Integrated in silico formulation design of self-emulsifying drug delivery systems

The drug formulation design of self-emulsifying drug delivery systems (SEDDS) often requires numerous experiments, which are time- and money-consuming. This research aimed to rationally design the SEDDS formulation by the integrated computational and experimental approaches. 4495 SEDDS formulation d...

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Autores principales:	Haoshi Gao, Haoyue Jia, Jie Dong, Xinggang Yang, Haifeng Li, Defang Ouyang
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Self-emulsifying drug delivery system Random forest Central composite design Molecular dynamic simulation Meloxicam Therapeutics. Pharmacology RM1-950
Acceso en línea:	https://doaj.org/article/17b2c78c190649319b847e19ceccbd6b
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spelling	oai:doaj.org-article:17b2c78c190649319b847e19ceccbd6b2021-12-02T05:01:22ZIntegrated in silico formulation design of self-emulsifying drug delivery systems2211-383510.1016/j.apsb.2021.04.017https://doaj.org/article/17b2c78c190649319b847e19ceccbd6b2021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2211383521001568https://doaj.org/toc/2211-3835The drug formulation design of self-emulsifying drug delivery systems (SEDDS) often requires numerous experiments, which are time- and money-consuming. This research aimed to rationally design the SEDDS formulation by the integrated computational and experimental approaches. 4495 SEDDS formulation datasets were collected to predict the pseudo-ternary phase diagram by the machine learning methods. Random forest (RF) showed the best prediction performance with 91.3% for accuracy, 92.0% for sensitivity and 90.7% for specificity in 5-fold cross-validation. The pseudo-ternary phase diagrams of meloxicam SEDDS were experimentally developed to validate the RF prediction model and achieved an excellent prediction accuracy (89.51%). The central composite design (CCD) was used to screen the best ratio of oil-surfactant-cosurfactant. Finally, molecular dynamic (MD) simulation was used to investigate the molecular interaction between excipients and drugs, which revealed the diffusion behavior in water and the role of cosurfactants. In conclusion, this research combined machine learning, central composite design, molecular modeling and experimental approaches for rational SEDDS formulation design. The integrated computer methodology can decrease traditional drug formulation design works and bring new ideas for future drug formulation design.Haoshi GaoHaoyue JiaJie DongXinggang YangHaifeng LiDefang OuyangElsevierarticleSelf-emulsifying drug delivery systemRandom forestCentral composite designMolecular dynamic simulationMeloxicamTherapeutics. PharmacologyRM1-950ENActa Pharmaceutica Sinica B, Vol 11, Iss 11, Pp 3585-3594 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Self-emulsifying drug delivery system Random forest Central composite design Molecular dynamic simulation Meloxicam Therapeutics. Pharmacology RM1-950
spellingShingle	Self-emulsifying drug delivery system Random forest Central composite design Molecular dynamic simulation Meloxicam Therapeutics. Pharmacology RM1-950 Haoshi Gao Haoyue Jia Jie Dong Xinggang Yang Haifeng Li Defang Ouyang Integrated in silico formulation design of self-emulsifying drug delivery systems
description	The drug formulation design of self-emulsifying drug delivery systems (SEDDS) often requires numerous experiments, which are time- and money-consuming. This research aimed to rationally design the SEDDS formulation by the integrated computational and experimental approaches. 4495 SEDDS formulation datasets were collected to predict the pseudo-ternary phase diagram by the machine learning methods. Random forest (RF) showed the best prediction performance with 91.3% for accuracy, 92.0% for sensitivity and 90.7% for specificity in 5-fold cross-validation. The pseudo-ternary phase diagrams of meloxicam SEDDS were experimentally developed to validate the RF prediction model and achieved an excellent prediction accuracy (89.51%). The central composite design (CCD) was used to screen the best ratio of oil-surfactant-cosurfactant. Finally, molecular dynamic (MD) simulation was used to investigate the molecular interaction between excipients and drugs, which revealed the diffusion behavior in water and the role of cosurfactants. In conclusion, this research combined machine learning, central composite design, molecular modeling and experimental approaches for rational SEDDS formulation design. The integrated computer methodology can decrease traditional drug formulation design works and bring new ideas for future drug formulation design.
format	article
author	Haoshi Gao Haoyue Jia Jie Dong Xinggang Yang Haifeng Li Defang Ouyang
author_facet	Haoshi Gao Haoyue Jia Jie Dong Xinggang Yang Haifeng Li Defang Ouyang
author_sort	Haoshi Gao
title	Integrated in silico formulation design of self-emulsifying drug delivery systems
title_short	Integrated in silico formulation design of self-emulsifying drug delivery systems
title_full	Integrated in silico formulation design of self-emulsifying drug delivery systems
title_fullStr	Integrated in silico formulation design of self-emulsifying drug delivery systems
title_full_unstemmed	Integrated in silico formulation design of self-emulsifying drug delivery systems
title_sort	integrated in silico formulation design of self-emulsifying drug delivery systems
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/17b2c78c190649319b847e19ceccbd6b
work_keys_str_mv	AT haoshigao integratedinsilicoformulationdesignofselfemulsifyingdrugdeliverysystems AT haoyuejia integratedinsilicoformulationdesignofselfemulsifyingdrugdeliverysystems AT jiedong integratedinsilicoformulationdesignofselfemulsifyingdrugdeliverysystems AT xinggangyang integratedinsilicoformulationdesignofselfemulsifyingdrugdeliverysystems AT haifengli integratedinsilicoformulationdesignofselfemulsifyingdrugdeliverysystems AT defangouyang integratedinsilicoformulationdesignofselfemulsifyingdrugdeliverysystems
_version_	1718400815430893568

Integrated in silico formulation design of self-emulsifying drug delivery systems

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