A mechanistic framework for in vitro-in vivo extrapolation of liver membrane transporters: prediction of drug-drug interaction between rosuvastatin and cyclosporine

Evaluation of BCRP-Related DDIs Between Methotrexate and Cyclosporin A Using Physiologically Based Pharmacokinetic Modelling

BACKGROUND AND OBJECTIVE

This study provides a physiologically based pharmacokinetic (PBPK) model-based analysis of the potential drug-drug interaction (DDI) between cyclosporin A (CsA), a breast cancer resistance protein transporter (BCRP) inhibitor, and methotrexate (MTX), a putative BCRP substrate.

METHODS

PBPK models for CsA and MTX were built using open-source tools and published data for both model building and for model verification and validation. The MTX and CsA PBPK models were evaluated for their application in simulating BCRP-related DDIs. A qualification of an introduced empirical uniform in vitro scaling factor of Ki values for transporter inhibition by CsA was conducted by using a previously developed model of rosuvastatin (sensitive index BCRP substrate), and assessing if corresponding DDI ratios were well captured.

RESULTS

Within the simulated DDI scenarios for MTX in the presence of CsA, the developed models could capture the observed changes in PK parameters as changes in the area under the curve ratios (area under the curve during DDI/area under the curve control) of 1.30 versus 1.31 observed and the DDI peak plasma concentration ratios (peak plasma concentration during DDI/peak plasma concentration control) of 1.07 versus 1.28 observed. The originally reported in vitro Ki values of CsA were scaled with the uniform qualified scaling factor for their use in the in vivo DDI simulations to correct for the low intracellular unbound fraction of the CsA effector concentration. The resulting predicted versus observed ratios of peak plasma concentration and area under the curve DDI ratios with MTX were 0.82 and 0.99, respectively, indicating adequate model accuracy and choice of a scaling factor to capture the observed DDI.

CONCLUSIONS

All models have been comprehensively documented and made publicly available as tools to support the drug development and clinical research community and further community-driven model development.

Using PBPK modeling to supplement clinical data and support the safe and effective use of dolutegravir in pregnant and lactating women

Optimal dosing in pregnant and lactating women requires an understanding of the pharmacokinetics in the mother, fetus, and breastfed infant. Physiologically-based pharmacokinetic (PBPK) modeling can be used to simulate untested scenarios and hence supplement clinical data to support dosing decisions. A PBPK model for the antiretroviral dolutegravir (mainly metabolized by UGT1A1) was verified using reported exposures in non-pregnant healthy volunteers, pregnant women, and the umbilical cord, lactating mothers, and breastfed neonates. The model was then applied to predict the impact of UGT1A1 phenotypes in extensive (EM), poor (PM), and ultra-rapid metabolizers (UM). The predicted dolutegravir maternal plasma and umbilical cord AUC in UGT1A1 PMs was 1.6-fold higher than in EMs. The predicted dolutegravir maternal plasma and umbilical cord AUC in UGT1A1 UMs mothers was 1.3-fold lower than in EMs. The predicted mean systemic and umbilical vein concentrations were in excess of the dolutegravir IC90 at 17, 28, and 40 gestational weeks, regardless of UGT1A1 phenotype, indicating that the standard dose of dolutegravir (50 mg q.d., fed state) is generally appropriate in late pregnancy, across UGT1A1 phenotypes. Applying the model in breastfed infants, a 1.5-, 1.7-, and 2.2-fold higher exposure in 2-day-old neonates, 10-day-old neonates, and infants who were UGT1A1 PMs, respectively, compared with EMs of the same age. However, it should be noted that the exposure in breastfed infants who were UGT1A1 PMs was still an order of magnitude lower than maternal exposure with a relative infant daily dose of <2%, suggesting safe use of dolutegravir in breastfeeding women.

A guide to developing population files for physiologically-based pharmacokinetic modeling in the Simcyp Simulator

The Simcyp Simulator is a software platform widely used in the pharmaceutical industry to conduct stochastic physiologically-based pharmacokinetic (PBPK) modeling. This approach has the advantage of combining routinely generated in vitro data on drugs and drug products with knowledge of biology and physiology parameters to predict a priori potential pharmacokinetic changes in absorption, distribution, metabolism, and excretion for populations of interest. Combining such information with pharmacodynamic knowledge of drugs enables planning for potential dosage adjustment when clinical studies are feasible. Although the conduct of dedicated clinical studies in some patient groups (e.g., with hepatic or renal diseases) is part of the regulatory path for drug development, clinical studies for all permutations of covariates potentially affecting pharmacokinetics are impossible to perform. The role of PBPK in filling the latter gap is becoming more appreciated. This tutorial describes the different input parameters required for the creation of a virtual population giving robust predictions of likely changes in pharmacokinetics. It also highlights the considerations needed to qualify the models for such contexts of use. Two case studies showing the step-by-step development and application of population files for obese or morbidly obese patients and individuals with Crohn's disease are provided as the backbone of our tutorial to give some hands-on and real-world examples.

Ulgen K. Advances in Physiologically Based Pharmacokinetic (PBPK) Modeling of Nanomaterials

Nanoparticles (NPs) have been widely used to improve the pharmacokinetic properties and tissue distribution of small molecules such as targeting to a specific tissue of interest, enhancing their systemic circulation, and enlarging their therapeutic properties. NPs have unique and complicated in vivo disposition properties compared to small molecule drugs due to their complex multifunctionality. Physiologically based pharmacokinetic (PBPK) modeling has been a powerful tool in the simulation of the absorption, distribution, metabolism, and elimination (ADME) characteristics of the materials, and it can be used in the characterization and prediction of the systemic disposition, toxicity, efficacy, and target exposure of various types of nanoparticles. In this review, recent advances in PBPK model applications related to the nanoparticles with unique properties, and dispositional features in the biological systems, ADME characteristics, the description of transport processes of nanoparticles in the PBPK model, and the challenges in PBPK model development of nanoparticles are delineated and juxtaposed with those encountered in small molecule models. Nanoparticle related, non-nanoparticle-related, and interspecies-scaling methods applied in PBPK modeling are reviewed. In vitro to in vivo extrapolation (IVIVE) methods being a promising computational tool to provide in vivo predictions from the results of in vitro and in silico studies are discussed. Finally, as a recent advancement ML/AI-based approaches and challenges in PBPK modeling in the estimation of ADME parameters and pharmacokinetic (PK) analysis results are introduced.

Mechanistic Determinants of Daprodustat Drug-Drug Interactions and Pharmacokinetics in Hepatic Dysfunction and Chronic Kidney Disease: Significance of OATP1B-CYP2C8 Interplay

Daprodustat is the first oral hypoxia-inducible factor prolyl hydroxylase inhibitor approved recently for the treatment of anemia caused by chronic kidney disease (CKD) in adults receiving dialysis. We evaluated the role of organic anion transporting polypeptide (OATP)1B-mediated hepatic uptake transport in the pharmacokinetics (PKs) of daprodustat using in vitro and in vivo studies, and physiologically-based PK (PBPK) modeling of its drug-drug interactions (DDIs) with inhibitor drugs. In vitro, daprodustat showed specific transport by OATP1B1/1B3 in the transfected cell systems and primary human and monkey hepatocytes. A single-dose oral rifampin (OATP1B inhibitor) reduced daprodustat intravenous clearance by a notable 9.9 ± 1.2-fold (P < 0.05) in cynomolgus monkeys. Correspondingly, volume of distribution at steady-state was also reduced by 5.0 ± 1.1-fold, whereas the half-life change was minimal (1.5-fold), corroborating daprodustat hepatic uptake inhibition by rifampin. A PBPK model accounting for OATP1B-CYP2C8 interplay was developed, which well described daprodustat PK and DDIs with gemfibrozil (CYP2C8 and OATP1B inhibitor) and trimethoprim (weak CYP2C8 inhibitor) within 25% error of the observed data in healthy subjects. About 18-fold increase in daprodustat area under the curve (AUC) following gemfibrozil treatment was found to be associated with strong CYP2C8 inhibition and moderate OATP1B inhibition. Moreover, PK modulation in hepatic dysfunction and subjects with CKD, in comparison to healthy control, was well-captured by the model. CYP2C8 and/or OATP1B inhibitor drugs (e.g., gemfibrozil, clopidogrel, rifampin, and cyclosporine) were predicted to perpetrate moderate-to-strong DDIs in healthy subjects, as well as, in target CKD population. Daprodustat can be used as a sensitive CYP2C8 index substrate in the absence of OATP1B modulation.

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

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Predicting acute paraquat toxicity using physiologically based kinetic modelling incorporating in vitro active renal excretion via the OCT2 transporter

Including active renal excretion in physiologically based kinetic (PBK) models can improve their use in quantitative in vitro- in vivo extrapolation (QIVIVE) as a new approach methodology (NAM) for predicting the acute toxicity of organic cation transporter 2 (OCT2) substrates like paraquat (PQ). To realise this NAM, kinetic parameters Vmax and Km for in vitro OCT2 transport of PQ were obtained from the literature. Appropriate scaling factors were applied to translate the in vitro Vmax to an in vivo Vmax. in vitro cytotoxicity data were defined in the rat RLE-6TN and L2 cell lines and the human A549 cell line. The developed PQ PBK model was used to apply reverse dosimetry for QIVIVE translating the in vitro cytotoxicity concentration-response curves to predicted in vivo toxicity dose-response curves after which the lower and upper bound benchmark dose (BMD) for 50% lethality (BMDL50 and BMDU50) were derived by applying BMD analysis. Comparing the predictions to the in vivo reported LD50 values resulted in a conservative prediction for rat and a comparable prediction for human showing proof of principle on the inclusion of active renal excretion and prediction of PQ acute toxicity for the developed NAM.

Characterization of Bile Acid Sulfate Conjugates as Substrates of Human Organic Anion Transporting Polypeptides

Drug interactions involving the inhibition of hepatic organic anion transporting polypeptides (OATPs) 1B1 and OATP1B3 are considered important. Therefore, we sought to study various sulfated bile acids (BA-S) as potential clinical OATP1B1/3 biomarkers. It was determined that BA-S [e.g., glycochenodeoxycholic acid 3-O-sulfate (GCDCA-S) and glycodeoxycholic acid 3-O-sulfate (GDCA-S)] are substrates of OATP1B1, OATP1B3, and sodium-dependent taurocholic acid cotransporting polypeptide (NTCP) transfected into human embryonic kidney 293 cells, with minimal uptake evident for other solute carriers (SLCs) like OATP2B1, organic anion transporter 2, and organic cation transporter 1. It was also shown that BA-S uptake by plated human hepatocytes (PHH) was inhibited (≥96%) by a pan-SLC inhibitor (rifamycin SV), and there was greater inhibition (≥77% versus ≤12%) with rifampicin (OATP1B1/3-selective inhibitor) than a hepatitis B virus myristoylated-preS1 peptide (NTCP-selective inhibitor). Estrone 3-sulfate was also used as an OATP1B1-selective inhibitor. In this instance, greater inhibition was observed with GDCA-S (76%) than GCDCA-S (52%). The study was expanded to encompass the measurement of GCDCA-S and GDCA-S in plasma of SLCO1B1 genotyped subjects. The geometric mean GDCA-S concentration was 2.6-fold (90% confidence interval 1.6, 4.3; P = 2.1 × 10^-4) and 1.3-fold (1.1, 1.7; P = 0.001) higher in individuals homozygous and heterozygous for the SLCO1B1 c.521T > C loss-of-function allele, respectively. For GCDCA-S, no significant difference was noted [1.2-fold (0.8, 1.7; P = 0.384) and 0.9-fold (0.8, 1.1; P = 0.190), respectively]. This supported the in vitro data indicating that GDCA-S is a more OATP1B1-selective substrate (versus GCDCA-S). It is concluded that GCDCA-S and GDCA-S are viable plasma-based OATP1B1/3 biomarkers, but they are both less OATP1B1-selective when compared to their corresponding 3-O-glucuronides (GCDCA-3G and GDCA-3G). Additional studies are needed to determine their utility versus more established biomarkers, such as coproporphyrin I, for assessing inhibitors with different OATP1B1 (versus OATP1B3) inhibition signatures.

Mechanistic Middle-Out Physiologically Based Toxicokinetic Modeling of Transporter-Dependent Disposition of Perfluorooctanoic Acid in Humans

Perfluorooctanoic acid (PFOA) is an environmental toxicant exhibiting a years-long biological half-life (t_1/2) in humans and is linked with adverse health effects. However, limited understanding of its toxicokinetics (TK) has obstructed the necessary risk assessment. Here, we constructed the first middle-out physiologically based toxicokinetic (PBTK) model to mechanistically explain the persistence of PFOA in humans. In vitro transporter kinetics were thoroughly characterized and scaled up to in vivo clearances using quantitative proteomics-based in vitro-to-in vivo extrapolation. These data and physicochemical parameters of PFOA were used to parameterize our model. We uncovered a novel uptake transporter for PFOA, highly likely to be monocarboxylate transporter 1 which is ubiquitously expressed in body tissues and may mediate broad tissue penetration. Our model was able to recapitulate clinical data from a phase I dose-escalation trial and divergent half-lives from clinical trial and biomonitoring studies. Simulations and sensitivity analyses confirmed the importance of renal transporters in driving extensive PFOA reabsorption, reducing its clearance and augmenting its t_1/2. Crucially, the inclusion of a hypothetical, saturable renal basolateral efflux transporter provided the first unified explanation for the divergent t_1/2 of PFOA reported in clinical (116 days) versus biomonitoring studies (1.3-3.9 years). Efforts are underway to build PBTK models for other perfluoroalkyl substances using similar workflows to assess their TK profiles and facilitate risk assessments.

Predictive Performance of Physiologically Based Pharmacokinetic Modelling of Beta-Lactam Antibiotic Concentrations in Adipose, Bone, and Muscle Tissues

Physiologically based pharmacokinetic (PBPK) models consist of compartments representing different tissues. As most models are only verified based on plasma concentrations, it is unclear how reliable associated tissue profiles are. This study aimed to assess the accuracy of PBPK-predicted beta-lactam antibiotic concentrations in different tissues and assess the impact of using effect site concentrations for evaluation of target attainment. Adipose, bone, and muscle concentrations of five beta-lactams (piperacillin, cefazolin, cefuroxime, ceftazidime, and meropenem) in healthy adults were collected from literature and compared with PBPK predictions. Model performance was evaluated with average fold errors (AFEs) and absolute AFEs (AAFEs) between predicted and observed concentrations. In total, 26 studies were included, 14 of which reported total tissue concentrations and 12 unbound interstitial fluid (uISF) concentrations. Concurrent plasma concentrations, used as baseline verification of the models, were fairly accurate (AFE: 1.14, AAFE: 1.50). Predicted total tissue concentrations were less accurate (AFE: 0.68, AAFE: 1.89). A slight trend for underprediction was observed but none of the studies had AFE or AAFE values outside threefold. Similarly, predictions of microdialysis-derived uISF concentrations were less accurate than plasma concentration predictions (AFE: 1.52, AAFE: 2.32). uISF concentrations tended to be overpredicted and two studies had AFEs and AAFEs outside threefold. Pharmacodynamic simulations in our case showed only a limited impact of using uISF concentrations instead of unbound plasma concentrations on target attainment rates. The results of this study illustrate the limitations of current PBPK models to predict tissue concentrations and the associated need for more accurate models. SIGNIFICANCE STATEMENT: Clinical inaccessibility of local effect site concentrations precipitates a need for predictive methods for the estimation of tissue concentrations. This is the first study in which the accuracy of PBPK-predicted tissue concentrations of beta-lactam antibiotics in humans were assessed. Predicted tissue concentrations were found to be less accurate than concurrent predicted plasma concentrations. When using PBPK models to predict tissue concentrations, this potential relative loss of accuracy should be acknowledged when clinical tissue concentrations are unavailable to verify predictions.

Use of In Vivo Imaging and Physiologically-Based Kinetic Modelling to Predict Hepatic Transporter Mediated Drug-Drug Interactions in Rats

Gadoxetate, a magnetic resonance imaging (MRI) contrast agent, is a substrate of organic-anion-transporting polypeptide 1B1 and multidrug resistance-associated protein 2. Six drugs, with varying degrees of transporter inhibition, were used to assess gadoxetate dynamic contrast enhanced MRI biomarkers for transporter inhibition in rats. Prospective prediction of changes in gadoxetate systemic and liver AUC (AUCR), resulting from transporter modulation, were performed by physiologically-based pharmacokinetic (PBPK) modelling. A tracer-kinetic model was used to estimate rate constants for hepatic uptake (khe), and biliary excretion (kbh). The observed median fold-decreases in gadoxetate liver AUC were 3.8- and 1.5-fold for ciclosporin and rifampicin, respectively. Ketoconazole unexpectedly decreased systemic and liver gadoxetate AUCs; the remaining drugs investigated (asunaprevir, bosentan, and pioglitazone) caused marginal changes. Ciclosporin decreased gadoxetate khe and kbh by 3.78 and 0.09 mL/min/mL, while decreases for rifampicin were 7.20 and 0.07 mL/min/mL, respectively. The relative decrease in khe (e.g., 96% for ciclosporin) was similar to PBPK-predicted inhibition of uptake (97-98%). PBPK modelling correctly predicted changes in gadoxetate systemic AUCR, whereas underprediction of decreases in liver AUCs was evident. The current study illustrates the modelling framework and integration of liver imaging data, PBPK, and tracer-kinetic models for prospective quantification of hepatic transporter-mediated DDI in humans.

SimRFlow: An R-based workflow for automated high-throughput PBPK simulation with the Simcyp® simulator

SimRFlow is a high-throughput physiologically based pharmacokinetic (PBPK) modelling tool which uses Certara’s Simcyp® simulator. The workflow is comprised of three main modules: 1) a Data Collection module for automated curation of physicochemical (from ChEMBL and the Norman Suspect List databases) and experimental data (i.e.: clearance, plasma-protein binding, and blood-to-plasma ratio, from httk-R package databases), 2) a Simulation module which activates the Simcyp® simulator and runs Monte Carlo simulations on virtual subjects using the curated data, and 3) a Data Visualisation module for understanding the simulated compound-specific profiles and predictions. SimRFlow has three administration routes (oral, intravenous, dermal) and allows users to change some simulation parameters including the number of subjects, simulation duration, and dosing. Users are only expected to provide a file of the compounds they wish to simulate, and in return the workflow provides summary statistics, concentration-time profiles of various tissue types, and a database file (containing in-depth results) for each simulated compound. This is presented within a guided and easy-to-use R Shiny interface which provides many plotting options for the visualisation of concentration-time profiles, parameter distributions, trends between the different parameters, as well as comparison of predicted parameters across all batch-simulated compounds. The in-built R functions can be assembled in user-customised scripts which allows for the modification of the workflow for different purposes. SimRFlow proves to be a time-efficient tool for simulating a large number of compounds without any manual curation of physicochemical or experimental data necessary to run Simcyp® simulations.

Quantitative Proteomics in Translational Absorption, Distribution, Metabolism, and Excretion and Precision Medicine

A reliable translation of in vitro and preclinical data on drug absorption, distribution, metabolism, and excretion (ADME) to humans is important for safe and effective drug development. Precision medicine that is expected to provide the right clinical dose for the right patient at the right time requires a comprehensive understanding of population factors affecting drug disposition and response. Characterization of drug-metabolizing enzymes and transporters for the protein abundance and their interindividual as well as differential tissue and cross-species variabilities is important for translational ADME and precision medicine. This review first provides a brief overview of quantitative proteomics principles including liquid chromatography-tandem mass spectrometry tools, data acquisition approaches, proteomics sample preparation techniques, and quality controls for ensuring rigor and reproducibility in protein quantification data. Then, potential applications of quantitative proteomics in the translation of in vitro and preclinical data as well as prediction of interindividual variability are discussed in detail with tabulated examples. The applications of quantitative proteomics data in physiologically based pharmacokinetic modeling for ADME prediction are discussed with representative case examples. Finally, various considerations for reliable quantitative proteomics analysis for translational ADME and precision medicine and the future directions are discussed. SIGNIFICANCE STATEMENT: Quantitative proteomics analysis of drug-metabolizing enzymes and transporters in humans and preclinical species provides key physiological information that assists in the translation of in vitro and preclinical data to humans. This review provides the principles and applications of quantitative proteomics in characterizing in vitro, ex vivo, and preclinical models for translational research and interindividual variability prediction. Integration of these data into physiologically based pharmacokinetic modeling is proving to be critical for safe, effective, timely, and cost-effective drug development.

Prediction of CYP‐mediated DDIs involving inhibition: Approaches to address the requirements for system qualification of the Simcyp Simulator

Physiologically‐based pharmacokinetic (PBPK) modeling is being increasingly used in drug development to avoid unnecessary clinical drug–drug interaction (DDI) studies and inform drug labels. Thus, regulatory agencies are recommending, or indeed requesting, more rigorous demonstration of the prediction accuracy of PBPK platforms in the area of their intended use. We describe a framework for qualification of the Simcyp Simulator with respect to competitive and mechanism‐based inhibition (MBI) of CYP1A2, CYP2D6, CYP2C8, CYP2C9, CYP2C19, and CYP3A4/5. Initially, a DDI matrix, consisting of a range of weak, moderate, and strong inhibitors and substrates with varying fraction metabolized by specific CYP enzymes that were susceptible to different degrees of inhibition, were identified. Simulations were run with 123 clinical DDI studies involving competitive inhibition and 78 clinical DDI studies involving MBI. For competitive inhibition, the overall prediction accuracy was good with an average fold error (AFE) of 0.91 and 0.92 for changes in the maximum plasma concentration (C_max) and area under the plasma concentration (AUC) time profile, respectively, as a consequence of the DDI. For MBI, an AFE of 1.03 was determined for both C_max and AUC. The prediction accuracy was generally comparable across all CYP enzymes, irrespective of the isozyme and mechanism of inhibition. These findings provide confidence in application of the Simcyp Simulator (V19 R1) for assessment of the DDI potential of drugs in development either as inhibitors or victim drugs of CYP‐mediated interactions. The approach described herein and the identified DDI matrix can be used to qualify subsequent versions of the platform.

Population PBPK modeling using parametric and nonparametric methods of the Simcyp Simulator, and Bayesian samplers

Physiologically‐based pharmacokinetic (PBPK) models usually include a large number of parameters whose values are obtained using in vitro to in vivo extrapolation. However, such extrapolations can be uncertain and may benefit from inclusion of evidence from clinical observations via parametric inference. When clinical interindividual variability is high, or the data sparse, it is essential to use a population pharmacokinetics inferential framework to estimate unknown or uncertain parameters. Several approaches are available for that purpose, but their relative advantages for PBPK modeling are unclear. We compare the results obtained using a minimal PBPK model of a canonical theophylline dataset with quasi‐random parametric expectation maximization (QRPEM), nonparametric adaptive grid estimation (NPAG), Bayesian Metropolis‐Hastings (MH), and Hamiltonian Markov Chain Monte Carlo sampling. QRPEM and NPAG gave consistent population and individual parameter estimates, mostly agreeing with Bayesian estimates. MH simulations ran faster than the others methods, which together had similar performance.

Application of a Physiologically Based Pharmacokinetic Model to Predict Cefazolin and Cefuroxime Disposition in Obese Pregnant Women Undergoing Caesarean Section

Intravenous (IV) cefuroxime and cefazolin are used prophylactically in caesarean sections (CS). Currently, there are concerns regarding sub-optimal dosing in obese pregnant women compared to lean pregnant women prior to CS. The current study used a physiologically based pharmacokinetic (PBPK) approach to predict cefazolin and cefuroxime pharmacokinetics in obese pregnant women at the time of CS as well as the duration that these drug concentrations remain above a target concentration (2, 4 or 8 µg/mL or µg/g) in plasma or adipose tissue. Cefazolin and cefuroxime PBPK models were first built using clinical data in lean and in obese non–pregnant populations. Models were then used to predict cefazolin and cefuroxime pharmacokinetics data in lean and obese pregnant populations. Both cefazolin and cefuroxime models sufficiently described their total and free levels in the plasma and in the adipose interstitial fluid (ISF) in non–pregnant and pregnant populations. The obese pregnant cefazolin model predicted adipose exposure adequately at different reference time points and indicated that an IV dose of 2000 mg can maintain unbound plasma and adipose ISF concentration above 8 µg/mL for 3.5 h post dose. Predictions indicated that an IV 1500 mg cefuroxime dose can achieve unbound plasma and unbound ISF cefuroxime concentration of ≥8 µg/mL up to 2 h post dose in obese pregnant women. Re-dosing should be considered if CS was not completed within 2 h post cefuroxime administration for both lean or obese pregnant if cefuroxime concentrations of ≥8 µg/mL is required. A clinical study to measure cefuroxime adipose concentration in pregnant and obese pregnant women is warranted.

Streamlining physiologically‐based pharmacokinetic model design for intravenous delivery of nanoparticle drugs

Physiologically‐based pharmacokinetic (PBPK) modeling for nanoparticles elucidates the nanoparticle drug’s disposition in the body and serves a vital role in drug development and clinical studies. This paper offers a systematic and tutorial‐like approach to developing a model structure and writing distribution ordinary differential equations based on asking binary questions involving the physicochemical nature of the drug in question. Further, by synthesizing existing knowledge, we summarize pertinent aspects in PBPK modeling and create a guide for building model structure and distribution equations, optimizing nanoparticle and non‐nanoparticle specific parameters, and performing sensitivity analysis and model validation. The purpose of this paper is to facilitate a streamlined model development process for students and practitioners in the field.

Prediction of the human pharmacokinetics of epyrifenacil and its major metabolite, S-3100-CA, by a physiologically based pharmacokinetic modeling using chimeric mice with humanized liver

Human internal dosimetry of pesticides is essential in the risk assessment when toxicity has been confirmed in laboratory animals. While human toxicokinetics data of pesticides are hardly obtained intendedly, the use of physiologically based pharmacokinetic (PBPK) modeling has become important for predicting human internal dosimetry. Especially, when the compound exhibits complicated pharmacokinetics via active uptake, metabolism, and biliary excretion in liver, it is difficult to obtain these hepatic parameters only by the in vitro experiments. Epyrifenacil, a new herbicide, is rapidly metabolized to S-3100-CA (CA) in mammals and causes hepatotoxicity in mice. CA is eliminated from the systemic circulation by biliary excretion and metabolism in liver. Although uptake of CA by transporters is observed in mouse primary hepatocytes, significantly less of it is observed in human primary hepatocytes. In order to evaluate human internal dosimetry of CA, a precise PBPK model was developed. To obtain human hepatic parameters, i.e., hepatic elimination intrinsic clearance via biliary excretion and metabolism, we used chimeric mice with humanized liver as a model to reproduce the complicated pharmacokinetics of CA in humans. After we developed a mouse PBPK model, by replacing mouse parameters with those of humans, we calculated CA concentration in human liver. Comparing the predicted CA exposure in human liver with the measured values in mice, we demonstrated a clear interspecies difference of approximately 4 times lower C_max and AUC in humans. This result suggested that the risk of hepatotoxicity is less in humans than in mice.

Physiologically Based Pharmacokinetic Modeling of Rosuvastatin to Predict Transporter-Mediated Drug-Drug Interactions

Purpose

To build a physiologically based pharmacokinetic (PBPK) model of the clinical OATP1B1/OATP1B3/BCRP victim drug rosuvastatin for the investigation and prediction of its transporter-mediated drug-drug interactions (DDIs).

Methods

The Rosuvastatin model was developed using the open-source PBPK software PK-Sim®, following a middle-out approach. 42 clinical studies (dosing range 0.002–80.0 mg), providing rosuvastatin plasma, urine and feces data, positron emission tomography (PET) measurements of tissue concentrations and 7 different rosuvastatin DDI studies with rifampicin, gemfibrozil and probenecid as the perpetrator drugs, were included to build and qualify the model.

Results

The carefully developed and thoroughly evaluated model adequately describes the analyzed clinical data, including blood, liver, feces and urine measurements. The processes implemented to describe the rosuvastatin pharmacokinetics and DDIs are active uptake by OATP2B1, OATP1B1/OATP1B3 and OAT3, active efflux by BCRP and Pgp, metabolism by CYP2C9 and passive glomerular filtration. The available clinical rifampicin, gemfibrozil and probenecid DDI studies were modeled using in vitro inhibition constants without adjustments. The good prediction of DDIs was demonstrated by simulated rosuvastatin plasma profiles, DDI AUC_last ratios (AUC_last during DDI/AUC_last without co-administration) and DDI C_max ratios (C_max during DDI/C_max without co-administration), with all simulated DDI ratios within 1.6-fold of the observed values.

Conclusions

A whole-body PBPK model of rosuvastatin was built and qualified for the prediction of rosuvastatin pharmacokinetics and transporter-mediated DDIs. The model is freely available in the Open Systems Pharmacology model repository, to support future investigations of rosuvastatin pharmacokinetics, rosuvastatin therapy and DDI studies during model-informed drug discovery and development (MID3).

Supplementary Information

The online version contains supplementary material available at 10.1007/s11095-021-03109-6.

Interactions of anti-COVID-19 drug candidates with hepatic transporters may cause liver toxicity and affect pharmacokinetics

Transporters in the human liver play a major role in the clearance of endo- and xenobiotics. Apical (canalicular) transporters extrude compounds to the bile, while basolateral hepatocyte transporters promote the uptake of, or expel, various compounds from/into the venous blood stream. In the present work we have examined the in vitro interactions of some key repurposed drugs advocated to treat COVID-19 (lopinavir, ritonavir, ivermectin, remdesivir and favipiravir), with the key drug transporters of hepatocytes. These transporters included ABCB11/BSEP, ABCC2/MRP2, and SLC47A1/MATE1 in the canalicular membrane, as well as ABCC3/MRP3, ABCC4/MRP4, SLC22A1/OCT1, SLCO1B1/OATP1B1, SLCO1B3/OATP1B3, and SLC10A1/NTCP, residing in the basolateral membrane. Lopinavir and ritonavir in low micromolar concentrations inhibited BSEP and MATE1 exporters, as well as OATP1B1/1B3 uptake transporters. Ritonavir had a similar inhibitory pattern, also inhibiting OCT1. Remdesivir strongly inhibited MRP4, OATP1B1/1B3, MATE1 and OCT1. Favipiravir had no significant effect on any of these transporters. Since both general drug metabolism and drug-induced liver toxicity are strongly dependent on the functioning of these transporters, the various interactions reported here may have important clinical relevance in the drug treatment of this viral disease and the existing co-morbidities.

Quantitative prediction of breast cancer resistant protein mediated drug-drug interactions using physiologically-based pharmacokinetic modeling

Quantitative assessment of drug‐drug interactions (DDIs) involving breast cancer resistance protein (BCRP) inhibition is challenged by overlapping substrate/inhibitor specificity. This study used physiologically‐based pharmacokinetic (PBPK) modeling to delineate the effects of inhibitor drugs on BCRP‐ and organic anion transporting polypeptide (OATP)1B‐mediated disposition of rosuvastatin, which is a recommended BCRP clinical probe. Initial static model analysis using in vitro inhibition data suggested BCRP/OATP1B DDI risk while considering regulatory cutoff criteria for a majority of inhibitors assessed (25 of 27), which increased rosuvastatin plasma exposure to varying degree (~ 0–600%). However, rosuvastatin area under plasma concentration‐time curve (AUC) was minimally impacted by BCRP inhibitors with calculated G‐value (= gut concentration/inhibition potency) below 100. A comprehensive PBPK model accounting for intestinal (OATP2B1 and BCRP), hepatic (OATP1B, BCRP, and MRP4), and renal (OAT3) transport mechanisms was developed for rosuvastatin. Adopting in vitro inhibition data, rosuvastatin plasma AUC changes were predicted within 25% error for 9 of 12 inhibitors evaluated via PBPK modeling. This study illustrates the adequacy and utility of a mechanistic model‐informed approach in quantitatively assessing BCRP‐mediated DDIs.

A Physiologically Based Pharmacokinetic and Drug-Drug Interaction Model for the CB2 Agonist Lenabasum

BACKGROUND AND OBJECTIVES

Lenabasum is a synthetic agonist of the cannabinoid receptor type 2 (CB2) with anti-inflammatory and antifibrotic properties. Utilizing Simcyp, we developed a physiologically based pharmacokinetic (PBPK) model based on physicochemical properties, cell culture data, and cytochrome P450 (CYP) phenotyping, inhibition, and induction data.

METHODS

Clinical data from healthy volunteers treated with 20 mg of lenabasum in a single ascending dose (SAD) study were used for model development. The model was verified using lenabasum SAD (10 and 40 mg) data as well as multiple dose (20 mg three times per day) data. Lenabasum is a CYP substrate, and the model predicted lenabasum clearance of 51% by CYP2C9, 37% by CYP2C8, and 12% by CYP3A4. Lenabasum is also an inhibitor of these isozymes.

RESULTS

The model accurately described the area under the plasma concentration-time curve (AUC) and maximum plasma concentration (C_max) for lenabasum within 1.19-fold and 1.25-fold accuracy, respectively, of the observed clinical values. The simulations of CYP inducers predicted that the strongest interaction would occur with rifampin, with the AUC decreasing to 0.36 of the control value, whereas the simulations of CYP inhibitors predicted that the greatest effect would occur with fluconazole, with a 1.43-fold increase in AUC.

CONCLUSIONS

Our model is a useful tool for predicting the pharmacokinetics of lenabasum and adjustments to its dosing in possible drug-drug interaction scenarios.

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.

Drug-Drug Interaction Between Oxycodone and Diazepam by a Combined in Silico Pharmacokinetic and Pharmacodynamic Modeling Approach

Opioids and benzodiazepines have complex drug-drug interactions (DDIs), which serve as an important source of adverse drug effects. In this work, we predicted the DDI between oxycodone (OXY) and diazepam (DZP) in the human body by applying in silico pharmacokinetic (PK) and pharmacodynamic (PD) modeling and simulation. First, we studied the PK interaction between OXY and DZP with a physiologically based pharmacokinetic (PBPK) model. Second, we applied molecular modeling techniques including molecular docking, molecular dynamics (MD) simulation, and the molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) free energy method to predict the PD-DDI between these two drugs. The PK interaction between OXY and DZP predicted by the PBPK model was not obvious. No significant interaction was observed between the two drugs at normal doses, though very high doses of DZP demonstrated a non-negligible inhibitory effect on OXY metabolism. On the contrary, the molecular modeling study shows that DZP has potential to compete with OXY at the same binding pocket of the active μ-opioid receptor (MOR) and κ-opioid receptor (KOR). MD simulation and MM-PBSA calculation results demonstrated that there is likely a synergetic effect between OXY and DZP binding to opioid receptors, as OXY is likely to target the active MOR while DZP selectively binds to the active KOR. Thus, pharmacokinetics contributes slightly to the DDI between OXY and DZP although an overdose of DZP has been brought to attention. Pharmacodynamics is likely to play a more important role than pharmacokinetics in revealing the mechanism of DDI between OXY and DZP.

PBPK modeling to predict drug-drug interactions of ivosidenib as a perpetrator in cancer patients and qualification of the Simcyp platform for CYP3A4 induction

Ivosidenib is a potent, targeted, orally active, small-molecule inhibitor of mutant isocitrate dehydrogenase 1 (IDH1) that has been approved in the United States for the treatment of adults with newly diagnosed acute myeloid leukemia (AML) who are greater than or equal to 75 years of age or ineligible for intensive chemotherapy, and those with relapsed or refractory AML, with a susceptible IDH1 mutation. Ivosidenib is an inducer of the CYP2B6, CYP2C8, CYP2C9, and CYP3A4 and an inhibitor of P-glycoprotein (P-gp), organic anion transporting polypeptide-1B1/1B3 (OATP1B1/1B3), and organic anion transporter-3 (OAT3) in vitro. A physiologically-based pharmacokinetic (PK) model was developed to predict drug-drug interactions (DDIs) of ivosidenib in patients with AML. The in vivo CYP3A4 induction effect of ivosidenib was quantified using 4β-hydroxycholesterol and was subsequently verified with the PK data from an ivosidenib and venetoclax combination study. The verified model was prospectively applied to assess the effect of multiple doses of ivosidenib on a sensitive CYP3A4 substrate, midazolam. The simulated midazolam geometric mean area under the curve (AUC) and maximum plasma concentration (C_max ) ratios were 0.18 and 0.27, respectively, suggesting ivosidenib is a strong inducer. The model was also used to predict the DDIs of ivosidenib with CYP2B6, CYP2C8, CYP2C9, P-gp, OATP1B1/1B3, and OAT3 substrates. The AUC ratios following multiple doses of ivosidenib and a single dose of CYP2B6 (bupropion), CYP2C8 (repaglinide), CYP2C9 (warfarin), P-gp (digoxin), OATP1B1/1B3 (rosuvastatin), and OAT3 (methotrexate) substrates were 0.90, 0.52, 0.84, 1.01, 1.02, and 1.27, respectively. Finally, in accordance with regulatory guidelines, the Simcyp modeling platform was qualified to predict CYP3A4 induction using known inducers and sensitive substrates.

Overcoming the shortcomings of the extended-clearance concept: a framework for developing a physiologically-based pharmacokinetic (PBPK) model to select drug candidates involving transporter-mediated clearance

Introduction:Human pharmacokinetic (PK) prediction can be a significant challenge to drug candidates undergoing transporter-mediated clearance, when only animal data and in vitro human parameters are available in the drug discovery stage.Areas covered:The extended clearance concept (ECC) that incorporates the processes of hepatic uptake, passive diffusion, metabolism and biliary secretion has been adapted to determine the rate-determining process of hepatic clearance and drug-drug interactions (DDIs). However, since the ECC is derived from the well-stirred model and does not consider the liver as a drug distribution organ to reflect the time-dependent variation of drug concentrations between the liver and plasma, it can be misused for compound selection in drug discovery.Expert opinion:The PBPK model consists of a set of differential equations of drug mass balance, and can overcome the shortcomings of the ECC in predicting human PK. The predictability, relevance and reliability of the model and the scaling factors for IVIVE must be validated using either the measured liver concentrations or DDI data with known transporter inhibitors, or both, in monkeys. A human PBPK model that incorporates in vitro human data and SFs obtained from the validated monkey PBPK model can be used for compound selection in the drug discovery phase.

Incorporating renal excretion via the OCT2 transporter in physiologically based kinetic modelling to predict in vivo kinetics of mepiquat in rat

The present study aimed at incorporating active renal excretion via the organic cation transporter 2 (OCT2) into a generic rat physiologically based kinetic (PBK) model using an in vitro human renal proximal tubular epithelial cell line (SA7K) and mepiquat chloride (MQ) as the model compound. The Vmax (10.5 pmol/min/mg protein) and Km (20.6 μM) of OCT2 transport of MQ were determined by concentration-dependent uptake in SA7K cells using doxepin as inhibitor. PBK model predictions incorporating these values in the PBK model were 6.7-8.4-fold different from the reported in vivo data on the blood concentration of MQ in rat. Applying an overall scaling factor that also corrects for potential differences in OCT2 activity in the SA7K cells and in vivo kidney cortex and species differences resulted in adequate predictions for in vivo kinetics of MQ in rat (2.3-3.2-fold). The results indicate that using SA7K cells to define PBK parameters for active renal OCT2 mediated excretion with adequate scaling enables incorporation of renal excretion via the OCT2 transporter in PBK modelling to predict in vivo kinetics of mepiquat in rat. This study demonstrates a proof-of-principle on how to include active renal excretion into generic PBK models.

In Silico Simulation of the Systemic Drug Exposure Following the Topical Application of Opioid Analgesics in Patients with Cutaneous Lesions

The use of opioid analgesics in treating severe pain is frequently associated with putative adverse effects in humans. Topical agents that are shown to have high efficacy with a favorable safety profile in clinical settings are great alternatives for pain management of multimodal analgesia. However, the risk of side effects induced by transdermal absorption and systemic exposure is of great concern as they are challenging to predict. The present study aimed to use "BIOiSIM" an artificial intelligence-integrated biosimulation platform to predict the transdermal disposition of opioid analgesics. The model successfully predicted their exposure following the topical application of central opioid agonist buprenorphine and peripheral agonist oxycodone in healthy human subjects with simulation of intra-skin exposure in subjects with burns and pressure wounds. The predicted plasma levels of analgesics were used to evaluate the safety of the therapeutic pain control in patients with the dermal structural impairments caused by acute (burns) or chronic cutaneous lesions (pressure wounds) with topical opioid analgesics.

Application of Individualized PBPK Modeling of Rate Data to Evaluate the Effect of Hemodialysis on Nonrenal Clearance Pathways

The aim of this study was to apply individualized, physiologically based pharmacokinetic modeling of ¹⁴ CO₂ production rates (iPBPK-R) measured by the erythromycin breath test to characterize the effect of hemodialysis on the function of nonrenal clearance pathways in patients with end-stage renal disease. Twelve patients previously received ¹⁴ C-erythromycin intravenously pre- and post-hemodialysis. Serial breath samples were collected after each dose over 2 hours. Eight PBPK parameters were co-estimated across periods, whereas activity of cytochrome P450 (CYP) 3A4 clearance was independently estimated for each period. Inhibition coefficients for organic anion transporting polypeptide (OATP), P-glycoprotein, and multidrug resistance-associated protein 2 activities were also estimated. Nonrenal clearance parameter estimates were explored regarding sex differences and correlations with uremic toxins and were used in hierarchical cluster analysis (HCA). Relationships between the function of nonrenal clearance pathways and uremic toxin concentrations were explored. Mean CYP 3A4 clearance increased by 2.2% post-hemodialysis. Uptake transporter activity was highly intervariable across hemodialysis. Females had 22% and 30% higher median CYP3A4 activity than males pre- and post-hemodialysis, respectively. Exploratory HCA indicated that using both CYP3A4 and OATP activity parameters at pre- and post-hemodialysis best identifies heterogeneous patients. This is the first study to use the iPBPK-R approach to simultaneously estimate multiple in vivo nonrenal elimination pathways in individual patients with kidney disease and to assess the effect of hemodialysis.

Hepatic transporter-mediated pharmacokinetic drug-drug interactions: Recent studies and regulatory recommendations

Transporter-mediated drug-drug interactions are one of the major mechanisms in pharmacokinetic-based drug interactions and correspondingly affecting drugs' safety and efficacy. Regulatory bodies underlined the importance of the evaluation of transporter-mediated interactions as a part of the drug development process. The liver is responsible for the elimination of a wide range of endogenous and exogenous compounds via metabolism and biliary excretion. Therefore, hepatic uptake transporters, expressed on the sinusoidal membranes of hepatocytes, and efflux transporters mediating the transport from hepatocytes to the bile are determinant factors for pharmacokinetics of drugs, and hence, drug-drug interactions. In parallel with the growing research interest in this area, regulatory guidances have been updated with detailed assay models and criteria. According to well-established preclinical results, observed or expected hepatic transporter-mediated drug-drug interactions can be taken into account for clinical studies. In this paper, various methods including in vitro, in situ, in vivo, in silico approaches, and combinational concepts and several clinical studies on the assessment of transporter-mediated drug-drug interactions were reviewed. Informative and effective evaluation by preclinical tools together with the integration of pharmacokinetic modeling and simulation can reduce unexpected clinical outcomes and enhance the success rate in drug development.

Novel testing strategy for prediction of rat biliary excretion of intravenously administered estradiol-17β glucuronide

The aim of the present study was to develop a generic rat physiologically based kinetic (PBK) model that includes a novel testing strategy where active biliary excretion is incorporated using estradiol-17β glucuronide (E217βG) as the model substance. A major challenge was the definition of the scaling factor for the in vitro to in vivo conversion of the PBK-model parameter Vmax. In vitro values for the Vmax and Km for transport of E217βG were found in the literature in four different studies based on experiments with primary rat hepatocytes. The required scaling factor was defined based on fitting the PBK model-based predicted values to reported experimental data on E217βG blood levels and cumulative biliary E217βG excretion. This resulted in a scaling factor of 129 mg protein/g liver. With this scaling factor the PBK model predicted the in vivo data for blood and cumulative biliary E217βG levels with on average of less than 1.8-fold deviation. The study provides a proof of principle on how biliary excretion can be included in a generic PBK model using primary hepatocytes to define the kinetic parameters that describe the biliary excretion.

Multi-factorial pharmacokinetic interactions: unraveling complexities in precision drug therapy

Introduction: Precision drug therapy requires accounting for pertinent factors in pharmacokinetic (PK) inter-individual variability (i.e., pharmacogenetics, diseases, polypharmacy, and natural product use) that can cause sub-therapeutic or adverse effects. Although each of these individual factors can alter victim drug PK, multi-factorial interactions can cause additive, synergistic, or opposing effects. Determining the magnitude and direction of these complex multi-factorial effects requires understanding the rate-limiting redundant and/or sequential PK processes for each drug.Areas covered: Perturbations in drug-metabolizing enzymes and/or transporters are integral to single- and multi-factorial PK interactions. Examples of single factor PK interactions presented include gene-drug (pharmacogenetic), disease-drug, drug-drug, and natural product-drug interactions. Examples of multi-factorial PK interactions presented include drug-gene-drug, natural product-gene-drug, gene-gene-drug, disease-natural product-drug, and disease-gene-drug interactions. Clear interpretation of multi-factorial interactions can be complicated by study design, complexity in victim drug PK, and incomplete mechanistic understanding of victim drug PK.Expert opinion: Incorporation of complex multi-factorial PK interactions into precision drug therapy requires advances in clinical decision tools, intentional PK study designs, drug-metabolizing enzyme and transporter fractional contribution determinations, systems and computational approaches (e.g., physiologically-based pharmacokinetic modeling), and PK phenotyping of progressive diseases.

Examination of Physiologically-Based Pharmacokinetic Models of Rosuvastatin

Physiologically‐based pharmacokinetic (PBPK) modeling is increasingly used to predict drug disposition and drug–drug interactions (DDIs). However, accurately predicting the pharmacokinetics of transporter substrates and transporter‐mediated DDIs (tDDIs) is still challenging. Rosuvastatin is a commonly used substrate probe in DDI risk assessment for new molecular entities (NMEs) that are potential organic anion transporting polypeptide 1B or breast cancer resistance protein transporter inhibitors, and as such, several rosuvastatin PBPK models have been developed to try to predict the clinical DDI and support NME drug labeling. In this review, we examine five representative PBPK rosuvastatin models, discuss common challenges that the models have come across, and note remaining gaps. These shared learnings will help with the continuing efforts of rosuvastatin model validation, provide more information to understand transporter‐mediated drug disposition, and increase confidence in tDDI prediction.

Dose Optimization in Kidney Disease: Opportunities for PBPK Modeling and Simulation

Kidney disease affects pharmacokinetic (PK) profiles of not only renally cleared drugs but also nonrenally cleared drugs. The impact of kidney disease on drug disposition has not been fully elucidated, but describing the extent of such impact is essential for conducting dose optimization in kidney disease. Accurate evaluation of kidney function has been a clinical interest for dose optimization, and more scientists pay attention and conduct research for clarifying the role of drug transporters, metabolic enzymes, and their interplay in drug disposition as kidney disease progresses. Physiologically based pharmacokinetic (PBPK) modeling and simulation can provide valuable insights for dose optimization in kidney disease. It is a powerful tool to integrate discrete knowledge from preclinical and clinical research and mechanistically investigate system- and drug-dependent factors that may contribute to the changes in PK profiles. PBPK-based prediction of drug exposures may be used a priori to adjust dosing regimens and thereby minimize the likelihood of drug-related toxicity. With real-time clinical studies, parameter estimation may be performed with PBPK approaches that can facilitate identification of sources of interindividual variability. PBPK modeling may also facilitate biomarker research that aids dose optimization in kidney disease. U.S. Food and Drug Administration guidances related to conduction of PK studies in kidney impairment and PBPK documentation provide the foundation for facilitating model-based dose-finding research in kidney disease.

Proteomics-Informed Prediction of Rosuvastatin Plasma Profiles in Patients With a Wide Range of Body Weight

Rosuvastatin is a frequently used probe to study transporter‐mediated hepatic uptake. Pharmacokinetic models have therefore been developed to predict transporter impact on rosuvastatin disposition in vivo. However, the interindividual differences in transporter concentrations were not considered in these models, and the predicted transporter impact was compared with historical in vivo data. In this study, we investigated the influence of interindividual transporter concentrations on the hepatic uptake clearance of rosuvastatin in 54 patients covering a wide range of body weight. The 54 patients were given an oral dose of rosuvastatin the day before undergoing gastric bypass or cholecystectomy, and pharmacokinetic (PK) parameters were established from each patient’s individual time‐concentration profiles. Liver biopsies were sampled from each patient and their individual hepatic transporter concentrations were quantified. We combined the transporter concentrations with in vitro uptake kinetics determined in HEK293‐transfected cells, and developed a semimechanistic model with a bottom‐up approach to predict the plasma concentration profiles of the single dose of rosuvastatin in each patient. The predicted PK parameters were evaluated against the measured in vivo plasma PKs from the same 54 patients. The developed model predicted the rosuvastatin PKs within two‐fold error for rosuvastatin area under the plasma concentration versus time curve (AUC; 78% of the patients; average fold error (AFE): 0.96), peak plasma concentration (C_max; 76%; AFE: 1.05), and terminal half‐life (t_1/2; 98%; AFE: 0.89), and captured differences in the rosuvastatin PKs in patients with the OATP1B1 521T<C polymorphism. This demonstrates that hepatic uptake clearance determined in transfected cell lines, together with proteomics scaling, provides a useful tool for prediction models, without the need for empirical scaling factors.

A Study of the Effect of Cyclosporine on Fevipiprant Pharmacokinetics and its Absolute Bioavailability Using an Intravenous Microdose Approach

This drug-drug interaction study determined the effect of cyclosporine, an inhibitor of organic anion transporting polypeptide (OATP) 1B3 and P-gp, on the pharmacokinetics (PK) of fevipiprant, an oral, highly selective, competitive antagonist of the prostaglandin D₂ receptor 2 and a substrate of the two transporters. The concomitant administration of an intravenous microdose of stable isotope-labeled fevipiprant provided the absolute bioavailability of fevipiprant as well as mechanistic insights into its PK and sensitivity to drug interactions. Liquid chromatography-mass spectrometry/mass spectrometry was used to measure plasma and urine concentrations. Geometric mean ratios [90% confidence interval (CI)] for oral fevipiprant with or without cyclosporine were 3.02 (2.38, 3.82) for C _max, 2.50 (2.17, 2.88) for AUC_last, and 2.35 (1.99, 2.77) for AUC_inf The geometric mean ratios (90% CI) for fevipiprant intravenous microdose with or without cyclosporine were 1.04 (0.86, 1.25) for C _max, 2.04 (1.83, 2.28) for AUC_last, and 1.95 (1.76, 2.16) for AUC_inf The absolute bioavailability for fevipiprant was approximately 0.3 to 0.4 in the absence and 0.5 in the presence of cyclosporine. The intravenous microdose allowed differentiation between systemic and presystemic effects of cyclosporine on fevipiprant, demonstrating a small (approximately 1.2-fold) presystemic effect of cyclosporine and a larger (approximately twofold) effect on systemic elimination of fevipiprant. Uptake by OATP1B3 appears to be the rate-limiting step in the hepatic elimination of fevipiprant, whereas P-gp does not have a relevant effect on oral absorption. SIGNIFICANCE STATEMENT: The drug interaction investigated here with cyclosporine, an inhibitor of several drug transporters, provides a refined quantitative understanding of the role of active transport processes in liver and intestine for the absorption and elimination of fevipiprant as well as the basis to assess the need for dose adjustment in the presence of transporter inhibitors. The applied intravenous microdose approach presents a strategy to maximize learnings from a trial, limit the number and duration of clinical trials, and enhance mechanistic drug-drug interaction understanding.

Impact of Disease on Plasma and Lung Exposure of Chloroquine, Hydroxychloroquine and Azithromycin: Application of PBPK Modeling

We use a mechanistic lung model to demonstrate that accumulation of chloroquine (CQ), hydroxychloroquine (HCQ), and azithromycin (AZ) in the lungs is sensitive to changes in lung pH, a parameter that can be affected in patients with coronavirus disease 2019 (COVID-19). A reduction in pH from 6.7 to 6 in the lungs, as observed in respiratory disease, led to 20-fold, 4.0-fold, and 2.7-fold increases in lung exposure of CQ, HCQ, and AZ, respectively. Simulations indicated that the relatively high concentrations of CQ and HCQ in lung tissue were sustained long after administration of the drugs had stopped. Patients with COVID-19 often present with kidney failure. Our simulations indicate that renal impairment (plus lung pH reduction) caused 30-fold, 8.0-fold, and 3.4-fold increases in lung exposures for CQ, HCQ, and AZ, respectively, with relatively small accompanying increases (20 to 30%) in systemic exposure. Although a number of different dosage regimens were assessed, the purpose of our study was not to provide recommendations for a dosing strategy, but to demonstrate the utility of a physiologically-based pharmacokinetic modeling approach to estimate lung concentrations. This, used in conjunction with robust in vitro and clinical data, can help in the assessment of COVID-19 therapeutics going forward.

A physiologically based pharmacokinetic - pharmacodynamic modelling approach to predict incidence of neutropenia as a result of drug-drug interactions of paclitaxel in cancer patients

Paclitaxel is the backbone of standard chemotherapeutic regimens used in a number of malignancies and is frequently given with concomitant medications. Newly developed oncolytic agents, including tyrosine kinase inhibitors are often shown to be CYP3A4 and P-gp inhibitors. The aim of this study was to develop a PBPK model for intravenously administered paclitaxel in order to predict the incidence of neutropenia and to estimate the DDI potential as a victim drug. The dose-dependent effects on paclitaxel plasma protein binding, volume of distribution and drug clearance were considered for dose levels of 80 mg/m², 135 mg/m² and 175 mg/m². A pharmacodynamics model that incorporate the impact of paclitaxel on the neutrophil was developed. The relative metabolic clearance via CYP3A4 and CYP2C8, the renal clearance as well as P-gp mediated biliary clearance were incorporated in the model in order to assess the neutropenia in the presence of DDI. The developed PBPK-PD model was able to recover the drop in neutrophils observed after the administration of 175mg/m2 of paclitaxel over a 3-h duration. The mean nadir observed was 1.9 × 10⁹ neutrophils/L and was attained after 10 days of treatment, and a fraction of 47% of the population was predicted to have at some point a neutropenia including 12% with severe neutropenia. In the case of concomitant administration of ketoconazole, 39% of the population was predicted to suffer from severe neutropenia. In summary, PBPK-PD modeling allows a priori prediction of DDIs and safety events involving complex combination therapies which are often utilized in an oncology setting.

Quantitative prediction of human pharmacokinetic responses to drugs via fluidically coupled vascularized organ chips

Analyses of drug pharmacokinetics (PKs) and pharmacodynamics (PDs) performed in animals are often not predictive of drug PKs and PDs in humans, and in vitro PK and PD modelling does not provide quantitative PK parameters. Here, we show that physiological PK modelling of first-pass drug absorption, metabolism and excretion in humans-using computationally scaled data from multiple fluidically linked two-channel organ chips-predicts PK parameters for orally administered nicotine (using gut, liver and kidney chips) and for intravenously injected cisplatin (using coupled bone marrow, liver and kidney chips). The chips are linked through sequential robotic liquid transfers of a common blood substitute by their endothelium-lined channels (as reported by Novak et al. in an associated Article) and share an arteriovenous fluid-mixing reservoir. We also show that predictions of cisplatin PDs match previously reported patient data. The quantitative in-vitro-to-in-vivo translation of PK and PD parameters and the prediction of drug absorption, distribution, metabolism, excretion and toxicity through fluidically coupled organ chips may improve the design of drug-administration regimens for phase-I clinical trials.

The Segregated Intestinal Flow Model (SFM) for Drug Absorption and Drug Metabolism: Implications on Intestinal and Liver Metabolism and Drug-Drug Interactions

The properties of the segregated flow model (SFM), which considers split intestinal flow patterns perfusing an active enterocyte region that houses enzymes and transporters (<20% of the total intestinal blood flow) and an inactive serosal region (>80%), were compared to those of the traditional model (TM), wherein 100% of the flow perfuses the non-segregated intestine tissue. The appropriateness of the SFM model is important in terms of drug absorption and intestinal and liver drug metabolism. Model behaviors were examined with respect to intestinally (M1) versus hepatically (M2) formed metabolites and the availabilities in the intestine (F_I) and liver (F_H) and the route of drug administration. The %contribution of the intestine to total first-pass metabolism bears a reciprocal relation to that for the liver, since the intestine, a gateway tissue, regulates the flow of substrate to the liver. The SFM predicts the highest and lowest M1 formed with oral (po) and intravenous (iv) dosing, respectively, whereas the extent of M1 formation is similar for the drug administered po or iv according to the TM, and these values sit intermediate those of the SFM. The SFM is significant, as this drug metabolism model explains route-dependent intestinal metabolism, describing a higher extent of intestinal metabolism with po versus the much reduced or absence of intestinal metabolism with iv dosing. A similar pattern exists for drug–drug interactions (DDIs). The inhibitor or inducer exerts its greatest effect on victim drugs when both inhibitor/inducer and drug are given po. With po dosing, more drug or inhibitor/inducer is brought into the intestine for DDIs. The bypass of flow and drug to the enterocyte region of the intestine after intravenous administration adds complications to in vitro–in vivo extrapolations (IVIVE).

Mechanistic Multiscale Pharmacokinetic Model for the Anticancer Drug 2',2'-difluorodeoxycytidine (Gemcitabine) in Pancreatic Cancer

The aim of this work is to build a mechanistic multiscale pharmacokinetic model for the anticancer drug 2’,2’‐difluorodeoxycytidine (gemcitabine, dFdC), able to describe the concentrations of dFdC metabolites in the pancreatic tumor tissue in dependence of physiological and genetic patient characteristics, and, more in general, to explore the capabilities and limitations of this kind of modeling strategy. A mechanistic model characterizing dFdC metabolic pathway (metabolic network) was developed using in vitro literature data from two pancreatic cancer cell lines. The network was able to describe the time course of extracellular and intracellular dFdC metabolites concentrations. Moreover, a physiologically‐based pharmacokinetic model was developed to describe clinical dFdC profiles by using enzymatic and physiological information available in the literature. This model was then coupled with the metabolic network to describe the dFdC active metabolite profile in the pancreatic tumor tissue. Finally, global sensitivity analysis was performed to identify the parameters that mainly drive the interindividual variability for the area under the curve (AUC) of dFdC in plasma and of its active metabolite (dFdCTP) in tumor tissue. From this analysis, cytidine deaminase (CDA) concentration was identified as the main driver of plasma dFdC AUC interindividual variability, whereas CDA and deoxycytidine kinase concentration mainly explained the tumor dFdCTP AUC variability. However, the lack of in vitro and in vivo information needed to characterize key model parameters hampers the development of this kind of mechanistic approach. Further studies to better characterize pancreatic cell lines and patient enzymes polymorphisms are encouraged to refine and validate the current model.

Prediction of Drug-Drug Interactions Between Opioids and Overdosed Benzodiazepines Using Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation

Background

Researchers have long been interested in the potential drug–drug interactions (DDIs) between opioids and benzodiazepines. However, much remains unknown concerning the interactions between these two drug classes. The objective of this work is to study the mechanism underlying the DDIs between opioids and benzodiazepines from the perspective of their pharmacokinetic (PK) interactions. A PK interaction occurs when two drugs are metabolized by the same cytochrome P450 enzymes and is one of the most common reasons for DDIs.

Methods

We quantitatively predicted the DDIs between three opioids (fentanyl, oxycodone and buprenorphine) and four benzodiazepines (alprazolam, diazepam, midazolam and triazolam) using a physiologically based pharmacokinetic (PBPK) modeling approach. A set of PBPK models was first constructed for these common opioids and benzodiazepines using SimCYP software, and the DDIs between them were then explored at various dosages.

Results

Our simulation results suggested there were no PK interactions between normal doses of opioids and benzodiazepines; but weak interactions can be expected with the combination of opioids and overdosed benzodiazepines. Particular attention should be given to the combination of fentanyl and overdosed alprazolam since a PK interaction can be observed between them.

Conclusion

Our results appear to indicate that pharmacodynamics may play a more important role than PKs in causing DDIs between opioids and benzodiazepines. This study also demonstrated that molecular modeling can be a very useful tool to mitigate the problem of “missing metabolic reaction parameters” in PK modeling and simulation.

Electronic supplementary material

The online version of this article (10.1007/s40268-019-00282-3) contains supplementary material, which is available to authorized users.

Physiologically-Based Pharmacokinetic Models for Evaluating Membrane Transporter Mediated Drug-Drug Interactions: Current Capabilities, Case Studies, Future Opportunities, and Recommendations

Physiologically-based pharmacokinetic (PBPK) modeling has been extensively used to quantitatively translate in vitro data and evaluate temporal effects from drug-drug interactions (DDIs), arising due to reversible enzyme and transporter inhibition, irreversible time-dependent inhibition, enzyme induction, and/or suppression. PBPK modeling has now gained reasonable acceptance with the regulatory authorities for the cytochrome-P450-mediated DDIs and is routinely used. However, the application of PBPK for transporter-mediated DDIs (tDDI) in drug development is relatively uncommon. Because the predictive performance of PBPK models for tDDI is not well established, here, we represent and discuss examples of PBPK analyses included in regulatory submission (the US Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the Pharmaceuticals and Medical Devices Agency (PMDA)) across various tDDIs. The goal of this collaborative effort (involving scientists representing 17 pharmaceutical companies in the Consortium and from academia) is to reflect on the use of current databases and models to address tDDIs. This challenges the common perceptions on applications of PBPK for tDDIs and further delves into the requirements to improve such PBPK predictions. This review provides a reflection on the current trends in PBPK modeling for tDDIs and provides a framework to promote continuous use, verification, and improvement in industrialization of the transporter PBPK modeling.