Nanoparticles and convergence of artificial intelligence for targeted drug delivery for cancer therapy: Current progress and challenges

The paper addresses the challenge of cancer treatment amid substantial tumor heterogeneity and limitations of conventional chemotherapy (poor bioavailability, adverse effects, non-specific targeting). It motivates targeted drug delivery as a key pillar of precision oncology, where nanoparticle (NP)-mediated systems can improve localization, protect and solubilize drugs, and enhance pharmacokinetics/pharmacodynamics. Given the diversity of biomarkers and unique molecular profiles for patients, the study explores how integrating AI with nanotechnology can support biomarker detection, optimize NP properties and interactions, analyze complex multi-omics and clinical data, and guide patient-specific therapy planning. The goal is to review advances in NP-based drug delivery, outline how AI can enhance these systems (e.g., classification, prediction, optimization), and identify current gaps and opportunities for improving personalized cancer therapy.

Background sections survey nanotechnology for drug delivery and precision medicine, highlighting classes of NPs (lipid, polymeric, inorganic, carbon-based, metals) and their pros/cons for targeted delivery and imaging. NPs can enhance stability/solubility, membrane transport, circulation time, and tumor localization but translation is limited by interspecies differences, patient heterogeneity, and incomplete understanding of NP–biological interactions. Targeted delivery strategies include passive (EPR effect) and active targeting; while EPR underpins many designs, its variability and disputes in NP contexts necessitate tumor-specific systems and consideration of normal–tumor differences. Challenges persist: limited knowledge of NP components/characteristics, non-uniform toxicity and assays, lack of standard synthesis protocols and in vivo monitoring, and incomplete models of NP–biological interactions. Existing NP-based systems in the clinic include Abraxane (albumin-bound paclitaxel), Intralipid emulsions, polymeric NPs (e.g., PLGA), Vyxeos (liposomal daunorubicin/cytarabine), Myocet (non-PEGylated doxorubicin), and NBTXR3 (hafnium oxide radioenhancer), illustrating clinical promise and ongoing trials. The review also examines AI’s growing role in bionanotechnology and drug development, including simulations at the nanoscale, ANN-based prediction of physicochemical properties, ADME/Tox modeling, drug synergy prediction, de novo molecule design (GANs), and integration with imaging and biomarker sensing NPs for diagnosis, prognosis, and therapy planning.

A narrative literature review following PRISMA guidance was conducted to address research questions on AI integration with NP-mediated targeted drug delivery. Searches spanned computer science, biomedical, and biotechnology sources (IEEE, SAGE, Springer, Elsevier, MDPI, Frontiers) and NLM/NCBI. Key themes included Drug Delivery and Drug Design, NPs for Targeted Drug Delivery, AI for Drug Delivery, and AI plus bionanotechnology. Due to limited literature specifically on AI-integrated NP drug delivery, a narrative synthesis was chosen over comparative meta-analysis. Inclusion focused on the past 10 years and excluded preprints. From ~150 records screened, 100 publications were included. Studies were analyzed to extract themes on applications/challenges in drug delivery, NP platforms for cancer therapy, and AI’s scope to address delivery challenges and improve efficacy.

- NP-mediated drug delivery can improve localization, protect labile drugs, enhance solubility, and optimize PK/PD, reducing dose frequency and side effects compared to conventional chemotherapy. - Targeting strategies encompass passive (EPR) and active mechanisms, but EPR variability in patients limits reliability, underscoring need for tumor-specific, patient-informed delivery systems. - Clinically relevant NP systems include: Abraxane (albumin-paclitaxel), Intralipid (emulsion improving bioavailability and reducing cytotoxicity), polymeric NPs (e.g., PLGA), Vyxeos (liposomal daunorubicin/cytarabine with synergy benefits), Myocet (liposomal doxorubicin), and NBTXR3 (hafnium oxide radioenhancer enhancing radiotherapy dose deposition). - AI contributes across the pipeline: predicting physicochemical properties and ADME/Tox; modeling drug–drug interactions; predicting drug sensitivity from genomic/chemical features; and forecasting drug synergy using ANNs and deep learning (e.g., DeepSynergy). - Generative models (GANs) enable de novo molecular design with predefined anticancer properties, handling large molecular datasets. - AI-enhanced imaging and analysis (e.g., deep learning for nanoparticle tracking and holographic characterization) can support pharmacokinetics/biodistribution assessment and validate EPR. - Biomarker detection with NPs (quantum dots, gold NPs, carbon nanotubes) offers high sensitivity/specificity for in vivo/in vitro sensing. AI can process complex biomarker and omics data for classification, prognosis, and therapy selection (e.g., KRAS mutation profiling), and optimize NP–drug–membrane interactions and release kinetics. - Emerging intelligent delivery concepts include AI-guided nanorobots/nanomotors and fuzzy logic for dose estimation, with PET-based tracking of radiolabeled nanomotors demonstrating real-time in vivo monitoring. - Despite promise, AI models face challenges of data diversity, bias, overfitting, validation, and computational demands; and NP translation is hampered by incomplete biological understanding, assay standardization gaps, and patient heterogeneity.

The review’s synthesis indicates that integrating AI with NP-based delivery directly addresses core challenges in precision oncology: heterogeneity in biomarkers and tumor microenvironments, EPR variability, and complex multi-parameter optimization of formulations and dosing. AI methods can rapidly analyze high-dimensional clinical and omics data to stratify patients, predict drug sensitivity and synergy, and guide selection of NP carriers and targeting ligands. In parallel, AI-enhanced imaging and particle tracking improve the assessment of PK/PD, biodistribution, and on-target accumulation, enabling feedback loops to refine delivery strategies. NP-enabled biomarker sensing provides richer, localized diagnostic signals that AI can interpret to tailor therapy. Collectively, these advances support the research question that AI–NP convergence can enhance targeted drug delivery effectiveness, reduce failures, and move toward personalized, image-guided cancer therapy, while highlighting the need for standardized datasets, robust validation, and clinically translatable designs.

Nanomedicine offers versatile carriers that can improve localized delivery to tumor sites, and hybrid approaches continue to enhance cancer treatment efficacy. However, persistent issues such as high failure/low response rates, recurrence, and EPR variability necessitate patient-specific strategies grounded in molecular profiling. AI can process complex clinical and omics data, support real-time monitoring and image-guided delivery, classify patients by molecular signatures, and generate actionable insights on treatment response. Although AI’s application specifically to targeted drug delivery remains limited compared to its role in drug discovery, this review outlines how AI can overcome fabrication and delivery constraints, advance NP imaging and tracking, and enable biomarker-driven personalization. Future work should develop intelligent systems for biomarker detection and nanoparticle tracking/analysis, establish standardized datasets and protocols, and validate AI-enabled delivery strategies clinically.

- The literature specifically on AI-integrated NP-mediated targeted drug delivery is limited, prompting a narrative rather than comparative/meta-analytic review. - Potential biases arise from data scarcity, heterogeneous study designs, and lack of standardized assays and protocols in nanomedicine. - Translation gaps persist due to interspecies differences, patient heterogeneity, and incomplete understanding of NP–biological interactions. - AI model constraints include risks of overfitting, bias, validation challenges, high computational requirements, and limited availability of large, diverse, multimodal clinical datasets. - Imaging agents (e.g., short half-life radiotracers) and EPR variability can complicate consistent evaluation of delivery performance.