The evolution of strategies to minimise the risk of human drug-induced liver injury (DILI) in drug discovery and development

Loss of Ninjurin1 alleviates acetaminophen-induced liver injury via enhancing AMPKα-NRF2 pathway

Acetaminophen (APAP), a widely used pain and fever reliever, is a major contributor to drug-induced liver injury, as its toxic metabolites such as NAPQI induce oxidative stress and hepatic necrosis. While N-acetylcysteine serves as the primary treatment for APAP-induced liver injury (AILI), its efficacy is confined to a narrow window of 8-24 h post-APAP overdose. Beyond this window, liver transplantation emerges as the final recourse, prompting ongoing research to pinpoint novel therapeutic targets aimed at enhancing AILI treatment outcomes. Nerve injury-induced protein 1 (Ninjurin1; Ninj1), initially recognized as an adhesion molecule, has been implicated in liver damage stemming from factors like TNFα and ischemia-reperfusion. Nonetheless, its role in oxidative stress-related liver diseases, including AILI, remains unexplored. In this study, we observed up-regulation of Ninj1 expression in the livers of both human DILI patients and the AILI mouse model. Through the utilization of Ninj1 null mice, hepatocyte-specific Ninj1 KO mice, and myeloid-specific Ninj1 KO mice, we unveiled that the loss of Ninj1 in hepatocytes, rather than myeloid cells, exerts alleviative effects on AILI irrespective of sex dependency. Further in vitro experiments demonstrated that Ninj1 deficiency shields hepatocytes from APAP-induced oxidative stress, mitochondrial dysfunctions, and cell death by bolstering NRF2 stability via activation of AMPKα. In summary, our findings imply that Ninj1 likely plays a role in AILI, and its deficiency confers protection against APAP-induced hepatotoxicity through the AMPKα-NRF2 pathway.

Beyond Cytotoxic Potency: Disposition Features Required to Design ADC Payload

Antibody-drug conjugates (ADCs) have demonstrated impressive clinical usefulness in treating several types of cancer, with the notion of widening of the therapeutic index of the cytotoxic payload through the minimization of the systemic toxicity. Therefore, choosing the most appropriate payload molecule is a particularly important part of the early design phase of ADC development, especially given the highly competitive environment ADCs find themselves in today. The focus of the current review is to describe critical attributes/considerations needed in the discovery and ultimately development of cytotoxic payloads in support of ADC design. In addition to potency, several key dispositional characteristics including solubility, permeability and bystander effect, pharmacokinetics, metabolism, and drug-drug interactions, are described as being an integral part of the integrated activities required in the design of clinically safe and useful ADC therapeutic agents.

In-depth mechanistic analysis including high-throughput RNA sequencing in the prediction of functional and structural cardiotoxicants using hiPSC cardiomyocytes

BACKGROUND

Cardiotoxicity remains one of the most reported adverse drug reactions that lead to drug attrition during pre-clinical and clinical drug development. Drug-induced cardiotoxicity may develop as a functional change in cardiac electrophysiology (acute alteration of the mechanical function of the myocardium) and/or as a structural change, resulting in loss of viability and morphological damage to cardiac tissue.

RESEARCH DESIGN AND METHODS

Non-clinical models with better predictive value need to be established to improve cardiac safety pharmacology. To this end, high-throughput RNA sequencing (ScreenSeq) was combined with high-content imaging (HCI) and Ca2+ transience (CaT) to analyze compound-treated human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs).

RESULTS

Analysis of hiPSC-CMs treated with 33 cardiotoxicants and 9 non-cardiotoxicants of mixed therapeutic indications facilitated compound clustering by mechanism of action, scoring of pathway activities related to cardiomyocyte contractility, mitochondrial integrity, metabolic state, diverse stress responses and the prediction of cardiotoxicity risk. The combination of ScreenSeq, HCI and CaT provided a high cardiotoxicity prediction performance with 89% specificity, 91% sensitivity and 90% accuracy.

CONCLUSIONS

Overall, this study introduces mechanism-driven risk assessment approach combining structural, functional and molecular high-throughput methods for pre-clinical risk assessment of novel compounds.

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.

Control compounds for preclinical drug-induced liver injury assessment: Consensus-driven systematic review by the ProEuroDILI network

BACKGROUND & AIMS

Idiosyncratic drug-induced liver injury (DILI) is a complex and unpredictable event caused by drugs, and herbal or dietary supplements. Early identification of human hepatotoxicity at preclinical stages remains a major challenge, in which the selection of validated in vitro systems and test drugs has a significant impact. In this systematic review, we analyzed the compounds used in hepatotoxicity assays and established a list of DILI-positive and -negative control drugs for validation of in vitro models of DILI, supported by literature and clinical evidence and endorsed by an expert committee from the COST Action ProEuroDILI Network (CA17112).

METHODS

Following 2020 PRISMA guidelines, original research articles focusing on DILI which used in vitro human models and performed at least one hepatotoxicity assay with positive and negative control compounds, were included. Bias of the studies was assessed by a modified 'Toxicological Data Reliability Assessment Tool'.

RESULTS

A total of 51 studies (out of 2,936) met the inclusion criteria, with 30 categorized as reliable without restrictions. Although there was a broad consensus on positive compounds, the selection of negative compounds lacked clarity. 2D monoculture, short exposure times and cytotoxicity endpoints were the most tested, although there was no consensus on drug concentrations.

CONCLUSIONS

Extensive analysis highlighted the lack of agreement on control compounds for in vitro DILI assessment. Following comprehensive in vitro and clinical data analysis together with input from the expert committee, an evidence-based consensus-driven list of 10 positive and negative control drugs for validation of in vitro models of DILI is proposed.

IMPACT AND IMPLICATIONS

Prediction of human toxicity early in the drug development process remains a major challenge, necessitating the development of more physiologically relevant liver models and careful selection of drug-induced liver injury (DILI)-positive and -negative control drugs to better predict the risk of DILI associated with new drug candidates. Thus, this systematic study has crucial implications for standardizing the validation of new in vitro models of DILI. By establishing a consensus-driven list of positive and negative control drugs, the study provides a scientifically justified framework for enhancing the consistency of preclinical testing, thereby addressing a significant challenge in early hepatotoxicity identification. Practically, these findings can guide researchers in evaluating safety profiles of new drugs, refining in vitro models, and informing regulatory agencies on potential improvements to regulatory guidelines, ensuring a more systematic and efficient approach to drug safety assessment.

In vitro-in silico pharmacology and chemistry of Stercularin, isolated from Sterculia diversifolia

Stercularin is a coumarin, isolated from the ethyl acetate fraction of stem bark and leaves of S. diversifolia. Pharmacologically it is active against cancer, diabetes, and inflammation etc. The molecule is further screened for in vitro pharmacological activities. In addition, a detailed description on its drug likeness and pharmacokinetic profile has been established to further explore its fate as a drug candidate. Stercularin exhibited antiglycation, immunomodulatory, and leishmanicidal activity in three different in vitro models. The IC50 values obtained in these three assays were 80.22 ± 0.46 mg/ml, 12.8 ± 1.6 μg/ml, and 8.32 ± 0.42 μg/ml, respectively. In case of drug likeness evaluation, Stercularin has acceptable physicochemical properties and compliant with major drug likeness descriptors i.e., Lipinski rule, Pfizer rule, GSK rule, and "golden triangle". Accepting Lipinski rule implies the oral drug development of Stercularin. Pharmacokinetically, Stercularin is permeable to Caco-2 and MDCK cell lines. 'Boiled-egg' plot suggest intestinal route of absorption, blood brain barrier nonpermeating, and not affected by p-glycoprotein. Stercularin has high plasma protein binding with low free fraction circulating in the plasma. Stercularin proved to be the substrate and/or inhibitor of CYP 450 system with a moderate half-life and clearance rate to allow flexible dosing regimen. Finally, slight risk of toxicity exists for Stercularin, but not being limiting factors of drug knock out. A nature isolated Stercularin possess pharmacological activities and is predicted to have acceptable pharmacokinetic profile. Further drug development and in vivo studies are desirable for optimization.

Utility of human cytochrome P450 inhibition data in the assessment of drug-induced liver injury

1. Drug-induced liver injury (DILI) is a major cause of drug development discontinuation and drug withdrawal from the market, but there are no golden standard methods for DILI risk evaluation. Since we had found the association between DILI and CYP1A1 or CYP1B1 inhibition, we further evaluated the utility of cytochrome P450 (P450) inhibition assay data for DILI risk evaluation using decision tree analysis.2. The inhibitory activity of drugs with DILI concern (DILI drugs) and no DILI concern (no-DILI drugs) against 10 human P450s was assessed using recombinant enzymes and luminescent substrates. The drugs were also subjected to cytotoxicity assays and high-content analysis using HepG2 cells. Molecular descriptors were calculated by alvaDesc.3. Decision tree analysis was performed with the data obtained as variables with or without P450-inhibitory activity to discriminate between DILI drugs and no-DILI drugs. The accuracy was significantly higher when P450-inhibitory activity was included. After the decision tree discrimination, the drugs were further discriminated with the P450-inhibitory activity. The results demonstrated that many false-positive and false-negative drugs were correctly discriminated by using the P450 inhibition data.4. These results suggest that P450 inhibition assay data are useful for DILI risk evaluation.

Understanding exosomes: Part 2-Emerging leaders in regenerative medicine

Exosomes are the smallest subset of extracellular signaling vesicles secreted by most cells with the ability to communicate with other tissues and cell types over long distances. Their use in regenerative medicine has gained tremendous momentum recently due to their ability to be utilized as therapeutic options for a wide array of diseases/conditions. Over 5000 publications are currently being published yearly on this topic, and this number is only expected to dramatically increase as novel therapeutic strategies continue to be developed. Today exosomes have been applied in numerous contexts including neurodegenerative disorders (Alzheimer's disease, central nervous system, depression, multiple sclerosis, Parkinson's disease, post-traumatic stress disorders, traumatic brain injury, peripheral nerve injury), damaged organs (heart, kidney, liver, stroke, myocardial infarctions, myocardial infarctions, ovaries), degenerative processes (atherosclerosis, diabetes, hematology disorders, musculoskeletal degeneration, osteoradionecrosis, respiratory disease), infectious diseases (COVID-19, hepatitis), regenerative procedures (antiaging, bone regeneration, cartilage/joint regeneration, osteoarthritis, cutaneous wounds, dental regeneration, dermatology/skin regeneration, erectile dysfunction, hair regrowth, intervertebral disc repair, spinal cord injury, vascular regeneration), and cancer therapy (breast, colorectal, gastric cancer and osteosarcomas), immune function (allergy, autoimmune disorders, immune regulation, inflammatory diseases, lupus, rheumatoid arthritis). This scoping review is a first of its kind aimed at summarizing the extensive regenerative potential of exosomes over a broad range of diseases and disorders.

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.

AI/ML Models to Predict the Severity of Drug-Induced Liver Injury for Small Molecules

Drug-induced liver injury (DILI), believed to be a multifactorial toxicity, has been a leading cause of attrition of small molecules during discovery, clinical development, and postmarketing. Identification of DILI risk early reduces the costs and cycle times associated with drug development. In recent years, several groups have reported predictive models that use physicochemical properties or in vitro and in vivo assay endpoints; however, these approaches have not accounted for liver-expressed proteins and drug molecules. To address this gap, we have developed an integrated artificial intelligence/machine learning (AI/ML) model to predict DILI severity for small molecules using a combination of physicochemical properties and off-target interactions predicted in silico. We compiled a data set of 603 diverse compounds from public databases. Among them, 164 were categorized as Most DILI (M-DILI), 245 as Less DILI (L-DILI), and 194 as No DILI (N-DILI) by the FDA. Six machine learning methods were used to create a consensus model for predicting the DILI potential. These methods include k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), Naïve Bayes (NB), artificial neural network (ANN), logistic regression (LR), weighted average ensemble learning (WA) and penalized logistic regression (PLR). Among the analyzed ML methods, SVM, RF, LR, WA, and PLR identified M-DILI and N-DILI compounds, achieving a receiver operating characteristic area under the curve of 0.88, sensitivity of 0.73, and specificity of 0.9. Approximately 43 off-targets, along with physicochemical properties (fsp³, log S, basicity, reactive functional groups, and predicted metabolites), were identified as significant factors in distinguishing between M-DILI and N-DILI compounds. The key off-targets that we identified include: PTGS1, PTGS2, SLC22A12, PPARγ, RXRA, CYP2C9, AKR1C3, MGLL, RET, AR, and ABCC4. The present AI/ML computational approach therefore demonstrates that the integration of physicochemical properties and predicted on- and off-target biological interactions can significantly improve DILI predictivity compared to chemical properties alone.

Physicochemical Properties, Drug Likeness, ADMET, DFT Studies and in vitro antioxidant activity of Oxindole Derivatives

Poor pharmacokinetic and safety profiles create significant hurdles in the drug development process. This work focuses on a detailed understanding of drug discovery interplay among physicochemical, pharmacokinetic, toxicity endpoints, and antioxidant properties of oxindole derivatives. DFT compıutations were also performed at B3LYP/6-311G** level to evaluate the physicochemical properties, global reactivity features, and intramolecular interactions. The BOILED-Egg pharmacokinetic model envisaged gastrointestinal absorption, blood-brain barrier penetration, and no interaction with p-glycoprotein for compounds C1 and C2. The physicochemical evaluation revealed that C1 possesses superior drug-like properties fit for oral absorption. Both derivatives were predicted to have high plasma protein binding, efficient distribution, and inhibiting CYP 450 major isoforms but serve as substrates only for a few of them. Both molecules have mild to moderate clearance rates. Out of ten toxicity parameters, only hepatotoxicity was predicted. DFT results implied that the meta position of the -OH group made the possibility of charge transfer greater than -para positioned -OH, due to the ΔN_max (eV) values of molecules C1 and C2 being calculated at 2.596 and 2.477, respectively. Both C1 and C2 exhibited a concentration dependant DPPH and ABTS radical scavenging activity. The chemical structure-physicochemical-pharmacokinetic relationship identified the meta position as the favorite for the electron-withdrawing hydroxyl group. This provides useful insight to medicinal chemists to design 6-chlorooxindole derivatives with an acceptable drug-like and pharmacokinetic property.

Administration of Secretome Derived from Human Mesenchymal Stem Cells Induces Hepatoprotective Effects in Models of Idiosyncratic Drug-Induced Liver Injury Caused by Amiodarone or Tamoxifen

Drug-induced liver injury (DILI) is one of the leading causes of acute liver injury. While many factors may contribute to the susceptibility to DILI, obese patients with hepatic steatosis are particularly prone to suffer DILI. The secretome derived from mesenchymal stem cell has been shown to have hepatoprotective effects in diverse in vitro and in vivo models. In this study, we evaluate whether MSC secretome could improve DILI mediated by amiodarone (AMI) or tamoxifen (TMX). Hepatic HepG2 and HepaRG cells were incubated with AMI or TMX, alone or with the secretome of MSCs obtained from human adipose tissue. These studies demonstrate that coincubation of AMI or TMX with MSC secretome increases cell viability, prevents the activation of apoptosis pathways, and stimulates the expression of priming phase genes, leading to higher proliferation rates. As proof of concept, in a C57BL/6 mouse model of hepatic steatosis and chronic exposure to AMI, the MSC secretome was administered endovenously. In this study, liver injury was significantly attenuated, with a decrease in cell infiltration and stimulation of the regenerative response. The present results indicate that MSC secretome administration has the potential to be an adjunctive cell-free therapy to prevent liver failure derived from DILI caused by TMX or AMI.

Discovery of new inhibitors of Mycobacterium tuberculosis EPSP synthase - A computational study

Tuberculosis (TB) is a highly infectious disease caused by the pathogen Mycobacterium tuberculosis (Mtb). EPSP Synthase (MtEPSPS), the enzyme responsible for the sixth step of the shikimate pathway, is a potential target for the development of new drugs for the treatment of TB, as it is essential in mycobacteria but absent in humans. In this work, we performed virtual screening using sets of molecules from two databases and three crystallographic structures of MtEPSPS. The initial hits obtained from molecular docking were filtered based on predicted binding affinity and interactions with binding site residues. Subsequently, molecular dynamics simulations were carried out to analyze the stability of protein-ligand complexes. We have found that MtEPSPS forms stable interactions with several candidates, including already approved pharmaceutical drugs such as Conivaptan and Ribavirin monophosphate. In particular, Conivaptan had the highest estimated binding affinity with the open conformation of the enzyme. The complex formed between MtEPSPS and Ribavirin monophosphate was also energetically stable as shown by RMSD, Rg and FEL analyses, and the ligand was stabilized by hydrogen bonds with important residues of the binding site. The findings reported in this work could serve as the basis of promising scaffolds for the discovery, design, and development of new anti-TB drugs.

Three-Dimensional Spheroids for Cancer Research

In vitro cell culture is one of the most widely used tools used today for increasing our understanding of various things such as protein production, mechanisms of drug action, tissue engineering, and overall cellular biology. For the past decades, however, cancer researchers have relied heavily on conventional two-dimensional (2D) monolayer culture techniques to test a variety of aspects of cancer research ranging from the cytotoxic effects of antitumor drugs to the toxicity of diagnostic dyes and contact tracers. However, many promising cancer therapies have either weak or no efficacy in real-life conditions, therefore delaying or stopping altogether their translating to the clinic. This is, in part, due to the reductionist 2D cultures used to test these materials, which lack appropriate cell-cell contacts, have altered signaling, do not represent the natural tumor microenvironment, and have different drug responses, due to their reduced malignant phenotype when compared to real in vivo tumors. With the most recent advances, cancer research has moved into 3D biological investigation. Three-dimensional (3D) cultures of cancer cells not only recapitulate the in vivo environment better than their 2D counterparts, but they have, in recent years, emerged as a relatively low-cost and scientifically accurate methodology for studying cancer. In this chapter, we highlight the importance of 3D culture, specifically 3D spheroid culture, reviewing some key methodologies for forming 3D spheroids, discussing the experimental tools that can be used in conjunction with 3D spheroids and finally their applications in cancer research.

Computational pharmacology and computational chemistry of 4-hydroxyisoleucine: Physicochemical, pharmacokinetic, and DFT-based approaches

Computational pharmacology and chemistry of drug-like properties along with pharmacokinetic studies have made it more amenable to decide or predict a potential drug candidate. 4-Hydroxyisoleucine is a pharmacologically active natural product with prominent antidiabetic properties. In this study, ADMETLab 2.0 was used to determine its important drug-related properties. 4-Hydroxyisoleucine is compliant with important drug-like physicochemical properties and pharma giants' drug-ability rules like Lipinski's, Pfizer, and GlaxoSmithKline (GSK) rules. Pharmacokinetically, it has been predicted to have satisfactory cell permeability. Blood-brain barrier permeation may add central nervous system (CNS) effects, while a very slight probability of being CYP2C9 substrate exists. None of the well-known toxicities were predicted in silico, being congruent with wet lab results, except for a "very slight risk" for respiratory toxicity predicted. The molecule is non ecotoxic as analyzed with common indicators such as bioconcentration and LC₅₀ for fathead minnow and daphnia magna. The toxicity parameters identified 4-hydroxyisoleucine as non-toxic to androgen receptors, PPAR-γ, mitochondrial membrane receptor, heat shock element, and p53. However, out of seven parameters, not even a single toxicophore was found. The density functional theory (DFT) study provided support to the findings obtained from drug-like property predictions. Hence, it is a very logical approach to proceed further with a detailed pharmacokinetics and drug development process for 4-hydroxyisoleucine.

The Potential Role of Metabolomics in Drug-Induced Liver Injury (DILI) Assessment

Drug-induced liver injury (DILI) is one of the most frequent adverse clinical reactions and a relevant cause of morbidity and mortality. Hepatotoxicity is among the major reasons for drug withdrawal during post-market and late development stages, representing a major concern to the pharmaceutical industry. The current biochemical parameters for the detection of DILI are based on enzymes (alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transpeptidase (GGT), alkaline phosphatase (ALP)) and bilirubin serum levels that are not specific of DILI and therefore there is an increasing interest on novel, specific, DILI biomarkers discovery. Metabolomics has emerged as a tool with a great potential for biomarker discovery, especially in disease diagnosis, and assessment of drug toxicity or efficacy. This review summarizes the multistep approaches in DILI biomarker research and discovery based on metabolomics and the principal outcomes from the research performed in this field. For that purpose, we have reviewed the recent scientific literature from PubMed, Web of Science, EMBASE, and PubTator using the terms “metabolomics”, “DILI”, and “humans”. Despite the undoubted contribution of metabolomics to our understanding of the underlying mechanisms of DILI and the identification of promising novel metabolite biomarkers, there are still some inconsistencies and limitations that hinder the translation of these research findings into general clinical practice, probably due to the variability of the methods used as well to the different mechanisms elicited by the DILI causing agent.

Setup of human liver-chips integrating 3D models, microwells and a standardized microfluidic platform as proof-of-concept study to support drug evaluation

Human 3D liver microtissues/spheroids are powerful in vitro models to study drug-induced liver injury (DILI) but the small number of cells per spheroid limits the models' usefulness to study drug metabolism. In this work, we scale up the number of spheroids on both a plate and a standardized organ-chip platform by factor 100 using a basic method which requires only limited technical expertise. We successfully generated up to 100 spheroids using polymer-coated microwells in a 96-well plate (= liver-plate) or organ-chip (= liver-chip). Liver-chips display a comparable cellular CYP3A4 activity, viability, and biomarker expression as liver spheroids for at least one week, while liver-plate cultures display an overall reduced hepatic functionality. To prove its applicability to drug discovery and development, the liver-chip was used to test selected reference compounds. The test system could discriminate toxicity of the DILI-positive compound tolcapone from its less hepatotoxic structural analogue entacapone, using biochemical and morphological readouts. Following incubation with diclofenac, the liver-chips had an increased metabolite formation compared to standard spheroid cultures. In summary, we generated a human liver-chip model using a standardized organ-chip platform which combines up to 100 spheroids and can be used for the evaluation of both drug safety and metabolism.

Early Drug-Induced Liver Injury (DILI) Risk Screening: "Free", as good as it gets

For all the promise of and need for clinical drug-induced liver injury (DILI) risk screening systems, demonstrating the predictive value of these systems versus readily available physicochemical properties and inherent dosing information has not been thoroughly evaluated. Therefore, we utilized a systematic approach to evaluate the predictive value of in vitro safety assays including Bile Salt Export Pump (BSEP) transporter inhibition and cytotoxicity in HepG2 and transformed human liver epithelial (THLE) along with physicochemical properties. We also evaluated the predictive value of in vitro ADME assays including hepatic partition coefficient (Kp) and its unbound counterpart since they provide insight on hepatic accumulation potential. The datasets comprised of 569 marketed drugs with FDA DILIrank annotation (Most vs Less/None), dose and physicochemical information, 384 drugs with Kp and plasma protein binding data, and 279 drugs with safety assay data. For each dataset and combination of input parameters, we developed random forest machine learning models and measured model performance using the receiver operator characteristic area-under-the-curve (ROC AUC). The median ROC AUC across the various data and parameters sets ranged from 0.67 to 0.77 with little evidence of additive predictivity when including safety or ADME assay data. Subsequent machine learning models consistently demonstrated daily dose, fraction sp3 or ionization, and cLogP/D inputs produced the best, simplest model for predicting clinical DILI risk with an ROC AUC of 0.75. This systematic framework should be used for future assay predictive value assessments and highlights the need for continued improvements to clinical DILI risk annotation.

Highly Sensitive Two-Photon Lipid Droplet Tracker for In Vivo Screening of Drug Induced Liver Injury

Lipid droplets (LDs) are lipid-abundant organelles found in most cell lines and primarily consist of neutral lipids. They serve as a repository of various lipids and are associated with many cellular metabolic processes, including energy storage, membrane synthesis, and protein homeostasis. LDs are prominent in a variety of diseases related to lipid regulation, including obesity, fatty liver disease, diabetes, and atherosclerosis. To monitor LD dynamics in live samples, we developed a highly selective two-photon fluorescent tracker for LDs (LD1). It exhibited outstanding sensitivity with a remarkable two-photon-action cross section (Φδ_max > 600 GM), photostability, and low cytotoxicity. In human hepatocytes and in vivo mouse liver tissue imaging, LD1 showed very bright fluorescence with high LD selectivity and minimized background signal to evaluate the stages of nonalcoholic fatty liver disease. Interestingly, we demonstrated that the liver sinusoid morphology became narrower with increasing LD size and visualized the dynamics including fusion of the LDs in vivo. Moreover, real-time and dual-color TPM imaging with LD1 and a two-photon lysosome tracker could be a useful predictive screening tool in the drug development process to monitor impending drug-induced liver injury inducing drug candidates.

In vitro cell-based models of drug-induced hepatotoxicity screening: progress and limitation

Drug-induced liver injury (DILI) is one of the major causes of post-approval withdrawal of therapeutics. As a result, there is an increasing need for accurate predictive in vitro assays that reliably detect hepatotoxic drug candidates while reducing drug discovery time, costs, and the number of animal experiments. In vitro hepatocyte-based research has led to an improved comprehension of the underlying mechanisms of chemical toxicity and can assist the prioritization of therapeutic choices with low hepatotoxicity risk. Therefore, several in vitro systems have been generated over the last few decades. This review aims to comprehensively present the development and validation of 2D (two-dimensional) and 3D (three-dimensional) culture approaches on hepatotoxicity screening of compounds and highlight the main factors affecting predictive power of experiments. To this end, we first summarize some of the recognized hepatotoxicity mechanisms and related assays used to appraise DILI mechanisms and then discuss the challenges and limitations of in vitro models.

New Perspectives to Improve Mesenchymal Stem Cell Therapies for Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is one of the leading causes of acute liver injury. Many factors may contribute to the susceptibility of patients to this condition, making DILI a global medical problem that has an impact on public health and the pharmaceutical industry. The use of mesenchymal stem cells (MSCs) has been at the forefront of regenerative medicine therapies for many years, including MSCs for the treatment of liver diseases. However, there is currently a huge gap between these experimental approaches and their application in clinical practice. In this concise review, we focus on the pathophysiology of DILI and highlight new experimental approaches conceived to improve cell-based therapy by the in vitro preconditioning of MSCs and/or the use of cell-free products as treatment for this liver condition. Finally, we discuss the advantages of new approaches, but also the current challenges that must be addressed in order to develop safer and more effective procedures that will allow cell-based therapies to reach clinical practice, enhancing the quality of life and prolonging the survival time of patients with DILI.

Machine Learning from Omics Data

Machine learning (ML) already accelerates discoveries in many scientific fields and is the driver behind several new products. Recently, growing sample sizes enabled the use of ML approaches in larger omics studies. This work provides a guide through a typical analysis of an omics dataset using ML. As an example, this chapter demonstrates how to build a model predicting Drug-Induced Liver Injury based on transcriptomics data contained in the LINCS L1000 dataset. Each section covers best practices and pitfalls starting from data exploration and model training including hyperparameter search to validation and analysis of the final model. The code to reproduce the results is available at https://github.com/Evotec-Bioinformatics/ml-from-omics .

Combatting AMR: A molecular approach to the discovery of potent and non-toxic rhenium complexes active against C. albicans-MRSA co-infection

Antimicrobial resistance (AMR) is a major emerging threat to public health, causing serious issues in the successful prevention and treatment of persistent diseases. While the problem escalates, lack of financial incentive has lead major pharmaceutical companies to interrupt their antibiotic drug discovery programs. The World Health Organisation (WHO) has called for novel solutions outside the traditional development pathway, with emphasis on new classes of active compounds with non-classical mechanisms of action. Metal complexes are an untapped source of antibiotic potential owing to unique modes of action and a wider range of three-dimensional geometries as compared to purely organic compounds. In this study, we present the antimicrobial and antifungal efficacy of a family of rhenium tricarbonyl diimine complexes with varying ligands, charge and lipophilicity. Our study allowed the identification of potent and non-toxic complexes active in vivo against S. aureus infections at MIC doses as low as 300 ng/mL, as well as against C. albicans-MRSA mixed co-infection. The compounds are capable of suppressing the C. albicans morphogenetic yeast-to-hyphal transition, eradicating fungal-S. aureus co-infection, while showing no sign of cardio-, hepato-, hematotoxiciy or teratogenicity.

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.

Generation of a Transgenic Zebrafish Line for In Vivo Assessment of Hepatic Apoptosis

Hepatic apoptosis is involved in a variety of pathophysiologic conditions in the liver, including hepatitis, steatosis, and drug-induced liver injury. The development of easy-to-perform and reliable in vivo assays would thus greatly enhance the efforts to understand liver diseases and identify associated genes and potential drugs. In this study, we developed a transgenic zebrafish line that was suitable for the assessment of caspase 3 activity in the liver by using in vivo fluorescence imaging. The larvae of transgenic zebrafish dominantly expressed Casper3GR in the liver under control of the promoter of the phosphoenolpyruvate carboxykinase 1 gene. Casper3GR is composed of two fluorescent proteins, tagGFP and tagRFP, which are connected via a peptide linker that can be cleaved by activated caspase 3. Under tagGFP excitation conditions in zebrafish that were exposed to the well-characterized hepatotoxicant isoniazid, we detected increased and decreased fluorescence associated with tagGFP and tagRFP, respectively. This result suggests that isoniazid activates caspase 3 in the zebrafish liver, which digests the linker between tagGFP and tagRFP, resulting in a reduction in the Förster resonance energy transfer to tagRFP upon tagGFP excitation. We also detected isoniazid-induced inhibition of caspase 3 activity in zebrafish that were treated with the hepatoprotectants ursodeoxycholic acid and obeticholic acid. The transgenic zebrafish that were developed in this study could be a powerful tool for identifying both hepatotoxic and hepatoprotective drugs, as well as for analyzing the effects of the genes of interest to hepatic apoptosis.

A decade of machine learning-based predictive models for human pharmacokinetics: Advances and challenges

Traditionally, in vitro and in vivo methods are useful for estimating human pharmacokinetics (PK) parameters; however, it is impractical to perform these complex and expensive experiments on a large number of compounds. The integration of publicly available chemical, or medical Big Data and artificial intelligence (AI)-based approaches led to qualitative and quantitative prediction of human PK of a candidate drug. However, predicting drug response with these approaches is challenging, partially because of the adaptation of algorithmic and limitations related to experimental data. In this report, we provide an overview of machine learning (ML)-based quantitative structure-activity relationship (QSAR) models used in the assessment or prediction of PK values as well as databases available for obtaining such data.

Pharmacokinetics-On-a-Chip: In Vitro Microphysiological Models for Emulating of Drugs ADME

Despite many ongoing efforts across the full spectrum of pharmaceutical and biotech industries, drug development is still a costly undertaking that involves a high risk of failure during clinical trials. Animal models played vital roles in understanding the mechanism of human diseases. However, the use of these models has been a subject of heated debate, particularly due to ethical matters and the inevitable pathophysiological differences between animals and humans. Current in vitro models lack the sufficient functionality and predictivity of human pharmacokinetics and toxicity, therefore, are not capable to fully replace animal models. The recent development of micro-physiological systems has shown great potential as indispensable tools for recapitulating key physiological parameters of humans and providing in vitro methods for predicting the pharmacokinetics and pharmacodynamics in humans. Integration of Absorption, Distribution, Metabolism, and Excretion (ADME) processes within one close in vitro system is a paramount development that would meet important unmet pharmaceutical industry needs. In this review paper, synthesis of the ADME-centered organ-on-a-chip technology is systemically presented from what is achieved to what needs to be done, emphasizing the requirements of in vitro models that meet industrial needs in terms of the structure and functions.

Tissue-Specific Microparticles Improve Organoid Microenvironment for Efficient Maturation of Pluripotent Stem-Cell-Derived Hepatocytes

Liver organoids (LOs) are receiving considerable attention for their potential use in drug screening, disease modeling, and transplantable constructs. Hepatocytes, as the key component of LOs, are isolated from the liver or differentiated from pluripotent stem cells (PSCs). PSC-derived hepatocytes are preferable because of their availability and scalability. However, efficient maturation of the PSC-derived hepatocytes towards functional units in LOs remains a challenging subject. The incorporation of cell-sized microparticles (MPs) derived from liver extracellular matrix (ECM), could provide an enriched tissue-specific microenvironment for further maturation of hepatocytes inside the LOs. In the present study, the MPs were fabricated by chemical cross-linking of a water-in-oil dispersion of digested decellularized sheep liver. These MPs were mixed with human PSC-derived hepatic endoderm, human umbilical vein endothelial cells, and mesenchymal stromal cells to produce homogenous bioengineered LOs (BLOs). This approach led to the improvement of hepatocyte-like cells in terms of gene expression and function, CYP activities, albumin secretion, and metabolism of xenobiotics. The intraperitoneal transplantation of BLOs in an acute liver injury mouse model led to an enhancement in survival rate. Furthermore, efficient hepatic maturation was demonstrated after ex ovo transplantation. In conclusion, the incorporation of cell-sized tissue-specific MPs in BLOs improved the maturation of human PSC-derived hepatocyte-like cells compared to LOs. This approach provides a versatile strategy to produce functional organoids from different tissues and offers a novel tool for biomedical applications.

Converging global crises are forcing the rapid adoption of disruptive changes in drug discovery

Spiralling research costs combined with urgent pressures from the Coronavirus 2019 (COVID-19) pandemic and the consequences of climate disruption are forcing changes in drug discovery. Increasing the predictive power of in vitro human assays and using them earlier in discovery would refocus resources on more successful research strategies and reduce animal studies. Increasing laboratory automation enables effective social distancing for researchers, while allowing integrated data capture from remote laboratory networks. Such disruptive changes would not only enable more cost-effective drug discovery, but could also reduce the overall carbon footprint of discovering new drugs.

Characterizing the reproducibility in using a liver microphysiological system for assaying drug toxicity, metabolism, and accumulation

Liver microphysiological systems (MPSs) are promising models for predicting hepatic drug effects. Yet, after a decade since their introduction, MPSs are not routinely used in drug development due to lack of criteria for ensuring reproducibility of results. We characterized the feasibility of a liver MPS to yield reproducible outcomes of experiments assaying drug toxicity, metabolism, and intracellular accumulation. The ability of the liver MPS to reproduce hepatotoxic effects was assessed using trovafloxacin, which increased lactate dehydrogenase (LDH) release and reduced cytochrome P450 3A4 (CYP3A4) activity. These observations were made in two test sites and with different batches of Kupffer cells. Upon culturing equivalent hepatocytes in the MPS, spheroids, and sandwich cultures, differences between culture formats were detected in CYP3A4 activity and albumin production. Cells in all culture formats exhibited different sensitivities to hepatotoxicant exposure. Hepatocytes in the MPS were more functionally stable than those of other culture platforms, as CYP3A4 activity and albumin secretion remained prominent for greater than 18 days in culture, whereas functional decline occurred earlier in spheroids (12 days) and sandwich cultures (7 days). The MPS was also demonstrated to be suitable for metabolism studies, where CYP3A4 activity, troglitazone metabolites, diclofenac clearance, and intracellular accumulation of chloroquine were quantified. To ensure reproducibility between studies with the MPS, the combined use of LDH and CYP3A4 assays were implemented as quality control metrics. Overall results indicated that the liver MPS can be used reproducibly in general drug evaluation applications. Study outcomes led to general considerations and recommendations for using liver MPSs. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? Microphysiological systems (MPSs) have been designed to recreate organ- or tissue-specific characteristics of extracellular microenvironments that enhance the physiological relevance of cells in culture. Liver MPSs enable long-lasting and stable culture of hepatic cells by culturing them in three-dimensions and exposing them to fluid flow. WHAT QUESTION DID THIS STUDY ADDRESS? What is the functional performance relative to other cell culture platforms and the reproducibility of a liver MPS for assessing drug development and evaluation questions, such as toxicity, metabolism, and pharmacokinetics? WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE? The liver MPS systematically detected the toxicity of trovafloxacin. When compared with spheroids and sandwich cultures, this system had a more stable function and different sensitivity to troglitazone, tamoxifen, and digoxin. Quantifying phase II metabolism of troglitazone and intracellular accumulation of chloroquine demonstrated the potential use of the liver MPS for studying drug metabolism and pharmacokinetics. Quality control criteria for assessing chip function were key for reliably using the liver MPS. HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE? Due to its functional robustness and physiological relevance (3D culture, cells expose to fluid flow and co-culture of different cell types), the liver MPS can, in a reproducible manner: (i) detect inflammatory-induced drug toxicity, as demonstrated with trovafloxacin, (ii) detect the toxicity of other drugs, such as troglitazone, tamoxifen, and digoxin, with different effects than those detected in spheroids and sandwich cultures, (iii) enable studies of hepatic function that rely on prolonged cellular activity, and (iv) detect phase II metabolites and drug accumulation to potentially support the interpretation of clinical data. The integration of MPSs in drug development will be facilitated by careful evaluation of performance and reproducibility as performed in this study.

A Critical Perspective on 3D Liver Models for Drug Metabolism and Toxicology Studies

The poor predictability of human liver toxicity is still causing high attrition rates of drug candidates in the pharmaceutical industry at the non-clinical, clinical, and post-marketing authorization stages. This is in part caused by animal models that fail to predict various human adverse drug reactions (ADRs), resulting in undetected hepatotoxicity at the non-clinical phase of drug development. In an effort to increase the prediction of human hepatotoxicity, different approaches to enhance the physiological relevance of hepatic in vitro systems are being pursued. Three-dimensional (3D) or microfluidic technologies allow to better recapitulate hepatocyte organization and cell-matrix contacts, to include additional cell types, to incorporate fluid flow and to create gradients of oxygen and nutrients, which have led to improved differentiated cell phenotype and functionality. This comprehensive review addresses the drug-induced hepatotoxicity mechanisms and the currently available 3D liver in vitro models, their characteristics, as well as their advantages and limitations for human hepatotoxicity assessment. In addition, since toxic responses are greatly dependent on the culture model, a comparative analysis of the toxicity studies performed using two-dimensional (2D) and 3D in vitro strategies with recognized hepatotoxic compounds, such as paracetamol, diclofenac, and troglitazone is performed, further highlighting the need for harmonization of the respective characterization methods. Finally, taking a step forward, we propose a roadmap for the assessment of drugs hepatotoxicity based on fully characterized fit-for-purpose in vitro models, taking advantage of the best of each model, which will ultimately contribute to more informed decision-making in the drug development and risk assessment fields.

Analysis of reproducibility and robustness of a human microfluidic four-cell liver acinus microphysiology system (LAMPS)

A human microfluidic four-cell liver acinus microphysiology system (LAMPS), was evaluated for reproducibility and robustness as a model for drug pharmacokinetics and toxicology. The model was constructed using primary human hepatocytes or human induced pluripotent stem cell (iPSC)-derived hepatocytes and 3 human cell lines for the endothelial, Kupffer and stellate cells. The model was tested in two laboratories and demonstrated to be reproducible in terms of basal function of hepatocytes, Terfenadine metabolism, and effects of Tolcapone (88 μM), Troglitazone (150 μM), and caffeine (600 μM) over 9 days in culture. Additional experiments compared basal outputs of albumin, urea, lactate dehydrogenase (LDH) and tumor necrosis factor (TNF)α, as well as drug metabolism and toxicity in the LAMPS model, and in 2D cultures seeded with either primary hepatocytes or iPSC-hepatocytes. Further experiments to study the effects of Terfenadine (10 μM), Tolcapone (88 μM), Trovafloxacin (150 μM with or without 1 μg/mL lipopolysaccharide), Troglitazone (28 μM), Rosiglitazone (0.8 μM), Pioglitazone (3 μM), and caffeine (600 μM) were carried out over 10 days. We found that both primary human hepatocytes and iPSC-derived hepatocytes in 3D culture maintained excellent basal liver function and Terfenadine metabolism over 10 days compared the same cells in 2D cultures. In 2D, non-overlay monolayer cultures, both cell types lost hepatocyte phenotypes after 48 h. With respect to drug effects, both cell types demonstrated comparable and more human-relevant effects in LAMPS, as compared to 2D cultures. Overall, these studies show that LAMPS is a robust and reproducible in vitro liver model, comparable in performance when seeded with either primary human hepatocytes or iPSC-derived hepatocytes, and more physiologically and clinically relevant than 2D monolayer cultures.

The potential diagnostic and therapeutic applications of exosomes in drug-induced liver injury

Drug-induced liver injury (DILI) has gradually become a global public medical problem, which can be caused by more than 1000 currently available drugs. Unfortunately, the diagnosis and treatment of DILI are limited and imperfect. Exosomes can be secreted by a variety of cells and tissues in the body, rich in cell-type specific proteins, nucleic acids and lipids, which has been widely studied as an important intercellular communication vehicle in liver diseases. Emerging data suggest that circulating exosomes and their cargos can be used as minimally-invasive sources of potential molecular biomarkers for the early detection, monitoring and evaluation of DILI. Exosomes in the urine were also found to contain proteins or RNAs that were indicative of DILI. In addition, exosomes derived from mesenchymal stem cell or hepatocyte are considered potential therapeutic agents to promote liver regenerative responses, modulate inflammatory response and deduce hepatocytes apoptosis. Based on the current findings, we suggest the potential applications of exosomes as biomarkers and therapeutics for DILI.

Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is one of the most cited reasons for the high drug attrition rate and drug withdrawal from the market. The accumulated large amount of high throughput transcriptomic profiles and advances in deep learning provide an unprecedented opportunity to improve the suboptimal performance of DILI prediction. In this study, we developed an eight-layer Deep Neural Network (DNN) model for DILI prediction using transcriptomic profiles of human cell lines (LINCS L1000 dataset) with the current largest binary DILI annotation data [i.e., DILI severity and toxicity (DILIst)]. The developed models were evaluated by Monte Carlo cross-validation (MCCV), permutation test, and an independent validation (IV) set. The developed DNN model achieved the area under the receiver operating characteristic curve (AUC) of 0.802 and 0.798, and balanced accuracy of 0.741 and 0.721 for training and an IV set, respectively, outperforming the conventional machine learning algorithms, including K-nearest neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). Moreover, the developed DNN model provided a more balanced sensitivity of 0.839 and specificity of 0.603. Besides, we found the developed DNN model had a superior predictive performance for oncology drugs. Also, the functional and network analysis of genes driving the predictions revealed their relevance to the underlying mechanisms of DILI. The proposed DNN model could be a promising tool for early detection of DILI potential in the pre-clinical setting.

Autophagy alleviates amiodarone-induced hepatotoxicity

Amiodarone is a widely used antiarrhythmic drug that can cause the development of steatohepatitis as well as liver fibrosis and cirrhosis. The molecular mechanisms of amiodarone-mediated liver injury remain largely unknown. We therefore analyzed amiodarone-mediated hepatocellular injury in patients with chronic heart failure, in primary hepatocytes and HepG2 cells. We found that amiodarone-treated patients with chronic heart failure revealed significantly higher serum levels of caspase-cleaved keratin-18, an apoptosis biomarker, compared to healthy individuals or patients not receiving amiodarone. Furthermore, amiodarone treatment of hepatocytes resulted in apoptosis associated with lipid accumulation and ER-stress induction. Liver cell steatosis was accompanied by enhanced de novo lipogenesis which, after reaching peak levels, declined together with decreased activation of ER stress. The decline of amiodarone-mediated lipotoxicity was associated with protective autophagy induction. In contrast, in hepatocytes treated with the autophagy inhibitor chloroquine as well as in autophagy gene (ATG5 or ATG7)-deficient hepatocytes, amiodarone-triggered toxicity was increased. In conclusion, we demonstrate that amiodarone induces lipid accumulation associated with ER stress and apoptosis in hepatocytes, which is mirrored by increased keratin-18 fragment serum levels in amiodarone-treated patients. Autophagy reduces amiodarone-mediated lipotoxicity and could provide a therapeutic strategy for protection from drug-induced liver injury.