Physiologically Based Pharmacokinetic Modeling to Investigate the Disease-Drug-Drug Interactions between Voriconazole and Nirmatrelvir/Ritonavir in COVID-19 Patients with CYP2C19 Phenotypes

Coronavirus disease 2019 (COVID-19)-associated pulmonary aspergillosis superinfection with cytokine storm is associated with increased mortality. This study aimed to establish a physiologically-based pharmacokinetic (PK) model to investigate the disease-drug-drug interactions between voriconazole and nirmatrelvir/ritonavir in patients with COVID-19 with elevated interleukin-6 (IL-6) levels carrying various CYP2C19 phenotypes. The model was constructed and validated using PK data on voriconazole, ritonavir, and IL-6, and was subsequently verified against clinical data from 78 patients with COVID-19. As a result, the model predicted voriconazole, ritonavir, and IL-6 PK parameters and drug-drug interaction-related fold changes in healthy subjects and patients with COVID-19 with acceptable prediction error, demonstrating its predictive capability. Simulations indicated ritonavir could increase voriconazole exposure to CYP2C19 intermediate and poor metabolizers rather than decrease it, in contrast to what is indicated in the drug package insert. However, the predicted ritonavir exposures were comparable across subjects. In patients with COVID-19, both ritonavir and IL-6 increased voriconazole trough concentrations, which may lead to CYP2C19 phenotype-dependent overexposure. In conclusion, COVID-19-induced IL-6 elevation and ritonavir increased voriconazole exposure, and the magnitude of interactions was influenced by CYP2C19 phenotype. Thus, caution is warranted when prescribing voriconazole concomitantly with Paxlovid in patients with COVID-19.

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 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.

A commentary on the use of pharmacoenhancers in the pharmaceutical industry and the implication for DMPK drug discovery strategies

Paxlovid, a drug combining nirmatrelvir and ritonavir, was designed for the treatment of COVID-19 and its rapid development has led to emergency use approval by the FDA to reduce the impact of COVID-19 infection on patients.In order to overcome potentially suboptimal therapeutic exposures, nirmatrelvir is dosed in combination with ritonavir to boost the pharmacokinetics of the active product.Here we consider examples of drugs co-administered with pharmacoenhancers.Pharmacoenhancers have been adopted for multiple purposes such as ensuring therapeutic exposure of the active product, reducing formation of toxic metabolites, changing the route of administration, and increasing the cost-effectiveness of a therapy.We weigh the benefits and risks of this approach, examining the impact of technology developments on drug design and how enhanced integration between cross-discipline teams can improve the outcome of drug discovery.

Pharmacokinetics of asciminib in the presence of CYP3A or P-gp inhibitors, CYP3A inducers, and acid-reducing agents

Asciminib is a first‐in‐class inhibitor of BCR::ABL1, specifically targeting the ABL myristoyl pocket. Asciminib is a substrate of CYP3A4 and P‐glycoprotein (P‐gp) and possesses pH‐dependent solubility in aqueous solution. This report summarizes the results of two phase I studies in healthy subjects aimed at assessing the impact of CYP3A and P‐gp inhibitors, CYP3A inducers and acid‐reducing agents (ARAs) on the pharmacokinetics (PK) of asciminib (single dose of 40 mg). Asciminib exposure (area under the curve [AUC]) unexpectedly decreased by ~40% when administered concomitantly with the strong CYP3A inhibitor itraconazole oral solution, whereas maximum plasma concentration (C_max) decreased by ~50%. However, asciminib exposure was slightly increased in subjects receiving an itraconazole capsule (~3%) or clarithromycin (~35%), another strong CYP3A inhibitor. Macroflux studies showed that cyclodextrin (present in high quantities as excipient [40‐fold excess to itraconazole] in the oral solution formulation of itraconazole) decreased asciminib flux through a lipid membrane by ~80%. The AUC of asciminib was marginally decreased by concomitant administration with the strong CYP3A inducer rifampicin (by ~13–15%) and the strong P‐gp inhibitor quinidine (by ~13–16%). Concomitant administration of the ARA rabeprazole had little or no effect on asciminib AUC, with a 9% decrease in C_max. The treatments were generally well tolerated. Taking into account the large therapeutic window of asciminib, the observed changes in asciminib PK following multiple doses of P‐gp, CYP3A inhibitors, CYP3A inducers, or ARAs are not considered to be clinically meaningful. Care should be exercised when administering asciminib concomitantly with cyclodextrin‐containing drug formulations.

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.

Novel Bruton's Tyrosine Kinase inhibitor remibrutinib: Drug-drug interaction potential as a victim of CYP3A4 inhibitors based on clinical data and PBPK modeling

Remibrutinib, a novel oral Bruton’s Tyrosine Kinase inhibitor (BTKi) is highly selective for BTK, potentially mitigating the side effects of other BTKis. Enzyme phenotyping identified CYP3A4 to be the predominant elimination pathway of remibrutinib. The impact of concomitant treatment with CYP3A4 inhibitors, grapefruit juice and ritonavir (RTV), was investigated in this study in combination with an intravenous microtracer approach. Pharmacokinetic (PK) parameters, including the fraction absorbed, the fractions escaping intestinal and hepatic first‐pass metabolism, the absolute bioavailability, systemic clearance, volume of distribution at steady‐state, and the fraction metabolized via CYP3A4 were evaluated. Oral remibrutinib exposure increased in the presence of RTV 4.27‐fold, suggesting that remibrutinib is not a sensitive CYP3A4 substrate. The rich PK dataset supported the building of a robust physiologically‐based pharmacokinetic (PBPK) model, which well‐described the therapeutic dose range of 25–100 mg. Simulations of untested scenarios revealed an absence of drug‐drug interaction (DDI) risk between remibrutinib and the weak CYP3A4 inhibitor fluvoxamine (area under the concentration‐time curve ratio [AUCR] <1.25), and a moderate effect with the CYP3A4 inhibitor erythromycin (AUCR: 2.71). Predictions with the moderate and strong CYP3A4 inducers efavirenz and rifampicin, suggested a distinct remibrutinib exposure decrease of 64% and 89%. Oral bioavailability of remibrutinib was 34%. The inclusion of an intravenous microtracer allowed the determination of all relevant remibrutinib PK parameters, which facilitated construction of the PBPK model. This will provide guidance on the selection or restriction of comedications and prediction of DDI risks.

Non-clinical Models to Determine Drug Passage into Human Breast Milk

BACKGROUND

Successful practice of clinical perinatal pharmacology requires a thorough understanding of the pronounced physiological changes during lactation and how these changes affect various drug disposition processes. In addition, pharmacokinetic processes unique to lactation have remained understudied. Hence, determination of drug disposition mechanisms in lactating women and their babies remains a domain with important knowledge gaps. Indeed, lack of data regarding infant risk during breastfeeding far too often results in discontinuation of breastfeeding and subsequent loss of all the associated benefits to the breastfed infant. In the absence of age-specific toxicity data, human lactation data alone are considered insufficient to rapidly generate the required evidence regarding risks associated with medication use during lactation.

METHODS

Systematic review of literature to summarize state-of-the art non-clinical approaches that have been developed to explore the mechanisms underlying drug milk excretion.

RESULTS

Several studies have reported methods to predict (to some extent) milk drug excretion rates based on physicochemical properties of the compounds. In vitro studies with primary mammary epithelial cells appear excellent approaches to determine transepithelial drug transport rates across the mammary epithelium. Several of these in vitro tools have been characterized in terms of transporter expression and activity as compared to the mammary gland tissue. In addition, with the advent of physiology-based pharmacokinetic (PBPK) modelling, these in vitro transport data may prove instrumental in predicting drug milk concentration time profiles prior to the availability of data from clinical lactation studies. In vivo studies in lactating animals have proven their utility in elucidating the mechanisms underlying drug milk excretion.

CONCLUSION

By combining various non-clinical tools (physicochemistry-based, in vitro and PBPK, in vivo animal) for drug milk excretion, valuable and unique information regarding drug milk concentrations during lactation can be obtained. The recently approved IMI project ConcePTION will address several of the challenges outlined in this review.

The Herb-Drug Pharmacokinetic Interaction of Fluoxetine and Its Metabolite Norfluoxetine with a Traditional Chinese Medicine in Rats by LC-MS/MS

Background

Fluoxetine (FLU) is the first-line and widely used medication for depression. The combination of Chaihu Shugan san (CSGS) and FLU is commonly used to enhance antidepressant effects and reduce side effects.

Objective

The primary objective of this study was to investigate the potential pharmacokinetic effect of CSGS on FLU.

Materials and Methods

Thirty-two healthy adult male Sprague-Dawley (SD) rats were randomly divided into four groups, the fluoxetine group and multiple dose groups A, B, and C. The rats in the different groups were orally administered with a combination of FLU and different doses of CSGS for 14 d. On the fifteenth day, serial blood samples were taken from the caudal vein before the administration and at 0.25, 0.5, 0.75, 1, 2, 4, 6, 8, 10, 12, 24, 36, and 48 h after the administration. A liquid-liquid extraction method was applied to extract the analytes from serum. Then, the concentrations of FLU and its metabolite, norfluoxetine (NOF), were determined using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The pharmacokinetic parameters were calculated by DAS 3.2.8 program and compared by statistic analysis.

Results

Compared with the FLU group, the FLU and NOF area under the plasma concentration-time curve (AUC) (0–∞) in multiple dose group C was significantly increased, while the NOF AUCs (0–∞) in multiple dose group A and multiple dose group B were decreased. Compared with the FLU group, the NOF clearance (CL) in multiple dose group C was decreased, while the CL in multiple dose groups A and B was increased.

Discussion and Conclusion

There were some differences in pharmacokinetic parameters between the FLU group and multiple dose groups, and CSGS can affect the pharmacokinetics of fluoxetine.