Artificial Intelligence, Microrobots/Nanorobots and Pharmaceutically Optimized Nanocarrier Systems for Precision Tumor Multitargeting

Abstract

Precision oncology increasingly requires multi-target strategies addressing tumor heterogeneity, microenvironmental barriers, immune evasion, and drug resistance. This narrative review critically examines the convergence of artificial intelligence (AI), microrobotic/nanorobotic systems, and pharmaceutically optimized nanocarriers for precision tumor multi-targeting. It highlights emerging intelligent nanomedicine strategies while distinguishing clinical evidence for selected nanocarrier products from preclinical and conceptual integration. This review explores AI-guided formulation design and patient stratification; microrobots and nano/micromotors for active delivery and intratumoral penetration; and optimized, stimuli-responsive nanocarriers for co-delivery of chemotherapeutics, immunomodulators, nucleic acids, and gene-editing agents. Tumor multi-targeting, microenvironment-responsive release, and synchronized drug delivery are critically assessed. AI-guided formulation studies include computational prediction and prospective laboratory validation, whereas active delivery platforms have predominantly been evaluated in vitro and in animal models. Preclinical and conceptual findings suggest that integrating AI-guided design, active navigation, and responsive nanocarriers may enhance spatial and temporal delivery control; reductions in systemic toxicity require system-specific validation. Study-specific quantitative matrices compare model validation, actuation conditions, payloads, release characteristics, and exposure measures, explicitly identifying missing data and non-comparable endpoints. Target prioritization incorporates biological validity, druggability, delivery feasibility, and clinical actionability. Investigational digital twins require longitudinal data quality control, calibrated uncertainty, drift surveillance, and clinician oversight. Although selected robotic–carrier combinations demonstrate preclinical benefits, fully integrated platforms enabling patient-specific adaptive treatment lack clinical validation. Translation requires indication-specific comparisons with simpler formulations, reproducible manufacturing, validated navigation, and prospective benefit–risk assessment. Future research should prioritize pharmaco-nanotechnologies, refined cancer molecular classification, and clinician-governed decision support before adaptive AI-assisted multi-target nanomedicine.

1. Introduction

Cancer treatment has shifted from organ-based therapy toward molecularly stratified and increasingly patient-specific intervention. Nevertheless, the clinical performance of many targeted therapies is constrained by intra- and intertumoral heterogeneity, adaptive resistance, stromal exclusion, abnormal vasculature, immunosuppressive niches and pharmacokinetic variability. Nanomedicine was introduced partly to overcome the limitations of freely circulating drugs by improving solubility, biodistribution, payload protection and controlled release [1,2,3,4]. Yet, the clinical translation of nanocarriers remains limited by variable tumor deposition, insufficient tissue penetration, batch-to-batch complexity and incomplete prediction of nano–bio interactions [5,6,7,8,9,10,11].

Precision tumor multi-targeting may benefit from coordinated target selection, formulation design and transport; however, integrating these functions is a research objective rather than an established requirement for clinically effective treatment. A proposed integrated platform would aim to (i) select target combinations from high-dimensional patient data; (ii) engineer nanocarriers with reproducible critical quality attributes (CQAs); (iii) deliver payloads across vascular, stromal, cellular and subcellular barriers; and (iv) support clinician-reviewed treatment adaptation after validation. AI, microrobots and pharmaceutically optimized nanocarriers address complementary parts of this problem. AI supports computational design, patient stratification and adaptive decision-making. Microrobots introduce active, externally or biologically powered transport. Nanocarriers provide the dosage-form architecture necessary for payload stabilization, biodistribution control and synchronized multi-agent release.

This review examines how these technologies can be combined in a translational Pharmaceutics framework. The focus is not simply on futuristic “nanorobots” but on realistic convergence: AI-guided product design, microactuated local delivery and manufacturable nanocarrier systems that can satisfy safety, quality and regulatory requirements.

2. Scope and Narrative Search Approach

This focused narrative review employed targeted, up-to-date literature searches to address three interconnected questions: how artificial intelligence (AI) guides tumor target and formulation design; how microrobots and nanorobots achieve active delivery; and how pharmaceutical nanocarriers support precision, multi-agent treatment. PubMed and Crossref searches combined (cancer OR tumor OR tumour OR oncology) with three topic blocks: (artificial intelligence OR machine learning OR Bayesian optimization OR quality by design) AND (nanocarrier OR nanoparticle OR formulation); (microrobot OR nanorobot OR micro/nanomotor OR magnetic microrobot OR biohybrid robot) AND (drug delivery OR targeted delivery); and (nanocarrier OR liposome OR lipid nanoparticle OR polymeric nanoparticle) AND (co-delivery OR combination therapy OR stimuli-responsive OR immunotherapy).

Related terminology and backward citation tracking were used to identify foundational studies, including papers predating current terminology. Recent literature from 2025–2026 was considered alongside earlier primary studies. Targeted bibliographic and numerical source checks for this revision were completed on 13 October 2026; this focused update is not a systematic quantitative synthesis.

Records were assessed for relevance from title and abstract, with methods and full text examined where needed. Eligible sources included experimental and clinical studies, pharmaceutical development work, methodological papers, and critical reviews with a direct bearing on at least one of the three questions. Selection favored clear mechanisms, adequate description of formulation or propulsion methods, evaluation of tumor delivery barriers, product quality, safety, and translational relevance. Duplicate records, papers outside the oncology and drug-delivery scope, reports with insufficient methods for interpretation, unresolved bibliographic identifiers, and unsupported speculative claims were set aside. Foundational studies and well-substantiated limitations were retained alongside recent advances.

Figure 1 summarizes this purposeful narrative selection and the reasons for retaining or excluding publications. The included studies were qualitatively synthesized based on theme and the maturity of the evidence, distinguishing between approved products and clinical findings, and animal experiments and conceptual proposals.

Figure 1. Targeted literature identification and qualitative synthesis. Note: This figure was prepared with assistance from ChatGPT 6.1 (OpenAI), based on the authors’ concepts and the cited literature. The authors reviewed and edited the figure and take full responsibility for its scientific content.

Searches in PubMed and Crossref, backward citation tracking, and a focused 2025–2026 update were used to identify literature across three interconnected areas: AI-guided nanocarrier design, active micro/nanorobotics for cancer drug delivery, and pharmaceutical nanocarriers for combination or responsive therapy. After duplicate records were removed, titles and abstracts were assessed for relevance and interpretability; methods and full texts were examined when needed. Experimental and clinical studies, pharmaceutical development reports, methods papers, critical reviews, and foundational work were considered. Records outside the oncology or drug-delivery scope, reports with insufficient methods, and records with unresolved bibliographic identifiers were set aside. The retained literature was synthesized qualitatively according to the review question and maturity of evidence: established or clinical findings, experimental animal and laboratory findings, and conceptual proposals or future directions.

The synthesis distinguishes four evidence settings: H, human clinical evidence for the specified product or decision-support application; A, animal experiments; L, in vitro or ex vivo experiments; and C, computational studies. P denotes a proposed integration for which end-to-end experimental validation has not been established in the cited evidence. These labels describe the setting of evidence, not a formal GRADE (Grading of Recommendations Assessment, Development and Evaluation) certainty assessment. A component may carry several labels because computational development can be followed by laboratory or animal testing. Human evidence for one component does not establish human evidence for their combination. Table 1 applies this framework to representative components and interfaces.

Table 1. Evidence settings for components and their integration.

Component or Interface	Evidence Setting and Source	What Is Demonstrated	What Remains Unproven
Selected oncology liposomes	H [3,4,9] (clinical evidence summarized in reviews)	Clinical use of particular formulations; product-specific PK and safety experience.	A clinical result cannot be transferred to a new multi-agent or robotic product.
AI-guided formulation	C/L/A [12]	Prediction of nanoparticle formation and composition, followed by laboratory validation and selected mouse PK experiments.	Human efficacy of a model-selected formulation or adaptive integrated therapy.
AI plus robotic experimentation	C/L [12] (laboratory automation)	Automation refers to laboratory liquid handling for formulation screening; animal PK validation concerns selected formulations, not an in vivo therapeutic robot.	This is not evidence of an AI-controlled therapeutic robot inside a patient.
Acoustic robot with drug-containing hydrogel	L/A [13]	Acoustic propulsion, magnetic guidance, ultrasound imaging and local 5-fluorouracil delivery after intravesical administration; antitumor activity in an orthotopic mouse bladder-tumor model.	Systemic human tumor navigation, human safety and efficacy, and patient-specific AI control are not established by this study.
Biohybrid robot carrying nanoparticles	L/A [14]	Algae–nanoparticle delivery after intratracheal administration in mice; reduced melanoma lung metastatic burden and longer survival than free-drug and passive nanoparticle controls.	Human route feasibility, scalable manufacture and clinical benefit–risk remain unestablished.
Mechanistic PK and QSP modeling	C [15,16] (using experimental or clinical data)	PBPK analysis of published nanoparticle tumor-delivery data [15]; discussion and illustrative modeling of virtual patients and digital twins in immuno-oncology [16]. Neither source establishes an AI–robot interface.	Prospective patient-specific control of an integrated nanorobotic product; mechanistic modeling alone does not demonstrate AI integration.
Full AI–robot–carrier adaptive platform	P (author-proposed synthesis; conceptual background [16,17,18,19])	A proposed architecture informed by complementary component studies and reviews; these sources do not demonstrate the complete adaptive platform.	End-to-end clinical validation and superiority over simpler treatment are not established in the cited sources.

Legend: A, animal; AI, artificial intelligence; C, computational; H, human; L, laboratory or ex vivo; P, proposed; PBPK, physiologically based pharmacokinetic; PK, pharmacokinetics; QSP, quantitative systems pharmacology. Notes: The codes describe the evidence setting, not a formal certainty-of-evidence score. H denotes human evidence summarized by the cited reviews, not a new clinical trial conducted by those reviews. Human cells, human-derived data or clinical inputs to a model do not constitute prospective clinical validation. “Not established” refers to the cited sources and does not assert that no other evidence exists. The two rows citing [12] describe different aspects of the same study and are not independent evidence.

The quantitative comparison is study-specific rather than a pooled meta-analysis. Model architecture, dataset size, split strategy and validation setting are considered alongside performance. Actuation and propulsion are interpreted within their measurement medium and administration route. Encapsulation, loading, plasma exposure, tumor uptake and release are separated by denominator, time point and assay. NR denotes a parameter not reported for the stated comparison in the selected source; NV denotes a parameter that could not be verified from the source material accessed for this revision; NA denotes a parameter not applicable to the endpoint. Missing values are not imputed. No cross-platform superiority is inferred from heterogeneous studies.

3. AI-Driven Design for Precision Tumor Multi-Targeting

3.1. AI for Formulation Optimization and Quality by Design (QbD)

Nanocarrier development is a multidimensional optimization problem. Particle size, polydispersity index (PDI), morphology, surface charge, lipid/polymer composition, polyethylene glycol (PEG) density, ligand density, encapsulation efficiency, drug ratio, release kinetics, osmolality, sterility, residual solvent and storage stability can all influence biological performance [2,8,10,20]. Classical design of experiments (DoE) remains essential, but AI and machine learning (ML) can extend QbD by learning nonlinear relationships among material attributes, process parameters and CQAs [12,17,20,21,22,23].

In an AI-supported QbD workflow, historical formulation data, automated high-throughput experiments and mechanistic priors are used to define a design space. Supervised models predict CQAs; Bayesian optimization proposes new experiments; active learning reduces the number of wet-lab iterations; and uncertainty-aware models identify regions requiring confirmatory testing [17,22]. For regulatory credibility, such workflows must preserve traceability between input variables, model predictions, experimental confirmation and clinically relevant product performance [15,20,23].

Four representative primary studies illustrate different validation questions (Table 2). TuNa-AI predicts formulation success from molecular and compositional information; a prospective formulation result is stronger than random cross-validation but does not establish clinical benefit [12]. Reker et al. screen drug–excipient co-assembly [24]. Ortiz-Perez et al. optimize cellular uptake through an experimental active-learning loop [25]. Chou et al. predict kinetic parameters for a mechanistic mouse PBPK (physiologically based pharmacokinetic) model [26]. These endpoints are not interchangeable: ROC-AUC (Area Under the Receiver Operating Characteristic Curve) evaluates ranking of binary outcomes, R² measures explained variation for a regression endpoint, and RMSE (Root Mean Square Error) depends on the endpoint scale. Uptake gain and plasma AUC are experimental outcomes, not measures of algorithmic discrimination.

Table 2. Comparative validation and evidence limits of representative AI studies.

Primary Study and Task	Architecture and Dataset	Validation and Numerical Result	Reproducibility and Evidence Limits
TuNa-AI [12]	Hybrid-kernel SVM: molecular fingerprints, physicochemical descriptors and ratio kernels. 1275 formulations, 17 drugs × 15 excipients × 5 ratios; expanded training set 2717, including 1442 published formulations.	Drug/excipient/pair holdouts; SVM ROC-AUC 0.87/0.91/0.86, respectively. Random/stratified fivefold CV also reported. Prospective venetoclax and trametinib formulation validation; mouse PK for the latter.	Code and data reported at https://github.com/RekerLab/TuNa-AI (accessed on 19 October 2026). Added literature data support training; they do not establish independent external validation. Prospective wet-lab validation exists; no clinical validation. Generalization beyond the investigated formulation domain remains unproven.
Drug–excipient co-assembly [24]	Random forest; chemical and MD descriptors. 1440 measured pairs, 16 drugs × 90 excipients.	94/1440 initial positive pairs (6.5%); computational screening of 2.1 million pairs and experimental discovery of 100 formulations. A harmonized ROC-AUC for cross-study comparison is not presented here.	Code and data: https://github.com/DanReker/CoAggregators (accessed on 19 October 2026). Prospective experiments and selected ex vivo/in vivo studies. Performance is formulation-domain dependent; class imbalance and related drug–excipient pairs warrant caution when assessing generalization.
Active-learning PLGA–PEG design [25]	Bayesian neural network for uptake; XGBoost for size/PDI. Cycle 0: 28–29 formulations (inconsistently reported in the source); then 10 + 10. Virtual library: 100,000.	Fivefold 80/20 CV; prospective active learning. Best uptake increased from approximately 5-fold to 15-fold relative to the uptake standard, not a 15-fold gain over cycle 0. No common cross-study test set.	Code/models: https://github.com/molML/Nano_Particles_Active_Learning; raw data: https://zenodo.org/records/8289605 (accessed on 19 October 2026). In vitro only. Uptake is not tumor efficacy. The source has an unresolved initial-count discrepancy.
AI-assisted mouse PBPK [26]	AI-QSAR, including DNN prediction of tumor kinetic parameters, coupled to PBPK; 378 tumor datasets.	80/20 train/test split with nested fivefold CV within training data. Reported PBPK delivery results: at 24 h, R2 0.83, RMSE 3.01; at 168 h, R2 0.56, RMSE 2.27; maximum, R2 0.82, RMSE 3.51. R2 ≥0.70 for 133/288 time profiles.	Code/data: https://github.com/UFPBPK/Nano-AI-PBPK-G1 (accessed on 19 October 2026). Retrospective mouse evidence. Delivery metrics are not the DNN parameter-prediction test scores. A random holdout from the same database does not establish independent clinical validation.

Abbreviations and symbols: AI, artificial intelligence; CV, cross-validation; DNN, deep neural network; MD, molecular dynamics; PBPK, physiologically based pharmacokinetic; PDI, polydispersity index; PEG, poly(ethylene glycol); PLGA, poly(lactic-co-glycolic acid); PK, pharmacokinetics; QSAR, quantitative structure–activity relationship; R², coefficient of determination; RMSE, root mean squared error; ROC-AUC, area under the receiver operating characteristic curve; SVM, support vector machine; TuNa-AI, tunable nanoparticle platform guided by AI; XGBoost, extreme gradient boosting; h, hour; n, number of formulations. Notes: Dataset sizes refer to formulations, drug–excipient pairs or tumor datasets, not patients. ROC-AUC and R² are dimensionless. PBPK delivery-efficiency RMSE values are expressed on the reported percentage-of-injected-dose scale (percentage points), not as normalized errors. Uptake-fold values are assay-relative measurements and cannot be compared directly with these errors. No harmonized ranking of the four studies is justified. Repository availability does not demonstrate computational reproducibility. Published results were checked against accessible primary-source text and author repository information; the models were not rerun. Interpretive cautions in the final column are critical appraisal, rather than measured failure rates.

TuNa-AI has prospective laboratory validation, rather than being a formulation-screening concept alone. Nevertheless, colloidal formation does not establish sterility, storage stability, tolerated excipient exposure or efficacy in heterogeneous tumors [12]. More generally, splits should keep related compounds, batches and source studies together when those dependencies could leak into a test set. Code availability permits inspection but does not substitute for independent replication. Model uncertainty must be tested for calibration, not inferred from model architecture. Failures to form nanoparticles and unsuccessful biological experiments should be retained in the dataset.

The cited QbD sources are reviews and therefore do not provide a common primary experimental dataset from which a valid numerical league table of DoE performance can be reconstructed [20,23]. QbD/DoE should report design terms, lack of fit, residual diagnostics, adjusted and predicted R² and independent confirmation of the design space. Mechanistic PBPK modeling must be evaluated separately from purely statistical formulation models. The original tumor-delivery analysis used 376 datasets and reported a median terminal delivery efficiency of 0.76% of injected dose [15]; this population summary is neither an individual formulation value nor an AI accuracy metric. The AI-PBPK results in Table 2 show that performance also depends on the observation horizon [26].

3.2. Multi-Omics-Guided Target Selection

Multi-omics analysis nominates candidate targets; it does not itself establish a treatment indication. Four distinct questions must be answered: is the target a causal and sufficiently prevalent disease dependency? Can a therapeutic modality alter it selectively? Can that modality reach the relevant tissue, cell and intracellular compartment at an active concentration? Does clinical evidence support a favorable benefit–risk profile in the specified indication? Genomic findings can be ranked using ESCAT (ESMO Scale for Clinical Actionability of Molecular Targets, where ESMO = European Society for Medical Oncology), but its clinical actionability tiers should not be assigned directly to exploratory transcriptomic signatures or delivery mechanisms [27]. Table 3 and Figure 2 provide a separate pharmaceutical delivery gate.

Table 3. Gates from multi-omics target nomination to an actionable treatment.

Gate	Evidence to Require	Failure Signal	Decision and Source
Combination value	Prespecify drug ratio, sequence and incremental benefit against each single agent; assess tolerability and compare with the corresponding free-drug combination where relevant.	Two payloads described as synergistic without a dose–response interaction test.	Retain combinations only if their added complexity yields measurable benefit, including efficacy or tolerability. The suggested evaluation criteria are based on the general principles discussed in nanocarrier reviews [1,11].
Biological and delivery validation	Confirm target engagement and pharmacologically relevant exposure at the intended site of action; verify intracellular delivery when the therapeutic target is intracellular.	Tumor fluorescence alone, without evidence that active payload reaches and engages its intended target.	Apply formulation and biological-barrier controls before claiming delivery feasibility; this does not by itself establish clinical actionability [6,10,11].
Clinical context	Specify histology, alteration, prior treatment and level of biomarker–drug evidence.	Exploratory expression signature treated as an approved indication.	Use ESCAT to rank genomic alteration–drug evidence in its relevant tumor context; ESCAT is not a regulatory approval or a validation of non-genomic signatures or delivery feasibility [27].
Access to a matched treatment	Track enrolled → biopsied → molecularly profiled → potentially actionable → treated denominators and reasons for attrition.	Target list without accessible drug, trial or patient eligibility.	MOSCATO 01: 1035 enrolled; 948 biopsied; 843 with a molecular portrait; 411 with an actionable alteration; 199 received matched therapy. This prospective, non-randomized study illustrates attrition and does not establish comparative treatment benefit [28].
Proof of clinical benefit	Require indication-specific prospective evidence; compare against appropriate standard care.	Drug–alteration match assumed to guarantee benefit.	SHIVA found no significant progression-free survival improvement for the tested off-label matching strategy versus physician-choice treatment: median 2.3 versus 2.0 months; hazard ratio 0.88; 95% confidence interval 0.65–1.19; p = 0.41. This result should not be generalized to all biomarker-directed therapies [29].

Abbreviations and trial names: ESCAT, ESMO Scale for Clinical Actionability of molecular Targets; ESMO, European Society for Medical Oncology; MOSCATO 01, Molecular Screening for Cancer Treatment Optimization 01; SHIVA, trial name used by Le Tourneau et al. [29] (not expanded as a conventional acronym in the cited report). Notes: The pharmaceutical gates are an author-proposed translational framework, not a validated clinical score. ESCAT supplies a clinical evidence vocabulary; delivery feasibility requires separate experiments. The multi-omics scope is conceptual: the cited studies do not validate an integrated multi-omics-to-nanomedicine treatment pathway. Recommendations about drug ratios, sequencing and experimental controls are the authors’ synthesis, rather than a published validated gate system. Rows are ordered by their earliest cited reference; citation groups are in ascending numerical order.

Figure 2. From multi-omics target nomination to an actionable intervention. Notes: Each candidate passes biological, therapeutic-modulation, delivery and clinical-evidence gates. Failure at a gate redirects the candidate to research or reassessment. The diagram is an original author-proposed translational synthesis; the clinical evidence vocabulary is informed by ESCAT [27]. It is not a validated prediction algorithm or decision score. This figure was prepared with assistance from ChatGPT 6.1 (OpenAI), based on the authors’ concepts and the cited literature. The authors reviewed and edited the figure and take full responsibility for its scientific content.

Clinical experience demonstrates substantial attrition between profiling and treatment. In MOSCATO 01 (Molecular Screening for Cancer Treatment Optimization 01 trial), 843 patients obtained a molecular portrait, 411 had an actionable alteration and 199 received matched treatment; 63/193 evaluable patients achieved a progression-free-survival ratio greater than 1.3 [28]. This was a selected, non-randomized experience with an intrapatient comparison. In the randomized SHIVA trial (NCT01771458), the tested off-label molecular-matching strategy did not significantly improve progression-free survival over physician-selected therapy (2.3 versus 2.0 months; hazard ratio 0.88, 95% CI 0.65–1.19; p = 0.41) [29]. These studies do not invalidate established biomarker–drug indications, but they show why an association or a drug match cannot be assumed to predict benefit.

For a proposed combination, each payload needs a measurable exposure–target-engagement relation and evidence that the second target adds benefit at jointly tolerated doses. Expression of a receptor may facilitate delivery while failing to define a therapeutic dependency. Conversely, a compelling intracellular target may remain inaccessible because of endosomal trapping, heterogeneous perfusion or insufficient tissue exposure. Candidate combinations should progress only after matched controls establish target engagement, incremental efficacy and acceptable toxicity; unsupported targets should remain hypotheses [6,10,11,27].

3.3. Predictive PK and PD Models and Clinician-Governed Digital Decision Support

Combined pharmacokinetic/pharmacodynamic (PK/PD) modeling connects formulation design to therapeutic effect. Nanocarriers often exhibit complex biodistribution, nonlinear clearance, mononuclear phagocyte system uptake and tumor-type-dependent extravasation [5,8,10,15]. Mechanistic PBPK models can characterize nanoparticle biodistribution and tumor delivery [15], while QSP models can simulate treatment scenarios and virtual patients [16]. AI-assisted extensions may help estimate uncertain parameters and test dosing schedules [21].

A digital twin is a candidate patient-specific model whose state can be updated as new observations become available; it should not be equated with a validated autonomous treatment controller. Existing mechanistic QSP work supports virtual-patient simulations, but patient-specific identifiability, longitudinal validation and uncertainty remain limiting [16,21]. For a nanomedicine twin, the observations must distinguish carrier-associated and released drug, exposure at the target compartment and evidence of target engagement. Tumor size or a circulating marker alone may not identify the processes governing delivery or resistance.

Longitudinal integration should preserve specimen and acquisition timestamps, provenance, units, missingness and assay versions. An event-driven ingestion service can update fast physiological or imaging measurements, while laboratory and omics inputs will generally arrive asynchronously. Only quality-checked observations should update a versioned state estimator; stale or contradictory data should suspend recommendations. A deployment would require secure electronic-record interfaces, an auditable data store, reproducible model containers, access control, monitoring and tested fallback procedures. CPU-based inference may suffice for some mechanistic models; GPU training or image analysis may be needed for others. Infrastructure and latency targets must be justified for the model and clinical task rather than presented as universal requirements [16,30].

Data drift should be separated from concept drift and calibration failure. Prespecified surveillance should evaluate missingness, input distributions, subgroup coverage, prediction residuals and interval coverage as outcome labels become available. Alarm limits must be set on validation data and tied to a safe response: stop recommendations, investigate, recalibrate or retrain offline, then revalidate a locked version. Automatic online retraining should not alter the deployed therapeutic policy without review. Clinicians should receive the predicted outcome range, assumptions, data freshness and out-of-domain status, including an explicit option to abstain. Closed-loop faults and mitigations are summarized in Table 4 as design proposals, not experimentally validated safeguards [30].

Table 4. Failure modes and proposed safeguards for digital decision support.

Failure Mode	Detection and Uncertainty Display	Proposed Safe Response	Evidence Basis
Loss of localization or actuation mismatch	Show confidence and time since last usable image; detect tracking failure.	Stop actuation; use a prevalidated safe-hold or retrieval pathway where feasible.	Robot-specific risk; safeguards proposed, not clinically established [13,18].
Missing or stale observations	Show timestamp, completeness and the unsupported state variables.	Abstain; request confirmation; fall back to standard clinician assessment.	QSP limitations and author-proposed operational controls [16,30].
State non-identifiability	Expose alternative parameter sets and wide prediction intervals.	Obtain informative measurements; avoid dose adaptation from ambiguous states.	Mechanistic-model limitations [16].
Population or assay drift	Monitor input distributions, subgroup residuals and interval coverage.	Freeze model recommendations; investigate and validate updates offline.	Proposed implementation monitored under clinical evaluation principles [30].
Incorrect dose or delayed feedback	Independent dose reconciliation and hard protocol exposure limits.	Prevent escalation; trigger clinician review and record the override.	Proposed safety architecture and human-factor evaluation [30].
Automation bias or conflicting objectives	Show benefit–harm uncertainty and the basis of each recommendation.	Require authorization; support abstention; preserve the audit trail.	Clinician-facing evaluation and comparator limits [30,31].
False trial match or outdated criteria	Display matching rule, evidence and unresolved eligibility fields.	Trial team and clinician confirm eligibility before a treatment decision.	Real clinical prescreening comparator [31].

Abbreviations: AI, artificial intelligence; DECIDE-AI, Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence; QSP, quantitative systems pharmacology. Notes: This is an author-proposed design-risk matrix, not a validated clinical safety protocol. No quantitative alarm threshold or clinical effectiveness of the listed safeguards is established by the cited references. DECIDE-AI is a reporting guideline for early clinical evaluation. Stopping an external actuation field does not necessarily stop passive displacement, biological self-propulsion, or drug release; safe holding, retrieval, clearance and biodegradation must be assessed for each platform.

MatchMiner, a platform for matching patients to clinical trials based on molecular and clinical data, is a more realistic clinical comparator than an autonomous tumor robot. It matches genomic and clinical data to curated trial criteria with daily updates and clinician eligibility review. In its institutional deployment, it facilitated 166 trial consents in 159 patients; consent occurred 55 days earlier than in the observational comparison group [31]. This supports a workflow endpoint, not a causal survival benefit, automatic prescribing or robotic control. Curated rules, incomplete eligibility fields, local trial availability and patient readiness limit its recommendations. A future twin should demonstrate added value over this type of rule-based support and a molecular tumor board, then undergo prospective silent-mode and clinician-assisted evaluation using DECIDE-AI (Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence) reporting principles before any controlled adaptive intervention [30,31].

4. Microrobots and Nano/Micromotors as Active Delivery Vectors

Passive nanocarrier delivery depends heavily on circulation time, vascular permeability and tumor retention. These parameters vary substantially among tumors and patients [7,15]. Microrobots and nano/micromotors seek to add an active transport component by using external fields, chemical gradients, biological propulsion or hybrid cell-based motility [13,14,18,19,32,33,34].

4.1. Magnetic Microrobots

Magnetically actuated microrobots are attractive because magnetic fields can penetrate tissue and provide remote control without relying on local chemical fuel [18,19,32,33]. Designs include helical swimmers, magnetic beads, soft hydrogel carriers, magnetic bacteria-based systems and magnetically steerable cell-particle hybrids [18,19,32,33]. Payloads can be incorporated by adsorption, encapsulation, covalent conjugation or attachment of drug-loaded nanoparticles [18,19,32,33,34]. Magnetic systems are comparatively strong candidates for translation because clinical magnetic resonance and magnetic-navigation infrastructure provide conceptual precedents; however, steering in deep, heterogeneous human anatomy remains a major engineering challenge [18,19,32,33].

4.2. Acoustic, Enzymatic and Chemically Powered Systems

Acoustic propulsion can move microstructures using ultrasound-induced streaming, cavitation or asymmetric oscillation [18,19]. Ultrasound is clinically established and can be combined with sonosensitive release systems, but spatial control and heating effects must be managed [18,19,35]. An imaging-guided bioresorbable hydrogel microrobot combines acoustic propulsion with magnetic guidance and ultrasound tracking in a preclinical bladder-tumor model [13]. Enzymatic micromotors exploit biochemical substrates such as urea, glucose or hydrogen peroxide [18,19,33,34]. While elegant in vitro, enzyme-powered systems face variable substrate availability, immunogenicity and safety concerns [18,34]. Chemically powered catalytic micromotors using peroxide are generally unsuitable for systemic human use unless redesigned around biocompatible fuels [18,19].

4.3. Biohybrid Microrobots

Biohybrid systems can use living bacteria, algae, sperm cells or selected immune cells for motility or homing; red blood cell membranes can provide camouflage for attached cargo [14,18,34]. These systems are particularly relevant for oncology because some cells naturally migrate toward hypoxic, inflamed or tumor-associated niches [14,18,34]. For example, bacteria-based carriers may exploit tumor tropism, while immune-cell carriers may combine homing capacity with immunological function [14,18,34]. Biohybrid approaches also introduce complex safety questions: replication control, genetic stability, immune activation, off-target colonization, sterilization, storage and release specifications [14,18,34].

4.4. Payload Integration and Regulatory Feasibility

For pharmaceutical translation, microrobots may require evaluation as devices, drug–device, biologic–device or other combination products, depending on their components and intended use [18,19,32]. Critical questions include the following: Can the propulsion mechanism be validated in clinically relevant anatomy? Can the carrier be manufactured reproducibly? Can the dose be quantified? What happens after payload release? Is the robot biodegradable, recoverable or permanently retained? Can imaging confirm localization? Regulatory feasibility will favor biodegradable materials, clinically familiar actuation modalities, externally measurable localization and clear release mechanisms [13,14,18,19,32,33,34,35].

Actuation strength is meaningful only alongside robot size, magnetic moment, gradient, flow and imaging geometry (Table 5). Magnetic torque aligns a moment in a field; translational pulling additionally requires a spatial field gradient. Magnetic penetration through tissue is therefore not equivalent to sufficient force at a human-scale target. Landers et al. report a human-scale navigation architecture with a 30 mT field and gradients up to 350 mT/m for a specified flow-bifurcation experiment; branch selection was demonstrated against a background flow of 37 cm/s [36]. These are engineering benchmarks in the reported anatomy/model, not a universal clinical field prescription or proof of treatment in human tumors.

Table 5. Study-specific quantitative benchmarks for active delivery.

Study and Setting	Actuation and Speed	Payload and Therapeutic Readout	Imaging Depth and Accuracy	Force and Material Fate
Acoustic BAM; laboratory and murine bladder [13]	30 μm robot; PBS peak mean speed 2043 ± 280 μm/s. Separate FUS experiment: 480 kHz, 626 kPa peak-to-peak.	5-FU: 183.1 ng/1000 BAMs; 62% of theoretical loading. About 60% released in urine at 12 h.	US tracking. Mouse imaging/FUS focal setting 5 mm; not maximum depth. Quantitative localization error NV.	Thrust NV. Subcutaneous breakdown by week 6; not a bladder clearance time.
Algae–NP(DOX); murine melanoma lung metastasis [14]	Biological motility. In simulated lung fluid at 37 °C, mean speed decreased from 100.3 to 46.7 μm/s after 2 h. These are in vitro values; numerical speed in living lung NV.	RBC membrane-coated PLGA–DOX cargo. Loading up to 27 μg DOX/106 microalgae under tested conditions. Median survival 37 vs. 27 days for free DOX and NP(DOX) controls; four 75 μg DOX doses, every other day.	Fluorescence/bioluminescence assessments; no standardized navigation accuracy or tissue-depth maximum verified.	Thrust and complete degradation time NV. Intratracheal administration; macrophage uptake and degradation supported, without a quantified whole-system clearance time.
Magnetic capsule; human-scale flow models and large-animal translation [36]	30 mT; gradient up to 350 mT/m. Navigation into the anterior cerebral artery in a model at 37 cm/s background flow within 53 ms. Flow speed is not propulsion speed.	Drug-containing dissolvable capsule; targeted release of a thrombolytic agent demonstrated in a clot model. Payload mass per capsule NV in this comparison; no clinical tumor efficacy established.	Radiopaque image-guided system. Human-scale anatomical access does not define a maximum tumor penetration depth. Localization error NV.	Absolute force NV in this comparison. Capsule dissolved within 40 s under high-frequency magnetic stimulation in vitro. Does not establish clinical tumor efficacy.
MC-1 bacteria–liposome hybrid; mouse colorectal xenograft [37]	Magnetic directional guidance plus aerotaxis; numerical applied field and in-tumor speed NV in this comparison. Magnetic guidance directs bacterial swimming rather than demonstrating gradient-driven propulsion.	Approximately 70 SN-38 liposomes (~170 nm) attached per cell. Mean tumor targeting ratio >50% for liposome-bearing bacteria; a delivery endpoint, not demonstrated tumor regression or survival benefit.	Microscopy/histological tumor localization. Targeting fraction is not % injected drug dose or positional error. Maximum tissue depth NV.	Force and whole-system degradation time NV. Peritumoral administration near HCT116 colorectal xenografts.
Urease nanobots; orthotopic mouse bladder [38]	Urease/urea propulsion after intravesical instillation; 300 mM urea in the active treatment formulation. In vivo speed NV.	131I therapeutic payload. About eightfold tumor accumulation increase reported in a separate 18F-PET experiment. At 18.5 MBq 131I in urea, tumor volume was ~90% lower than in untreated animals, under the reported regimen.	18F-PET for accumulation; magnetic resonance imaging for tumor volume; ex vivo scattered light-sheet microscopy for penetration. Accumulation is not navigation accuracy. Maximum in vivo imaging depth NV.	Thrust and complete material degradation time NV; mesoporous silica-based carriers administered locally with urea substrate.

Abbreviations and symbols: BAM, bioresorbable acoustic microrobot; DOX, doxorubicin; FUS, focused ultrasound; MC-1, Magnetococcus marinus strain MC-1; NP, nanoparticle; NP(DOX), doxorubicin-loaded nanoparticles; NV, not verified quantitatively in the primary material accessed for this comparison (not equivalent to not reported); PBS, phosphate-buffered saline; PET, positron emission tomography; PLGA, poly(lactic-co-glycolic acid); RBC, red blood cell; SD, standard deviation; SN-38, 7-ethyl-10-hydroxycamptothecin, the active metabolite of irinotecan; 5-FU, 5-fluorouracil; ¹⁸F, fluorine-18; ¹³¹I, iodine-131. Units: μm, micrometer; nm, nanometer; cm, centimeter; mm, millimeter; ng, nanogram; μg, microgram; kHz, kilohertz; kPa, kilopascal; mT, millitesla; mT/m, millitesla per meter; mM, millimolar; MBq, megabecquerel; s, second; ms, millisecond; h, hour; °C, degrees Celsius; wt%, percentage by mass. Notes: NV denotes information not verified in the primary material accessed for this comparison and does not imply its absence from the publication; speeds measured in buffer or simulated biological fluids must not be relabeled as in vivo speeds; the quoted acoustic pressure is peak-to-peak; payload mass, theoretical loading percentage, radionuclide activity and liposome count represent different quantities with distinct units or reference bases; no pooled ranking of speed, force or payload is valid across these delivery routes; all listed studies provide preclinical evidence, and none establishes clinical anticancer efficacy in patients.

The acoustic BAM study separates fast laboratory propulsion from local animal navigation. Table 5 retains the measured PBS speed, payload and ultrasound settings and the 5 mm focal-depth setting used for the mouse experiment [13]. That depth is a setup condition, not a measured maximum through human tissue. Imaging visibility is not a validated numerical localization error. Thrust, human-scale maximum depth and quantitative path-tracking accuracy remain unverified for this comparison and are marked accordingly. Hydrogel breakdown in a subcutaneous implantation experiment is also distinct from drug-release time or urinary clearance.

Locally administered biohybrids and enzyme-powered systems address different environments. Magneto-aerotactic bacteria transported attached liposomes into mouse tumor regions [37], and intravesical urease-powered nanobots increased tumor accumulation and reduced tumor volume in mice [38]. Algae-associated nanoparticle delivery was demonstrated after pulmonary administration, with median survival of 37 days versus 27 days for the free-drug and nanoparticle comparator groups [14]. None of these results establishes safe intravenous access to arbitrary human lesions. Translation requires route-specific perfused anatomy, field and heating maps, imaging error distributions, dose accounting, off-target capture and clearance measurements.

5. Pharmaceutically Optimized Nanocarriers for Multi-Agent Oncology

5.1. Liposomes and Lipid Nanoparticles

Liposomes remain one of the most clinically established nanocarrier classes [3,4,9]. Their advantages include biocompatibility, capacity for hydrophilic and lipophilic drugs, surface modification and controlled release engineering [1,2,3,4]. PEGylated liposomes can extend circulation time, while ligand-functionalized liposomes can promote cellular uptake [3,4]. For multi-targeting, liposomes may co-encapsulate synergistic drug ratios or separately carry agents with complementary release profiles [3,4,11]. Lipid nanoparticles (LNPs), particularly ionizable lipid systems, have expanded interest in ribonucleic acid (RNA) delivery, including messenger RNA (mRNA) and siRNA [35,39]. For ionizable lipids, the negative logarithm of the acid dissociation constant (pKa) is a relevant parameter [39].

5.2. Polymeric Nanoparticles and Micelles

Polymeric nanoparticles offer tunable degradation, mechanical properties and release kinetics [8,35,40]. Materials such as poly(lactic-co-glycolic acid) (PLGA), poly(lactic acid) (PLA), PEG–polyester block copolymers and stimuli-responsive polymers can be used to tailor drug loading and release [35,40,41,42,43,44,45]. Polymeric micelles are useful for hydrophobic drugs, although dilution instability remains a common challenge [41]. Polymer chemistry can be engineered to respond to pH (a logarithmic measure of acidity), redox gradients, enzymes, hypoxia or reactive oxygen species (ROS) in the tumor microenvironment [40,44,45]. Polymer molecular weight (MW) is another formulation variable [40].

5.3. Lipid–Polymer Hybrids and Extracellular Vesicles

Lipid–polymer hybrids combine polymeric core stability with lipid-shell biocompatibility and surface functionalization [42]. Their core–shell architecture can accommodate different types of payloads, with release behavior depending on the formulation [42]. Extracellular vesicles carry biological cargo and mediate cell interactions; engineered targeting must be demonstrated for a given preparation [43]. Cell-membrane-coated nanoparticles are a related but distinct biomimetic platform [14,43].

The translational challenge for extracellular vesicles lies in source variability, purification, potency assays and scalable manufacturing [43].

5.4. Stimuli-Responsive Release

Stimuli-responsive nanocarriers exploit endogenous tumor features—acidic pH, high glutathione, overexpressed enzymes, hypoxia or ROS—or exogenous triggers such as ultrasound, light, heat and magnetic fields [44,45]. For multi-target therapy, stimuli-responsiveness may reduce systemic exposure while synchronizing release at the desired site [44,45]. The central design issue is specificity: the trigger threshold must distinguish diseased from healthy tissues while preserving stability during manufacturing, storage and circulation [10,20,44,45].

5.5. Co-Delivery and Multi-Drug Synchronization

Combination therapy requires attention to drug ratio, sequence and temporal exposure [1,3]. A nanocarrier that delivers two agents at a fixed synergistic ratio may outperform separate administration if the drugs share an optimal exposure window [1,3]. Conversely, some combinations require sequential release, such as stromal modulation before cytotoxic therapy or immune priming before checkpoint blockade [1,3,40]. AI-guided release modeling can help select materials and architectures that produce desired temporal profiles [12,17,22].

Table 6 provides the qualitative pharmaceutical comparison; the study-specific quantitative benchmarks in Table 7 complement it without treating formulation-dependent values as class-wide constants.

Table 6. Pharmaceutically optimized nanocarrier platforms for targeted and combination therapy.

Nanocarrier Class	Suitable Payloads	Targeting and Release Mechanisms	Pharmaceutical Advantages	Major Constraints	Optimization Priorities and Reference
Liposomes	Hydrophilic and hydrophobic anticancer drugs; imaging agents in theranostic formulations	Variable passive tumor accumulation; ligand-mediated targeting; formulation-dependent release triggered by pH, enzymes, heat, light or ultrasound	Clinical PK and safety advantages for selected products; therapeutic benefit is product- and indication-specific	Premature leakage; limited encapsulation with some loading methods; clearance and PEG-related uptake trade-offs; heterogeneous tumor delivery	Lipid/cholesterol composition; particle size and surface charge; PEG and targeting-ligand presentation; loading efficiency, drug retention and storage stability [3]. https://doi.org/10.3390/pharmaceutics17020276
Ionizable lipid nanoparticles	siRNA and mRNA; other nucleic-acid cargos when demonstrated	Cellular uptake and endosomal RNA delivery; tissue targeting depends on lipid composition	Efficient nucleic-acid encapsulation; adaptable lipid composition	Predominant liver delivery for many formulations; inflammatory responses; inefficient endosomal escape	Ionizable-lipid pKa, helper-lipid composition, particle size, endosomal escape [39] https://doi.org/10.1038/s41578-021-00358-0
Polymeric nanoparticles	Small-molecule drugs; selected proteins or nucleic acids, depending on formulation	Controlled release; ligand or subcellular targeting when engineered	Tunable composition, degradation and release	Formulation-dependent burst release; manufacturing and characterization complexity	Polymer composition and molecular weight, drug–polymer compatibility, release profile, manufacturing controls [40] https://doi.org/10.3389/fphar.2021.601626
Polymeric micelles	Primarily poorly water-soluble small molecules	Solubilization; optional ligand targeting or responsive release	Improved apparent solubility; small particle size	Dilution-dependent instability; premature drug release	Critical micelle concentration, drug–core compatibility, stability after dilution and storage [41] https://doi.org/10.1016/j.addr.2020.09.009
Lipid–polymer hybrid nanoparticles	Small-molecule drugs, selected nucleic acids and imaging agents	Core–shell loading; surface functionalization; formulation-dependent controlled release	Combines polymer-core stability with a modifiable lipid surface	More complex preparation and characterization; possible aggregation	Lipid-to-polymer ratio, mixing conditions, lipid coverage and colloidal stability [42] https://doi.org/10.3390/jfb14090437
Extracellular vesicles	Native bioactive proteins and nucleic acids; engineered small-molecule drugs, RNA and protein cargo	Source-dependent cellular interactions and cargo transfer; engineered surface targeting; sustained release when incorporated into suitable delivery materials	Biological membrane protects cargo; endogenous bioactivity and engineered delivery functionality; potential biocompatibility depending on source and product	Source and cargo heterogeneity; purification and impurity-control challenges; limited manufacturing consistency; complex potency and biodistribution assessment	Parent-cell source and culture conditions; isolation/purification; identity, membrane integrity, purity and cargo content; batch consistency; mechanism-linked potency assays [43]. https://doi.org/10.1038/s41392-025-02312-w
Stimuli-responsive nanocarriers	Drugs, photosensitizers and other formulation-dependent cargos	Release or activation in response to pH, redox state, enzymes, hypoxia, temperature, light, ultrasound or magnetic fields	Potential spatial or temporal control	Variable trigger specificity; activation and safety constraints	Trigger threshold, release kinetics, off-target activation, compatibility with activation device [44] https://doi.org/10.7150/thno.38069

Notes: This qualitative comparison summarizes pharmaceutical selection criteria. It does not rank classes by a common numerical therapeutic index; product-specific quantitative benchmarks are reported in Table 7.

Table 7. Quantitative formulation benchmarks with preserved endpoints and denominators.

Formulation and Source	Encapsulation and Loading	Exposure or Tumor Delivery	Release and Matched Comparison	Evidence Boundary
PLGA and PLGA–PEG docetaxel particles [46]	At 5:100 drug:PLGA input by mass: PLGA 37 ± 4%; PLGA–PEG 19 ± 3% EE (mean ± SD; n = 3).	Plasma half-life and tumor %ID NR.	At ~3 wt% loading: 50% release at ~7 h (PLGA) or ~10 h (PLGA–PEG).	In vitro comparison; no therapeutic-index estimate.
Lipid–polymer hybrid docetaxel particles [46]	At the same input: 59 ± 4% EE (mean ± SD; n = 3). Reported loading yields: 3.0, 6.1 and 8.5% at 5, 10 and 15% drug/PLGA input, respectively.	Plasma half-life and tumor %ID NR.	At ~3 wt% loading: 50% release at ~20 h; sustained release over 120 h.	Same study comparator; release changes above ~6% loading; no therapeutic-index estimate.
NK105 paclitaxel micelles [47]	~23% w/w drug in the formulation; EE not substituted for loading.	In Colon 26 tumor-bearing mice at 50 or 100 mg/kg paclitaxel-equivalent doses, plasma paclitaxel AUC was ~50–86-fold higher than with free paclitaxel; tumor AUC was ~25-fold higher. Neither is tumor %ID.	Prolonged exposure and antitumor activity; no common release t50 extracted (NV).	Preclinical PK/efficacy [47]. Phase III: non-inferiority for progression-free survival was not demonstrated for NK105 65 mg/m2 versus paclitaxel 80 mg/m2, despite less peripheral sensory neuropathy; clinical caution described separately [48].
DOX/IR-780 temperature-sensitive liposomes [49]	DOX EE 92.52% reported. Carrier-mass loading fraction NV.	Intratumoral administration in mice bearing 4T1 breast tumors; systemic half-life/tumor %ID not inferred.	In vitro: 808 nm, 0.8 W/cm2, 80.1 ± 3.9% release after 5 min; ~43.5% at 20 s versus 1.9% for DOX-only temperature-sensitive liposomes. In vivo therapy used 1 W/cm2 for 5 min.	Triggered co-delivery; heat, optical access and route are integral to the comparison.
64Cu-MM-302 HER2-targeted liposome [50]	No EE/loading value transferred from DOXIL.	Human lesion uptake 0.52–18.5%ID/kg at 24–48 h, about 35-fold variability.	PET signal indicates radiolabeled carrier disposition, not directly active intracellular DOX.	Clinical imaging; %ID/kg cannot be compared directly with mouse %ID or %ID/g.
DOXIL PEGylated liposome [51]	>90% doxorubicin encapsulated. Doxorubicin hydrochloride 2 mg/mL (doxorubicin base equivalent 1.87 mg/mL); summed carrier lipids 15.96 mg/mL, calculated from label components.	Total plasma doxorubicin terminal half-life: 52.3 ± 5.6 h at 10 mg/m2 and 55.0 ± 4.8 h at 20 mg/m2 (mean ± SE; label cohort N = 23), in AIDS-related Kaposi sarcoma. Tumor %ID NR in label.	A manufacturing/clinical PK benchmark; no matched release curve supplied here.	Human clinical product. These are dose/indication-specific label values.
iRGD-engineered exosomes carrying DOX [52]	Up to 20% EE after electroporation; drug/carrier loading percentage NV.	Tumor delivery and inhibition in mice; plasma half-life and quantitative tumor %ID NV.	No standardized release curve verified for this comparison.	Source-specific EVs from mouse immature dendritic cells expressing a Lamp2b–iRGD fusion; no class-wide potency or loading range.
Ionizable CRISPR LNPs [53]	Cas9 mRNA and sgRNA EE >90% for L6/L8/L10 and MC3 comparators; ~65% for L1.	Tumor gene editing in mouse models; particle tumor %ID and plasma half-life NV.	RNA delivery and editing are functional endpoints, not a small-molecule release t50.	Laboratory/animal oncology evidence; encapsulation alone does not establish cytosolic delivery.

Abbreviations and notations: AIDS, acquired immunodeficiency syndrome; AUC, area under the concentration–time curve; Cas9, CRISPR-associated protein 9; CRISPR, clustered regularly interspaced short palindromic repeats; DOX, doxorubicin; EE, encapsulation efficiency (amount of encapsulated drug or RNA relative to the amount initially added); EVs, extracellular vesicles; Fig., figure; h, hours; HER2, human epidermal growth factor receptor 2; iRGD, internalizing Arg–Gly–Asp tumor-penetrating peptide; Lamp2b, lysosome-associated membrane protein 2b; LNPs, lipid nanoparticles; mRNA, messenger RNA; NR, not reported in the cited source for the specified endpoint; NV, not verified or quantitatively extracted for this table (not evidence that the source lacks the measurement); PEG, poly(ethylene glycol); PET, positron emission tomography; PK, pharmacokinetics; PLGA, poly(lactic-co-glycolic acid); RNA, ribonucleic acid; SD, standard deviation; SE, standard error; sgRNA, single-guide RNA; t₅₀, time to 50% cumulative drug release; w/w, mass fraction; wt%, percentage by mass; %ID, percentage of injected dose; %ID/kg and %ID/g, percentage of injected dose per kilogram and per gram of tissue, respectively; ⁶⁴Cu, copper-64. The notation n/N indicates sample size; n denotes the number of replicates where specified. NA is defined in Section 2.

5.6. Quantitative Performance and Matched Comparisons

Encapsulation efficiency is the fraction of input drug recovered within the carrier, whereas loading is the mass of drug relative to the stated carrier or formulation denominator. A high encapsulation percentage can coexist with low drug loading. Plasma carrier-associated half-life, free-drug exposure, tumor % injected dose and intracellular active drug are different endpoints. Table 7 preserves reported denominators and experimental conditions; the entries illustrate feasible formulation performance, not typical values for all members of a class. Stimuli-responsiveness is a design feature spanning carrier classes rather than an eighth mutually exclusive material class.

Matched comparisons provide firmer evidence than cross-study averages. In the docetaxel comparison, hybrid particles had greater encapsulation and slower release than the PLGA and PLGA–PEG comparators at the reported input and loading conditions; this is a formulation-level result, not proof of improved in vivo therapeutic index [46]. NK105 increased plasma and tumor paclitaxel AUC relative to free paclitaxel in the mouse experiment [47]. However, its later phase III study did not establish the prespecified progression-free-survival non-inferiority endpoint [48]. Improved preclinical exposure is therefore insufficient to establish a clinically superior platform.

For triggered co-delivery, DOX/IR-780 thermosensitive liposomes provide a matched release and efficacy experiment, but the laser settings and local administration route are part of the intervention [49]. They cannot be generalized to a deep systemic tumor. Human PET imaging of HER2-targeted liposomes showed 0.52–18.5% injected dose per kg in lesions at 24–48 h, illustrating substantial heterogeneity rather than a universal liposome accumulation percentage [50]. A label-based clinical PK value for DOXIL and an in vitro loading value for another liposome should never be merged into the profile of a fictitious common product [50,51].

No representative dataset here supplies matched tumor uptake, plasma half-life, release and toxicity-adjusted efficacy for every class. Consequently, a quantitative class-wide therapeutic-index ranking would be unsupported. Therapeutic index should be evaluated with a prespecified efficacy-to-toxicity exposure or dose measure, rather than inferred from tumor-growth inhibition or body-weight preservation alone. For co-delivery, the indication-specific choice should prioritize maintained active drug ratio, intracellular target engagement and benefit against both single agents and their free combination, while accounting for batch reproducibility and the accessible delivery route. Fixed-ratio small-molecule combinations may favor a liposome or hybrid architecture if the measured ratio is maintained; RNA payloads require an LNP or other experimentally validated cytosolic delivery system. Hydrophobic drugs may benefit from micellar solubilization provided dilution stability is demonstrated. EV selection requires source-specific purity and potency evidence. Externally triggered carriers are relevant when optical or acoustic access and release specificity can be demonstrated. These are payload- and route-dependent selection criteria, not a numerical ranking of classes [3,39,41,42,43].

6. Integrated Platform: From AI-Guided Design to Microactuated Nanomedicine

A proposed integrated system would connect target prioritization, pharmaceutical design, active transport and monitoring. The component studies support particular interfaces, including laboratory AI–formulation optimization and preclinical robot–carrier delivery, but do not establish the efficacy of the complete adaptive platform [12,13,14]. The value of adding a robot or a predictive model should be demonstrated against a matched optimized carrier and a simpler clinical workflow. Figure 3 presents the investigational architecture; Table 1 identifies the evidence settings supporting its constituent functions. AI-enabled extracellular-vesicle delivery is also discussed as a research direction rather than a clinically validated interface [54].

Figure 3. Proposed conceptual workflow for the AI-assisted design and clinician-reviewed evaluation of nanocarrier–microrobot systems. Note: Arrows indicate dependencies between successive stages; they do not represent demonstrated clinical efficacy or a clinically validated integrated platform. This figure was prepared with assistance from ChatGPT 6.1 (OpenAI), based on the authors’ concepts and the cited literature. The authors reviewed and edited the figure and take full responsibility for its scientific content.

Patient and tumor measurements inform target nomination, followed by assessment of biological relevance, druggability, delivery feasibility, and clinical actionability (Figure 3). Formulation design, evaluation of CQAs, manufacturing reproducibility, microrobot integration, and preclinical actuation and imaging tests precede any investigational administration under an authorized protocol. Localization, payload release, interactions with the tumor microenvironment, and therapeutic and safety endpoints are subsequently evaluated within the investigational setting. Longitudinal monitoring supports clinician-reviewed assessment of pharmacokinetics and pharmacodynamics, potential benefits and harms, and uncertainty.

Selected components and interfaces have been investigated in laboratory or animal studies [12,13,14]; however, the complete integrated, patient-specific workflow remains conceptual and has not been clinically validated. Clinician-reviewed findings could inform a subsequent protocol-bound evaluation, although this feedback connection is not explicitly depicted. Any treatment selection or subsequent adaptation requires clinician authorization within the applicable investigational protocol.

7. Multi-Target Therapeutic Strategies

7.1. Targeting Tumor Cells and Resistance Pathways

A core multi-targeting use case is co-delivery of a cytotoxic agent with a resistance-pathway inhibitor [6,11]. Examples include chemotherapy plus siRNA against efflux pumps, kinase inhibitors plus apoptosis sensitizers, or deoxyribonucleic acid (DNA)-damaging drugs plus DNA repair inhibitors [6,11]. Co-encapsulation can coordinate administered drug ratios, but cellular co-localization and active intracellular exposure must be measured. AI may nominate resistance-related target combinations; indication-specific validation is required before those predictions guide treatment [6,11,17,22].

Figure 4 maps tumor, stromal, vascular, and immune components to potential targets for coordinated delivery of complementary therapeutic payloads.

Figure 4. Tumor microenvironment and potential targets for coordinated therapeutic delivery. Note: This figure was prepared with assistance from ChatGPT 6.1 (OpenAI), based on the authors’ concepts and the cited literature. The authors reviewed and edited the figure and take full responsibility for its scientific content.

The schematic diagram depicts a vascularized tumor containing malignant cells, cancer-associated fibroblasts (CAFs), extracellular matrix (ECM), and immune-cell populations. Hypoxia and low pH are shown as features of the local microenvironment, while drug efflux, DNA repair, antigen loss, PD-1/PD-L1 signaling, and suppression by regulatory T cells (Tregs) and tumor-associated macrophages (TAMs) illustrate potential mechanisms of treatment resistance or immune evasion. Three therapeutic payloads—a cytotoxic agent, a stromal modulator, and an immune-directed or RNA-based agent—are shown as a possible coordinated strategy targeting tumor cells, stromal barriers, and the immune niche. They may be administered at the same time, although their release kinetics and effective doses need not be identical. Effects on vascular access, distribution, and resistance are additional targets for evaluation. The diagram is conceptual; the delivery, the therapeutic benefit, and safety require validation for each specific formulation and tumor setting. Target nomination does not establish druggability, delivery feasibility or clinical actionability; these are assessed separately in Figure 2 and Table 3.

7.2. Targeting the Tumor Microenvironment

The tumor microenvironment (TME) is not a passive barrier. Cancer-associated fibroblasts, extracellular matrix, hypoxia, abnormal vasculature, tumor-associated macrophages and immunosuppressive cytokines shape therapeutic response [5,6,7,11,15,35]. Multi-target nanomedicine can combine matrix modulation, vascular normalization, macrophage repolarization or hypoxia-activated release with direct tumor-cell killing [6,11,35,44,45]. Microrobotic transport may be especially valuable where stromal density or interstitial pressure limits diffusion [14,18,19,32,33,34].

7.3. Targeting Immune Evasion

Nanocarriers can deliver immunomodulators, antigens, adjuvants, checkpoint inhibitors, cytokines or RNA constructs to reprogram antitumor immunity [2,6,11,35]. Spatial targeting matters because systemic immune activation can cause severe toxicity [2,6,35]. AI-supported patient stratification can help identify immune-cold tumors requiring priming, immune-excluded tumors requiring stromal intervention and inflamed tumors suitable for checkpoint augmentation [16,17,21,22].

7.4. Subcellular and Temporal Targeting

Some payloads require cytosolic, nuclear, mitochondrial or lysosomal delivery [6]. Nanocarrier surface chemistry, endosomal escape strategies and stimuli-responsive release can be tuned accordingly [6,44,45]. Temporal targeting is equally important: a tumor-penetrating peptide or stromal modulator may need to act before chemotherapy; an immunogenic cell death inducer may need to precede checkpoint blockade [6,11,35]. AI-driven optimization can model these schedules, but biological validation remains indispensable [17,20,22,23].

8. Safety, Chemistry, Manufacturing, and Controls (CMC), and Regulatory Considerations

8.1. Safety and Biodistribution

Integrated microrobot–nanocarrier systems introduce layered safety questions [14,18,19,32,33,34]. Nanocarrier risks include complement activation, infusion reactions, liver and spleen accumulation, accelerated blood clearance, unexpected immunogenicity and long-term material persistence [4,8,9,10]. Microrobot-specific risks include embolization, off-target navigation, incomplete degradation, local inflammation, biohybrid replication, magnetic-material retention and uncertain interactions with implanted devices [14,18,19,32,33,34].

8.2. Manufacturing and Quality Control

Manufacturing must define reproducible CQAs for both carrier and actuation components [10,20,23]. For nanocarriers, CQAs include size distribution, morphology, surface chemistry, drug loading, release kinetics, sterility, endotoxin, residual solvents and storage stability [10,20,23]. For microrobots, additional specifications may include shape, magnetic moment, propulsion response, biodegradation rate, imaging visibility, payload attachment strength and actuation-field thresholds [18,19,32,33]. Combination systems will need integrated release and navigation assays, not isolated material characterization alone [18,19,20,23].

8.3. Regulatory Classification

Depending on design, the final product may be regulated as a drug, biologic, device or combination product [10,18,19]. Early regulatory dialogue is essential because product classification affects preclinical testing, manufacturing controls, clinical endpoints and post-market surveillance [10,18,20,23]. Explainable AI documentation will also be needed if algorithmic predictions influence formulation design, patient selection or treatment adaptation [16,17,21,22]. Figure 5 depicts a proposed clinician-governed decision-support architecture with explicit data-quality, uncertainty and stopping gates.

Figure 5. Proposed conceptual architecture for clinician-governed digital decision support and investigational delivery. Note: Solid arrows indicate the supervised workflow; dashed blue arrows indicate quality-checked feedback, whereas dashed orange arrows indicate pathways for abstention from model-guided action and clinician-directed use of standard care. This figure was prepared with assistance from ChatGPT 6.1 (OpenAI), based on the authors’ concepts and the cited literature. The authors reviewed and edited the figure and take full responsibility for its scientific content.

Figure 5 distinguishes data assimilation, model recommendation, clinician authorization and treatment execution. A locked version, prespecified uncertainty display, input-quality gates and an auditable override should be part of any future clinical evaluation [30]. An image-guided experimental carrier should not proceed on a predicted location alone. Loss of reliable localization requires a robot-specific fail-safe plan; payload exposure, field heating and dose limits must remain independently constrained. These are proposed safeguards whose effectiveness must be tested, not features already validated for the complete integrated platform.

Longitudinal observations, including imaging, exposure measurements, and laboratory and omics data, undergo checks for provenance, freshness, and consistency before informing a versioned patient model. Model predictions are accompanied by prediction intervals and explicit applicability limits. A clinician assesses potential benefits and harms, reviews the applicable protocol, and authorizes any investigational action. Protocol-bound execution includes investigational delivery and monitoring, which may generate quality-checked feedback for a subsequent, controlled model update. Stale inputs, model drift, uncertain localization, or unacceptable risk prompt abstention from model-guided action and clinician-directed use of standard care.

The diagram is an original conceptual synthesis drawing on mechanistic quantitative systems pharmacology (QSP) approaches to virtual patients and digital twins in immuno-oncology [16] and on the DECIDE-AI reporting framework for early stage clinical evaluation of AI-based decision support systems [30]. These references inform the conceptual rationale and evaluation considerations; they do not establish the clinical validity of the proposed integrated architecture.

Any future implementation would require locked model versions during each evaluation phase, predefined dose limits, an auditable record of decisions and updates, and clinician authorization of treatment changes. Autonomous prescribing is not part of the proposed architecture.

9. Future Directions

The next phase of precision tumor multi-targeting should prioritize validation over novelty. Four directions are especially important [10,16,17,18,20,21,23].

First, standardized datasets are required for AI-driven nanocarrier design. Public and industrial datasets should capture composition, process conditions, CQAs, biological assays and negative results using harmonized ontologies [17,20,22,23]. Second, microrobot studies need clinically relevant models, including tissue phantoms, organoids, perfused tumor models, immune-competent animal models and imaging-guided biodistribution studies [14,18,19,32,33,34]. Third, nanocarrier optimization should shift from single CQA maximization toward multi-objective therapeutic performance, including manufacturability, stability, payload ratio and patient-specific safety [10,20,23]. Fourth, closed-loop systems should be introduced gradually, beginning with decision support and prospective observational validation before adaptive interventional trials [16,21].

Prospective evaluation should first compare a locked model against a simple statistical or rule-based baseline in silent mode, then measure how clinician-assisted use changes decisions, workload and adverse events. Every carrier study should state whether the payload reaches its target in an active form; every robot study should report route-specific navigation success, tracking error and material fate. Additional components should be retained only if ablation comparisons demonstrate incremental benefit. Standardization should preserve negative experiments and uncertainty, rather than selecting only successful integrations [30].

10. Synthesis of Convergent Evidence

The convergent evidence supports complementary functions with different maturity levels (Table 1). Clinical carrier precedents do not validate an AI-selected robotic combination; laboratory formulation prediction does not validate in vivo robotic control; and local animal delivery does not establish systemic human navigation. The complete AI–robot–carrier feedback platform remains a proposed integration. Its most credible evaluation path is an indication-specific, clinician-governed product with measured target engagement, reproducible CMC, validated image-guided transport and comparative benefit–risk assessment.

11. Recent Advances in 2025–2026

Recent work on artificial intelligence in nanomedicine includes primary formulation studies summarized in Table 2 and broader reviews of AI-assisted design and stimuli-responsive carriers [12,55,56,57,58,59,60,61,62,63,64]. Computational studies also discuss molecular imaging and mathematical modeling of carrier behavior [65,66]. General deep-learning methods and adjacent neurological nanomedicine applications provide methodological context, not direct evidence of tumor efficacy [67,68,69]. The correct validation unit is a locked formulation tested independently, with calibration and failures reported. Figure 6 summarizes how molecular stratification and immune-evasion assessment could inform coordinated payload delivery, together with the barriers that must be addressed before clinical translation.

Figure 6. Molecular stratification, immune evasion, coordinated payload delivery, and barriers to clinical translation. Note: This figure was prepared with assistance from ChatGPT 6.1 (OpenAI), based on the authors’ concepts and the cited literature. The authors reviewed and edited the figure and take full responsibility for its scientific content.

Tumor tissue and liquid-biopsy data may be integrated with genomic, transcriptomic, proteomic, single-cell, and spatial analyses to identify molecular subtypes, heterogeneous cell populations, immune states, and potential resistance mechanisms. These findings can inform AI-assisted patient stratification and target selection. The figure highlights candidate mechanisms of immune evasion, including antigen loss or impaired presentation, PD-1/PD-L1 signaling, suppressive immune-cell populations, and limited cytotoxic T-cell infiltration. Microrobots and nanorobots are shown as distinct, investigational delivery platforms with potential roles in guided transport, tumor localization, barrier penetration, and controlled payload release. Co-delivery or programmed sequencing of a barrier-modulating agent, a cytotoxic drug, and an immune-directed or RNA-based payload illustrates one possible multi-target strategy; its timing and doses require optimization for each system. Translation to clinical use further depends on reproducible manufacture, sterility, biodistribution and clearance, safety, reliable actuation and imaging, and appropriate preclinical, clinical, and regulatory evidence. The workflow in Figure 6 is conceptual and does not imply demonstrated efficacy of the integrated approach.

Motion-enhanced delivery, magnetic actuation and biohybrid transport offer proof of concept in preclinical or selected compartmental settings [13,14,18,33,34,70,71,72,73,74,75,76,77]. Acoustic hydrogel microrobots [13], self-propelled magnetic nanorobots [74] and bacteria-based systems [75] represent distinct engineering approaches; they should not be presented as equivalent to validated human tumor navigation. DNA nanostructures provide programmable drug-delivery approaches but do not by themselves demonstrate robotic locomotion [78,79].

Human-scale magnetic navigation advances should be separated from validated human oncology outcomes. A clinically oriented capsule architecture provides engineering benchmarks [36], while the primary acoustic, algal and urease examples remain route-specific animal studies [13,14,38]. The comparative evidence is summarized in Table 5 and Table 7; the conceptual pathways in Figure 3, Figure 5 and Figure 6 are not additional independent demonstrations of integration.

Recent carrier reviews and experimental reports address tumor barriers, resistance, co-delivery, nanovaccines, gene therapy and immunotherapy [42,55,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120]. The broader use of vascular-targeted carriers is relevant to endothelial interactions, although it does not establish tumor-specific benefit [121]. Multiple payloads should be justified by response at measured intratumoral concentrations, and a multi-agent label alone does not establish synergy [6,11,88,89]. Clinical precedents for liposomes are stronger than those for bespoke integrated microrobot–carrier systems [3,4,9,10,92,122].

12. Practical Research Agenda and Limitations

A practical translational study should specify a tumor indication and accessible delivery route; select two or more complementary target compartments; establish the comparative value of every added component; and define a target product profile before optimization [10,18,20,23,35,85,86]. Benchmarks should include free drug, optimized carrier alone, propelled vehicle alone and the integrated combination. Measurements should report drug ratio over time, tissue-level and cell-level exposure, clearance, material persistence, immunogenicity and clinically meaningful efficacy alongside tumor size [5,7,14,15,18,34,85].

The evidence base has important limitations. Carrier retention in rodent xenografts does not establish human uptake, nominal drug loading is not delivered dose, and observed motility in simplified media does not establish navigation in living tumors [5,7,14,15,18,19,32,33,34,85,123]. Digital twins, autonomous feedback and personalized in vivo robotic control are research concepts rather than routine oncology interventions [16,21]. Narrative selection may omit relevant negative studies; the present synthesis should therefore guide hypothesis formation, not be interpreted as comparative proof of superiority. Future clinical studies need prespecified endpoints, independent replication and transparent reporting of manufacturing variability and failures [10,20,23,103]. Where photoactivated therapies are incorporated into these platforms, their evaluation should also address the coordination of targeted delivery with light exposure, heterogeneous intratumoral distribution, and modality-specific biological constraints, drawing on the opportunities and translational limitations identified in photoimmunotherapy and nanotechnology-assisted photodynamic therapy [124,125].

The quantitative matrices are purposive, study-level comparisons. They do not constitute a systematic quantitative synthesis and cannot estimate a pooled superiority or therapeutic index across platforms. Some requested metrics are missing or could not be verified for the stated experimental setting. Complete source datasets, independent model replication, standardized biodistribution denominators and matched efficacy–toxicity comparisons are needed to close these gaps. Experimental or clinical validation of the full adaptive AI–microrobot–nanocarrier architecture has not been established in the reviewed evidence.

13. Conclusions

AI-guided formulation, active micro/nanorobotic transport and optimized nanocarriers have been demonstrated in different computational, laboratory, animal and selected component-level clinical settings. Their full adaptive integration has not been clinically validated. A target is useful only when it is biologically valid, therapeutically modifiable, deliverable and actionable in the specified indication. Translational progress should be judged by matched improvements in target engagement, efficacy, safety and manufacturing reproducibility, with quantitative reporting of model uncertainty, navigation and delivered payload. The near-term digital comparator is clinician-governed decision support; autonomous personalized tumor therapy remains investigational.