image: (a) Examples of three major ML model types: tree‐based models (e.g., random forests, gradient boosted trees), kernel‐based models (e.g., support vector machines, Gaussian processes), and deep learning models (e.g., neural networks, graph neural networks), each using molecular descriptors such as logP, pKa, and melting point to predict properties like solubility. (b) Generalized ML pipeline showing stages of data acquisition and cleaning (with features and targets), model evaluation (e.g., regression fitting and performance), and interpretation (e.g., feature importance analysis).
Credit: Rohan Chand Sahu
This article provides a comprehensive review of the integration of machine learning (ML) and nanomedicine in cancer drug delivery, highlighting their transformative potential to address longstanding challenges in the field. It begins by outlining the global burden of cancer, with projections of rising incidence and mortality, and the limitations of conventional therapies—such as non-selectivity, off-target toxicity, and adverse effects—that drive the need for advanced delivery systems. Nanomedicines, characterized by nanoscale carriers (1–200 nm) like liposomes, polymeric nanoparticles, and dendrimers, offer advantages including enhanced site-specific delivery, improved pharmacokinetics, and reduced systemic toxicity, yet their development remains hindered by inefficient trial-and-error formulation methods and limited clinical translation.
The core of the review focuses on how ML—an subset of artificial intelligence (AI)—streamlines nanomedicine development through data-driven prediction and optimization. It details key ML models (e.g., linear regression, decision trees, random forests, support vector machines, artificial neural networks, deep learning, and the Levenberg-Marquardt algorithm) along with their strengths, limitations, and applications in nanomedicine, such as predicting nanoparticle parameters (size, polydispersity index), optimizing drug release profiles, selecting drug-excipient combinations, and personalizing therapeutic regimens. The article describes the operational workflow of ML models, including data collection and cleaning, dataset splitting (training, validation, testing), model construction and validation, and experimental interpretation, emphasizing the critical role of high-quality, standardized data in ensuring model robustness.
Several case studies illustrate practical applications: ML-assisted optimization of multi-drug nanocarriers for melanoma therapy, modeling stimuli-responsive drug release from hydrogels, predicting nanocrystal quality attributes, assessing nanoparticle tumor delivery efficiency via meta-analysis, and using ML to analyze nanoparticle cellular internalization for breast cancer diagnosis. The review also explores broader pharmaceutical applications of AI/ML, including aiding cancer diagnosis, selecting compatible excipients, predicting formulation parameters, and advancing precision medicine by integrating genomic and clinical data to tailor treatments for individual patients.
Despite significant preclinical progress, the article identifies key challenges to translational readiness: data heterogeneity and lack of standardization, model complexity and "black-box" interpretability, regulatory hurdles, patient data privacy concerns, limited clinical expertise integration, biological complexity (e.g., tumor heterogeneity, protein corona formation), and technical barriers in cancer drug delivery. It underscores the need for standardized datasets aligned with FAIR principles, explainable AI frameworks, interdisciplinary collaboration, and regulatory alignment to facilitate clinical adoption.
The future direction section proposes a roadmap: short-term efforts to establish standardized data and robust validation protocols, mid-term collaboration with regulators to develop ML-specific guidelines and clinical decision support systems, and long-term generation of large-scale clinical evidence and globally harmonized standards. Concluding that ML-driven nanomedicine represents a paradigm shift in cancer therapy, the article emphasizes that overcoming technical, regulatory, and ethical hurdles—through transparency, reproducibility, and evidence-based practice—will be pivotal to translating preclinical promise into safe, effective, and personalized patient care.
Journal
Med Research
Method of Research
Literature review
Subject of Research
Not applicable
Article Title
Machine Learning for Predictive Modeling in Nanomedicine‐Based Cancer Drug Delivery
Article Publication Date
17-Nov-2025
COI Statement
The authors declare no conflicts of interest.