Relevant exposure routes need to be taken into account when performing chemical risk assessments for humans. Chemical risks are often assessed by performing route-to-route extrapolations based on oral repeated dose toxicity studies if route-specific toxicity data are unavailable. When performing a route-to-route extrapolation, an extrapolation factor derived from differences in absorption after exposure through different routes needs to be estimated. In this study, we used a machine learning (ML) and regression-based approach to estimate extrapolation-factor-like coefficients for oral-to-inhalation extrapolations using chemical structures and physicochemical properties. We used well-reviewed chemicals with human chronic toxicity values for specific administration routes (oral reference dose and inhalation reference concentration) available. ML regression models for predicting inhalation reference concentrations were developed using oral reference doses and molecular features as descriptors. The ML-based regression models gave better predictions than models using only molecular features or even single constant extrapolation factors, suggesting that the ML-based approach offers advantages over other oral-to-inhalation extrapolation methods.

Medication-induced hiccups, although uncommon, can significantly reduce patients’ quality of life. We had previously identified nicotine as a potential trigger of drug-induced hiccups. However, the mechanisms and risk factors, particularly those related to the route of administration, remain unclear. This study used the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) to investigate nicotine-induced hiccups, particularly focusing on the routes of administration and patient factors. We analyzed 14,836,467 cases reported between January 1, 2004, and March 31, 2022, which were downloaded from the FDA website. Of the 198,556 adverse event reports for nicotine, 970 involved hiccups. We performed univariate analyses on routes of administration and drug formulations to determine their influence on the occurrence of hiccups. Furthermore, we analyzed patient information related to nicotine-induced hiccups. Men were more frequently affected by nicotine-induced hiccups than women, with a higher incidence in older patients. Oral nicotine administration via gum and lozenges was more significantly associated with the occurrence of hiccups than other routes. Nicotine-induced hiccups are influenced by the administration route, particularly oral formulations, such as gum and lozenges. These findings indicate the need for further studies to elucidate the mechanisms of nicotine-induced hiccups and to develop preventive strategies.

Accurate prediction of drug response in cancer treatment remains a critical challenge due to the complex biological interactions underlying tumor sensitivity and resistance. In this work, we introduce OT-GNN, a novel graph neural network framework that leverages optimal transport theory to integrate prior drug-target interaction knowledge with gene expression profiles for interpretable and robust drug response prediction. By embedding an optimal transport-based alignment mechanism into the GNN architecture, OT-GNN dynamically reweights gene importance tailored to each drug–cell line pair, enhancing both predictive accuracy and biological interpretability. We evaluate OT-GNN on a processed NCI-60 dataset under zero-shot learning settings, demonstrating superior performance compared to traditional machine learning models, recent deep learning methods, and standard GNN variants without our proposed alignment. OT-GNN achieves state-of-the-art ROC-AUC and PR-AUC scores, with improved stability across multiple runs, highlighting its potential as a reliable tool for precision oncology applications. Our approach bridges the gap between data-driven modeling and biological prior knowledge, providing a pathway toward more transparent and effective drug response prediction.

The cost and time required for drug discovery have increased, prompting the need for more efficient methods to predict compound properties using artificial intelligence (AI). In particular, the prediction of absorption, distribution, metabolism, and excretion (ADME) properties is crucial. Intrinsic metabolic clearance (CL_int) is an essential property in ADME because it affects the side effects or the dosage schedule. In silico machine learning (ML) models for estimating CL_int have been developed, but due to the inherent complexity of AI techniques, it is difficult to obtain ideas for a more effective structure. Therefore, we employed SHAP (SHapley Additive exPlanations), an explainable AI (XAI) method, to elucidate the contributions of molecular substructures to CL_int predictions. We constructed a random forest model, classified into low and high clearance categories. SHAP values were calculated to visualize the importance of features, and significant substructures influencing CL_int were identified. The model demonstrated a high recall for predicting low-CL_int compounds; however, its overall accuracy for high-CL_int classification was low. This highlights its potential for filtering out low-CL_int compounds rather than accurately identifying those with high-CL_int. Visualization of SHAP values provided insights into substructure modifications to improve CL_int, thus providing valuable guidance for drug optimization. This approach highlights the effectiveness of integrating explainable AI methods to improve the interpretability of ML models in drug discovery.

Big data and artificial intelligence (AI) are now creating a whirlwind in the general methodology of both medical research and healthcare practice. It fundamentally transforms the entire paradigm of medical field, which could be called “the third revolution of medicine”, where the first one was caused by invention of antibiotics, contributed to eradicate the bacterial infection and the second one was brought by the invention of biopharmaceutical, such as molecular-targeted drug and antibody agent, introducing innovative treatment　methods for cancer and several incurable diseases. This article discusses the future form of medicine which the third revolution will bring about. As for the methodological innovation of medical research, this revolution will bring about the data-driven approach for medical science, promoting the reverse science, which will be supported by big data and inductive AI. As for the medical practice and healthcare, this revolution will advance the current mobile health and real world medicine, which, incorporating the cutting edge molecular instrumental methods, will realize PM (precision medicine) mobile health and PM real world medicine. Moreover, on account of current progress in natural language processing as seen in the large language model, AI method will expect to develop to understand the interrelationship among the clinical events described in EMR to comprehend disease progression course, which will realize the “predictive control medicine”. Those innovation in both medical research and clinical practice will contribute to reduce the disparity of medical cure level of the clinical practice.