Predicting the Human Hepatic Clearance of Acidic and Zwitterionic Drugs

Which Unbound Fraction Should We Use in the Well-Stirred Model for More Accurately Predicting Hepatic Clearance of Drugs for Humans?

As the hepatic clearance (CLH) of drugs becomes independent of hepatic blood flow rate, CLH depends primarily on intrinsic clearance (CLint). Incubations of microsomes or hepatocytes are commonly used to generate CLint. Therefore, CLint estimates corrected for binding to the in vitro systems and scaled to whole liver, are applied to a well-stirred liver model to obtain CLH predictions for drugs. In other words, CLint is extrapolated with the ratio of unbound fraction between the plasma (fup) and incubation medium (fuinc). However, this binding correction resulted to an important underprediction bias of CLH. Therefore, the approach considering fup and fuinc needs to be better understood for more precisely scaling CLint. The objective of this study was to explain the underprediction bias of CLH based on physicochemical properties of drugs. Analysis-ready datasets have been collected so that evaluation of the data generates a mechanistic understanding of the impact of unbound fraction on the prediction of CLH of human for 128 drugs. Experimental values of fuinc for liver are quantifying solely the binding to lipids in microsomes or hepatocytes in the absence of plasma proteins in the incubations. However, the experimental values of fup for plasma can estimate the binding to lipids and plasma proteins. Therefore, drugs binding primarily to lipids in the liver and plasma showed a less pronounced underprediction bias of CLH by using the ratios of fup/fuinc in the well-stirred model. In contrast, drugs binding primarily to the plasma proteins in the liver and plasma showed a larger underprediction bias of CLH. Furthermore, for the ionized drugs, values of fup and fuinc are not covering the pH gradient effect between plasma and hepatocytes, which also impacted the CLH predictions. For these reasons, a mechanistic approach was proposed to replace the conventional fup value with an adjusted fup (fu-adjusted) that accounts for differences in proteins/lipids binding and pH gradient effect between the liver and plasma. Hence, replacing fup with fu-adjusted did provide a universal and mechanisms-based approach removing the underprediction bias for all datasets of drugs. Overall, this study indicates which drug properties generated the largest underprediction bias of CLH and suggests that the Poulin et al. method referring to fu-adjusted could be the most proper unbound fraction to reduce that bias with the well-stirred model.

Development of PRC1 Inhibitors Employing Fragment-Based Approach and NMR-Guided Optimization

Polycomb Repressive Complex 1 (PRC1) is associated with transcriptional silencing, and its dysregulation plays an important role in various cancers. Well-characterized PRC1 inhibitors can facilitate the exploration of PRC1 inhibition as therapeutic agents. By employing an NMR-based fragment screening approach, we have previously identified a very weak millimolar ligand RB-1, which directly binds to RING1B-BMI1. Then, we reported a low-micromolar PRC1 inhibitor, RB-3, which is active in leukemic cells, showing inhibition of H2A ubiquitylation and modulation of target genes. Here, we describe details of the optimization campaign of RB-1 into potent PRC1 inhibitors by guiding the SAR employing two NMR approaches and a probe-based biochemical assay. These efforts, combined with medicinal chemistry optimization, resulted in the development of RB-3 and slightly improved RB-4. We have demonstrated that RB-4 binds to both RING1A and RING1B proteins and inhibits the activity of RING1B-BMI1 and RING1B-PCGF1, representing both canonical and noncanonical PRC1 complexes.

A membrane permeability database for nonpeptidic macrocycles

The process of developing new drugs is arduous and costly, particularly for targets classified as "difficult-to-drug." Macrocycles show a particular ability to modulate difficult-to-drug targets, including protein-protein interactions, while still allowing oral administration. However, the determination of membrane permeability, critical for reaching intracellular targets and for oral bioavailability, is laborious and expensive. In silico methods are a cost-effective alternative, enabling predictions prior to compound synthesis. Here, we present a comprehensive online database ( https://swemacrocycledb.com/ ), housing 5638 membrane permeability datapoints for 4216 nonpeptidic macrocycles, curated from the literature, patents, and bioactivity repositories. In addition, we present a new descriptor, the "amide ratio" (AR), that quantifies the peptidic nature of macrocyclic compounds, enabling the classification of peptidic, semipeptidic, and nonpeptidic macrocycles. Overall, this resource fills a gap among existing databases, offering valuable insights into the membrane permeability of nonpeptidic and semipeptidic macrocycles, and facilitating predictions for drug discovery projects.

Predicting the Intravenous Pharmacokinetics of Covalent Drugs in Animals and Humans

30 covalent drugs were used to assess clearance (CL) prediction reliability in animals and humans. In animals, marked CL underprediction was observed using cryopreserved hepatocytes or liver microsomes (LMs) supplemented for cytochrome P450 activity. Improved quantitative performance was observed by combining metabolic stability data from LMs and liver S9 fractions, the latter supplemented with reduced glutathione for glutathione transferase activity. While human LMs provided reliable human CL predictions, prediction statistics were improved further by incorporating S9 stability data. CL predictions with allometric scaling were less robust compared to in vitro drug metabolism methods; the best results were obtained using the fu-corrected intercept model. Human volume of distribution (Vd) was well predicted using allometric scaling of animal pharmacokinetic data; the most reliable results were achieved using simple allometric scaling of unbound Vd values. These results provide a quantitative framework to guide appropriate method selection for human PK prediction with covalent drugs.

Using the Dynamic Well-Stirred Model to Extrapolate Hepatic Clearance of Organic Anion-Transporting Polypeptide Transporter Substrates without Assuming Albumin-Mediated Hepatic Drug Uptake

Extrapolating in vivo hepatic clearance from in vitro uptake data is a known challenge, especially for organic anion-transporting polypeptide transporter (OATP) substrates, and the well-stirred model (WSM) commonly yields systematic underpredictions for those anionic drugs, hypothetically due to "albumin-mediated hepatic drug uptake". In the present study, we demonstrate that the WSM incorporating the dynamic free fraction (f D), a measure of drug protein binding affinity, performs reasonably well in predicting hepatic clearance of OATP substrates. For a selection of anionic drugs, including atorvastatin, fluvastatin, pravastatin, rosuvastatin, pitavastatin, cerivastatin, and repaglinide, this dynamic well-stirred model (dWSM) correctly predicts hepatic plasma clearance within 2-fold error for six out of seven OATP substrates examined. The geometric mean of clearance ratios between the predicted and the observed values falls in the range of 1.21-1.38. As expected, the WSM with unbound fraction (f u) systematically underpredicts hepatic clearance with greater than 2-fold error for five out of seven drugs, and the geometric mean of clearance ratios between the predicted and the observed values is in the range of 0.20-0.29. The results suggest that, despite its simplicity, the dWSM operates well for transporter-mediated uptake clearance, and that clearance under-prediction of OATP substrates may not necessarily be associated with the chemical class of the anionic drugs, nor is it a result of albumin-mediated hepatic drug uptake as currently hypothesized. Instead, the superior prediction power of the dWSM confirms the utility of the dynamic free fraction in clearance prediction and the importance of drug plasma binding kinetics in hepatic uptake clearance. SIGNIFICANCE STATEMENT: The traditional well-stirred model (WSM) consistently underpredicts organin anion-transporting polypeptide transporter (OATP)-mediated hepatic uptake clearance, hypothetically due to the albumin-mediated hepatic drug uptake. In this manuscript, we apply the dynamic WSM to extrapolate hepatic clearance of the OATP substrates, and our results show significant improvements in clearance prediction without assuming albumin-mediated hepatic drug uptake.

Drug Design and Success of Prospective Mouse In Vitro-In Vivo Extrapolation (IVIVE) for Predictions of Plasma Clearance (CLp) from Hepatocyte Intrinsic Clearance (CLint)

Hepatocyte intrinsic clearance (CL_int) and methods of in vitro-in vivo extrapolation (IVIVE) are often used to predict plasma clearance (CL_p) in drug discovery. While the prediction success of this approach is dependent on the chemotype, specific molecular properties and drug design features that govern these outcomes are poorly understood. To address this challenge, we investigated the success of prospective mouse CL_p IVIVE across 2142 chemically diverse compounds. Dilution scaling, which assumes that the free fraction in hepatocyte incubations (f_u,inc) is governed by binding to the 10% of serum in the incubation medium, was used as our default CL_p IVIVE approach. Results show that predictions of CL_p are better for smaller (molecular weight (MW) < 500 Da), less polar (total polar surface area (TPSA) < 100 Ų, hydrogen bond donor (HBD) ≤1, hydrogen bond acceptor (HBA) ≤ 6), lipophilic (log D > 3), and neutral compounds, with low HBD count playing the key role. If compounds are classified according to their chemical space, predictions were good for compounds resembling central nervous system (CNS) drugs [average absolute fold error (AAFE) of 2.05, average fold error (AFE) of 0.90], moderate for classical druglike compounds (according to Lipinski, Veber, and Ghose guidelines; AAFE of 2.55; AFE of 0.68), and poor for nonclassical "beyond the rule of 5" compounds (AAFE of 3.31; AFE of 0.41). From the perspective of measured druglike properties, predictions of CL_p were better for compounds with moderate-to-high hepatocyte CL_int (>10 μL/min/10⁶ cells), high passive cellular permeability (P_app > 100 nm/s), and moderate observed CL_p (5-50 mL/min/kg). Influences of plasma protein binding (f_u,p) and P-glycoprotein (Pgp) apical efflux ratio (AP-ER) were less pronounced. If the extended clearance classification system (ECCS) is applied, predictions were good for class 2 (P_app > 50 nm/s; neutral or basic; AAFE of 2.35; AFE of 0.70) and acceptable for class 1A compounds (AAFE of 2.98; AFE of 0.70). Classes 1B, 3 A/B, and 4 showed poor outcomes (AAFE > 3.80; AFE < 0.60). Functional groups trending toward weaker CL_p IVIVE were esters, carbamates, sulfonamides, carboxylic acids, ketones, primary and secondary amines, primary alcohols, oxetanes, and compounds liable to aldehyde oxidase metabolism, likely due to multifactorial reasons. Multivariate analysis showed that multiple properties are relevant, combining together to define the overall success of CL_p IVIVE. Our results indicate that the current practice of prospective CL_p IVIVE is suitable only for CNS-like compounds and well-behaved classical druglike space (e.g., high permeability or ECCS class 2) without challenging functional groups. Unfortunately, based on existing mouse data, prospective CL_p IVIVE for complex and nonclassical chemotypes is poor and hardly better than random guessing. This is likely due to complexities such as extrahepatic metabolism and transporter-mediated disposition which are poorly captured by this methodology. With small-molecule drug discovery increasingly evolving toward nonclassical and complex chemotypes, existing CL_p IVIVE methodology will require improvement. While empirical correction factors may bridge the gap in the near future, improved and new in vitro assays, data integration models, and machine learning (ML) methods are increasingly needed to address this challenge and reduce the number of nonclinical pharmacokinetic (PK) studies.

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.

Discovery of Ervogastat (PF-06865571): A Potent and Selective Inhibitor of Diacylglycerol Acyltransferase 2 for the Treatment of Non-alcoholic Steatohepatitis

Discovery efforts leading to the identification of ervogastat (PF-06865571), a systemically acting diacylglycerol acyltransferase (DGAT2) inhibitor that has advanced into clinical trials for the treatment of non-alcoholic steatohepatitis (NASH) with liver fibrosis, are described herein. Ervogastat is a first-in-class DGAT2 inhibitor that addressed potential development risks of the prototype liver-targeted DGAT2 inhibitor PF-06427878. Key design elements that culminated in the discovery of ervogastat are (1) replacement of the metabolically labile motif with a 3,5-disubstituted pyridine system, which addressed potential safety risks arising from a cytochrome P450-mediated O-dearylation of PF-06427878 to a reactive quinone metabolite precursor, and (2) modifications of the amide group to a 3-THF group, guided by metabolite identification studies coupled with property-based drug design.

Improved Predictability of Hepatic Clearance with Optimal pH for Acyl-Glucuronidation in Liver Microsomes

The purpose of this study was to investigate the optimal pH for acyl-glucuronidation formation with carboxylic acid-containing compounds in human and rat liver microsomes to improve the predictability of their hepatic clearance. The optimal pH for acyl-glucuronidation of all 17 compounds was around pH 6.0 in human and rat liver microsomes. Correlation analysis was done with the predicted in vitro intrinsic clearance (CL_{int,in vitro}) and in vivo intrinsic clearance (CL_{int,in vivo}) calculated from available reported data of total clearance (CL_tot) of 11 compounds in humans. For 8 of the 11 compounds, under the pH 6.0 condition, the CL_{int,in vitro} were within 1/3 to 3-fold error of the observed CL_{int,in vivo} whereas, the error was within 1/3 to 3-fold of the observed CL_{int,in vivo} for only 3 of the 11 under the pH 7.4 condition. The intracellular pH in human and rat hepatocytes decreased in the presence of a carboxylic acid-containing compound. These findings suggest that acyl-glucuronidation in liver microsomes at pH 6.0 is closer to physiological conditions in the presence of carboxylic acid compounds, and thus, use of this pH condition is important for physiological interpretation and predictability of intrinsic clearance.

LipMetE (Lipophilic Metabolism Efficiency) as a Simple Guide for Half-Life and Dosing Regimen Prediction of Oral Drugs

The in vivo half-life is a key property of every drug molecule, as it determines dosing regimens, peak-to-trough ratios and often dose. However, half-life optimization can be challenging due to its multifactorial nature, with in vitro metabolic turnover, plasma protein binding and volume of distribution all impacting half-life. We here propose that the medicinal chemistry design parameter Lipophilic Metabolism Efficiency (LipMetE) can greatly simplify half-life optimization of neutral and basic compounds. Using mathematical transformations, examples from preclinical GABAA projects and clinical compounds with human pharmacokinetic data, we show that LipMetE is directly proportional to the logarithm of half-life. As the design parameter LipMetE can be swiftly calculated using the readily available parameters LogD, intrinsic clearance and fraction unbound in human liver microsomes or hepatocytes, this approach enables rational half-life optimization from the early stages of drug discovery projects.

Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data

Pharmacokinetic research plays an important role in the development of new drugs. Accurate predictions of human pharmacokinetic parameters are essential for the success of clinical trials. Clearance (CL) and volume of distribution (Vd) are important factors for evaluating pharmacokinetic properties, and many previous studies have attempted to use computational methods to extrapolate these values from nonclinical laboratory animal models to human subjects. However, it is difficult to obtain sufficient, comprehensive experimental data from these animal models, and many studies are missing critical values. This means that studies using nonclinical data as explanatory variables can only apply a small number of compounds to their model training. In this study, we perform missing-value imputation and feature selection on nonclinical data to increase the number of training compounds and nonclinical datasets available for these kinds of studies. We could obtain novel models for total body clearance (CL_tot) and steady-state Vd (Vd_ss) (CL_tot: geometric mean fold error [GMFE], 1.92; percentage within 2-fold error, 66.5%; Vd_ss: GMFE, 1.64; percentage within 2-fold error, 71.1%). These accuracies were comparable to the conventional animal scale-up models. Then, this method differs from animal scale-up methods because it does not require animal experiments, which continue to become more strictly regulated as time passes.

Pharmacokinetics, Mass Balance, Metabolism, and Excretion of the Liver-Targeted Acetyl-CoA Carboxylase Inhibitor PF-05221304 (Clesacostat) in Humans

The disposition of the hepatoselective ACC inhibitor PF-05221304 (Clesacostat) was studied after a single 50-mg oral dose of [14C]-PF-05221304 to healthy human subjects.Mass balance was achieved with 89.9% of the administered dose recovered in urine and faeces, over the 11-day study period. The total administered radioactivity excreted in faeces and urine was 81.7% and 8.2%, respectively. Unchanged PF-05221304 accounted for 35.6% of the radioactive dose in faeces, suggesting ∼64% of the administered dose was absorbed.PF-05221304 was principally metabolized via oxidative and reductive pathways involving: (a) N-dealkylation, (b) isopropyl group monohydroxylation to yield enantiomeric metabolites (M2a and M2b), (c) hydroxylation on the 3-azaspiro[5.5]undecan-8-one moiety to metabolites M5 and 519c, and (d) carbonyl group reduction to enantiomeric alcohol metabolites M3, and M4. Secondary metabolites (521a, 521b, and 533), derived from a combination of oxidation and reduction of the primary metabolites accounted for ∼14.8% of the dose. In plasma, unchanged PF-05221304 represented 96.1% circulating radioactivity. Metabolites M1, M2b, and M2a represented 1.94, 1.76, and 0.18% of circulating radioactivity, respectively.Overall, these data suggest that PF-05221304 is well absorbed in humans and eliminated largely via phase I metabolism.

Exploration and application of a liver-on-a-chip device in combination with modelling and simulation for quantitative drug metabolism studies

Microphysiological systems (MPS) are complex and more physiologically realistic cellular in vitro tools that aim to provide more relevant human in vitro data for quantitative prediction of clinical pharmacokinetics while also reducing the need for animal testing. The PhysioMimix liver-on-a-chip integrates medium flow with hepatocyte culture and has the potential to be adopted for in vitro studies investigating the hepatic disposition characteristics of drug candidates. The current study focusses on liver-on-a-chip system exploration for multiple drug metabolism applications. Characterization of cytochrome P450 (CYP), UDP-glucuronosyl transferase (UGT) and aldehyde oxidase (AO) activities was performed using 15 drugs and in vitro to in vivo extrapolation (IVIVE) was assessed for 12 of them. Next, the utility of the liver-on-a-chip for estimation of the fraction metabolized (f_m) via specific biotransformation pathways of quinidine and diclofenac was established. Finally, the metabolite identification opportunities were also explored using efavirenz as an example drug with complex primary and secondary metabolism involving a combination of CYP, UGT and sulfotransferase enzymes. A key aspect of these investigations was the application of mathematical modelling for improved parameter calculation. Such approaches will be required for quantitative assessment of metabolism and/or transporter processes in systems where medium flow and system compartments result in non-homogeneous drug concentrations. In particular, modelling was used to explore the effect of evaporation from the medium and it was found that the intrinsic clearance (CL_int) might be underestimated by up to 40% for low clearance compounds if evaporation is not accounted for. Modelling of liver-on-a-chip in vitro data also enhanced the approach to f_m estimation allowing objective assessment of metabolism models of different complexity. The resultant diclofenac f_m,UGT of 0.64 was highly comparable with values reported previously in the literature. The current study demonstrates the integration of mathematical modelling with experimental liver-on-a-chip studies and illustrates how this approach supports generation of high quality of data from complex in vitro cellular systems.

In Vitro - in Vivo Extrapolation of Hepatic Clearance in Preclinical Species

Accurate prediction of human clearance is of critical importance in drug discovery. In this study, in vitro - in vivo extrapolation (IVIVE) of hepatic clearance was established using large sets of compounds for four preclinical species (mouse, rat, dog, and non-human primate) to enable better understanding of clearance mechanisms and human translation. In vitro intrinsic clearances were obtained using pooled liver microsomes (LMs) or hepatocytes (HEPs) and scaled to hepatic clearance using the parallel-tube and well-stirred models. Subsequently, IVIVE scaling factors (SFs) were derived to best predict in vivo clearance. The SFs for extended clearance classification system (ECCS) class 2/4 compounds, involving metabolic clearance, were generally small (≤ 2.6) using both LMs and HEPs with parallel-tube model, with the exception of the rodents (~ 2.4-4.6), suggesting in vitro reagents represent in vivo reasonably well. SFs for ECCS class 1A and 1B are generally higher than class 2/4 across the species, likely due to the contribution of transporter-mediated clearance that is under-represented with in vitro reagents. The parallel-tube model offered lower variability in clearance predictions over the well-stirred model. For compounds that likely demonstrate passive permeability-limited clearance in vitro, rat LM predicted in vivo clearance more accurately than HEP. This comprehensive analysis demonstrated reliable IVIVE can be achieved using LMs and HEPs. Evaluation of clearance IVIVE in preclinical species helps to better understand clearance mechanisms, establish more reliable IVIVE in human, and enhance our confidence in human clearance and PK prediction, while considering species differences in drug metabolizing enzymes and transporters.

Discovery and Optimization of Orally Bioavailable Phthalazone and Cinnolone Carboxylic Acid Derivatives as S1P2 Antagonists against Fibrotic Diseases

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive lung disease. Current treatments only slow down disease progression, making new therapeutic strategies compelling. Increasing evidence suggests that S1P2 antagonists could be effective agents against fibrotic diseases. Our compound collection was mined for molecules possessing substructure features associated with S1P2 activity. The weakly potent indole hit 6 evolved into a potent phthalazone series, bearing a carboxylic acid, with the aid of a homology model. Suboptimal pharmacokinetics of a benzimidazole subseries were improved by modifications targeting potential interactions with transporters, based on concepts deriving from the extended clearance classification system (ECCS). Scaffold hopping, as a part of a chemical enablement strategy, permitted the rapid exploration of the position adjacent to the carboxylic acid. Compound 38, with good pharmacokinetics and in vitro potency, was efficacious at 10 mg/kg BID in three different in vivo mouse models of fibrotic diseases in a therapeutic setting.

Organic Anion-Transporting Polypeptide 1B1/1B3-Mediated Hepatic Uptake Determines the Pharmacokinetics of Large Lipophilic Acids: In Vitro-In Vivo Evaluation in Cynomolgus Monkey

It is generally presumed that uptake transport mechanisms are of limited significance in hepatic clearance for lipophilic or high passive-permeability drugs. In this study, we evaluated the mechanistic role of the hepato-selective organic anion-transporting polypeptides (OATPs) 1B1/1B3 in the pharmacokinetics of compounds representing large lipophilic acid space. Intravenous pharmacokinetics of 16 compounds with molecular mass ∼400-730 Da, logP ∼3.5-8, and acid pKa <6 were obtained in cynomolgus monkey after dosing without and with a single-dose rifampicin-OATP1B1/1B3 probe inhibitor. Rifampicin (30 mg/kg oral) significantly (P < 0.05) reduced monkey clearance and/or steady-state volume of distribution (VDss) for 15 of 16 acids evaluated. Additionally, clearance of danoprevir was reduced by about 35%, although statistical significance was not reached. A significant linear relationship was noted between the clearance ratio (i.e., ratio of control to treatment groups) and VDss ratio, suggesting hepatic uptake contributes to the systemic clearance and distribution simultaneously. In vitro transport studies using primary monkey and human hepatocytes showed uptake inhibition by rifampicin (100 µM) for compounds with logP ≤6.5 but not for the very lipophilic acids (logP > 6.5), which generally showed high nonspecific binding in hepatocyte incubations. In vitro uptake clearance and fraction transported by OATP1B1/1B3 (ft,OATP1B) were found to be similar in monkey and human hepatocytes. Finally, for compounds with logP ≤6.5, good agreement was noted between in vitro ft,OATP1B and clearance ratio (as well as VDss ratio) in cynomolgus monkey. In conclusion, this study provides mechanistic evidence for the pivotal role of OATP1B-mediated hepatic uptake in the pharmacokinetics across a wide, large lipophilic acid space. SIGNIFICANCE STATEMENT: This study provides mechanistic insight into the pharmacokinetics of a broad range of large lipophilic acids. Organic anion-transporting polypeptides 1B1/1B3-mediated hepatic uptake is of key importance in the pharmacokinetics and drug-drug interactions of almost all drugs and new molecular entities in this space. Diligent in vitro and in vivo transport characterization is needed to avoid the false negatives often noted because of general limitations in the in vitro assays while handling compounds with such physicochemical attributes.