Optimal Design of Aqueous Two-Phase Systems for Biomolecule Partitioning

Abstract

Aqueous two-phase systems (ATPS) have exhibited superior performance in many biotechnological applications. To promote the implementation of these powerful platforms by industry in the downstream processing, an optimal design method is developed to tailor high-performance ATPS for partitioning biomolecules in this work. In this design method, two machine learning (ML) models that combine the artificial neural network (ANN) algorithm and group contribution (GC) method are respectively employed to predict the phase equilibrium composition of polymer-electrolyte ATPS and the partition of biomolecules in these aqueous systems. By integrating these two ANN-GC models into the computer-aided design technique, the optimal ATPS is identified by solving an optimization-based mixed-integer non-linear programming (MINLP) problem. As a proof of concept, results of partitioning cefazolin and β-amylase are presented. In the case of cefazolin, the partitioning performance of our tailored ATPS (PPG600 + KNaSO4 + H2O) is nearly 7 times greater than that of the reported ATPS (PEG6000 + Na3C6H5O7 + H2O). Meanwhile, the ATPS of PPG600 + KNaSO4 + H2O gives a cefazolin recovery of 95.0 wt % and an agent input of 0.154 kg/kg aqueous solution, and for the ATPS of PEG6000 + Na3C6H5O7 + H2O, these values are 90.6 and 0.233, respectively. For the second case, the partition coefficient of β-amylase in our proposed ATPS (PPG400 + KNaHPO4 + H2O) is about 13.5 times higher than that of the reported ATPS (PEG10000 + KH2PO4 + H2O). In addition, the ATPS of PPG600 + KNaSO4 + H2O gives an β-amylase recovery of 97.3 wt % at a cost of 0.387 kg agent input/kg aqueous solution, and for the ATPS of PEG6000 + Na3C6H5O7 + H2O, they are 66.3 and 0.252, respectively.

Toward microfluidic continuous-flow and intelligent downstream processing of biopharmaceuticals

“…ML models have also been intensively used for development of membranes for UF 136,137 and APTS. 138–140…”

Investigating and Optimizing Insulin Partitioning with Conjugated Au Nanoparticles in Aqueous Two-Phase Systems Using Response Surface Methodology

“…There is a direct correlation between the partitioning of biomolecules, which is quantified by the partition coefficient ( K ), and their physicochemical properties. These properties include isoelectric points, sizes, surface hydrophobicity, polymer molecular weight, polymer/salt concentrations, pH, and temperature. ,, The separations’ selectivity in ATPS has been enhanced under various conditions, which are problematically associated with the need to make structural changes in ATPSs or to modify biomolecules. , It is therefore becoming more necessary to find techniques that do not require the reformulation of system structures or biomolecules. By creating a mechanism that facilitates the transfer of the biomolecule between the two phases, biomolecules partitioning enhances in ATPS. , Nanoparticles fulfill this demand and improve biomolecule partitioning.…”

Modeling Study on the Density and Viscosity of Ionic Liquid–Organic Solvent–Water Ternary Mixtures

“…Recently, machine learning (ML) algorithms such as artificial neural networks (ANNs) and support vector machine (SVM) have been employed to develop nonlinear GC models for predicting CO 2 solubility in ionic liquids, toxicity of ionic liquids, and physical properties of IL–H 2 O mixtures such as viscosity and surface tension . Additionally, hybrid modeling methods that combine GC methods and ANN algorithms have been introduced to model the phase equilibrium behavior of aqueous two-phase systems (ATPSs). , Recent works have also demonstrated the seamless integration of ANN-based GC models with computer-aided design methods for the optimal design of solvent and aqueous biphasic systems. − Inspired by these successful applications, this study aims to combine the GC method with three popular ML algorithms, namely, ANN, XGBoost, and LightGBM, to build models for predicting the density and viscosity of IL–os–water mixtures. In our previous work, we studied the density, viscosity, and other properties of the IL–IL binary hybrid system and concluded that ANN and GC have a good combination .…”