Prediction of the performance of pre‐packed purification columns through machine learning

Jiang, Qihao; Seth, Sohan; Scharl, Theresa; Schröeder, Tim; Jungbauer, Alois; Dimartino, Simone

doi:10.1002/jssc.202100864

Cited by 8 publications

(1 citation statement)

References 72 publications

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“…The best results were achieved for the prediction of solvents and solvent ratio with ECFP LSTM auto-encoder with FFNN as the supervised machine-learning method with an accuracy of 0.95 for first task and 0.982 for second task respectively ( Vaškevičius et al., 2021 ). Several ML models have been developed so far to address some of the challenges in downstream processing such as XGboost for the prediction of column performance ( Jiang et al., 2022b ), PeakBot for chromatographic peak prediction ( Bueschl et al., 2022 ), DeepRT for peptide retention time prediction ( Ma et al., 2017 ) and an algorithm to predict the HCPs elution behavior ( Buyel et al., 2013 ).…”

Section: Ai-based ML Algorithms In Recombinant Protein Productionmentioning

confidence: 99%

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

Parthiban,

Vijeesh,

Gayathri

et al. 2023

Front. Plant Sci.

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Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.

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Section: Ai-based ML Algorithms In Recombinant Protein Productionmentioning

confidence: 99%

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

Parthiban,

Vijeesh,

Gayathri

et al. 2023

Front. Plant Sci.

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Current state of implementation of in silico tools in the biopharmaceutical industry—Proceedings of the 5th modeling workshop

Wittkopp,

Welsh,

Todd

et al. 2024

Biotech & Bioengineering

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The fifth modeling workshop (5MW) was held in June 2023 at Favrholm, Denmark and sponsored by Recovery of Biological Products Conference Series. The goal of the workshop was to assemble modeling practitioners to review and discuss the current state, progress since the last fourth mini modeling workshop (4MMW), gaps and opportunities for development, deployment and maintenance of models in bioprocess applications. Areas of focus were four categories: biophysics and molecular modeling, mechanistic modeling, computational fluid dynamics (CFD) and plant modeling. Highlights of the workshop included significant advancements in biophysical/molecular modeling to novel protein constructs, mechanistic models for filtration and initial forays into modeling of multiphase systems using CFD for a bioreactor and mapped strategically to cell line selection/facility fit. A significant impediment to more fully quantitative and calibrated models for biophysics is the lack of large, anonymized datasets. A potential solution would be the use of specific descriptors in a database that would allow for detailed analyzes without sharing proprietary information. Another gap identified was the lack of a consistent framework for use of models that are included or support a regulatory filing beyond the high‐level guidance in ICH Q8–Q11. One perspective is that modeling can be viewed as a component or precursor of machine learning (ML) and artificial intelligence (AI). Another outcome was alignment on a key definition for “mechanistic modeling.” Feedback from participants was that there was progression in all of the fields of modeling within scope of the conference. Some areas (e.g., biophysics and molecular modeling) have opportunities for significant research investment to realize full impact. However, the need for ongoing research and development for all model types does not preclude the application to support process development, manufacturing and use in regulatory filings. Analogous to ML and AI, given the current state of the four modeling types, a prospective investment in educating inter‐disciplinary subject matter experts (e.g., data science, chromatography) is essential to advancing the modeling community.

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Continuous packed bed column removal of Cr⁶⁺, Cd²⁺, and Pb²⁺ ions from synthetic wastewater using polymeric ultra‐permeable and biodegradable ferromagnetic nanocomposite membrane

Suter,

Rutto,

Omwoyo

2024

Can J Chem Eng

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Water purification techniques, including membrane technologies, ion exchange and adsorption, chemical/biochemical reduction, and electrochemical processes, have been developed to remove/recover metal ions species from polluted wastewater. This work assessed the efficiency of polymeric, biodegradable, ultra‐permeable and magnetic nanocomposite membrane (CNCs/N6@Fe3O4‐CT) in a continuous packed bed column for the removal of Cd(II), Cr(VI), and Pb(II) metal ions from synthetic wastewaters. The eco‐compatibility of CNCs/N6@Fe3O4‐CT was increased using chitosan biopolymer. Fe3O4 nanoparticles increased the surface area and improved the separation process. CNCs and N6 polymeric materials enhanced their strength, porosity, and additional binding sites. The CNCs/N6@Fe3O4‐CT nanocomposite membrane was employed as packing material in a fixed‐bed lab‐scale column (height 30 cm, diameter 1.5 cm) to constantly remove Cd(II), Cr(VI), and Pb(II) metal ions from synthetic wastewaters and actual hexavalent chromium tannery effluent. The studies were carried out with different initial metal ion concentrations (10, 20, and 30 mg/L), input flow rates (2, 4, and 6 mL/min), and solution pH values (2.0, 5.0, and 8.0). The obtained experimental data from the breakthrough curves was fitted to the traditional dynamic Thomas model, Yoon‐Nelson, and Bed Depth Service Time (BDST) model.

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Prediction of the performance of pre‐packed purification columns through machine learning

Cited by 8 publications

References 72 publications

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

Current state of implementation of in silico tools in the biopharmaceutical industry—Proceedings of the 5th modeling workshop

Continuous packed bed column removal of Cr⁶⁺, Cd²⁺, and Pb²⁺ ions from synthetic wastewater using polymeric ultra‐permeable and biodegradable ferromagnetic nanocomposite membrane

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Prediction of the performance of pre‐packed purification columns through machine learning

Cited by 8 publications

References 72 publications

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

Current state of implementation of in silico tools in the biopharmaceutical industry—Proceedings of the 5th modeling workshop

Continuous packed bed column removal of Cr6+, Cd2+, and Pb2+ ions from synthetic wastewater using polymeric ultra‐permeable and biodegradable ferromagnetic nanocomposite membrane

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Continuous packed bed column removal of Cr⁶⁺, Cd²⁺, and Pb²⁺ ions from synthetic wastewater using polymeric ultra‐permeable and biodegradable ferromagnetic nanocomposite membrane