Human neurons flourish in mouse brains, offering a new view of neurodevelopmental disorders.
By Zoe Beketova
In this article, the author discusses a paper published in Nature where researchers transplanted mice with human stem-cell-derived cortical organoids (hCO), meaning part of the mice brain was actually composed of human cells and neurons. This model could reveal underlying biological causes of disease and even point to potential treatments. The team is hoping to use these kinds of in vivo transplantation approaches to tell what is missing in the in vitro model to spare live animals from research uses.
A genomic-led strategy to anticipate drug safety effects.
By Brian R Ferolito et al.
In this study, authors developed a genetics-led strategy leveraging Mendelian Randomization (MR) across large-scale genetic datasets. This MR analysis demonstrated the capacity to predict hundreds of known ADR for approved medications, with approximately 40% corroborated by FDA. The comprehensive atlas provides a robust predictive framework for anticipating drug safety, potentially enhancing decision-making in drug development and pharmacovigilance, but prospective implementation data remain sparse, external validation is uncommon, and evidence that these tools improve real-world antibiotic safety outcomes remains limited.
Safety Monitoring of High-Risk Antibiotics Using Artificial Intelligence: A Narrative Review with Focus on Real-World Evidence.
By Mila Kostić et al.
This narrative review synthesises literature from several scientific publications database between 2019 and April 2026 on Artificial intelligence and real-world evidence used as complementary approaches to address High-risk antibiotics toxicity, therapeutic drug monitoring complexity, and antimicrobial stewardship workload. Current evidence indicates that artificial intelligence may improve safety monitoring when embedded within clinically rich data environments, but evidence that these tools improve real-world antibiotic safety outcomes, as opposed to predictive discrimination alone, remains limited.
Detection of seizurogenic compounds in human and rat neurons in vitro: A multi-laboratory, multi-model assessment.
By Roberts et al.
Authors present the results of a multi-laboratory MEA assessment of seizurogenic detection in both human iPSC-derived neural cultures and primary rat cortical neurons. Their aim was to determine the optimal methods, models, and parameters for larger scale nonclinical assessment for seizure liability, as it remains a key risk for drug development and following chemical exposure. Evaluating the impact of both pro-convulsants compounds and negative controls on spontaneous electrical activity in both types of neurons utilizing MEA, they noted that overall the human model was more consistent across sites.
Artificial intelligence in drug discovery – what it is, where we stand and the path forward.
By Bender et al.
In this review, authors discuss potential reasons for the disappointingly limited evidence of clinically relevant impact of the use of AI in drug discovery. Mentioning the insufficient focus on clinical translation during model development and the underspecification of computational models for real-world use cases, among other underlying factors, Bender et al. finish this review by providing recommendations for the development of AI in drug discovery, i.e. moving on from model validation and focusing instead on the ability to improve decision making.
Recommendations on the measurement of cardiovascular parameters in non-rodent species for regulatory safety assessment of pharmaceuticals.
By Valentin et al.
Cardiovascular monitoring in non-rodent species remains a key component of regulatory safety assessment and translational risk evaluation in pharmaceutical development. Authors wrote this review to examine whether cardiovascular endpoints are required in every repeat-dose non-rodent toxicology study and evaluates the strengths and limitations of currently used methodologies, since approaches used to collect these endpoints remain heterogeneous across sponsors and study designs, with important consequences for data quality and interpretation.
The First Practical Guidance for Review of NAMs-Supported Trials Is Here!
By Michael Phelan
One of, if not the first guidance document for first-in-human trials supported by NAMs was released in July 2026 by an Australian team and is described in this article as a critical step to the widespread adoption of NAMs globally. The author discusses how this guidance documents directly answers the question of “how to evaluate a submission where NAMs replace or reduce conventional animal data?” by introducing a four-tier risk framework and has parallels with key concepts from the FDA NAMs draft guidance. This could lower the practical barrier to initiating NAMs-supported clinical trials in Australia.
Advancing safety pharmacology: Emerging technologies, innovative methods, and future directions.
By Pugsley et al.
This editorial introduces the annual themed issue of the Journal of Pharmacological and Toxicological Methods (JPTM) dedicated to methodologies in safety pharmacology. The issue draws on content presented at the 2025 Safety Pharmacology Society Annual Meeting. The manuscripts primarily focus on advances in core battery safety evaluations and data analysis methodologies, while also highlighting emerging areas of investigation. Continued publication of the same themed issues over decades underscores the importance of methodological innovation and evaluation.
Proteome-guided drug discovery maps and mitigates therapeutic degrader toxicity.
By Basu et al.
Authors of this paper present their work in optimising Heterobifunctional targeted degraders (HBDs) to decrease their de novo toxicity, often a hindrance to clinical success. They introduce a drug discovery framework that exploits high-throughput proteomics to map and mitigate toxicity mechanisms of emerging drug modalities. Using HBDs indicated for treatment-resistant prostate cancer and machine learning, they identified safer analogs. Their findings establish a versatile framework for developing safer medicines and highlight the transformative potential of proteome-guided drug discovery.