes drug–drug interactions with some antiretrovirals, but the potential interaction between dolutegravir and levonorgestrel has not been examined. We aimed to evaluate cytochrome P450 (CYP)‐mediated levonorgestrel metabolism and quantify the effects of dolutegravir on levonorgestrel apparent intrinsic clearance (CLint.app.) and CYP gene expression. Methods: In vitro CYP‐mediated CLint.app. of levonorgestrel was quantified using a recombinant human CYP (rhCYP) enzyme system. A primary human hepatocyte model of drug metabolism was used to assess the effects of dolutegravir on (1) levonorgestrel CLint.app., using liquid chromatography‐tandem mass spectrometry, and (2) the expression of specific CYP enzymes, using quantitative real‐time polymerase chain reaction. Results: Levonorgestrel clearance was mediated by multiple rhCYPs, including rhCYP3A4. Under control conditions, levonorgestrel CLint.app. was 22.4 ± 5.0 μL/min/106 hepatocytes. Incubation with 43.1 nM of unbound dolutegravir elevated levonorgestrel CLint.app. to 31.4 ± 7.8 µL/min/106 hepatocytes (P = 0.168), while 142.23 nM increased levonorgestrel CLint.app. to 37.0 ± 2.9 µL/min/106 hepatocytes (P = 0.012). Unbound dolutegravir ≥ 431 nM induced expression of CYP3A4 (≥ two‐fold) in a dose‐dependent manner, while 1.44 μM of unbound dolutegravir induced CYP2B6 expression 2.2 ± 0.3‐fold (P = 0.0004). Conclusions: In summary, this in vitro study suggests that dolutegravir has the potential to increase hepatic clearance of levonorgestrel by inducing both CYP3A and non‐CYP3A enzymes. The observed in vitro dolutegravir–levonorgestrel drug–drug interaction should be further examined in clinical studies. [ABSTRACT FROM AUTHOR]

Background: We previously demonstrated that etonogestrel concentrations were 82% lower in women using etonogestrel contraceptive implants plus efavirenz-based ART compared with women not receiving ART.Objectives: To investigate the genetic contribution to this previously observed drug-drug interaction through studying SNPs in genes known to be involved in efavirenz, nevirapine or etonogestrel metabolism in the same group of women.Patients and Methods: Here, we present a secondary analysis evaluating SNPs involved in efavirenz, nevirapine and etonogestrel metabolism and associated etonogestrel pharmacokinetics among 57 women, 19 not receiving ART (control group), 19 receiving efavirenz- (600 mg daily) based ART and 19 receiving nevirapine- (200 mg twice daily) based ART. Associations between patient genotype and etonogestrel pharmacokinetic parameters were determined through univariate and multivariate linear regression. This study was registered at clinicaltrials.gov (NCT02082652).Results: Within the control group, CYP2B6 983 T>C was associated with 27% higher etonogestrel Cmax and 28% higher AUC0-24weeks. In the efavirenz group CYP2B6 516 G>T was associated with 43% lower etonogestrel Cmin and 34% lower AUC0-24weeks. For participants receiving nevirapine, NR1I2 63396 C>T was associated with 39% lower etonogestrel Cmin and 37% lower AUC0-24weeks.Conclusions: This study demonstrates the influence of pharmacogenetics on the extent of drug-drug interactions between etonogestrel and efavirenz- or nevirapine-based ART. Efavirenz plus the etonogestrel contraceptive implant results in a detrimental drug-drug interaction irrespective of patient genetics, which is worsened in women possessing variant alleles for these CYP2B6 SNPs. [ABSTRACT FROM AUTHOR]

Background: HIV-positive women receiving efavirenz-based ART and levonorgestrel contraceptive implants are at risk of low levonorgestrel exposure and unintended pregnancy.Objectives: To investigate clinically applicable dose-adjustment strategies to overcome the known drug-drug interaction (DDI) between levonorgestrel and efavirenz, using a physiologically based pharmacokinetic (PBPK) modelling-based approach.Methods: A PBPK model was qualified against clinical data to predict levonorgestrel plasma concentrations when standard-dose (150 mg) levonorgestrel implants were administered alone (control group), as well as when standard-dose or increased-dose (300 mg) levonorgestrel implants were coadministered with either 600 or 400 mg of efavirenz.Results: No difference was seen between in vivo clinical and PBPK-model-simulated levonorgestrel plasma concentrations (P > 0.05). Simulated levonorgestrel plasma concentrations were ∼50% lower at 48 weeks post-implant-placement in virtual individuals receiving standard-dose levonorgestrel with either 600 or 400 mg of efavirenz compared with the control group (efavirenz:control geometric mean ratio = 0.42 and 0.49, respectively). Conversely, increased-dose levonorgestrel in combination with either 600 or 400 mg of efavirenz was sufficient to restore levonorgestrel concentrations to levels similar to those observed in the 150 mg levonorgestrel control group 48 weeks post-implant-placement (efavirenz:control geometric mean ratio = 0.86 and 1.03, respectively).Conclusions: These results suggest that the clinically significant DDI between efavirenz and levonorgestrel is likely to persist despite efavirenz dose reduction, whereas dose escalation of implantable levonorgestrel may represent a successful clinical strategy to circumvent efavirenz-levonorgestrel DDIs and will be of use to inform clinical trial design to assess coadministration of efavirenz and levonorgestrel implants. [ABSTRACT FROM AUTHOR]

Background: Antiretroviral drugs are among the therapeutic agents with the highest potential for drug-drug interactions (DDIs). In the absence of clinical data, DDIs are mainly predicted based on preclinical data and knowledge of the disposition of individual drugs. Predictions can be challenging, especially when antiretroviral drugs induce and inhibit multiple cytochrome P450 (CYP) isoenzymes simultaneously.Methods: This study predicted the magnitude of the DDI between efavirenz, an inducer of CYP3A4 and inhibitor of CYP2C8, and dual CYP3A4/CYP2C8 substrates (repaglinide, montelukast, pioglitazone, paclitaxel) using a physiologically based pharmacokinetic (PBPK) modeling approach integrating concurrent effects on CYPs. In vitro data describing the physicochemical properties, absorption, distribution, metabolism, and elimination of efavirenz and CYP3A4/CYP2C8 substrates as well as the CYP-inducing and -inhibitory potential of efavirenz were obtained from published literature. The data were integrated in a PBPK model developed using mathematical descriptions of molecular, physiological, and anatomical processes defining pharmacokinetics. Plasma drug-concentration profiles were simulated at steady state in virtual individuals for each drug given alone or in combination with efavirenz. The simulated pharmacokinetic parameters of drugs given alone were compared against existing clinical data. The effect of efavirenz on CYP was compared with published DDI data.Results: The predictions indicate that the overall effect of efavirenz on dual CYP3A4/CYP2C8 substrates is induction of metabolism. The magnitude of induction tends to be less pronounced for dual CYP3A4/CYP2C8 substrates with predominant CYP2C8 metabolism.Conclusion: PBPK modeling constitutes a useful mechanistic approach for the quantitative prediction of DDI involving simultaneous inducing or inhibitory effects on multiple CYPs as often encountered with antiretroviral drugs. [ABSTRACT FROM AUTHOR]

The images or other third-party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. Correction to: Predicting Drug-Drug Interactions between Rifampicin and Ritonavir-Boosted Atazanavir Using PBPK Modelling If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. [Extracted from the article]

Background and Objective: The rate of depression in patients with HIV is higher than in the general population. The use of antidepressants can have a beneficial effect, improving antiretroviral therapy adherence and consequently their efficacy and safety. Efavirenz and protease inhibitor boosted with ritonavir are major components of the antiretroviral therapy and are inducers and/or inhibitors of several cytochrome P450 (CYP) isoforms. Although antidepressants are prescribed to a significant proportion of patients treated with antiretrovirals, there are limited clinical data on drug-drug interactions. The aim of this study was to predict the magnitude of drug-drug interactions among efavirenz, boosted protease inhibitors and the most commonly prescribed antidepressants using an in vitro-in vivo extrapolation (IVIVE) model simulating virtual clinical trials. Methods: In vitro data describing the chemical characteristics, and absorption, distribution, metabolism and elimination (ADME) properties of efavirenz, boosted protease inhibitors and the most commonly prescribed antidepressants were obtained from published literature or generated by standard methods. Pharmacokinetics and drug-drug interaction were simulated using the full physiologically based pharmacokinetic model implemented in the Simcyp™ ADME simulator. The robustness of our modeling approach was assessed by comparing the magnitude of simulated drug-drug interactions using probe drugs to that observed in clinical studies. Results: Simulated pharmacokinetics and drug-drug interactions were in concordance with available clinical data. Although the simulated drug-drug interactions with antidepressants were overall weak to moderate according to the classification of the US FDA, fluoxetine and venlafaxine represent better candidates from a pharmacokinetic standpoint for patients on efavirenz and venlafaxine or citalopram for patients on boosted protease inhibitors. Conclusion: The modest magnitude of interaction could be explained by the fact that antidepressants are substrates of multiple isoforms and thus metabolism can still occur through CYPs that are weakly impacted by efavirenz or boosted protease inhibitors. These findings indicate that IVIVE is a useful tool for predicting drug-drug interactions and designing prospective clinical trials, giving insight into the variability of exposure, sample size and time-dependent induction or inhibition. [ABSTRACT FROM AUTHOR]

Objectives To describe the pharmacokinetics of maraviroc when dosed at 150 or 300 mg once daily with 800/100 mg of darunavir/ritonavir. Methods A retrospective case-note review of HIV-infected adults taking maraviroc was conducted. Patients on a maraviroc-based regimen for a minimum of 5 weeks were grouped as receiving: (i) 300 mg of maraviroc twice daily with 245 mg of tenofovir/200 mg of emtricitabine; (ii) 300 mg of maraviroc once daily with 800/100 mg of darunavir/ritonavir once daily; and (iii) 150 mg of maraviroc once daily with 800/100 mg of darunavir/ritonavir once daily. Ctrough and Cpeak data were collected at 2, 12 or 24 h post-dose. Results Sixty-six patients were included, providing 115 samples. The median (IQR) Cpeak was 378 (350–640) ng/mL for 300 mg of maraviroc twice daily with 245 mg of tenofovir/200 mg of emtricitabine (n = 9), 728 (378–935) ng/mL for 300 mg of maraviroc once daily with darunavir/ritonavir (n = 29) and 364 (104–624) ng/mL for 150 mg of maraviroc once daily with darunavir/ritonavir (n = 2; P = 0.24). The median (IQR) Ctrough was 46 (33–61) ng/mL for 300 mg of maraviroc twice daily with 245 mg of tenofovir/200 mg of emtricitabine (n = 12), 70 (49–97) ng/mL for 300 mg of maraviroc once daily with darunavir/ritonavir (n = 34) and 43 (35–55) ng/mL for 150 mg of maraviroc once daily with darunavir/ritonavir (n = 17; P = 0.001). The maraviroc Ctrough in black patients (n = 34) was 61 (45–110) ng/mL and in white patients (n = 29) it was 49 (42–70) ng/mL (P = 0.04). The Cpeak in black patients (n = 20) was 800 (397–1060) ng/mL versus 387 (336–723) ng/mL in white patients (n = 20; P = 0.02). Conclusions Once daily coadministration of 300 mg of maraviroc with 800/100 mg of darunavir/ritonavir was well tolerated and had favourable pharmacokinetics when compared with 300 mg of maraviroc twice daily with 245 mg of tenofovir/200 mg of emtricitabine. A 24% higher Ctrough and 107% higher Cpeak was seen in black patients compared with white patients. [ABSTRACT FROM PUBLISHER]

A letter to the editor is presented in response to the article about HIV protease inhibitor tipranavir, which appeared in the previous issue.

Levonorgestrel (LNG) is a synthetic progestin that is available in oral contraceptive tablets, a subdermal implant, and an intrauterine system for contraception. LNG pharmacokinetics are a pivotal determinant of contraceptive efficacy and essential in assessing drug-drug interactions influencing LNG exposure following different routes of LNG administration. A highly sensitive LC-MS/MS method was developed and validated to quantify levonorgestrel in human plasma. Liquid-liquid extraction was utilized with a sample volume of 500 μL to extract levonorgestrel from plasma. Chromatographic separation of LNG was achieved with a Fortis™ C18 (3 μm: 100 mm × 2.1 mm) reverse phase analytical column. The mobile phases consisted of de-ionized water plus 0.1% NH 4 OH (100:0.1%, v/v) (A), and methanol plus 0.1% NH 4 OH (100:0.1%, v/v) (B) delivered as a gradient at a flow rate of 400 μL/min. Detection of LNG and internal standard (D-(−)-norgestrel-d7) was achieved using positive polarity mode monitoring at 313.2–245.2 amu and 320.1–251.2 amu, respectively. The assay was linear over the calibration range of 49.6 to 1500 pg/mL. This method was used to quantify plasma LNG released by subdermal implant in support of a drug interaction study among women with HIV receiving efavirenz- or nevirapine-based antiretroviral therapy. [ABSTRACT FROM AUTHOR]

We evaluated the pharmacokinetics of double-dose levonorgestrel (LNG) implants to overcome the drug–drug interaction with efavirenz-based antiretroviral therapy (ART). We conducted a nonrandomized, open-label, parallel-group, longitudinal pharmacokinetic study among Ugandan women ages 18–45 years. Participants with HIV on ART containing efavirenz 600 mg received 300 mg of LNG implants (Jadelle®, Bayer, New Zealand): 300LNG+ART group. We compared our outcomes with women without HIV using standard dose, 150 mg of LNG implants: 150LNG group. The implant was placed on day zero in both groups, and we quantified plasma LNG concentrations over 48 weeks post implant insertion. LNG pharmacokinetic parameters were estimated using noncompartmental techniques. Our primary outcome was the geometric mean ratio with 90% confidence intervals of LNG area under the concentration–time curve over 24 weeks (AUC 0–24w) between groups. Demographic data were described as median (interquartile range). A secondary outcome compared between-group percent of LNG concentrations ≥300 pg/mL, a minimum threshold selected a priori based on observed pregnancies in Ugandan women on standard-dose LNG implants plus efavirenz. We enrolled 27 women in the 300LNG+ART group (34 [28.0 to 40.5] years and 61.0 [49.8–66.0] kg) and 19 women in the 150LNG group (33 [30.0 to 34.5] years and 64.9 [59.0 to 74.5] kg). LNG AUC 0–24w was 34% lower for 300LNG+ART versus 150LNG (geometric mean 9998 vs. 15,231 pg*week/mL, respectively [geometric mean ratio 0.66 (90% confidence intervals, 0.54 to 0.80)]). The percentage of participants with LNG concentrations ≥300 pg/mL was not statistically different between groups at week 24 (300LNG+ART: 74.1%; 150LNG: 89.5%; p = 0.27). Double-dose LNG implant did not completely overcome the drug–drug interaction with efavirenz. In women using ART containing efavirenz, placing two implant systems (300 mg) did not normalize LNG pharmacokinetics compared with the standard-dose implant (150 mg), and some women had evidence of ovulatory activity. Alternative ART without drug–drug interactions, such as dolutegravir, is recommended with contraceptive implants. [ABSTRACT FROM AUTHOR]