Comedications alter drug-induced liver injury reporting frequency: Data mining in the WHO VigiBase™

A pharmacovigilance study on drug-induced liver injury associated with antibody-drug conjugates (ADCs) based on the food and drug administration adverse event reporting system

BACKGROUND

This study aimed to assess the association between drug-induced liver injury (DILI) and antibody-drug conjugates (ADCs) by comprehensively evaluating spontaneous reports submitted to the Food and Drug Administration Adverse Event Reporting System (FAERS) database from 2004Q1 to 2022Q3.

RESEARCH DESIGN AND METHODS

All DILI cases with ADCs as primary suspected drugs were extracted from the FAERS database from 2004Q1 to 2022Q3 using OpenVigil 2.1. The reporting odds ratio (ROR) and the proportional reporting ratio (PRR) for reporting the association between different drugs and DILI risk were calculated.

RESULTS

A total of 504 DILI cases were attributed to ADCs during the study period. Patients with ADCs-related DILI (n = 504) had a mean age of 56.2 ± 18.4 years, with 167 cases not reporting patients' age. Females and males comprised 42.5% and 44.0% of the cases, respectively, while there was no information on gender in 13.5% of the cases. The DILI signals were detected in trastuzumab emtansine, enfortumab vedotin, brentuximab vedotin, polatuzumab vedotin, gemtuzumab ozogamicin, inotuzumab ozogamicin, and trastuzumab deruxtecan.

CONCLUSIONS

The FAERS data mining suggested an association between DILI and some ADCs. Further studies are warranted to unraveling the underlying mechanisms and taking preventive measures for ADCs-related DILI.

Liver injury associated with endothelin receptor antagonists: a pharmacovigilance study based on FDA adverse event reporting system data

BACKGROUND

Endothelin receptor antagonists are commonly used in clinical practice, with concerns about their hepatotoxicity.

AIM

This study aimed to conduct a comprehensive pharmacovigilance study based on FDA adverse event reporting system data to evaluate the possible association between endothelin receptor antagonists and drug-induced liver injury.

METHOD

Adverse event reports from FDA adverse event reporting system between January 2004 and December 2022 were analyzed. Disproportionality algorithms, including reporting odds ratio and information component, were used to evaluate the association between endothelin receptor antagonists and liver injury. Sex- and age-stratified analyses of drug-induced liver injury events were also conducted in relation to endothelin receptor antagonists.

RESULTS

Significant associations between bosentan, macitentan, and liver injury were identified. Bosentan showed a strong link with liver injury, with reporting odds ratios for cholestatic injury at 7.59 (95% confidence interval: 6.90-8.35), hepatocellular injury at 5.63 (5.29-6.00), and serious drug-related hepatic disorders events at 1.33 (1.24-1.43). Drug-induced liver injury signals associated with bosentan were detected in all age groups. Macitentan was associated with liver injury, with reporting odds ratios for hepatic failure at 1.64 (1.39-1.94), cholestatic injury at 1.62 (1.43-1.83), and serious drug-related hepatic disorders events at 1.40 (1.29-1.51). No drug-induced liver injury signal was detected for ambrisentan, and no significant sex differences were observed in drug-induced liver injury events.

CONCLUSION

Both bosentan and macitentan are associated with liver injury. Routine monitoring of serum aminotransferase levels is recommended, especially in patients at higher risk of liver injury. Further research into drug-drug interactions involving endothelin receptor antagonists is warranted.

Folic acid protects against isoniazid-induced liver injury via the m6A RNA methylation of cytochrome P450 2E1 in mice

Background

Cytochrome P450 2E1 (CYP2E1) converts isoniazid (INH) to toxic metabolites and is critical in INH-induced liver injury. The aim is to investigate the effect of folic acid (FA) on CYP2E1 and INH-induced liver injury.

Methods

Male Balb/c mice were used. The mice in the control group only received an AIN-93M diet. The AIN-93M diet was supplemented with 0.66 g INH/kg diet for the mice in the INH and FA groups. The mice in the FA group were treated with additional 0.01 g FA/kg diet. The one-carbon cycle metabolites, the expressions of CYP2E1 and the DNA and RNA methylation levels were detected to reveal the potential mechanism.

Results

FA treatment significantly reduced the alanine aminotransferase level and alleviated the liver necrosis. The mRNA and protein expressions of CYP2E1 were significantly lower in the FA group than those in the INH group. The N6-methyladenosine RNA methylation level of Cyp2e1 significantly increased in the FA group compared with the INH group, while the DNA methylation levels of Cyp2e1 were similar between groups. Additionally, the liver S-adenosyl methionine (SAM)/S-adenosyl homocysteine (SAH) was elevated in the FA group and tended to be positively correlated with the RNA methylation level of Cyp2e1.

Conclusion

FA alleviated INH-induced liver injury which was potentially attributed to its inhibitory effect on CYP2E1 expressions through enhancing liver SAM/SAH and RNA methylation.

Outlier analysis for accelerating clinical discovery: An augmented intelligence framework and a systematic review

Clinical discoveries largely depend on dedicated clinicians and scientists to identify and pursue unique and unusual clinical encounters with patients and communicate these through case reports and case series. This process has remained essentially unchanged throughout the history of modern medicine. However, these traditional methods are inefficient, especially considering the modern-day availability of health-related data and the sophistication of computer processing. Outlier analysis has been used in various fields to uncover unique observations, including fraud detection in finance and quality control in manufacturing. We propose that clinical discovery can be formulated as an outlier problem within an augmented intelligence framework to be implemented on any health-related data. Such an augmented intelligence approach would accelerate the identification and pursuit of clinical discoveries, advancing our medical knowledge and uncovering new therapies and management approaches. We define clinical discoveries as contextual outliers measured through an information-based approach and with a novelty-based root cause. Our augmented intelligence framework has five steps: define a patient population with a desired clinical outcome, build a predictive model, identify outliers through appropriate measures, investigate outliers through domain content experts, and generate scientific hypotheses. Recognizing that the field of obstetrics can particularly benefit from this approach, as it is traditionally neglected in commercial research, we conducted a systematic review to explore how outlier analysis is implemented in obstetric research. We identified two obstetrics-related studies that assessed outliers at an aggregate level for purposes outside of clinical discovery. Our findings indicate that using outlier analysis in clinical research in obstetrics and clinical research, in general, requires further development.

Exploring the impact of co-exposure timing on drug-drug interactions in signal detection through spontaneous reporting system databases: a scoping review

INTRODUCTION

Drug-drug interactions (DDIs) are defined as the pharmacological effects produced by the concomitant administration of two or more drugs. To minimize false positive signals and ensure their validity when analyzing Spontaneous Reporting System (SRS) databases, it has been suggested to incorporate key pharmacological principles, such as temporal plausibility.

AREAS COVERED

The scoping review of the literature was completed using MEDLINE from inception to March 2023. Included studies had to provide detailed methods for identifying DDIs in SRS databases. Any methodological approach and adverse event were accepted. Descriptive analyzes were excluded as we focused on automatic signal detection methods. The result is an overview of all the available methods for DDI signal detection in SRS databases, with a specific focus on the evaluation of the co-exposure time of the interacting drugs. It is worth noting that only a limited number of studies (n = 3) have attempted to address the issue of overlapping drug administration times.

EXPERT OPINION

Current guidelines for signal validation focus on factors like the number of reports and temporal association, but they lack guidance on addressing overlapping drug administration times, highlighting a need for further research and method development.

Analysis of death avoidance by concomitant use of prednisone in patients with renal transplant using the Food and Drug Administration Adverse Event Reporting System

BACKGROUND

Patients with renal transplant are frequently administered immunosuppressants to prevent transplant-related adverse events. There are mainly nine immunosuppressants on the market, and multiple immunosuppressants are frequently administered for patients with renal transplant. Identifying which immunosuppressant was responsible when efficacy or safety was observed in patients taking multiple immunosuppressants is difficult. This study aimed to identify the immunosuppressant that was effective in reducing death in patients with renal transplant. A very large sample size was required to conduct prospective clinical trials of immunosuppressant combinations, which is impractical. We investigated cases wherein death occurred despite immunosuppressant administration in patients with renal transplant using Food and Drug Administration Adverse Event Reporting System (FAERS) data.

MATERIAL AND METHOD

We used FAERS data reported between January 2004 and December 2022 in patients with renal transplant who received one or more immunosuppressants. Groups were defined for each combination of immunosuppressants. Comparison between two identical groups except for the presence or absence of prednisone was performed using the reporting odds ratio (ROR) and the adjusted ROR (aROR) controlling for differences in patient background.

RESULTS

When the group without prednisone was set as the reference, the aROR for death was significantly <1.000 in several cases in the group to which prednisone was added.

CONCLUSIONS

The inclusion of prednisone in the immunosuppressant combinations was suggested to be effective in reducing death. We provided the sample code of software R that can reproduce the results.

Nutritional intervention is promising in alleviating liver injury during tuberculosis treatment: a review

Liver injury is a main adverse effect of first-line tuberculosis drugs. Current management of tuberculosis-drug-induced liver injury (TBLI) mainly relies on withdrawing tuberculosis drugs when necessary. No effective treatment exists. Various nutrients and functional food ingredients may play a protective role in TBLI. However, a comprehensive review has not been conducted to compare the effects of these nutrients and functional food ingredients. We searched Pubmed and Web of Science databases from the earliest date of the database to March 2023. All available in-vitro, animal and clinical studies that examined the effects of nutritional intervention on TBLI were included. The underlying mechanism was briefly reviewed. Folic acid, quercetin, curcumin, Lactobacillus casei, spirulina and Moringa oleifera possessed moderate evidence to have a beneficial effect on alleviating TBLI mostly based on animal studies. The evidence of other nutritional interventions on TBLI was weak. Alleviating oxidative stress and apoptosis were the leading mechanisms for the beneficial effects of nutritional intervention on TBLI. In conclusion, a few nutritional interventions are promising for alleviating TBLI including folic acid, quercetin, curcumin, L. casei, spirulina and M. oleifera, the effectiveness and safety of which need further confirmation by well-designed randomized controlled trials. The mechanisms for the protective role of these nutritional interventions on TBLI warrant further study, particularly by establishing the animal model of TBLI using the tuberculosis drugs separately.

Statistical methods for exploring spontaneous adverse event reporting databases for drug-host factor interactions

BACKGROUND

Drug toxicity does not affect patients equally; the toxicity may only exert in patients who possess certain attributes of susceptibility to specific drug properties (i.e., drug-host interaction). This concept is crucial for personalized drug safety but remains under-studied, primarily due to methodological challenges and limited data availability. By monitoring a large volume of adverse event reports in the postmarket stage, spontaneous adverse event reporting systems provide an unparalleled resource of information for adverse events and could be utilized to explore risk disparities of specific adverse events by age, sex, and other host factors. However, well-formulated statistical methods to formally address such risk disparities are currently lacking.

METHODS

In this paper, we present a statistical framework to explore spontaneous adverse event reporting databases for drug-host interactions and detect risk disparities in adverse drug events by various host factors, adapting methods for safety signal detection. We proposed four different methods, including likelihood ratio test, normal approximation test, and two tests using subgroup ratios. We applied our proposed methods to simulated data and Food and Drug Administration (FDA) Adverse Event Reporting Systems (FAERS) and explored sex-/age-disparities in reported liver events associated with specific drug classes.

RESULTS

The simulation result demonstrates that two tests (likelihood ratio, normal approximation) can detect disparities in adverse drug events associated with host factors while controlling the family wise error rate. Application to real data on drug liver toxicity shows that the proposed method can be used to detect drugs with unusually high level of disparity regarding a host factor (sex or age) for liver toxicity or to determine whether an adverse event demonstrates a significant unbalance regarding the host factor relative to other events for the drug.

CONCLUSION

Though spontaneous adverse event reporting databases require careful data processing and inference, the sheer size of the databases with diverse data from different countries provides unique resources for exploring various questions for drug safety that are otherwise impossible to address. Our proposed methods can be used to facilitate future investigation on drug-host interactions in drug toxicity using a large number of reported adverse events.

Acetaminophen-Induced Hepatotoxicity in Obesity and Nonalcoholic Fatty Liver Disease: A Critical Review

The epidemic of obesity, type 2 diabetes and nonalcoholic liver disease (NAFLD) favors drug consumption, which augments the risk of adverse events including liver injury. For more than 30 years, a series of experimental and clinical investigations reported or suggested that the common pain reliever acetaminophen (APAP) could be more hepatotoxic in obesity and related metabolic diseases, at least after an overdose. Nonetheless, several investigations did not reproduce these data. This discrepancy might come from the extent of obesity and steatosis, accumulation of specific lipid species, mitochondrial dysfunction and diabetes-related parameters such as ketonemia and hyperglycemia. Among these factors, some of them seem pivotal for the induction of cytochrome P450 2E1 (CYP2E1), which favors the conversion of APAP to the toxic metabolite N-acetyl-p-benzoquinone imine (NAPQI). In contrast, other factors might explain why obesity and NAFLD are not always associated with more frequent or more severe APAP-induced acute hepatotoxicity, such as increased volume of distribution in the body, higher hepatic glucuronidation and reduced CYP3A4 activity. Accordingly, the occurrence and outcome of APAP-induced liver injury in an obese individual with NAFLD would depend on a delicate balance between metabolic factors that augment the generation of NAPQI and others that can mitigate hepatotoxicity.

Folic acid protects against tuberculosis-drug-induced liver injury in rats and its potential mechanism by metabolomics

Observational study indicated that folic acid (FA) supplementation may protect against tuberculosis-drug-induced liver injury (TBLI). The aim is to investigate the effect and mechanism of FA on TBLI in rats. Liver injury was induced by a daily gavage of isoniazid (INH) and rifampicin (RIF) in the model and FA groups. Rats in the FA group were also treated with 2.5 mg/kg body weight FA. Rats in the control group were not treated. Eight rats were used in each group. The severity of liver injury was measured by the serum levels of hepatic enzymes and histological score. The metabolites in serum and liver tissues were analyzed by HPLC-Q-TOF-MS/MS. FA treatment significantly reduced alanine aminotransferase and liver necrosis. Seventy-nine differential metabolites in the serum and liver tissues were identified among the three groups. N-acylethanolamines, INH and RIF metabolites, phosphatidylcholines, lysophosphatidylcholines, monoglycerides, diglycerides and bile acids were regulated by FA treatment, involving key metabolic pathways, such as N-acylethanolamine metabolism, INH and RIF metabolism, liver regeneration, inflammation alleviation and bile acid metabolism. RT-PCR and western blotting results confirmed the altered N-acylethanolamine metabolism and improved drug metabolism by FA. In conclusion, FA was protective against TBLI, which may be related to the regulation of N-acylethanolamine metabolism and drug detoxification by FA.

Identifying Drug-Induced Liver Injury Associated With Inflammation-Drug and Drug-Drug Interactions in Pharmacologic Treatments for COVID-19 by Bioinformatics and System Biology Analyses: The Role of Pregnane X Receptor

Of the patients infected with coronavirus disease 2019 (COVID-19), approximately 14–53% developed liver injury resulting in poor outcomes. Drug-induced liver injury (DILI) is the primary cause of liver injury in COVID-19 patients. In this study, we elucidated liver injury mechanism induced by drugs of pharmacologic treatments against SARS-CoV-2 (DPTS) using bioinformatics and systems biology. Totally, 1209 genes directly related to 216 DPTS (DPTSGs) were genes encoding pharmacokinetics and therapeutic targets of DPTS and enriched in the pathways related to drug metabolism of CYP450s, pregnane X receptor (PXR), and COVID-19 adverse outcome. A network, constructed by 110 candidate targets which were the shared part of DPTSGs and 445 DILI targets, identified 49 key targets and four Molecular Complex Detection clusters. Enrichment results revealed that the 4 clusters were related to inflammatory responses, CYP450s regulated by PXR, NRF2-regualted oxidative stress, and HLA-related adaptive immunity respectively. In cluster 1, IL6, IL1B, TNF, and CCL2 of the top ten key targets were enriched in COVID-19 adverse outcomes pathway, indicating the exacerbation of COVID-19 inflammation on DILI. PXR-CYP3A4 expression of cluster 2 caused DILI through inflammation-drug interaction and drug-drug interactions among pharmaco-immunomodulatory agents, including tocilizumab, glucocorticoids (dexamethasone, methylprednisolone, and hydrocortisone), and ritonavir. NRF2 of cluster 3 and HLA targets of cluster four promoted DILI, being related to ritonavir/glucocorticoids and clavulanate/vancomycin. This study showed the pivotal role of PXR associated with inflammation-drug and drug-drug interactions on DILI and highlighted the cautious clinical decision-making for pharmacotherapy to avoid DILI in the treatment of COVID-19 patients.

Detection of Synergistic Interaction on an Additive Scale Between Two Drugs on Abnormal Elevation of Serum Alanine Aminotransferase Using Machine-Learning Algorithms

Drug-induced liver injury (DILI) is a common adverse drug reaction, with abnormal elevation of serum alanine aminotransferase (ALT). Several clinical studies have investigated whether a combination of two drugs alters the reporting frequency of DILI using traditional statistical methods such as multiple logistic regression (MLR), but this model may over-fit the data. This study aimed to detect a synergistic interaction between two drugs on the risk of abnormal elevation of serum ALT in Japanese adult patients using three machine-learning algorithms: MLR, logistic least absolute shrinkage and selection operator (LASSO) regression, and extreme gradient boosting (XGBoost) algorithms. A total of 58,413 patients were extracted from Nihon University School of Medicine's Clinical Data Warehouse and assigned to case (N = 4,152) and control (N = 54,261) groups. The MLR model over-fitted a training set. In the logistic LASSO regression model, three combinations showed relative excess risk due to interaction (RERI) for abnormal elevation of serum ALT: diclofenac and famotidine (RERI 2.427, 95% bootstrap confidence interval 1.226-11.003), acetaminophen and ambroxol (0.540, 0.087-4.625), and aspirin and cilostazol (0.188, 0.135-3.010). Moreover, diclofenac (adjusted odds ratio 1.319, 95% bootstrap confidence interval 1.189-2.821) and famotidine (1.643, 1.332-2.071) individually affected the risk of abnormal elevation of serum ALT. In the XGBoost model, not only the individual effects of diclofenac (feature importance 0.004) and famotidine (0.016), but also the interaction term (0.004) was included in important predictors. Although further study is needed, the combination of diclofenac and famotidine appears to increase the risk of abnormal elevation of serum ALT in the real world.

A Comprehensive Review of Computational Methods For Drug-Drug Interaction Detection

The detection of drug-drug interactions (DDIs) is a crucial task for drug safety surveillance, which provides effective and safe co-prescriptions of multiple drugs. Since laboratory researches are often complicated, costly and time-consuming, it's urgent to develop computational approaches to detect drug-drug interactions. In this paper, we conduct a comprehensive review of state-of-the-art computational methods falling into three categories: literature-based extraction methods, machine learning-based prediction methods and pharmacovigilance-based data mining methods. Literature-based extraction methods detect DDIs from published literature using natural language processing techniques; machine learning-based prediction methods build prediction models based on the known DDIs in databases and predict novel ones; pharmacovigilance-based data mining methods usually apply statistical techniques on various electronic data to detect drug-drug interaction signals. We first present the taxonomy of drug-drug interaction detection methods and provide the outlines of three categories of methods. Afterwards, we respectively introduce research backgrounds and data sources of three categories, and illustrate their representative approaches as well as evaluation metrics. Finally, we discuss the current challenges of existing methods and highlight potential opportunities for future directions.

A pharmacovigilance study of the association between tetracyclines and hepatotoxicity based on Food and Drug Administration adverse event reporting system data

Background While tetracycline antibiotics are commonly prescribed in practice, the risk of drug-induced liver injury (DILI) remains controversial. Aim To evaluate the association of DILI with tetracycline antibiotics. Method All DILI cases of tetracycline antibiotics as primary suspected drugs were extracted from the US Food and Drug Administration adverse event reporting system (FAERS). The outcomes included severe DILI, hepatocellular injury, cholestatic injury, and liver failure. Disproportionality analyses were conducted by estimating the reporting odds ratio (ROR) and the information component (IC). Results A total of 1,435 liver injury cases associated with tetracycline antibiotics were identified. The DILI signal was detected in tigecycline, minocycline, and doxycycline. The RORs and the 95% confidence intervals (95% CI) of tigecycline, minocycline, and doxycycline were (ROR 5.85, 95% CI 4.96-6.91), (ROR 6.4, 95% CI 5.76-7.11), and (ROR 2.07, 95% CI 1.86-2.31), respectively. Compared to minocycline (ROR 5.5, 95% CI 4.94-6.12; IC 2.35, 95% CI 1.98-2.68) and doxycycline (ROR 1.91, 95% CI 1.71-2.12; IC 0.91, 95% CI 0.55-1.26), tigecycline showed a stronger association with hepatocellular injury (ROR 7.11, 95% CI 6.13-8.23; IC 2.68, 95% CI 2.16-3.13). Tigecycline also showed a stronger association with cholestatic injury (ROR 12.16, 95% CI 10.13-14.61; IC 3.51, 95% CI 2.79-4) than minocycline (ROR 3.23, 95% CI 2.59-4.04; IC 1.67, 95% CI 0.9-2.37) or doxycycline (ROR 2.86, 95% CI 2.47-3.31; IC 1.5, 95% CI 1-1.97). Tigecycline (ROR 6.56, 95% CI 4.57-9.41; IC 2.69, 95% CI 1.28-3.64) and minocycline (ROR 4.22, 95% CI 3.14-5.66; IC 2.06, 95% CI 1-2.93) showed a significant association with liver failure. Conclusion The data mining of FAERS suggested an association between DILI and tigecycline, minocycline, and doxycycline.

Risk assessment of micafungin-induced liver injury using spontaneous reporting system data and electronic medical records

INTRODUCTION

There is limited information regarding antifungal-induced liver injuries, which have high mortality rates. Therefore, we used the Japanese Adverse Drug Event Report (JADER) database for signal detection associated with antifungal-induced liver injuries and medical records for risk assessment.

METHODS

Reports of antifungal-induced liver injuries from JADER data were analyzed to calculate the reporting odds ratio (ROR) and 95% confidence interval (CI). A medical record-based study involving 109 adult patients treated with micafungin shows liver injury as the primary outcome in patients treated with micafungin. The albumin-bilirubin (ALBI) score was calculated based on albumin and total bilirubin levels. We selected five explanatory factors for multivariable logistic regression: alanine aminotransferase ≥20 IU/L, alkaline phosphatase ≥372 IU/L, aspartate aminotransferase ≥25 IU/L, ALBI score ≥ -1.290, and age ≥65 years.

RESULTS

Signal detection for micafungin was observed in both, hepatocellular and cholestatic injuries, as per data from JADER. Univariate analyses performed on medical records suggest that alanine aminotransferase (p = 0.008), aspartate aminotransferase (p = 0.036), alkaline phosphatase (p = 0.045), and ALBI score (p = 0.028) may be factors associated with micafungin-induced liver injury. Based on multivariable logistic regression, the adjusted odds ratio for micafungin-induced liver injury in patients with ALBI score ≥ -1.290 was 2.78 (95% CI: 1.014-7.605, p = 0.047), suggesting that low hepatic functional reserve could be a risk factor for micafungin-induced liver injury.

CONCLUSIONS

Careful monitoring of liver function may be necessary for micafungin administration in patients with low hepatic functional reserve.

BERT-Based Natural Language Processing of Drug Labeling Documents: A Case Study for Classifying Drug-Induced Liver Injury Risk

Background & Aims: The United States Food and Drug Administration (FDA) regulates a broad range of consumer products, which account for about 25% of the United States market. The FDA regulatory activities often involve producing and reading of a large number of documents, which is time consuming and labor intensive. To support regulatory science at FDA, we evaluated artificial intelligence (AI)-based natural language processing (NLP) of regulatory documents for text classification and compared deep learning-based models with a conventional keywords-based model.

Methods: FDA drug labeling documents were used as a representative regulatory data source to classify drug-induced liver injury (DILI) risk by employing the state-of-the-art language model BERT. The resulting NLP-DILI classification model was statistically validated with both internal and external validation procedures and applied to the labeling data from the European Medicines Agency (EMA) for cross-agency application.

Results: The NLP-DILI model developed using FDA labeling documents and evaluated by cross-validations in this study showed remarkable performance in DILI classification with a recall of 1 and a precision of 0.78. When cross-agency data were used to validate the model, the performance remained comparable, demonstrating that the model was portable across agencies. Results also suggested that the model was able to capture the semantic meanings of sentences in drug labeling.

Conclusion: Deep learning-based NLP models performed well in DILI classification of drug labeling documents and learned the meanings of complex text in drug labeling. This proof-of-concept work demonstrated that using AI technologies to assist regulatory activities is a promising approach to modernize and advance regulatory science.

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 systematic comparison of hepatobiliary adverse drug reactions in FDA and EMA drug labeling reveals discrepancies

Drug labeling informs physicians and patients on the safe and effective use of medication. However, recent studies suggested discrepancies in labeling of the same drug between different regulatory agencies. Here, we evaluated the hepatic safety information in labeling for 549 medications approved by the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). Limited discrepancies were found regarding risk for hepatic adverse drug reactions (ADRs) (8.7% in hepatic ADR warnings and 21.3% in contraindication for liver disease), while caution should be exercised over drugs with inconsistencies in contraindications for liver disease and evidence for hepatotoxicity (4.9%). Most discrepancies were attributable to less-severe hepatic events and low-frequency hepatic ADR reports and had limited implication on clinical outcomes.

Verification of the "Upward Variation in the Reporting Odds Ratio Scores" to Detect the Signals of Drug-Drug Interactions

The reporting odds ratio (ROR) is easy to calculate, and there have been several examples of its use because of its potential to speed up the detection of drug-drug interaction signals by using the "upward variation of ROR score". However, since the validity of the detection method is unknown, this study followed previous studies to investigate the detection trend. The statistics models (the Ω shrinkage measure and the "upward variation of ROR score") were compared using the verification dataset created from the Japanese Adverse Drug Event Report database (JADER). The drugs registered as "suspect drugs" in the verification dataset were considered as the drugs to be investigated, and the target adverse event in this study was Stevens-Johnson syndrome (SJS), as in previous studies. Of 3924 pairs that reported SJS, the number of positive signals detected by the Ω shrinkage measure and the "upward variation of ROR score" (Model 1, the Susuta Model, and Model 2) was 712, 2112, 1758, and 637, respectively. Furthermore, 1239 positive signals were detected when the Haldane-Anscombe 1/2 correction was applied to Model 2, the statistical model that showed the most conservative detection trend. This result indicated the instability of the positive signal detected in Model 2. The ROR scores based on the frequency-based statistics are easily inflated; thus, the use of the "upward variation of ROR scores" to search for drug-drug interaction signals increases the likelihood of false-positive signal detection. Consequently, the active use of the "upward variation of ROR scores" is not recommended, despite the existence of the Ω shrinkage measure, which shows a conservative detection trend.

Treatment with the combination of clavulanic acid and valproic acid led to recovery of neuronal and behavioral deficits in an epilepsy rat model

Epilepsy, which is caused by abnormal neuronal firing in the brain, is a common neurological disease and affects motor and cognitive functions. Excessive levels of glutamate and insufficient levels of inhibitory GABA are involved in its pathophysiology. Valproic acid (Val), a GABAergic agonist, is one of the first-line antiepileptic drugs, but it shows many adverse side effects at the clinical dose. Clavulanic acid (CA), a β-lactamase inhibitor, has been demonstrated to increase glutamate transporter-1 expression. This study evaluated the effects of CA and Val in an epilepsy rat model. Male Wistar rats received intraperitoneal injections of pentylenetetrazol (PTZ, 35 mg/kg, every other day, IP, for 13 days) to induce kindling epilepsy. After four times of PTZ injection, rats received daily treatment with CA (1 or 10 mg/kg, IP), Val (50 or 100 mg/kg, IP), or the combination of CA (1 mg/kg) and Val (50 mg/kg) for 7 consecutive days. Motor, learning, and memory functions were measured. Rats with PTZ-induced kindling exhibited seizures, motor dysfunction, cognitive impairment, and cell loss and reduction of neurogenesis in the hippocampus. Neither 1 mg/kg CA nor 50 mg/kg Val treatment was effective in alleviating behavioral and neuronal deficits. However, treatment with 10 mg/kg CA, 100 mg/kg Val, and the combination of 1 mg/kg CA and 50 mg/kg Val improved these behavioral and neuronal deficits. Particularly, the combination of CA and Val showed synergistic effects on seizure suppression, suggesting the potential for treating epilepsy and related neuronal damage and motor and cognitive deficits.

Dietary Intake of Vegetables and Cooking Oil Was Associated With Drug-Induced Liver Injury During Tuberculosis Treatment: A Preliminary Cohort Study

Background and Purpose: Drug-induced liver injury is challenging during tuberculosis treatment. There is no epidemiological data investigating the relation between dietary intake and the risk of drug-induced liver injury during tuberculosis treatment. The aim of this study is to investigate the association of food and nutrient intake with the incidence of tuberculosis-drug-induced liver injury.

Methods: A cohort study was conducted in two city-level tuberculosis-specialized hospitals in Linyi City and Qingdao City, China from January 2011 to December 2013. The dietary intake was assessed by a 3-day 24-h food recall survey and a standard food-frequency questionnaire. The liver functions including aspartate aminotransferase (AST) and alanine aminotransferase (ALT) were monitored throughout the 6-month tuberculosis therapy. Liver injury was defined as ALT or AST higher than two times of the upper limit of normal (ULN). Liver dysfunction was defined as ALT or AST higher than the ULN. The ULN for ALT and AST is 40 U/L. Multivariate logistic regression analyses were performed to determine the dietary factors associated with the incidence of liver injury and liver dysfunction.

Results: A total of 605 patients were included in the analysis. During the treatment, 8.1% patients exhibited liver injury and 23.3% patients exhibited liver dysfunction. A lower intake of vegetables was associated with a higher risk of liver injury [OR (95% CI): 3.50 (1.52–8.08), P = 0.003) and liver dysfunction [OR (95% CI): 2.37 (1.31–4.29), P = 0.004], while a lower intake of cooking oil was associated with a lower risk of liver injury [OR (95% CI): 0.44 (0.20–0.96), P = 0.040)] and liver dysfunction [OR (95% CI): 0.51 (0.31–0.85), P = 0.009].

Conclusion: The current study indicated that the higher risks of tuberculosis-drug-induced liver injury and liver dysfunction were statistically associated with decreased vegetable intake and increased cooking oil intake.

Second-Generation Cephalosporins-Associated Drug-Induced Liver Disease: A Study in VigiBase with a Focus on the Elderly

Background: The objective of this study was to characterize individual case safety reports (ICSRs) and adverse drug reactions (ADRs) related to second-generation cephalosporins and resulting in hepatobiliary disorders, in VigiBase, WHO global database. Methods: All second-generation cephalosporins hepatobiliary ADRs reported up to July 2019 were included. Characteristic of cephalosporins and ADRs, aside from disproportionality data were evaluated. Results: A total of 1343 ICSRs containing 1585 ADRs were analyzed. Cefuroxime was suspected to have caused hepatobiliary disorders in most cases—in 38% of adults and in 35% of elderly. Abnormal hepatic function was the most frequent ADR, followed by jaundice and hepatitis. For 49% of the ADRs reported in the elderly and 51% in the adult population, the outcome was favorable, with fatal outcome for 2% of the adults and 10% of the elderly. Higher proportional reporting ration (PRR) values were reported in the elderly for cefotetan-associated jaundice, cefuroxime-associated acute hepatitis and hepatitis cholestatic as well as for cefotiam and cefmetazole-associated liver disorder. Conclusion: Hepatobiliary ADRs were reported for 2nd generation cephalosporins, with over 50% of cases in adults, without gender differences. Cholestatic hepatitis was predominately reported in the elderly and this category was more prone to specific hepatic reactions.

Spontaneous Reports of Serious Adverse Drug Reactions Resulting From Drug-Drug Interactions: An Analysis From the French Pharmacovigilance Database

Few data are available on the clinical impact of drug-drug interactions (DDIs). Most of the studies are limited to the analysis of exposure to potential DDI or the targeted impact of the combination of a few drugs or therapeutic classes. The analysis of adverse drug reaction (ADR) reports could be a mean to study generally the adverse effects identified due to a DDI. Our objective was to describe the characteristics of ADRs resulting from DDIs reported to the French Pharmacovigilance system and to identify the drugs most often implicated in these ADRs. Considering all ADR reports from January 01, 2012, to December 31, 2016, we identified all cases of ADR resulting from a DDI (DDI-ADRs). We then described these in terms of patients' characteristics, ADR seriousness, drugs involved (two or more per case), and ADR type. Of the 4,027 reports relating to DDI-ADRs, 3,303 were related to serious ADRs. Patients with serious DDI-ADRs had a median age of 76 years (interquartile range: 63-84); 53% were male. Of all serious DDI-ADRs, 11% were life-threatening and 8% fatal. In 36% of cases, the DDI causing the ADR involved at least three drugs. Overall, 8,424 different drugs were mentioned in the 3,303 serious DDI-ADRs considered. Altogether, drugs from the "antithrombotic agents" subgroup were incriminated in 34% of serious DDI-ADRs. Antidepressants were the second most represented therapeutic/pharmacological subgroup (5% of serious DDI-ADRs). Among the 3,843 ADR types reported in the 3,303 serious DDI-ADRs considered, the most frequently represented were hemorrhage (40% clinical hemorrhage; 6% biological hemorrhage), renal failure (8%), pharmacokinetic alteration (5%), and cardiac arrhythmias (4%). Hemorrhagic accidents are still an important part of serious ADRs resulting from DDIs reported in France. The other clinical consequences of DDIs seem less well identified by pharmacovigilance. Moreover, more than one-third of serious DDI-ADRs involved at least three drugs.

Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach

Drug-induced liver injury (DILI) presentation varies biochemically and histologically. Certain drugs present quite consistent injury patterns, i.e., DILI signatures. In contrast, others are manifested as broader types of liver injury. The variety of DILI presentations by a single drug suggests that both drugs and host factors may contribute to the phenotype. However, factors determining the DILI types have not been yet elucidated. Identifying such factors may help to accurately predict the injury types based on drugs and host information and assist the clinical diagnosis of DILI. Using prospective DILI registry datasets, we sought to explore and validate the associations of biochemical injury types at the time of DILI recognition with comprehensive information on drug properties and host factors. Random forest models identified a set of drug properties and host factors that differentiate hepatocellular from cholestatic damage with reasonable accuracy (69-84%). A simplified logistic regression model developed for practical use, consisting of patient's age, drug's lipoaffinity, and hybridization ratio, achieved a fair prediction (68-74%), but suggested potential clinical usability, computing the likelihood of liver injury type based on two properties of drugs taken by a patient and patient's age. In summary, considering both drug and host factors in evaluating DILI risk and phenotypes open an avenue for future DILI research and aid in the refinement of causality assessment.

Improved Detection Criteria for Detecting Drug-Drug Interaction Signals Using the Proportional Reporting Ratio

There is a current demand for “safety signal” screening, not only for single drugs but also for drug-drug interactions. The detection of drug-drug interaction signals using the proportional reporting ratio (PRR) has been reported, such as through using the combination risk ratio (CRR). However, the CRR does not consider the overlap between the lower limit of the 95% confidence interval of the PRR of concomitant-use drugs and the upper limit of the 95% confidence interval of the PRR of single drugs. In this study, we proposed the concomitant signal score (CSS), with the improved detection criteria, to overcome the issues associated with the CRR. “Hypothetical” true data were generated through a combination of signals detected using three detection algorithms. The signal detection accuracy of the analytical model under investigation was verified using machine learning indicators. The CSS presented improved signal detection when the number of reports was ≥3, with respect to the following metrics: accuracy (CRR: 0.752 → CSS: 0.817), Youden’s index (CRR: 0.555 → CSS: 0.661), and F-measure (CRR: 0.780 → CSS: 0.820). The proposed model significantly improved the accuracy of signal detection for drug-drug interactions using the PRR.

Integrating Regulatory Drug Label Information to Facilitate Evaluation of Adverse Events in Pharmacovigilance

Background:

Efficiency and accuracy for signal detection and evaluation activities are integral components of routine Pharmacovigilance (PV) practices. However, an Individual Case Safety Report (ICSR) may consist of a variety of confounders such as Concomitant Medications (CM), Past Medical History (PMH), and concurrent medical conditions that influence a safety officer’s evaluation of a potential Adverse Event (AE). Limited pharmacovigilance systems are currently available as a tool designed to enhance the efficiency and accuracy of signal detection and management.

Objective:

To introduce a systemic approach to make critical safety information readily available for users in order to discern possible interferences from CM and make informed decisions on the signal evaluation process – saving time while improving quality.

Methods:

Oracle Empirica Signal software was utilized to extract cases with CM that are Known Implicating Medications (KIM) for each AE according to public regulatory information from drug labels – FDA Structured Product Labeling (SPL) or EMA Summary of Product Characteristics (SPC). SAS Enterprise Guide was used to further process the data generated from Oracle Empirica Signal software.

Results:

For any target drug being evaluated for safety purposes, a KIM reference table can be generated, which summarizes all potential causality contributions from CMs.

Conclusion:

In addition to providing standalone KIM table as reference, adoption of this concept and automation may also be fully integrated into commercial signal detection and management software packages for easy use and accessibility and may even lead to reduced False Positive rate in signal detection within the PV space.

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.

Drug-induced liver injury

Drug-induced liver injury (DILI) is an adverse reaction to drugs or other xenobiotics that occurs either as a predictable event when an individual is exposed to toxic doses of some compounds or as an unpredictable event with many drugs in common use. Drugs can be harmful to the liver in susceptible individuals owing to genetic and environmental risk factors. These risk factors modify hepatic metabolism and excretion of the DILI-causative agent leading to cellular stress, cell death, activation of an adaptive immune response and a failure to adapt, with progression to overt liver injury. Idiosyncratic DILI is a relative rare hepatic disorder but can be severe and, in some cases, fatal, presenting with a variety of phenotypes, which mimic other hepatic diseases. The diagnosis of DILI relies on the exclusion of other aetiologies of liver disease as specific biomarkers are still lacking. Clinical scales such as CIOMS/RUCAM can support the diagnostic process but need refinement. A number of clinical variables, validated in prospective cohorts, can be used to predict a more severe DILI outcome. Although no pharmacological therapy has been adequately tested in randomized clinical trials, corticosteroids can be useful, particularly in the emergent form of DILI related to immune-checkpoint inhibitors in patients with cancer.

Drug-Drug Combinations Can Enhance Toxicity as Shown by Monocyte-Derived Hepatocyte-like Cells From Patients With Idiosyncratic Drug-Induced Liver Injury

Study identifier

ClinicalTrials.gov NCT 02353455.

EASL Clinical Practice Guidelines: Drug-induced liver injury

Idiosyncratic (unpredictable) drug-induced liver injury is one of the most challenging liver disorders faced by hepatologists, because of the myriad of drugs used in clinical practice, available herbs and dietary supplements with hepatotoxic potential, the ability of the condition to present with a variety of clinical and pathological phenotypes and the current absence of specific biomarkers. This makes the diagnosis of drug-induced liver injury an uncertain process, requiring a high degree of awareness of the condition and the careful exclusion of alternative aetiologies of liver disease. Idiosyncratic hepatotoxicity can be severe, leading to a particularly serious variety of acute liver failure for which no effective therapy has yet been developed. These Clinical Practice Guidelines summarize the available evidence on risk factors, diagnosis, management and risk minimization strategies for drug-induced liver jury.

Safety profile of traditional Chinese herbal injection: An analysis of a spontaneous reporting system in China

PURPOSE

Although a series of serious adverse events have continually raised concerns about the potential toxicity of traditional Chinese medicine injections (TCM injections), studies on this subject are still sparse. We conducted a descriptive analysis of a spontaneous reporting system in China to describe the safety profile of TCM injections.

METHODS

The safety profile of TCM injections is described by descriptive analysis of 559 066 adverse reports collected from Guangdong Provincial Center for adverse drug reaction (ADR) Monitoring in China during 2003 to 2017.

RESULTS

The percentage of new or serious ADRs of TCM injections is much higher than average percentage of China's spontaneous reporting system (SRS) as a whole (48.70% vs <25%). Compared with conventional injections, TCM injections have a slightly lower percentage of serious ADRs (6.02% vs 6.72%) and much higher percentage of unknown (new) ADRs (46.74% vs 24.13%). The gender and age distribution for TCM injections are similar to conventional injections. The reporting rates of ADRs increased with age. Anaphylactic shock and anaphylactoid reaction are high-risk ADRs for TCM injections and, anaphylactic shock is ranked number 1 in causing deaths (50.00%).

CONCLUSIONS

There are some differences and similarities on the safety profile between TCM injections and conventional injections. TCM injections have higher risk of adverse effects than any other dosage forms of TCM medications and higher percentage of new or serious adverse effects than conventional injections. A lot of work need to be done to clarify the huge amount of potential unknown adverse effects related to TCM injections.

Review article: drug-induced liver injury in the context of nonalcoholic fatty liver disease - a physiopathological and clinical integrated view

BACKGROUND

Nonalcoholic fatty disease (NAFLD) is the most common liver disease, since it is strongly associated with obesity and metabolic syndrome pandemics. NAFLD may affect drug disposal and has common pathophysiological mechanisms with drug-induced liver injury (DILI); this may predispose to hepatoxicity induced by certain drugs that share these pathophysiological mechanisms. In addition, drugs may trigger fatty liver and inflammation per se by mimicking NAFLD pathophysiological mechanisms.

AIMS

To provide a comprehensive update on (a) potential mechanisms whereby certain drugs can be more hepatotoxic in NAFLD patients, (b) the steatogenic effects of drugs, and (c) the mechanism involved in drug-induced steatohepatitis (DISH).

METHODS

A language- and date-unrestricted Medline literature search was conducted to identify pertinent basic and clinical studies on the topic.

RESULTS

Drugs can induce macrovesicular steatosis by mimicking NAFLD pathogenic factors, including insulin resistance and imbalance between fat gain and loss. Other forms of hepatic fat accumulation exist, such as microvesicular steatosis and phospholipidosis, and are mostly associated with acute mitochondrial dysfunction and defective lipophagy, respectively. Drug-induced mitochondrial dysfunction is also commonly involved in DISH. Patients with pre-existing NAFLD may be at higher risk of DILI induced by certain drugs, and polypharmacy in obese individuals to treat their comorbidities may be a contributing factor.

CONCLUSIONS

The relationship between DILI and NAFLD may be reciprocal: drugs can cause NAFLD by acting as steatogenic factors, and pre-existing NAFLD could be a predisposing condition for certain drugs to cause DILI. Polypharmacy associated with obesity might potentiate the association between this condition and DILI.

Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media

Drug-drug interactions (DDIs) constitute an important concern in drug development and postmarketing pharmacovigilance. They are considered the cause of many adverse drug effects exposing patients to higher risks and increasing public health system costs. Methods to follow-up and discover possible DDIs causing harm to the population are a primary aim of drug safety researchers. Here, we review different methodologies and recent advances using data mining to detect DDIs with impact on patients. We focus on data mining of different pharmacovigilance sources, such as the US Food and Drug Administration Adverse Event Reporting System and electronic health records from medical institutions, as well as on the diverse data mining studies that use narrative text available in the scientific biomedical literature and social media. We pay attention to the strengths but also further explain challenges related to these methods. Data mining has important applications in the analysis of DDIs showing the impact of the interactions as a cause of adverse effects, extracting interactions to create knowledge data sets and gold standards and in the discovery of novel and dangerous DDIs.

Severe Drug-Induced Liver Injury as an Adverse Drug Event of Antibiotics: A Case Report and Review of the Literature

Drug-induced liver injury is one of the main reasons for acute liver failure. We report the case of a young patient who experienced a drug-induced liver injury resulting in life-threatening acute liver failure after treatment with different antibiotics (amoxicillin, ciprofloxacin, cefazolin, clindamycin) and acetaminophen, or a combination of these drugs. Moreover, we provide an overview of the hepatotoxic potential of these drugs.

Comparison of efficacy of folic acid and silymarin in the management of antiepileptic drug induced liver injury: a randomized clinical trial

BACKGROUND

Liver injury associated with antiepileptic drugs accounts for a large proportion of drug-induced liver injuries (DILI) in children. Although withdrawal of the causative agent is the only proved treatment for DILI, in some clinical situations it is not possible. Recent studies have reported promising results of using hepatoprotective drugs with antioxidant actions for the management of DILI. This study aimed to evaluate the efficacy of folic acid versus silymarin treatment in relation to decreasing liver enzymes in patients with DILI due to antiepileptic therapy.

METHODS

This randomized, open-label, clinical trial evaluated 55 children with epilepsy who were on antiepileptic treatment and experienced DILI. The children were randomized to receive either silymarin (5 mg/kg per day) or folic acid (1 mg per day) for one month and were followed up for three months.

RESULTS

Liver enzymes significantly decreased in both groups. The decrease trend in alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were stronger in the folic acid group compared to silymarin group (P=0.04 and P=0.007, respectively). At the end of the study patients in the folic acid group had significantly lower ALT (P=0.04), AST (P=0.02), and gamma-glutamyl transferase (GGT) (P<0.001) levels and also higher percentage of normal ALT (30.7% vs 3.4%, P=0.009) and AST (42.3% vs 0%, P<0.001), and GGT (23.1% vs 0%, P=0.008) values compared to the patients in the silymarin group. No rebound elevations in ALT, AST and GGT levels or adverse reactions were noted in neither of the study groups.

CONCLUSION

Although both treatments were safe and effective in decreasing liver enzymes, folic acid seems to be superior to silymarin in the management of DILI.

Application and Exploration of Big Data Mining in Clinical Medicine

OBJECTIVE

To review theories and technologies of big data mining and their application in clinical medicine.

DATA SOURCES

Literatures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015.

STUDY SELECTION

Original articles regarding big data mining theory/technology and big data mining's application in the medical field were selected.

RESULTS

This review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster-Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine.

CONCLUSION

Big data mining has the potential to play an important role in clinical medicine.