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<p>P ediatric cancers pose unique challenges due to their distinct genetic profiles, aggressive progression, and limited treatment options. While conventional therapies can be effective, they are often associated with long-term side effects, including growth delays, cognitive impairment, and secondary malignancies, necessitating a shift toward precision medicine. Nanoinformatics, an emerging interdisciplinary field combining nanotechnology, bioinformatics, artificial intelligence (AI), and computational modeling, offers promising solutions to enhance drug discovery and nanoparticle (NP)-based drug delivery in pediatric oncology. This review supplements current literature with applied nanoinformatics strategies specifically focused on pediatric neuroblastoma. We highlight the use of cBioPortal for target identification, siDirect for siRNA design, and HADDOCK for molecular docking of transferrin (Tf) with transferrin receptor 1 (TfR1). Additionally, we discuss the roles of AI-driven drug discovery, machine learning algorithms, molecular docking, and molecular dynamics (MD) simulations in predicting NP interactions and refining target specificity. In our case study, lipid nanoparticles (LNPs) were functionalized with Tf for targeted siRNA delivery to silence the MYCN oncogene in neuroblastoma cells overexpressing TfR1. The review also explores advancements in MD simulations, multi-omics data integration, and real-time monitoring to improve therapeutic precision. Despite challenges related to data standardization, ethical concerns, and clinical translation, progress in AI, adaptive modeling, and digital twin technologies fosters an optimistic outlook. Ultimately, nanoinformatics holds transformative potential to improve pediatric cancer treatment by enabling precision targeting, reducing toxicity, and enhancing therapeutic efficacy and outcomes.</p> Graphical Abstract <p></p>

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Nanoinformatics: A Potential Tool for Precision Targeting in Pediatric Cancer

  • 期刊论文

  • 发表日期：

    2025年9月3日

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  • Muhammad Irfan,
  • Urooj Fatima,
  • Aqsa Sajjad,
  • Muhammad Naveed

摘要

P ediatric cancers pose unique challenges due to their distinct genetic profiles, aggressive progression, and limited treatment options. While conventional therapies can be effective, they are often associated with long-term side effects, including growth delays, cognitive impairment, and secondary malignancies, necessitating a shift toward precision medicine. Nanoinformatics, an emerging interdisciplinary field combining nanotechnology, bioinformatics, artificial intelligence (AI), and computational modeling, offers promising solutions to enhance drug discovery and nanoparticle (NP)-based drug delivery in pediatric oncology. This review supplements current literature with applied nanoinformatics strategies specifically focused on pediatric neuroblastoma. We highlight the use of cBioPortal for target identification, siDirect for siRNA design, and HADDOCK for molecular docking of transferrin (Tf) with transferrin receptor 1 (TfR1). Additionally, we discuss the roles of AI-driven drug discovery, machine learning algorithms, molecular docking, and molecular dynamics (MD) simulations in predicting NP interactions and refining target specificity. In our case study, lipid nanoparticles (LNPs) were functionalized with Tf for targeted siRNA delivery to silence the MYCN oncogene in neuroblastoma cells overexpressing TfR1. The review also explores advancements in MD simulations, multi-omics data integration, and real-time monitoring to improve therapeutic precision. Despite challenges related to data standardization, ethical concerns, and clinical translation, progress in AI, adaptive modeling, and digital twin technologies fosters an optimistic outlook. Ultimately, nanoinformatics holds transformative potential to improve pediatric cancer treatment by enabling precision targeting, reducing toxicity, and enhancing therapeutic efficacy and outcomes.

Graphical Abstract