Machine Learning Approaches to Predict Hepatotoxicity Risk in Patients Receiving Nilotinib

Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model

Purpose

Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions related to first-line anti-tuberculosis drugs in pediatric tuberculosis patients. This study aims to develop an automatic machine learning (AutoML) model for predicting the risk of anti-tuberculosis drug-induced liver injury (ATB-DILI) in children.

Methods

A retrospective study was performed on the clinical data and therapeutic drug monitoring (TDM) results of children initially treated for tuberculosis at the affiliated Changsha Central Hospital of University of South China. After the features were screened by univariate risk factor analysis, AutoML technology was used to establish predictive models. The area under the receiver operating characteristic curve (AUC) was used to evaluate model's performance, and then the TreeShap algorithm was employed to interpret the variable contributions.

Results

A total of 184 children were enrolled in this study, of whom 19 (10.33%) developed ATB-DILI. Univariate analysis showed that seven variables were risk factors for ATB-DILI, including the plasma peak concentration (Cmax) of rifampicin, body mass index (BMI), alanine aminotransferase, total bilirubin, total bile acids, aspartate aminotransferase and creatinine. Among the numerous predictive models constructed by the "H2O" AutoML platform, the gradient boost machine (GBM) model exhibited the superior performance with AUCs of 0.838 and 0.784 on the training and testing sets, respectively. The TreeShap algorithm showed that Cmax of rifampicin and BMI were important features that affect the AutoML model's performance.

Conclusion

The GBM model established by AutoML technology shows high predictive accuracy and interpretability for ATB-DILI in children. The prediction model can assist clinicians to implement timely interventions and mitigation strategies, and formulate personalized medication regimens, thereby minimizing potential harm to high-risk children of ATB-DILI.

Construction of an explanatory model for predicting hepatotoxicity: a case study of the potentially hepatotoxic components of Gardenia jasminoides

It is well-known that the hepatotoxicity of drugs can significantly influence their clinical use. Despite their effective therapeutic efficacy, many drugs are severely limited in clinical applications due to significant hepatotoxicity. In response, researchers have created several machine learning-based hepatotoxicity prediction models for use in drug discovery and development. Researchers aim to predict the potential hepatotoxicity of drugs to enhance their utility. However, current hepatotoxicity prediction models often suffer from being unverified, and they fail to capture the detailed toxicological structures of predicted hepatotoxic compounds. Using the 56 chemical constituents of Gardenia jasminoides as examples, we validated the trained hepatotoxicity prediction model through literature reviews, principal component analysis (PCA), and structural comparison methods. Ultimately, we successfully developed a model with strong predictive performance and conducted visual validation. Interestingly, we discovered that the predicted hepatotoxic chemical constituents of Gardenia possess both toxic and therapeutic effects, which are likely dose-dependent. This discovery greatly contributes to our understanding of the dual nature of drug-induced hepatotoxicity.

Predicting adverse drug event using machine learning based on electronic health records: a systematic review and meta-analysis

Introduction

Adverse drug events (ADEs) pose a significant challenge in current clinical practice. Machine learning (ML) has been increasingly used to predict specific ADEs using electronic health record (EHR) data. This systematic review provides a comprehensive overview of the application of ML in predicting specific ADEs based on EHR data.

Methods

A systematic search of PubMed, Web of Science, Embase, and IEEE Xplore was conducted to identify relevant articles published from the inception to 20 May 2024. Studies that developed ML models for predicting specific ADEs or ADEs associated with particular drugs were included using EHR data.

Results

A total of 59 studies met the inclusion criteria, covering 15 drugs and 15 ADEs. In total, 38 machine learning algorithms were reported, with random forest (RF) being the most frequently used, followed by support vector machine (SVM), eXtreme gradient boosting (XGBoost), decision tree (DT), and light gradient boosting machine (LightGBM). The performance of the ML models was generally strong, with an average area under the curve (AUC) of 76.68% ± 10.73, accuracy of 76.00% ± 11.26, precision of 60.13% ± 24.81, sensitivity of 62.35% ± 20.19, specificity of 75.13% ± 16.60, and an F1 score of 52.60% ± 21.10. The combined sensitivity, specificity, diagnostic odds ratio (DOR), and AUC from the summary receiver operating characteristic (SROC) curve using a random effects model were 0.65 (95% CI: 0.65-0.66), 0.89 (95% CI: 0.89-0.90), 12.11 (95% CI: 8.17-17.95), and 0.8069, respectively. The risk factors associated with different drugs and ADEs varied.

Discussion

Future research should focus on improving standardization, conducting multicenter studies that incorporate diverse data types, and evaluating the impact of artificial intelligence predictive models in real-world clinical settings.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024565842, identifier CRD42024565842.

Effects of Platycladus orientalis Leaf Extract on the Growth Performance, Fur-Production, Serum Parameters, and Intestinal Microbiota of Raccoon Dogs

Platycladus orientalis leaves are rich in flavonoids and polysaccharides, which offer high medicinal and nutritional benefits. This study aimed to investigate the impact of P. orientalis leaf extract (PLE) on the growth performance, fur quality, serum parameters, and intestinal microbiota of raccoon dogs. Sixty healthy male black raccoon dogs, aged 85 (±5) days, were randomly assigned to four groups and fed a basal diet supplemented with 0, 0.25, 0.50, and 1.00 g/kg PLE for 125 days (designated as groups P0, P1, P2, and P3, respectively). The results revealed that the raccoon dogs in group P1 exhibited increased average daily gain and underfur length while showing a decreased feed/gain ratio compared to group P0 (p < 0.05). However, the heart index in group P2 was significantly lower than in group P0 (p < 0.05), and the kidney index and serum alanine aminotransferase activities in group P3 were higher than in groups P2 and P0 (p < 0.05), suggesting potential adverse effects at higher PLE dosages. Notably, dietary PLE supplementation led to a reduction in serum glucose concentrations (p < 0.05), which may have implications for glucose regulation. Furthermore, the study explored the impact of dietary supplementation with 0.25 g/kg PLE on the raccoon dogs' intestinal microbiota using high-throughput sequencing. The results showed significant alterations in the microbial community structure, with a notable decrease in the abundance of Prevotella copri in response to 0.25 g/kg PLE supplementation (p < 0.05). In conclusion, supplementing raccoon dogs' diet with 0.25 g/kg PLE can lead to improved growth performance and a positive influence on the intestinal microbiota. However, caution should be exercised regarding higher dosages, as they may have adverse effects on certain parameters. As a result, PLE holds promise as a potential feed additive for fur animal production.

Machine-learning-based adverse drug event prediction from observational health data: A review

Adverse drug events (ADEs) are responsible for a significant number of hospital admissions and fatalities. Machine learning models have been developed to assess the individual patient risk of having an ADE. In this article, we have reviewed studies addressing the prediction of ADEs in observational health data with machine learning. The field of individualised ADE prediction is rapidly emerging through the increasing availability of additional data modalities (e.g., genetic data, screening data, wearables data) and advanced deep learning models such as transformers. Consequently, personalised adverse drug event predictions are becoming more feasible and tangible.

Screening of major hepatotoxic components of Tripterygium wilfordii based on hepatotoxic injury patterns

BACKGROUND

Tripterygium wilfordii Hook. F. (TwHF), a traditional Chinese medicine, is widely used in the treatment of rheumatoid arthritis. Due to multiorgan toxicity, particularly hepatotoxicity, the application of TwHF is restricted. To clarify the hepatotoxic substances, zebrafish, hepatocytes and macrophages were used for screening based on hepatotoxic injury patterns. This study provides a basis for further elucidation of the hepatotoxic mechanism of TwHF.

METHODS

First, 12 compounds were selected according to the chemical categories of TwHF. The fluorescence area and fluorescence intensity of zebrafish livers were observed and calculated. The viability of two hepatocyte lines was detected by CCK₈ assay. TNF-α and IL-1β mRNA expression in bone marrow-derived macrophages was used to evaluate macrophage activation, a factor of potential indirect hepatotoxicity. Finally, the hepatotoxic characteristics of 4 representative components were verified in mice in vivo.

RESULTS

Parthenolide, triptolide, triptonide, triptobenzene H, celastrol, demethylzeylasteral, wilforlide A, triptotriterpenic acid A and regelidine significantly reduced the fluorescence area and fluorescence intensity of zebrafish livers. The viability of L-02 or AML-12 cells was significantly inhibited by parthenolide, triptolide, triptonide, celastrol, demethylzeylasteral, and triptotriterpenic acid A. Parthenolide, triptolide, triptonide, celastrol, demethylzeylasteral and triptobenzene H significantly increased TNF-α and IL-1β mRNA levels in macrophages, while triptophenolide, hypodiolide and wilforine significantly reduced TNF-α and IL-1β mRNA levels. Triptotriterpenic acid A, celastrol and triptobenzene H at a dose of 10 mg/kg significantly increased the levels of mouse serum alanine aminotransferase and aspartate aminotransferase and aggravated liver inflammation.

CONCLUSIONS

Parthenolide, triptolide, triptonide, celastrol, demethylzeylasteral, triptotriterpenic acid A and triptobenzene H might be the main hepatotoxic components of TwFH. Among them, only triptotriterpenic acid A presents direct hepatotoxicity. Triptobenzene H exerts indirect liver damage by activating macrophages. Parthenolide, triptolide, triptonide, celastrol, and demethylzeylasteral can directly and indirectly cause liver injury.