Comparison of the Efficacy and Safety of Disease-Modifying Antirheumatic Drugs Combination Therapies: A Systematic Review and Network Meta-Analysis.

There are several disease-modifying antirheumatic drugs currently available to treat rheumatoid arthritis (RA). However, the optimal combination therapy with methotrexate for treating RA remains unclear. We aimed to identify combination therapies with high-efficacy and safety by employing the Bayesian method in a network meta-analysis.

Prediction of Cisplatin-Induced Acute Kidney Injury Using an Interpretable Machine Learning Model and Electronic Medical Record Information.

Predicting cisplatin-induced acute kidney injury (Cis-AKI) before its onset is important. We aimed to develop a predictive model for Cis-AKI using patient clinical information based on an interpretable machine learning algorithm. This single-center retrospective study included hospitalized patients aged ≥ 18 years who received the first course of cisplatin chemotherapy from January 1, 2011, to December 31, 2020, at Nagoya City University Hospital.

Machine Learning-Based Prediction of the Inhibitory Activity of Chemical Substances Against Rat and Human Cytochrome P450s.

The prediction of cytochrome P450 inhibition by a computational (quantitative) structure-activity relationship approach using chemical structure information and machine learning would be useful for toxicity research as a simple and rapid tool. However, there are few models focusing on the species differences between rat and human in the P450s inhibition. This study aimed to establish models to classify chemical substances as inhibitors or non-inhibitors of various rat and human P450s, using only molecular descriptors.

Streamlining Considerations for Safety Measures: A Predictive Model for Addition of Clinically Significant Adverse Reactions to Japanese Drug Package Inserts.

The addition of clinically significant adverse reactions (CSARs) to Japanese package inserts (PIs) is an important safety measure that can be used to inform medical personnel of potential health risks; however, determining the necessity of their addition can be lengthy and complex. Therefore, we aimed to construct a machine learning-based model that can predict the addition of CSARs at an early stage due to the accumulation of both Japanese and overseas adverse drug reaction (ADR) cases. The target comprised CSARs added to PIs from August 2011 to March 2022.

Predicting the Addition of Information Regarding Clinically Significant Adverse Drug Reactions to Japanese Drug Package Inserts Using a Machine-Learning Model.

Purpose: To develop a machine learning (ML)-based model for predicting the addition of clinically significant adverse reaction (CSAR)-associated information to drug package inserts (PIs) based on information of adverse drug reaction (ADR) cases during the post-marketing stage in Japan.

Methods: We collected data on CSARs added to PIs from August 2011 to March 2020. ADR cases that led to CSARs resulting in PI revisions were considered as a positive case, and ML was used to construct a binary classification model to predict the PI revisions.

Epidemiology and predictors of hyponatremia in a contemporary cohort of patients with malignancy: a retrospective cohort study.

Background: Hyponatremia is associated with worse outcomes among patients with malignancy. However, contemporary cohort data on epidemiology and risk factors are lacking.

Methods: In this single-centre, retrospective cohort study, patients who received intravenous antineoplastic agents from 2018 to 2020 at Nagoya City University Hospital were enrolled.

Comparison of Efficacy and Safety of Direct Oral Anticoagulants and Warfarin between Patients in Asian and Non-Asian Regions: A Systematic Review and Meta-Regression Analysis.

Direct oral anticoagulants (DOACs) have increasingly replaced warfarin for treating patients with non-valvular atrial fibrillation (NVAF). DOACs have been demonstrated to be more useful than warfarin, which was highlighted at its ethnic differences in efficacy and safety; however, the regional differences of DOACs remain unclear. We conducted a systematic review, meta-analysis, and meta-regression to evaluate the efficacy and safety of DOACs in patients from Asian and non-Asian regions with NVAF.

Development of quantitative model of a local lymph node assay for evaluating skin sensitization potency applying machine learning CatBoost.

The estimated concentrations for a stimulation index of 3 (EC3) in murine local lymph node assay (LLNA) is an important quantitative value for determining the strength of skin sensitization to chemicals, including cosmetic ingredients. However, animal testing bans on cosmetics in Europe necessitate the development of alternative testing methods to LLNA. A machine learning-based prediction method can predict complex toxicity risks from multiple variables.

In Silico Approach to Predict Severe Cutaneous Adverse Reactions Using the Japanese Adverse Drug Event Report Database.

Severe cutaneous adverse reactions (SCARs), such as Stevens-Johnson syndrome/toxic epidermal necrolysis and drug-induced hypersensitivity syndrome, are rare and occasionally fatal. However, it is difficult to detect SCARs at the drug development stage, necessitating a new approach for prediction. Therefore, in this study, using the chemical structure information of SCAR-causative drugs from the Japanese Adverse Drug Event Report (JADER) database, we tried to develop a predictive classification model of SCAR through deep learning.

Comparison of Efficacy of Dipeptidyl Peptidase-4 Inhibitors and Sodium-Glucose Co-Transporter 2 Inhibitors Between Japanese and Non-Japanese Patients: A Meta-Analysis.

We explored efficacy of dipeptidyl peptidase-4 inhibitors (DPP-4is) and sodium-glucose co-transporter 2 inhibitors (SGLT2is) between Japanese and non-Japanese patients with type 2 diabetes mellitus by conducting a systematic review and meta-analysis. A literature search of public databases before May 2017 identified 91 (DPP-4i) and 63 (SGLT2i) randomized placebo-controlled trials (> 12-week treatment). Multivariate meta-regression analysis identified baseline hemoglobin A1c (HbA1c) levels and placebo responses as covariates affecting efficacy of two agent classes independently of study region (Japanese/non-Japanese).

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.

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.