Advances in photoactivated carbon-based nanostructured materials for targeted cancer therapy

Nano ribbon Gold, Silica PTT Structural optimisation for enhanced PTT conversion efficiency Supervised ML models (e.g., RF, SVM) In vivo testing on mice: efficacy in ablation ML predicting optimal preparation of silica-coated gold nanorods for photothermal ablation. [182] Nanosheet rGO and Metal ions (Co, Fe, Cu, Ce) NA Interactions of various gases (acetone, isoprene, ammonia) IBM SPSS for stat. analyses, PCA and LDA pattern recognition ANN classification EB analysis from volunteers for lung cancer diagnosis using a sensor array to detect specific biomarkers Constructing an E-Nose Using Metal-Ion-Induced Assembly of Graphene Oxide for Diagnosis of Lung Cancer via Exhaled Breath. [183] Nanodot CND NA Interactions of CNDs with metal ions (Co2 + and Fe3 + ) Active Adaptive Method with machine learning Used in dental diagnosis, treatment, early caries detection, remineralisation therapies. Expediting CND synthesis by the active adaptive method with ML and applications in dental diagnosis and treatment. [184] Nanodot CND functionalised with boron and nitrogen NA Optimisation of synthesis parameters for enhanced fluorescence intensity ML models (e.g., polynomial regression, random forest) In vivo validation using Wistar rats to monitor fluorescence behaviour after intravenous injection Graphene Quantum Dots with Improved Fluorescence Activity via ML: Implications for Fluorescence Monitoring. [185] Nanotube CNT; Aryl groups; Diazonium chemistry NA Interaction of OCC-DNA nano-sensors with serum biomarkers ML (e.g., SVM) Serum analysis for ovarian cancer detection Detection of ovarian cancer via spectral fingerprinting of quantum-defect-modified CNTs in serum by ML. [186] Nanotube SWCNTs functionalised with ssDNA NA Protein adsorption dynamics influenced by surface properties of SWCNTs Supervised learning
(Random Forest Classifier) Validation of adsorption predictions using (LC-MS/MS) and corona exchange Supervised learning model predicts protein adsorption to carbon nanotubes. [187] Nano particle mixture Various NPs (metallic, polymeric, liposomal) NA Protein corona formation on nanoparticles and subsequent cellular interactions. ML (e.g., RF) Validation through proteomics and cellular uptake studies ML predicts the composition of the protein corona and cellular recognition of nanoparticles. [188] Nanotube MWCNTs with systematically reduced oxygen functional groups NA Interaction of MWNTs with embryonic zebrafish (surface charge-related mortality) Statistical modelling (e.g., Logistic regression model) Toxicity testing on zebrafish embryos to determine the lethal and sub-lethal impacts of MWNTs Toward safer MWCNT design: establishing a statistical model that relates surface charge and embryonic zebrafish mortality. [189] Nanodot CND NA Prediction and control of optical properties such as FL colour and excitation dependence for enhanced bioimaging Supervised learning (e.g., RF, Gradient Boosting) Deep Convolutional Neural Network (DCNN) Exploiting deep learning for predictable carbon dot design. [190] Nanodot CD NA Integration of CND with biological systems for imaging and therapy. Optimisation of biocompatibility, and binding specificity ML models (e.g., XGBoost, logistic regression, PCA) In vivo studies on bioimaging, pH monitoring, FL-based bacterial tracking in Gram-positive or negative systems Utilising ML to expedite the fabrication and biological application of CNDs. [174] Light-Driven Nanorobot CHL integrated with GCS-PPy nanoparticles PT, PS, PTH Interaction of CHL-GCS-PPy nanorobots with the TME AI algorithms for navigation and therapy optimisation In vitro and in vivo testing for bladder cancer treatment efficacy Light‐Driven, Green‐Fabricated AI‐Enabled Nanorobots for Multimodal Phototherapeutic Management of Bladder Cancer. [191] Nano particle mixture Gold, Meta- mTHPC PTT + PDT Prediction of optimal phototherapy parameters e.g., laser power, wavelength, exposure time ML (regression, interpolation, analytical function fitting) In vitro neuroblastoma SH-SY5Y cell treatment using AuNP-mTHPC with PDT and PTT A predictive model for personalisation of nanotechnology-based phototherapy in cancer treatment. [192] Nanotube MWCNT NA MWCNTs interactions with biological systems that could lead to DNA damage or other toxic effects ML modelling (e.g., Random Forest, Logistic Regression) In vivo (DNA strand breaks via Comet assay) in vitro (gene mutation assessment via micronucleus test) Machine learning methods for MWCNT genotoxicity prediction. [193] Nano particle (other) Gold chloride and TDTAA PDT Dynamic effects of PDT on cancer cells e.g., morphological changes, circularity, and curvature Deep learning (Cell pose algorithm for segmentation) PDT treatment on HepG2 hepatocellular carcinoma cells Deep Learning Insights into the Dynamic Effects of Photodynamic Therapy on Cancer Cells. [194] Nanotube Oxidised CNT
(–OH and –COOH) NA Effect of pristine and oxidised CNTs on mitochondrial respiration, oxygen consumption, proton gradient disruption, and ATP synthesis Nano-QSPR modelling using Raman spectroscopy, ML algorithms In vitro assays using isolated rat liver mitochondria Experimental–computational study of CNT effects on mitochondrial respiration: In silico nano-QSPR ML models based on new Raman spectra [195] Nanotube MWCNT Pristine, –OH, –COOH and –NH₂ NA Genotoxicity assessment focusing on DNA damage and mutagenicity by physicochemical properties of MWCNTs Ensemble super learning (RF, Gradient Boosting, SVM) In vitro (e.g., Comet assay, micronucleus test),
in vivo toxicity profiling studies Ensemble super learner-based genotoxicity prediction of MWCNT. [196] Nanotube MWCNT functionalised with mTHPC PTT + PDT Mechanistic constructive collaboration between PDT and PTT effects causing mitochondrial damage, ROS generation, and oxidative stress leading to apoptosis Genomic and proteomic analysis of oxidative stress pathways and apoptosis-related proteins In vitro studies using SKOV3 ovarian cancer cells (cell viability assays, flow cytometry, microscopy, and molecular analyses) Synergic mechanisms of photothermal and photodynamic therapies mediated by photosensitizer/carbon nanotube complexes. [197] Nanotube Carbon, Boron-Nitride, Silicon-Carbide nanotubes functionalised with azacitidine NA Azacitidine Encapsulation efficiency and binding mechanisms in nanotube cavities through adsorption energies and charge transfer Density Functional Theory (DFT), Molecular Dynamics (MD) simulations In silico study, no experimental validation included Comparative study of the efficiency of silicon carbide, boron nitride and carbon nanotube to deliver cancerous drug, azacitidine: A DFT study. [198] Nanotube CNT NA Interaction of fluorouracil anticancer drug with CNTs Computational modelling (Ab initio methods, Monte Carlo simulations) In silico study, no experimental validation included Studies of ab initio and Monte Carlo simulation on interaction of fluorouracil anticancer drug with CNT. [199] Nanotube SWCNT with functional groups, Pristine NA Physisorption of penicillamine drug on nanotubes; functionalisation effect on adsorption energy, solubility, and intermolecular interactions Computational modelling (DFT and MD Simulation) In silico study, no experimental validation included Modelling the interaction between anti-cancer drug penicillamine and pristine and functionalised CNT for medical app.: DFT and MD. [200]