Comparison of the developmental/reproductive toxicity and hepatotoxicity of phthalate esters in rats using an open toxicity data source.

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), di-n-butyl phthalate (DBP), di(2-ethylhexyl) phthalate (DEHP), di-isodecyl phthalate (DIDP), di-isononyl phthalate (DINP), and di-n-octyl 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.

Possible Causes of Failing to Meet Primary Endpoints: A Systematic Review of Randomized Controlled Phase 3 Clinical Trials in Patients With Non-Small Cell Lung Cancer.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the most common cause of cancer-related mortality worldwide and represents a huge unmet medical need. Despite the favorable results of phase 2 clinical trials, many phase 3 clinical trials fail to meet primary endpoints. Therefore, we investigated the causes of failure to meet primary endpoints in phase 3 clinical trials. METHODS: We performed a systematic review of phase 3 clinical trials in patients with NSCLC. The results of phase 3 clinical trials collected from the survey were categorized as "negative" (failed to meet the primary endpoint) or "positive" (met the primary endpoint). RESULTS: Of a total of 106 trials collected from this survey, 40 positive trials (38%) and 66 negative trials (62%) were identified. The majority of the primary endpoints were overall survival (OS) or progression-free survival (PFS) (94%). More trials using OS as the primary endpoint were negative (42 of 56 trials), and more trials using PFS as the primary endpoint were positive (24 of 44 trials). The median OS in the control arm in negative trials was significantly longer than the pretrial estimate ( P < .001), whereas the median PFS in the control arm in positive trials was relatively consistent with the pretrial estimate. CONCLUSIONS: Our findings suggest that the selection of the primary endpoint and the pretrial estimate can potentially impact the results of phase 3 clinical trials in patients with NSCLC and are critical success factors when planning phase 3 clinical trials.

In Silico Prediction of Chemical-Induced Hepatocellular Hypertrophy Using Molecular Descriptors.

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.