TY - JOUR
T1 - Design of Spherical Crystallization of Active Pharmaceutical Ingredients via a Highly Efficient Strategy
T2 - From Screening to Preparation
AU - Ma, Yiming
AU - Sun, Mengmeng
AU - Liu, Yanbo
AU - Chen, Mingyang
AU - Wu, Songgu
AU - Wang, Mengwei
AU - Wang, Lingyu
AU - Gao, Zhenguo
AU - Han, Dandan
AU - Liu, Lande
AU - Wang, Jingkang
AU - Gong, Junbo
N1 - Publisher Copyright:
© 2021 American Chemical Society.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/7/12
Y1 - 2021/7/12
N2 - This work aims to develop a highly efficient spherical crystallization from screening to preparation stage based on liquid-liquid phase separation (LLPS). Mixtures than can undergo an LLPS split into two liquid phases with different physical properties, and the oil droplets formed during that process make LLPS a promising approach to prepare spherical particles of an active pharmaceutical ingredient (API). In the screening stage, three machine learning (ML) models (artificial neural network, support vector machine, and logistic regression) were established for predicting LLPS for an API. Two linear models, a simple linear model and a machine learning-based linear model, were also constructed to produce further optimization. The ML-based prediction of LLPS was first established in this work and showed high accuracy and reliability. Also, when compared to a method where the screening depended on the results of experiments, the prediction model highly reduced the use of chemical substances and saved labor and time. In the preparation stage, water and ethanol, which have low toxicity to mammals and have environmental advantages over other organic solvents, were applied as the solvents of LLPS-based spherical crystallization. The LLPS-based preparation process of spherical particles possesses advantages in terms of reduction of the number of unit operations as well as energy consumption and processing cost.
AB - This work aims to develop a highly efficient spherical crystallization from screening to preparation stage based on liquid-liquid phase separation (LLPS). Mixtures than can undergo an LLPS split into two liquid phases with different physical properties, and the oil droplets formed during that process make LLPS a promising approach to prepare spherical particles of an active pharmaceutical ingredient (API). In the screening stage, three machine learning (ML) models (artificial neural network, support vector machine, and logistic regression) were established for predicting LLPS for an API. Two linear models, a simple linear model and a machine learning-based linear model, were also constructed to produce further optimization. The ML-based prediction of LLPS was first established in this work and showed high accuracy and reliability. Also, when compared to a method where the screening depended on the results of experiments, the prediction model highly reduced the use of chemical substances and saved labor and time. In the preparation stage, water and ethanol, which have low toxicity to mammals and have environmental advantages over other organic solvents, were applied as the solvents of LLPS-based spherical crystallization. The LLPS-based preparation process of spherical particles possesses advantages in terms of reduction of the number of unit operations as well as energy consumption and processing cost.
KW - Artificial neural network
KW - Liquid-liquid phase separation
KW - Logistics regression
KW - Pharmaceutical crystallization
KW - Spherical crystallization
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85110390490&partnerID=8YFLogxK
U2 - 10.1021/acssuschemeng.1c01973
DO - 10.1021/acssuschemeng.1c01973
M3 - Article
AN - SCOPUS:85110390490
VL - 9
SP - 9018
EP - 9032
JO - ACS Sustainable Chemistry and Engineering
JF - ACS Sustainable Chemistry and Engineering
SN - 2168-0485
IS - 27
ER -