Cambridge, MA — 12/02/2025 — Insilico Medicine (“Insilico”), a global leader in AI-powered drug discovery, and Atossa Therapeutics (“Atossa”) (Nasdaq: ATOS), a clinical-stage biopharmaceutical company developing novel treatments for breast cancer and other serious conditions, announce the publication of a joint study evaluating the potential of (Z)-endoxifen for glioblastoma multiforme (GBM). The peer-reviewed article, now published in Nature’s Scientific Reports, represents one of the most comprehensive AI-enabled analyses to date exploring whether endoxifen, an active metabolite of tamoxifen with known activity in endocrine-resistant breast cancer, may offer new therapeutic opportunities for one of the deadliest malignant brain tumors in adults. The study aimed to identify new oncology indications with high therapeutic potential for endoxifen, as monotherapy or in combination, by applying Insilico’s AI-powered PandaOmics platform across a wide range of cancer types based on its mechanisms of action. Through this systematic evaluation, GBM emerged as a top candidate for further investigation.

Archaerhodopsin 3 (AR3) is a microbial proton pump that increases alkalinity inside a cell when exposed to green light. Scientists from Okayama University successfully inserted AR3 genes into two mouse cancer cell lines. Upon exposure to green laser light, these modified cells died due to increased alkalinity, both in vitro and in mouse tumor model. This mechanism points the way to highly specific and minimally toxic anti-cancer therapies.

First immune-transcriptomic map of lung cancer plus tuberculosis.

CD4/CD8 ratio and NK cells up; antigen presentation pathways enriched.

Six-gene signature validated; four-gene model AUC 0.94 for LC-PTB.

MTB remodels tumor microenvironment via metabolic-immune crosstalk.

This review systematically examines the integration of machine learning (ML) and artificial intelligence (AI) in nanomedicine for cancer drug delivery. It demonstrates how ML algorithms—including support vector machines, neural networks, and deep learning models—are revolutionizing nanoparticle design, drug release prediction, and personalized therapy planning. The article outlines the complete ML workflow from data acquisition to model interpretation, compares key algorithms, and presents real-world case studies spanning multidrug carrier optimization and cancer diagnostics. While highlighting substantial preclinical advances, the authors identify critical barriers to clinical translation such as data heterogeneity, model opacity, and regulatory challenges. The review concludes with a forward-looking roadmap emphasizing data standardization, explainable AI, and clinical validation to bridge the gap between computational innovation and patient-ready nanomedicine.