Magnitude Of Drug-drug Interactions Research Articles

Understanding CYP3A4 and P-gp mediated drug-drug interactions through PBPK modeling - Case example of pralsetinib.

Pralsetinib, a potent and selective inhibitor of oncogenic RET fusion and RET mutant proteins, is a substrate of the drug metabolizing enzyme CYP3A4 and a substrate of the efflux transporter P-gp based on invitro data. Therefore, its pharmacokinetics (PKs) may be affected by co-administration of potent CYP3A4 inhibitors and inducers, P-gp inhibitors, and combined CYP3A4 and P-gp inhibitors. With the frequent overlap between CYP3A4 and P-gp substrates/inhibitors, pralsetinib is a challenging and representative example of the need to more quantitatively characterize transporter-enzyme interplay. A physiologically-based PK (PBPK) model for pralsetinib was developed to understand the victim drug-drug interaction (DDI) risk for pralsetinib. The key parameters driving the magnitude of pralsetinib DDIs, the P-gp intrinsic clearance and the fraction metabolized by CYP3A4, were determined from PBPK simulations that best captured observed DDIs from three clinical studies. Sensitivity analyses and scenario simulations were also conducted to ensure these key parameters were determined with sound mechanistic rationale based on current knowledge, including the worst-case scenarios. The verified pralsetinib PBPK model was then applied to predict the effect of other inhibitors and inducers on the PKs of pralsetinib. This work highlights the challenges in understanding DDIs when enzyme-transporter interplay occurs, and demonstrates an important strategy for differentiating enzyme/transporter contributions to enable PBPK predictions for untested scenarios and to inform labeling.

Assessing the relative contribution of CYP3A-and P-gp-mediated pathways to the overall disposition and drug-drug interaction of dabigatran etexilate using a comprehensive mechanistic physiological-based pharmacokinetic model.

Dabigatran etexilate (DABE) is a clinical probe substrate for studying drug-drug interaction (DDI) through an intestinal P-glycoprotein (P-gp). A recent in vitro study, however, has suggested a potentially significant involvement of CYP3A-mediated oxidative metabolism of DABE and its intermediate monoester BIBR0951 in DDI following microdose administration of DABE. In this study, the relative significance of CYP3A- and P-gp-mediated pathways to the overall disposition of DABE has been explored using mechanistic physiologically based pharmacokinetic (PBPK) modeling approach. The developed PBPK model linked DABE with its 2 intermediate (BIBR0951 and BIBR1087) and active (dabigatran, DAB) metabolites, and with all relevant drug-specific properties known to date included. The model was successfully qualified against several datasets of DABE single/multiple dose pharmacokinetics and DDIs with CYP3A/P-gp inhibitors. Simulations using the qualified model supported that the intestinal CYP3A-mediated oxidation of BIBR0951, and not the gut P-gp-mediated efflux of DABE, was a key contributing factor to an observed difference in the DDI magnitude following the micro-versus therapeutic doses of DABE with clarithromycin. Both the saturable CYP3A-mediated metabolism of BIBR0951 and the solubility-limited DABE absorption contributed to the relatively modest nonlinearity in DAB exposure observed with increasing doses of DABE. Furthermore, the results suggested a limited role of the gut P-gp, but an appreciable, albeit small, contribution of gut CYP3A in mediating the DDIs following the therapeutic dose of DABE with dual CYP3A/P-gp inhibitors. Thus, a possibility exists for a varying extent of CYP3A involvement when using DABE as a clinical probe in the DDI assessment, across DABE dose levels.

Projections of Drug-Drug Interactions Caused by Time-Dependent Inhibitors of Cytochrome P450 1A2, 2B6, 2C8, 2C9, 2C19, and 2D6 Using In Vitro Data in Static and Dynamic Models.

In vitro time-dependent inhibition (TDI) kinetic parameters for cytochrome P450 (CYP) 1A2, 2B6, 2C8, 2C9, 2C19, and 2D6, were determined in pooled human liver microsomes for 19 drugs (and 2 metabolites) for which clinical drug-drug interactions (DDI) are known. In vitro TDI data were incorporated into the projection of the magnitude of DDIs using mechanistic static models and Simcyp®. Results suggest that for the mechanistic static model, use of estimated average unbound exit concentration of the inhibitor from the liver resulted in a successful prediction of observed magnitude of clinical DDIs and was similar to Simcyp®. Overall, predictions of DDI magnitude (i.e., fold increase in AUC of a CYP-specific marker substrate) were within 2-fold of actual values. Geometric mean-fold errors were 1.7 and 1.6 for static and dynamic models, respectively. Projections of DDI from both models were also highly correlated to each other (r2 = 0.92). This investigation demonstrates that DDI can be reliably predicted from in vitro TDI data generated in HLM for several CYP enzymes. Simple mechanistic static model equations as well as more complex dynamic PBPK models can be employed in this process. Significance Statement Cytochrome P450 time-dependent inhibitors (TDI) can cause drug-drug interactions (DDI). An ability to reliably assess the potential for a new drug candidate to cause DDI is essential during drug development. In this report, TDI data for 19 drugs (and 2 metabolites) were measured and used in static and dynamic models to reliably project the magnitude of DDI resulting from inhibition of CYP1A2, 2B6, 2C8, 2C9, 2C19, and 2D6.

Inhibitory Effects of Tricyclic Antidepressants on Human Liver Microsomal Morphine Glucuronidation: Application of IVIVE to Predict Potential Drug-Drug Interactions in Humans.

Tricyclic antidepressants (TCAs) are commonly co-administered with morphine as an adjuvant analgesic. Nevertheless, there remains a lack of information concerning metabolic drug-drug interactions (DDIs) resulting from TCA inhibition on morphine glucuronidation. This study aimed to (i) examine the inhibitory effects of TCAs (viz., amitriptyline, clomipramine, imipramine, and nortriptyline) on human liver microsomal morphine 3- and 6-glucuronidation and (ii) evaluate the potential of DDI in humans by employing in vitro-in vivo extrapolation (IVIVE) approaches. The inhibition parameters for TCA inhibition on morphine glucuronidation were derived from the in vitro system containing 2% BSA. The Ki values were employed to predict the DDI magnitude in vivo by using static and dynamic mechanistic PBPK approaches Results: TCAs moderately inhibited human liver microsomal morphine glucuronidation, with clomipramine exhibiting the most potent inhibition potency. Amitriptyline, clomipramine, imipramine, and nortriptyline competitively inhibited morphine 3- and 6-glucuronide formation with the respective Ki values of 91 ± 7.5 and 82 ± 11 μM, 23 ± 1.3 and 14 ± 0.7 μM, 103 ± 5 and 90 ± 7 μM, and 115 ± 5 and 110 ± 3 μM. Employing the static mechanistic IVIVE, a prediction showed an estimated 20% elevation in the morphine AUC when co-administered with either clomipramine or imipramine, whereas the predicted increase was <5% for amitriptyline or nortriptyline. PBPK modelling predicted an increase of less than 10% in the morphine AUC due to the inhibition of clomipramine and imipramine in both virtual healthy and cirrhotic populations. The results suggest that the likelihood of potential clinical DDIs arising from tricyclic antidepressant inhibition on morphine glucuronidation is low.

Mechanistic Static Model based Prediction of Transporter Substrate Drug-Drug Interactions Utilizing Atorvastatin and Rifampicin.

An in vitro relative activity factor (RAF) technique combined with mechanistic static modeling was examined to predict drug-drug interaction (DDI) magnitude and analyze contributions of different clearance pathways in complex DDIs involving transporter substrates. Atorvastatin and rifampicin were used as a model substrate and inhibitor pair. In vitro studies were conducted with transfected HEK293 cells, hepatocytes and human liver microsomes. Prediction success was defined as predictions being within twofold of observations. The RAF method successfully translated atorvastatin uptake from transfected cells to hepatocytes, demonstrating its ability to quantify transporter contributions to uptake. Successful translation of atorvastatin's in vivo intrinsic hepatic clearance (CLint,h,in vivo) from hepatocytes to liver was only achieved through consideration of albumin facilitated uptake or through application of empirical scaling factors to transporter-mediated clearances. Transporter protein expression differences between hepatocytes and liver did not affect CLint,h,in vivo predictions. By integrating cis and trans inhibition of OATP1B1/OATP1B3, atorvastatin-rifampicin (single dose) DDI magnitude could be accurately predicted (predictions within 0.77-1.0 fold of observations). Simulations indicated that concurrent inhibition of both OATP1B1 and OATP1B3 caused approximately 80% of atorvastatin exposure increases (AUCR) in the presence of rifampicin. Inhibiting biliary elimination, hepatic metabolism, OATP2B1, NTCP, and basolateral efflux are predicted to have minimal to no effect on AUCR. This study demonstrates the effective application of a RAF-based translation method combined with mechanistic static modeling for transporter substrate DDI predictions and subsequent mechanistic interpretation.

Development of 4-Pyridoxic Acid PBPK Model to Support Biomarker-Informed Evaluation of OAT1/3 Inhibition and Effect of Chronic Kidney Disease.

Monitoring endogenous biomarkers is increasingly used to evaluate transporter-mediated drug-drug interactions (DDIs) in early drug development and may be applied to elucidate changes in transporter activity in disease. 4-pyridoxic acid (PDA) has been identified as the most sensitive plasma endogenous biomarker of renal organic anion transporters (OAT1/3). Increase in PDA baseline concentrations was observed after administration of probenecid, a strong clinical inhibitor of OAT1/3 and also in patients with chronic kidney disease (CKD). The aim of this study was to develop and verify a physiologically-based pharmacokinetic (PBPK) model of PDA, to predict the magnitude of probenecid DDI and predict the CKD-related changes in PDA baseline. The PBPK model for PDA was first developed in healthy population, building on from previous population pharmacokinetic modeling, and incorporating a mechanistic kidney model to consider OAT1/3-mediated renal secretion. Probenecid PBPK model was adapted from the Simcyp database and re-verified to capture its dose-dependent pharmacokinetics (n = 9 studies). The PBPK model successfully predicted the PDA plasma concentrations, area under the curve, and renal clearance in healthy subjects at baseline and after single/multiple probenecid doses. Prospective simulations in severe CKD predicted successfully the increase in PDA plasma concentration relative to healthy (within 2-fold of observed data) after accounting for 60% increase in fraction unbound in plasma and additional 50% decline in OAT1/3 activity beyond the decrease in glomerular filtration rate. The verified PDA PBPK model supports future robust evaluation of OAT1/3 DDI in drug development and increases our confidence in predicting exposure and renal secretion in patients with CKD.

Impact of Obesity on the Drug-Drug Interaction Between Dolutegravir and Rifampicin or Any Other Strong Inducers.

Obesity is increasingly prevalent among people with HIV. Obesity can impact drug pharmacokinetics and consequently the magnitude of drug-drug interactions (DDIs) and, thus, the related recommendations for dose adjustment. Virtual clinical DDI studies were conducted using physiologically based pharmacokinetic (PBPK) modeling to compare the magnitude of the DDI between dolutegravir and rifampicin in nonobese, obese, and morbidly obese individuals. Each DDI scenario included a cohort of virtual individuals (50% female) between 20 and 50 years of age. Drug models for dolutegravir and rifampicin were verified against clinical observed data. The verified models were used to simulate the concurrent administration of rifampicin (600 mg) at steady state with dolutegravir (50 mg) administered twice daily in normal-weight (BMI 18.5-30 kg/m2), obese (BMI 30-40 kg/m2), and morbidly obese (BMI 40-50 kg/m2) individuals. Rifampicin was predicted to decrease dolutegravir area under the curve (AUC) by 72% in obese and 77% in morbidly obese vs 68% in nonobese individuals; however, dolutegravir trough concentrations were reduced to a similar extent (83% and 85% vs 85%). Twice-daily dolutegravir with rifampicin resulted in trough concentrations always above the protein-adjusted 90% inhibitory concentration for all BMI groups and above the 300 ng/mL threshold in a similar proportion for all BMI groups. The combined effect of obesity and induction by rifampicin was predicted to further decrease dolutegravir exposure but not the minimal concentration at the end of the dosing interval. Thus, dolutegravir 50 mg twice daily with rifampicin can be used in individuals with a high BMI up to 50 kg/m2.

Physiologically-based pharmacokinetic modeling to predict drug-drug interaction of enzalutamide with combined P-gp and CYP3A substrates

Enzalutamide is known to strongly induce cytochrome P450 3A4 (CYP3A4). Furthermore, enzalutamide showed induction and inhibition of P-glycoprotein (P-gp) in in vitro studies. A clinical drug-drug interaction (DDI) study between enzalutamide and digoxin, a typical P-gp substrate, suggested enzalutamide has weak inhibitory effect on P-gp substrates. Direct oral anticoagulants (DOACs), such as apixaban and rivaroxaban, are dual substrates of CYP3A4 and P-gp, and hence it is recommended to avoid co-administration of these DOACs with combined P-gp and strong CYP3A inducers. Enzalutamide’s net effect on P-gp and CYP3A for apixaban and rivaroxaban plasma exposures is of interest to physicians who treat patients for venous thromboembolism with prostate cancer. Accordingly, a physiologically-based pharmacokinetic (PBPK) analysis was performed to predict the magnitude of DDI on apixaban and rivaroxaban exposures in the presence of 160 mg once-daily dosing of enzalutamide. The PBPK models of enzalutamide and M2, a major metabolite of enzalutamide which also has potential to induce CYP3A and P-gp and inhibit P-gp, were developed and verified as perpetrators of CYP3A-and P-gp-mediated interaction. Simulation results predicted a 31% decrease in AUC and no change in Cmax for apixaban and a 45% decrease in AUC and a 25% decrease in Cmax for rivaroxaban when 160 mg multiple doses of enzalutamide were co-administered. In summary, enzalutamide is considered to decrease apixaban and rivaroxaban exposure through the combined effects of CYP3A induction and net P-gp inhibition. Concurrent use of these drugs warrants careful monitoring for efficacy and safety.

The Potentially Significant Role of CYP3A-Mediated Oxidative Metabolism of Dabigatran Etexilate and its Intermediate Metabolites in Drug-Drug Interaction Assessments Using Microdose Dabigatran Etexilate.

Dabigatran etexilate (DABE), a double ester prodrug of dabigatran (DAB), is a probe substrate of intestinal P-glycoprotein (P-gp) commonly used in clinical drug-drug interaction (DDI) studies. When compared to its therapeutic dose at 150 mg, microdose DABE (375 µg) showed approximately 2-fold higher in DDI magnitudes with CYP3A/P-gp inhibitors. In this study, we conducted several in vitro metabolism studies to demonstrate that DABE at a theoretical gut concentration after microdosing significantly underwent NADPH-dependent oxidation (~40-50%) in parallel to carboxylesterase-mediated hydrolysis in human intestinal microsomes. Furthermore, NADPH-dependent metabolism of its intermediate monoester BIBR0951 was also observed in both human intestinal and liver microsomes, accounting for 100% and 50% of total metabolism, respectively. Metabolite profiling using LC-MS/MS confirmed the presence of several novel oxidative metabolites of DABE and of BIBR0951 in the NADPH-fortified incubations. CYP3A was identified as the major enzyme catalyzing the oxidation of both compounds. The metabolism of DABE and BIBR0951 was well described by Michaelis-Menten kinetics, with Km ranging 1-3 µM, significantly below the expected concentrations following the therapeutic dose of DABE. Overall, the present results suggested that CYP3A played a significant role in the presystemic metabolism of DABE and BIBR0951 following microdose DABE administration, thus attributing partly to the apparent overestimation in the DDI magnitude observed with the CYP3A/P-gp inhibitors. Therefore, DABE at the microdose, unlike the therapeutic dose, would likely be a less predictive tool and should be considered as a clinical dual substrate for P-gp and CYP3A when assessing potential P-gp-mediated impacts by dual CYP3A/P-gp inhibitors. Significance Statement This is the first study demonstrating a potentially significant role of CYP-mediated metabolism of the prodrug DABE following a microdose, but not a therapeutic dose. This additional pathway coupled with its susceptibility to P-gp may make DABE a clinical dual substrate for both P-gp and CYP3A at a microdose. The study also highlights the need for better characterization of the pharmacokinetics and metabolism of a clinical DDI probe substrate over the intended study dose range for proper result interpretations.

Effect of doravirine on dolutegravir trough concentrations in people with HIV switched from darunavir/cobicistat.

Dolutegravir and doravirine are individually safe and potent ART components, but their combined use has not been studied in clinical trials. Real-life experiences in small cohorts of highly treatment-experienced people with HIV have shown that combined treatment of dolutegravir with doravirine was well tolerated, achieving virological suppression in most cases.1,2 The potential pharmacokinetic drug–drug interaction (DDI) between the two drugs has been also investigated by Anderson et al.3 They found that doravirine co-administration increased dolutegravir exposure by about 25%–35%, a difference that was considered as not clinically relevant. However, it cannot be excluded that the co-administration of dolutegravir and doravirine in people with HIV instead of healthy volunteers may result in a different magnitude of DDI, as previously reported for other antiretroviral drugs.4–6 To address this issue, we considered people with HIV switched to doravirine from darunavir/cobicistat because we have previously shown that these antiretroviral drugs do not interact with dolutegravir.7,8

General Framework to Quantitatively Predict Pharmacokinetic Induction Drug-Drug Interactions Using In Vitro Data.

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

Individual dose recommendations for drug interaction between tacrolimus and voriconazole in adult liver transplant recipients: A semiphysiologically based population pharmacokinetic modeling approach

The magnitude of drug-drug interaction between tacrolimus and voriconazole is highly variable, and individually tailoring the tacrolimus dose when concomitantly administered with voriconazole remains difficult. This study aimed to develop a semiphysiologically based population pharmacokinetic (semi-PBPK) model and a web-based dashboard to identify the dynamic inhibition of tacrolimus metabolism caused by voriconazole and provide individual tacrolimus regimens for Chinese adult liver transplant recipients. A total of 264 tacrolimus concentrations and 146 voriconazole concentrations were prospectively collected from 32 transplant recipients. A semi-PBPK model with physiological compartments including the gut wall, portal vein, and liver was developed using the nonlinear mixed-effects modeling software NONMEM (version 7.4). A web-based dashboard was established in R software (version 3.6.1) to recommend the individual tacrolimus regimens when concomitantly administered with voriconazole. The reversible inhibition of tacrolimus metabolism caused by voriconazole was investigated in both the liver and the gut wall. Moreover, voriconazole could highly inhibit the CYP3A activity in the gut wall more than in the liver. BMI and postoperative days were identified as significant covariates on intrinsic intestinal and hepatic clearance of tacrolimus, respectively. Age and postoperative days were identified as significant covariates on the volume of distribution of voriconazole. The individual tacrolimus regimens when concomitantly administered with voriconazole could be recommended in the dashboard (https://tac-vor-ddi.shinyapps.io/shinyapp3/). In conclusion, the semi-PBPK model successfully described the dynamic inhibition process between tacrolimus and voriconazole, and the web-based dashboard could provide individual tacrolimus regimens when concomitantly administered with voriconazole.

Robust physiologically based pharmacokinetic model of rifampicin for predicting drug-drug interactions via P-glycoprotein induction and inhibition in the intestine, liver, and kidney.

P‐glycoprotein (P‐gp) is an efflux transporter that plays an important role in the pharmacokinetics of its substrate, and P‐gp activities can be altered by induction and inhibition effects of rifampicin. This study aimed to establish a physiologically based pharmacokinetic (PBPK) model of rifampicin to predict the P‐gp‐mediated drug–drug interactions (DDIs) and assess the DDI impact in the intestine, liver, and kidney. The induction and inhibition parameters of rifampicin for P‐gp were estimated using two of seven DDI cases of rifampicin and digoxin and incorporated into our previously constructed PBPK model of rifampicin. The constructed rifampicin model was verified using the remaining five DDI cases with digoxin and five DDI cases with other P‐gp substrates (talinolol and quinidine). Based on the established PBPK model, following repeated dosing of 600 mg rifampicin, the deduced net effect was an approximately threefold induction in P‐gp activities in the intestine, liver, and kidney. Furthermore, in all 12 cases the predicted area under the plasma concentration–time curve ratios of the P‐gp substrates were within the predefined acceptance criteria with various dosing regimens. Intestinal effects of P‐gp–mediated DDIs had their greatest impact on the pharmacokinetics of digoxin and talinolol, with a minimal impact on the liver and kidney. For quinidine, predicted intestinal P‐gp/cytochrome P450 3A–mediated DDIs were slightly underestimated because of the complexity of nonlinearity and transporter‐enzyme interplay. These findings demonstrate that our rifampicin model can be applicable to quantitatively predict the net impact of P‐gp induction and/or inhibition on diverse P‐gp substrates and investigate the magnitude of DDIs in each tissue.

Understanding Interindividual Variability in the Drug Interaction of a Highly Extracted CYP1A2 Substrate Tizanidine: Application of a Permeability-Limited Multicompartment Liver Model in a Population Based Physiologically Based Pharmacokinetic Framework

Tizanidine, a centrally acting skeletal muscle relaxant, is predominantly metabolized by CYP1A2 and undergoes extensive hepatic first-pass metabolism after oral administration. As a highly extracted drug, the systemic exposure to tizanidine exhibits considerable interindividual variability and is altered substantially when coadministered with CYP1A2 inhibitors or inducers. The aim of the current study was to compare the performance of a permeability-limited multicompartment liver (PerMCL) model, which operates as an approximation of the dispersion model, and the well stirred model (WSM) for predicting tizanidine drug-drug interactions (DDIs). Physiologically based pharmacokinetic models were developed for tizanidine, incorporating the PerMCL model and the WSM, respectively, to simulate the interaction of tizanidine with a range of CYP1A2 inhibitors and inducers. Whereas the WSM showed a tendency to underpredict the fold change of tizanidine area under the plasma concentration-time curve (AUC ratio) in the presence of perpetrators, the use of PerMCL model increased precision (absolute average-fold error: 1.32-1.42 versus 1.58) and decreased bias (average-fold error: 0.97-1.25 versus 0.63) for the predictions of mean AUC ratios as compared with the WSM. The PerMCL model captured the observed range of individual AUC ratios of tizanidine as well as the correlation between individual AUC ratios and CYP1A2 activities without interactions, whereas the WSM was not able to capture these. The results demonstrate the advantage of using the PerMCL model over the WSM in predicting the magnitude and interindividual variability of DDIs for a highly extracted sensitive substrate tizanidine. SIGNIFICANCE STATEMENT: This study demonstrates the advantages of the PerMCL model, which operates as an approximation of the dispersion model, in mitigating the tendency of the WSM to underpredict the magnitude and variability of DDIs of a highly extracted CYP1A2 substrate tizanidine when it is administered with CYP1A2 inhibitors or inducers. The physiologically based pharmacokinetic modeling approach described herein is valuable to the understanding of drug interactions of highly extracted substrates and the source of its interindividual variability.

Assessing the Effect of Organic Anion Transporter Inhibition on Circulating Pyridoxic Acid, an Endogenous Transporter Biomarker, using Physiologically‐based Pharmacokinetic Modeling

Transporter‐mediated renal secretion plays a critical role in the systemic clearance of ~30% of the Food and Drug Administration (FDA)‐approved drugs and is attributed to clinically significant drug‐drug interactions (DDIs). For example, organic anion transporters (OATs) are inhibited by perpetrator drugs (e.g., probenecid), leading to higher circulating levels of OAT substrates (e.g., furosemide).1 Recently, OAT inhibition has been shown to be associated with increased plasma levels of endogenous substrates, e.g., pyridoxic acid (PDA) and homovanillic acid, which can serve as potential OAT biomarkers.2 The objective of our study was to develop and validate a physiologically‐based pharmacokinetic (PBPK) model of PDA to allow prospective evaluation of the magnitude of potential DDI of an OAT inhibitor‐substrate pair. Probenecid and furosemide were used as selective inhibitor and substrate of OATs, respectively. Physicochemical properties and in vitro data pertaining to the pharmacokinetics of probenecid and PDA were collated from literature. A PBPK model was first constructed to predict the plasma concentration‐time profiles of probenecid after oral administration using Simcyp (v20). Then, utilizing the fraction secreted versus filtered as well as the unbound fraction (fu = 0.092) data of PDA,2 the total elimination and synthesis rates of PDA at steady‐state were used to develop a baseline PBPK model. The effect of OAT inhibition by probenecid on the PDA levels was estimated and the predicted area under the plasma concentration‐time profile (AUC), change in clearance, and the highest plasma concentration (Cmax) values were compared with the observed data.2 The model was able to capture the changes within 2‐fold of the clinical data. Using the predicted AUC profile of PDA and assuming 100% inhibition at Cmax, the magnitude of inhibition was estimated across different time points. The biomarker‐informed magnitude of inhibition showed significant correlation (r2 = 0.9336) with the observed change in furosemide concentrations in the presence of probenecid (Fig. 1). PDA‐informed PBPK model developed here can be used to prospectively predict the magnitude of OAT inhibition, which is particularly applicable to assessing DDI potential of investigational drugs or prescribed drugs in special populations with sparse samples, without the requirement of an exogenous probe substrate.

Effect of Cyclosporin A and Impact of Dose Staggering on OATP1B1/1B3 Endogenous Substrates and Drug Probes for Assessing Clinical Drug Interactions.

This study was designed to assess the quantitative performance of endogenous biomarkers for organic anion transporting polypeptide (OATP) 1B1/1B3‐mediated drug‐drug interactions (DDIs). Ten healthy volunteers orally received OATP1B1/1B3 probe cocktail (0.2 mg pitavastatin, 1 mg rosuvastatin, and 2 mg valsartan) and an oral dose of cyclosporin A (CysA, 20 mg and 75 mg) separated by a 1‐hour interval (20 mg (−1 hour), and 75 mg (−1 hour)). CysA 75 mg was also given with a 3‐hour interval (75 mg (−3 hours)) to examine the persistence of OATP1B1/1B3 inhibition. The area under the plasma concentration‐time curve ratios (AUCRs) were 1.63, 3.46, and 2.38 (pitavastatin), 1.39, 2.16, and 1.81 (rosuvastatin), and 1.42, 1.77, and 1.85 (valsartan), at 20 mg, 75 mg (−1 hour) and 75 mg (−3 hours) of CysA, respectively. CysA effect on OATP1B1/1B3 was unlikely to persist at the dose examined. Among 26 putative OATP1B1/1B3 biomarkers evaluated, AUCR and maximum concentration ratio (C maxR) of CP‐I showed the highest Pearson’s correlation coefficient with CysA AUC (0.94 and 0.93, respectively). Correlation between AUCR of pitavastatin, and C maxR or AUCR of CP‐I were consistent between this study and our previous study using rifampicin as an OATP1B1/1B3 inhibitor. Nonlinear regression analysis of AUCR−1 of pitavastatin and CP‐I against CysA C max yielded K i,OATP1B1/1B3,app (109 ± 35 and 176 ± 42 nM, respectively), similar to the Ki ,OATP1B1/1B3 estimated by our physiologically‐based pharmacokinetic model analysis described previously (107 nM). The endogenous OATP1B1/1B3 biomarkers, particularly C maxR and AUCR of CP‐I, corroborates OATP1B1/1B3 inhibition and yields valuable information that improve accurate DDI predictions in drug development, and enhance our understanding of interindividual variability in the magnitude of DDIs.

Magnitude of Drug-Drug Interactions in Special Populations.

Drug–drug interactions (DDIs) are one of the most frequent causes of adverse drug reactions or loss of treatment efficacy. The risk of DDIs increases with polypharmacy and is therefore of particular concern in individuals likely to present comorbidities (i.e., elderly or obese individuals). These special populations, and the population of pregnant women, are characterized by physiological changes that can impact drug pharmacokinetics and consequently the magnitude of DDIs. This review compiles existing DDI studies in elderly, obese, and pregnant populations that include a control group without the condition of interest. The impact of physiological changes on the magnitude of DDIs was then analyzed by comparing the exposure of a medication in presence and absence of an interacting drug for the special population relative to the control population. Aging does not alter the magnitude of DDIs as the related physiological changes impact the victim and perpetrator drugs to a similar extent, regardless of their elimination pathway. Conversely, the magnitude of DDIs can be changed in obese individuals or pregnant women, as these conditions impact drugs to different extents depending on their metabolic pathway.

In Vitro Assessment of Transporter Mediated Perpetrator DDIs for Several Hepatitis C Virus Direct-Acting Antiviral Drugs and Prediction of DDIs with Statins Using Static Models.

Inhibitory effects of asunaprevir, daclatasvir, grazoprevir, paritaprevir, simeprevir, and voxilaprevir, direct-acting antiviral (DAA) drugs for the treatment of chronic hepatitis C virus (HCV) infection, were evaluated in vitro against a range of clinically important drug transporters. In vitro inhibition studies were conducted using transporter transfected cells and membrane vesicles. The risk of clinical drug-drug interactions (DDIs) was assessed using simplified static models recommended by regulatory agencies. Furthermore, we refined and developed static models to predict complex DDIs with several statins (pitavastatin, rosuvastatin, atorvastatin, and pravastatin) by mechanistically assessing differential inhibitory effects of perpetrator drugs on multiple transporters, such as organic anion transporting polypeptides (OATP1B), breast cancer resistance protein (BCRP), multidrug resistance protein 2 (MRP2), organic anion transporter 3 (OAT3), and cytochrome P450 CYP3A enzyme, as they are known to contribute to absorption, distribution, metabolism and excretion (ADME) of above statins. These models successfully predicted a total of 46 statin DDIs, including above DAA drugs and their fix-dose combination regimens. Predicted plasma area under curve ratio (AUCR) with and without perpetrator drugs was within ~ 2-fold of observed values. In contrast, simplified static R-value model resulted in increased false negative and false positive predictions when different prediction cut-off values were applied. Our studies suggest that mechanistic static model is a promising and useful tool to provide more accurate prediction of the risk and magnitude of DDIs with statins in early drug development and may help to improve the management of clinical DDIs for HCV drugs to ensure effective and safe HCV therapy. GRAPHICAL ABSTRACT.

Unraveling pleiotropic effects of rifampicin by using physiologically based pharmacokinetic modeling: Assessing the induction magnitude of P-glycoprotein-cytochrome P450 3A4 dual substrates.

Rifampicin induces both P‐glycoprotein (P‐gp) and cytochrome P450 3A4 (CYP3A4) through regulating common nuclear receptors (e.g., pregnane X receptor). The interplay of P‐gp and CYP3A4 has emerged to be an important factor in clinical drug–drug interactions (DDIs) with P‐gp–CYP3A4 dual substrates and requires qualitative and quantitative understanding. Although physiologically based pharmacokinetic (PBPK) modeling has become a widely accepted approach to assess DDIs and is able to reasonably predict DDIs caused by CYP3A4 induction and P‐gp induction individually, the predictability of PBPK models for the effect of simultaneous P‐gp and CYP3A4 induction on P‐gp‐CYP3A4 dual substrates remains to be systematically evaluated. In this study, we used a PBPK modeling approach for the assessment of DDIs between rifampicin and 12 drugs: three sensitive P‐gp substrates, seven P‐gp–CYP3A4 dual substrates, and two P‐gp–CYP3A4 dual substrates and inhibitors. A 3.5‐fold increase of intestinal P‐gp abundance was incorporated in the PBPK models to account for rifampicin‐mediated P‐gp induction at steady state. The simulation results showed that accounting for P‐gp induction in addition to CYP3A4 induction improved the prediction accuracy of the area under the concentration‐time curve and maximum (peak) plasma drug concentration ratios compared with considering CYP3A4 induction alone. Furthermore, the interplay of relevant drug‐specific parameters and its impact on the magnitude of DDIs were evaluated using sensitivity analysis. The PBPK approach described herein, in conjunction with robust in vitro and clinical data, can help in the prospective assessment of DDIs involving other P‐gp and CYP3A4 dual substrates. The database reported in the present study provides a valuable aid in understanding the combined effect of P‐gp and CYP3A4 induction during drug development.

An experience on the model-based evaluation of pharmacokinetic drug-drug interaction for a long half-life drug.

Fixed-dose combinations development requires pharmacokinetic drug-drug interaction (DDI) studies between active ingredients. For some drugs, pharmacokinetic properties such as long half-life or delayed distribution, make it difficult to conduct such clinical trials and to estimate the exact magnitude of DDI. In this study, the conventional (non-compartmental analysis and bioequivalence [BE]) and model-based analyses were compared for their performance to evaluate DDI using amlodipine as an example. Raw data without DDI or simulated data using pharmacokinetic models were compared to the data obtained after concomitant administration. Regardless of the methodology, all the results fell within the classical BE limit. It was shown that the model-based approach may be valid as the conventional approach and reduce the possibility of DDI overestimation. Several advantages (i.e., quantitative changes in parameters and precision of confidence interval) of the model-based approach were demonstrated, and possible application methods were proposed. Therefore, it is expected that the model-based analysis is appropriately utilized according to the situation and purpose.