scholarly journals Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

2020 ◽  
Vol 21 (6) ◽  
pp. 2114
Author(s):  
Robert Ancuceanu ◽  
Marilena Viorica Hovanet ◽  
Adriana Iuliana Anghel ◽  
Florentina Furtunescu ◽  
Monica Neagu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Injury ◽  
Computational Models ◽  
Liver Toxicity ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Drug Induced ◽  
Reference Drug ◽  
Drug Induced Liver Injury

Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans” (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

scholarly journals Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

2020 ◽  
Author(s):  
Robert Ancuceanu ◽  
Marilena Viorica Hovanet ◽  
Adriana Iuliana Anghel ◽  
Florentina Furtunescu ◽  
Monica Neagu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Injury ◽  
Computational Models ◽  
Liver Toxicity ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Drug Induced ◽  
Reference Drug ◽  
Drug Induced Liver Injury

Drug induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized drugs and candidate drugs and predicting hepatotoxicity from the chemical structure of a substance remains a challenge worth pursuing, being also coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016 a group of researchers from FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans”, DILIrank. This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A number of 78 models with reasonable performance have been selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

scholarly journals Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI)

2020 ◽  
Vol 17 (7) ◽  
pp. 2628-2637 ◽  
Author(s):  
Eni Minerali ◽  
Daniel H. Foil ◽  
Kimberley M. Zorn ◽  
Thomas R. Lane ◽  
Sean Ekins
Keyword(s):  
Machine Learning ◽  
Liver Injury ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Drug Induced ◽  
Drug Induced Liver Injury

scholarly journals Machine-Learning Prediction of Oral Drug-Induced Liver Injury (DILI) via Multiple Features and Endpoints

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiaobin Liu ◽  
Danhua Zheng ◽  
Yi Zhong ◽  
Zhaofan Xia ◽  
Heng Luo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Liver Injury ◽  
Computational Models ◽  
Characteristic Curve ◽  
Prediction Performance ◽  
Oral Drug ◽  
Good Prediction ◽  
Drug Induced ◽  
Drug Induced Liver Injury

Drug discovery is a costly process which usually takes more than 10 years and billions of dollars for one successful drug to enter the market. Despite all the safety tests, drugs may still cause adverse reactions and be restricted in use or even withdrawn from the market. Drug-induced liver injury (DILI) is one of the major adverse drug reactions, and computational models may be used to predict and reduce it. To assess the computational prediction performance of DILI, we curated DILI endpoints from three databases and prepared drug features including chemical descriptors, therapeutic classifications, gene expressions, and binding proteins. We trained machine-learning models to predict the various DILI endpoints using different drug features. Using the optimal feature sets, the top-performing models obtained areas under the receiver operating characteristic curve (AUC) around 0.8 for some DILI endpoints. We found that some features, including therapeutic classifications and proteins, have good prediction performance towards DILI. We also discovered that the severity of DILI endpoints as well as the selection of negative samples may significantly affect the prediction results. Overall, our study provided a comprehensive collection, curation, and prediction of DILI endpoints using various drug features, which may help the drug researchers to better understand and prevent DILI during the drug discovery process.

scholarly journals Pervasive Lying Posture Tracking

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5953 ◽  
Author(s):  
Parastoo Alinia ◽  
Ali Samadani ◽  
Mladen Milosevic ◽  
Hassan Ghasemzadeh ◽  
Saman Parvaneh
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Computational Models ◽  
Learning Algorithms ◽  
Pressure Sensors ◽  
Machine Learning Algorithms ◽  
Sensor System ◽  
Accurate Detection ◽  
Research Questions ◽  
Posture Tracking

Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article we propose a comprehensive approach for designing a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are the most effective in the accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F1 score that ranges from 95.2% to 97.8% with a coefficient of variation from 0.03 to 0.05. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that, because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.

Drug-induced liver injury classification model based on in vitro human transcriptomics and in vivo rat clinical chemistry data

2014 ◽  
Vol 2 (4) ◽  
pp. 63-70 ◽  
Author(s):  
Danyel Jennen ◽  
Jan Polman ◽  
Mark Bessem ◽  
Maarten Coonen ◽  
Joost van Delft ◽  
...  
Keyword(s):  
Liver Injury ◽  
Clinical Chemistry ◽  
Classification Model ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Chemistry Data ◽  
Model Based ◽  
Injury Classification

scholarly journals A case report of a drug‐induced liver injury (DILI) caused by multiple antidepressants with causality established by the updated Roussel Uclaf causality assessment method (RUCAM) and in vitro testing

2020 ◽  
Vol 8 (12) ◽  
pp. 3105-3109
Author(s):  
Miguel González‐Muñoz ◽  
Jaime Monserrat Villatoro ◽  
Eva Marín‐Serrano ◽  
Stefan Stewart ◽  
Belén Bardón Rivera ◽  
...  
Keyword(s):  
Case Report ◽  
Liver Injury ◽  
Causality Assessment ◽  
Assessment Method ◽  
In Vitro Testing ◽  
Drug Induced ◽  
Drug Induced Liver Injury

scholarly journals Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury

2020 ◽  
Vol 8 ◽  
Author(s):  
Ting Li ◽  
Weida Tong ◽  
Ruth Roberts ◽  
Zhichao Liu ◽  
Shraddha Thakkar
Keyword(s):  
Deep Learning ◽  
Liver Injury ◽  
High Throughput ◽  
Permutation Test ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Attrition Rate ◽  
Support Vector ◽  
Drug Induced ◽  
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.

scholarly journals Evaluation of the relevance of DILI predictive hypotheses in early drug development: review of in vitro methodologies vs. BDDCS classification

2018 ◽  
Vol 7 (3) ◽  
pp. 358-370 ◽  
Author(s):  
Rosa Chan ◽  
Leslie Z. Benet
Keyword(s):  
Liver Injury ◽  
Drug Development ◽  
Safety Concern ◽  
Drug Induced ◽  
Development Review ◽  
Drug Induced Liver Injury ◽  
Underlying Mechanisms ◽  
Early Drug

Drug-induced liver injury (DILI) is a major safety concern; it occurs frequently; it is idiosyncratic; it cannot be adequately predicted; and a multitude of underlying mechanisms has been postulated.

scholarly journals The evolution of strategies to minimise the risk of human drug-induced liver injury (DILI) in drug discovery and development

2020 ◽  
Vol 94 (8) ◽  
pp. 2559-2585 ◽  
Author(s):  
Paul A. Walker ◽  
Stephanie Ryder ◽  
Andrea Lavado ◽  
Clive Dilworth ◽  
Robert J. Riley
Keyword(s):  
Drug Discovery ◽  
Liver Injury ◽  
Drug Uptake ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
New Chemical Entities ◽  
Preclinical Animal Models ◽  
Uptake Transporters

Abstract Early identification of toxicity associated with new chemical entities (NCEs) is critical in preventing late-stage drug development attrition. Liver injury remains a leading cause of drug failures in clinical trials and post-approval withdrawals reflecting the poor translation between traditional preclinical animal models and human clinical outcomes. For this reason, preclinical strategies have evolved over recent years to incorporate more sophisticated human in vitro cell-based models with multi-parametric endpoints. This review aims to highlight the evolution of the strategies adopted to improve human hepatotoxicity prediction in drug discovery and compares/contrasts these with recent activities in our lab. The key role of human exposure and hepatic drug uptake transporters (e.g. OATPs, OAT2) is also elaborated.

Sign in / Sign up

Export Citation Format

Share Document