Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations-Reference-Cited by-同舟云学术

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations

Published:2017-10-17 Issue:1 Volume:44 Page:135-143
ISSN:0363-9045
Container-title:Drug Development and Industrial Pharmacy
language:en
Short-container-title:Drug Development and Industrial Pharmacy

Author:

León Blanco José M.¹,González-R Pedro L.¹,Arroyo García Carmen Martina²,Cózar-Bernal María José²,Calle Suárez Marcos¹,Canca Ortiz David¹,Rabasco Álvarez Antonio María²,González Rodríguez María Luisa²

Affiliation:

1. Department of Industrial Management Science, School of Engineering, Universidad de Sevilla, Seville, Spain;

2. Department of Pharmaceutical Technology, Faculty of Pharmacy, Universidad de Sevilla, Seville, Spain

Publisher

Informa UK Limited

Subject

Organic Chemistry,Drug Discovery,Pharmaceutical Science,Pharmacology

Link

https://www.tandfonline.com/doi/pdf/10.1080/03639045.2017.1386201

Reference26 articles.

1. Applying the taguchi method to optimize sumatriptan succinate niosomes as drug carriers for skin delivery

2. A simple Ultraviolet spectrophotometric method for the determination of etoricoxib in dosage formulations

3. Artificial neural network as an alternative to multiple regression analysis in optimizing formulation parmaeters of cytarabine liposomes

4. Performance comparison of neural network training algorithms in modeling of bimodal drug delivery

5. Pharmaceutical Drug Design Using Dynamic Connectionist Ensemble Networks

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Optimizing nanoliposomal formulations: Assessing factors affecting entrapment efficiency of curcumin-loaded liposomes using machine learning;International Journal of Pharmaceutics;2023-11

2. Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles;Scientific Reports;2023-10-21

3. State-of-the-art review of neural network applications in pharmaceutical manufacturing: current state and future directions;Journal of Intelligent Manufacturing;2023-09-20

4. Artificial neural network–based inference of drug–target interactions;Nanotechnology Principles in Drug Targeting and Diagnosis;2023

5. Artificial Intelligence and Its Applications in Drug Discovery, Formulation Development, and Healthcare;Computer Aided Pharmaceutics and Drug Delivery;2022