Breast cancer faces significant therapeutic challenges, particularly for triple-negative breast cancer (TNBC), due to limited targeted therapies and drug resistance. Drug repositioning leverages existing safety and pharmacokinetic data to expedite the identification of new indications with cost-effective benefits compared to de novo drug discovery. In this critical narrative review, we examine recent advances in computational repositioning strategies for breast cancer, focusing on network-based methods, computer-aided drug design, artificial intelligence and machine learning, transcriptomic signature matching, and multi-omics integration. We highlight key case studies that have progressed to preclinical validation or clinical evaluation. We assess comparative performance metrics, experimental validation outcomes, and real-world success rates. We also present critical methodological challenges, including data heterogeneity, bias in real-world data, and the need for study reproducibility. Our review emphasizes the importance of window-of-opportunity trials and the need for standardized data sharing and reproducible pipelines. These insights highlight the groundbreaking potential of in silico repositioning in addressing unmet needs in breast cancer therapy.

Background: This study evaluates the translational relevance of free computer-assisted ADMET platforms in drug discovery by comparing SwissADME and FAF-Drugs4 predictions with regulatory and curated reference data for approved anticancer drugs. Methods: Fourteen approved anticancer agents representing diverse chemical classes were analyzed using SwissADME and FAF-Drugs4. We compared predicted physicochemical, pharmacokinetic, and toxicity-related properties with information extracted from FDA- and EMA-approved product information, DrugBank, and PubChem. We evaluated the concordance for oral absorption, permeability, metabolic stability, and toxicity-related trends. Results: The platforms showed good concordance for broad descriptor-driven properties, particularly oral suitability and physicochemical trends. Strong agreement was observed for intravenously administered taxanes, which displayed unfavorable oral drug characteristics, and for several orally active small molecules with generally compatible profiles. Partial concordance was observed for compounds such as temozolomide, whose clinical behavior is influenced by factors not fully captured by descriptor-based models. Toxicity outputs were informative as early warning signals, with vandetanib showing the clearest alignment between predicted elevated risk and documented safety concerns. Conclusions: Free computational ADMET tools are valuable computer-assisted drug discovery resources for early triage, predictive toxicology, and prioritization of repositioning candidates, but they should complement rigorous experimental and clinical evaluation.

Background/Objectives: Amb a 1 is the major allergenic component of Ambrosia artemisiifolia pollen and a clinically relevant marker of genuine ragweed sensitization. Heart failure is increasingly recognized as a systemic syndrome shaped by immune activation, endothelial dysfunction, fibrosis, neurohormonal imbalance, pulmonary comorbidity, and environmental exposures. This narrative review aims to synthesize the translational evidence linking Amb a 1-specific IgE, IgE-mediated inflammation, allergic airway disease, and cardiovascular remodeling in heart failure. Methods: A targeted narrative review was performed, integrating evidence on component-resolved ragweed diagnosis, IgE-FcεRI signaling, mast cell and eosinophil biology, pollen exposure, cardiovascular inflammation, and heart failure pathophysiology. Results: No dedicated clinical studies have validated Amb a 1-specific IgE as a diagnostic, prognostic, or therapeutic biomarker in heart failure. However, adjacent evidence supports biologically plausible links between allergen-specific IgE responses and cardiovascular dysfunction, including mast cell activation, cytokine release, endothelial perturbation, oxidative stress, microvascular dysfunction, pulmonary-cardiac interaction, and myocardial fibrosis. Amb a 1-specific IgE may therefore identify a seasonally vulnerable heart failure phenotype, particularly in patients with allergic rhinitis, asthma, eosinophilic inflammation, or recurrent symptom worsening during ragweed season. A systemic/indirect pathway operating through allergic airway disease is distinguished from a postulated direct cardiac pathway; the latter remains strictly speculative, as no direct evidence demonstrates that inhaled Amb a 1 reaches or activates cardiac mast cells in vivo. Conclusions: Amb a 1-specific IgE should not currently be used to infer cardiac causality or modify heart failure therapy. Prospective, phenotype-rich, exposure-informed studies are needed to determine whether ragweed sensitization has clinically meaningful implications for heart failure endotyping, seasonal risk assessment, and cardio-allergology care. These findings may inform patient-centered heart failure management by improving the interpretation of seasonal dyspnea, allergic comorbidity, and symptom fluctuations in ragweed-endemic regions.

Background/Objectives: General cardio-oncology guidance describes cardiovascular toxicity from BRAF/MEK inhibitors and immune checkpoint inhibitors (ICIs), but it does not resolve how treatment decisions should change when heart failure (HF) is already present. This critical narrative review separates direct HF-specific evidence from indirect evidence and proposes an HF-centered framework for systemic melanoma. Methods: MEDLINE/PubMed, Embase, and the Cochrane Library were searched for English-language sources published from January 2015 to April 2026, with targeted guideline, prescribing-information, interaction, and geriatric-oncology updates through July 2026. Sources were classified as direct, indirect, or extrapolated. This review follows SANRA principles but does not claim systematic-review conduct, pooled estimates, formal risk-of-bias assessment, or GRADE certainty. Results: Only case-level evidence directly describes systemic melanoma treatment in established HF; most cardiotoxicity rates are derived from selected longitudinal and real-world cohorts. We therefore organize decisions across four domains: HF phenotype and current stability; oncological urgency and alternatives; treatment-specific toxicity phenotype; and detectability, reversibility, and patient priorities. Each domain is graded ordinally and generates its own decision output, and explicit precedence rules resolve situations in which several domains are simultaneously abnormal; the pathway from assessment to documented output is presented as a decision flowchart and applied to illustrative clinical cases. This approach modifies interpretation of symptoms, biomarkers, ventricular function, surveillance, drug interactions, treatment interruption, and rechallenge. Every actionable statement is labeled as guideline-supported or author-proposed, therapy-specific baseline and follow-up monitoring are tabulated separately for each treatment class, and heart failure with preserved ejection fraction is addressed as a phenotype in which clinical deterioration may occur without any change in ejection fraction. Patient-level priorities in older adults—survival, quality of life, avoidance of hospitalization, tolerance of frequent monitoring, oral versus infusion treatment, and functional expectation—are specified as items to be recorded rather than inferred. Conclusions: Stable HF is not an automatic contraindication to effective melanoma therapy, whereas recent or active decompensation requires stabilization or monitored treatment when oncological delay is unsafe. The proposed framework operationalizes guideline principles for an understudied population while making the limits of the evidence explicit. It makes a procedural rather than an empirical claim: it is a transparent structure for documenting a decision that must be made anyway, not an algorithm derived from or validated against outcome data.

Background: Azelaic acid, an established dermatologic dicarboxylic acid, was previously nominated as an unusual anticancer repurposing candidate. However, it remains unclear whether physicochemical properties, predicted ADME, or structural proximity to established endocrine anticancer drugs support this hypothesis. This study positioned azelaic acid relative to a clinically anchored endocrine anticancer reference panel using reference-defined space based on physicochemical and predicted ADME descriptors and two-dimensional structural similarity analyses. Methods: Sixteen clinically used endocrine anticancer drugs defined the reference set, with finasteride and dutasteride included as mechanistic comparators. SwissADME descriptors were screened for redundancy, standardized, and analyzed by principal component analysis and Euclidean nearest neighbor distances. Robustness was assessed by leave-one-reference-out, bootstrap, and WLOGP sensitivity analyses. Structural similarity was evaluated independently using Morgan fingerprints and Tanimoto coefficients. Results: The first two principal components explained 75.31% of reference variance. Flutamide was the nearest Clinical Reference to azelaic acid in physicochemical and predicted ADME space (d = 3.0063), but this distance exceeded the reference $q_{90}$ threshold (2.6521), classifying azelaic acid as extrapolative. This classification persisted in all 16 leave-one-reference-out analyses and in 73.69% of 992 valid bootstrap replicates. Structurally, the highest reference similarity was observed with toremifene (T = 0.08696), below the minimum internal Clinical Reference nearest-neighbor similarity (0.13761); radius-3 sensitivity analysis preserved this result. Conclusions: Azelaic acid is neither a close structural analog nor a physicochemical and predicted ADME neighbor of established endocrine anticancer drugs. Its network-derived nomination therefore represents a non-canonical repurposing hypothesis requiring further experimental validation.