ArticleInternational journal of molecular sciences2022
Deep Learning-Based Artificial Intelligence to Investigate Targeted Nanoparticles' Uptake in TNBC Cells.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Engineering smart polymeric lipid nanoparticles for breast cancer: AI-guided formulation design, biological barriers, and translational constraints.Journal of nanobiotechnology · 2026Review
- Artificial Intelligence in the Design and Development of Nanoparticle Drug Delivery Systems: A Systematic Review.Advances in pharmacological and pharmaceutical sciences · 2026Review
- Nanomaterials in Drug Delivery: Leveraging Artificial Intelligence and Big Data for Predictive Design.International journal of molecular sciences · 2025Review
- Tailored therapies for triple-negative breast cancer: current landscape and future perceptions.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Machine Learning-Enhanced Nanoparticle Design for Precision Cancer Drug Delivery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Nanoradiopharmaceuticals: Design Principles, Radiolabeling Strategies, and Biomedicine Applications.Pharmaceutics · 2025Review
- Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues.Journal of imaging · 2025Review
- Artificial Intelligence-Driven Innovations in Pharmaceutical Development and Drug Delivery Systems.Current topics in medicinal chemistry · 2025Review
- Advancements in triple-negative breast cancer sub-typing, diagnosis and treatment with assistance of artificial intelligence : a focused review.Journal of cancer research and clinical oncology · 2024Review
- Machine learning assisted-nanomedicine using magnetic nanoparticles for central nervous system diseases.Nanoscale advances · 2023Review
- Application of Computing as a High-Practicability and -Efficiency Auxiliary Tool in Nanodrugs Discovery.Pharmaceutics · 2023Review
- Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges.Frontiers in pharmacology · 2023Review
- Circular RNAs in gynecologic cancers: mechanisms and implications for chemotherapy resistance.Frontiers in pharmacology · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.