From hazard to risk prioritization: a case study to predict drug-induced cholestasis using physiologically based kinetic modeling

Cholestasis is characterized by hepatic accumulation of bile acids. Clinical manifestation of cholestasis only occurs in a small proportion of exposed individuals. The present study aims to develop a new approach methodology (NAM) to predict drug-induced cholestasis as a result of drug-induced hepatic bile acid efflux inhibition and the resulting bile acid accumulation. To this end, hepatic concentrations of a panel of drugs were predicted by a generic physiologically based kinetic (PBK) drug model. Their effects on hepatic bile acid efflux were incorporated in a PBK model for bile acids. The predicted bile acid accumulation was used as a measure for a drug's cholestatic potency. The selected drugs were known to inhibit hepatic bile acid efflux in an assay with primary suspension-cultured hepatocytes and classified as common, rare, or no for cholestasis incidence. Common cholestasis drugs included were atorvastatin, chlorpromazine, cyclosporine, glimepiride, ketoconazole, and ritonavir. The cholestasis incidence of the drugs appeared not to be adequately predicted by their Ki for inhibition of hepatic bile acid efflux, but rather by the AUC of the PBK model predicted internal hepatic drug concentration at therapeutic dose level above this Ki. People with slower drug clearance, a larger bile acid pool, reduced bile salt export pump (BSEP) abundance, or given higher than therapeutic dose levels were predicted to be at higher risk to develop drug-induced cholestasis. The results provide a proof-of-principle of using a PBK-based NAM for cholestasis risk prioritization as a result of transporter inhibition and identification of individual risk factors.

Identification of reversible OATP1B1 and time-dependent CYP3A4 inhibition as the major risk factors for drug-induced cholestasis (DIC)

Hepatic bile acid regulation is a multifaceted process modulated by several hepatic transporters and enzymes. Drug-induced cholestasis (DIC), a main type of drug-induced liver injury (DILI), denotes any drug-mediated condition in which hepatic bile flow is impaired. Our ability in translating preclinical toxicological findings to human DIC risk is currently very limited, mainly due to important interspecies differences. Accordingly, the anticipation of clinical DIC with available in vitro or in silico models is also challenging, due to the complexity of the bile acid homeostasis. Herein, we assessed the in vitro inhibition potential of 47 marketed drugs with various degrees of reported DILI severity towards all metabolic and transport mechanisms currently known to be involved in the hepatic regulation of bile acids. The reported DILI concern and/or cholestatic annotation correlated with the number of investigated processes being inhibited. Furthermore, we employed univariate and multivariate statistical methods to determine the important processes for DILI discrimination. We identified time-dependent inhibition (TDI) of cytochrome P450 (CYP) 3A4 and reversible inhibition of the organic anion transporting polypeptide (OATP) 1B1 as the major risk factors for DIC among the tested mechanisms related to bile acid transport and metabolism. These results were consistent across multiple statistical methods and DILI classification systems applied in our dataset. We anticipate that our assessment of the two most important processes in the development of cholestasis will enable a risk assessment for DIC to be efficiently integrated into the preclinical development process.

Simplifying the Extended Clearance Concept Classification System (EC3S) to Guide Clearance Prediction in Drug Discovery

PURPOSE

The Extended Clearance Concept Classification System was established as a development-stage tool to provide a framework for identifying fundamental mechanism(s) governing drug disposition in humans. In the present study, the applicability of the EC3S in drug discovery has been investigated. In its current format, the EC3S relies on low-throughput hepatocyte uptake data, which are not frequently generated in a discovery setting.

METHODS

A relationship between hepatocyte uptake clearance and MDCK permeability was first established along with intrinsic clearance from human liver microsomes. The performance of this approach was examined by categorizing 64 drugs into EC3S classes and comparing the predicted major elimination pathway(s) to that observed in humans. As an extension of the work, the ability of the simplified EC3S to predict human systemic clearance based on intrinsic clearance generated using in-vitro metabolic systems was evaluated.

RESULTS

The assessment enabled the use of MDCK permeability and unscaled unbound intrinsic clearance to generate cut-off criteria to categorize compounds into four EC3S classes: Class 12ab, 2cd, 34ab, and 34cd, with major elimination mechanism(s) assigned to each class. The predictivity analysis suggested that systemic clearance could generally be predicted within threefold for EC3S class 12ab and 34ab compounds. For classes 2cd and 34cd, systemic clearance was poorly predicted using in-vitro systems explored in this study.

CONCLUSION

Collectively, our simplified classification approach is expected to facilitate the identification of mechanism(s) involved in drug elimination, faster resolution of in-vitro to in-vivo disconnects, and better design of mechanistic pharmacokinetic studies in drug discovery.

Role of Hepatocyte Transporters in Drug-Induced Liver Injury (DILI)-In Vitro Testing

Bile acids and bile salts (BA/BS) are substrates of both influx and efflux transporters on hepatocytes. Canalicular efflux transporters, such as BSEP and MRP2, are crucial for the removal of BA/BS to the bile. Basolateral influx transporters, such as NTCP, OATP1B1/1B3, and OSTα/β, cooperate with canalicular transporters in the transcellular vectorial flux of BA/BS from the sinusoids to the bile. The blockage of canalicular transporters not only impairs the bile flow but also causes the intracellular accumulation of BA/BS in hepatocytes that contributes to, or even triggers, liver injury. In the case of BA/BS overload, the efflux of these toxic substances back to the blood via MRP3, MRP4, and OST α/β is considered a relief function. FXR, a key regulator of defense against BA/BS toxicity suppresses de novo bile acid synthesis and bile acid uptake, and promotes bile acid removal via increased efflux. In drug development, the early testing of the inhibition of these transporters, BSEP in particular, is important to flag compounds that could potentially inflict drug-induced liver injury (DILI). In vitro test systems for efflux transporters employ membrane vesicles, whereas those for influx transporters employ whole cells. Additional in vitro pharmaceutical testing panels usually include cellular toxicity tests using hepatocytes, as well as assessments of the mitochondrial toxicity and accumulation of reactive oxygen species (ROS). Primary hepatocytes are the cells of choice for toxicity testing, with HepaRG cells emerging as an alternative. Inhibition of the FXR function is also included in some testing panels. The molecular weight and hydrophobicity of the drug, as well as the steady-state total plasma levels, may positively correlate with the DILI potential. Depending on the phase of drug development, the physicochemical properties, dosing, and cut-off values of BSEP IC₅₀ ≤ 25-50 µM or total C_ss,plasma/BSEP IC₅₀ ≥ 0.1 may be an indication for further testing to minimize the risk of DILI liability.

Inhibition of canalicular and sinusoidal taurocholate efflux by cholestatic drugs in human hepatoma HepaRG cells

HepaRG cells are highly-differentiated human hepatoma cells, which are increasingly recognized as a convenient cellular model for in vitro evaluation of hepatic metabolism, transport, and/or toxicity of drugs. The present study was designed to evaluate whether HepaRG cells can also be useful for studying drug-mediated inhibition of canalicular and/or sinusoidal hepatic efflux of bile acids, which constitutes a major mechanism of drug-induced liver toxicity. For this purpose, HepaRG cells, initially loaded with the bile acid taurocholate (TC), were reincubated in TC-free transport assay medium, in the presence or absence of calcium or drugs, before analysis of TC retention. This method allowed us to objectivize and quantitatively measure biliary and sinusoidal efflux of TC from HepaRG cells, through distinguishing cellular and canalicular compartments. In particular, time-course analysis of the TC-free reincubation period of HepaRG cells, that is, the efflux period, indicated that a 20 min-efflux period allowed reaching biliary and sinusoidal excretion indexes for TC around 80% and 60%, respectively. Addition of the prototypical cholestatic drugs bosentan, cyclosporin A, glibenclamide, or troglitazone during the TC-free efflux phase period was demonstrated to markedly inhibit canalicular and sinusoidal secretion of TC, whereas, by contrast, incubation with the noncholestatic compounds salicylic acid or flumazenil was without effect. Such data therefore support the use of human HepaRG cells for in vitro predicting drug-induced liver toxicity (DILI) due to the inhibition of hepatic bile acid secretion, using a biphasic TC loading/efflux assay.

Advancing Estimation of Hepatobiliary Clearances in Physiologically Based Pharmacokinetic Models of Rosuvastatin Using Human Hepatic Concentrations

PURPOSE

To estimate hepatobiliary clearances of rosuvastatin via simultaneously fitting to reported human positron emission tomography (PET) data in the liver and gallbladder.

METHODS

A hepatobiliary model incorporating five intrinsic hepatobiliary clearances (active uptake clearance at the sinusoidal membrane, efflux clearance by passive diffusion through the sinusoidal membrane, influx clearance by passive diffusion through sinusoidal membrane, clearance of biliary excretion at the canalicular membrane, and intercompartment clearance from the intrahepatic bile duct to the gallbladder) and three compartments (liver, intrahepatic bile duct, and gallbladder) was developed to simultaneously fit rosuvastatin liver and gallbladder data from a representative subject reported by Billington et al. (1). Two liver blood supply input functions, arterial input function and dual input function (using peripheral venous as an alternative to portal vein), were assessed. Additionally, the predictive performance between the established model and four reported models trained with only systemic exposure data, was evaluated by comparing simulated liver and gallbladder profiles with observations.

RESULTS

The established hepatobiliary model well captured the kinetic profiles of rosuvastatin in the liver and gallbladder during the PET scans. Application of dual input function led to a marked underestimation of liver concentrations at the initial stage after i.v. dosing which cannot be offset by altering model parameter values. The simulated hepatobiliary profiles from three of the reported models demonstrated substantial deviation from the observed data.

CONCLUSIONS

The present study highlights the necessity of using hepatobiliary data to verify and improve the predictive performance of hepatic disposition of rosuvastatin.

Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology

BACKGROUND

Adverse drug reactions (ADRs) are one of the leading causes of morbidity and mortality in health care. Understanding which drug targets are linked to ADRs can lead to the development of safer medicines.

METHODS

Here, we analyse in vitro secondary pharmacology of common (off) targets for 2134 marketed drugs. To associate these drugs with human ADRs, we utilized FDA Adverse Event Reports and developed random forest models that predict ADR occurrences from in vitro pharmacological profiles.

FINDINGS

By evaluating Gini importance scores of model features, we identify 221 target-ADR associations, which co-occur in PubMed abstracts to a greater extent than expected by chance. Amongst these are established relations, such as the association of in vitro hERG binding with cardiac arrhythmias, which further validate our machine learning approach. Evidence on bile acid metabolism supports our identification of associations between the Bile Salt Export Pump and renal, thyroid, lipid metabolism, respiratory tract and central nervous system disorders. Unexpectedly, our model suggests PDE3 is associated with 40 ADRs.

INTERPRETATION

These associations provide a comprehensive resource to support drug development and human biology studies.

FUNDING

This study was not supported by any formal funding bodies.

Drug Induced Liver Injury (DILI). Mechanisms and Medicinal Chemistry Avoidance/Mitigation Strategies

Adverse drug reactions (ADRs) are a common cause of attrition in drug discovery and development and drug-induced liver injury (DILI) is a leading cause of preclinical and clinical drug terminations. This perspective outlines many of the known DILI mechanisms and assessment methods used to evaluate and mitigate DILI risk. Literature assessments and retrospective analyses using verified DILI-associated drugs from the Liver Tox Knowledge Base (LTKB) have been used to derive the predictive value of each end point, along with combination approaches of multiple methods. In vitro assays to assess inhibition of the bile salt export pump (BSEP), mitotoxicity, reactive metabolite (RM) formation, and hepatocyte cytolethality, along with physicochemical properties and clinical dose provide useful DILI predictivity. This Perspective also highlights some of the strategies used by medicinal chemists to reduce DILI risk during the optimization of drug candidates.

Challenging the Relevance of Unbound Tissue-to-Blood Partition Coefficient (Kpuu) on Prediction of Drug-Drug Interactions

PURPOSE

To examine the theoretical/practical utility of the liver-to-blood partition coefficient (Kp_uu) for predicting drug-drug interactions (DDIs), and compare the Kp_uu-approach to the extended clearance concept AUC_R-approach.

METHODS

The Kp_uu relationship was derived from first principles. Theoretical simulations investigated the impact of changes in a single hepatic-disposition process on unbound systemic (AUC_B,u) and hepatic exposure (AUC_H,u) versus Kp_uu. Practical aspects regarding Kp_uu utilization were examined by predicting the magnitude of DDI between ketoconazole and midazolam employing published ketoconazole Kp_uu values.

RESULTS

The Kp_uu hepatic-disposition relationship is based on the well-stirred model. Simulations emphasize that changes in influx/efflux intrinsic clearances result in Kp_uu changes, however AUC_H,u remains unchanged. Although incorporation of Kp_uu is believed to improve DDI-predictions, utilizing published ketoconazole Kp_uu values resulted in prediction errors for a midazolam DDI.

CONCLUSIONS

There is limited benefit in using Kp_uu for DDI-predictions as the AUC_R-based approach can reasonably predict DDIs without measurement of intracellular drug concentrations, a difficult task hindered by experimental variability. Further, Kp_uu changes can mislead as they may not correlate with changes in AUC_B,u or AUC_H,u. The well-stirred model basis of Kp_uu when applied to hepatic-disposition implies that nuances of intracellular drug distribution are not considered by the Kp_uu model.

Robustness testing and optimization of an adverse outcome pathway on cholestatic liver injury

Adverse outcome pathways (AOPs) have been recently introduced as tools to map the mechanisms underlying toxic events relevant for chemical risk assessment. AOPs particularly depict the linkage between a molecular initiating event and an adverse outcome through a number of intermediate key events. An AOP has been previously introduced for cholestatic liver injury. The objective of this study was to test the robustness of this AOP for different types of cholestatic insult and the in vitro to in vivo extrapolation. For this purpose, in vitro samples from human hepatoma HepaRG cell cultures were exposed to cholestatic drugs (i.e. intrahepatic cholestasis), while in vivo samples were obtained from livers of cholestatic mice (i.e. extrahepatic cholestasis). The occurrence of cholestasis in vitro was confirmed through analysis of bile transporter functionality and bile acid analysis. Transcriptomic analysis revealed inflammation and oxidative stress as key events in both types of cholestatic liver injury. Major transcriptional differences between intrahepatic and extrahepatic cholestatic liver insults were observed at the level of cell death and metabolism. Novel key events identified by pathway analysis included endoplasmic reticulum stress in intrahepatic cholestasis, and autophagy and necroptosis in both intrahepatic as extrahepatic cholestasis. This study demonstrates that AOPs constitute dynamic tools that should be frequently updated with new input information.

Advancing Predictions of Tissue and Intracellular Drug Concentrations Using In Vitro, Imaging and Physiologically Based Pharmacokinetic Modeling Approaches

This white paper examines recent progress, applications, and challenges in predicting unbound and total tissue and intra/subcellular drug concentrations using in vitro and preclinical models, imaging techniques, and physiologically based pharmacokinetic (PBPK) modeling. Published examples, regulatory submissions, and case studies illustrate the application of different types of data in drug development to support modeling and decision making for compounds with transporter-mediated disposition, and likely disconnects between tissue and systemic drug exposure. The goals of this article are to illustrate current best practices and outline practical strategies for selecting appropriate in vitro and in vivo experimental methods to estimate or predict tissue and plasma concentrations, and to use these data in the application of PBPK modeling for human pharmacokinetic (PK), efficacy, and safety assessment in drug development.

When Does the Rate-Determining Step in the Hepatic Clearance of a Drug Switch from Sinusoidal Uptake to All Hepatobiliary Clearances? Implications for Predicting Drug-Drug Interactions

For dual transporter-enzyme substrate drugs, the extended clearance model can be used to predict the rate-determining step(s) (RDS) of a drug and hence predict its drug-drug interaction (DDI) liabilities (i.e., transport, metabolism, or both). If the RDS of the hepatic clearance of the drug is sinusoidal uptake clearance (CL^s _in), even if the drug is eliminated mainly by hepatic metabolism, its DDI liability (as viewed from changes to systemic drug concentrations) is expected to be inhibition or induction of uptake transporters but not hepatic enzymes; however, this is true only if the condition required to maintain CL^s _in as the RDS is maintained. Here, we illustrate through theoretical simulations that the RDS condition may be violated in the presence of a DDI. That is, the RDS of a drug can switch from CL^s _in to all hepatobiliary clearances [i.e., metabolic/biliary clearance (CL_{met + bile}) and CL^s _in], leading to unexpected systemic DDIs, such as metabolic DDIs, when only transporter DDIs were anticipated. As expected, these analyses revealed that the RDS switch depends on the ratio of CL_{met + bile} to sinusoidal efflux clearance (CL^s _ef). Additional analyses revealed that for intravenously administered drugs, the RDS switch also depends on the magnitude of CL^s _in We analyzed published in vitro quantified hepatobiliary clearances and observed that most drugs have a CL_{met + bile}/CL^s _ef ratio < 4; hence, in practice, the magnitude of CL^s _in must be considered when establishing the RDS. These analyses provide insights previously not appreciated and a theoretical framework to predict DDI liabilities for drugs that are dual transporter-enzyme substrates.

Comparison of Species and Cell-Type Differences in Fraction Unbound of Liver Tissues, Hepatocytes, and Cell Lines

Fraction unbound (f_u) of liver tissue, hepatocytes, and other cell types is an essential parameter used to estimate unbound liver drug concentration and intracellular free drug concentration. f_u,liver and f_u,cell are frequently measured in multiple species and cell types in drug discovery and development for various applications. A comparison study of 12 matrices for f_u,liver and f_u,cell of hepatocytes in five different species (mouse, rat, dog, monkey, and human), as well as f_u,cell of Huh7 and human embryonic kidney 293 cell lines, was conducted for 22 structurally diverse compounds with the equilibrium dialysis method. Using an average bioequivalence approach, our results show that the average difference in binding to liver tissue, hepatocytes, or different cell types was within 2-fold of that of the rat f_u,liver Therefore, we recommend using rat f_u,liver as a surrogate for liver binding in other species and cell types in drug discovery. This strategy offers the potential to simplify binding studies and reduce cost, thereby enabling a more effective and practical determination of f_u for liver tissues, hepatocytes, and other cell types. In addition, f_u under hepatocyte stability incubation conditions should not be confused with f_u,cell, as one is a diluted f_u and the other is an undiluted f_u Cell density also plays a critical role in the accurate measurement of f_u,cell.