The role of quantitative systems pharmacology modeling in the prediction and explanation of idiosyncratic drug-induced liver injury

Beyond the hype and toward application: liver complex in vitro models in preclinical drug safety

INTRODUCTION

Drug induced Liver-Injury (DILI) is a leading cause of drug attrition and complex in vitro models (CIVMs), including three dimensional (3D) spheroids, 3D bio printed tissues and flow-based systems, could improve preclinical prediction. Although CIVMs have demonstrated good sensitivity and specificity in DILI detection their adoption remains limited.

AREAS COVERED

This article describes DILI, the challenges with its prediction and the current strategies and models that are being used. It reviews data from industry-FDA collaborations and strategic partnerships and finishes with an outlook of CIVMs in preclinical toxicity testing. Literature searches were performed using PubMed and Google Scholar while product information was collected from manufacturer websites.

EXPERT OPINION

Liver CIVMs are promising models for predicting DILI although, a decade after their introduction, routine use by the pharmaceutical industry is limited. To accelerate their adoption, several industry-regulator-developer partnerships or consortia have been established to guide the development and qualification. Beyond this, liver CIVMs should continue evolving to capture greater immunological mimicry while partnering with computational approaches to deliver systems that change the paradigm of predicting DILI.

Prediction of the liver safety profile of a first-in-class myeloperoxidase inhibitor using quantitative systems toxicology modeling

The novel myeloperoxidase inhibitor verdiperstat was developed as a treatment for neuroinflammatory and neurodegenerative diseases. During development, a computational prediction of verdiperstat liver safety was performed using DILIsym v8A, a quantitative systems toxicology (QST) model of liver safety.A physiologically-based pharmacokinetic (PBPK) model of verdiperstat was constructed in GastroPlus 9.8, and outputs for liver and plasma time courses of verdiperstat were input into DILIsym. In vitro experiments measured the likelihood that verdiperstat would inhibit mitochondrial function, inhibit bile acid transporters, and generate reactive oxygen species (ROS); these results were used as inputs into DILIsym, with two alternate sets of parameters used in order to fully explore the sensitivity of model predictions. Verdiperstat dosing protocols up to 600 mg BID were simulated for up to 48 weeks using a simulated population (SimPops) in DILIsym.Verdiperstat was predicted to be safe, with only very rare, mild liver enzyme increases as a potential possibility in highly sensitive individuals. Subsequent Phase 3 clinical trials found that ALT elevations in the verdiperstat treatment group were generally similar to those in the placebo group. This validates the DILIsym simulation results and demonstrates the power of QST modelling to predict the liver safety profile of novel therapeutics.

Quantitative Systems Toxicology Identifies Independent Mechanisms for Hepatotoxicity and Bilirubin Elevations Due to AKR1C3 Inhibitor BAY1128688

BAY1128688 is a selective inhibitor of AKR1C3, investigated recently in a trial that was prematurely terminated due to drug-induced liver injury. These unexpected observations prompted use of the quantitative systems toxicology model, DILIsym, to determine possible mechanisms of hepatotoxicity. Using mechanistic in vitro toxicity data as well as clinical exposure data, DILIsym predicted the potential for BAY1128688 to cause liver toxicity (elevations in serum alanine aminotransferase (ALT)) and elevations in serum bilirubin. Initial simulations overpredicted hepatotoxicity and bilirubin elevations, so the BAY1128688 representation within DILIsym underwent optimization. The liver partition coefficient Kp was altered to align simulated bilirubin elevations with those observed clinically. Altering the mode of bile acid canalicular and basolateral efflux inhibition was necessary to accurately predict ALT elevations. Optimization results support that bilirubin elevations observed early during treatment are due to altered bilirubin metabolism and transporter inhibition, which is independent of liver injury. The modeling further supports that on-treatment ALT elevations result from inhibition of bile acid transporters, particularly the bile salt excretory pump, leading to accumulation of toxic bile acids. The predicted dose-dependent intrinsic hepatotoxicity may increase patient susceptibility to an adaptive immune response, accounting for ALT elevations observed after completion of treatment. These BAY1128688 simulations provide insight into the mechanisms behind hepatotoxicity and bilirubin elevations and may inform the potential risk posed by future compounds.

Advances in the study of acetaminophen-induced liver injury

Acetaminophen (APAP) overdose is a significant cause of drug-induced liver injury and acute liver failure. The diagnosis, screening, and management of APAP-induced liver injury (AILI) is challenging because of the complex mechanisms involved. Starting from the current studies on the mechanisms of AILI, this review focuses on novel findings in the field of diagnosis, screening, and management of AILI. It highlights the current issues that need to be addressed. This review is supposed to summarize the recent research progress and make recommendations for future research.

Cell state-directed therapy - epigenetic modulation of gene transcription demonstrated with a quantitative systems pharmacology model of temozolomide

Cancer therapy continues to be plagued by modest therapeutic advances. This is particularly evident in glioblastoma multiforme (GBM) wherein treatment failures are attributed to intratumoral heterogeneity (ITH), a dynamic process of cell state transitions or plasticity. To address ITH, we introduce the concept of cell state-directed (CSD) therapy through a quantitative systems pharmacology model of temozolomide (TMZ), a cornerstone of GBM drug therapy. The model consisting of multiple modules incorporated an epigenetic-based gene transcription-translation module that enabled CSD therapy. Numerous model simulations were conducted to demonstrate the potential impact of CSD therapy on TMZ activity. The simulations included those based on global sensitivity analyses to identify fragile nodes - MDM2 and XIAP - in the network, and also how an epigenetic modifier (birabresib) could overcome a mechanism of TMZ resistance. The positive results of CSD therapy on TMZ activity supports continued efforts to develop CSD therapy as a new anticancer approach.

Role of Curculigo Orchioides in the Proliferation and Apoptosis of Breast Cancer Michigan Cancer Foundation-7 (MCF-7) Cells Under Network Pharmacology

Based on network pharmacology (NP), the effects of curculigo orchioides (CO) granules alone or CO granules along with tamoxifen on the proliferation and apoptosis (P&A) of human breast cancer (BC) cells were explored. A search database was constructed to obtain the active components (ACs) of CO and related target genes to perform disease mapping, and a BC target map was constructed to perform related pathway enrichment analysis (PEA). The blank control group (CG), estrogen group (EG), and tamoxifen group (TG) were set as controls to observe the effects of CO granules alone and CO granules along with tamoxifen. The PEA showed that the effect of CO on BC may be related to the cooperation of its ACs. The target of action may be related to cell cycle and proliferation, growth factors, metabolic pathways, etc. Different concentrations of CO had different effects, CO granules of various concentrations had no obvious growth-promoting effect (P < 0.05), and so did CO combined with tamoxifen (P > 0.05); high-dose (H-D) CO combined with tamoxifen can induce cell apoptosis (P < 0.05). Through NP, the ACs of CO and related targets of BC were predicted and analyzed. At the same time, it was proved that CO granules are safe in clinical use, and the combined effect of tamoxifen is better in the treatment of estrogen-positive patients, providing reference for the follow-up use of CO in the treatment of BC.

Preclinical models of idiosyncratic drug-induced liver injury (iDILI): Moving towards prediction

Idiosyncratic drug-induced liver injury (iDILI) encompasses the unexpected harms that prescription and non-prescription drugs, herbal and dietary supplements can cause to the liver. iDILI remains a major public health problem and a major cause of drug attrition. Given the lack of biomarkers for iDILI prediction, diagnosis and prognosis, searching new models to predict and study mechanisms of iDILI is necessary. One of the major limitations of iDILI preclinical assessment has been the lack of correlation between the markers of hepatotoxicity in animal toxicological studies and clinically significant iDILI. Thus, major advances in the understanding of iDILI susceptibility and pathogenesis have come from the study of well-phenotyped iDILI patients. However, there are many gaps for explaining all the complexity of iDILI susceptibility and mechanisms. Therefore, there is a need to optimize preclinical human in vitro models to reduce the risk of iDILI during drug development. Here, the current experimental models and the future directions in iDILI modelling are thoroughly discussed, focusing on the human cellular models available to study the pathophysiological mechanisms of the disease and the most used in vivo animal iDILI models. We also comment about in silico approaches and the increasing relevance of patient-derived cellular models.

In-Vitro Approaches to Predict and Study T-Cell Mediated Hypersensitivity to Drugs

Mitigating the risk of drug hypersensitivity reactions is an important facet of a given pharmaceutical, with poor performance in this area of safety often leading to warnings, restrictions and withdrawals. In the last 50 years, efforts to diagnose, manage, and circumvent these obscure, iatrogenic diseases have resulted in the development of assays at all stages of a drugs lifespan. Indeed, this begins with intelligent lead compound selection/design to minimize the existence of deleterious chemical reactivity through exclusion of ominous structural moieties. Preclinical studies then investigate how compounds interact with biological systems, with emphasis placed on modeling immunological/toxicological liabilities. During clinical use, competent and accurate diagnoses are sought to effectively manage patients with such ailments, and pharmacovigilance datasets can be used for stratification of patient populations in order to optimise safety profiles. Herein, an overview of some of the in-vitro approaches to predict intrinsic immunogenicity of drugs and diagnose culprit drugs in allergic patients after exposure is detailed, with current perspectives and opportunities provided.

Application of the DILIsym® Quantitative Systems Toxicology drug-induced liver injury model to evaluate the carcinogenic hazard potential of acetaminophen

In 2019, the California Office of Environmental Health Hazard Assessment (OEHHA) initiated a review of the carcinogenic hazard potential of acetaminophen. The objective of the analysis herein was to inform this review by assessing whether variability in patient baseline characteristics (e.g. baseline glutathione (GSH) levels, pharmacokinetics, and capacity of hepatic antioxidants) leads to potential differences in carcinogenic hazard potential at different dosing schemes: maximum labeled doses of 4 g/day, repeated doses above the maximum labeled dose (>4-12 g/day), and acute overdoses of acetaminophen (>15 g). This was achieved by performing simulations of acetaminophen exposure in thousands of diverse virtual patients scenarios using the DILIsym® Quantitative Systems Toxicology (QST) model. Simulations included assessments of the dose and exposure response for toxicity and mode of cell death based on evaluations of the kinetics of changes of: GSH, N-acetyl-p-benzoquinone-imine (NAPQI), protein adducts, mitochondrial dysfunction, and hepatic cell death. Results support that, at therapeutic doses, cellular GSH binds to NAPQI providing sufficient buffering capacity to limit protein adduct formation and subsequent oxidative stress. Simulations evaluating repeated high-level supratherapeutic exposures or acute overdoses indicate that cell death precedes DNA damage that could result in carcinogenicity and thus acetaminophen does not present a carcinogenicity hazard to humans at any dose.

A Review on Drug-Induced Nephrotoxicity: Pathophysiological Mechanisms, Drug Classes, Clinical Management, and Recent Advances in Mathematical Modeling and Simulation Approaches

A variety of marketed drugs belonging to various therapeutic classes are known to cause nephrotoxicity. Nephrotoxicity can manifest itself in several forms depending on the specific site involved as well as the underlying pathophysiological mechanisms. As they often coexist with other pathophysiological conditions, the steps that can be taken to treat them are often limited. Thus, drug-induced nephrotoxicity remains a major clinical challenge. Prior knowledge of risk factors associated with special patient populations and specific classes of drugs, combined with early diagnosis, therapeutic drug monitoring with dose adjustments, as well as timely prospective treatments are essential to prevent and manage them better. Most incident drug-induced renal toxicity is reversible only if diagnosed at an early stage and treated promptly. Hence, diagnosis at an early stage is the need of the hour to counter it. Significant recent advances in the identification of novel early biomarkers of nephrotoxicity are not beyond limitations. In such a scenario, mathematical modeling and simulation (M&S) approaches may help to better understand and predict toxicities in a clinical setting. This review summarizes pathophysiological mechanisms of drug-induced nephrotoxicity, classes of nephrotoxic drugs, management, prevention, and diagnosis in clinics. Finally, it also highlights some of the recent advancements in mathematical M&S approaches that could be used to better understand and predict drug-induced nephrotoxicity.

Mechanistic Investigations Support Liver Safety of Ubrogepant

Small-molecule calcitonin gene-related peptide (CGRP) receptor antagonists have demonstrated therapeutic efficacy for the treatment of migraine. However, previously investigated CGRP receptor antagonists, telcagepant and MK-3207, were discontinued during clinical development because of concerns about drug-induced liver injury. A subsequent effort to identify novel CGRP receptor antagonists less likely to cause hepatotoxicity led to the development of ubrogepant. The selection of ubrogepant, following a series of mechanistic studies conducted with MK-3207 and telcagepant, was focused on key structural modifications suggesting that ubrogepant was less prone to forming reactive metabolites than previous compounds. The potential for each drug to cause liver toxicity was subsequently assessed using a quantitative systems toxicology approach (DILIsym) that incorporates quantitative assessments of mitochondrial dysfunction, disruption of bile acid homeostasis, and oxidative stress, along with estimates of dose-dependent drug exposure to and within liver cells. DILIsym successfully modeled liver toxicity for telcagepant and MK-3207 at the dosing regimens used in clinical trials. In contrast, DILIsym predicted no hepatotoxicity during treatment with ubrogepant, even at daily doses up to 1000 mg (10-fold higher than the approved clinical dose of 100 mg). These predictions are consistent with clinical trial experience showing that ubrogepant has lower potential to cause hepatotoxicity than has been observed with telcagepant and MK-3207.

Drug Dosing Recommendations for All Patients: A Roadmap for Change

Most drug labels do not contain dosing recommendations for a significant portion of real‐world patients for whom the drug is prescribed. Current label recommendations predominately reflect the population studied in pivotal trials that typically exclude patients who are very young or old, emaciated or morbidly obese, pregnant, or have multiple characteristics likely to influence dosing. As a result, physicians may need to guess the correct dose and regimen for these patients. It is now feasible to provide dose and regimen recommendations for these patients by integrating available scientific knowledge and by utilizing or modifying current regulatory agency‐industry practices. The purpose of this commentary is to explore several factors that should be considered in creating a process that will provide more effective, safe, and timely drug dosing recommendations for most, if not all, patients. These factors include the availability of real‐world data, development of predictive models, experience with the US Food and Drug Administration (FDA)’s pediatric exclusivity program, development of clinical decision software, funding mechanisms like the Prescription Drug Users Fee Act (PDUFA), and harmonization of global regulatory policies. From an examination of these factors, we recommend a relatively simple, efficient expansion of current practices designed to predict, confirm, and continuously improve drug dosing for more patients. We believe implementing these recommendations will benefit patients, payers, industry, and regulatory agencies.

The DILI-sim Initiative: Insights into Hepatotoxicity Mechanisms and Biomarker Interpretation

The drug‐induced liver injury (DILI)‐sim Initiative is a public‐private partnership involving scientists from industry, academia, and the US Food and Drug Administration (FDA). The Initiative uses quantitative systems toxicology (QST) to build and refine a model (DILIsym) capable of understanding and predicting liver safety liabilities in new drug candidates and to optimize interpretation of liver safety biomarkers used in clinical studies. Insights gained to date include the observation that most dose‐dependent hepatotoxicity can be accounted for by combinations of just three mechanisms (oxidative stress, interference with mitochondrial respiration, and alterations in bile acid homeostasis) and the importance of noncompetitive inhibition of bile acid transporters. The effort has also provided novel insight into species and interpatient differences in susceptibility, structure‐activity relationships, and the role of nonimmune mechanisms in delayed idiosyncratic hepatotoxicity. The model is increasingly used to evaluate new drug candidates and several clinical trials are underway that will test the model's ability to prospectively predict liver safety. With more refinement, in the future, it may be possible to use the DILIsym predictions to justify reduction in the size of some clinical trials. The mature model could also potentially assist physicians in managing the liver safety of their patients as well as aid in the diagnosis of DILI.

Understanding Idiosyncratic Toxicity: Lessons Learned from Drug-Induced Liver Injury

Idiosyncratic adverse drug reactions (IADRs) encompass a diverse group of toxicities that can vary by drug and patient. The complex and unpredictable nature of IADRs combined with the fact that they are rare makes them particularly difficult to predict, diagnose, and treat. Common clinical characteristics, the identification of human leukocyte antigen risk alleles, and drug-induced proliferation of lymphocytes isolated from patients support a role for the adaptive immune system in the pathogenesis of IADRs. Significant evidence also suggests a requirement for direct, drug-induced stress, neoantigen formation, and stimulation of an innate response, which can be influenced by properties intrinsic to both the drug and the patient. This Perspective will provide an overview of the clinical profile, mechanisms, and risk factors underlying IADRs as well as new approaches to study these reactions, focusing on idiosyncratic drug-induced liver injury.

Drug-Induced Liver Injury: Highlights of the Recent Literature

Drug-induced liver injury (DILI), herbal-induced liver injury, and herbal and dietary supplement (HDS)-induced liver injury are an important aspect of drug safety. Knowledge regarding responsible drugs, mechanisms, risk factors, and the diagnostic tools to detect liver injury have continued to grow in the past year. This review highlights what we considered the most significant publications from among more than 1800 articles relating to liver injury from medications, herbal products, and dietary supplements in 2017 and 2018. The US Drug-Induced Liver Injury Network (DILIN) prospective study highlighted several areas of ongoing study, including the potential utility of human leukocyte antigens and microRNAs as DILI risk factors and new data on racial differences, the role of alcohol consumption, factors associated with prognosis, and updates on the clinical signatures of autoimmune DILI, thiopurines, and HDS agents. Novel data were also generated from the Spanish and Latin American DILI registries as well as from Chinese and Korean case series. A few new agents causing DILI were added to the growing list in the past 2 years, including sodium-glucose co-transporter-2 inhibitors, as were new aspects of chemotherapy-associated liver injury. A number of cases reported previously described hepatotoxins confirmed via the Roussel Uclaf Causality Assessment Method (RUCAM; e.g., norethisterone, methylprednisolone, glatiramer acetate) and/or the DILIN method (e.g., celecoxib, dimethyl fumarate). Additionally, much work centered on elucidating the pathophysiology of DILI, including the importance of bile salt export pumps and immune-mediated mechanisms. Finally, it must be noted that, while hundreds of new studies described DILI in 2017-2018, the quality of such reports must always be addressed. Björnsson reminds us to remain very critical of the data when addressing the future utility of a study, which is why it is so important to adhere to a standardized method such as RUCAM when determining DILI causality. While drug-induced hepatotoxicity remains a diagnosis of exclusion, the diverse array of publications that appeared in 2017 and 2018 provided important advances in our understanding of DILI, paving the way for our improved ability to make a more definitive diagnosis and risk assessment.

Quantitative Systems Toxicology Analysis of In Vitro Mechanistic Assays Reveals Importance of Bile Acid Accumulation and Mitochondrial Dysfunction in TAK-875-Induced Liver Injury

TAK-875 (fasiglifam), a GPR40 agonist in development for the treatment of type 2 diabetes (T2D), was voluntarily terminated in Phase III trials due to adverse liver effects. The potential mechanisms of TAK-875 toxicity were explored by combining in vitro experiments with quantitative systems toxicology (QST) using DILIsym, a mathematical representation of drug-induced liver injury. In vitro assays revealed that bile acid transporters were inhibited by both TAK-875 and its metabolite, TAK-875-Glu. Experimental data indicated that human bile salt export pump (BSEP) inhibition by TAK-875 was mixed whereas sodium taurocholate co-transporting polypeptide (NTCP) inhibition by TAK-875 was competitive. Furthermore, experimental data demonstrated that both TAK-875 and TAK-875-Glu inhibit mitochondrial electron transport chain (ETC) enzymes. These mechanistic data were combined with a physiologically based pharmacokinetic (PBPK) model constructed within DILIsym to estimate liver exposure of TAK-875 and TAK-875-Glu. In a simulated population (SimPops) constructed to reflect T2D patients, 16/245 (6.5%) simulated individuals developed alanine aminotransferase (ALT) elevations, an incidence similar to that observed with 200 mg daily dosing in clinical trials. Determining the mode of bile acid transporter inhibition (K_i) was critical to accurate predictions. In addition, simulations conducted on a sensitive subset of individuals (SimCohorts) revealed that when either BSEP or ETC inhibition was inactive, ALT elevations were not predicted to occur, suggesting that the two mechanisms operate synergistically to produce the observed clinical response. These results demonstrate how utilizing QST methods to interpret in vitro experimental results can lead to an improved understanding of the clinically relevant mechanisms underlying drug-induced toxicity.

Comparison of the Hepatotoxic Potential of Two Treatments for Autosomal-Dominant Polycystic Kidney DiseaseUsing Quantitative Systems Toxicology Modeling

Purpose

Autosomal-dominant polycystic kidney disease (ADPKD) is an orphan disease with few current treatment options. The vasopressin V₂ receptor antagonist tolvaptan is approved in multiple countries for the treatment of ADPKD, however its use is associated with clinically significant drug-induced liver injury.

Methods

In prior studies, the potential for hepatotoxicity of tolvaptan was correctly predicted using DILIsym®, a quantitative systems toxicology (QST) mathematical model of drug-induced liver injury. In the current study, we evaluated lixivaptan, another proposed ADPKD treatment and vasopressin V₂ receptor antagonist, using DILIsym®. Simulations were conducted that assessed the potential for lixivaptan and its three main metabolites to cause hepatotoxicity due to three injury mechanisms: bile acid accumulation, mitochondrial dysfunction, and oxidative stress generation. Results of these simulations were compared to previously published DILIsym results for tolvaptan.

Results

No ALT elevations were predicted to occur at the proposed clinical dose for lixivaptan, in contrast to previously published simulation results for tolvaptan. As such, lixivaptan was predicted to have a markedly lower risk of hepatotoxicity compared to tolvaptan with respect to the hepatotoxicity mechanisms represented in DILIsym.

Conclusions

These results demonstrate the potential for using QST methods to differentiate drugs in the same class for their potential to cause hepatotoxicity.

Electronic supplementary material

The online version of this article (10.1007/s11095-019-2726-0) contains supplementary material, which is available to authorized users.

Study on the extraction, purification, partial chemical characterization and anti-alcohol liver injury activity of Mori Fructus polysaccharides

Mori Fructus (MF) is a fruit rich in many nutrients.

Drug-Induced Liver Injury: Biomarkers, Requirements, Candidates, and Validation

The hepatotoxicity of drugs is the main cause of drug withdrawal from the pharmaceutical market and interruption of the development of new molecules. Biomarkers are useful in several situations. In case of suspected drug-induced liver injury (DILI), biomarkers can be used to confirm liver damage, its severity, prognosis, confirm drug causality, or define the type of DILI. In this review, we will first present the currently used biomarkers and candidate biomarkers for the future. The current biomarkers are certainly very helpful including with the assistance of diagnostic method such the Roussel Uclaf Causality Assessment Method, but provide a limited information for the early detection of liver injury, the role of specific drug and the prediction of DILI. Some biomarkers are promising but they are not yet available for routine use. Studies are still needed to confirm their interest, particularly in comparison to Roussel Uclaf Causality Assessment Method.

Applications of Quantitative Systems Pharmacology in Model-Informed Drug Discovery: Perspective on Impact and Opportunities

Quantitative systems pharmacology (QSP) approaches have been increasingly applied in the pharmaceutical since the landmark white paper published in 2011 by a National Institutes of Health working group brought attention to the discipline. In this perspective, we discuss QSP in the context of other modeling approaches and highlight the impact of QSP across various stages of drug development and therapeutic areas. We discuss challenges to the field as well as future opportunities.

Current insights in the complexities underlying drug-induced cholestasis

Drug-induced cholestasis (DIC) poses a major challenge to the pharmaceutical industry and regulatory agencies. It causes both drug attrition and post-approval withdrawal of drugs. DIC represents itself as an impaired secretion and flow of bile, leading to the pathological hepatic and/or systemic accumulation of bile acids (BAs) and their conjugate bile salts. Due to the high number of mechanisms underlying DIC, predicting a compound's cholestatic potential during early stages of drug development remains elusive. A profound understanding of the different molecular mechanisms of DIC is, therefore, of utmost importance. Although many knowledge gaps and caveats still exist, it is generally accepted that alterations of certain hepatobiliary membrane transporters and changes in hepatocellular morphology may cause DIC. Consequently, liver models, which represent most of these mechanisms, are valuable tools to predict human DIC. Some of these models, such as membrane-based in vitro models, are exceptionally well-suited to investigate specific mechanisms (i.e. transporter inhibition) of DIC, while others, such as liver slices, encompass all relevant biological processes and, therefore, offer a better representation of the in vivo situation. In the current review, we highlight the principal molecular mechanisms associated with DIC and offer an overview and critical appraisal of the different liver models that are currently being used to predict the cholestatic potential of drugs.

Analyzing the Mechanisms Behind Macrolide Antibiotic-Induced Liver Injury Using Quantitative Systems Toxicology Modeling

PURPOSE

Macrolide antibiotics are commonly prescribed treatments for drug-resistant bacterial infections; however, many macrolides have been shown to cause liver enzyme elevations and one macrolide, telithromycin, has been pulled from the market by its provider due to liver toxicity. This work seeks to assess the mechanisms responsible for the toxicity of macrolide antibiotics.

METHODS

Five macrolides were assessed in in vitro systems designed to test for bile acid transporter inhibition, mitochondrial dysfunction, and oxidative stress. The macrolides were then represented in DILIsym, a quantitative systems pharmacology (QST) model of drug-induced liver injury, placing the in vitro results in context with each compound's predicted liver exposure and known biochemistry.

RESULTS

DILIsym results suggest that solithromycin and clarithromycin toxicity is primarily due to inhibition of the mitochondrial electron transport chain (ETC) while erythromycin toxicity is primarily due to bile acid transporter inhibition. Telithromycin and azithromycin toxicity was not predicted by DILIsym and may be caused by mechanisms not currently incorporated into DILIsym or by unknown metabolite effects.

CONCLUSIONS

The mechanisms responsible for toxicity can be significantly different within a class of drugs, despite the structural similarity among the drugs. QST modeling can provide valuable insight into the nature of these mechanistic differences.

Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology

Two technologies that have emerged in the last decade offer a new paradigm for modern pharmacology, as well as drug discovery and development. Quantitative systems pharmacology (QSP) is a complementary approach to traditional, target-centric pharmacology and drug discovery and is based on an iterative application of computational and systems biology methods with multiscale experimental methods, both of which include models of ADME-Tox and disease. QSP has emerged as a new approach due to the low efficiency of success in developing therapeutics based on the existing target-centric paradigm. Likewise, human microphysiology systems (MPS) are experimental models complementary to existing animal models and are based on the use of human primary cells, adult stem cells, and/or induced pluripotent stem cells (iPSCs) to mimic human tissues and organ functions/structures involved in disease and ADME-Tox. Human MPS experimental models have been developed to address the relatively low concordance of human disease and ADME-Tox with engineered, experimental animal models of disease. The integration of the QSP paradigm with the use of human MPS has the potential to enhance the process of drug discovery and development.

Latest advances in diagnosing and predicting DILI: what was new in 2017?

Drug-induced liver injury (DILI) remains an increasingly recognized cause of hepatotoxicity and liver failure worldwide. In 2017, we continued to learn about predicting, diagnosing, and prognosticating drug hepatotoxicity. Areas covered: In this review, we selected from over 1200 articles from 2017 to synopsize updates in DILI. There were new HLA haplotypes associated with medications including HLA-C0401 and HLA-B*14. There has been continued work with quantitative systems pharmacology, particularly with the DILIsym® initiative, which employs mathematical representations of DILI mechanisms to predict hepatotoxicity in simulated populations. Additionally, knowledge regarding microRNAs (miRNAs) continues to expand. Some new miRNAs this past year include miRNA-223 and miRNA-605. Aside from miRNAs, other biomarkers for diagnosis, prognosis, and even prediction of DILI were explored. Studies on K18, OPN, and MCSFR have correlated DILI and liver-associated death within 6 months. Conversely, a new prognostic panel using apolipoportein-A1 and haptoglobin has been proposed to predict recovery. Further study of CDH5 has also provided researchers a possible new biomarker for prediction and susceptibility to DILI. Expert commentary: Although research on DILI remains quite promising, there is yet to be a reliable, simple method to predict, diagnose, and risk assess this form of hepatotoxicity.

Use of quantitative clinical pharmacology to improve early clinical development success in neurodegenerative diseases

INTRODUCTION

The success rate of pharmaceutical Research & Development (R&D) is much lower compared to other industries such as micro-electronics or aeronautics with the probability of a successful clinical development to approval in central nervous system (CNS) disorders hovering in the single digits (7%). Areas covered: Inspired by adjacent engineering-based industries, we argue that quantitative modeling in CNS R&D might improve success rates. We will focus on quantitative techniques in early clinical development, such as PharmacoKinetic-PharmacoDynamic modeling, clinical trial simulation, model-based meta-analysis and the mechanism-based physiology-based pharmacokinetic modeling, and quantitative systems pharmacology. Expert commentary: Mechanism-based computer modeling rely less on existing clinical datasets, therefore can better generalize than Big Data analytics, including prospectively and quantitatively predicting the clinical outcome of new drugs. More specifically, exhaustive post-hoc analysis of failed trials using individual virtual human trial simulation could illuminate underlying causes such as lack of sufficient functional target engagement, negative pharmacodynamic interactions with comedications and genotypes, and mismatched patient population. These insights are beyond the capacity of artificial intelligence (AI) methods as they are many more possible combinations than subjects. Unlike 'black box' approaches in AI, mechanism-based platforms are transparent and based on biologically sound assumptions that can be interrogated.

Harvesting the promise of AOPs: An assessment and recommendations

The Adverse Outcome Pathway (AOP) concept is a knowledge assembly and communication tool to facilitate the transparent translation of mechanistic information into outcomes meaningful to the regulatory assessment of chemicals. The AOP framework and associated knowledgebases (KBs) have received significant attention and use in the regulatory toxicology community. However, it is increasingly apparent that the potential stakeholder community for the AOP concept and AOP KBs is broader than scientists and regulators directly involved in chemical safety assessment. In this paper we identify and describe those stakeholders who currently-or in the future-could benefit from the application of the AOP framework and knowledge to specific problems. We also summarize the challenges faced in implementing pathway-based approaches such as the AOP framework in biological sciences, and provide a series of recommendations to meet critical needs to ensure further progression of the framework as a useful, sustainable and dependable tool supporting assessments of both human health and the environment. Although the AOP concept has the potential to significantly impact the organization and interpretation of biological information in a variety of disciplines/applications, this promise can only be fully realized through the active engagement of, and input from multiple stakeholders, requiring multi-pronged substantive long-term planning and strategies.

Maximizing the impact of microphysiological systems with in vitro-in vivo translation

Microphysiological systems (MPS) hold promise for improving therapeutic drug approval rates by providing more physiological, human-based, in vitro assays for preclinical drug development activities compared to traditional in vitro and animal models. Here, we first summarize why MPSs are needed in pharmaceutical development, and examine how MPS technologies can be utilized to improve preclinical efforts. We then provide the perspective that the full impact of MPS technologies will be realized only when robust approaches for in vitro-in vivo (MPS-to-human) translation are developed and utilized, and explain how the burgeoning field of quantitative systems pharmacology (QSP) can fill that need.

DeCoST: A New Approach in Drug Repurposing From Control System Theory

In this paper, we propose DeCoST (Drug Repurposing from Control System Theory) framework to apply control system paradigm for drug repurposing purpose. Drug repurposing has become one of the most active areas in pharmacology since the last decade. Compared to traditional drug development, drug repurposing may provide more systematic and significantly less expensive approaches in discovering new treatments for complex diseases. Although drug repurposing techniques rapidly evolve from "one: disease-gene-drug" to "multi: gene, dru" and from "lazy guilt-by-association" to "systematic model-based pattern matching," mathematical system and control paradigm has not been widely applied to model the system biology connectivity among drugs, genes, and diseases. In this paradigm, our DeCoST framework, which is among the earliest approaches in drug repurposing with control theory paradigm, applies biological and pharmaceutical knowledge to quantify rich connective data sources among drugs, genes, and diseases to construct disease-specific mathematical model. We use linear-quadratic regulator control technique to assess the therapeutic effect of a drug in disease-specific treatment. DeCoST framework could classify between FDA-approved drugs and rejected/withdrawn drug, which is the foundation to apply DeCoST in recommending potentially new treatment. Applying DeCoST in Breast Cancer and Bladder Cancer, we reprofiled 8 promising candidate drugs for Breast Cancer ER+ (Erbitux, Flutamide, etc.), 2 drugs for Breast Cancer ER- (Daunorubicin and Donepezil) and 10 drugs for Bladder Cancer repurposing (Zafirlukast, Tenofovir, etc.).

Oxaloacetate Ameliorates Chemical Liver Injury via Oxidative Stress Reduction and Enhancement of Bioenergetic Fluxes

Chemical injury is partly due to free radical lipid peroxidation, which can induce oxidative stress and produce a large number of reactive oxygen species (ROS). Oxaloacetic acid is an important intermediary in the tricarboxylic acid cycle (TCA cycle) and participates in metabolism and energy production. In our study, we found that oxaloacetate (OA) effectively alleviated liver injury which was induced by hydrogen peroxide (H₂O₂) in vitro and carbon tetrachloride (CCl₄) in vivo. OA scavenged ROS, prevented oxidative damage and maintained the normal structure of mitochondria. We further confirmed that OA increased adenosine triphosphate (ATP) by promoting the TCA production cycle and oxidative phosphorylation (OXPHOS). Finally, OA inhibited the mitogen-activated protein kinase (MAPK) and apoptotic pathways by suppressing tumor necrosis factor-α (TNF-α). Our findings reveal a mechanism for OA ameliorating chemical liver injury and suggest a possible implementation for preventing the chemical liver injury.

Advancing Predictive Hepatotoxicity at the Intersection of Experimental, in Silico, and Artificial Intelligence Technologies

Adverse drug reactions, particularly those that result in drug-induced liver injury (DILI), are a major cause of drug failure in clinical trials and drug withdrawals. Hepatotoxicity-mediated drug attrition occurs despite substantial investments of time and money in developing cellular assays, animal models, and computational models to predict its occurrence in humans. Underperformance in predicting hepatotoxicity associated with drugs and drug candidates has been attributed to existing gaps in our understanding of the mechanisms involved in driving hepatic injury after these compounds perfuse and are metabolized by the liver. Herein we assess in vitro, in vivo (animal), and in silico strategies used to develop predictive DILI models. We address the effectiveness of several two- and three-dimensional in vitro cellular methods that are frequently employed in hepatotoxicity screens and how they can be used to predict DILI in humans. We also explore how humanized animal models can recapitulate human drug metabolic profiles and associated liver injury. Finally, we highlight the maturation of computational methods for predicting hepatotoxicity, the untapped potential of artificial intelligence for improving in silico DILI screens, and how knowledge acquired from these predictions can shape the refinement of experimental methods.

Elevation of the serum liver enzyme levels during tolvaptan treatment in patients with autosomal dominant polycystic kidney disease (ADPKD)

BACKGROUND

In 2014, tolvaptan, a vasopressin receptor antagonist, was approved for the treatment of autosomal dominant polycystic kidney disease (ADPKD) in Japan. Clinical trials of tolvaptan revealed frequent occurrence of the liver function abnormality. According to the package insert in Japan, liver function tests should be performed once a month in patients receiving tolvaptan. Furthermore, immediate discontinuation of tolvaptan is recommended in the appearance of liver function abnormalities.

METHODS

Seven patients of ADPKD who was discontinued tolvaptan because of elevation of the serum liver enzyme levels were described in detail and analyzed.

RESULTS

None of them fulfilled the criteria for applicability of Hy's law, which predicts a high risk of severe, potentially fatal, drug-induced liver injury (DILI). In our patients, the rate of increase of total kidney volume (TKV) significantly decreased during tolvaptan administration, but increased after discontinuation; in Cases 1-5, mean annual growth rate of TKV during administration was - 10.15%/year, and during discontinuation was + 23.72%/year. After the serum liver enzyme levels returned to normal range, tolvaptan was resumed in six patients with informed consent. Except one patient, tolvaptan has been continued without increase of the serum liver enzyme levels.

CONCLUSION

In patients with mild elevation of the serum liver enzyme, as is less than three times the upper limit of normal (ULN), resumption of tolvaptan may be considered after the serum liver enzyme levels return to normal range.

Mechanistic Systems Modeling to Improve Understanding and Prediction of Cardiotoxicity Caused by Targeted Cancer Therapeutics

Tyrosine kinase inhibitors (TKIs) are highly potent cancer therapeutics that have been linked with serious cardiotoxicity, including left ventricular dysfunction, heart failure, and QT prolongation. TKI-induced cardiotoxicity is thought to result from interference with tyrosine kinase activity in cardiomyocytes, where these signaling pathways help to control critical processes such as survival signaling, energy homeostasis, and excitation–contraction coupling. However, mechanistic understanding is limited at present due to the complexities of tyrosine kinase signaling, and the wide range of targets inhibited by TKIs. Here, we review the use of TKIs in cancer and the cardiotoxicities that have been reported, discuss potential mechanisms underlying cardiotoxicity, and describe recent progress in achieving a more systematic understanding of cardiotoxicity via the use of mechanistic models. In particular, we argue that future advances are likely to be enabled by studies that combine large-scale experimental measurements with Quantitative Systems Pharmacology (QSP) models describing biological mechanisms and dynamics. As such approaches have proven extremely valuable for understanding and predicting other drug toxicities, it is likely that QSP modeling can be successfully applied to cardiotoxicity induced by TKIs. We conclude by discussing a potential strategy for integrating genome-wide expression measurements with models, illustrate initial advances in applying this approach to cardiotoxicity, and describe challenges that must be overcome to truly develop a mechanistic and systematic understanding of cardiotoxicity caused by TKIs.

Liver Effects of Clinical Drugs Differentiated in Human Liver Slices

Drugs with clinical adverse effects are compared in an ex vivo 3-dimensional multi-cellular human liver slice model. Functional markers of oxidative stress and mitochondrial function, glutathione GSH and ATP levels, were affected by acetaminophen (APAP, 1 mM), diclofenac (DCF, 1 mM) and etomoxir (ETM, 100 μM). Drugs targeting mitochondria more than GSH were dantrolene (DTL, 10 μM) and cyclosporin A (CSA, 10 μM), while GSH was affected more than ATP by methimazole (MMI, 500 μM), terbinafine (TBF, 100 μM), and carbamazepine (CBZ 100 μM). Oxidative stress genes were affected by TBF (18%), CBZ, APAP, and ETM (12%–11%), and mitochondrial genes were altered by CBZ, APAP, MMI, and ETM (8%–6%). Apoptosis genes were affected by DCF (14%), while apoptosis plus necrosis were altered by APAP and ETM (15%). Activation of oxidative stress, mitochondrial energy, heat shock, ER stress, apoptosis, necrosis, DNA damage, immune and inflammation genes ranked CSA (75%), ETM (66%), DCF, TBF, MMI (61%–60%), APAP, CBZ (57%–56%), and DTL (48%). Gene changes in fatty acid metabolism, cholestasis, immune and inflammation were affected by DTL (51%), CBZ and ETM (44%–43%), APAP and DCF (40%–38%), MMI, TBF and CSA (37%–35%). This model advances multiple dosing in a human ex vivo model, plus functional markers and gene profile markers of drug induced human liver side-effects.