Explicating the transformative role of artificial intelligence in designing targeted nanomedicine DOI

Masheera Akhtar,

Nida Nehal,

Azka Gull

и другие.

Expert Opinion on Drug Delivery, Год журнала: 2025, Номер unknown

Опубликована: Май 5, 2025

Artificial intelligence (AI) has emerged as a transformative force in nanomedicine. revolutionizing drug delivery, diagnostics, and personalized treatment. While nanomedicine offers precise targeted delivery reduced toxic effects, its clinical translation is hindered by biological complexity, unpredictable vivo behavior, inefficient trial-and-error approaches. This review covers the application of AI Machine Learning (ML) across development pipeline, starting from target identification to nanoparticle design, toxicity prediction, dosing. Different AI/ML models like QSAR, MTK-QSBER, Alchemite, along with data sources high-throughput screening methods, have been explored. Real-world applications are critically discussed, including AI-assisted repurposing, controlled-release formulations, cancer-specific systems. an essential component designing next-generation Efficiently handling multidimensional datasets, optimizing personalizing treatment regimens, it sped up innovation process. However, challenges heterogeneity, model transparency, regulatory gaps remain. Addressing these hurdles through interdisciplinary efforts emerging innovations explainable federated learning will pave way for AI-driven

Язык: Английский

Electrochemical‐Genetic Programming of Protein‐Based Magnetic Soft Robots for Active Drug Delivery DOI Creative Commons

Hang Zhao,

Bo Yu,

Dingyi Yu

и другие.

Advanced Science, Год журнала: 2025, Номер unknown

Опубликована: Апрель 29, 2025

Abstract Magnetic soft robots have the potential to revolutionize field of drug delivery owing their capability execute tasks in hard‐to‐reach regions living organisms. Advancing functionality perform active and related necessitates innovation smart substrate materials that satisfy both mechanical biocompatibility requirements while offering stimuli‐responsive properties. Optimization interaction between magnetic components is also critical as it ensures robust actuation robot complex physiological environments. To address these issues, a facile strategy presented synergistically combines genetic programming electrochemical engineering achieve on‐demand release with protein‐magnetite robots. As robot, genetically engineered silk‐elastin‐like protein (SELP) encoded thermo‐responsive motifs, serving dynamic unit respond temperature changes. Ultrafine magnetite (Fe 3 O 4 ) nanocrystals are electrochemically nucleated situ grown on Fe‐protein coordination sites within SELP hydrogel network, endowing reinforced strength, superparamagnetic property, photothermal conversion capability. These can navigate confined spaces, target specific sites, payloads ex vivo an intestinal model. Taken together, proposed offers innovative approach tailoring protein‐based toward precision systems.

Язык: Английский

Процитировано

0

Revolutionizing Gangrene Therapy: Nanoparticle-Based Interventions and Biomarker Applications DOI

Elizabeth Rani Edwin,

S. Jayaprakash,

Yamuna Gopi

и другие.

Deleted Journal, Год журнала: 2025, Номер unknown

Опубликована: Май 14, 2025

Язык: Английский

Процитировано

0

Synthesis, characterization, and exosomal corona formation of self-assembled dipeptide nanomaterials DOI Creative Commons
Burcu Önal Acet, Ömür Acet, Madita Wandrey

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 19, 2025

Abstract Exosomes (Exos), also known as small extracellular vesicles, are naturally occurring nanoparticles (NPs), which characterized by their nanometer size and negative charged in physiological environments. While it is widely accepted that proteins biological compounds adhere to different nanomaterials (NMs), forming an outer layer the biomolecule corona (BC), detailed understanding of factors contributing BC formation well its effects remains limited. Studies have shown can affect physicochemical properties synthetic natural NPs once contacting fluids. Here, we present a study investigating novel concept exosomal formation, contrast well-documented mainly consists Exos/exosomal components. For this purpose, peptide-based Fmoc-Lysine (Fmoc-Lys) NMs were synthesized characterized, interaction studies with (cancer) cell-derived Exos performed. Measurements size, zeta potential, colloidal stability indicate formation. Furthermore, cell viability experiments showed Exo-NM resulted reduced nanotoxicity profile indicating practical relevance for applications these NMs. In summary, here provide first evidence supporting around should become part evaluating interactions at nano-bio-interfaces.

Язык: Английский

Процитировано

0

Innovative nanoparticle strategies for treating oral cancers DOI

Shahryar Irannejadrankouhi,

Hassan Mivehchi,

Aisan Eskandari-Yaghbastlo

и другие.

Medical Oncology, Год журнала: 2025, Номер 42(6)

Опубликована: Апрель 26, 2025

Язык: Английский

Процитировано

0

Explicating the transformative role of artificial intelligence in designing targeted nanomedicine DOI

Masheera Akhtar,

Nida Nehal,

Azka Gull

и другие.

Expert Opinion on Drug Delivery, Год журнала: 2025, Номер unknown

Опубликована: Май 5, 2025

Artificial intelligence (AI) has emerged as a transformative force in nanomedicine. revolutionizing drug delivery, diagnostics, and personalized treatment. While nanomedicine offers precise targeted delivery reduced toxic effects, its clinical translation is hindered by biological complexity, unpredictable vivo behavior, inefficient trial-and-error approaches. This review covers the application of AI Machine Learning (ML) across development pipeline, starting from target identification to nanoparticle design, toxicity prediction, dosing. Different AI/ML models like QSAR, MTK-QSBER, Alchemite, along with data sources high-throughput screening methods, have been explored. Real-world applications are critically discussed, including AI-assisted repurposing, controlled-release formulations, cancer-specific systems. an essential component designing next-generation Efficiently handling multidimensional datasets, optimizing personalizing treatment regimens, it sped up innovation process. However, challenges heterogeneity, model transparency, regulatory gaps remain. Addressing these hurdles through interdisciplinary efforts emerging innovations explainable federated learning will pave way for AI-driven

Язык: Английский

Процитировано

0