Prediction of Transporter-Mediated Rosuvastatin Hepatic Uptake Clearance and Drug Interaction in Humans Using Proteomics-Informed REF Approach

ISTransbase: an online database for inhibitor and substrate of drug transporters

Drug transporters, integral membrane proteins found throughout the human body, play critical roles in physiological and biochemical processes through interactions with ligands, such as substrates and inhibitors. The extensive and disparate data on drug transporters complicate understanding their complex relationships with ligands. To address this challenge, it is essential to gather and summarize information on drug transporters, inhibitors and substrates, and simultaneously develop a comprehensive and user-friendly database. Current online resources often provide fragmented information and have limited coverage of drug transporter substrates and inhibitors, highlighting the need for a specialized, comprehensive and openly accessible database. ISTransbase addresses this gap by amassing a substantial amount of data from literature, government documents and open databases. It includes 16 528 inhibitors and 4465 substrates of 163 drug transporters from 18 different species, resulting in a total of 93 841 inhibitor records and 51 053 substrate records. ISTransbase provides detailed insights into drug transporters and their inhibitors/substrates, encompassing transporter and molecule structure, transporter function and distribution, as well as experimental methods and results from transport or inhibition experiments. Furthermore, ISTransbase offers three search strategies that allow users to retrieve drugs and transporters based on multiple selectable constraints, as well as perform checks for drug-drug interactions. Users can also browse and download data. In summary, ISTransbase (https://istransbase.scbdd.com/) serves as a valuable resource for accurately and efficiently accessing information on drug transporter inhibitors and substrates, aiding researchers in exploring drug transporter mechanisms and assisting clinicians in mitigating adverse drug reactions Database URL: https://istransbase.scbdd.com/.

Prediction of Human Clearance Using In Silico Models with Reduced Bias

Predicting human clearance with high accuracy from in silico-derived parameters alone is highly desirable, as it is fast, saves in vitro resources, and is animal-sparing. We derived random forest (RF) models from 1340 compounds with human intravenous pharmacokinetic (PK) data, the largest data set publicly available today. To assess the general applicability of the RF models, we systematically removed structural-therapeutic class analogues and other compounds with structural similarity from the training sets. For a quasi-prospective test set of 343 compounds, we show that RF models devoid of structurally similar compounds in the training set predict human clearance with a geometric mean fold error (GMFE) of 3.3. While the observed GMFE illustrates how difficult it is to generate a useful model that is broadly applicable, we posit that our RF models yield a more realistic assessment of how well human clearance can be predicted prospectively. We deployed the conformal prediction formalism to assess the model applicability and to determine the prediction confidence intervals for each prediction. We observed that clearance can be predicted better for renally cleared compounds than for other clearance mechanisms. We show that applying a classification model for predicting renal clearance identifies a subset of compounds for which clearance can be predicted with higher accuracy, yielding a GMFE of 2.3. In addition, our in silico RF human clearance models compared well to models derived from scaling human hepatocytes or preclinical in vivo data.

Successful Prediction of Human Hepatic Concentrations of Transported Drugs Using the Proteomics-Informed Relative Expression Factor Approach

Tissue drug concentrations determine the efficacy and toxicity of drugs. When a drug is the substrate of transporters that are present at the blood:tissue barrier, the steady-state unbound tissue drug concentrations cannot be predicted from their corresponding plasma concentrations. To accurately predict transporter-modulated tissue drug concentrations, all clearances (CLs) mediating the drug's entry and exit (including metabolism) from the tissue must be accurately predicted. Because primary cells of most tissues are not available, we have proposed an alternative approach to predict such CLs, that is the use of transporter-expressing cells/vesicles (TECs/TEVs) and relative expression factor (REF). The REF represents the abundance of the relevant transporters in the tissue vs. in the TECs/TEVs. Here, we determined the transporter-based intrinsic CL of glyburide (GLB) and pitavastatin (PTV) in OATP1B1, OATP1B3, OATP2B1, and NTCP-expressing cells and MRP3-, BCRP-, P-gp-, and MRP2-expressing vesicles and scaled these CLs to in vivo using REF. These predictions fell within a priori set twofold range of the hepatobiliary CLs of GLB and PTV, estimated from their hepatic positron emission tomography imaging data: 272.3 and 607.8 mL/min for in vivo hepatic sinusoidal uptake CL, 47.8 and 17.4 mL/min for sinusoidal efflux CL, and 0 and 4.20 mL/min for biliary efflux CL, respectively. Moreover, their predicted hepatic concentrations (area under the hepatic concentration-time curve (AUC) and maximum plasma concentration (Cmax )), fell within twofold of their mean observed data. These data, together with our previous findings, confirm that the REF approach can successfully predict transporter-based drug CLs and tissue concentrations to enhance success in drug development.

Building a Predictive PBPK Model for Human OATP Substrates: a Strategic Framework for Early Evaluation of Clinical Pharmacokinetic Variations Using Pitavastatin as an Example

To select a drug candidate for clinical development, accurately and promptly predicting human pharmacokinetic (PK) profiles, assessing drug-drug interactions (DDIs), and anticipating potential PK variations in disease populations are crucial steps in drug discovery. The complexity of predicting human PK significantly increases when hepatic transporters are involved in drug clearance (CL) and volume of distribution (Vss). A strategic framework is developed here, utilizing pitavastatin as an example. The framework includes the construction of a monkey physiologically-based PK (PBPK) model, model calibration to obtain scaling factors (SF) of in vitro-in vivo extrapolation (IVIVE) for various clearance parameters, human model development and validation, and assessment of DDIs and PK variations in disease populations. Through incorporating in vitro human parameters and calibrated SFs from the monkey model of 3.45, 0.14, and 1.17 for CLint,active, CLint,passive, and CLint,bile, respectively, and together with the relative fraction transported by individual transporters obtained from in vitro studies and the optimized Ki values for OATP inhibition, the model reasonably captured observed pitavastatin PK profiles, DDIs and PK variations in human subjects carrying genetic polymorphisms, i.e., AUC within 20%. Lastly, when applying the functional reduction based on measured OATP1B biomarkers, the model adequately predicted PK changes in the hepatic impairment population. The present study presents a strategic framework for early-stage drug development, enabling the prediction of PK profiles and assessment of PK variations in scenarios like DDIs, genetic polymorphism, and hepatic impairment-related disease states, specifically focusing on OATP substrates.

Utilizing Pharmacogenomic Data for a Safer Use of Statins among the Emirati Population

BACKGROUND

Statins are the most prescribed lipid-lowering drugs worldwide. The associated adverse events, especially muscle symptoms, have been frequently reported despite their perceived safety. Three pharmacogenes, the solute carrier organic anion transporter family member 1B1 (SLCO1B1), ATP-binding cassette subfamily G member 2 (ABCG2), and cytochrome P450 2C9 (CYP2C9) are suggested as safety biomarkers for statins. The Clinical Pharmacogenomic Implementation Consortium (CPIC) issued clinical guidelines for statin use based on these three genes.

OBJECTIVES

The present study aimed to examine variants in these pharmacogenes to predict the safety of statin use among the Emirati population.

METHODS

Analyzing 242 whole exome sequencing data at the three genes enabled the determination of the frequencies of the single nucleotide polymorphisms (SNPs), annotating the haplotypes and the predicted functions of their proteins.

RESULTS

In our cohort, 29.8% and 5.4% had SLCO1B1 decreased and poor function, respectively. The high frequency warns of the possibility of significant side effects of some statins and the importance of pharmacogenomic testing. We found a low frequency (6%) of the ABCG2:rs2231142 variant, which indicates the low probability of Emirati patients being recommended against higher rosuvastatin doses compared with other populations with higher frequencies of this variant. In contrast, we found high frequencies of the functionally impaired CYP2C9 alleles, which makes fluvastatin a less favorable choice.

CONCLUSION

Among the sparse studies available, the present one demonstrates all SLCO1B1 and CYP2C9 function-impairing alleles among Emiratis. We highlighted how population-specific pharmacogenomic data can predict safer choices of statins, especially in understudied populations.

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.

Drug transporters in drug disposition - the year 2022 in review

On behalf of all the authors, I am pleased to share our third annual review on drug transporter science with an emphasis on articles published and deemed influential in signifying drug transporters' role in drug disposition in the year 2022. As the drug transporter field is rapidly evolving several key findings were noted including promising endogenous biomarkers, rhythmic activity, IVIVE approaches in transporter-mediated clearance, new modality interaction, and transporter effect on gut microbiome. As identified previously (Chothe et Cal. 2021, 2022) the goal of this review is to highlight key findings without a comprehensive overview of each article and to this end, each coauthor independently selected 1-3 peer-reviewed articles published or available online in the year 2022 (Table 1). Each article is summarized in synopsis and commentary with unbiased viewpoints by each coauthor. We strongly encourage readers to consult original articles for specifics of the study. Finally, I would like to thank all coauthors for their continued support in writing this annual review on drug transporters and invite anyone interested in contributing to future versions of this review.

Interpretation of Protein-Mediated Uptake of Statins by Hepatocytes Is Confounded by the Residual Statin-Protein Complex

Inclusion of plasma (or plasma proteins) in human hepatocyte uptake studies narrows, but does not close, the gap in in vitro to in vivo extrapolation (IVIVE) of organic anion transporting polypeptide (OATP)-mediated hepatic clearance (CLh) of statins. We have previously shown that this "apparent" protein-mediated uptake effect (PMUE) of statins by OATP1B1-expressing cells, in the presence of 5% human serum albumin (HSA), is mostly an artifact caused by residual statin-HSA complex remaining in the uptake assay. We determined if the same was true with plated human hepatocytes (PHH) and if this artifact can be reduced using suspended human hepatocytes (SHH) and the oil-spin method. We quantified the uptake of a cocktail of five statins by PHH and SHH in the absence and presence of 5% HSA. After terminating the uptake assay, the amount of residual HSA was quantified by quantitative targeted proteomics. For both PHH and SHH, except for atorvastatin and cerivastatin, the increase in total, active, and passive uptake of the statins, in the presence of 5% HSA, was explained by the estimated residual stain-HSA complex. In addition, the increase in active statin uptake by SHH, where present, was marginal (<50%), much smaller than that observed with PHH. Such a marginal increase cannot bridge the gap in IVIVE of CLh of statins. These data disprove the prevailing hypotheses for the in vitro PMUE. A true PMUE should be evaluated using the uptake data corrected for the residual drug-protein complex. SIGNIFICANCE STATEMENT: We show that the apparent protein-mediated uptake (PMUE) of statins by human hepatocytes is largely confounded by residual statin when plated or suspended human hepatocytes are used. Therefore, mechanisms other than PMUE need to be explored to explain the underprediction of the in vivo human hepatic clearance of statins by human hepatocyte uptake assays.

Evaluation of the Selectivity of Several Organic Anion Transporting Polypeptide 1B Biomarkers Using Relative Activity Factor Method

In recent years, some endogenous substrates of organic anion transporting polypeptide 1B (OATP1B) have been identified and characterized as potential biomarkers to assess OATP1B-mediated clinical drug-drug interactions (DDIs). However, quantitative determination of their selectivity to OATP1B is still limited. In this study, we developed a relative activity factor (RAF) method to determine the relative contribution of hepatic uptake transporters OATP1B1, OATP1B3, OATP2B1, and sodium-taurocholate co-transporting polypeptide (NTCP) on hepatic uptake of several OATP1B biomarkers, including coproporphyrin I (CPI), coproporphyrin I CPIII, and sulfate conjugates of bile acids: glycochenodeoxycholic acid sulfate (GCDCA-S), glycodeoxycholic acid sulfate (GDCA-S), and taurochenodeoxycholic acid sulfate (TCDCA-S). RAF values for OATP1B1, OATP1B3, OATP2B1, and NTCP were determined in cryopreserved human hepatocytes and transporter transfected cells using pitavastatin, cholecystokinin, resveratrol-3-O-β-D-glucuronide, and taurocholic acid (TCA) as reference compounds, respectively. OATP1B1-specific pitavastatin uptake in hepatocytes was measured in the absence and presence of 1 µM estropipate, whereas NTCP-specific TCA uptake was measured in the presence of 10 µM rifampin. Our studies suggested that CPI was a more selective biomarker for OATP1B1 than CPIII, whereas GCDCA-S and TCDCA-S were more selective to OATP1B3. OATP1B1 and OATP1B3 equally contributed to hepatic uptake of GDCA-S. The mechanistic static model, incorporating the fraction transported of CPI/III estimated by RAF and in vivo elimination data, predicted several perpetrator interactions with CPI/III. Overall, RAF method combined with pharmacogenomic and DDI studies is a useful tool to determine the selectivity of transporter biomarkers and facilitate the selection of appropriate biomarkers for DDI evaluation. SIGNIFICANCE STATEMENT: The authors developed a new relative activity factor (RAF) method to quantify the contribution of hepatic uptake transporters organic anion transporting polypeptide (OATP)1B1, OATP1B3, OATP2B1, and sodium taurocholate co-transporting polypeptide (NTCP) on several OATP1B biomarkers and evaluated their predictive value on drug-drug interactions (DDI). These studies suggest that the RAF method is a useful tool to determine the selectivity of transporter biomarkers. This method combined with pharmacogenomic and DDI studies will mechanistically facilitate the selection of appropriate biomarkers for DDI prediction.

In Vitro-In Vivo Extrapolation and Scaling Factors for Clearance of Human and Preclinical Species with Liver Microsomes and Hepatocytes

In vitro-in vivo extrapolation ((IVIVE) and empirical scaling factors (SF) of human intrinsic clearance (CL_int) were developed using one of the largest dataset of 455 compounds with data from human liver microsomes (HLM) and human hepatocytes (HHEP). For extended clearance classification system (ECCS) class 2/4 compounds, linear SFs (SF_lin) are approximately 1, suggesting enzyme activities in HLM and HHEP are similar to those in vivo under physiological conditions. For ECCS class 1A/1B compounds, a unified set of SFs was developed for CL_int. These SFs contain both SF_lin and an exponential SF (SF_β) of fraction unbound in plasma (f_u,p). The unified SFs for class 1A/1B eliminate the need to identify the transporters involved prior to clearance prediction. The underlying mechanisms of these SFs are not entirely clear at this point, but they serve practical purposes to reduce biases and increase prediction accuracy. Similar SFs have also been developed for preclinical species. For HLM-HHEP disconnect (HLM > HHEP) ECCS class 2/4 compounds that are mainly metabolized by cytochrome P450s/FMO, HLM significantly overpredicted in vivo CL_int, while HHEP slightly underpredicted and geometric mean of HLM and HHEP slightly overpredicted in vivo CL_int. This observation is different than in rats, where rat liver microsomal CL_int correlates well with in vivo CL_int for compounds demonstrating permeability-limited metabolism. The good CL_int IVIVE developed using HLM and HHEP helps build confidence for prospective predictions of human clearance and supports the continued utilization of these assays to guide structure-activity relationships to improve metabolic stability.

Predicting transporter mediated drug-drug interactions via static and dynamic physiologically based pharmacokinetic modeling: A comprehensive insight on where we are now and the way forward

The greater utilization and acceptance of physiologically-based pharmacokinetic (PBPK) modeling to evaluate the potential metabolic drug-drug interactions is evident by the plethora of literature, guidance's, and regulatory dossiers available in the literature. In contrast, it is not widely used to predict transporter-mediated DDI (tDDI). This is attributed to the unavailability of accurate transporter tissue expression levels, the absence of accurate in vitro to in vivo extrapolations (IVIVE), enzyme-transporter interplay, and a lack of specific probe substrates. Additionally, poor understanding of the inhibition/induction mechanisms coupled with the inability to determine unbound concentrations at the interaction site made tDDI assessment challenging. Despite these challenges, continuous improvements in IVIVE approaches enabled accurate tDDI predictions. Furthermore, the necessity of extrapolating tDDI's to special (pediatrics, pregnant, geriatrics) and diseased (renal, hepatic impaired) populations is gaining impetus and is encouraged by regulatory authorities. This review aims to visit the current state-of-the-art and summarizes contemporary knowledge on tDDI predictions. The current understanding and ability of static and dynamic PBPK models to predict tDDI are portrayed in detail. Peer-reviewed transporter abundance data in special and diseased populations from recent publications were compiled, enabling direct input into modeling tools for accurate tDDI predictions. A compilation of regulatory guidance's for tDDI's assessment and success stories from regulatory submissions are presented. Future perspectives and challenges of predicting tDDI in terms of in vitro system considerations, endogenous biomarkers, the use of empirical scaling factors, enzyme-transporter interplay, and acceptance criteria for model validation to meet the regulatory expectations were discussed.

Sinusoidal Organic Anion-Transporting Polypeptide 1B1/1B3 and Bile Canalicular Multidrug Resistance-Associated Protein 2 Play an Essential Role in the Hepatobiliary Disposition of a Synthetic Cyclic Dinucleotide (STING Agonist)

The liver is central to the elimination of many drugs from the body involving multiple processes and understanding of these processes is important to quantitively assess hepatic clearance of drugs. The synthetic STING (STimulator of INterferon Genes protein) agonist is a new class of drugs currently being evaluated in clinical trials as a potential anticancer therapy. In this study, we used ML00960317 (synthetic STING agonist) to investigate the hepatobiliary disposition of this novel molecular entity. A bile-duct cannulated (BDC) rat study indicated that biliary excretion is the major route of elimination for ML00960317 (84% of parent dose in bile). The human biliary clearance using in vitro sandwich cultured human hepatocyte model predicted significant biliary excretion of ML00960317 (biliary excretion index (BEI) of 47%). Moreover, the transport studies using transporter expressing cell lines, hepatocytes, and membrane vesicles indicated that ML00960317 is a robust substrate of OATP1B1, OATP1B3, and MRP2. Using relative expression factor approach, the combined contribution of OATP1B1 (fraction transported (ft) = 0.62) and OATP1B3 (ft = 0.31) was found to be 93% of the active uptake clearance of ML00960317 into the liver. Furthermore, OATP1B1 and OATP1B3-mediated uptake of ML00960317 was inhibited by rifampicin with IC50 of 6.5 and 2.3 μM, respectively indicating an in vivo DDI risk (R value of 1.5 and 2.5 for OATP1B1 and OATP1B3, respectively). These results highlighted an important role of OATP1B1, OATP1B3, and MRP2 in the hepatobiliary disposition of ML00960317. These pathways may act as rate-determining steps in the hepatic clearance of ML00960317 thus presenting clinical DDI risk.

Use of selective substrates and inhibitors to rapidly characterise batches of cryopreserved primary human hepatocytes for assessment of active uptake liability in drug discovery and development

The use of hepatocytes to predict human hepatic metabolic clearance is the gold standard approach. However whilst enzymes are well characterised, knowledge gaps remain for transporters. Furthermore, methods to study specific transporter involvement are often complicated by overlapping substrate specificity. Selective substrates and inhibitors would aid investigations into clinically relevant pharmacokinetic effects. However, to date no consensus has been reached.This work defines selective hepatic uptake transporter substrates and inhibitors for the six main human hepatocyte transporters (OATP1B1, OATP1B3, OATP2B1, NTCP, OAT2 & OCT1), and demonstrates their use to rapidly characterise batches of human hepatocytes for uptake transporter activity. Hepatic uptake was determined across a range of substrate concentrations, allowing the definition of kinetic parameters and hence active and passive components. Systematic investigations identified a specific substrate and inhibitor for each transporter, with no overlap between the specificity of substrate and inhibitor for any given transporter.Early characterisation of compound interactions with uptake transporters will aid in early risk assessment and chemistry design. Hence, this work further highlights the feasibility of a refined methodology for rapid compound characterisation for the application of static and dynamic models, for early clinical risk assessment and guidance for the clinical development plan.

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.

Novel Insights in Drug Transporter Sciences: the Year 2021 in Review

On behalf of the team I am pleased to present the second annual 'novel insights into drug transporter sciences review' focused on peer-reviewed articles that were published in the year 2021. In compiling the articles for inclusion, preprints available in 2021 but officially published in 2022 were considered to be in scope. To support this review the contributing authors independently selected one or two articles that were thought to be impactful and of interest to the broader research community. A similar approach as published last year was adopted whereby key observations, methods and analysis of each paper is concisely summarized in the synopsis followed by a commentary highlighting the impact of the paper in understanding of drug transporters' role in drug disposition.As the goal of this review is not to provide a comprehensive overview of each paper but rather highlight important findings that are well supported by the data, the reader is encouraged to consult the original articles for additional information. Further, and keeping in line with the goals of this review, it should be noted that all authors actively contributed by writing synopsis and commentary for individual papers and no attempt was made to standardize language or writing styles. In this way, the review article is reflective of not only the diversity of the articles but also that of the contributors. I extend my thanks to the authors for their continued support and also welcome Diane Ramsden and Pallabi Mitra as contributing authors for this issue.

Recent advances in the translation of drug metabolism and pharmacokinetics science for drug discovery and development

Drug metabolism and pharmacokinetics (DMPK) is an important branch of pharmaceutical sciences. The nature of ADME (absorption, distribution, metabolism, excretion) and PK (pharmacokinetics) inquiries during drug discovery and development has evolved in recent years from being largely descriptive to seeking a more quantitative and mechanistic understanding of the fate of drug candidates in biological systems. Tremendous progress has been made in the past decade, not only in the characterization of physiochemical properties of drugs that influence their ADME, target organ exposure, and toxicity, but also in the identification of design principles that can minimize drug-drug interaction (DDI) potentials and reduce the attritions. The importance of membrane transporters in drug disposition, efficacy, and safety, as well as the interplay with metabolic processes, has been increasingly recognized. Dramatic increases in investments on new modalities beyond traditional small and large molecule drugs, such as peptides, oligonucleotides, and antibody-drug conjugates, necessitated further innovations in bioanalytical and experimental tools for the characterization of their ADME properties. In this review, we highlight some of the most notable advances in the last decade, and provide future perspectives on potential major breakthroughs and innovations in the translation of DMPK science in various stages of drug discovery and development.

Recent developments in in vitro and in vivo models for improved translation of preclinical pharmacokinetics and pharmacodynamics data

Improved pharmacokinetics/pharmacodynamics (PK/PD) prediction in the early stages of drug development is essential to inform lead optimization strategies and reduce attrition rates. Recently, there have been significant advancements in the development of new in vitro and in vivo strategies to better characterize pharmacokinetic properties and efficacy of drug leads. Herein, we review advances in experimental and mathematical models for clearance predictions, advancements in developing novel tools to capture slowly metabolized drugs, in vivo model developments to capture human etiology for supporting drug development, limitations and gaps in these efforts, and a perspective on the future in the field.

Successful Prediction of Human Steady-State Unbound Brain-to-Plasma Concentration Ratio of P-gp Substrates Using the Proteomics-Informed Relative Expression Factor Approach

In order to optimize central nervous system (CNS) drug development, accurate prediction of the drug's human steady-state unbound brain interstitial fluid-to-plasma concentration ratio (K_p,uu,brain ) is critical, especially for drugs that are effluxed by the multiple drug resistance transporters (e.g., P-glycoprotein, P-gp). Due to lack of good in vitro human blood-brain barrier models, we and others have advocated the use of a proteomics-informed relative expressive factor (REF) approach to predict K_p,uu,brain . Therefore, we tested the success of this approach in humans, with a focus on P-gp substrates, using brain positron emission tomography imaging data for verification. To do so, the efflux ratio (ER) of verapamil, N-desmethyl loperamide, and metoclopramide was determined in human P-gp-transfected MDCKII cells using the Transwell assay. Then, using the ER estimate, K_p,uu,brain of the drug was predicted using REF (ER approach). Alternatively, in vitro passive and P-gp-mediated intrinsic clearances (CLs) of these drugs, estimated using a five-compartmental model, were extrapolated to in vivo using REF (active CL) and brain microvascular endothelial cells protein content (passive CL). The ER approach successfully predicted K_p,uu,brain of all three drugs within twofold of observed data and within 95% confidence interval of the observed data for verapamil and N-desmethyl loperamide. Using the in vitro-to-in vivo extrapolated clearance approach, K_p,uu,brain was reasonably well predicted but not the brain unbound interstitial fluid drug concentration-time profile. Therefore, we propose that the ER approach be used to predict K_p,uu,brain of CNS candidate drugs to enhance their success in development.

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.