Integrating Ensemble NSGA‐II for Multi‐Objective Process Optimization: Refolding of Proinsulin as a Case Study

ABSTRACT In‐vitro refolding of biotherapeutic inclusion bodies has long been recognized as a bottleneck in protein production in host systems such as Escherichia coli . Low throughput, costly reagents, and offline analysis often plague refolding development efforts. Refolding optimization typically employs statistical approaches such as Design of Experiments (DoE). While DOE offers advantage over univariate one‐factor‐at‐a‐time analysis, but it requires large subset sampling, which is cost‐inefficient and labour‐intensive. This paper demonstrates a knowledge‐based refolding optimization, contrasted to the typical DoE‐based protocol for proinsulin. The reaction is monitored and segmented into two parts (segment 1: 0–2 h and segment 2:2–6 h) based on the Fourier transform infrared (FTIR), Oxidation Reduction Potential (ORP) and Reverse Phase‐ High Performance Liquid Chromatography (RP‐HPLC) analysis. The data is fed to a multi‐objective optimization (MOO) method that utilize XGBoost, coupled with an NSGA‐II optimizer. Based on the Pareto front, a linear correlation between parameters was observed in segments 1 and 2. An ensemble coupled non‐dominated sorting genetic algorithm II (NSGA‐II) was developed to optimize the reaction conditions beforehand. The proposed optimizer was then compared with the traditional DoE‐based optimization. The developed optimization framework increased the yield to 65% ± 1.78% compared to 54% ± 2.62% in the traditional DoE‐based approach (relatively 20% higher). The approach could combine screening and optimization analysis in a single step, dramatically reducing the overall experimental efforts by ∼50%.