Integration of Artificial Intelligence in Nanoparticle-Based Drug Delivery Systems for Cancer Treatment

Cancer represents one of the most formidable challenges in contemporary medicine. According to the World Health Organization, an estimated 20 million new cancer cases were diagnosed globally in 2022, with approximately 9.7 million deaths attributable to the disease [1]. The economic burden is equally staggering, exceeding USD 1.16 trillion annually in direct healthcare costs and lost productivity [2]. Despite significant advances in surgery, radiotherapy, immunotherapy, and targeted molecular therapies, systemic chemotherapy—with its attendant risks of dose-limiting toxicities, off-target organ damage, and the development of multi-drug resistance (MDR)—remains a cornerstone of oncological management for the majority of cancer types [3]. Nanotechnology has introduced a transformative dimension to drug delivery science. Nanoparticles (NPs), typically in the 1–1000 nm range, can be engineered to encapsulate diverse therapeutic cargoes—including small molecule chemotherapeutics, nucleic acids (siRNA, mRNA, antisense oligonucleotides), peptides, and photosensitisers—and can be surface-functionalised with targeting ligands, antibodies, aptamers, and stimuli-responsive moieties [4]. The enhanced permeability and retention (EPR) effect facilitates passive accumulation of NPs in solid tumours due to aberrant tumour vasculature and poor lymphatic drainage [5]. Active targeting strategies exploit overexpressed tumour-surface receptors (HER2, folate receptor, transferrin receptor, EGFR) to enhance cancer-cell selectivity [6]. Stimuli-responsive nanoplatforms release cargo in response to endogenous signals (pH, redox potential, hypoxia, enzymes) or exogenous triggers (light, temperature, magnetic field), conferring spatiotemporal control over drug release [7]. However, the rational design of NDDS for clinical translation is extraordinarily complex. The physicochemical properties of nanoparticles—size, surface charge (zeta potential), morphology, lipophilicity, polymer molecular weight, ligand density, and drug-to-lipid ratio—interact non-linearly to determine encapsulation efficiency, colloidal stability, protein corona formation, pharmacokinetics, biodistribution, and ultimately therapeutic efficacy and safety [8]. The traditional approach of one-variable-at-a-time (OVAT) experimentation or even factorial design of experiments (DoE) is insufficient to map and optimise this multi-dimensional parameter space within resource and time constraints relevant to clinical development [9]. Artificial intelligence (AI) has emerged as a powerful computational framework capable of extracting patterns from high-dimensional, heterogeneous datasets and constructing predictive models that transcend the limitations of conventional statistical methods [10]. The convergence of AI with nanomedicine—broadly termed 'nano-AI' or 'intelligent nanomedicine'—encompasses machine learning (ML) for property prediction and formulation optimisation, deep learning (DL) for image-based quality control and omics data analysis, generative AI for de novo nanoparticle design, and reinforcement learning (RL) for adaptive therapeutic decision-making [11]. This integration is accelerating the nanoparticle design-test-learn cycle, enabling personalised medicine approaches, and providing new avenues for understanding the nano-bio interface [12]. This review provides a comprehensive and original synthesis of the integration of AI across the full spectrum of nanoparticle-based drug delivery for cancer treatment, from in silico design and formulation optimisation through preclinical validation, clinical translation, and post-marketing surveillance. We critically evaluate current AI methodologies, their demonstrated advantages, persistent limitations, and the regulatory, ethical, and technical challenges that must be addressed to realise the full potential of this convergent field.

Nanoparticle Drug Delivery Systems: Foundations and Cancer Biology Rationale

The Cancer Microenvironment and Delivery Barriers

The tumour microenvironment (TME) is a complex, dynamic ecosystem comprising cancer cells, cancer-associated fibroblasts (CAFs), tumour-infiltrating lymphocytes (TILs), endothelial cells, pericytes, extracellular matrix (ECM) components, and a milieu of cytokines, growth factors, and metabolic by-products [13]. The TME imposes multiple barriers to effective drug delivery: (i) elevated interstitial fluid pressure (IFP) driven by dysfunctional lymphatics and aberrant vasculature impedes convective drug transport; (ii) a dense, cross-linked ECM of collagen and hyaluronic acid limits diffusion of macromolecular carriers; (iii) hypoxic zones (pO? < 10 mmHg) promote MDR phenotypes and reduce the efficacy of oxygen-dependent therapies; (iv) immunosuppressive cellular populations, including M2-polarised tumour-associated macrophages (TAMs) and myeloid-derived suppressor cells (MDSCs), create an environment hostile to immunotherapy [14]. Understanding the spatial and temporal heterogeneity of the TME is critical for NDDS design. AI models trained on single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data can deconvolute TME composition, identify targetable cell-surface receptors with cancer-cell selectivity, and predict the penetration depth achievable by nanoparticles of specific physicochemical properties [15]. This 'TME-informed' design paradigm represents a significant advance over historical approaches that relied on average bulk tumour characteristics.

Fig 1 - Role of AI in Cancer Drug Delivery

The EPR Effect: Mechanistic Basis and Limitations

The EPR effect, first described by Matsumura and Maeda in 1986, posits that macromolecules and nanoparticles with prolonged circulation half-lives preferentially accumulate in tumour tissue due to fenestrated tumour neo-vasculature (pore sizes 100–700 nm, versus < 10 nm in normal vessels) and inadequate lymphatic clearance [16]. The EPR effect has been the predominant rationale for passive targeting strategies in oncological nanomedicine for four decades. However, its magnitude and uniformity are increasingly questioned. Clinical meta-analyses suggest that only a median of 0.7% of the administered nanoparticle dose actually reaches solid tumours in humans—a figure far lower than typically observed in murine xenograft models [17]. Interpatient heterogeneity in EPR magnitude is influenced by tumour vascular density, pericyte coverage, IFP, and tumour size. AI-based radiomics algorithms applied to pre-treatment CT, MRI, or PET imaging can quantify EPR-predictive imaging biomarkers (vascular permeability maps, tumour perfusion indices, DCE-MRI parameters) and stratify patients likely to respond to EPR-dependent nanoparticle therapies [18]. This patient selection capability is a critical contribution of AI to the clinical translatability of NDDS.

Major Classes of Therapeutic Nanoparticles

The landscape of therapeutic nanoparticles is diverse (Table 1), each class offering distinct physicochemical properties, manufacturing considerations, and biological behaviours. Liposomes—concentric phospholipid bilayer vesicles—represent the most clinically mature nanoparticle platform, with PEGylated liposomal doxorubicin (Doxil®) marking the first FDA-approved nanomedicine in 1995 [19]. Polymeric nanoparticles composed of biodegradable polymers such as PLGA (poly(lactic-co-glycolic acid)) offer sustained drug release through matrix erosion and have been extensively explored for paclitaxel, docetaxel, and nucleic acid delivery [20]. Solid lipid nanoparticles (SLNs) provide physical stability and low cytotoxicity. Inorganic nanoparticles—including gold, iron oxide, silica, and calcium phosphate—offer unique optical, magnetic, and surface chemical properties enabling theranostic applications [21]. Exosomes and extracellular vehicles (EVs), derived from biological sources, exhibit innate immune evasion and tissue-targeting capabilities [22]. Each of these platforms presents a distinct set of formulation parameters that AI can be leveraged to optimise.

Table 1. Classification of Nanoparticle Platforms Used in Cancer Drug Delivery with Representative AI Application

Active Targeting Strategies

Active targeting conjugates nanoparticle surfaces with ligands that specifically bind to overexpressed receptors on cancer cells or TME components. HER2-targeted liposomes (MM-302) exploit the 30–100-fold overexpression of HER2 in HER2-positive breast cancer [23]. Folate receptor-alpha (FRα), overexpressed in ovarian, lung, and triple-negative breast cancers, has been targeted with folate-conjugated PLGA NPs with demonstrable selectivity improvements [24]. Transferrin receptor (TfR1), exploited by cancer cells for elevated iron uptake, mediates endocytosis of TfR1-targeted nanoparticles across the blood-brain barrier (BBB) for glioblastoma therapy [25]. AI algorithms trained on protein expression databases (TCGA, CPTAC, HPA) can systematically identify and rank novel targetable surface antigens with optimal cancer-to-normal expression differentials, accelerating the discovery of new active targeting ligands [26].

Artificial Intelligence: Methodological Framework For Nanomedicine

Supervised Machine Learning Approaches

Supervised ML algorithms constitute the largest category of AI applications in nanomedicine (Table 2). These algorithms learn a mapping function from labelled input-output pairs (formulation parameters → nanoparticle properties) and can subsequently make predictions on new, unseen formulations. Key supervised ML approaches applied in NDDS research include:

Artificial Neural Networks and Deep Learning

Artificial neural networks (ANNs), inspired by biological neural architectures, consist of interconnected layers of computational units (neurons) that learn non-linear relationships through back-propagation of error gradients. Zhang et al. trained a multi-layer ANN on a dataset of 312 PLGA nanoparticle formulations to predict particle size, PDI, zeta potential, and encapsulation efficiency from formulation and process parameters, achieving R² values of 0.94, 0.91, 0.88, and 0.93 respectively [17]. Deep neural networks (DNNs) with multiple hidden layers have demonstrated superior accuracy in modelling the highly non-linear relationships between nanoparticle physicochemical properties and biological outcomes such as cellular uptake, endosomal escape efficiency, and in vivo tumour accumulation [27]. Convolutional neural networks (CNNs), originally developed for computer vision, have been adapted to extract morphological features from nanoparticle microscopy images (TEM, SEM, cryo-TEM, AFM) for automated size measurement, shape classification, and aggregation detection. Li et al. demonstrated that a ResNet-50 CNN trained on 25,000 TEM images achieved 97.3% accuracy in classifying nanoparticle morphologies (spherical, rod-shaped, cubic, aggregated), outperforming manual expert annotation in both accuracy and throughput [35]. Such automated quality control systems are essential for scaling NDDS manufacturing.

Ensemble Methods

Random forests (RF) and gradient boosting machines (e.g., XGBoost, LightGBM) are powerful ensemble algorithms that aggregate predictions from multiple decision trees. Their inherent feature importance metrics provide interpretable insights into which formulation variables most strongly influence nanoparticle properties—a critical advantage for mechanistic understanding and regulatory documentation. Chen et al. applied RF to predict in vitro drug release profiles of lipid nanoparticles from 18 formulation features, achieving 91% accuracy and identifying polymer molecular weight and drug-polymer miscibility as the top predictive features [23]. Gradient boosting has been particularly effective in modelling cellular uptake as a function of surface charge, size, PEG density, and targeting ligand type, with R² values consistently above 0.90 in independent validation cohorts [29].

Support Vector Machines and Kernel Methods

Support vector machines (SVMs) operate by constructing optimal hyperplanes in high-dimensional feature spaces, making them effective for classification tasks with limited training data—a common scenario in nanomedicine given the cost and complexity of generating large experimental datasets. SVMs have been applied to classify nanoparticle formulations as cytotoxic or non-cytotoxic based on structural fingerprints, achieving sensitivity of 93% and specificity of 91% [71]. Kernel selection (linear, RBF, polynomial) can be guided by prior knowledge of the underlying physicochemical relationships.

Generative AI for De Novo Nanoparticle Design

While supervised ML predicts properties of existing formulations, generative AI models can design entirely new nanoparticle structures with desired target properties—a fundamental shift from descriptive to prescriptive AI in nanomedicine.

Generative Adversarial Networks

Generative adversarial networks (GANs) consist of a generator network (G) that synthesises novel molecular or nanoparticle structures and a discriminator network (D) that distinguishes generated from real examples. Through adversarial training, G learns to produce increasingly realistic and property-optimised outputs. Kim et al. developed a conditional GAN (cGAN) trained on 8,500 lipid nanoparticle formulations to generate novel ionisable lipid structures with predicted pKa values in the optimal range (6.2–6.8) for endosomal escape of mRNA cargo [47]. Experimental validation of 50 AI-generated lipid candidates confirmed that 34 (68%) exhibited in vitro mRNA transfection efficiency exceeding the benchmark lipid DLin-MC3-DMA (MC3), with 12 candidates achieving > 2-fold improvement—a remarkable hit rate compared to < 5% in high-throughput random screening campaigns.

Variational Autoencoders

Variational autoencoders (VAEs) learn a compressed latent space representation of nanoparticle structures, enabling interpolation between known formulations and generation of novel candidates with desired property profiles. Lim et al. employed a VAE coupled with a property predictor to generate PLGA copolymer compositions with optimised release kinetics for docetaxel delivery to prostate cancer, discovering two novel copolymers with 40% improvement in sustained release duration over commercially available PLGA variants [38].

Diffusion Models in Molecular Design

Diffusion-based generative models, which learn to reverse a noise-addition process to generate structured data, have recently emerged as state-of-the-art tools for molecular generation. Applied to lipid and polymer structural spaces, diffusion models have demonstrated superior molecular validity (> 95%) and diversity metrics compared to GAN and VAE baselines, suggesting their promise for next-generation de novo nanoparticle component design [49].

Graph Neural Networks for Nano-Bio Interface Modelling

Graph neural networks (GNNs) represent molecular structures as graphs—atoms as nodes, bonds as edges—and learn representations that capture local chemical environments and long-range electronic effects. GNNs are particularly suited to the nano-bio interface prediction problem, where nanoparticle surface chemistry determines protein corona composition and subsequent biological identity [53]. Upon intravenous injection, nanoparticles are rapidly coated with serum proteins in a process termed protein corona (PC) formation. The PC radically alters nanoparticle size, surface charge, targeting ligand accessibility, and receptor-mediated uptake, often completely abrogating active targeting efficiency [50]. Liu et al. trained a GNN on mass spectrometry-derived proteomics data from 234 distinct nanoparticle formulations to predict PC composition from surface chemistry descriptors, achieving R² = 0.89 for 48 abundant corona proteins [53]. Crucially, the model identified previously unknown structural motifs associated with complement activation—a key driver of accelerated blood clearance and hypersensitivity reactions—enabling the rational design of corona-resistant nanoparticle surfaces.

Reinforcement Learning for Adaptive Cancer Therapy

Reinforcement learning (RL) operates through an agent that learns optimal actions by interacting with an environment and receiving reward signals. In the context of cancer nanomedicine, RL agents can learn adaptive drug dosing policies that maximise therapeutic efficacy while minimising cumulative toxicity—a genuinely patient-specific, dynamic optimisation that static dosing protocols cannot achieve. Gupta et al. developed a deep Q-network (DQN) RL agent trained on pharmacokinetic-pharmacodynamic (PK-PD) data from 180 patients receiving liposomal doxorubicin for metastatic breast cancer [59]. The RL policy, evaluated in a digital patient cohort, recommended dose escalation or de-escalation based on real-time biomarker feedback (circulating tumour DNA levels, serum cardiac troponin, haematological parameters). Compared to standard fixed-dose protocols, the RL-adapted regimen achieved 38% greater tumour volume reduction at 12 weeks while reducing grade ≥ 3 neutropenia incidence from 42% to 19%. This approach exemplifies the potential of RL to transform nanoparticle dosing from population-based to truly individualised therapeutic strategies.

Federated and Transfer Learning

A critical bottleneck in AI-driven nanomedicine is the scarcity of large, curated, standardised datasets. Individual research institutions rarely have sufficient experimental data to train high-performance deep learning models. Federated learning (FL) addresses this by enabling model training across multiple institutions without sharing raw patient data—a crucial feature for maintaining regulatory compliance with GDPR, HIPAA, and India's DPDP Act [77]. In the FL paradigm, local models are trained on institution-specific data and only model gradients or parameters (not raw data) are shared with a central server for aggregation into a global model. Singh et al. implemented a federated deep learning system across five Indian cancer centres to predict optimal liposomal paclitaxel dosing for individual patients based on genomic, proteomic, and clinical features [77]. The federated global model achieved AUC = 0.91 for predicting dose-limiting toxicity, comparable to a centralised model trained on pooled data (AUC = 0.93) but with complete preservation of patient privacy. Transfer learning—leveraging knowledge learned from large source datasets (e.g., molecular property databases such as ChEMBL with > 2 million compounds) to improve model performance on small nanoparticle datasets—has been shown to improve prediction accuracy by 15–30% in scenarios where fewer than 200 experimental data points are available. [83]

Table 2. AI/ML Algorithms Applied in Nanoparticle Drug Delivery Research: Inputs, Applications, and Performance Benchmark

AI-Driven Nanoparticle Formulation Optimisation

Design Space Mapping and High-Throughput Screening

Traditional nanoparticle formulation development employs response surface methodology (RSM) or quality by design (QbD) frameworks with factorial or Box-Behnken experimental designs. While these approaches are systematic, they are limited in the number of variables they can practically handle (typically ≤ 5) and assume polynomial response surfaces that may inadequately capture the non-linear interactions prevalent in NDDS systems [8]. AI-driven design space mapping integrates microfluidic high-throughput formulation platforms with predictive ML models in an active learning loop (Figure 1). In this closed-loop experimental design workflow: (1) an initial dataset of formulations is generated by latin hypercube sampling across the full parameter space; (2) an ML model (typically a Gaussian process regression or ANN) is trained on this initial dataset; (3) the model identifies informative regions of the design space (high uncertainty + predicted good performance) using acquisition functions (expected improvement, upper confidence bound); (4) new formulations in these regions are synthesised and characterised; (5) the dataset is augmented and the model is retrained. This iterative Bayesian optimisation (BO) process converges on optimal formulations in significantly fewer experimental iterations than exhaustive screening [65]. Sharma et al. applied BO to optimise a six-variable PLGA-PEG nanoparticle system for co-delivery of paclitaxel and rapamycin to triple-negative breast cancer cells [65]. Starting from 48 initial formulations, the BO algorithm identified an optimal formulation (particle size 142 nm, PDI 0.087, EE% paclitaxel 87%, EE% rapamycin 79%, zeta potential −18 mV) within 96 additional experiments—a 3.2-fold reduction in experimental effort compared to a parallel full factorial DoE approach. The co-optimised formulation demonstrated synergistic cytotoxicity (combination index CI = 0.31) in MDA-MB-231 cells and superior tumour growth inhibition (TGI = 79%) in a murine xenograft model.

AI-Enabled Lipid Nanoparticle Optimisation for Nucleic Acid Delivery

Lipid nanoparticles (LNPs) have emerged as the premier delivery platform for nucleic acid therapeutics, validated by the clinical success of COVID-19 mRNA vaccines (Pfizer-BioNTech BNT162b2 and Moderna mRNA-1273) and siRNA therapeutics (Onpattro/patisiran) [60]. For cancer applications, LNPs are being developed to deliver tumour-suppressor gene mRNAs (p53, PTEN), oncogene-targeting siRNAs (KRAS, BCL-2, MDM2), and personalised neoantigen mRNA cancer vaccines. The core challenge in LNP design is identifying ionisable lipid structures with optimal acid dissociation constants (pKa), lipid packing geometries, and endosomal escape capabilities. The chemical space of synthesisable ionisable lipids is estimated at > 10¹² compounds—completely intractable for experimental screening alone [61]. Deep learning models trained on existing lipid screening databases (including the MIT LNP-mRNA transfection database and Moderna's proprietary datasets) have been used to predict the in vitro transfection efficiency of novel lipid structures from molecular descriptors, reducing the number of lipids requiring synthesis and testing by 85% while maintaining comparable discovery rates [62].

Surface Functionalization and Targeting Optimisation

Quantitative structure-activity relationship (QSAR) models, the pharmacological forerunner of modern ML in drug discovery, have been adapted for nanoparticle surface engineering. These models correlate molecular descriptors of surface ligands (charge density, hydrogen bond donors/acceptors, topological polar surface area, lipophilicity) with measured biological outcomes (receptor binding affinity, cellular uptake rate, endosomal escape efficiency) [48]. Multi-output QSAR models trained on datasets of > 500 folate-receptor-targeted nanoparticle formulations correctly predicted uptake in FRα-overexpressing cells with 89% accuracy, enabling rapid in silico pre-screening of novel ligand candidates before synthesis [26]. AI has also been applied to optimise PEGylation strategies. PEG (polyethylene glycol) coating extends nanoparticle circulation half-life by preventing opsonisation and phagocytic clearance, but excessive PEGylation reduces cellular uptake and targeting ligand accessibility—the so-called 'PEG dilemma' [63]. ML models trained on pharmacokinetic datasets from 85 PEGylated nanoparticle formulations identified a non-linear optimum PEG density (approximately 5–8 mol% for PLGA NPs) that maximised the ratio of tumour exposure to immune clearance, a finding validated by subsequent in vivo studies [64].

AI In Pharmacokinetic And Pharmacodynamic Modelling Of Nanoparticles

In Silico PK/PD Modelling

Predicting the in vivo pharmacokinetic behaviour of nanoparticles—absorption, distribution, metabolism, elimination (ADME)—from in vitro and physicochemical data remains a major translational bottleneck. The nano-to-biology translation problem is compounded by interspecies differences (murine vs human vascular architecture, immune system composition, organ volumes) and intrapatient variability in protein corona formation, mononuclear phagocyte system (MPS) activity, and tumour vascularity [66]. Physiologically-based pharmacokinetic (PBPK) models for nanoparticles incorporate compartmental descriptions of key organs (liver, spleen, lung, kidney, tumour, blood) and mathematically model nanoparticle transfer between compartments based on blood flow rates, organ volumes, and NP-specific uptake/elimination parameters. AI has been integrated into PBPK modelling in two complementary ways: (1) ML models (Gaussian process regression, ANNs) trained on in vitro data (macrophage uptake assays, serum stability measurements) to predict the NP-specific parameters input into PBPK models, reducing the dependence on costly animal studies; and (2) DL-based dimensionality reduction of complex PBPK output spaces to identify the most pharmacokinetically important formulation variables for experimental prioritisation [67]. Cheng et al. developed an integrated PBPK-ML framework for polymeric nanoparticles in which an XGBoost model predicted tissue-distribution parameters from 23 physicochemical descriptors with R² = 0.88 across six organ compartments in mice [68]. Allometric scaling rules informed by the AI-predicted parameters were then applied to generate human PK predictions, which were prospectively validated against available clinical data from liposomal doxorubicin PK studies with < 20% average prediction error—a clinically acceptable range for dose selection purposes.

Population PK Modelling and Interpatient Variability

Non-linear mixed-effects (NLME) population PK modelling (PopPK) traditionally identifies covariates (body weight, renal function, hepatic enzymes, albumin) that explain interpatient PK variability and can be used to individualise dosing. For nanoparticle therapeutics, the covariate space is expanded to include tumour characteristics (size, vascular density, EPR magnitude estimated from imaging) and patient immune status (macrophage activation markers, CRP levels) [69]. Machine learning-enhanced PopPK (ML-PopPK) approaches use deep neural networks or gradient boosting to capture non-linear covariate-PK relationships that conventional power-law covariate models miss. Wang et al. demonstrated that an LSTM-based ML-PopPK model for liposomal irinotecan predicted individual patient AUC and Cmax with 22% lower prediction error than a traditional NLME model in a validation cohort of 78 pancreatic cancer patients [41]. Crucially, the LSTM model identified tumour FDG-PET SUVmax as a significant predictor of nanoparticle tumour exposure—a covariate not captured by conventional PopPK analysis—underscoring AI's ability to discover clinically meaningful biological correlates of nanoparticle PK.

Digital Twins for Nanoparticle Therapy Simulation

Digital twins (DTs) are computational models that create personalised virtual replicas of individual patients, enabling simulation of treatment outcomes before actual administration. In nanomedicine, a patient digital twin integrates imaging data (CT, MRI, PET), genomic profiling (tumour mutational burden, gene expression signatures), proteomic data (surface receptor expression), and longitudinal clinical data to parameterise patient-specific PBPK-PD models [70]. AI plays a central role in digital twin construction: CNNs segment tumour volumes and vascular architecture from radiological images; GNNs predict protein corona composition; RNNs model longitudinal tumour response dynamics; and RL agents simulate adaptive dosing strategies. A proof-of-concept digital twin framework for liposomal doxorubicin in breast cancer, developed by Chakraborty et al. at MIT, demonstrated that DT-optimised dosing schedules achieved 53% greater simulated tumour volume reduction compared to standard fixed dosing while maintaining equivalent cardiotoxicity risk—an encouraging demonstration of personalised nanomedicine at the computational level [70].

Fig 2 - Base drug delivery system

AI in The Tumour Microenvironment Analysis For Drug Delivery

Multiomics Integration And Time Characterisation

Comprehensive characterisation of the TME is essential for identifying nanoparticle delivery barriers and rational targeting strategy selection. Single-cell RNA sequencing (scRNA-seq) generates transcriptomic profiles at the individual cell level, enabling deconvolution of TME cellular composition, identification of cancer cell subclones with distinct drug sensitivity profiles, and mapping of receptor expression heterogeneity across tumour cells [72]. However, scRNA-seq datasets for a single tumour biopsy can contain expression profiles for tens of thousands of individual cells, with each cell characterised by thousands of gene expression values—a high-dimensional dataset requiring sophisticated computational analysis. Deep learning models, including variational autoencoders and graph-based neural networks, have been developed specifically for scRNA-seq data analysis. These tools perform cell clustering (identifying cell types), trajectory inference (pseudotime analysis of cell differentiation states), and gene regulatory network reconstruction [73]. For NDDS applications, AI analysis of scRNA-seq data from patient tumour biopsies has been used to: (i) rank targetable surface receptors by expression level and cancer cell selectivity; (ii) identify CAF subtypes that promote nanoparticle exclusion from tumour parenchyma; and (iii) predict the immunosuppressive landscape that would limit immuno-oncology combination strategies [74].

Spatial Transcriptomics and Drug Penetration Modelling

Spatial transcriptomics (ST) platforms (10X Visium, SLIDE-seq, Stereo-seq) preserve the spatial coordinates of gene expression within intact tissue sections, providing information that scRNA-seq—which disrupts tissue architecture—cannot capture. AI analysis of ST data generates spatially resolved maps of receptor expression, immune infiltration, hypoxia markers, and stromal composition within the three-dimensional tumour architecture [75]. These spatial gene expression maps have been computationally fused with nanoparticle biodistribution data (from fluorescence microscopy or mass cytometry) to build predictive models of drug penetration depth as a function of TME spatial architecture. Kim et al. trained a 3D CNN on matched ST and fluorescence nanoparticle distribution images from 45 tumour specimens, producing models that predicted nanoparticle penetration depth from ST-derived TME features with correlation coefficient r = 0.84 [47]. These models can guide NDDS design toward formulations capable of achieving therapeutic drug concentrations throughout heterogeneous tumour masses, not just the perivascular regions where EPR is dominant.

AI for Immunogenic Cell Death and Immuno-Oncology Combinations

Nanoparticle-delivered chemotherapy and photodynamic agents can induce immunogenic cell death (ICD)—a form of apoptosis characterised by surface exposure of calreticulin (eat-me signal), release of HMGB1 and ATP (danger signals), and subsequent dendritic cell maturation and tumour-specific T-cell activation [76]. ICD-inducing nanoparticle formulations are increasingly being combined with immune checkpoint inhibitors (anti-PD-1, anti-CTLA-4) to convert immunologically 'cold' tumours to 'hot' tumours amenable to immunotherapy. AI models trained on in vitro ICD readouts (calreticulin exposure by flow cytometry, ATP release assays, HMGB1 ELISA) from panels of nanoparticle formulations have identified physicochemical and drug-related predictors of ICD induction, facilitating rational selection of ICD-competent nanomedicines for immuno-oncology combination studies [76]. Transformer-based AI models have further been used to mine the published literature (> 15,000 preclinical studies) for combinations of nanoparticle platforms and checkpoint inhibitors with the highest probability of synergy, generating ranked shortlists for experimental validation.

Advanced Nanoparticle Platforms And AI Interactions

Stimuli-Responsive Smart Nanoparticles

Stimuli-responsive nanoparticles are engineered to release their cargo preferentially in the tumour microenvironment in response to specific physico-chemical triggers. pH-responsive nanoparticles exploit the acidic tumour interstitial pH (6.4–7.0, vs physiological 7.4) to trigger conformational changes in pH-sensitive polymers (polyacids such as poly(L-histidine)) or pH-cleavable linkers (hydrazone, acetal) that initiate drug release [77]. Redox-responsive systems exploit the elevated glutathione concentration in tumour cells (2–10 mM intracellular, vs 2–20 μM extracellular) to cleave disulfide bonds and release encapsulated cargo. Enzyme-responsive nanoparticles utilise upregulated tumour proteases (MMP-2, MMP-9, cathepsins, urokinase plasminogen activator) to cleave peptide or oligonucleotide linkers. AI has been applied to design optimal stimuli-responsive elements by training ML models on structure-activity datasets correlating linker chemistry with cleavage kinetics under simulated tumour microenvironmental conditions. Deep learning models (specifically sequence-based RNNs and transformer architectures) have been used to design novel protease-cleavable peptide sequences with optimised MMP-2 substrate specificity and cleavage efficiency, as validated by fluorescence resonance energy transfer (FRET) assays [78]. This AI-guided approach reduced the peptide screening effort by 60% while identifying cleavable sequences with 2.5-fold faster cleavage rates than literature benchmarks.

Theranostic Nanoparticles and AI-Guided Imaging

Theranostic nanoparticles integrate therapeutic and diagnostic functions within a single nanoplatform, enabling real-time monitoring of drug biodistribution, therapeutic response, and dose adaptation [79]. Iron oxide nanoparticles (IONPs) provide T2-weighted MRI contrast, enabling tracking of nanoparticle accumulation in tumours. Gold nanoparticles (AuNPs) enhance CT contrast and can serve as photothermal transducers. Fluorescent quantum dots enable optical imaging for intraoperative tumour margin delineation. Radiolabelled nanoparticles enable quantitative PET/SPECT biodistribution imaging. AI enhances theranostic nanoparticle utility through several mechanisms: (1) Deep learning algorithms analyse theranostic imaging data (MRI, PET) to automatically segment nanoparticle-accumulating tumour regions, calculate tumour-to-background ratios, and map drug distribution heterogeneity within tumours [80]; (2) Radiomics AI models extract hundreds of quantitative imaging features from theranostic NP scans to predict histopathological response to therapy before surgery or rebiopsy; (3) AI models trained on serial theranostic imaging data can predict therapeutic response (tumour volume reduction, metabolic activity changes) at early treatment timepoints—enabling timely adaptive treatment modifications in non-responders.

Exosomes and Biological Nanoparticles

Exosomes and other extracellular vesicles (EVs) have attracted intense research interest as natural drug delivery vehicles because of their inherent ability to traverse biological barriers (including the BBB), their tropism for specific recipient cell types, and their intrinsic immunological stealth properties [81]. However, EV biogenesis is extraordinarily complex, involving hundreds of proteins (tetraspanins, flotillins, heat shock proteins), lipids, and nucleic acids whose composition varies with the cellular source, physiological state, and culture conditions. AI has been applied to EV research across several dimensions: (1) ML models trained on proteomics data from EV preparations identify the minimal surface protein signatures required for organ-specific tropism, enabling the engineering of designer EVs with programmed tissue targeting [82]; (2) Recurrent neural networks predict the cargo loading efficiency of specific therapeutic nucleic acids (siRNA, miRNA) into EVs from RNA secondary structure features and EV membrane composition, guiding co-incubation or electroporation loading protocols; (3) Transfer learning from general protein folding models (AlphaFold2) has been applied to model EV membrane protein structures and identify novel rational surface engineering targets [83].

Clinical Translation: Challenges and AI Solutions

The Translational Gap in Nanomedicine

Despite the vast preclinical literature on nanoparticle drug delivery, the number of NDDS reaching clinical approval has been disappointingly small relative to research investment. As of 2025, fewer than 60 nanomedicines have received regulatory approval globally, and of these, fewer than 20 are for oncological indications [84]. The major contributors to translational failure include: (1) poor correlation between murine xenograft pharmacokinetics and human PK; (2) overestimation of EPR magnitude in clinical settings; (3) inadequately predictive in vitro cancer models used for pre-IND screening; (4) manufacturing scalability challenges; and (5) insufficient biomarker development for patient selection [85]. AI addresses several of these gaps. Human-relevant in vitro models—including 3D tumour spheroids, organoids, and organ-on-chip systems—generate richer, more clinically predictive datasets than 2D cell culture, but their complexity demands AI-based image analysis and multi-parameter data integration tools to extract meaningful endpoints [85]. AI-driven humanised mouse models, incorporating patient-derived xenografts (PDX) and human immune system reconstitution, generate translational datasets upon which AI PK scaling models achieve significantly lower prediction errors than allometric scaling alone [86]. Table 3 summarises key clinical trials and advanced preclinical programmes that have integrated AI with nanoparticle drug delivery.

Fig 3 - Integrated System

AI in Biomarker Discovery for Patient Stratification

A persistent challenge in nanomedicine clinical trials has been the enrolment of unselected patient populations in which only a subset may benefit from NDDS therapy (typically those with high EPR magnitude, specific receptor expression, or tumour penetrability). Without predictive biomarkers, clinical trials are underpowered to detect efficacy signals in responsive subgroups and may result in failed trials despite genuine efficacy in biomarker-defined cohorts [87]. AI-driven biomarker discovery approaches applied to NDDS trials include: (1) Radiomics—quantitative extraction of > 1,000 imaging features from pre-treatment CT/MRI—to identify imaging biomarkers of EPR magnitude and tumour vascular permeability; (2) Genomic ML models trained on TCGA data to identify gene expression signatures associated with receptor overexpression relevant to active targeting; (3) Liquid biopsy AI platforms analysing circulating tumour DNA (ctDNA), circulating tumour cells (CTCs), and exosomal cargo to provide non-invasive tumour profiling for patient selection and response monitoring. Park et al. demonstrated that a radiomics ML model based on pre-treatment DCE-MRI predicted response to liposomal doxorubicin in metastatic breast cancer with AUC = 0.84, compared to AUC = 0.61 for conventional clinical parameters alone [29].

Manufacturing Scale-Up and AI Quality Control

GMP (Good Manufacturing Practice) scale-up of nanoparticle production from laboratory to clinical-grade manufacturing is a critical bottleneck associated with significant batch-to-batch variability in size, PDI, encapsulation efficiency, and surface properties. Process analytical technology (PAT) frameworks, mandated by regulatory agencies (FDA, EMA) for pharmaceutical manufacturing, require real-time monitoring and control of critical process parameters (CPPs) and critical quality attributes (CQAs) [88]. AI-enabled PAT for NDDS manufacturing integrates: (1) Real-time multivariate spectroscopy (NIR, Raman) data analysed by partial least squares (PLS) or CNN models to predict CQAs (particle size, drug content) from spectral fingerprints without offline sampling; (2) Process control algorithms (model predictive control, MPC) trained on historical manufacturing data to maintain CPPs within specification despite raw material variability; (3) Computer vision systems applying CNNs to vial inspection images to detect sub-visible particles, aggregates, and fill-volume deviations with sensitivity superior to human inspectors. Implementing AI-PAT at a GMP liposome manufacturing facility reduced batch failure rate from 12% to 2.8% and decreased quality control testing cycle time by 65% [88].

Regulatory Landscape For AI-Nanoparticle Drug Delivery

Evolving Regulatory Frameworks

The integration of AI into both the development and clinical application of nanoparticle therapeutics introduces novel regulatory challenges that existing frameworks were not designed to address. Regulatory agencies globally are developing dedicated guidance documents for AI/ML-enabled medical devices and pharmaceutical applications. The FDA's Action Plan for AI/ML-Based Software as a Medical Device (SaMD) and the EU's AI Act provide overarching regulatory principles, but specific guidance for AI embedded in pharmaceutical manufacturing or clinical decision support for nanomedicine remains nascent [89]. Key regulatory considerations for AI-NDDS integration include: (1) Model validation and qualification: AI models used to make decisions about NDDS formulations or dosing must be rigorously validated on independent datasets, with demonstrated robustness to dataset shift (changes in patient population, manufacturing process, or analytical instrumentation); (2) Transparency and explainability: Regulatory reviewers require understanding of how AI models reach their recommendations—a challenge for deep learning 'black boxes' that can be partially addressed by explainability tools (SHAP values, LIME, saliency maps, attention mechanisms); (3) Continuous learning and model drift: AI models that update themselves based on real-world post-marketing data require change management frameworks specifying acceptable performance drift boundaries and revalidation triggers; (4) Cybersecurity: AI-enabled clinical decision support systems for nanoparticle dosing must meet rigorous cybersecurity standards to prevent adversarial attacks that could compromise patient safety [89].

Good Machine Learning Practice (GMLP)

The concept of Good Machine Learning Practice (GMLP)—analogous to GMP and GCP—encompasses standards for data quality, model development, validation, deployment, and monitoring of AI systems in healthcare. For AI applications in nanomedicine, GMLP principles encompass: representative and bias-free training datasets; pre-specified model validation metrics and acceptance criteria; version-controlled model development pipelines; prospective clinical validation studies; post-deployment performance monitoring; and clear delineation of human oversight responsibilities in AI-assisted decision-making workflows [90]. India's Central Drugs Standard Control Organisation (CDSCO) has initiated consultation processes for regulating AI-enabled diagnostic and therapeutic devices, representing an important step toward a domestically coherent regulatory environment for AI-driven nanomedicine innovations developed and manufactured in India—a growing global force in nanomedicine production [90].

Ethical Considerations In AI-Driven Nanoparticle Cancer Therapy

Algorithmic Bias and Health Equity

AI models trained predominantly on data from patients of European ancestry—a significant bias in many clinical genomics and pharmacological databases (e.g., UK Biobank, TCGA)—may have suboptimal predictive performance in patients from other ethnic backgrounds due to differences in pharmacogenomics, tumour biology, and comorbidity profiles [91]. For nanomedicine AI applications, this manifests as potential disparities in AI-recommended dosing, biomarker-based patient selection, and imaging biomarker interpretation across different populations. Addressing algorithmic bias in nanomedicine AI requires: (1) prospective diversification of clinical trial enrolment with attention to ancestry representation; (2) federated learning across geographically and ethnically diverse data sources; (3) fairness-aware ML training objectives that penalise disparate prediction errors across demographic subgroups; and (4) mandatory bias audits for AI systems used in oncology decision support [91]. India, with its extraordinary genetic diversity, is uniquely positioned to contribute population-diverse datasets that will improve the global generalisability of nanomedicine AI models.

Informed Consent, Data Privacy, and Patient Autonomy

The deployment of AI in nanomedicine clinical practice raises important questions about patient consent. When an AI model recommends a specific nanoparticle dosing schedule, patients must be adequately informed about the role of AI in treatment recommendations, the model's performance characteristics and limitations, and their right to opt for conventional non-AI-assisted care [92]. The complexity of AI decision-making creates a new dimension of the informed consent challenge: even clinicians may not fully understand the mechanistic basis of an AI recommendation, requiring novel consent frameworks that emphasise transparency about the overall process rather than the mathematical specifics of any individual model. Patient data used to train nanomedicine AI models—including genomic data, imaging, liquid biopsy results, and longitudinal treatment outcomes—represents some of the most sensitive personal health information conceivable. Data governance frameworks must ensure patients are informed about how their data will be used for AI training, implement technical safeguards (differential privacy, secure multi-party computation) to prevent re-identification, and provide meaningful data access and deletion rights consistent with applicable privacy regulations [92].

Intellectual Property and Open Science

The convergence of AI and nanomedicine is generating a complex intellectual property landscape. Pharmaceutical companies and AI firms are filing patents on AI-generated nanoparticle compositions, ML-derived biomarker algorithms, and AI-assisted manufacturing processes. The novel inventor status of AI systems themselves remains legally contested across jurisdictions. Open science advocates argue that AI-generated scientific insights should be shared freely to accelerate global progress against cancer, while commercial innovators argue that IP protection is essential for incentivising the substantial investment required for clinical translation [93]. Balanced approaches—including publication of model architectures and training methodologies alongside proprietary datasets, time-limited exclusivity periods, and compulsory licensing provisions for essential nanomedicines in low- and middle-income countries—are being discussed in international forums.

Future Perspectives and Emerging Frontiers

Large Language Models and Scientific Discovery

Large language models (LLMs) trained on vast corpora of scientific literature, chemical structures, and clinical trial data are beginning to demonstrate capacity for scientific reasoning and hypothesis generation in nanomedicine [94]. Models such as GPT-4, Claude (Anthropic), Gemini (Google DeepMind), and domain-specific scientific LLMs (BioMedLM, ChemBERTa, MolT5) can assist researchers in: (1) automated literature mining to extract and synthesise experimental results from thousands of papers on nanoparticle drug delivery; (2) hypothesis generation for novel nanoparticle designs based on analogy to existing chemical and biological knowledge; (3) automated scientific writing and protocol generation; and (4) interactive question-answering systems that allow clinical oncologists to query AI models about nanoparticle formulation rationale in natural language. While LLMs show remarkable promise, important limitations must be acknowledged: they can generate plausible-sounding but factually incorrect information ('hallucinations'); they do not inherently reason mechanistically; and their outputs require expert validation before integration into clinical workflows [94]. Nevertheless, LLM-assisted scientific discovery represents a genuine near-term transformative opportunity for nanomedicine research.

AI-Driven Personalised Neoantigen Nanoparticle Vaccines

Personalised cancer vaccines targeting patient-specific tumour neoantigens—peptides derived from somatic mutations absent in normal tissues—represent a frontier where AI and nanoparticle delivery are converging with striking clinical promise [95]. The workflow involves: (1) whole-exome sequencing of tumour and normal tissue; (2) AI neoantigen prediction algorithms (NetMHCpan, MHCflurry, pVACtools) that identify somatic mutations likely to produce peptides capable of binding to the patient's HLA alleles and eliciting T-cell responses; (3) LNP or polymeric NP formulation of the identified neoantigen peptides or mRNA sequences, often in combination with adjuvants (TLR agonists, STING agonists); (4) AI-optimised dosing and administration schedules. BioNTech's individualised neoantigen vaccine mRNA-4157/V940, developed with Moderna and delivered via LNPs, demonstrated in Phase 2b trials a 44% reduction in distant recurrence or death in resected high-risk melanoma patients when combined with pembrolizumab [95]. AI played a central role in neoantigen prediction and LNP formulation optimisation. As AI neoantigen prediction algorithms continue to improve and LNP manufacturing becomes more scalable, personalised neoantigen vaccine approaches are expected to expand across a wider range of tumour types.

Quantum Computing and Nanoparticle Molecular Simulation

Quantum computing promises to revolutionise molecular simulation by exponentially accelerating quantum chemical calculations that are intractable on classical computers. For nanomedicine, this means that the nano-bio interface—protein-nanoparticle interactions, lipid bilayer dynamics, DNA-nanoparticle binding—could be modelled at quantum mechanical accuracy for systems of thousands of atoms, enabling truly physics-based nanoparticle design [96]. While practical quantum advantage for molecular simulation of NDDS-relevant systems may still be 5–15 years away, hybrid quantum-classical algorithms (variational quantum eigensolvers, quantum approximate optimisation algorithms) are already being explored for small-molecule drug-receptor interaction calculations that inform nanoparticle cargo selection.

The Internet of Nano-Things and Closed-Loop Therapy

The Internet of Nano-Things (IoNT) envisions a future where nanosensors, nanocomputers, and therapeutic nanoparticles communicate wirelessly within the human body to enable real-time sensing of tumour biomarkers, local drug concentration monitoring, and automatic dose adjustment [97]. AI is the intelligence layer of this vision: machine learning algorithms residing in wearable edge-computing devices or implantable biosensors would continuously analyse nanosensor data streams, compare measured biomarker levels against personalised therapeutic target windows, and communicate with implanted drug depot nanodevices to adjust drug release rates in real-time. While fully realised IoNT cancer therapy remains speculative, important building blocks are already demonstrated: implantable biosensors for continuous tumour microenvironment monitoring (pH, O?, glucose), externally programmable stimuli-responsive nanoparticle depots, and edge AI systems for real-time biomarker analysis [97]. The convergence of these technologies over the coming decade may make true closed-loop cancer nanotherapy a clinical reality.

Table 3. Clinical Trials and Advanced Preclinical Studies Integrating AI with Nanoparticle Drug Delivery in Oncology (2020–2025)