Deep Learning-Based Artificial Intelligence to Investigate Targeted Nanoparticles’ Uptake in TNBC Cells

Abstract

Triple negative breast cancer (TNBC) is the most aggressive subtype of breast cancer in women. It has the poorest prognosis along with limited therapeutic options. Smart nano-based carriers are emerging as promising approaches in treating TNBC due to their favourable characteristics such as specifically delivering different cargos to cancer cells. However, nanoparticles’ tumour cell uptake, and subsequent drug release, are essential factors considered during the drug development process. Contemporary qualitative analyses based on imaging are cumbersome and prone to human biases. Deep learning-based algorithms have been well-established in various healthcare settings with promising scope in drug discovery and development. In this study, the performance of five different convolutional neural network models was evaluated. In this research, we investigated two sequential models from scratch and three pre-trained models, VGG16, ResNet50, and Inception V3. These models were trained using confocal images of nanoparticle-treated cells loaded with a fluorescent anticancer agent. Comparative and cross-validation analyses were further conducted across all models to obtain more meaningful results. Our models showed high accuracy in predicting either high or low drug uptake and release into TNBC cells, indicating great translational potential into practice to aid in determining cellular uptake at the early stages of drug development in any area of research.

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

“…With this in mind, DL algorithms are being used in precision medicine to predict anticancer drug response in patient-derived cancer cell lines, as in the case of the DeepDRK framework, which is freely available ( Wang Y et al, 2021 ). A method based on DL was developed to study the uptake of targeted nanoparticles in triple-negative breast cancer, which could be useful for proper dosing in clinical practice ( Ali et al, 2022 ). A nanodiamond biosensor platform using DL was developed to rapidly assess individual specific sensitivity to oxidative phosphorylation inhibitors in patients with hepatocellular carcinoma ( Xu et al, 2023 ).…”

Classification of Healthy and Frozen Pomegranates Using Hyperspectral Imaging and Deep Learning

“…Accuracy is the overall correctness of the model prediction and is calculated according to Equation (2) [32].…”

Circular RNAs in gynecologic cancers: mechanisms and implications for chemotherapy resistance

“…Nonetheless, prolonged CDDP exposure leads tumor cells to activate a variety of mechanisms to obstruct cisplatin, which is manifested at the molecular, organelle, and cellular levels ( Lugones et al, 2022 ; Romani, 2022 ; Tang et al, 2023 ). These mechanisms involve reducing platinum compound accumulation through active efflux/isolation or suppression of endocytosis; increasing oncogene mutagenesis; detoxifying through metallothionein, GSH conjugates, and other antioxidants; modulating DNA methylation status; increasing DNA-damage repair levels; altering protein post-translational modifications; over-expressing chaperone molecules; reinforcing compensatory signaling communication between organelles; suppressing apoptotic pathways; and activating the EMT pathway, among others ( Ali et al, 2022 ; Domingo et al, 2022 ; Tsvetkova and Ivanova, 2022 ). Numerous studies have now demonstrated that certain circular RNAs (circRNAs) are also involved in drug resistance of gynecologic cancer cells to CDDP ( Table 1 ).…”