{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T17:05:57Z","timestamp":1773507957456,"version":"3.50.1"},"reference-count":66,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T00:00:00Z","timestamp":1764720000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T00:00:00Z","timestamp":1764720000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Biotech &amp; Bioengineering"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    In\u2010vitro refolding of biotherapeutic inclusion bodies has long been recognized as a bottleneck in protein production in host systems such as\n                    <jats:italic>Escherichia coli<\/jats:italic>\n                    . 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\u2010factor\u2010at\u2010a\u2010time analysis, but it requires large subset sampling, which is cost\u2010inefficient and labour\u2010intensive. This paper demonstrates a knowledge\u2010based refolding optimization, contrasted to the typical DoE\u2010based protocol for proinsulin. The reaction is monitored and segmented into two parts (segment 1: 0\u20132\u2009h and segment 2:2\u20136\u2009h) based on the Fourier transform infrared (FTIR), Oxidation Reduction Potential (ORP) and Reverse Phase\u2010 High Performance Liquid Chromatography (RP\u2010HPLC) analysis. The data is fed to a multi\u2010objective optimization (MOO) method that utilize XGBoost, coupled with an NSGA\u2010II optimizer. Based on the Pareto front, a linear correlation between parameters was observed in segments 1 and 2. An ensemble coupled non\u2010dominated sorting genetic algorithm II (NSGA\u2010II) was developed to optimize the reaction conditions beforehand. The proposed optimizer was then compared with the traditional DoE\u2010based optimization. The developed optimization framework increased the yield to 65%\u2009\u00b1\u20091.78% compared to 54%\u2009\u00b1\u20092.62% in the traditional DoE\u2010based approach (relatively 20% higher). The approach could combine screening and optimization analysis in a single step, dramatically reducing the overall experimental efforts by \u223c50%.\n                  <\/jats:p>","DOI":"10.1002\/bit.70119","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T12:26:12Z","timestamp":1764764772000},"page":"670-691","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Integrating Ensemble NSGA\u2010II for Multi\u2010Objective Process Optimization: Refolding of Proinsulin as a Case Study"],"prefix":"10.1002","volume":"123","author":[{"given":"Rashmi","family":"Sharma","sequence":"first","affiliation":[{"name":"School of Interdisciplinary Research Indian Institute of Technology Delhi  New Delhi India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naveen G.","family":"Jesubalan","sequence":"additional","affiliation":[{"name":"School of Interdisciplinary Research Indian Institute of Technology Delhi  New Delhi India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5913-4244","authenticated-orcid":false,"given":"Anurag S.","family":"Rathore","sequence":"additional","affiliation":[{"name":"School of Interdisciplinary Research Indian Institute of Technology Delhi  New Delhi India"},{"name":"Department of Chemical Engineering Indian Institute of Technology Delhi  New Delhi India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,12,3]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/BTPR.1635"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.17762\/TURCOMAT.V12I2.1870"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/JSSC.201200476"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781315183176\u201012"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.SEPPUR.2024.126579"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.2022.2067548"},{"key":"e_1_2_9_8_1","doi-asserted-by":"crossref","unstructured":"Botalb A. M.Moinuddin U. M.Al\u2010Saggaf andS. S. A.Ali.2018. \u201cContrasting Convolutional Neural Network (CNN) With Multi\u2010Layer Perceptron (MLP) for Big Data Analysis.\u201d International Conference on Intelligent and Advanced System ICIAS 2018.https:\/\/doi.org\/10.1109\/ICIAS.2018.8540626.","DOI":"10.1109\/ICIAS.2018.8540626"},{"key":"e_1_2_9_9_1","volume-title":"Statistics for Experimenters: Design, Innovation, and Discovery","author":"Box G. E.","year":"2005"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0076\u20106879(09)63017\u20102"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.BIOTECHADV.2022.108050"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.PEP.2016.01.015"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/3\u2010540\u201045356\u20103_83"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.PEP.2010.09.008"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2021.754693"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.24382\/swfr\u2010wr17"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.2174\/092986610791760414"},{"key":"e_1_2_9_19_1","first-page":"17","article-title":"Biomacromolecular Journal Overexpression and Purification of the Synthetic Human Proinsulin to Efficient Produce Glargine Insulin in E. coli","volume":"6","author":"Feizi L.","year":"2020","journal-title":"Biomacromolecules Journal"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1590\/S2175\u201097902018000001006"},{"key":"e_1_2_9_21_1","unstructured":"Gallagher W.n.d.FTIR Analysis of Protein Structure. Accessed February 7 2024.http:\/\/ftp.pdb.bnl.gov."},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1080\/23311916.2018.1502242"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.BBAPAP.2005.07.015"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1002\/BIT.26900"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/S00216\u2010024\u201005249\u20101\/FIGURES\/4"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.PROCBIO.2023.11.022"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978\u20100\u201008\u2010100623\u20108.00003\u20107"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/0076\u20106879(87)43048\u20106"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/MERCON.2019.8818915"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chemrev.0c01251"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.PROCS.2020.03.214"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.JBIOTEC.2022.01.002"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.4014\/JMB.1504.04062"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/978\u20101\u20104419\u20107273\u20106_6\/COVER"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.3389\/FGENE.2019.01077\/FULL"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/BFB0103030"},{"key":"e_1_2_9_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.SAA.2018.10.028"},{"key":"e_1_2_9_38_1","doi-asserted-by":"publisher","DOI":"10.1039\/D2RE00440B"},{"key":"e_1_2_9_39_1","doi-asserted-by":"publisher","DOI":"10.1111\/J.1742\u20104658.2011.08039.X"},{"key":"e_1_2_9_40_1","doi-asserted-by":"publisher","DOI":"10.3389\/FNMOL.2018.00426\/FULL"},{"key":"e_1_2_9_41_1","unstructured":"PalJ.2021.Hyperparameter Tuning in XGBoost Using RandomizedSearchCV. Medium.https:\/\/jayant017.medium.com\/hyperparameter\u2010tuning\u2010in\u2010xgboost\u2010using\u2010randomizedsearchcv\u201088fcb5b58a73."},{"key":"e_1_2_9_42_1","doi-asserted-by":"publisher","DOI":"10.1088\/1755\u20101315\/113\/1\/012127"},{"key":"e_1_2_9_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.JPBA.2016.05.001"},{"key":"e_1_2_9_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.CES.2024.119774"},{"key":"e_1_2_9_45_1","doi-asserted-by":"publisher","DOI":"10.1007\/S00253\u2010021\u201011151\u2010Y"},{"key":"e_1_2_9_46_1","doi-asserted-by":"publisher","DOI":"10.30657\/PEA.2020.26.33"},{"key":"e_1_2_9_47_1","doi-asserted-by":"publisher","DOI":"10.1002\/BIT.22382"},{"key":"e_1_2_9_48_1","doi-asserted-by":"publisher","DOI":"10.1074\/JBC.M300906200\/ASSET\/C6C83A1A\u2010C118\u201042FE\u20109769\u201090760A4B5C87\/MAIN.ASSETS\/GR10.JPG"},{"key":"e_1_2_9_49_1","doi-asserted-by":"publisher","DOI":"10.1039\/D0MA00760A"},{"key":"e_1_2_9_50_1","doi-asserted-by":"publisher","DOI":"10.1002\/JCTB.4152"},{"key":"e_1_2_9_51_1","doi-asserted-by":"publisher","DOI":"10.1186\/S12934\u2010022\u201001778\u20105\/FIGURES\/7"},{"key":"e_1_2_9_52_1","doi-asserted-by":"publisher","DOI":"10.1007\/S00018\u2010010\u20100344\u20104"},{"key":"e_1_2_9_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.IJBIOMAC.2024.133951"},{"key":"e_1_2_9_54_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.BBAGEN.2016.11.014"},{"key":"e_1_2_9_55_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.BEJ.2022.108601"},{"key":"e_1_2_9_56_1","doi-asserted-by":"publisher","DOI":"10.1186\/S40643\u2010021\u201000419\u2010W\/TABLES\/14"},{"key":"e_1_2_9_57_1","doi-asserted-by":"publisher","DOI":"10.1021\/bi00254a009"},{"key":"e_1_2_9_58_1","doi-asserted-by":"publisher","DOI":"10.2174\/138920309789630534"},{"key":"e_1_2_9_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3070634"},{"key":"e_1_2_9_60_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.IJPHARM.2010.02.025"},{"key":"e_1_2_9_61_1","unstructured":"Weiss M. D.Steiner andL.Philipson Insulin Biosynthesis Secretion Structure and Structure\u2010Activity Relationships (2015).https:\/\/europepmc.org\/article\/nbk\/nbk279029."},{"key":"e_1_2_9_62_1","doi-asserted-by":"publisher","DOI":"10.1111\/J.1574\u20106968.2002.TB11310.X"},{"key":"e_1_2_9_63_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0003\u20102697(02)00287\u20107"},{"key":"e_1_2_9_64_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01025049\/METRICS"},{"key":"e_1_2_9_65_1","doi-asserted-by":"publisher","DOI":"10.3390\/biom4010235"},{"key":"e_1_2_9_66_1","doi-asserted-by":"publisher","DOI":"10.1002\/JPS.23746"},{"key":"e_1_2_9_67_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.PROENG.2011.08.745"}],"container-title":["Biotechnology and Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/analyticalsciencejournals.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/bit.70119","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/analyticalsciencejournals.onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/bit.70119","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/analyticalsciencejournals.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/bit.70119","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T06:14:33Z","timestamp":1771049673000},"score":1,"resource":{"primary":{"URL":"https:\/\/analyticalsciencejournals.onlinelibrary.wiley.com\/doi\/10.1002\/bit.70119"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,3]]},"references-count":66,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["10.1002\/bit.70119"],"URL":"https:\/\/doi.org\/10.1002\/bit.70119","archive":["Portico"],"relation":{},"ISSN":["0006-3592","1097-0290"],"issn-type":[{"value":"0006-3592","type":"print"},{"value":"1097-0290","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,3]]},"assertion":[{"value":"2025-07-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-20","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}