Deep Learning for Drug-Induced Liver Injury

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

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

Frontiers in Artificial Intelligence ◽

10.3389/frai.2021.729834 ◽

2021 ◽

Vol 4 ◽

Author(s):

Yue Wu ◽

Zhichao Liu ◽

Leihong Wu ◽

Minjun Chen ◽

Weida Tong

Keyword(s):

United States ◽

Deep Learning ◽

Natural Language Processing ◽

Liver Injury ◽

Natural Language ◽

Language Processing ◽

The United States ◽

Drug Induced ◽

Drug Induced Liver Injury ◽

Drug Labeling

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.

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Leberschaden unter oraler Antikoagulation, Statin- und ACE-Hemmertherapie

Praxis ◽

10.1024/1661-8157/a000298 ◽

2010 ◽

Vol 99 (21) ◽

pp. 1259-1265 ◽

Cited By ~ 2

Author(s):

Bruggisser ◽

Terraciano ◽

Rätz Bravo ◽

Haschke

Keyword(s):

Liver Injury ◽

Wird Eine ◽

Drug Induced ◽

Drug Induced Liver Injury

Ein 71-jähriger Patient stellt sich mit Epistaxis und ikterischen Skleren auf der Notfallstation vor. Der Patient steht unter einer Therapie mit Phenprocoumon, Atorvastatin und Perindopril. Anamnestisch besteht ein langjähriger Alkoholabusus. Laborchemisch werden massiv erhöhte Leberwerte (ALAT, Bilirubin) gesehen. Der INR ist unter oraler Antikoagulation und bei akuter Leberinsuffizienz >12. Die weiterführenden Abklärungen schliessen eine Virushepatitis und eine Autoimmunhepatitis aus. Nachdem eine Leberbiopsie durchgeführt werden kann, wird eine medikamentös-toxische Hepatitis, ausgelöst durch die Komedikation von Atorvastatin, Phenprocoumon und Perindopril bei durch Alkohol bereits vorgeschädigter Leber diagnostiziert. Epidemiologie, Pathophysiologie und Klink der medikamentös induzierten Leberschäden (drug induced liver injury, DILI), speziell von Coumarinen, Statinen und ACE-Hemmern werden im Anschluss an den Fallbericht diskutiert.

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