Adequate translation of parameters from in vitro experiments to in vivo requires a proper characterisation and mechanistic representation of the systems. The aim of this study was to apply a mechanistic model to better characterise the data from experiments involving in vitro cell lines transfected with ABCB1 genes. In vitro bi-directional transport data (compound recoveries, apparent permeabilities (Papp) and efflux ratios (ER)) were generated from mock and transfected cell lines of Madin-Darby canine kidney type 1 (MDCK I) and type II (MDCK II), as well as a pig kidney-derived cell lines (LLC-PK1). These were transfected/transduced with either rat (LLC-PK1/Mdr1a and MDCK I/Mdr1a) or human (LLC-PK1/MDR1 and MDCK II/MDR1) P-glycoprotein (P-gp) cDNAs. The compounds tested included donepezil, loperamide, phenytoin, quinidine, risperidone, and verapamil. Also, a mechanistic model was used to analyse the data. ER was <2 for mock cell lines and >2 for transfected cell lines, particularly for compounds that are P-gp substrates. The mechanistic model provided a better fit to the observed data, enabling more accurate estimation of both passive and active transport parameters. Nonspecific binding estimates were slightly higher on the apical side (mean: 0.76; range: 0.49-1.00) compared to the basolateral side (mean: 0.71; range: 0.43-0.90). Passive and active clearance values were approximately two-fold higher with the mechanistic model than with the conventional model (passive: 0.17-5.65; active: 1.31-4.88). Overall, the mechanistic model provides parameter estimates that are consistent across species and cell lines, making them more suitable for translation to in vivo systems.

In vitro to in vivo extrapolation (IVIVE) methods for hepatic clearance (CLH) prediction often underpredict, partly due to reliance on mathematical liver disposition models such as the well-stirred model (WSM) or parallel tube model (PTM). The ex vivo isolated perfused rat liver (IPRL) model bridges in vitro and in vivo data, providing mechanistic insights into the predictive accuracy of IVIVE models. This study evaluates the IPRL model across a diverse selection of 16 compounds, and benchmarks results against in vitro and in vivo data to verify the predictive performance of the WSM and PTM. Results demonstrate that both the IPRL and in vivo clearance conflict with assumptions of the WSM (AAFE = 2.85) or PTM (AAFE = 1.74), which consider the liver outlet concentration as a driver for the hepatic elimination rate. However, except for terfenadine, IPRL clearance predictions were within two-fold (AAFE = 1.59) of in vivo clearance when the liver inlet concentration was utilized to calculate the CLH. When employing the WSM or PTM for in vitro to ex vivo extrapolation, underpredictions were observed for compounds with high plasma protein binding and subject to sinusoidal hepatic uptake, reflecting model oversimplification compared to in vivo dynamics. Our findings experimentally challenge the theoretical assumptions underlying the use of the WSM and PTM in IVIVE methods. Unique insights from the IPRL model point to the next steps needed to advance IVIVE: refining current liver disposition models through enhanced and next-generation in vitro assays, capturing dynamic in vivo disposition mechanisms, and exploring complementary models.

Microphysiological systems (MPS) are comprised of one or multiple cell types of human or animal origins that mimic the biochemical/electrical/mechanical responses and blood-tissue barrier properties of the cells observed within a complex organ. The goal of incorporating these in vitro systems is to expedite and advance the drug discovery and development paradigm with improved predictive and translational capabilities. Considering the industry need for improved efficiency and the broad challenges of model qualification and acceptance, the International Consortium for Innovation and Quality (IQ) founded an IQ MPS working group in 2014 and Affiliate in 2018. This group connects thought leaders and end users, provides a forum for crosspharma collaboration, and engages with regulators to qualify translationally relevant MPS models. To understand how pharmaceutical companies are using MPS, the IQ MPS Affiliate conducted two surveys in 2019, survey 1, and 2021, survey 2, which differed slightly in the scope of definition of the complex in vitro models under question. The surveys captured demographics, resourcing, rank order for organs of interest, compound modalities tested, and MPS organ-specific questions, including nonclinical species needs and cell types. The major focus of this manuscript is on results from survey 2, where we specifically highlight the context of use for MPS within safety, pharmacology, or absorption, disposition, metabolism, and excretion and discuss considerations for including MPS data in regulatory submissions. In summary, these data provide valuable insights for developers, regulators, and pharma, offering a view into current industry practices and future considerations while highlighting key challenges impacting MPS adoption. SIGNIFICANCE STATEMENT: The application of microphysiological systems (MPS) represents a growing area of interest in the drug discovery and development framework. This study surveyed 20+ pharma companies to understand resourcing, current areas of application, and the key challenges and barriers to internal MPS adoption. These results will provide regulators, tech providers, and pharma industry leaders a starting point to assess the current state of MPS applications along with key learnings to effectively realize the potential of MPS as an emerging technology.

The effect of membrane transporters on drug disposition, efficacy and safety is now well recognized. Since the initial publication from the International Transporter Consortium, significant progress has been made in understanding the roles and functions of transporters, as well as in the development of tools and models to assess and predict transporter-mediated activity, toxicity and drug–drug interactions (DDIs). Notable advances include an increased understanding of the effects of intrinsic and extrinsic factors on transporter activity, the application of physiologically based pharmacokinetic modelling in predicting transporter-mediated drug disposition, the identification of endogenous biomarkers to assess transporter-mediated DDIs and the determination of the cryogenic electron microscopy structures of SLC and ABC transporters. This article provides an overview of these key developments, highlighting unanswered questions, regulatory considerations and future directions.

Neuroblastoma (NB) is a heterogenous disease with solid tumors in infants and young children originating from neural crest tissue and frequently manifests in the adrenal glands of the thoracic, abdominal, or cervical paraspinal ganglia ([Rivera et al., 2023][1]). Patients are stratified in low-,

Hepatic impairment (HI) moderately (<5-fold) affects the systemic exposure (i.e., area under the plasma concentration-time curve [AUC]) of drugs that are substrates of the hepatic sinusoidal organic anion transporting polypeptide (OATP) transporters and are excreted unchanged in the bile and/or urine. However, the effect of HI on their AUC is much greater (>10-fold) for drugs that are also substrates of cytochrome P450 (CYP) 3A enzymes. Using the extended clearance model, through simulations, we identified the ratio of sinusoidal efflux clearance (CL) over the sum of metabolic and biliary CLs as important in predicting the impact of HI on the AUC of dual OATP/CYP3A substrates. Because HI may reduce hepatic CYP3A-mediated CL to a greater extent than biliary efflux CL, the greater the contribution of the former versus the latter, the greater the impact of HI on drug AUC ratio (AUCRHI ). Using physiologically-based pharmacokinetic modeling and simulation, we predicted relatively well the AUCRHI of OATP substrates that are not significantly metabolized (pitavastatin, rosuvastatin, valsartan, and gadoxetic acid). However, there was a trend toward underprediction of the AUCRHI of the dual OATP/CYP3A4 substrates fimasartan and atorvastatin. These predictions improved when the sinusoidal efflux CL of these two drugs was increased in healthy volunteers (i.e., before incorporating the effect of HI), and by modifying the directionality of its modulation by HI (i.e., increase or decrease). To accurately predict the effect of HI on AUC of hepatobiliary cleared drugs it is important to accurately predict all hepatobiliary pathways, including sinusoidal efflux CL.

Gefapixant (MK-7264, AF-219), a first-in-class P2X3 antagonist, is being developed as oral treatment for refractory or unexplained chronic cough. Based on in vitro data, gefapixant exerts inhibitory activity on the organic anion transporter (OAT) P1B1 transporter. Therefore, a drug-drug interaction study evaluating the potential effects of gefapixant on the OATP1B1 drug transporter, using pitavastatin as a sensitive probe substrate, was conducted. An open-label, 2-period, fixed-sequence study in 20 healthy adults 18 to 55 years old was conducted. In period 1, a 1-mg oral dose of pitavastatin was administered to each participant. After a ≥4-day washout, in period 2 participants received a 45-mg oral dose of gefapixant twice daily on days 1 through 4. On day 2 of period 2, pitavastatin was coadministered with the morning dose of gefapixant. Pitavastatin exposures following single-dose administration with and without multiple doses of gefapixant were similar: geometric mean ratio (90% confidence interval) of pitavastatin area under the plasma concentration–time curve from time 0 to infinity (AUC 0-∞ ) (pitavastatin + gefapixant/pitavastatin alone) was 0.97 (0.93-1.02). The ratio of pitavastatin lactone AUC 0-∞ to pitavastatin AUC 0-∞ was also comparable between treatments. Administration of gefapixant and pitavastatin was generally well tolerated, with no safety findings of concern. These results support that gefapixant has a low potential to inhibit the OATP1B1 transporter.

Metabolites of perpetrators may contribute to drug–drug interactions (DDIs) mediated by inhibition/induction of drug-metabolizing enzymes and transporters. Using physiologically-based pharmacokinetic (PBPK) modeling, we provide our perspective on the model-predicted site-of-action concentrations for metabolites of cytochrome P4503A (CYP3A) and P-glycoprotein (P-gp) dual inhibitors, itraconazole and verapamil, in DDI studies with midazolam (CYP3A substrate) and dabigatran etexilate (P-gp substrate), as representative examples. We focus on whether applying the free drug hypothesis improves DDI predictions in these cases. PBPK modeling is a mechanistic approach to quantitatively describe in vivo drug concentration-time profiles, and thus is widely applied to predict clinical outcomes including DDIs.1, 2 This modeling approach is one of the critical components in model-informed drug discovery and development (MID3). Regulatory authorities in general accept the modeling outcomes of drug-metabolizing enzyme-mediated DDIs, especially CYP enzymes, whereas sufficient confidence levels for predictive model-performance have not yet been reached for transporter-mediated DDIs due to, for instance, uncertainties around in vitro-to-in vivo scaling factors of transporter kinetics and drug concentrations at the site-of-action.3-5 The uncertainties are typically addressed by sensitivity analyses to evaluate effects of the given parameters on overall outcomes.5-7 Itraconazole and verapamil are dual inhibitors of the major drug-metabolizing enzyme, CYP3A, and the efflux transporter, P-gp.8, 9 Their primary metabolites, hydroxyitraconazole and norverapamil, also inhibit both CYP3A and P-gp. We recently reported PBPK modeling with in vitro-to-in vivo extrapolation (IVIVE) of P-gp kinetics in clinical DDI studies between itraconazole and verapamil as perpetrator drugs and digoxin, dabigatran etexilate, and quinidine as victim drugs.7 We used the Simcyp population-based simulator with the advanced dissolution, absorption, and metabolism (ADAM) model to predict DDIs, including the fraction of the dose absorbed (Fa) and the fraction of the dose escaping intestinal first-pass metabolism (Fg). The results revealed that the DDI results were reasonably described by the PBPK-IVIVE approach with P-gp kinetic parameters determined in vitro, such as inhibitor Ki. We incorporated the inhibition parameters of parent drugs in both liver and intestine whereas those of metabolites were only applied to the liver. The reason for this difference was because of the model-predicted different site-of-action concentrations between parent drugs and metabolites. That is, the predicted site-of-action concentrations for parent drugs were the ADAM-predicted unbound enterocyte concentrations (Cgut,u), whereas those for metabolites were the model-predicted unbound portal vein concentrations (Cportal,u). In most reported itraconazole and verapamil PBPK models, the unbound fractions in gut enterocytes (fu,gut) of both the parent drugs and the metabolites are set at unity as often indicated as default values.7-9 Several other perpetrator metabolite PBPK models accounting for intestinal DDIs on CYP3A also assume metabolite fu,gut of unity, as illustrated in Table S1. This implies that the reported PBPK models have utilized the model-predicted total portal vein concentrations (Cportal) of metabolites as the site-of-action concentration, which is not in line with the commonly applied free drug hypothesis.10 Hence, the question arises whether the free drug hypothesis for metabolites should be applied to the PBPK modeling (i.e., fu,gut ≈ fu,plasma [unbound fraction in plasma]). To address this question, we focused on DDI prediction between itraconazole and verapamil as perpetrators and midazolam and dabigatran etexilate as victims, as representative DDI cases. First, the sensitivity analyses for the inhibition parameters for both the parent drugs and the metabolites were simultaneously performed in two scenarios with the metabolites’ fu,gut of unity and fu,plasma. Furthermore, the sensitivity analyses for the metabolite fu,gut ranging from 0.0001 to 1 were performed in these DDI studies. We believe that the present results could help understand the effects of the site-of-action concentrations of metabolites on DDI prediction. PBPK modeling outlines and results are summarized in the Supplementary Material. In the midazolam DDI studies, the sensitivity analyses for CYP3A4 inhibition parameters of the parent drugs and the metabolites showed modest differences in the predicted midazolam Fg between the metabolite fu,gut of unity and fu,plasma (Figures 1 and 2). The differences were pronounced in the areas around the weaker inhibition potency of the parent drugs than the metabolites. Noteworthily, the midazolam Fg at the original inputs of parent drugs (itraconazole Ki of 0.001 μM and verapamil kinact of 1.2 h−1) was near-unity among the ranges of the metabolite inhibition potency tested due to the parent drug-mediated near-complete CYP3A4 inhibition. This led to the negligible differences in the predicted Fg at the metabolite fu,gut of 0.0001 to 1 between the two sets of analyses with and without the metabolite inhibition parameters (Figure S2). Thus, the differences in the predicted ratios of the maximal plasma concentrations and the area under the plasma concentration–time curves (CmaxR and AUCR, respectively) suggested that the contribution of metabolites to the overall DDIs would be mainly due to hepatic CYP3A4 inhibition. The ADAM-predicted maximal Cgut,u for both itraconazole and verapamil reached around 1–10 μM (Figure S1). Accordingly, in these cases, additional effects of the metabolites on intestinal DDIs were negligible due to near-complete intestinal CYP3A4 inhibition by the parent drug alone. In the dabigatran etexilate DDI studies, the sensitivity analyses for the parent drug and metabolite P-gp Ki showed considerable differences in the predicted substrate Fa between the metabolite fu,gut of unity and fu,plasma (Figures 1 and 2). Notably, the predicted Fa was nearly independent of the metabolite P-gp Ki when the metabolite fu,gut was set at fu,plasma, suggesting the negligible contributions of metabolites to the intestinal DDIs. This is mostly explained by the model-predicted steady-state site-of-action concentrations of metabolites (i.e., Cportal,u) not reaching the P-gp Ki values (Figure S1). When the metabolite fu,gut was set at unity, the predicted site-of-action concentrations (i.e., Cportal) were two to three-fold higher than the P-gp Ki (Figure S1). This resulted in pronounced differences in the predicted substrate Fa in the sensitivity analyses where the metabolites were more potent than the parent drugs. In the case of itraconazole DDIs, there were negligible differences in the DDI prediction (CmaxR ≈ 8 and AUCR ≈ 7) among the range of hydroxyitraconazole fu,gut largely due to that hydroxyitraconazole was approximately four-fold less potent than itraconazole (P-gp Ki of 0.8 vs. 0.22 μM; Figure S2). In contrast, the contribution of metabolite to the overall results depended on the metabolite fu,gut in the case of verapamil DDIs because norverapamil was approximately 10-fold more potent than verapamil (P-gp Ki of 0.15 vs. 2.0 μM; Figure S2). The predicted Fa increased from ~0.1 at a norverapamil fu,gut of less than 0.1 to ~0.2 at fu,gut of unity, causing an increase in CmaxR and AUCR from 1.5 to 2.5. Norverapamil fu,gut corresponding to fu,plasma of 0.083 was around the point where the predicted Fa increased. To exemplify DDI prediction depending on the inhibition potency of parent drugs versus metabolites, additional sensitivity analyses were performed by changing the parent drug and metabolite P-gp Ki. When itraconazole and hydroxyitraconazole P-gp Ki values were changed to 10-fold higher and lower values, respectively, the effects of hydroxyitraconazole fu,gut were significantly more pronounced on the DDI prediction with increasing the predicted Fa up to ~0.3 (Figure S3). This example appeared to be similar to the DDI prediction between verapamil and dabigatran etexilate because of more potent inhibition by metabolites than parent drugs. Reversely, when verapamil and norverapamil P-gp Ki values were changed to 10-fold lower and higher values, respectively, the effects of norverapamil fu,gut on the DDI prediction disappeared from the original analyses (Figures S2 and S3). The predicted Fa of ~0.4 in the test group was higher than that in the original analyses (0.1–0.2) because of the strong P-gp inhibition by the parent drug. As expected, one of the key parameters for DDI prediction is the ratios of unbound inhibitor concentrations over inhibition potency. The contribution of metabolites to overall DDIs could depend on the differences in these ratios between parent drugs and metabolites. The metabolite contribution could also underlie the baselines of substrate Fa and Fg, such as near-complete intestinal absorption or availability in control groups without inhibitors or test groups without metabolites. As the main MID3 component, PBPK modeling is a powerful tool to address key questions in clinical studies, including untested scenarios. Currently, it is challenging to predict unbound metabolite concentrations at the site-of-action. To overcome uncertainty in the prediction outcomes, sensitivity analyses for key parameters, such as metabolite fu,gut, should be performed to evaluate their effects on overall results. When the degree of predicted overall DDIs depend on metabolite fu,gut, the input values of fu,gut should be considered carefully. In some cases, such as where potential safety concerns should primarily be considered, the fu,gut of unity could be assumed as the most conservative scenario to predict maximal intestinal DDIs by metabolites. In this case, particular attention should also be paid to the differences in outcomes over the range of fu,gut, (e.g., fu,gut ≈ 1 vs. fu,plasma). Depending on metabolite fu,plasma, the predicted site-of-action concentrations could be significantly different in some cases, for example, ~100-fold for hydroxyitraconazole (fu,plasma ≈ 0.012) and ~10-fold for norverapamil (fu,plasma ≈ 0.083). Although the difference in the modeling results between fu,gut of unity and fu,plasma were minimal in three out of the four cases presented, the modeling approach utilizing metabolite Cportal with fu,gut of unity should not be simply extrapolated to other DDI scenarios, especially when metabolites are more potent inhibitors than parent drugs. As presented in this study, verapamil DDIs with dabigatran etexilate were overpredicted when norverapamil fu,gut was set at unity, whereas those were reasonably predicted assuming fu,gut equal to fu,plasma. This case underscores that the metabolite fu,gut is a critical parameter for predicting the relevant site-of-action concentrations. Furthermore, PBPK modeling with the metabolite fu,gut equal to fu,plasma sufficiently predicted the DDI results in all the cases tested. It is our belief that, unless there is any direct evidence to the contrary, such as low parent drug Fg, metabolite Cportal,u (i.e., fu,gut ≈ fu,plasma) should be the more appropriate site-of-action concentration than Cportal (i.e., fu,gut ≈ unity). This is in line with the free drug hypothesis generally assumed for modeling.10 To quantitatively predict metabolite Cgut,u, the model should account for the metabolite disposition profiles, such as the metabolite formation in the gastrointestinal (GI) tract and the distribution between the GI tract and portal vein (including recirculation if occurring). Hence, more mechanistic intestinal PBPK models will be required to further support decision making on MID3. This study was sponsored by Janssen Research & Development, LLC. All authors are employees of Janssen Research & Development, LLC, and are shareholders in the parent company (Johnson & Johnson). Appendix S1 Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Gefapixant (MK-7264, AF-219), a first-in-class P2X3 antagonist, is being developed as oral treatment for refractory or unexplained chronic cough. Based on in vitro data, gefapixant exerts inhibitory activity on the organic anion transporter (OAT) P1B1 transporter. Therefore, a drug-drug interaction study evaluating the potential effects of gefapixant on the OATP1B1 drug transporter, using pitavastatin as a sensitive probe substrate, was conducted. An open-label, 2-period, fixed-sequence study in 20 healthy adults 18 to 55 years old was conducted. In period 1, a 1-mg oral dose of pitavastatin was administered to each participant. After a ≥4-day washout, in period 2 participants received a 45-mg oral dose of gefapixant twice daily on days 1 through 4. On day 2 of period 2, pitavastatin was coadministered with the morning dose of gefapixant. Pitavastatin exposures following single-dose administration with and without multiple doses of gefapixant were similar: geometric mean ratio (90% confidence interval) of pitavastatin area under the plasma concentration-time curve from time 0 to infinity (AUC0-∞ ) (pitavastatin + gefapixant/pitavastatin alone) was 0.97 (0.93-1.02). The ratio of pitavastatin lactone AUC0-∞ to pitavastatin AUC0-∞ was also comparable between treatments. Administration of gefapixant and pitavastatin was generally well tolerated, with no safety findings of concern. These results support that gefapixant has a low potential to inhibit the OATP1B1 transporter.

As one of the key components in model‐informed drug discovery and development, physiologically‐based pharmacokinetic (PBPK) modeling linked with in vitro‐to‐in vivo extrapolation (IVIVE) is widely applied to quantitatively predict drug–drug interactions (DDIs) on drug‐metabolizing enzymes and transporters. This study aimed to investigate an IVIVE for intestinal P‐glycoprotein (Pgp, ABCB1)‐mediated DDIs among three Pgp substrates, digoxin, dabigatran etexilate, and quinidine, and two Pgp inhibitors, itraconazole and verapamil, via PBPK modeling. For Pgp substrates, assuming unbound Michaelis‐Menten constant ( K m ) to be intrinsic, in vitro‐to‐in vivo scaling factors for maximal Pgp‐mediated efflux rate ( J max ) were optimized based on the clinically observed results without co‐administration of Pgp inhibitors. For Pgp inhibitors, PBPK models utilized the reported in vitro values of Pgp inhibition constants ( K i ), 1.0 μM for itraconazole and 2.0 μM for verapamil. Overall, the PBPK modeling sufficiently described Pgp‐mediated DDIs between these substrates and inhibitors with the prediction errors of less than or equal to ±25% in most cases, suggesting a reasonable IVIVE for Pgp kinetics in the clinical DDI results. The modeling results also suggest that Pgp kinetic parameters of both the substrates ( K m and J max ) and the inhibitors ( K i ) are sensitive to Pgp‐mediated DDIs, thus being key for successful DDI prediction. It would also be critical to incorporate appropriate unbound inhibitor concentrations at the site of action into PBPK models. The present results support a quantitative prediction of Pgp‐mediated DDIs using in vitro parameters, which will significantly increase the value of in vitro studies to design and run clinical DDI studies safely and effectively.

Physiologically based pharmacokinetic models, populated with drug-metabolizing enzyme and transporter (DMET) abundance, can be used to predict the impact of hepatic impairment (HI) on the pharmacokinetics (PK) of drugs. To increase confidence in the predictive power of such models, they must be validated by comparing the predicted and observed PK of drugs in HI obtained by phenotyping (or probe drug) studies. Therefore, we first predicted the effect of all stages of HI (mild to severe) on the PK of drugs primarily metabolized by cytochrome P450 (CYP) 3A enzymes using the default HI module of Simcyp Version 21, populated with hepatic and intestinal CYP3A abundance data. Then, we validated the predictions using CYP3A probe drug phenotyping studies conducted in HI. Seven CYP3A substrates, metabolized primarily via CYP3A (fraction metabolized, 0.7-0.95), with low to high hepatic availability, were studied. For all stages of HI, the predicted PK parameters of drugs were within twofold of the observed data. This successful validation increases confidence in using the DMET abundance data in HI to predict the changes in the PK of drugs cleared by DMET for which phenotyping studies in HI are not available or cannot be conducted. In addition, using CYP3A drugs as an example, through simulations, we identified the salient PK factors that drive the major changes in exposure (area under the plasma concentration-time profile curve) to drugs in HI. This theoretical framework can be applied to any drug and DMET to quickly determine the likely magnitude of change in drug PK due to HI.

Membrane transport proteins are involved in the absorption, disposition, efficacy, and/or toxicity of many drugs. Numerous mechanisms (e.g., nuclear receptors, epigenetic gene regulation, microRNAs, alternative splicing, post-translational modifications, and trafficking) regulate transport protein levels, localization, and function. Various factors associated with disease, medications, and dietary constituents, for example, may alter the regulation and activity of transport proteins in the intestine, liver, kidneys, brain, lungs, placenta, and other important sites, such as tumor tissue. This white paper reviews key mechanisms and regulatory factors that alter the function of clinically relevant transport proteins involved in drug disposition. Current considerations with in vitro and in vivo models that are used to investigate transporter regulation are discussed, including strengths, limitations, and the inherent challenges in predicting the impact of changes due to regulation of one transporter on compensatory pathways and overall drug disposition. In addition, translation and scaling of in vitro observations to in vivo outcomes are considered. The importance of incorporating altered transporter regulation in modeling and simulation approaches to predict the clinical impact on drug disposition is also discussed. Regulation of transporters is highly complex and, therefore, identification of knowledge gaps will aid in directing future research to expand our understanding of clinically relevant molecular mechanisms of transporter regulation. This information is critical to the development of tools and approaches to improve therapeutic outcomes by predicting more accurately the impact of regulation-mediated changes in transporter function on drug disposition and response.

Predicting transporter-based drug clearance (CL) and tissue concentrations (TC) in humans is important to reduce the risk of failure during drug development. In addition, when transporters are present at the tissue:blood interface (e.g., in the liver, blood-brain barrier), predicting TC is important to predict the drug's efficacy and safety. With the advent of quantitative targeted proteomics, in vitro to in vivo extrapolation (IVIVE) of transporter-based drug CL and TC is now possible using transporter-expressing models (cells lines, membrane vesicles) and the in vivo to in vitro relative expression of transporters (REF) as a scaling factor. Unlike other approaches based on physiological scaling, the REF approach is not dependent on the availability of primary cells. Here, we review the REF approach and compare it with other IVIVE approaches such as the relative activity factor approach and physiological scaling. For each of these scaling approaches, we review their underlying principles, assumptions, methodology, predictive performance, as well as advantages and limitations. Finally, we discuss current gaps in IVIVE of transporter-based CL and TC and propose possible reasons for these gaps as well as areas to investigate to bridge these gaps.

Gefapixant (MK‐7264, AF‐219) is a first‐in‐class P2X3 antagonist in development for refractory or unexplained chronic cough. Gefapixant is primarily cleared by renal excretion. To assess the importance of the multidrug and toxin extrusion protein 1 (MATE1) and MATE2K transporters in the elimination of gefapixant, a drug‐drug interaction study was conducted evaluating the effect of coadministration of a single dose of pyrimethamine, a competitive inhibitor of MATE1 and MATE2K, on the single‐dose pharmacokinetics of gefapixant in healthy participants. Safety and tolerability were also assessed. In this open‐label, 2‐period, fixed‐sequence study, a 45‐mg dose of gefapixant was administered to 12 participants in period 1. After a 7‐day washout, a 50‐mg dose of pyrimethamine was administered 3 hours before a 45‐mg dose of gefapixant in period 2. Compared with the administration of gefapixant alone, concomitant dosing of gefapixant with pyrimethamine increased the total gefapixant plasma exposure (area under the plasma concentration–time curve from time 0 to infinity) by 24%, reduced gefapixant renal clearance by 30%, and increased gefapixant mean terminal half‐life from 7.7 to 10.3 hours. The most frequently reported adverse events were dysgeusia, hypogeusia, and dry mouth; all adverse events were considered of mild intensity and resolved by the end of the study. These results support that MATE1 and/or MATE2K contribute to the renal clearance of gefapixant, but the effect of inhibition of these transporters on gefapixant pharmacokinetics is not considered clinically meaningful.

Renal impairment (RI) is known to influence the pharmacokinetics of nonrenally eliminated drugs, although the mechanism and clinical impact is poorly understood. We assessed the impact of RI and single dose oral rifampin (RIF) on the pharmacokinetics of CYP3A, OATP1B, P‐gp, and BCRP substrates using a microdose cocktail and OATP1B endogenous biomarkers. RI alone had no impact on midazolam (MDZ), maximum plasma concentration (Cmax), and area under the curve (AUC), but a progressive increase in AUC with RI severity for dabigatran (DABI), and up to ~2‐fold higher AUC for pitavastatin (PTV), rosuvastatin (RSV), and atorvastatin (ATV) for all degrees of RI was observed. RIF did not impact MDZ, had a progressively smaller DABI drug‐drug interaction (DDI) with increasing RI severity, a similar 3.1‐fold to 4.4‐fold increase in PTV and RSV AUC in healthy volunteers and patients with RI, and a diminishing DDI with RI severity from 6.1‐fold to 4.7‐fold for ATV. Endogenous biomarkers of OATP1B (bilirubin, coproporphyrin I/III, and sulfated bile salts) were generally not impacted by RI, and RIF effects on these biomarkers in RI were comparable or larger than those in healthy volunteers. The lack of a trend with RI severity of PTV and several OATP1B biomarkers, suggests that mechanisms beyond RI directly impacting OATP1B activity could also be considered. The DABI, RSV, and ATV data suggest an impact of RI on intestinal P‐gp, and potentially BCRP activity. Therefore, DDI data from healthy volunteers may represent a worst‐case scenario for clinically derisking P‐gp and BCRP substrates in the setting of RI.

This annual review is the sixth of its kind since 2016 (see references). Our objective is to explore and share articles which we deem influential and significant in the field of biotransformation and bioactivation. These fields are constantly evolving with new molecular structures and discoveries of corresponding pathways for metabolism that impact relevant drug development with respect to efficacy and safety. Based on the selected articles, we created three sections: (1) drug design, (2) metabolites and drug metabolizing enzymes, and (3) bioactivation and safety (Table 1). Unlike in years past, more biotransformation experts have joined and contributed to this effort while striving to maintain a balance of authors from academic and industry settings.[Table: see text].

Hepatocellular accumulation of bile salts by inhibition of bile salt export pump (BSEP/ABCB11) may result in cholestasis and is one proposed mechanism of drug-induced liver injury (DILI). To understand the relationship between BSEP inhibition and DILI, we evaluated 64 DILI-positive and 57 DILI-negative compounds in BSEP, multidrug resistance protein (MRP) 2, MRP3, and MRP4 vesicular inhibition assays. An empirical cutoff (5 μM) for BSEP inhibition was established based on a relationship between BSEP IC50 values and the calculated maximal unbound concentration at the inlet of the human liver (fu*Iin,max, assay specificity = 98%). Including inhibition of MRP2–4 did not increase DILI predictivity. To further understand the potential to inhibit bile salt transport, a selected subset of 30 compounds were tested for inhibition of taurocholate (TCA) transport in a long-term human hepatocyte micropatterned co-culture (MPCC) system. The resulting IC50 for TCA in vitro biliary clearance and biliary excretion index (BEI) in MPCCs were compared with the compound’s fu*Iin,max to assess potential risk for bile salt transport perturbation. The data show high specificity (89%). Nine out of 15 compounds showed an IC50 value in the BSEP vesicular assay of <5μM, but the BEI IC50 was more than 10-fold the fu*Iin,max, suggesting that inhibition of BSEP in vivo is unlikely. The data indicate that although BSEP inhibition measured in membrane vesicles correlates with DILI risk, that measurement of this assay activity is insufficient. A two-tiered strategy incorporating MPCCs is presented to reduce BSEP inhibition potential and improve DILI risk. SIGNIFICANCE STATEMENT This work describes a two-tiered in vitro approach to de-risk compounds for potential bile salt export pump inhibition liabilities in drug discovery utilizing membrane vesicles and a long-term human hepatocyte micropatterned co-culture system. Cutoffs to maximize specificity were established based on in vitro data from a set of 121 DILI-positive and –negative compounds and associated calculated maximal unbound concentration at the inlet of the human liver based on the highest clinical dose.

In addition to drug-drug interactions (DDI) between small molecule drugs, it is recognized that the potential for interactions between small molecules and therapeutic proteins (TP) and other new modalities also needs to be considered. Thus far, focus for TP-DDIs has been on cytokines and cytokine modulators, because these are known to affect the expression of drug-metabolizing enzymes and transporters in hepatocytes. Multiple clinical TP-DDI studies have now been completed in a variety of autoimmune populations showing limited effects in several disease states. The number of modalities recently approved or explored for therapeutic interventions, such as gene, RNA, and cell therapies, is growing rapidly, especially in the oncology area. Therefore, an increased understanding for the potential for DDIs is warranted. In this chapter, we summarize our current understanding of TP-DDIs, both based on in vitro and clinical findings, and discuss the potential for new modalities to be victims or perpetrators of such DDIs.