Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 6 appraisals
Archives of toxicology
A review of machine learning in toxicology: current practices and reporting gaps
In recent years, machine learning and artificial intelligence approaches have been increasingly applied in the context of toxicological risk assessment. Many published overview, review, and comment papers discuss advantages, disadvantages, success stories, and open challenges for the application of machine learning models in toxicology. Machine learning methods using information from in vitro experiments can help to avoid animal experiments, thus allowing for larger numbers of experiments to be conducted. Drawbacks of machine learning models are the lack of mechanistic interpretability and the need for large amounts of high-quality data. In this work, we present a literature review of papers indexed in PubMed or published in the journal Computational Toxicology in the years 2022 to 2024, to assess the usage of machine learning methods in toxicology as well as the practices in reporting of methods and corresponding results. We do not address the suitability or the performance of methods, which is impossible to assess objectively without reanalysis on raw data, but focus on common practices and gaps in reporting. Major results are that many different machine learning methods are used in toxicology, often with appropriate internal validation. However, in only half of the cases, interpretation methods are used to address the problem that these models often make predictions as a black box. Moreover, there are very frequent gaps in reporting, in particular related to handling of missing values, and availability of data and code. Thus, this review can serve as a starting point for further tailored methodological research and guidance.
2 Aug 2026
Read appraisal →Cardiovascular toxicology
Mechanisms of Cardiovascular Toxicity Induced by Silver Nanoparticles: A Systematic Review of Preclinical Evidence
Silver nanoparticles (AgNPs) have emerged as one of the most widely used nanomaterials in medical and consumer products, yet their cardiovascular safety remains inadequately characterized. While their antimicrobial properties are well-documented, accumulating evidence reveals that AgNPs pose a significant threat to the cardiovascular system. Exposure to AgNPs through inhalation, ingestion, or dermal contact enables these particles to enter systemic circulation, where they accumulate in cardiac and vascular tissues in a dose-, time-, and size-dependent manner. Once deposited, AgNPs initiate a cascade of pathological events, including oxidative stress, mitochondrial dysfunction, DNA damage, and disruption of calcium and sodium channel signaling. These molecular disturbances translate into physiological alterations such as bradycardia, atrioventricular conduction block, impaired contractility, and exacerbated ischemia-reperfusion injury. AgNPs also trigger endothelial activation, leading to upregulation of adhesion molecules, namely vascular cell adhesion molecule-1 (VCAM-1), intracellular adhesion molecule-1 (ICAM-1) and recruitment of inflammatory cells, creating a proinflammatory and prothrombotic environment. Elevated cardiac biomarkers, including creatine kinase-MB fraction (CK-MB), brain natriuretic peptide (BNP), lactate dehydrogenase (LDH), and histopathological changes, including pericardial edema, myofibril disorganization, fibrosis, and inflammatory infiltration are consistently observed. Vascular dysfunction manifests as impaired vasorelaxation, enhanced vasoconstriction via endothelin-1 upregulation, thrombosis, and abnormal angiogenesis driven by vascular endothelial growth factor (VEGF) dysregulation. Although plant-based AgNPs have shown cardioprotective potential in limited studies, the overall evidence indicates significant cardiovascular risk. This systematic review synthesizes findings from 38 preclinical studies (35 in vivo, 3 in vitro), providing a comprehensive analysis of AgNP-induced cardiotoxicity with emphasis on oxidative stress, inflammation, and molecular dysregulation as core mechanistic drivers.
13 July 2026
Read appraisal →JMIR infodemiology
Self-Reported Tianeptine Experiences on Reddit: Natural Language Processing-Assisted Qualitative Study
This study, using natural language processing and manual thematic analysis of Reddit posts, revealed a rapid rise in discussions about tianeptine, with posts frequently reporting dependence, withdrawal, and coingestion with other unregulated substances, highlighting tianeptine as an emerging public health concern.
8 July 2026
Read appraisal →Journal of chemical information and modeling
BioTD: An Online Database of Biotoxins
Biotoxins, mainly produced by venomous animals, plants, and microorganisms, exhibit high physiological activity and unique effects such as lowering blood pressure and analgesia. A number of venom-derived drugs are already available on the market, with many more candidates currently undergoing clinical and laboratory studies. However, drug design resources related to biotoxins are insufficient, particularly because of a lack of accurate and extensive activity data. To fulfill this demand, we developed the Biotoxins Database (BioTD). BioTD is the largest open-source database for toxins, offering open access to 14,607 data records (8,185 activity records), covering 8,975 toxins sourced from 5,220 references and patents across over 900 species. The activity data in BioTD are categorized into five groups: Activity, Safety, Kinetics, Hemolysis, and other physiological indicators. Moreover, BioTD provides data on 1,532 mutants, refines the whole sequence and signal peptide sequences of toxins, and annotates disulfide-bond information. All of the data in the database can be downloaded for free. Given the importance of biotoxins and their associated data, this new database is expected to attract broad interest from diverse research fields in drug discovery. BioTD is freely accessible at http://biotoxin.net/.
23 June 2026
Read appraisal →European journal of medicinal chemistry
Machine learning-based prediction of drug lactation risk: Bridging molecular features and breastfeeding safety
BACKGROUND: Breastfeeding medication safety assessment presents critical challenges due to limited clinical evidence and ethical constraints on lactation studies. While existing computational models focus on predicting pharmacokinetic surrogates such as milk-to-plasma ratios, these endpoints lack direct clinical actionability for therapeutic decision-making. METHODS: We developed LRCpredictor, a computational framework for predicting drug lactation risk based on Dr. Thomas W. Hale's evidence-based Lactation Risk Categories (LRC) system. A dataset of 391 drugs (179 high-risk L4/L5, 212 low-risk L1/L2) was characterized using three complementary molecular representations. Systematic feature engineering with four selection algorithms identified optimal feature subsets. Five ensemble machine learning algorithms were evaluated through cross-validation and independent testing. Multi-level SHAP analysis and structural alert mining were employed to elucidate molecular determinants of lactation risk. RESULTS: The optimal Gradient Boosting Decision Tree (GBDT) model using 35 selected features achieved robust cross-validated performance (AUC = 0.80, MCC = 0.52) and demonstrated notably superior discrimination for extreme risk categories (L1 vs. L5: AUC = 0.85, MCC = 0.62). Analysis revealed lactation risk is governed by the complex interplay of electronic properties, structural topology, electrotopological characteristics, polarizability and drug-likeness attributes. Matched molecular pair analysis demonstrated how specific structural modifications translate into interpretable risk changes. We further identified 18 structural alerts, including five exclusive to high-risk drugs, enabling rapid toxicophore recognition during risk assessment and drug design. CONCLUSIONS: LRCpredictor provides the first interpretable framework for clinically actionable lactation risk prediction with mechanistic molecular insights. The freely accessible web platform (https://lrcpredictor.streamlit.app/) with integrated visual explanations supports evidence-based decision-making for healthcare providers and drug design optimization.
7 June 2026
Read appraisal →Intensive Care Med
Critical care management of the patient with pharmaceutical poisoning
Poisonings account for up to 10% of admissions to intensive care units and represent a common yet complex challenge in critical care. While most patients recover with supportive care alone, a significant proportion require targeted interventions and advanced organ support. This state-of-the-art review provides a practical, evidence-based guide to manage pharmaceutical poisoning in the intensive care unit, with emphasis on real-world applicability for intensivists. It highlights toxicant-specific considerations for early resuscitation, structured by the ABCDE approach. Essential principles of the diagnostic workup are also summarized. In addition, the review offers actionable guidance on when and how to escalate care, moving from supportive measures to specific interventions including gastrointestinal decontamination, extracorporeal toxicant elimination, antidote therapy, and rescue strategies such as intravenous lipid emulsion and extracorporeal membrane oxygenation. Key algorithms and decision pathways-such as the diagnostic evaluation, ABCDE-based stabilization, gastrointestinal decontamination, and extracorporeal elimination approaches-are presented with visually structured figures. A comprehensive table provides an overview of the most relevant antidotes used in critical care, including indications and dosing.
1 Dec 2025
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