In silico prediction for toxicity of chemicals is required to reduce cost, time, and animal testing. However, predicting hepatocellular hypertrophy, which often affects the derivation of the No-Observed-Adverse-Effect Level in repeated dose toxicity studies, is difficult because pathological findings are diverse, mechanisms are largely unknown, and a wide variety of chemical structures exists. Therefore, a method for predicting the hepatocellular hypertrophy of diverse chemicals without complete understanding of their mechanisms is necessary. In this study, we developed predictive classification models of hepatocellular hypertrophy using machine learning-specifically, deep learning, random forest, and support vector machine. We extracted hepatocellular hypertrophy data on rats from 2 toxicological databases, our original database developed from risk assessment reports such as pesticides, and the Hazard Evaluation Support System Integrated Platform. Then, we constructed prediction models based on molecular descriptors and evaluated their performance using independent test chemicals datasets, which differed from the training chemicals datasets. Further, we defined the applicability domain (AD), which generally limits the application for chemicals, as structurally similar to the training chemicals dataset. The best model was found to be the support vector machine model using the Hazard Evaluation Support System Integrated Platform dataset, which was trained with 251 chemicals and predicted 214 test chemicals inside the applicability domain. It afforded a prediction accuracy of 0.76, sensitivity of 0.90, and area under the curve of 0.81. These in silico predictive classification models could be reliable tools for hepatocellular hypertrophy assessments and can facilitate the development of in silico models for toxicity prediction.

Phthalate esters (PEs) are widely used as plasticizers in various kinds of plastic products. Some PEs have been known to induce developmental and reproductive toxicity (DART) as well as hepatotoxicity in laboratory animals. In some cases of DART, the strength of toxicity of PEs depends on the side chain lengths, while the relationship between hepatotoxicity and side chain length is unknown. Therefore, in this study, we compared DART and hepatotoxicity in rats, focusing on 6 PEs with different side chains. We collected toxicity data of 6 PEs, namely, n-butyl benzyl phthalate (BBP), din -butyl phthalate (DBP), di(2-ethylhexyl) phthalate (DEHP), di-isodecyl phthalate (DIDP), di-isononyl phthalate (DINP), and di-noctyl phthalate (DNOP), from open data source, then, we constructed the toxicity database to comprehensively and efficiently compare the toxicity effects. When we compared DART using the toxicity database, we found that BBP, DBP, and DEHP with short side chains showed strong toxicities against the reproductive organs of male offspring, and the No-Observed-Adverse-Effect Levels (NOAELs) of BBP, DBP, and DEHP were lower than DIDP, DINP, and DNOP with long side chains. Comparing hepatotoxicities, the lowest NOAEL was shown 14 mg/kg/day for DEHP, based on liver weight gain with histopathological changes. However, as BBP and DBP showed higher NOAEL than the other 3 PEs (DIDP, DINP, and DNOP), we conclude that hepatotoxicity does not depend on the length of side chain. Concerning side chain length of PEs, we effectively utilized our constructed database and found that DART and hepatotoxicity in rats showed different modes of toxicities.

Although it is important to determine the risk factors for postoperative delirium (POD) to prevent the onset of POD, most previous studies that focused on risk factors for POD analyzed small cohort groups. Therefore, we aimed to explore the risk factors for the onset of POD by analyzing the National Health Insurance Claims Database (NDB) in Japan, which covers most Japanese residents.We compiled the clinical data of patients who emerged from POD after eight types of surgical procedures from August 2015 to August 2016 from the NDB. We subsequently developed a logistic regression model to determine the factors that affected the onset of POD.From the logistic regression analysis, we found that four factors (being over 75 years old, previous history of delirium, administration of a blood product during the operation, and emergency hospitalization) increased the odds ratio of POD. These factors may decrease physiological function, thereby leading to the onset of POD. We also clarified that the odds ratio of POD in patients administered inhalation anesthetic alone was lower than in patients administered combined inhalation and intravenous anesthetics for coronary artery bypass grafting. This finding was in line with the results of a previous small cohort study.These findings imply that, to prevent the onset of POD, appropriate precaution for and appropriate selection of anesthetic agents is necessary for patients with the four POD risk factors.

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Introduction: Because a lot of cost, time, and laboratory animals are required in the repeated dose toxicity tests, in silico prediction of toxicity is desired. Chemical-induced hepatocellular hypertrophy often influences the No-Observed-Adverse-Effect Level in repeated dose toxicity tests. Since hepatocellular hypertrophy is caused by various chemicals and the mechanisms are mostly unknown, it is necessary to develop a prediction method which does not require comprehensive understanding of mechanism. In this study, we developed predictive classification models of hepatocellular hypertrophy in rats using machine learning methods. Methods: Hepatocellular hypertrophy data was extracted from two reliable databases. One is our original database from risk assessment reports such as pesticides (ORAD: Original Risk Assessment reports Database). The other is the Hazard Evaluation Support System Integrated Platform (HESS). We constructed predictive classification models based on molecular descriptors calculated by Dragon 6 software using machine learning methods known as deep learning (DL), random forest (RF), and support vector machine (SVM), respectively. Model performance were evaluated using test chemicals independent from training chemicals used to build the model. Further, we also developed consensus models, which accepted matching results for more than two machine learning models (DL, RF, and SVM). Results: Among DL, RF, and SVM, the SVM model showed the highest performance with a prediction accuracy of 0.75 or more both in the ORAD dataset and the HESS dataset. Although the prediction accuracy was similar between the ORAD dataset and the HESS dataset in any model, the sensitivity was higher in the HESS dataset. The consensus model which is based on the combined three models could not increase the prediction accuracy or sensitivity from the SVM model in either the ORAD dataset or the HESS dataset. In the consensus model, the sensitivity was higher in the HESS dataset than the ORAD dataset as same as the SVM model. Conclusions:We have developed in silico predictive classification models of hepatocellular hypertrophy of diverse chemicals. The best model was SVM regardless the dataset. These our models could be faster and more cost-effective assessments tools for hepatocellular hypertrophy.