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Advancements in microneedle fabrication techniques: artificial intelligence assisted 3D-printing technology.
Biswas, Anuj A; Dhondale, Madhukiran R; Agrawal, Ashish K; Serrano, Dolores R; Mishra, Brahmeshwar; Kumar, Dinesh.
Afiliación
  • Biswas AA; Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Uttar Pradesh, Varanasi, India.
  • Dhondale MR; Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Uttar Pradesh, Varanasi, India.
  • Agrawal AK; Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Uttar Pradesh, Varanasi, India.
  • Serrano DR; School of Pharmacy, Complutense University, Madrid, Spain.
  • Mishra B; Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Uttar Pradesh, Varanasi, India. bmishra.phe@itbhu.ac.in.
  • Kumar D; Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Uttar Pradesh, Varanasi, India. dinesh.phe@itbhu.ac.in.
Drug Deliv Transl Res ; 14(6): 1458-1479, 2024 Jun.
Article en En | MEDLINE | ID: mdl-38218999
ABSTRACT
Microneedles (MNs) are micron-scale needles that are a painless alternative to injections for delivering drugs through the skin. MNs find applications as biosensing devices and could serve as real-time diagnosis tools. There have been numerous fabrication techniques employed for producing quality MN-based systems, prominent among them is the three-dimensional (3D) printing. 3D printing enables the production of quality MNs of tuneable characteristics using a variety of materials. Further, the possible integration of artificial intelligence (AI) tools such as machine learning (ML) and deep learning (DL) with 3D printing makes it an indispensable tool for fabricating microneedles. Provided that these AI tools can be trained and act with minimal human intervention to control the quality of products produced, there is also a possibility of mass production of MNs using these tools in the future. This work reviews the specific role of AI in the 3D printing of MN-based devices discussing the use of AI in predicting drug release patterns, its role as a quality control tool, and in predicting the biomarker levels. Additionally, the autonomous 3D printing of microneedles using an integrated system of the internet of things (IoT) and machine learning (ML) is discussed in brief. Different categories of machine learning including supervised learning, semi-supervised learning, unsupervised learning, and reinforced learning have been discussed in brief. Lastly, a brief section is dedicated to the biosensing applications of MN-based devices.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Sistemas de Liberación de Medicamentos / Impresión Tridimensional / Agujas Tipo de estudio: Prognostic_studies Límite: Animals / Humans Idioma: En Revista: Drug Deliv Transl Res Año: 2024 Tipo del documento: Article País de afiliación: India Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Sistemas de Liberación de Medicamentos / Impresión Tridimensional / Agujas Tipo de estudio: Prognostic_studies Límite: Animals / Humans Idioma: En Revista: Drug Deliv Transl Res Año: 2024 Tipo del documento: Article País de afiliación: India Pais de publicación: Estados Unidos