New and incremental FDA black box warnings from 2008 to 2015

Description and Validation of a Novel AI Tool, LabelComp, for the Identification of Adverse Event Changes in FDA Labeling

INTRODUCTION

The accurate identification and timely updating of adverse reactions in drug labeling are crucial for patient safety and effective drug use. Postmarketing surveillance plays a pivotal role in identifying previously undetected adverse events (AEs) that emerge when a drug is used in broader and more diverse patient populations. However, traditional methods of updating drug labeling with new AE information have been manual, time consuming, and error prone. This paper introduces the LabelComp tool, an innovative artificial intelligence (AI) tool designed to enhance the efficiency and accuracy of postmarketing drug safety surveillance. Utilizing a combination of text analytics and a trained Bidirectional Encoder Representations from Transformers (BERT) model, the LabelComp tool automatically identifies changes in AE terms from updated drug labeling documents.

OBJECTIVE

Our objective was to create and validate an AI tool with high accuracy that could enable researchers and FDA reviewers to efficiently identify safety-related drug labeling changes.

RESULTS

Our validation study of 87 drug labeling PDF pairs demonstrates the tool's high accuracy, with F1 scores of overall performance ranging from 0.795 to 0.936 across different evaluation tiers and a recall of at least 0.997 with only one missed AE out of 483 total AEs detected, indicating the tool's efficacy in identifying new AEs.

CONCLUSION

The LabelComp tool can support drug safety surveillance and inform regulatory decision-making. The publication of this tool also aims to encourage further community-driven enhancements, aligning with broader interests in applying AI to advance regulatory science and public health.

Serum levels of IL-6/IL-10/GLDH may be early recognition markers of anti-tuberculosis drugs (ATB) -induced liver injury

To explore the potential value of serum glutamate dehydrogenase (GLDH) combined with inflammatory cytokines as diagnostic biomarkers for anti-tuberculosis drug -induced liver injury (ATB-DILI). We collected the residual serum from the patients who met the criteria after liver function tests. We have examined these parameters including GLDH which were determined by enzyme-linked immunosorbent assay and cytokines which were determined by cytokine combination detection kit. Multivariate logistics stepwise forward regression was applied to establish regression models. A total of 138 tuberculosis patients were included in the diagnostic markers study of ATB-DILI, including normal liver function group (n = 108) and ATB-DILI group(n = 30). Serum GLDH, IL-6 and IL-10 levels were significantly increased in the ATB-DILI group. Receiver operating characteristic curve (ROC) curve showed that the area under curve (AUC) of serum GLDH, IL-6 and IL-10 for the diagnosis of ATB-DILI were 0.870, 0.714 and 0.811, respectively. In logistic regression modeling, the AUC of GLDH combined with IL-10 as an ATB-DILI marker is 0.912. Serum IL-6、IL-10 and GLDH levels began to rise preceded the increase in ALT by 7 days, with significant differences in IL-6 compared with 7 days. Serum GLDH, IL-6 and IL-10 levels were correlated with the severity of liver injury. In conclusion, we found that GLDH, IL-6 and IL-10 alone as diagnostic markers of ATB-DILI had good diagnostic efficacy. Logistic regression model established by GLDH and IL-10 had better diagnostic efficacy and IL-6 may be an early predictor of liver injury in the setting of ATB poisoning.

Evaluating the Impact of Black Box Warning Updates on the Reporting of Drug-Related Adverse Events: a Cross Sectional Study of the FAERS Database

BACKGROUND

The Food and Drug Administration (FDA)'s Adverse Event Reporting System (FAERS) is a post-marketing surveillance system which relies on spontaneous reports of adverse drug reactions (ADRs). Our objective was to evaluate how black box warning (BBW) updates impact ADR reporting rates.

RESEARCH DESIGN AND METHODS

We searched MEDWATCH for all BBW updates issued between January 2014 and December 2016 and categorized them as new, major, and minor. Rates of relevant ADR reports from the FAERS database in the 4 years preceding and following a BBW update were assessed among the different BBW categories.

RESULTS

Forty BBW updates were included (16 major, 3 new, and 21 minor). A meaningful increase in the proportion of relevant ADRs of all ADRs reported following BBW updates was documented for 53% of new or major updates and 24% of minor updates (p = 0.06). The median percentage of reported relevant ADRs increased by 5% following new and major BBW updates and decreased by 30% following minor BBW updates (p = 0.3).

CONCLUSIONS

Reporting of adverse events to the FAERS database is affected by the severity and timing of related BBW updates, highlighting the drawbacks of spontaneous reporting systems. Regulators should promote proactive pharmacovigilance strategies to cope with these limitations.

Characterization and corroboration of safety signals identified from the US Food and Drug Administration Adverse Event Reporting System, 2008-19: cross sectional study

OBJECTIVE

To characterize potential drug safety signals identified from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), from 2008 to 2019, to determine how often these signals resulted in regulatory action by the FDA and whether these actions were corroborated by published research findings or public assessments by the Sentinel Initiative.

DESIGN

Cross sectional study.

SETTING

USA.

POPULATION

Safety signals identified from the FAERS and publicly reported by the FDA between 2008 and 2019; and review of the relevant literature published before and after safety signals were reported in 2014-15. Literature searches were performed in November 2019, Sentinel Initiative assessments were searched in December 2021, and data analysis was finalized in December 2021.

MAIN OUTCOME MEASURES

Safety signals and resulting regulatory actions; number and characteristics of published studies, including corroboration of regulatory action as evidenced by significant associations (or no associations) between the drug related to the signal and the adverse event.

RESULTS

From 2008 to 2019, 603 potential safety signals identified from the FAERS were reported by the FDA (median 48 annually, interquartile range 41-61), of which 413 (68.5%) were resolved as of December 2021 (372 of 399 (93.2%) signals ≥3 years old were resolved). Among the resolved safety signals, 91 (22.0%) led to no regulatory action and 322 (78.0%) resulted in regulatory action, including 319 (77.2%) changes to drug labeling and 59 (14.3%) drug safety communications or other public communications from the FDA. For a subset of 82 potential safety signals reported in 2014-15, a literature search identified 1712 relevant publications; 1201 (70.2%) were case reports or case series. Among these 82 safety signals, 76 (92.7%) were resolved, of which relevant published research was identified for 57 (75.0%) signals and relevant Sentinel Initiative assessments for four (5.3%) signals. Regulatory actions by the FDA were corroborated by at least one relevant published research study for 17 of the 57 (29.8%) resolved safety signals; none of the relevant Sentinel Initiative assessments corroborated FDA regulatory action.

CONCLUSIONS

Most potential safety signals identified from the FAERS led to regulatory action by the FDA. Only a third of regulatory actions were corroborated by published research, however, and none by public assessments from the Sentinel Initiative. These findings suggest that either the FDA is taking regulatory actions based on evidence not made publicly available or more comprehensive safety evaluations might be needed when potential safety signals are identified.

Adverse Drug Reactions Associated With Drugs Prescribed in Psychiatry: A Retrospective Descriptive Analysis in a Tertiary Care Hospital

Background Psychiatric disorders are chronic in nature which often require long and continuous medications. These medications are known to cause adverse effects on their use. Their monitoring and prevention are crucial for the practicing family and community physicians. Method This is a cross-sectional retrospective study conducted to analyze all the spontaneous adverse drug reactions (ADRs) reported from the psychiatry department to the ADR Monitoring Center, Department of Pharmacology, AIIMS Jodhpur during the time period from 2014 to 2020. Results A total of 334 ADRs were reported. The majority of the ADRs were reported from antipsychotics (60.6%) followed by antidepressants (25.5%) and antiepileptic drugs (5.8%). On further subgroup analysis of the drug classes among antipsychotics, Clozapine (15.8%) was the leading offending agent. Similarly, among Antidepressants, Escitalopram (6.1%) was causing the most side effects. The most common ADR reported was sedation (7.26%) followed by salivary hypersecretion (6.7%), akathisia (5.52%), and weight gain (5.52%). Conclusion Knowledge of common ADRs help in better management of the diseases and psychotropics as a class has various frequents ADRs. Early detection and suitable intervention can help the community physicians in the proper care of the patients and rational use of drugs.

The human hepatocyte TXG-MAPr: gene co-expression network modules to support mechanism-based risk assessment

Mechanism-based risk assessment is urged to advance and fully permeate into current safety assessment practices, possibly at early phases of drug safety testing. Toxicogenomics is a promising source of mechanisms-revealing data, but interpretative analysis tools specific for the testing systems (e.g. hepatocytes) are lacking. In this study, we present the TXG-MAPr webtool (available at https://txg-mapr.eu/WGCNA_PHH/TGGATEs_PHH/ ), an R-Shiny-based implementation of weighted gene co-expression network analysis (WGCNA) obtained from the Primary Human Hepatocytes (PHH) TG-GATEs dataset. The 398 gene co-expression networks (modules) were annotated with functional information (pathway enrichment, transcription factor) to reveal their mechanistic interpretation. Several well-known stress response pathways were captured in the modules, were perturbed by specific stressors and showed preservation in rat systems (rat primary hepatocytes and rat in vivo liver), with the exception of DNA damage and oxidative stress responses. A subset of 87 well-annotated and preserved modules was used to evaluate mechanisms of toxicity of endoplasmic reticulum (ER) stress and oxidative stress inducers, including cyclosporine A, tunicamycin and acetaminophen. In addition, module responses can be calculated from external datasets obtained with different hepatocyte cells and platforms, including targeted RNA-seq data, therefore, imputing biological responses from a limited gene set. As another application, donors' sensitivity towards tunicamycin was investigated with the TXG-MAPr, identifying higher basal level of intrinsic immune response in donors with pre-existing liver pathology. In conclusion, we demonstrated that gene co-expression analysis coupled to an interactive visualization environment, the TXG-MAPr, is a promising approach to achieve mechanistic relevant, cross-species and cross-platform evaluation of toxicogenomic data.

Using an Automated Algorithm to Identify Potential Drug-Induced Liver Injury Cases in a Pharmacovigilance Database

INTRODUCTION

Drug-induced liver injury (DILI) is the most frequent cause of acute liver failure in North America and Europe, but it is often missed because of unstandardized diagnostic methods and criteria. This study aimed to develop and validate an automated algorithm to identify potential DILI cases in routine pharmacovigilance (PV) activities.

METHODS

Post-marketing hepatic adverse events reported for a potentially hepatotoxic drug in a global PV database from 19 March 2017 to 18 June 2018 were assessed manually and with the automated algorithm. The algorithm provided case assessments by applying pre-specified criteria to all case data and narratives simultaneously.

RESULTS

A total of 1456 cases were included for analysis and assessed manually. Sufficient data for algorithm assessment were available for 476 cases (32.7%). Of these cases, manual assessment identified 312 (65.5%) potential DILI cases while algorithm assessment identified 305 (64.1%) potential DILI cases. Comparison of manual and algorithm assessments demonstrated a sensitivity of 97.8% and a specificity of 79.3% for the algorithm. Given the prevalence of potential DILI cases in the population studied, the algorithm was calculated to have positive predictive value 56.3% and negative predictive value 99.2%. The time required for manual review compared to algorithm review suggested that application of the algorithm prior to manual screening would have resulted in a time savings of 42.2%.

CONCLUSION

An automated algorithm to identify potential DILI cases was developed and successfully implemented. The algorithm demonstrated a high sensitivity, a high negative predictive value, along with significant efficiency and utility in a real-time PV database.

An audit of Black Box Warnings (BBWs) in the United States Food and Drug Administration (US-FDA) database - a five-year analysis

BACKGROUND

The black-box warning (BBW) is the most serious warning that US-FDA can ask for on a drug's labelling. BBWs represent key safety concerns uncovered either during dossier review or post-approval. We have conducted the present study with the primary objective of assessing BBWs issued by the US-FDA.

METHODS

BBW's were identified on US-FDA's website from 1st January 2015 to 31st December 2019. Prescribing information was used to identify and characterize BBWs into new and minor/major update on a previous BBW. The therapeutic class of the drug, nature [Biological/New Molecular entity (NME)], formulation type, expected duration of use along with the year of first approval of the molecule with BBWs were evaluated.

RESULTS

A total of n = 167 BBWs were issued by FDA of which 53 (31.7%) had major updates, 57(34.1%) had minor updates and 57(34.1%) were new BBWs. A total of 137(82%) of BBWs were with NME's whereas 30(18%) were with biologics. Drugs for neurology 40(25.5%)had the highest number of BBWs followed by oncology 38(24.2%). Among type of BBWs, cardiovascular risk 31 (15%) were the highest.

CONCLUSION

Practicing physicians need to understand that benefit-risk of a drug is dynamic and keep abreast of new data related to it.

Thinking Outside the Black Box: Current Perceptions on Breast Implant Safety and Utility

BACKGROUND

There is growing public concern surrounding breast implant safety. In fact, the U.S. Food and Drug Administration recently proposed changes to breast implant labeling, which include a boxed warning. Given such efforts to increase transparency on breast implant safety, this study assessed laywomen's perceptions of breast implant safety, and the impact of proposed changes to breast implant labeling on laywomen's decision-making regarding breast implants.

METHODS

This was a cross-sectional investigation of women recruited through Amazon Mechanical Turk. Discrete choice modeling was used to query opinions on breast implants after viewing proposed labeling changes. Chi-square and analysis of variance were used for subgroup analyses, and McNemar analyses were used to assess changes in participants' responses.

RESULTS

The authors received 500 survey responses. At baseline, 353 participants (70.6 percent) considered breast implants to be at least somewhat safe. After viewing the proposed boxed warning, 252 participants (50.4 percent) responded that they would be less likely to receive implants. In fact, a significantly greater proportion of participants considered breast implants to be either unsafe or very unsafe after viewing suggested changes to implant labeling than at baseline (58.4 percent versus 28.8 percent; p = 0.001). By the end of the survey, willingness to consider alternative options for breast augmentation/reconstruction increased significantly from baseline.

CONCLUSIONS

Improved labeling can enhance laywomen's understanding of breast implant safety and can impact decision-making. However, greater scrutiny of breast implants should not prevent women who need implants from receiving them. Transparency and objectivity in the surgeon-patient dialogue can ensure the appropriate use of medical devices such as breast implants.

Drug Safety Data Curation and Modeling in ChEMBL: Boxed Warnings and Withdrawn Drugs

The safety of marketed drugs is an ongoing concern, with some of the more frequently prescribed medicines resulting in serious or life-threatening adverse effects in some patients. Safety-related information for approved drugs has been curated to include the assignment of toxicity class(es) based on their withdrawn status and/or black box warning information described on medicinal product labels. The ChEMBL resource contains a wide range of bioactivity data types, from early "Discovery" stage preclinical data for individual compounds through to postclinical data on marketed drugs; the inclusion of the curated drug safety data set within this framework can support a wide range of safety-related drug discovery questions. The curated drug safety data set will be made freely available through ChEMBL and updated in future database releases.

Microengineered systems with iPSC-derived cardiac and hepatic cells to evaluate drug adverse effects

Hepatic and cardiac drug adverse effects are among the leading causes of attrition in drug development programs, in part due to predictive failures of current animal or in vitro models. Hepatocytes and cardiomyocytes differentiated from human induced pluripotent stem cells (iPSCs) hold promise for predicting clinical drug effects, given their human-specific properties and their ability to harbor genetically determined characteristics that underlie inter-individual variations in drug response. Currently, the fetal-like properties and heterogeneity of hepatocytes and cardiomyocytes differentiated from iPSCs make them physiologically different from their counterparts isolated from primary tissues and limit their use for predicting clinical drug effects. To address this hurdle, there have been ongoing advances in differentiation and maturation protocols to improve the quality and use of iPSC-differentiated lineages. Among these are in vitro hepatic and cardiac cellular microsystems that can further enhance the physiology of cultured cells, can be used to better predict drug adverse effects, and investigate drug metabolism, pharmacokinetics, and pharmacodynamics to facilitate successful drug development. In this article, we discuss how cellular microsystems can establish microenvironments for these applications and propose how they could be used for potentially controlling the differentiation of hepatocytes or cardiomyocytes. The physiological relevance of cells is enhanced in cellular microsystems by simulating properties of tissue microenvironments, such as structural dimensionality, media flow, microfluidic control of media composition, and co-cultures with interacting cell types. Recent studies demonstrated that these properties also affect iPSC differentiations and we further elaborate on how they could control differentiation efficiency in microengineered devices. In summary, we describe recent advances in the field of cellular microsystems that can control the differentiation and maturation of hepatocytes and cardiomyocytes for drug evaluation. We also propose how future research with iPSCs within engineered microenvironments could enable their differentiation for scalable evaluations of drug effects.

Medicare Prescription Drug Plan Formulary Restrictions After Postmarket FDA Black Box Warnings

BACKGROUND

The boxed warning (also known as "black box warning") is one of the FDA's strongest safety actions for pharmaceuticals. After the FDA issues black box warnings for drugs, prescribing changes have been inconsistent. Formulary management may provide an opportunity to restrict access to drugs with serious safety concerns.

OBJECTIVE

To examine Medicare prescription drug plan formulary changes after new FDA postmarket black box warnings and major updates to preexisting black box warnings.

METHODS

In this cohort study, we identified each drug that received a new FDA postmarket black box warning or a major update to a preexisting black box warning from January 2008 through June 2015 and examined its formulary coverage. The main outcome measure was the proportion of Medicare prescription drug plan formularies providing unrestrictive coverage immediately before the black box warning, at least 1 year after the warning and at least 2 years after the warning. Unrestrictive formulary coverage was defined as coverage of a drug without prior authorization or step-therapy requirements.

RESULTS

Of 101 new black box warnings and major updates to preexisting warnings affecting 68 unique drug formulations, the mean percentage of formularies providing unrestrictive coverage changed from 65.4% (95% CI = 59.6%-71.2%) prewarning; 62.6% (95% CI = 56.3%-68.9%, P = 0.04) at least 1 year postwarning; and 61.9% (95% CI = 55.4%-68.5%, P = 0.10) at least 2 years postwarning.

CONCLUSIONS

The mean percentage of Medicare prescription drug plan formularies providing unrestrictive coverage decreased modestly by approximately 3 percentage points after drugs received postmarket FDA black box warnings. Formulary restrictions may present an underused mechanism to reduce use of potentially unsafe medications.

DISCLOSURES

This study was supported by a student research grant received by Solotke and provided by the Yale School of Medicine Office of Student Research under National Institutes of Health training grant award T35DK104689. Karaca-Mandic, Shah, and Ross acknowledge support from Agency for Healthcare Research and Quality (AHRQ) grant R01 HS025164, which studies factors associated with de-adoption of drug therapies shown to be ineffective or unsafe. The content of this study is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors assume full responsibility for the accuracy and completeness of the ideas presented. Ross has received support from the following: the U.S. Food and Drug Administration (FDA) as part of the Centers for Excellence in Regulatory Science and Innovation (CERSI) program; Johnson and Johnson through Yale University to develop methods of clinical trial data sharing; Medtronic and the FDA to develop methods for postmarket surveillance of medical devices; the Blue Cross Blue Shield Association to better understand medical technology evaluation; the Centers for Medicare & Medicaid Services (CMS) to develop and maintain performance measures that are used for public reporting; the AHRQ to examine community predictors of health care quality; and the Laura and John Arnold Foundation, which established the Collaboration for Research Integrity and Transparency at Yale University. Shah has received support from the FDA as part of the CERSI program. In addition, he has received support through the Mayo Clinic from CMS, AHRQ, National Science Foundation, and Patient-centered Outcomes Research Institute. Karaca-Mandic has provided consulting services to Precision Health Economics and Tactile Medical for work unrelated to this manuscript. Dhruva and Solotke have no conflicts of interest to report.

MitoToxy assay: A novel cell-based method for the assessment of metabolic toxicity in a multiwell plate format using a lactate FRET nanosensor, Laconic

Mitochondrial toxicity is a primary source of pre-clinical drug attrition, black box warning and post-market drug withdrawal. Methods that detect mitochondrial toxicity as early as possible during the drug development process are required. Here we introduce a new method for detecting mitochondrial toxicity based on MDA-MB-231 cells stably expressing the genetically encoded FRET lactate indicator, Laconic. The method takes advantage of the high cytosolic lactate accumulation observed during mitochondrial stress, regardless of the specific toxicity mechanism, explained by compensatory glycolytic activation. Using a standard multi-well plate reader, dose-response curve experiments allowed the sensitivity of the methodology to detect metabolic toxicity induced by classical mitochondrial toxicants. Suitability for high-throughput screening applications was evaluated resulting in a Z’-factor > 0.5 and CV% < 20 inter-assay variability. A pilot screening allowed sensitive detection of commercial drugs that were previously withdrawn from the market due to liver/cardiac toxicity issues, such as camptothecin, ciglitazone, troglitazone, rosiglitazone, and terfenadine, in ten minutes. We envisage that the availability of this technology, based on a fluorescent genetically encoded indicator, will allow direct assessment of mitochondrial metabolism, and will make the early detection of mitochondrial toxicity in the drug development process possible, saving time and resources.

Analysis of Postapproval Clinical Trials of Therapeutics Approved by the US Food and Drug Administration Without Clinical Postmarketing Requirements or Commitments

Importance

The US Food and Drug Administration (FDA) can use postmarketing requirements to mandate pharmaceutical companies to conduct clinical trials after the approval of novel therapeutics. Pharmaceutical companies can also agree to conduct nonmandated clinical trials as postmarketing commitments. However, when therapeutics are approved by the FDA without postmarketing requirements or postmarketing commitments, it is not well known how often pharmaceutical companies voluntarily conduct trials and report results monitoring safety or efficacy after approval.

Objective

To characterize postapproval clinical trials sponsored by pharmaceutical companies of therapeutics initially approved by the FDA without clinical postmarketing requirements or commitments.

Design, Setting, and Participants

This cross-sectional analysis included postapproval clinical trials conducted with at least 1 site in the United States sponsored by pharmaceutical companies of therapeutics first approved by the FDA from 2009 through 2012. Analyses were conducted June 11, 2018, to November 30, 2018.

Main Outcomes and Measures

Postapproval clinical trials registered on ClinicalTrials.gov generating safety or efficacy data, characteristics including whether trials focused on approved or unapproved indications, study design elements, and rates of study completion and results reporting.

Results

From 2009 through 2012, the FDA approved 110 novel therapeutics for 120 indications, of which 37 novel therapeutics for 39 indications did not have postmarketing requirements or commitments for new clinical studies at the time of first approval. For 31 therapeutics (83.8%), there were 600 postapproval clinical trials sponsored by pharmaceutical companies. Most trials investigated therapeutics for new indications (363 [60.5%]) or expanded populations of the originally indicated disease (122 [20.3%]). Trials were often small (median [interquartile range] enrollment, 44 [21-131] participants), nonrandomized (359 [59.8%]), unblinded (455 [75.8%]), and lacked comparators (381 [63.5%]). Approximately half of the trials (311 [51.8%]) assessed at least 1 clinical outcome. Of 300 terminated or completed trials, 204 trials (68.0%) had reported results on ClinicalTrials.gov a median (interquartile range) 16 (13-25) months after their primary completion date. For the 96 trials (32.0%) without reported results, a median (interquartile range) 35 (13-62) months had passed since their primary completion date.

Conclusions and Relevance

Pharmaceutical companies frequently conducted clinical trials after approval, even when there were no clinical postmarketing requirements or commitments at approval. However, most of these trials evaluated new indications or expanded patient populations rather than monitored approved uses, and nearly half of the trials remained incomplete more than 5 years after original therapeutic approval.

Biomarkers of drug-induced liver injury

Drug-induced liver injury (DILI) is a major clinical and regulatory challenge. As a result, interest in DILI biomarkers is growing. So far, considerable progress has been made in identification of biomarkers for diagnosis (acetaminophen-cysteine protein adducts), prediction (genetic biomarkers), and prognosis (microRNA-122, high mobility group box 1 protein, keratin-18, glutamate dehydrogenase, mitochondrial DNA). Many of those biomarkers also provide mechanistic insight. The purpose of this chapter is to review major advances in DILI biomarker research over the last decade, and to highlight some of the challenges involved in implementation. Although much work has been done, more liver-specific biomarkers, more DILI-specific biomarkers, and better prognostic biomarkers for survival are all still needed. Furthermore, more work is needed to define reference intervals and medical decision limits.

Animal models of drug-induced liver injury

Drug-induced liver injury (DILI) presents unique challenges for consumers, clinicians, and regulators. It is the most common cause of acute liver failure in the US. It is also one of the most common reasons for termination of new drugs during pre-clinical testing and withdrawal of new drugs post-marketing. DILI is generally divided into two forms: intrinsic and idiosyncratic. Many of the challenges with DILI are due in large part to poor understanding of the mechanisms of toxicity. Although useful models of intrinsic DILI are available, they are frequently misused. Modeling idiosyncratic DILI presents greater challenges, but promising new models have recently been developed. The purpose of this manuscript is to provide a critical review of the most popular animal models of DILI, and to discuss the future of DILI research.

Biomarkers of drug-induced liver injury: progress and utility in research, medicine, and regulation

INTRODUCTION

The difficulty of understanding and diagnosing drug-induced liver injury (DILI) has led to proliferation of serum and genetic biomarkers. Many applications of these biomarkers have been proposed, including investigation of mechanisms, prediction of DILI during early trials or before initiation of therapy in patients, and diagnosis of DILI during therapy. Areas covered: We review the definition and categories of DILI, describe recent developments in DILI biomarker development, and provide guidance for future directions in DILI biomarker research. Expert commentary: There are major obstacles to DILI biomarker development and implementation, including the low prevalence of idiosyncratic DILI (IDILI), weak associations of IDILI with genetic variants, and lack of specificity of many biomarkers for the liver. Certain serum biomarkers, like miR-122, may have clinical utility in early-presenting patients with either intrinsic or idiosyncratic DILI in the future, while others likely will not find use. Future research should focus on implementation of biomarkers to predict later injury and outcome in early presenters with intrinsic DILI, and on development of biomarkers of adaptation and repair in the liver that can be used to determine if a liver test abnormality is likely to be clinically significant in IDILI.