Complex drug interactions of HIV protease inhibitors 1: inactivation, induction, and inhibition of cytochrome P450 3A by ritonavir or nelfinavir

Prediction of Drug-Drug Interactions for Highly Plasma Protein Bound Compounds

Accurate prediction of drug-drug interactions (DDI) from in vitro data is important, as it provides insights on clinical DDI risk and study design. Historically, the lower limit of plasma fraction unbound (fu,p) is set at 1% for DDI prediction of highly bound compounds by the regulatory agencies due to the uncertainty of the fu,p measurements. This leads to high false positive DDI predictions for highly bound compounds. The recently published ICH M12 DDI guideline allows the use of experimental fu,p for DDI prediction of highly bound compounds. To further build confidence in DDI prediction of highly bound compounds using experimental fu,p values, we evaluated a set of drugs with fu,p < 1% and clinical DDI > 20% using both basic and mechanistic static models. All the compounds evaluated were flagged for DDI risk with the mechanistic model using experimental fu,p values. There was no false negative DDI prediction. Similarly, using the basic model, the DDI risk of all the compounds was identified except for CYP2D6 inhibition of almorexant. The totality of the data demonstrates that the DDI potential of highly bound compounds can be predicted accurately when actual protein binding numbers are measured.

No dose adjustment of metformin or substrates of organic cation transporters (OCT)1 and OCT2 and multidrug and toxin extrusion protein (MATE)1/2K with fostemsavir coadministration based on modeling approaches

Fostemsavir is an approved gp120-directed attachment inhibitor and prodrug for the treatment of human immunodeficiency virus type 1 infection in combination with other antiretrovirals (ARVs) in heavily treatment-experienced adults with multi-drug resistance, intolerance, or safety concerns with their current ARV regimen. Initial in vitro studies indicated that temsavir, the active moiety of fostemsavir, and its metabolites, inhibited organic cation transporter (OCT)1, OCT2, and multidrug and toxin extrusion transporters (MATEs) at tested concentration of 100 uM, although risk assessment based on the current Food and Drug Administration in vitro drug-drug interaction (DDI) guidance using the mechanistic static model did not reveal any clinically relevant inhibition on OCTs and MATEs. However, a DDI risk was flagged with EMA static model predictions. Hence, a physiologically based pharmacokinetic (PBPK) model of fostemsavir/temsavir was developed to further assess the DDI risk potential of OCT and MATEs inhibition by temsavir and predict changes in metformin (a sensitive OCT and MATEs substrate) exposure. No clinically relevant impact on metformin concentrations across a wide range of temsavir concentrations was predicted; therefore, no dose adjustment is recommended for metformin when co-administered with fostemsavir.

Physiologically based Pharmacokinetic Model Validated to Enable Predictions Of Multiple Drugs in a Long-acting Drug-combination Nano-Particles (DcNP): Confirmation with 3 HIV Drugs, Lopinavir, Ritonavir, and Tenofovir in DcNP Products

Drug-Combination Nanoparticles (DcNP) are a novel drug delivery system designed for synchronized delivery of multiple drugs in a single, long-acting, and targeted dose. Unlike depot formulations, slowly releasing drug at the injection site into the blood, DcNP allows multiple-drug-in-combination to collectively distribute from the injection site into the lymphatic system. Two distinct classes of long-acting injectables products are proposed based on pharmacokinetic mechanisms. Class I involves sustained release at the injection site. Class II involves a drug-carrier complex composed of lopinavir, ritonavir, and tenofovir uptake and retention in the lymphatic system before systemic access as a part of the PBPK model validation. For clinical development, Class II long-acting drug-combination products, we leverage data from 3 nonhuman primate studies consisting of nine PK datasets: Study 1, varying fixed-dose ratios; Study 2, short multiple dosing with kinetic tails; Study 3, long multiple dosing (chronic). PBPK validation criteria were established to validate each scenario for all drugs. The models passed validation in 8 of 9 cases, specifically to predict Study 1 and 2, including PK tails, with ritonavir and tenofovir, fully passing Study 3 as well. PBPK model for lopinavir in Study 3 did not pass the validation due to an observable time-varying and delayed drug accumulation, which likely was due to ritonavir's CYP3A inhibitory effect building up during multiple dosing that triggered a mechanism-based drug-drug interaction (DDI). Subsequently, the final model enables us to account for this DDI scenario.

In vitro demonstration of antedrug mechanism of a pharmacokinetic booster to improve CYP3A4 substrates by CYP3A4-mediated metabolism inhibition

We previously reported novel benzyl-ether derivatives with an imidazole ring and a hydroxyl group (A-01) or carboxyl group (B-01) and esters (2 esters of A-01, and 7 esters of B-01) as pharmacokinetics (PK) boosters. This study demonstrates how these ester compounds embody the concept of a safe pharmacokinetic booster, with potent and transient inhibition of CYP3A4-mediated drug metabolism. As a model CYP3A4 substrate and CYP3A4 enzyme, midazolam (MDZ) and rat liver microsomes were used. A-01 inhibited MDZ metabolism significantly, while B-01 induced only slight inhibition. Although rat liver microsomes hydrolyzed the ester compounds over time, several ester compounds strongly inhibited MDZ metabolism. Due to the significant activity of A-01, A-01 esters affected MDZ metabolism, irrespective of hydrolysis state. Time-dependent inhibition evaluation indicated that the B-01 ester inhibition is not mechanism-based, as hydrolysis eliminated MDZ metabolism inhibition. We report that the B-01 esters significantly inhibit CYP3A4-mediated drug metabolism, and upon hydrolysis this property is eliminated. In conclusion, B-01 ester compounds may be safe PK boosters with antedrug characteristics.

Evaluating Drug Interactions between Ritonavir and Opioid Analgesics: Implications from Physiologically Based Pharmacokinetic Simulation

Several commonly used opioid analgesics, such as fentanyl, sufentanil, alfentanil, and hydrocodone, are by report primarily metabolized by the CYP3A4 enzyme. The concurrent use of ritonavir, a potent CYP3A4 inhibitor, can lead to significant drug interactions. Using physiologically based pharmacokinetic (PBPK) modeling and simulation, this study examines the effects of different dosing regimens of ritonavir on the pharmacokinetics of these opioids. The findings reveal that co-administration of ritonavir significantly increases the exposure of fentanyl analogs, with over a 10-fold increase in the exposure of alfentanil and sufentanil when given with ritonavir. Conversely, the effect of ritonavir on fentanyl exposure is modest, likely due to additional metabolism pathways. Additionally, the study demonstrates that the steady-state exposure of hydrocodone and its active metabolite hydromorphone can be increased by up to 87% and 95%, respectively, with concurrent use of ritonavir. The extended-release formulation of hydrocodone is particularly affected. These insights from PBPK modeling provide valuable guidance for optimizing opioid dosing and minimizing the risk of toxicity when used in combination with ritonavir-containing prescriptions.

Physiologically based pharmacokinetic modeling of ritonavir-oxycodone drug interactions and its implication for dosing strategy

The concomitant administration of ritonavir and oxycodone may significantly increase the plasma concentrations of oxycodone. This study was aimed to simulate DDI between ritonavir and oxycodone, a widely used opioid, and to formulate dosing protocols for oxycodone by using physiologically based pharmacokinetic (PBPK) modeling. We developed a ritonavir PBPK model incorporating induction and competitive and time-dependent inhibition of CYP3A4 and competitive inhibition of CYP2D6. The ritonavir model was evaluated with observed clinical pharmacokinetic data and validated for its CYP3A4 inhibition potency. We then used the model to simulate drug interactions between oxycodone and ritonavir under various dosing scenarios. The developed model captured the pharmacokinetic characteristics of ritonavir from clinical studies. The model also accurately predicts exposure changes of midazolam, triazolam, and oxycodone in the presence of ritonavir. According to model simulations, the steady-state maximum, minimum and average concentrations of oxycodone increased by up to 166% after co-administration with ritonavir, and the total exposure increased by approximately 120%. To achieve similar steady-state concentrations, halving the dose with an unchanged dosing interval or doubling the dosing interval with an unaltered single dose should be practical for oxycodone, whether formulated in uncoated or controlled-release tablets during long-term co-medication with ritonavir. The results revealed exposure-related risks of oxycodone-ritonavir interactions that have not been studied clinically and emphasized PBPK as a workable method to direct judicious dosage.

Effects of itraconazole and carbamazepine on the pharmacokinetics of nirmatrelvir/ritonavir in healthy adults

AIMS

The objective of this study was to evaluate the effects of a strong cytochrome P450 family (CYP) 3A4 inhibitor (itraconazole) and inducer (carbamazepine) on the pharmacokinetics and safety of nirmatrelvir/ritonavir.

METHODS

Pharmacokinetics were measured in two phase 1, open-label, fixed-sequence studies in healthy adults. During Period 1, oral nirmatrelvir/ritonavir 300 mg/100 mg twice daily was administered alone; during Period 2, it was administered with itraconazole or carbamazepine. Nirmatrelvir/ritonavir was administered as repeated doses or one dose in the itraconazole and carbamazepine studies, respectively. Nirmatrelvir and ritonavir plasma concentrations and adverse event (AE) rates in both periods were analysed.

RESULTS

Each study included 12 participants. Following administration of nirmatrelvir/ritonavir with itraconazole (Test) or alone (Reference), test/reference ratios of the adjusted geometric means (90% CIs) for nirmatrelvir AUCtau and Cmax were 138.82% (129.25%, 149.11%) and 118.57% (112.50%, 124.97%), respectively. After administration of nirmatrelvir/ritonavir with carbamazepine (Test) or alone (Reference), test/reference ratios (90% CIs) of the adjusted geometric means for nirmatrelvir AUCinf and Cmax were 44.50% (33.77%, 58.65%) and 56.82% (47.04%, 68.62%), respectively. Nirmatrelvir/ritonavir was generally safe when administered with or without itraconazole or carbamazepine. No serious or severe AEs were reported.

CONCLUSIONS

Coadministration of a strong CYP3A4 inhibitor with a strong CYP3A inhibitor used for pharmacokinetic enhancement (i.e., ritonavir) resulted in small increases in plasma nirmatrelvir exposure, whereas coadministration of a strong inducer substantially decreased systemic nirmatrelvir and ritonavir exposures suggesting a contraindication in the label with CYP3A4 strong inducers. Administration of nirmatrelvir/ritonavir alone or with itraconazole or carbamazepine was generally safe.

Leveraging physiologically based pharmacokinetic modeling to optimize dosing for lopinavir/ritonavir with rifampin in pediatric patients

STUDY OBJECTIVE

Treatment of HIV and tuberculosis co-infection leads to significant mortality in pediatric patients, and treatment can be challenging due to the clinically significant drug-drug interaction (DDI) between lopinavir/ritonavir (LPV/RTV) and rifampin. Doubling LPV/RTV results in insufficient lopinavir trough concentrations in pediatric patients. The objective of this study was to leverage physiologically based pharmacokinetic (PBPK) modeling to optimize the adjusted doses of LPV/RTV in children receiving the WHO-revised doses of rifampin (15 mg/kg daily).

DESIGN

Adult and pediatric PBPK models for LPV/RTV with rifampin were developed, including CYP3A and P-glycoprotein inhibition and induction.

SETTING (OR DATA SOURCE)

Data for LPV/RTV model development and evaluation were available from the pediatric AIDS Clinical Trials Group.

PATIENTS

Dosing simulations were next performed to optimize dosing in children (2 months to 8 years of age).

INTERVENTION

Exposure following super-boosted LPV/RTV with 10 and 15 mg/kg PO daily rifampin was simulated.

MEASUREMENTS AND MAIN RESULTS

Simulated parameters were within twofold observations for LPV, RTV, and rifampin in adults and children ≥2 weeks old. The model predicted that, in healthy adults receiving 400/100 mg oral LPV/RTV twice daily (BID), co-treatment with 600 mg oral rifampin daily decreased the steady-state area under the concentration vs. time curve of LPV by 79%, in line with the observed change of 75%. Simulated and observed concentration profiles were comparable for LPV/RTV (230/57.5 mg/m2 ) PO BID without rifampin and 230/230 mg/m2 LPV/RTV PO BID with 10 mg/kg PO daily rifampin in pediatric patients. Sixteen mg/kg of super-boosted LPV (LPV/RTV 1:1) PO BID with 15 mg/kg PO daily rifampin achieved simulated LPV troughs >1 mg/L in ≥93% of virtual children weighing 3.0-24.9 kg, which was comparable with 10 mg/kg PO daily rifampin.

CONCLUSIONS

Super-boosted LPV/RTV with 15 mg/kg rifampin achieves therapeutic LPV troughs in HIV/TB-infected simulated children.

The Potential Therapeutic Application of Simvastatin for Brain Complications and Mechanisms of Action

Statins are common drugs that are clinically used to reduce elevated plasma cholesterol levels. Based on their solubility, statins are considered to be either hydrophilic or lipophilic. Amongst them, simvastatin has the highest lipophilicity to facilitate its ability to cross the blood-brain barrier. Recent studies have suggested that simvastatin could be a promising therapeutic option for different brain complications and diseases ranging from brain tumors (i.e., medulloblastoma and glioblastoma) to neurological disorders (i.e., Alzheimer's disease, Parkinson's disease, and Huntington's disease). Specific mechanisms of disease amelioration, however, are still unclear. Independent studies suggest that simvastatin may reduce the risk of developing certain neurodegenerative disorders. Meanwhile, other studies point towards inducing cell death in brain tumor cell lines. In this review, we outline the potential therapeutic effects of simvastatin on brain complications and review the clinically relevant molecular mechanisms in different cases.

The inhibitory and inducing effects of ritonavir on hepatic and intestinal CYP3A and other drug-handling proteins

Ritonavir, originally developed as HIV protease inhibitor, is widely used as a booster in several HIV pharmacotherapy regimens and more recently in Covid-19 treatment (e.g., Paxlovid). Its boosting capacity is due to the highly potent irreversible inhibition of the cytochrome P450 (CYP) 3 A enzyme, thereby enhancing the plasma exposure to coadministered drugs metabolized by CYP3A. Typically used booster doses of ritonavir are 100-200 mg once or twice daily. This review aims to address several aspects of this booster drug, including the possibility to use lower ritonavir doses, 20 mg for instance, resulting in partial CYP3A inactivation in patients. If complete CYP3A inhibition is not needed, lower ritonavir doses could be used, thereby reducing unwanted side effects. In this context, there are contradictory reports on the actual recovery time of CYP3A activity after ritonavir discontinuation, but probably this will take at least one day. In addition to ritonavir's CYP3A inhibitory effect, it can also induce and/or inhibit other CYP enzymes and drug transporters, albeit to a lesser extent. Although ritonavir thus exhibits gene induction capacities, with respect to CYP3A activity the inhibition capacity clearly predominates. Another potent CYP3A inhibitor, the ritonavir analog cobicistat, has been reported to lack the ability to induce enzyme and transporter genes. This might result in a more favorable drug-drug interaction profile compared to ritonavir, although the actual benefit appears to be limited. Indeed, ritonavir is still the clinically most used pharmacokinetic enhancer, indicating that its side effects are well manageable, even in chronic administration regimens.

Drug-drug interactions that alter the exposure of glucuronidated drugs: Scope, UDP-glucuronosyltransferase (UGT) enzyme selectivity, mechanisms (inhibition and induction), and clinical significance

Drug-drug interactions (DDIs) arising from the perturbation of drug metabolising enzyme activities represent both a clinical problem and a potential economic loss for the pharmaceutical industry. DDIs involving glucuronidated drugs have historically attracted little attention and there is a perception that interactions are of minor clinical relevance. This review critically examines the scope and aetiology of DDIs that result in altered exposure of glucuronidated drugs. Interaction mechanisms, namely inhibition and induction of UDP-glucuronosyltransferase (UGT) enzymes and the potential interplay with drug transporters, are reviewed in detail, as is the clinical significance of known DDIs. Altered victim drug exposure arising from modulation of UGT enzyme activities is relatively common and, notably, the incidence and importance of UGT induction as a DDI mechanism is greater than generally believed. Numerous DDIs are clinically relevant, resulting in either loss of efficacy or an increased risk of adverse effects, necessitating dose individualisation. Several generalisations relating to the likelihood of DDIs can be drawn from the known substrate and inhibitor selectivities of UGT enzymes, highlighting the importance of comprehensive reaction phenotyping studies at an early stage of drug development. Further, rigorous assessment of the DDI liability of new chemical entities that undergo glucuronidation to a significant extent has been recommended recently by regulatory guidance. Although evidence-based approaches exist for the in vitro characterisation of UGT enzyme inhibition and induction, the availability of drugs considered appropriate for use as 'probe' substrates in clinical DDI studies is limited and this should be research priority.

Pharmacokinetic Drug-Drug Interactions Involving Antiretroviral Agents: An Update

Antiretroviral therapy is the recognized treatment for human immunodeficiency virus (HIV) infection involving several antiviral agents. Even though highly active antiretroviral therapy has been proven to be very effective in suppressing HIV replication, the antiretroviral drugs, belonging to different pharmacological classes, present quite complex pharmacokinetic properties such as extensive drug metabolism and transport by membrane-associated drug carriers. Moreover, due to uncomplications or complications in HIV-infected populations, an antiretroviralbased multiple-drug coadministration therapy strategy is usually applied for treatment effect, thus raising the possibility of drug-drug interactions between antiretroviral drugs and common drugs such as opioids, stains, and hormonal contraceptives. Herein, thirteen classical antiretroviral drugs approved by US Food and Drug Administration were summarized. Besides, relative drug metabolism enzymes and transporters known to interact with those antiretroviral drugs were detailed and described. Furthermore, one after the summarized antiretroviral drugs, the drug-drug interactions between two antiretroviral drugs or antiretroviral drug - conventional medical drugs of the past decade were discussed and summarized. This review is intended to deepen the pharmacological understanding of antiretroviral drugs and promote more secure clinical applications for antiretroviral drugs to treat HIV.

Studying the right transporter at the right time: an in vitro strategy for assessing drug-drug interaction risk during drug discovery and development

INTRODUCTION

Transporters are significant in dictating drug pharmacokinetics, thus inhibition of transporter function can alter drug concentrations resulting in drug-drug interactions (DDIs). Because they can impact drug toxicity, transporter DDIs are a regulatory concern for which prediction of clinical effect from in vitro data is critical to understanding risk.

AREA COVERED

The authors propose in vitro strategies to assist mitigating/removing transporter DDI risk during development by frontloading specific studies, or managing patient risk in the clinic. An overview of clinically relevant drug transporters and observed DDIs are provided, alongside presentation of key considerations/recommendations for in vitro study design evaluating drugs as inhibitors or substrates. Guidance on identifying critical co-medications, clinically relevant disposition pathways and using mechanistic static equations for quantitative prediction of DDI is compiled.

EXPERT OPINION

The strategies provided will facilitate project teams to study the right transporter at the right time to minimise development risks associated with DDIs. To truly alleviate or manage clinical risk, the industry will benefit from moving away from current qualitative basic static equation approaches to transporter DDI hazard assessment towards adopting the use of mechanistic models to enable quantitative DDI prediction, thereby contextualising risk to ascertain whether a transporter DDI is simply pharmacokinetic or clinically significant requiring intervention.

In Silico Prediction of Andrographolide Dosage Regimens for COVID-19 Treatment

Andrographolide (APE) has been used for COVID-19 treatment in various clinical settings in South-East Asia due to its benefits on reduction of viral clearance and prevention of disease progression. However, the limitation of APE clinical use is the high incidence of adverse events. The objective of this study was to find the optimal dosage regimens of APE for COVID-19 treatment. The whole-body physiologically-based pharmacokinetic (PBPK) models were constructed using data from the published articles and validated against clinical observations. The inhibitory effect of APE was determined for the potency of drug efficacy. For prevention of pneumonia, multiple oral doses such as 120[Formula: see text]mg for three doses, followed by 60[Formula: see text]mg three times daily for 4 consecutive days, or 200[Formula: see text]mg intravenous infusion at the rate of 20 mg/h once daily is advised in patients with mild COVID-19. For prevention of pneumonia and reduction of viral clearance time, the recommended dosage regimen is 500[Formula: see text]mg intravenous infusion at the rate of 25[Formula: see text]mg/h once daily in patients with mild-to-moderate COVID-19. One hundred virtual populations (50 males and 50 females) were simulated for oral and intravenous infusion formulations of APE. The eligible PBPK/PD models successfully predicted optimal dosage regimens and formulations of APE for prevention of disease progression and/or reduction of viral clearance time. Additionally, APE should be co-administered with other antiviral drugs to enhance therapeutic efficacy for COVID-19 treatment.

Clinically Relevant Interactions Between Ritonavir-Boosted Nirmatrelvir and Concomitant Antiseizure Medications: Implications for the Management of COVID-19 in Patients with Epilepsy

Ritonavir-boosted nirmatrelvir (RBN) has been authorized recently in several countries as an orally active anti-SARS-CoV-2 treatment for patients at high risk of progressing to severe COVID-19 disease. Nirmatrelvir is the active component against the SARS-CoV-2 virus, whereas ritonavir, a potent CYP3A inhibitor, is intended to boost the activity of nirmatrelvir by increasing its concentration in plasma to ensure persistence of antiviral concentrations during the 12-hour dosing interval. RBN is involved in many clinically important drug–drug interactions both as perpetrator and as victim, which can complicate its use in patients treated with antiseizure medications (ASMs). Interactions between RBN and ASMs are bidirectional. As perpetrator, RBN may increase the plasma concentration of a number of ASMs that are CYP3A4 substrates, possibly leading to toxicity. As victims, both nirmatrelvir and ritonavir are subject to metabolic induction by concomitant treatment with potent enzyme-inducing ASMs (carbamazepine, phenytoin, phenobarbital and primidone). According to US and European prescribing information, treatment with these ASMs is a contraindication to the use of RBN. Although remdesivir is a valuable alternative to RBN, it may not be readily accessible in some settings due to cost and/or need for intravenous administration. If remdesivir is not an appropriate option, either bebtelovimab or molnupiravir may be considered. However, evidence about the clinical efficacy of bebtelovimab is still limited, and molnupiravir, the only orally active alternative, is deemed to have appreciably lower efficacy than RBN and remdesivir.

Estimation of Fetal-to-Maternal Unbound Steady-State Plasma Concentration Ratio of P-Glycoprotein and/or Breast Cancer Resistance Protein Substrate Drugs Using a Maternal-Fetal Physiologically Based Pharmacokinetic Model

Pregnant women are frequently prescribed drugs to treat chronic diseases such as human immunodeficiency virus infection, but little is known about the benefits and risks of these drugs to the fetus that are driven by fetal drug exposure. The latter can be estimated by fetal-to-maternal unbound plasma concentration at steady state (Kp,uu,fetal). For drugs that are substrates of placental efflux transporters [i.e., P-glycoprotein (P-gp) or breast cancer resistance protein (BCRP)], Kp,uu,fetal is expected to be <1. Here, we estimated the in vivo Kp,uu,fetal of selective P-gp and BCRP substrate drugs by maternal-fetal physiologically based pharmacokinetic (m-f-PBPK) modeling of umbilical vein (UV) plasma and maternal plasma (MP) concentrations obtained simultaneously at term from multiple maternal-fetal dyads. To do so, three drugs were selected: nelfinavir (P-gp substrate), efavirenz (BCRP substrate), and imatinib (P-gp/BCRP substrate). An m-f-PBPK model for each drug was developed and validated for the nonpregnant population and pregnant women using the Simcyp simulator (v20). Then, after incorporating placental passive diffusion clearance, the in vivo Kp,uu,fetal of the drug was estimated by adjusting the placental efflux clearance until the predicted UV/MP values best matched the observed data (Kp,uu,fetal) of nelfinavir = 0.41, efavirenz = 0.39, and imatinib = 0.35. Furthermore, Kp,uu,fetal of nelfinavir and efavirenz at gestational weeks (GWs) 25 and 15 were predicted to be 0.34 and 0.23 (GW25) and 0.33 and 0.27 (GW15). These Kp,uu,fetal values can be used to adjust dosing regimens of these drugs to optimize maternal-fetal drug therapy throughout pregnancy, to assess fetal benefits and risks of these dosing regimens, and to determine if these estimated in vivo Kp,uu,fetal values can be predicted from in vitro studies. SIGNIFICANCE STATEMENT: The in vivo fetal-to-maternal unbound steady-state plasma concentration ratio (Kp,uu,fetal) of nelfinavir [P-glycoprotein (P-gp) substrate], efavirenz [breast cancer resistance protein (BCRP) substrate], and imatinib (P-gp and BCRP substrate) was successfully estimated using maternal-fetal physiologically based pharmacokinetic (m-f-PBPK) modeling. These Kp,uu,fetal values can be used to adjust dosing regimens of these drugs to optimize maternal-fetal drug therapy throughout pregnancy, to assess fetal benefits and risks of these dosing regimens, and to determine if these estimated in vivo Kp,uu,fetal values can be predicted from in vitro studies.

A Mechanistic Absorption and Disposition Model of Ritonavir to Predict Exposure and Drug-Drug Interaction Potential of CYP3A4/5 and CYP2D6 Substrates

BACKGROUND AND OBJECTIVES

Due to health authority warnings and the recommended limited use of ketoconazole as a model inhibitor of cytochrome P450 (CYP) 3A4 in clinical drug-drug interaction (DDI) studies, there is a need to search for alternatives. Ritonavir is a strong inhibitor for CYP3A4/5-mediated DDIs and has been proposed as a suitable alternative to ketoconazole. It can also be used as a weak inhibitor for CYP2D6-mediated DDIs. Most of the currently available physiologically based pharmacokinetic (PBPK) inhibitor models developed for predicting DDIs use first-order absorption models, which do not mechanistically capture the effect of formulations on the systemic exposure of the inhibitor. Thus, the main purpose of the current study was to verify the predictive performance of a mechanistic absorption and disposition model of ritonavir when it was applied to the inhibition of CYP2D6 and CYP3A4/5 by ritonavir.

METHODS

A PBPK model that incorporates formulation characteristics and enzyme kinetic parameters for post-absorptive pharmacokinetic processes of ritonavir was constructed. Key absorption-related parameters in the model were determined using mechanistic modelling of in vitro biopharmaceutics experiments. The model was verified for systemic exposure and DDI risk assessment using clinical observations from 13 and 18 studies, respectively.

RESULTS

Maximal inhibition of hepatic (3.53% of the activity remaining) and gut (5.16% of the activity remaining) CYP3A4 activity was observed when ritonavir was orally administered in doses of 100 mg or higher. The PBPK model accurately described the concentrations of ritonavir in the different simulated studies. The prediction accuracy for maximum concentration (C_max) and area under the plasma concentration versus time curve (AUC) were assessed. The bias (average fold error, AFE) for the prediction of C_max and AUC was 0.92 and 1.06, respectively, and the precision (absolute average fold error, AAFE) was 1.29 and 1.23, respectively. The PBPK model predictions for all C_max and AUC ratios when ritonavir was used as an inhibitor of CYP metabolism fell within twofold of the clinical observations. The prediction accuracy for C_max and AUC ratios had a bias (AFE) of 0.85 and 0.99, respectively, and a precision (AAFE) of 1.21 and 1.33, respectively.

CONCLUSIONS

The current model, which incorporates formulation characteristics and mechanistic disposition parameters, can be used to assess the DDI potential of CYP3A4/5 and CYP2D6 substrates administered with a twice-daily dose of 100 mg of ritonavir for 14 days.

Foerster K, Terstegen T, Vogt C, Said A, Schulz M, E. Haefeli W. Oral Drugs Against COVID-19

BACKGROUND

Five-day oral therapies against early COVID-19 infection have recently been conditionally approved in Europe. In the drug combination nirmatrelvir + ritonavir (nirmatrelvir/r), the active agent, nirmatrelvir, is made bioavailable in clinically adequate amounts by the additional administration of a potent inhibitor of its first-pass metabolism by way of cytochrome P450 [CYP] 3A in the gut and liver. In view of the central role of CYP3A in the clearance of many different kinds of drugs, and the fact that many patients with COVID-19 are taking multiple drugs to treat other conditions, it is important to assess the potential for drug interactions when nirmatrelvir/r is given, and to minimize the risks associated with such interactions.

METHODS

We defined the interaction profile of ritonavir on the basis of information derived from two databases (Medline, GoogleScholar), three standard electronic texts on drug interactions, and manufacturer-supplied drug information. We compiled a list of drugs and their potentially relevant interactions, developed a risk min - imization algorithm, and applied it to the substances in question. We also compiled a list of commonly prescribed drugs for which there is no risk of interaction with nirmatrelvir/r.

RESULTS

Out of 190 drugs and drug combinations, 57 do not need any special measures when given in combination with brief, low-dose ritonavir treatment, while 15 require dose modification or a therapeutic alternative, 8 can be temporarily discontinued, 9 contraindicate ritonavir use, and 102 should preferably be combined with a different treatment.

CONCLUSION

We have proposed measures that are simple to carry out for the main types of drug that can interact with ritonavir. These measures can be implemented under quarantine conditions before starting a 5-day treatment with nirmatrelvir/r.

Physiologically Based Pharmacokinetic Modeling of 3 HIV Drugs in Combination and the Role of Lymphatic System after Subcutaneous Dosing. Part 1: Model for the Free-Drug Mixture

Drug-combination nanoparticles (DcNP) allow the formulation of multiple HIV drugs in one injectable. In nonhuman primates (NHP), all drugs in DcNP have demonstrated long-acting pharmacokinetics (PK) in the blood and lymph nodes, rendering it suitable for a Targeted Long-acting Antiretroviral Therapy (TLC-ART). To support the translation of TLC-ART into the clinic, the objective is to present a physiologically based PK (PBPK) model tool to control mechanisms affecting the rather complex DcNP-drug PK. Two species contribute simultaneously to the drug PK: drugs that dissociate from DcNP (Part 1) and drugs retained in DcNP (Part 2, presented separately). Here, we describe the PBPK modeling of the nanoparticle-free drugs. The free-drug model was built on subcutaneous injections of suspended lopinavir, ritonavir, and tenofovir in NHP, and validated by external experiments. A novelty was the design of a lymphatic network as part of a whole-body PBPK system which included major lymphatic regions: the cervical, axillary, hilar, mesenteric, and inguinal nodes. This detailed/regionalized description of the lymphatic system and mononuclear cells represents an unprecedented level of prediction that renders the free-drug model extendible to other small-drug molecules targeting the lymphatic system at both the regional and cellular levels.

In vitro investigations into the roles of CYP450 enzymes and drug transporters in the drug interactions of zanubrutinib, a covalent Bruton's tyrosine kinase inhibitor

Zanubrutinib is a highly selective, potent, orally available, targeted covalent inhibitor (TCI) of Bruton's tyrosine kinase (BTK). This work investigated the in vitro drug metabolism and transport of zanubrutinib, and its potential for clinical drug-drug interactions (DDIs). Phenotyping studies indicated cytochrome P450 (CYP) 3A are the major CYP isoform responsible for zanubrutinib metabolism, which was confirmed by a clinical DDI study with itraconazole and rifampin. Zanubrutinib showed mild reversible inhibition with half maximal inhibitory concentration (IC50 ) of 4.03, 5.69, and 7.80 μM for CYP2C8, CYP2C9, and CYP2C19, respectively. Data in human hepatocytes disclosed induction potential for CYP3A4, CYP2B6, and CYP2C enzymes. Transport assays demonstrated that zanubrutinib is not a substrate of human breast cancer resistance protein (BCRP), organic anion transporting polypeptide (OATP)1B1/1B3, organic cation transporter (OCT)2, or organic anion transporter (OAT)1/3 but is a potential substrate of the efflux transporter P-glycoprotein (P-gp). Additionally, zanubrutinib is neither an inhibitor of P-gp at concentrations up to 10.0 μM nor an inhibitor of BCRP, OATP1B1, OATP1B3, OAT1, and OAT3 at concentrations up to 5.0 μM. The in vitro results with CYPs and transporters were correlated with the available clinical DDIs using basic models and mechanistic static models. Zanubrutinib is not likely to be involved in transporter-mediated DDIs. CYP3A inhibitors and inducers may impact systemic exposure of zanubrutinib. Dose adjustments may be warranted depending on the potency of CYP3A modulators.

Successful Prediction of Human Fetal Exposure to P-Glycoprotein Substrate Drugs Using the Proteomics-Informed Relative Expression Factor Approach and PBPK Modeling and Simulation

Many women take drugs during their pregnancy to treat a variety of clinical conditions. To optimize drug efficacy and reduce fetal toxicity, it is important to determine or predict fetal drug exposure throughout pregnancy. Previously, we developed and verified a maternal-fetal physiologically based pharmacokinetic (m-f PBPK) model to predict fetal Kp,uu (unbound fetal plasma AUC/unbound maternal plasma AUC) of drugs that passively cross the placenta. Here, we used in vitro transport studies in Transwell, in combination with our m-f PBPK model, to predict fetal Kp,uu of drugs that are effluxed by placental P-glycoprotein (P-gp)-namely, dexamethasone, betamethasone, darunavir, and lopinavir. Using Transwell, we determined the efflux ratio of these drugs in hMDR1-MDCKcP-gpKO cells, in which human P-gp was overexpressed and the endogenous P-gp was knocked out. Then, using the proteomics-informed efflux ratio-relative expressive factor approach, we predicted the fetal Kp,uu of these drugs at term. Finally, to verify our predictions, we compared them with the observed in vivo fetal Kp,uu at term. The latter was estimated using our m-f PBPK model and published fetal [umbilical vein (UV)]/maternal plasma drug concentrations obtained at term (UV/maternal plasma). Fetal Kp,uu predictions for dexamethasone (0.63), betamethasone (0.59), darunavir (0.17), and lopinavir (0.08) were successful, as they fell within the 90% confidence interval of the corresponding in vivo fetal Kp,uu (0.30-0.66, 0.29-0.71, 0.11-0.22, 0.04-0.19, respectively). This is the first demonstration of successful prediction of fetal Kp,uu of P-gp drug substrates from in vitro studies. SIGNIFICANCE STATEMENT: For the first time, using in vitro studies in cells, this study successfully predicted human fetal Kp,uu of P-gp substrate drugs. This success confirms that the m-f PBPK model, combined with the ER-REF approach, can successfully predict fetal drug exposure to P-gp substrates. This success provides increased confidence in the use of the ER-REF approach, combined with the m-f PBPK model, to predict fetal Kp,uu of drugs (transported by P-gp or other transporters), both at term and at earlier gestational ages.

Static and Dynamic Projections of Drug-Drug Interactions Caused by Cytochrome P450 3A Time-Dependent Inhibitors Measured in Human Liver Microsomes and Hepatocytes

Cytochrome P450 3A (CYP3A) is a frequent target for time-dependent inhibition (TDI) that can give rise to drug-drug interactions (DDI). Yet many drugs that exhibit in vitro TDI for CYP3A do not result in DDI. There were 23 drugs with published clinical DDI evaluated for CYP3A TDI in human liver microsomes (HLM) and hepatocytes (HHEP), and these data were used in static and dynamic models for projecting DDI caused by inactivation of CYP3A in both liver and intestine. TDI parameters measured in HHEP, particularly the maximal rate of enzyme inactivation, were generally lower than those measured in HLM. In static models, the use of estimated average unbound organ exit concentrations offered the most accurate projections of DDI with geometric mean fold errors of 2.0 and 1.7 for HLM and HHEP, respectively. Use of maximum organ entry concentrations yielded marked overestimates of DDI. When evaluated in a binary fashion (i.e., projection of DDI of 1.25-fold or greater), data from HLM offered the greatest sensitivity (100%) and specificity (67%) and yielded no missed DDI when average unbound organ exit concentrations were used. In dynamic physiologically based pharmacokinetic modeling, accurate projections of DDI were obtained with geometric mean fold errors of 1.7 and 1.6 for HLM and HHEP, respectively. Sensitivity and specificity were 100% and 67% when using TDI data generated in HLM and Simcyp modeling. Overall, DDI caused by CYP3A-mediated TDI can be reliably projected using dynamic or static models. For static models, average organ unbound exit concentrations should be used as input values otherwise DDI will be markedly overestimated. SIGNIFICANCE STATEMENT: CYP3A time-dependent inhibitors (TDI) are important in the design and development of new drugs. The prevalence of CYP3A TDI is high among newly synthesized drug candidates, and understanding the potential need for running clinical drug-drug interaction (DDI) studies is essential during drug development. Ability to reliably predict DDI caused by CYP3A TDI has been difficult to achieve. We report a thorough evaluation of CYP3A TDI and demonstrate that DDI can be predicted when using appropriate models and input parameters generated in human liver microsomes or hepatocytes.

Investigating the Utility of Humanized Pregnane X Receptor-Constitutive Androstane Receptor-CYP3A4/7 Mouse Model to Assess CYP3A-Mediated Induction

Clinical induction liability is assessed with human hepatocytes. However, underpredictions in the magnitude of clinical induction have been reported. Unfortunately, in vivo studies in animals do not provide additional insight because of species differences in drug metabolizing enzymes and their regulatory pathways. To circumvent this limitation, transgenic animals expressing human orthologs were developed. The aim of this work was to investigate the utility of mouse models expressing human orthologs of pregnane X receptor, constitutive androstane receptor, and CYP3A4/7 (Tg-Composite) in evaluating clinical induction. Rifampin, efavirenz, and pioglitazone, which were employed to represent strong, moderate, and weak inducers, were administered at multiple doses to Tg-Composite animals. In vivo CYP3A activity was monitored by measuring changes in the exposure of the CYP3A probe substrate triazolam. After the in vivo studies, microsomes were prepared from their livers to measure changes of in vitro CYP3A4 activity. In both in vivo and in vitro, distinction of clinic induction was recapitulated as rifampin yielded the greatest inductive effect followed by efavirenz and pioglitazone. Interestingly, with rifampin, in vivo CYP3A activity was approximately 4-fold higher than in vitro activity. Conversely, there was no difference between in vivo and in vitro CYP3A activity with efavirenz. These findings are consistent with the report that, although rifampin exhibits differential inductive effects between the intestines and liver, efavirenz does not. These data highlight the promise of transgenic models, such as Tg-Composite, to complement human hepatocytes to enhance the translatability of clinical induction as well as become a powerful tool to further study mechanisms of drug disposition. SIGNIFICANCE STATEMENT: Underprediction of the magnitude of clinical induction when using human hepatocytes has been reported, and transgenic models may improve clinical translatability. The work presented here showcases the human orthologs of pregnane X receptor, constitutive androstane receptor, and CYP3A4/7 model, which was able to recapitulate the magnitude of clinical induction and to differentiate tissue-dependent induction observed with rifampin but not with efavirenz. These results not only foreshadow the potential application of such transgenic models in assessing clinical induction but also in further investigation of the mechanism of drug disposition.

Physiologically Based Pharmacokinetic Modelling to Identify Pharmacokinetic Parameters Driving Drug Exposure Changes in the Elderly

BACKGROUND

Medication use is highly prevalent with advanced age, but clinical studies are rarely conducted in the elderly, leading to limited knowledge regarding age-related pharmacokinetic changes.

OBJECTIVE

The objective of this study was to investigate which pharmacokinetic parameters determine drug exposure changes in the elderly by conducting virtual clinical trials for ten drugs (midazolam, metoprolol, lisinopril, amlodipine, rivaroxaban, repaglinide, atorvastatin, rosuvastatin, clarithromycin and rifampicin) using our physiologically based pharmacokinetic (PBPK) framework.

METHODS

PBPK models for all ten drugs were developed in young adults (20-50 years) following the best practice approach, before predicting pharmacokinetics in the elderly (≥ 65 years) without any modification of drug parameters. A descriptive relationship between age and each investigated pharmacokinetic parameter (peak concentration [C_max], time to C_max [t_max], area under the curve [AUC], clearance, volume of distribution, elimination-half-life) was derived using the final PBPK models, and verified with independent clinically observed data from 52 drugs.

RESULTS

The age-related changes in drug exposure were successfully simulated for all ten drugs. Pharmacokinetic parameters were predicted within 1.25-fold (70%), 1.5-fold (86%) and 2-fold (100%) of clinical data. AUC increased progressively by 0.9% per year throughout adulthood from the age of 20 years, which was explained by decreased clearance, while C_max, t_max and volume of distribution were not affected by human aging. Additional clinical data of 52 drugs were contained within the estimated variability of the established age-dependent correlations for each pharmacokinetic parameter.

CONCLUSION

The progressive decrease in hepatic and renal blood flow, as well as glomerular filtration, rate led to a reduced clearance driving exposure changes in the healthy elderly, independent of the drug.

SARS-CoV-2 requires cholesterol for viral entry and pathological syncytia formation

Many enveloped viruses induce multinucleated cells (syncytia), reflective of membrane fusion events caused by the same machinery that underlies viral entry. These syncytia are thought to facilitate replication and evasion of the host immune response. Here, we report that co-culture of human cells expressing the receptor ACE2 with cells expressing SARS-CoV-2 spike, results in synapse-like intercellular contacts that initiate cell-cell fusion, producing syncytia resembling those we identify in lungs of COVID-19 patients. To assess the mechanism of spike/ACE2-driven membrane fusion, we developed a microscopy-based, cell-cell fusion assay to screen ~6000 drugs and >30 spike variants. Together with quantitative cell biology approaches, the screen reveals an essential role for biophysical aspects of the membrane, particularly cholesterol-rich regions, in spike-mediated fusion, which extends to replication-competent SARS-CoV-2 isolates. Our findings potentially provide a molecular basis for positive outcomes reported in COVID-19 patients taking statins and suggest new strategies for therapeutics targeting the membrane of SARS-CoV-2 and other fusogenic viruses.

Genetic Polymorphism Effect on Warfarin-Rifampin Interaction: A Case Report and Review of Literature

Warfarin-rifampin interaction has been reported since the 1970s. Due to rifampin's strong induction of CYP2C9, most cases could not attain the target international normalized ratio (INR) despite warfarin dose escalation. Genetic polymorphisms determine up to 50% of warfarin dose variability. A 38-year-old woman was started on warfarin and rifampin for cerebral venous sinus thrombosis and pulmonary tuberculosis. Over six weeks, the daily warfarin dose was increased from 3 to 10 mg to attain three consecutive in-clinic therapeutic INRs. She completed three complications-free months of warfarin treatment with time in therapeutic range (TTR) of 46%. We performed retrospective genetic testing to determine the patient's CYP2C9, CYP4F2, and VKORC1 genotypes and whether they had affected the interaction outcome. The analysis revealed that the subject carries CYP2C9*3*3 and VKORC1-1639 (GA) mutations, classifying her as a slow metabolizer and, hence, highly warfarin-sensitive. This was reflected on how the case responded to a relatively lower dose than previously reported cases that did not achieve the target on warfarin daily doses up to 35 mg. This is the first report addressing the genotype effect on this interaction. Patients with genetic variants requiring low warfarin doses are more likely to respond at a feasible dose while on rifampin. Future studies to evaluate warfarin-rifampin-gene interaction are warranted.

Inhibition and induction of CYP enzymes in humans: an update

The cytochrome P450 (CYP) enzyme family is the most important enzyme system catalyzing the phase 1 metabolism of pharmaceuticals and other xenobiotics such as herbal remedies and toxic compounds in the environment. The inhibition and induction of CYPs are major mechanisms causing pharmacokinetic drug–drug interactions. This review presents a comprehensive update on the inhibitors and inducers of the specific CYP enzymes in humans. The focus is on the more recent human in vitro and in vivo findings since the publication of our previous review on this topic in 2008. In addition to the general presentation of inhibitory drugs and inducers of human CYP enzymes by drugs, herbal remedies, and toxic compounds, an in-depth view on tyrosine-kinase inhibitors and antiretroviral HIV medications as victims and perpetrators of drug–drug interactions is provided as examples of the current trends in the field. Also, a concise overview of the mechanisms of CYP induction is presented to aid the understanding of the induction phenomena.

Numerical analysis of time-dependent inhibition kinetics: comparison between rat liver microsomes and rat hepatocyte data for mechanistic model fitting

Time-dependent inhibition (TDI) may confound drug interaction predictions. Recently, models were generated for an array of TDI kinetic schemes using numerical analysis of microsomal assays. Additionally, a distinct terminal inactivation step was identified for certain mechanism based inhibitors (MBI) following reversible metabolite intermediate complex (MIC) formation. Longer hepatocyte incubations potentially allow analysis of slow TDI and terminal inactivation. In the experiments presented here, we compared the quality of TDI parameterization by numerical analysis between hepatocyte and microsomal data. Rat liver microsomes (RLM), suspended rat hepatocytes (SRH) and sandwich-cultured rat hepatocytes (SCRH) were incubated with the prototypical CYP3A MBI troleandomycin and the substrate midazolam. Data from RLM provided a better model fit as compared to SRH. Increased CYP3A expression after dexamethasone (DEX) induction improved the fit for RLM and SRH. A novel sequential kinetic scheme, defining inhibitor metabolite production prior to MIC formation, improved the fit compared to direct MIC formation. Furthermore, terminal inactivation rate constants were parameterized for RLM and SRH samples with DEX-induced CYP3A. The low expression of CYP3A and experimental error in SCRH resulted in poor data for model fitting. Overall, RLM generated data better suited for elucidation of TDI mechanisms by numerical analysis.

Volume of Distribution is Unaffected by Metabolic Drug-Drug Interactions

Introduction

It has been recognized that significant transporter interactions result in volume of distribution changes in addition to potential changes in clearance. For drugs that are not clinically significant transporter substrates, it is expected that drug–drug interactions would not result in any changes in volume of distribution.

Methods

An evaluation of this hypothesis proceeded via an extensive analysis of published intravenous metabolic drug–drug interactions, based on clinically recommended index substrates and inhibitors of major cytochrome P450 (CYP) isoforms.

Results

Seventy-two metabolic drug interaction studies were identified where volume of distribution at steady-state (V_ss) values were available for the CYP index substrates caffeine (CYP1A2), metoprolol (CYP2D6), midazolam (CYP3A4), theophylline (CYP1A2), and tolbutamide (CYP2C9). Changes in exposure (area under the curve) up to 5.1-fold were observed; however, ratios of V_ss changes have a range of 0.70–1.26, with one outlier displaying a V_ss ratio of 0.57.

Discussion

These results support the widely held founding tenant of pharmacokinetics that clearance and V_ss are independent parameters. Knowledge that V_ss is unchanged in metabolic drug–drug interactions can be helpful in discriminating changes in clearance from changes in bioavailability (F) when only oral dosing data are available, as we have recently demonstrated. As V_ss remains unchanged for intravenous metabolic drug–drug interactions, following oral dosing changes in V_ss/F will reflect changes in F alone. This estimation of F change can subsequently be utilized to assess changes in clearance alone from calculations of apparent clearance. Utilization of this simple methodology for orally dosed drugs will have a significant impact on how drug–drug interactions are interpreted from drug development and regulatory perspectives.

COVID-19: therapeutic approaches description and discussion

COVID-19 emerged in December 2019 in China, and since then, has disrupted global public health and changed economic paradigms. In dealing with the new Coronavirus, SARS-CoV-2, the world has not faced such extreme global fragility since the "Spanish flu" pandemic in 1918. Researchers globally are dedicating efforts to the search for an effective treatment for COVID-19. Drugs already used in a clinical setting for other pathologies have been tested as a new therapeutic approach against SARS-CoV-2, setting off a frenzy over the preliminary data of different studies. This work aims to compile and discuss the data published thus far. Despite the potential effects of some antivirals and antiparasitic against COVID-19, clinical studies must confirm real effectiveness. However, non-pharmacological approaches have proven to be the most efficient strategy to date.

Cytochrome P450 3A Time-Dependent Inhibition Assays Are Too Sensitive for Identification of Drugs Causing Clinically Significant Drug-Drug Interactions: A Comparison of Human Liver Microsomes and Hepatocytes and Definition of Boundaries for Inactivation Rate Constants

Time-dependent inhibition (TDI) of CYP3A is an important mechanism underlying numerous drug-drug interactions (DDIs), and assays to measure this are done to support early drug research efforts. However, measuring TDI of CYP3A in human liver microsomes (HLMs) frequently yields overestimations of clinical DDIs and thus can lead to the erroneous elimination of many viable drug candidates from further development. In this investigation, 50 drugs were evaluated for TDI in HLMs and suspended human hepatocytes (HHEPs) to define appropriate boundary lines for the TDI parameter rate constant for inhibition (kobs) at a concentration of 30 µM. In HLMs, a kobs value of 0.002 minute-1 was statistically distinguishable from control; however, many drugs show kobs greater than this but do not cause DDI. A boundary line defined by the drug with the lowest kobs that causes a DDI (diltiazem) was established at 0.01 minute-1 Even with this boundary, of the 33 drugs above this value, only 61% cause a DDI (true positive rate). A corresponding analysis was done using HHEPs; kobs of 0.0015 minute-1 was statistically distinguishable from control, and the boundary was established at 0.006 minute-1 Values of kobs in HHEPs were almost always lower than those in HLMs. These findings offer a practical guide to the use of TDI data for CYP3A in early drug-discovery research. SIGNIFICANCE STATEMENT: Time-dependent inhibition of CYP3A is responsible for many drug interactions. In vitro assays are employed in early drug research to identify and remove CYP3A time-dependent inhibitors from further consideration. This analysis demonstrates suitable boundaries for inactivation rates to better delineate drug candidates for their potential to cause clinically significant drug interactions.

A Simple Methodology to Differentiate Changes in Bioavailability From Changes in Clearance Following Oral Dosing of Metabolized Drugs

Accurately discriminating changes in clearance (CL) from changes in bioavailability (F) following an oral drug-drug interaction is difficult without carrying out an intravenous interaction study. This may be true for drugs that are clinically significant transporter substrates; however, for interactions that are strictly metabolic, it has been recognized that volume of distribution remains unchanged between both phases of the interaction study. With the understanding that changes in volume of distribution will be minimal for metabolized drugs, the inverse of the change in apparent volume of distribution can provide adequate estimates of the change in bioavailability alone. Utilization of this estimate of F change in tandem with the observed apparent clearance (CL/F) change in an oral drug-drug interaction can provide an estimate of the change in clearance alone. Here, we examine drug-drug interactions involving five known inhibitors and inducers of cytochrome P450 3A4 isozyme on victim drugs midazolam and apixaban for which the interaction was carried out both orally and intravenously, allowing for evaluation of this methodology. Predictions of CL and F changes based on oral data were reasonably close to observed changes based on intravenous studies, demonstrating that this simple yet powerful methodology can reasonably differentiate changes in F from changes in CL for oral metabolic drug interactions when only oral data are available. Utilization of this relatively simple methodology to evaluate DDIs for orally dosed drugs will have a significant impact on how DDIs are interpreted from a drug development and regulatory perspective.

Drug Interactions of Psychiatric and COVID-19 Medications

INTRODUCTION

Coronavirus disease 2019 (COVID-19) has become a pandemic with 1771514 cases identified in the world and 70029 cases in Iran until April 12, 2020. The co-prescription of psychotropics with COVID-19 medication is not uncommon. Healthcare providers should be familiar with many Potential Drug-Drug Interactions (DDIs) between COVID-19 therapeutic agents and psychotropic drugs based on cytochrome P450 metabolism. This review comprehensively summarizes the current literature on DDIs between antiretroviral drugs and chloroquine/hydroxychloroquine, and psychotropics, including antidepressants, antipsychotics, mood stabilizers, and anxiolytics.

METHODS

Medical databases, including Google Scholar, PubMed, Web of Science, and Scopus were searched to identify studies in English with keywords related to psychiatric disorders, medications used in the treatment of psychiatric disorders and COVID-19 medications.

RESULTS

There is a great potential for DDIs between psychiatric and COVID-19 medications ranging from interactions that are not clinically apparent (minor) to those that produce life-threatening adverse drug reactions, or loss of treatment efficacy. The majority of interactions are pharmacokinetic interactions via the cytochrome P450 enzyme system.

CONCLUSION

DDIs are a major concern in the comorbidity of psychiatric disorders and COVID-19 infection resulting in the alteration of expected therapeutic outcomes. The risk of toxicity or lack of efficacy may occur due to a higher or lower plasma concentration of medications. However, psychiatric medication can be safely used in combination with COVID-19 pharmacotherapy with either a wise selection of medication with the least possibility of interaction or careful patient monitoring and management.

Physiologically-Based Pharmacokinetic Modeling for Optimal Dosage Prediction of Quinine Coadministered With Ritonavir-Boosted Lopinavir

The coformulated lopinavir/ritonavir significantly reduces quinine concentration in healthy volunteers due to potential drug-drug interactions (DDIs). However, DDI information in malaria and HIV coinfected patients are lacking. The objective of the study was to apply physiologically-based pharmacokinetic (PBPK) modeling to predict optimal dosage regimens of quinine when coadministered with lopinavir/ritonavir in malaria and HIV coinfected patients with different conditions. The developed model was validated against literature. Model verification was evaluated using the accepted method. The verified PBPK models successfully predicted unbound quinine disposition when coadministered with lopinavir/ritonavir in coinfected patients with different conditions. Suitable dose adjustments to counteract with the DDIs have identified in patients with various situations (i.e., a 7-day course at 1,800 mg t.i.d. in patients with malaria with HIV infection, 648 mg b.i.d. in chronic renal failure, 648 mg t.i.d. in hepatic insufficiency except for severe hepatic insufficiency (324 mg b.i.d.), and 648 mg t.i.d. in CYP3A4 polymorphism).

Safety implications of combined antiretroviral and anti-tuberculosis drugs

Introduction: Antiretroviral and anti-tuberculosis (TB) drugs are often co-administered in people living with HIV (PLWH). Early initiation of antiretroviral therapy (ART) during TB treatment improves survival in patients with advanced HIV disease. However, safety concerns related to clinically significant changes in drug exposure resulting from drug-drug interactions, development of overlapping toxicities and specific challenges related to co-administration during pregnancy represent barriers to successful combined treatment for HIV and TB.Areas covered: Pharmacokinetic interactions of different classes of ART when combined with anti-TB drugs used for sensitive-, drug-resistant (DR) and latent TB are discussed. Overlapping drug toxicities, implications of immune reconstitution inflammatory syndrome (IRIS), safety in pregnancy and research gaps are also explored.Expert opinion: New antiretroviral and anti-tuberculosis drugs have been recently introduced and international guidelines updated. A number of effective molecules and clinical data are now available to build treatment regimens for PLWH with latent or active TB. Adopting a systematic approach that also takes into account the need for individualized variations based on the available evidence is the key to successfully integrate ART and TB treatment and improve treatment outcomes.

In vitro-in vivo correlation of the drug-drug interaction potential of antiretroviral HIV treatment regimens on CYP1A1 substrate riociguat

Objectives: Riociguat is a soluble guanylate cyclase stimulator licensed for the treatment of pulmonary arterial hypertension (PAH), a potentially fatal complication of human immunodeficiency virus infection. This study investigated the inhibitory potency of selected antiretroviral regimens on the metabolic clearance of riociguat.Methods: The inhibitory potential of the components of six antiretroviral combinations (ATRIPLA® (efavirenz/emtricitabine/tenofovir disoproxil), COMPLERA® (rilpivirine/emtricitabine/tenofovir disoproxil), STRIBILD® (elvitegravir/cobicistat/emtricitabine/tenofovir disoproxil), TRIUMEQ® (abacavir/dolutegravir/lamivudine), and two ritonavir-boosted regimens) on riociguat metabolism were evaluated in recombinant human CYP1A1 and CYP3A4 as well as in human hepatocytes exhibiting both CYP1A1 and CYP3A4 activity. In vitro-in vivo correlation was performed between calculated and observed increases in riociguat exposure in vivo.Results: Using both in vitro systems, the predicted increase in exposure of riociguat was highest with components of TRIUMEQ® followed by COMPLERA®, ATRIPLA®, STRIBILD®, and the ritonavir-boosted regimens. Further experiments in human hepatocytes confirmed CYP1A1 to be the predominant enzyme in the metabolic clearance of riociguat.Conclusion: Antiretroviral treatment containing the potent CYP1A1 inhibitor abacavir had the greatest impact on riociguat metabolic clearance. The impact of comedications containing only strong CYP3A4 inhibitors e.g. ritonavir was less pronounced, suggesting a benefit of riociguat over PAH-targeting medications with contraindications for use with strong CYP3A4 inhibitors.

Expanded Physiologically-Based Pharmacokinetic Model of Rifampicin for Predicting Interactions With Drugs and an Endogenous Biomarker via Complex Mechanisms Including Organic Anion Transporting Polypeptide 1B Induction

As rifampicin can cause the induction and inhibition of multiple metabolizing enzymes and transporters, it has been challenging to accurately predict the complex drug–drug interactions (DDIs). We previously constructed a physiologically‐based pharmacokinetic (PBPK) model of rifampicin accounting for the components for the induction of cytochrome P450 (CYP) 3A/CYP2C9 and the inhibition of organic anion transporting polypeptide 1B (OATP1B). This study aimed to expand and verify the PBPK model for rifampicin by incorporating additional components for the induction of OATP1B and CYP2C8 and the inhibition of multidrug resistance protein 2. The established PBPK model was capable of accurately predicting complex rifampicin‐induced alterations in the profiles of glibenclamide, repaglinide, and coproporphyrin I (an endogenous biomarker of OATP1B activities) with various dosing regimens. Our comprehensive rifampicin PBPK model may enable quantitative prediction of DDIs across diverse potential victim drugs and endogenous biomarkers handled by multiple metabolizing enzymes and transporters.

Nanotechnology approaches for delivery of cytochrome P450 substrates in HIV treatment

Introduction: Antiretroviral therapy (ART) has led to a significant reduction in HIV-1 morbidity and mortality. Many antiretroviral drugs (ARVs) are metabolized by cytochrome P450 (CYP) pathway, and the majority of these drugs are also either CYP inhibitors or inducers and few possess both activities. These CYP substrates, when used for HIV treatment in the conventional dosage form, have limitations such as low systemic bioavailability, potential drug-drug interactions, and short half-lives. Thus, an alternative mode of delivery is needed in contrast to conventional ARVs. Areas covered: In this review, we summarized the limitations of conventional ARVs in HIV treatment, especially for ARVs which are CYP substrates. We also discussed the preclinical and clinical studies using the nanotechnology strategy to overcome the limitations of these CYP substrates. The preclinical studies and clinical studies published from 2000 to February 2019 were discussed. Expert opinion: Since preclinical and clinical studies for prevention and treatment of HIV using nanotechnology approaches have shown considerable promise in recent years, nanotechnology could become an alternative strategy for daily oral therapy as a future treatment.

Doravirine and the Potential for CYP3A-Mediated Drug-Drug Interactions

Identifying and understanding potential drug-drug interactions (DDIs) are vital for the treatment of human immunodeficiency virus type 1 (HIV-1) infection. This article discusses DDIs between doravirine, a nonnucleoside reverse transcriptase inhibitor (NNRTI), and cytochrome P450 3A (CYP3A) substrates and drugs that modulate CYP3A activity. Consistent with previously published in vitro data and DDI trials with the CYP3A substrates midazolam and atorvastatin, doravirine did not have any meaningful impact on the pharmacokinetics of the CYP3A substrates ethinyl estradiol and levonorgestrel. Coadministration of doravirine with CYP3A inhibitors (ritonavir or ketoconazole) increased doravirine exposure approximately 3-fold. However, these increases were not considered clinically meaningful. Conversely, previously published trials showed that coadministered CYP3A inducers (rifampin and rifabutin) decreased doravirine exposure by 88% and 50%, respectively (K. L. Yee, S. G. Khalilieh, R. I. Sanchez, R. Liu, et al., Clin Drug Investig 37:659-667, 2017 [https://doi.org/10.1007/s40261-017-0513-4]; S. G. Khalilieh, K. L. Yee, R. I. Sanchez, R. Liu, et al., J Clin Pharmacol 58:1044-1052, 2018 [https://doi.org/10.1002/jcph.1103]), while doravirine exposure following prior efavirenz administration led to an initial reduction in doravirine exposure of 62%, but the reduction became less pronounced with time (K. L. Yee, R. I. Sanchez, P. Auger, R. Liu, et al., Antimicrob Agents Chemother 61:e01757-16, 2017 [https://doi.org/10.1128/AAC.01757-16]). Overall, the coadministration of doravirine with CYP3A inhibitors and substrates is, therefore, supported by these data together with efficacy and safety data from clinical trials, while coadministration with strong CYP3A inducers, such as rifampin, cannot be recommended. Concomitant dosing with rifabutin (a CYP3A inducer less potent than rifampin) is acceptable if doravirine dosing is adjusted from once to twice daily; however, the effect of other moderate inducers on doravirine pharmacokinetics is unknown.

Drug-Drug Interaction Studies of Methadone and Antiviral Drugs: Lessons Learned

Different views appear in the literature on the extent of specific cytochrome P450 (CYP) involvement in methadone metabolism. The aim of this work is to leverage knowledge from drug-drug interaction (DDI) studies in new drug applications between methadone and antiviral medications to better understand methadone disposition and to inform design of future DDI studies with methadone. A database of DDI studies between all FDA-approved human immunodeficiency virus and hepatitis C virus medications and methadone was constructed. The database contains data from 29 DDI studies. Sixteen of the 29 studies had statistically significant changes in methadone area under the concentration-time curve. Methadone exposure was either decreased or unchanged when it was coadministered with weak to strong CYP3A inhibitors or a moderate CYP3A4 inducer. Methadone exposure was reduced when it was coadministered with CYP2B6 inducers. The role of other enzymes (CYP2C9, CYP2C19, and CYP2D6) cannot be fully elucidated from these studies. In conclusion, CYP2B6 plays a prominent role in methadone metabolism, although methadone exposure is not sensitive to CYP3A perturbation. In designing methadone DDI studies, (1) measuring R- and S-methadone is more informative than measuring total methadone, and (2) CYP2B6 genotyping of subjects enrolled in methadone DDI studies should be considered. Finally, there is a need for the development of predictive models to determine the influence of medications on methadone disposition.

Alternative switching strategies based on regimens with a low genetic barrier: do clinicians have a choice nowadays?

Clinicians sometimes use switching strategies based on regimens such as RAL + ABC/3TC or RPV + ABC/3TC in order to resolve tolerability or safety issues associated with conventional recommended first-line strategies. Despite the low genetic barrier of these regimens, high safety and efficacy rates have been reported in retrospective studies.

Considerations from the Innovation and Quality Induction Working Group in Response to Drug-Drug Interaction Guidances from Regulatory Agencies: Focus on CYP3A4 mRNA In Vitro Response Thresholds, Variability, and Clinical Relevance

The Innovation and Quality Induction Working Group presents an assessment of best practice for data interpretation of in vitro induction, specifically, response thresholds, variability, application of controls, and translation to clinical risk assessment with focus on CYP3A4 mRNA. Single concentration control data and Emax/EC50 data for prototypical CYP3A4 inducers were compiled from many human hepatocyte donors in different laboratories. Clinical CYP3A induction and in vitro data were gathered for 51 compounds, 16 of which were proprietary. A large degree of variability was observed in both the clinical and in vitro induction responses; however, analysis confirmed in vitro data are able to predict clinical induction risk. Following extensive examination of this large data set, the following recommendations are proposed. a) Cytochrome P450 induction should continue to be evaluated in three separate human donors in vitro. b) In light of empirically divergent responses in rifampicin control and most test inducers, normalization of data to percent positive control appears to be of limited benefit. c) With concentration dependence, 2-fold induction is an acceptable threshold for positive identification of in vitro CYP3A4 mRNA induction. d) To reduce the risk of false positives, in the absence of a concentration-dependent response, induction ≥ 2-fold should be observed in more than one donor to classify a compound as an in vitro inducer. e) If qualifying a compound as negative for CYP3A4 mRNA induction, the magnitude of maximal rifampicin response in that donor should be ≥ 10-fold. f) Inclusion of a negative control adds no value beyond that of the vehicle control.

Clopidogrel Increases Dasabuvir Exposure With or Without Ritonavir, and Ritonavir Inhibits the Bioactivation of Clopidogrel

Dasabuvir is mainly metabolized by cytochrome P450 (CYP) 2C8 and is predominantly used in a regimen containing ritonavir. Ritonavir and clopidogrel are inhibitors of CYP3A4 and CYP2C8, respectively. In a randomized, crossover study in 12 healthy subjects, we examined the impact of clinical doses of ritonavir (for 5 days), clopidogrel (for 3 days), and their combination on dasabuvir pharmacokinetics, and the effect of ritonavir on clopidogrel. Clopidogrel, but not ritonavir, increased the geometric mean AUC_0‐∞ of dasabuvir 4.7‐fold; range 2.0–10.1‐fold (P = 8·10⁻⁷), compared with placebo. Clopidogrel and ritonavir combination increased dasabuvir AUC_0‐∞ 3.9‐fold; range 2.1–7.9‐fold (P = 2·10⁻⁶), compared with ritonavir alone. Ritonavir decreased the AUC_0‐4h of clopidogrel active metabolite by 51% (P = 0.0001), and average platelet inhibition from 51% without ritonavir to 31% with ritonavir (P = 0.0007). In conclusion, clopidogrel markedly elevates dasabuvir concentrations, and patients receiving ritonavir are at risk for diminished clopidogrel response.

Analysis of Clinical Drug-Drug Interaction Data To Predict Magnitudes of Uncharacterized Interactions between Antiretroviral Drugs and Comedications

Despite their high potential for drug-drug interactions (DDI), clinical DDI studies of antiretroviral drugs (ARVs) are often lacking, because the full range of potential interactions cannot feasibly or pragmatically be studied, with some high-risk DDI studies also being ethically difficult to undertake. Thus, a robust method to screen and to predict the likelihood of DDIs is required. We developed a method to predict DDIs based on two parameters: the degree of metabolism by specific enzymes, such as CYP3A, and the strength of an inhibitor or inducer. These parameters were derived from existing studies utilizing paradigm substrates, inducers, and inhibitors of CYP3A to assess the predictive performance of this method by verifying predicted magnitudes of changes in drug exposure against clinical DDI studies involving ARVs. The derived parameters were consistent with the FDA classification of sensitive CYP3A substrates and the strength of CYP3A inhibitors and inducers. Characterized DDI magnitudes (n = 68) between ARVs and comedications were successfully quantified, meaning 53%, 85%, and 98% of the predictions were within 1.25-fold (0.80 to 1.25), 1.5-fold (0.66 to 1.48), and 2-fold (0.66 to 1.94) of the observed clinical data. In addition, the method identifies CYP3A substrates likely to be highly or, conversely, minimally impacted by CYP3A inhibitors or inducers, thus categorizing the magnitude of DDIs. The developed effective and robust method has the potential to support a more rational identification of dose adjustment to overcome DDIs, being particularly relevant in an HIV setting, given the treatment's complexity, high DDI risk, and limited guidance on the management of DDIs.

Guiding dose adjustment of amlodipine after co-administration with ritonavir containing regimens using a physiologically-based pharmacokinetic/pharmacodynamic model

Amlodipine, a commonly prescribed anti-hypertensive drug, shows increased systemic exposure with cytochrome P450 (CYP) 3A inhibitors. Ritonavir (RTV) is a potent mechanism-based and reversible CYP3A inhibitor and moderate inducer that is used as a pharmacokinetic enhancer in several antiviral treatment regimens. Drug-drug interaction (DDI) between RTV and amlodipine is due to mixed inhibition and induction of CYP3A4, which is challenging to predict without a mechanistic model that accounts for the complexity of both mechanisms occurring simultaneously. A novel physiologically-based pharmacokinetic (PBPK) model was developed for amlodipine, and the model was verified using published clinical PK and DDI data. The verified amlodipine PBPK model was linked to a pharmacodynamics model that describes changes in systolic blood pressure (SBP) during and after co-administration with RTV. The magnitude and time course of RTV effects on amlodipine plasma exposures and SBP were evaluated, to provide guidance on dose adjustment of amlodipine during and after co-administration with RTV-containing regimens. Model simulations suggested that the increase in amlodipine's plasma exposure by RTV diminishes by approximately 80% within 5 days after the last dose of RTV. PBPK simulations suggested that resuming a full dose of amlodipine [5 mg once daily (QD)] immediately after RTV's last dose would decrease daily average SBP by a maximum of 3.3 mmHg, while continuing with the reduced dose (2.5 mg QD) for 5 days after the last dose of RTV would increase daily average SBP by a maximum of 5.8 mmHg. Based on these results, either approach of resuming amlodipine's full dose could be appropriate when combined with appropriate clinical monitoring.