Drug-induced liver injury: Towards early prediction and risk stratification

Comparison of drug-induced liver injury risk between propylthiouracil and methimazole: A quantitative systems toxicology approach

Propylthiouracil (PTU) and methimazole (MMI), two classical antithyroid agents possess risk of drug-induced liver injury (DILI) with unknown mechanism of action. This study aimed to examine and compare their hepatic toxicity using a quantitative system toxicology approach. The impact of PTU and MMI on hepatocyte survival, oxidative stress, mitochondrial function and bile acid transporters were assessed in vitro. The physiologically based pharmacokinetic (PBPK) models of PTU and MMI were constructed while their risk of DILI was calculated by DILIsym, a quantitative systems toxicology (QST) model by integrating the results from in vitro toxicological studies and PBPK models. The simulated DILI (ALT >2 × ULN) incidence for PTU (300 mg/d) was 21.2%, which was within the range observed in clinical practice. Moreover, a threshold dose of 200 mg/d was predicted with oxidative stress proposed as an important toxic mechanism. However, DILIsym predicted a 0% incidence of hepatoxicity caused by MMI (30 mg/d), suggesting that the toxicity of MMI was not mediated through mechanism incorporated into DILIsym. In conclusion, DILIsym appears to be a practical tool to unveil hepatoxicity mechanism and predict clinical risk of DILI.

Comparative profiling of serum, urine, and feces bile acids in humans, rats, and mice

Bile acids (BAs) play important pathophysiological roles in both humans and mammalian animals. Laboratory rats and mice are widely used animal models for assessing pharmacological effects and their underlying molecular mechanisms. However, substantial physiological differences exist in BA composition between humans and murine rodents. Here, we comprehensively compare BA profiles, including primary and secondary BAs, along with their amino acid conjugates, and sulfated metabolites in serum, urine, and feces between humans and two murine rodents. We further analyze the capabilities in gut microbial transform BAs among three species and compare sex-dependent variations within each species. As a result, BAs undergo amidation predominately with glycine in humans and taurine in mice but are primarily unamidated in rats. BA sulfation is a unique characteristic in humans, whereas rats and mice primarily perform multiple hydroxylations during BA synthesis and metabolism. For gut microbial transformed BA capabilities, humans are comparable to those of rats, stronger than those of mice in deconjugation and 7α-dehydroxylation, while humans are weak than those of rats or mice in oxidation and epimerization. Such differences enhance our understanding of the divergent experimental outcomes observed in humans and murine rodents, necessitating caution when translating findings from these rodent species to humans.

Autoimmunity associates with severity of illness in elderly patients with drug-induced liver injury

Background: Drug-induced liver injury (DILI) is a potentially serious adverse drug reaction. Due to the lack of definite etiology, specific clinical manifestations, and diagnostic methods, its prediction and diagnosis are challenging. Elderly individuals are deemed to be at high risk for DILI due to abnormal pharmacokinetics, aging tissue repair function, comorbidities, and taking multiple drugs. This study aimed to identify the clinical characteristics and explore the risk factors associated with the severity of illness in elderly patients with DILI. Methods: In the present study, the clinical characteristics at the time of liver biopsy of consecutive patients with biopsy-proven DILI who presented at our hospital from June 2005 to September 2022 were evaluated. Hepatic inflammation and fibrosis were assessed according to the Scheuer scoring system. The presence of autoimmunity was considered if IgG level >1.1 × ULN (1826 mg/dL), or high titer (>1:80) of ANA, or SMA. Results: In total, 441 patients were enrolled, and the median age was 63.3 years (IQR, 61.0-66.0); 122 (27.7%), 195 (44.2%), or 124 (28.1%) were classified as having minor, moderate, or severe hepatic inflammation, respectively; and 188 (42.6%), 210 (47.6%) or 43 (9.8%) patients presented minor, significant fibrosis or cirrhosis, respectively. Female sex (73.5%) and the cholestatic pattern (47.6%) were dominant in elderly DILI patients. Autoimmunity existed in 201 patients (45.6%). Comorbidities were not directly associated with the severity of DILI. PLT (OR: 0.994, 95% CI: 0.991-0.997; p < 0.001), AST (OR: 1.001, 95% CI: 1.000-1.003, p = 0.012), TBIL (OR: 1.006, 95% CI: 1.003-1.010, p < 0.001), and autoimmunity (OR: 1.831, 95% CI: 1.258-2.672, p = 0.002) were associated with the degree of hepatic inflammation. Meanwhile, PLT (OR: 0.990, 95% CI: 0.986-0.993, p < 0.001), TBIL (OR: 1.004, 95% CI: 1.000-1.007, p = 0.028), age (OR: 1.123, 95% CI: 1.067-1.183, p < 0.001), and autoimmunity (OR: 1.760, 95% CI: 1.191-2.608, p = 0.005) were associated with the stage of hepatic fibrosis. Conclusion: This study revealed that the presence of autoimmunity represents a more serious illness state of DILI, deserving more intensive monitoring and progressive treatment.

Machine Learning Models for Predicting Liver Toxicity

Liver toxicity is a major adverse drug reaction that accounts for drug failure in clinical trials and withdrawal from the market. Therefore, predicting potential liver toxicity at an early stage in drug discovery is crucial to reduce costs and the potential for drug failure. However, current in vivo animal toxicity testing is very expensive and time consuming. As an alternative approach, various machine learning models have been developed to predict potential liver toxicity in humans. This chapter reviews current advances in the development and application of machine learning models for prediction of potential liver toxicity in humans and discusses possible improvements to liver toxicity prediction.

The LAC Score Indicates Significant Fibrosis in Patients With Chronic Drug-Induced Liver Injury: A Large Biopsy-Based Study

Currently, there are no satisfactory noninvasive methods for the diagnosis of fibrosis in patients with chronic drug-induced liver injury (DILI). Our goal was to develop an algorithm to improve the diagnostic accuracy of significant fibrosis in this population. In the present study, we retrospectively investigated the biochemical and pathological characteristics of consecutive patients with biopsy-proven chronic DILI, who presented at our hospital from January 2013 to December 2017. A noninvasive algorithm was developed by using multivariate logistic regression, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) to diagnose significant fibrosis in the training cohort, and the algorithm was subsequently validated in the validation cohort. Totally, 1,130 patients were enrolled and randomly assigned into a training cohort (n = 848) and a validation cohort (n = 282). Based on the multivariate analysis, LSM, CHE, and APRI were independently associated with significant fibrosis. A novel algorithm, LAC, was identified with the AUROC of 0.81, which was significantly higher than LSM (AUROC 0.78), CHE (AUROC 0.73), and APRI (AUROC 0.68), alone. The best cutoff value of LAC in the training cohort was 5.4. When the LAC score was used to diagnose advanced fibrosis and cirrhosis stages, the optimal cutoff values were 6.2 and 6.7, respectively, and the AUROC values were 0.84 and 0.90 in the training cohort and 0.81 and 0.83 in the validation cohort. This study proved that the LAC score can contribute to the accurate assessment of high-risk disease progression and the establishment of optimal treatment strategies for patients with chronic DILI.

Prediction of Alternative Drug-Induced Liver Injury Classifications Using Molecular Descriptors, Gene Expression Perturbation, and Toxicology Reports

Motivation: Drug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI, based on the chemical properties of substances and experiments performed on cell lines, would bring a significant reduction in the cost of clinical trials and faster development of drugs. The current study aims to build predictive models of risk of DILI for chemical compounds using multiple sources of information.

Methods: Using several supervised machine learning algorithms, we built predictive models for several alternative splits of compounds between DILI and non-DILI classes. To this end, we used chemical properties of the given compounds, their effects on gene expression levels in six human cell lines treated with them, as well as their toxicological profiles. First, we identified the most informative variables in all data sets. Then, these variables were used to build machine learning models. Finally, composite models were built with the Super Learner approach. All modeling was performed using multiple repeats of cross-validation for unbiased and precise estimates of performance.

Results: With one exception, gene expression profiles of human cell lines were non-informative and resulted in random models. Toxicological reports were not useful for prediction of DILI. The best results were obtained for models discerning between harmless compounds and those for which any level of DILI was observed (AUC = 0.75). These models were built with Random Forest algorithm that used molecular descriptors.

Liver Injury Associated with Metamizole Exposure: Features of an Underestimated Adverse Event

INTRODUCTION AND OBJECTIVE

The potential of metamizole to cause drug-induced liver injury (DILI) has received increasing attention. We investigated the distinguishing features of a case series comprising 32 patients with suspected metamizole-induced DILI.

METHODS

For the current analysis, 32 of 238 patients with DILI included in our prospective study on drugs potentially causing DILI were included. Diagnosis of DILI was based on expert opinion and RUCAM (Roussel Uclaf Causality Assessment Method) score and supported by an in vitro test using monocyte-derived hepatocyte-like cells.

RESULTS

Suspected metamizole-DILI was characterised by a female predominance, hepatocellular pattern of injury, high proportion of antinuclear antibody positivity, and predominance of eosinophilic cell infiltration and necrosis in the histopathological analysis. With 22%, a high proportion of these metamizole-associated liver injury cases developed acute liver failure, which was characterised by a longer latency of metamizole use and more pronounced liver biochemistry abnormalities at onset and peak levels. Furthermore, jaundice was a common finding in the metamizole-associated liver injury cases with 66% presenting with peak bilirubin levels of 3 mg/dL or higher, which was associated with a worse outcome and a higher frequency of acute liver failure.

CONCLUSIONS

Our analysis of a well-characterised DILI cohort further supports the potential of metamizole causing DILI and provides important features for the establishment of a signature pattern of liver injury observed in patients treated with metamizole.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov: NCT02353455.

Novel Antioxidant, Deethylated Ethoxyquin, Protects against Carbon Tetrachloride Induced Hepatotoxicity in Rats by Inhibiting NLRP3 Inflammasome Activation and Apoptosis

Inflammation and an increase in antioxidant responses mediated by oxidative stress play an important role in the pathogenesis of acute liver injury (ALI). We utilized in silico prediction of biological activity spectra for substances (PASS) analysis to estimate the potential biological activity profile of deethylated ethoxyquin (DEQ) and hypothesized that DEQ exhibits antioxidant and anti-inflammatory effects in a rat model of carbon tetrachloride (CCl₄)-induced ALI. Our results demonstrate that DEQ improved liver function which was indicated by the reduction of histopathological liver changes. Treatment with DEQ reduced CCl₄-induced elevation of gene expression, and the activity of antioxidant enzymes (AEs), as well as the expression of transcription factors Nfe2l2 and Nfkb2. Furthermore, DEQ treatment inhibited apoptosis, downregulated gene expression of pro-inflammatory cytokines (Tnf and Il6), cyclooxygenase 2 (Ptgs2), decreased glutathione (GSH) level and myeloperoxidase (MPO) activity in rats with ALI. Notably, DEQ treatment led to an inhibition of CCl₄-induced NLRP3-inflammasome activation which was indicated by the reduced protein expression of IL-1β, caspase-1, and NLRP3 in the liver. Our data suggest that DEQ has a hepatoprotective effect mediated by redox-homeostasis regulation, NLRP3 inflammasome, and apoptosis inhibition, which makes that compound a promising candidate for future clinical studies.

Strategies for Early Prediction and Timely Recognition of Drug-Induced Liver Injury: The Case of Cyclin-Dependent Kinase 4/6 Inhibitors

The idiosyncratic nature of drug-induced liver injury (DILI) represents a current challenge for drug developers, regulators and clinicians. The myriad of agents (including medications, herbals, and dietary supplements) with recognized DILI potential not only strengthens the importance of the post-marketing phase, when urgent withdrawal sometimes occurs for rare unanticipated liver toxicity, but also shows the imperfect predictivity of pre-clinical models and the lack of validated biomarkers beyond traditional, non-specific liver function tests. After briefly reviewing proposed key mechanisms of DILI, we will focus on drug-related risk factors (physiochemical and pharmacokinetic properties) recently proposed as predictors of DILI and use cyclin-dependent kinase 4/6 inhibitors, relatively novel oral anticancer medications approved for breast cancer, as a case study to discuss the feasibility of early detection of DILI signals during drug development: published data from pivotal clinical trials, unpublished post-marketing reports of liver adverse events, and pharmacokinetic properties will be used to provide a comparative evaluation of their liver safety and gain insight into drug-related risk factors likely to explain the observed differences.

The application of omics-based human liver platforms for investigating the mechanism of drug-induced hepatotoxicity in vitro

Drug-induced liver injury (DILI) complicates safety assessment for new drugs and poses major threats to both patient health and drug development in the pharmaceutical industry. A number of human liver cell-based in vitro models combined with toxicogenomics methods have been developed as an alternative to animal testing for studying human DILI mechanisms. In this review, we discuss the in vitro human liver systems and their applications in omics-based drug-induced hepatotoxicity studies. We furthermore present bioinformatic approaches that are useful for analyzing toxicogenomic data generated from these models and discuss their current and potential contributions to the understanding of mechanisms of DILI. Human pluripotent stem cells, carrying donor-specific genetic information, hold great potential for advancing the study of individual-specific toxicological responses. When co-cultured with other liver-derived non-parenchymal cells in a microfluidic device, the resulting dynamic platform enables us to study immune-mediated drug hypersensitivity and accelerates personalized drug toxicology studies. A flexible microfluidic platform would also support the assembly of a more advanced organs-on-a-chip device, further bridging gap between in vitro and in vivo conditions. The standard transcriptomic analysis of these cell systems can be complemented with causality-inferring approaches to improve the understanding of DILI mechanisms. These approaches involve statistical techniques capable of elucidating regulatory interactions in parts of these mechanisms. The use of more elaborated human liver models, in harmony with causality-inferring bioinformatic approaches will pave the way for establishing a powerful methodology to systematically assess DILI mechanisms across a wide range of conditions.

Idiosyncratic DILI: Analysis of 46,266 Cases Assessed for Causality by RUCAM and Published From 2014 to Early 2019

One of the most difficult challenges in clinical hepatology is the diagnosis of a drug-induced liver injury (DILI). The timing of the events, exclusion of alternative causes, and taking into account the clinical context should be systematically assessed and scored in a transparent manner. RUCAM (Roussel Uclaf Causality Assessment Method) is a well-established diagnostic algorithm and scale to assess causality in patients with suspected DILI. First published in 1993 and updated in 2016, RUCAM is now the worldwide most commonly used causality assessment method (CAM) for DILI. The following manuscript highlights the recent implementation of RUCAM around the world, by reviewing the literature for publications that utilized RUCAM, and provides a review of “best practices” for the use of RUCAM in cases of suspected DILI. The worldwide appreciation of RUCAM is substantiated by the current analysis of 46,266 DILI cases, all tested for causality using RUCAM. These cases derived from 31 reports published from 2014 to early 2019. Their first authors came from 10 countries, with China on top, followed by the US, and Germany on the third rank. Importantly, all RUCAM-based DILI reports were published in high profile journals. Many other reports were published earlier from 1993 up to 2013 in support of RUCAM. Although most of the studies were of high quality, the current case analysis revealed shortcomings in few studies, not at the level of RUCAM itself but rather associated with the work of the users. To ensure in future DILI cases a better performance by the users, a list of essential elements is proposed. As an example, all suspected DILI cases should be evaluated 1) by the updated RUCAM to facilitate result comparisons, 2) according to a prospective study protocol to ensure complete data sets, 3) after exclusion of cases with herb induced liver injury (HILI) from a DILI cohort to prevent confounding variables, and 4) according to inclusion of DILI cases with RUCAM-based causality gradings of highly probable or probable, in order to increase the specificity of the results. In conclusion, RUCAM benefits from its high appreciation and performs well provided the users adhere to published recommendations to prevent confounding variability.

Idiosyncratic DILI: Analysis of 46,266 Cases Assessed for Causality by RUCAM and Published From 2014 to Early 2019

One of the most difficult challenges in clinical hepatology is the diagnosis of a drug-induced liver injury (DILI). The timing of the events, exclusion of alternative causes, and taking into account the clinical context should be systematically assessed and scored in a transparent manner. RUCAM (Roussel Uclaf Causality Assessment Method) is a well-established diagnostic algorithm and scale to assess causality in patients with suspected DILI. First published in 1993 and updated in 2016, RUCAM is now the worldwide most commonly used causality assessment method (CAM) for DILI. The following manuscript highlights the recent implementation of RUCAM around the world, by reviewing the literature for publications that utilized RUCAM, and provides a review of "best practices" for the use of RUCAM in cases of suspected DILI. The worldwide appreciation of RUCAM is substantiated by the current analysis of 46,266 DILI cases, all tested for causality using RUCAM. These cases derived from 31 reports published from 2014 to early 2019. Their first authors came from 10 countries, with China on top, followed by the US, and Germany on the third rank. Importantly, all RUCAM-based DILI reports were published in high profile journals. Many other reports were published earlier from 1993 up to 2013 in support of RUCAM. Although most of the studies were of high quality, the current case analysis revealed shortcomings in few studies, not at the level of RUCAM itself but rather associated with the work of the users. To ensure in future DILI cases a better performance by the users, a list of essential elements is proposed. As an example, all suspected DILI cases should be evaluated 1) by the updated RUCAM to facilitate result comparisons, 2) according to a prospective study protocol to ensure complete data sets, 3) after exclusion of cases with herb induced liver injury (HILI) from a DILI cohort to prevent confounding variables, and 4) according to inclusion of DILI cases with RUCAM-based causality gradings of highly probable or probable, in order to increase the specificity of the results. In conclusion, RUCAM benefits from its high appreciation and performs well provided the users adhere to published recommendations to prevent confounding variability.

Drug-induced Liver Injury Caused by Phenprobamate: Strong Probability Due to Repeated Toxicity

Drug-induced liver injury (DILI) is an important cause of morbidity and mortality. DILI can even cause acute liver failure and the need for liver transplantation. Identifying DILI may be particularly difficult because it is actually an exclusion diagnosis and individuals are usually exposed to several drugs during a lifetime. Causality assessment methods are needed for objective diagnosis. The most common methods are; updated Roussel Uclaf causality assessment method (RUCAM), Narenjo adverse drug reaction probability scale and Maria and Victorino (M&V) causality assessment scale. Phenprobamate is a widely used muscle relaxant. Herein we report a rare case of repeated DILI caused by phenprobamate and review the objective diagnostic process for hepatotoxicities. Physicians should be aware of the potential adverse effects of this drug, including hepatotoxicity.

How to cite this article: Duzenli T, Tanoglu A, et al. Drug-induced Liver Injury Caused by Phenprobamate: Strong Probability Due to Repeated Toxicity. Euroasian J Hepatogastroenterol 2019;9(1) :49-51.

Top-ranking drugs out of 3312 drug-induced liver injury cases evaluated by the Roussel Uclaf Causality Assessment Method

INTRODUCTION

A list presenting a valid ranking of drugs most commonly implicated is hardly to be found. Areas covered: Published cases of drug-induced liver injury (DILI) with verified causality using RUCAM (Roussel Uclaf Causality Assessment Method) were used for a ranking of drugs most commonly implicated in causing DILI. Expert opinion: RUCAM-based DILI cases were retrieved from 15 reports published by six international databases of DILI registries and three large medical centers, which provided 3312 cases. Overall 48 drugs with the highest number of DILI cases were listed. Among the top 10 ranking drugs implicated in causing DILI were, in decreasing order: amoxicillin-clavulanate, flucloxacilllin, atorvastatin, disulfiram, diclofenac, simvastatin, carbamazepine, ibuprofen, erythromycin, and anabolic steroids as bodybuilding agents. For these 10 drugs, respective DILI case numbers were highest for Amoxicillin-clavulanate (n=333) and lowest for anabolic steroids (n=26). The author classifies the databases of national DILI registries and large medical centers as best sources of drugs implicated in DILI. Presently discouraged is the use of the LiverTox website because many cases were derived from published cases of poor quality and could previously not be classified as DILI, calling for the inclusion of DILI cases with established causality by the updated RUCAM.

Cytotoxicity of 34 FDA approved small-molecule kinase inhibitors in primary rat and human hepatocytes

Of the 34 FDA approved oral small-molecule kinase inhibitors (KI), 23 (68%) have warnings for hepatotoxicity in product labeling. To better understand the mechanisms of KI hepatotoxicity and whether such effects can be predicted, we examined 34 KIs for cytotoxicity in primary rat and human hepatocytes. The hepatocytes were treated with KIs at ten concentrations normalized to maximal therapeutic blood levels (Cmax). At 5 and 24 h post treatment, lactate dehydrogenase or alanine aminotransferase leakage, caspase 3/7 activities and cellular adenosine triphosphate levels were measured. At 1 to 100-fold Cmax, while 5 KIs were neither toxic to human nor rat hepatocytes, 3 KIs showed similar cytotoxicity in both species and 26 KIs showed species-biased cytotoxicity, with 16 KIs being more toxic to human hepatocytes and 10 KIs being more toxic to rat hepatocytes. At concentrations of 1-, 2.5-, 5-, 10-, 100-fold Cmax, the number of cytotoxic KIs in human hepatocytes was 4, 8, 11, 14 and 27, respectively, and the corresponding number in rat hepatocytes was 1, 4, 9, 12 and 27, respectively. When hepatocyte cytotoxicity at 100-fold Cmax was used to predict KI clinical hepatotoxicity reflected in product labeling, the accuracy was 0.65 with human hepatocytes and 0.59 with rat cells. When the criterion of daily dose ≥100 mg or Cmax ≥1.1 μM was used to predict KI hepatotoxicity, the accuracy was 0.56 or 0.47, respectively. These results suggest both indirect and direct drug-induced hepatocyte toxicity may contribute to the mechanisms of KI-induced hepatotoxicity seen clinically and use of primary hepatocytes is a useful in vitro model to help predict such toxicity.

Dynamic imaging of adaptive stress response pathway activation for prediction of drug induced liver injury

Drug-induced liver injury remains a concern during drug treatment and development. There is an urgent need for improved mechanistic understanding and prediction of DILI liabilities using in vitro approaches. We have established and characterized a panel of liver cell models containing mechanism-based fluorescent protein toxicity pathway reporters to quantitatively assess the dynamics of cellular stress response pathway activation at the single cell level using automated live cell imaging. We have systematically evaluated the application of four key adaptive stress pathway reporters for the prediction of DILI liability: SRXN1-GFP (oxidative stress), CHOP-GFP (ER stress/UPR response), p21 (p53-mediated DNA damage-related response) and ICAM1 (NF-κB-mediated inflammatory signaling). 118 FDA-labeled drugs in five human exposure relevant concentrations were evaluated for reporter activation using live cell confocal imaging. Quantitative data analysis revealed activation of single or multiple reporters by most drugs in a concentration and time dependent manner. Hierarchical clustering of time course dynamics and refined single cell analysis allowed the allusion of key events in DILI liability. Concentration response modeling was performed to calculate benchmark concentrations (BMCs). Extracted temporal dynamic parameters and BMCs were used to assess the predictive power of sub-lethal adaptive stress pathway activation. Although cellular adaptive responses were activated by non-DILI and severe-DILI compounds alike, dynamic behavior and lower BMCs of pathway activation were sufficiently distinct between these compound classes. The high-level detailed temporal- and concentration-dependent evaluation of the dynamics of adaptive stress pathway activation adds to the overall understanding and prediction of drug-induced liver liabilities.

Safety data and withdrawal of hepatotoxic drugs

BACKGROUND AND AIM

The occurrence of drug induced liver injury (DILI) is the most common reason of post-marketing withdrawals. DILI in humans is difficult to predict using in vitro cytotoxicity screening and animal studies. A review of hepatotoxicity data was performed with the aim of identifying relevant factors that could have predicted the occurrence of serious DILI.

METHODS

The drugs withdrawn from the market due to hepatotoxicity in Europe and/or in USA either by marketing authorization holders or by Regulatory agencies from 1997 to 2016 were selected. The liver safety data and the withdrawal decisions were identified from a search within the European medicine agency (EMA) website, the Food and drug administration (FDA) orange book and PubMed^®.

RESULTS

From 1997 to 2016, eight drugs were withdrawn from the market for hepatotoxicity reason: tolcapone, troglitazone, trovafloxacin, bromfenac, nefazodone, ximelagatran, lumiracoxib and sitaxentan. The safety data suggest that while liver test abnormalities have been detected during clinical trials, other relevant factors leading to the discontinuation of these drugs have been identified: lack of predictability of animal models, inappropriate liver function test, non-compliance with drug treatment, less attention paid to rare adverse drug reactions, unpredictable occurrence and irreversible outcome of liver toxicity.

CONCLUSION

Several relevant factors may contribute to an inadequate risk management leading to the discontinuation of the drugs. Preclinical safety data are not sufficient to allow early prediction of DILI in humans and post-marketing safety monitoring and signal detection still should be used to identify potential serious cases of DILI. However, it seems that changes in Pharmacovigilance legislation with a closer management of drug safety may have contributed to the improvement of the risk minimization.

Bioengineering considerations in liver regenerative medicine

Background

Liver disease contributes significantly to global disease burden and is associated with rising incidence and escalating costs. It is likely that innovative approaches, arising from the emerging field of liver regenerative medicine, will counter these trends.

Main body

Liver regenerative medicine is a rapidly expanding field based on a rich history of basic investigations into the nature of liver structure, physiology, development, regeneration, and function. With a bioengineering perspective, we discuss all major subfields within liver regenerative medicine, focusing on the history, seminal publications, recent progress within these fields, and commercialization efforts. The areas reviewed include fundamental aspects of liver transplantation, liver regeneration, primary hepatocyte cell culture, bioartificial liver, hepatocyte transplantation and liver cell therapies, mouse liver repopulation, adult liver stem cell/progenitor cells, pluripotent stem cells, hepatic microdevices, and decellularized liver grafts.

Conclusion

These studies highlight the creative directions of liver regenerative medicine, the collective efforts of scientists, engineers, and doctors, and the bright outlook for a wide range of approaches and applications which will impact patients with liver disease.

A Multinomial Logistic Regression Approach for Arrhythmia Detection

Cardiovascular diseases are the leading causes on mortality in the world. Consequently, tools and methods providing useful and applicable insights into their assessment play a crucial role in the prediction and managements of specific heart conditions. In this article, we introduce a method based on multi-class Logistic Regression as a classifier to provide a powerful and accurate insight into cardiac arrhythmia, which is one of the predictors of serious vascular diseases. As suggested by our evaluation, this provides a robust, scalable, and accurate system, which can successfully tackle the challenges posed by the utilisation of big data in the medical sector.

Highlights of drug - and herb- induced liver injury in the literature from 2016: how best to translate new information into clinical practice?

INTRODUCTION

Over 1500 papers on drug-induced liver injury (DILI) and herb-induced liver injury (HILI) were published in 2016, many of which have the potential to impact clinical practice. Areas covered: Clinical studies and case series that lent themselves to new concepts in diagnosing, and treating DILI were selected for inclusion. Epidemiology of DILI in large prospective registries was highlighted. Causality assessment of drug hepatotoxicity remains challenging, as seen with cases of OxyELITE Pro (OEP). In 2016 updates to the Roussel Uclaf Causality Assessment Method (RUCAM) were published to aid in the accuracy of diagnosing DILI/HILI. New reports of established hepatotoxins were again discussed in 2016, including genetic risk factors for DILI with respect to antituberculous agents. Expert opinion: 2016 marked a turning point in how much credence should be placed in the current causality assessment for DILI/HILI cases. Many recognized hepatotoxins are backed by a relatively few number of literature reports. Danan and Teschke make a strong case that an updated RUCAM should remain the gold standard for diagnosing DILI/HILI going forward, although the role of expert opinion is often still needed in cases where RUCAM falls short. The field of chemoinformatics continues to evolve while we await a truly predictive and diagnostic DILI biomarker.