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<p>Breast cancer remains one of the leading causes of cancer-related death in the world, and there is a great need for the development of new advanced targeted therapeutic techniques with higher efficiency and lower systemic toxic effects. The present study was directed towards the development and biological evaluation of novel nitro pyrimidine loaded solid lipid nanoparticles (SLNs) as a targeted anticancer drug delivery system with the aid of artificial intelligence. Ligand D14, which was identified as the most promising TACE inhibitor by the machine learning-based bioactivity prediction algorithm, exhibited the most favorable docking score of − 9.4&#xa0;kcal/mol and stable molecular interactions in 100 ns of molecular dynamics simulations. The optimized formulation of SLN showed good encapsulation efficiency (91.00 ± 2.3%) and the capacity of drug loading (9.01 ± 0.8%) which signified successful incorporation of the nitro-pyrimidine derivative in the lipid matrix. The <i>in vitro</i> drug-release assays showed that the cumulative drug-release percentage was 93.1% at pH 7.8 and 74.3% at pH 5.2 after 75&#xa0;h. Structural integrity, crystallinity, nanoscale particle distribution and formulation stability were confirmed by physicochemical characterization via FTIR, XRD, LC-MS, FE-SEM, and particle-size analysis. Biological assays performed in the breast cancer cells (MCF-7) showed substantial dose-dependent cytotoxicity and apoptosis induction, especially at 100&#xa0;µg/mL. Altogether, the results indicate that AI-supported nitro-pyrimidine loaded SLNs are a potential promising targeted nanotherapeutic platform for breast cancer management as translatable in precision oncology.</p>

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AI-Enhanced Design of Nitro-Pyrimidine-Loaded SLNs: Integrating Molecular Modeling, and Biological Characterization for Cancer Therapy

  • 期刊论文

  • 发表日期：

    2026年5月29日

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  • Dillibabu Krishnan,
  • Priya Manogar,
  • Panneerselvam Theivendren

摘要

Breast cancer remains one of the leading causes of cancer-related death in the world, and there is a great need for the development of new advanced targeted therapeutic techniques with higher efficiency and lower systemic toxic effects. The present study was directed towards the development and biological evaluation of novel nitro pyrimidine loaded solid lipid nanoparticles (SLNs) as a targeted anticancer drug delivery system with the aid of artificial intelligence. Ligand D14, which was identified as the most promising TACE inhibitor by the machine learning-based bioactivity prediction algorithm, exhibited the most favorable docking score of − 9.4 kcal/mol and stable molecular interactions in 100 ns of molecular dynamics simulations. The optimized formulation of SLN showed good encapsulation efficiency (91.00 ± 2.3%) and the capacity of drug loading (9.01 ± 0.8%) which signified successful incorporation of the nitro-pyrimidine derivative in the lipid matrix. The in vitro drug-release assays showed that the cumulative drug-release percentage was 93.1% at pH 7.8 and 74.3% at pH 5.2 after 75 h. Structural integrity, crystallinity, nanoscale particle distribution and formulation stability were confirmed by physicochemical characterization via FTIR, XRD, LC-MS, FE-SEM, and particle-size analysis. Biological assays performed in the breast cancer cells (MCF-7) showed substantial dose-dependent cytotoxicity and apoptosis induction, especially at 100 µg/mL. Altogether, the results indicate that AI-supported nitro-pyrimidine loaded SLNs are a potential promising targeted nanotherapeutic platform for breast cancer management as translatable in precision oncology.