Application of advanced machine learning algorithms for anomaly detection and quantitative prediction in protein A chromatography

Tiwari, Arvind; Bansode, Vikrant; Rathore, Anurag S.

doi:10.1016/j.chroma.2022.463486

Cited by 9 publications

(2 citation statements)

References 35 publications

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“…130 In addition to soft sensors, ML algorithms are also crucial components for constructing smart downstream processing platforms. ML models have vastly been employed to create accurate predictive models for mechanistic modeling 131 and parameter optimization [132][133][134][135][136] in the chromatography process, for enhancing efficiency and product quality while reducing experimentation time and cost. ML models have also been intensively used for development of membranes for UF 136,137 and APTS.…”

Section: Emergence Of Intelligent Biomanufacturing Processesmentioning

confidence: 99%

Toward microfluidic continuous-flow and intelligent downstream processing of biopharmaceuticals

Sharma,

Mottafegh,

Joo

et al. 2024

Lab Chip

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Section: Emergence Of Intelligent Biomanufacturing Processesmentioning

confidence: 99%

Toward microfluidic continuous-flow and intelligent downstream processing of biopharmaceuticals

Sharma,

Mottafegh,

Joo

et al. 2024

Lab Chip

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“…Recently, several teams have developed predictive tools for different attributes of protein drug candidates, and machine learning algorithms combined with in silico methods have proven to be a powerful approach (Bailly et al, 2020; Chen et al, 2020; Hebditch & Warwicker, 2019; Khetan et al, 2022; Lauer et al, 2012; Raybould et al, 2019; Tiwari et al, 2022). Machine learning is a sub-field of artificial intelligence that is focused on the development and usage of mathematical algorithms and statistical models that can learn from data and make predictions or decisions without being programmed explicitly.…”

Section: Introductionmentioning

confidence: 99%

Holistic in silico developability assessment of novel classes of small proteins using publicly available sequence-based predictors

Pais,

Mayer,

Felderer

et al. 2023

Preprint

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The development of novel therapeutic proteins is a lengthy and costly process, with an average attrition rate of 91% (Thomas et al., 2021). To increase the probability of success and ensure robust drug supply beyond approval, it is essential to assess the developability profile of new potential drug candidates as early and broadly as possible in development (Jain et al., 2023). Predicting these propertiesin silicois expected to be the next leap in innovation as it would enable significantly reduced development timelines combined with broader screens at lower costs. However, developing predictive algorithms typically requires substantial datasets generated under very defined conditions, a limiting factor especially for new classes of therapeutic proteins that hold immense clinical promise.Here we describe a strategy for assessing the developability of a novel class of small therapeutic Anticalin® proteins using machine learning in conjunction with a knowledge-driven approach. The knowledge-driven approach considers developability attributes such as aggregation propensity, charge variants, immunogenicity, specificity, thermal stability, hydrophobicity, and potential post-translational modifications, to calculate a holistic developability score. Based on sequence-derived descriptors as input parameters we established novel statistical models designed to predict the developability scores for Anticalin proteins. The best models yielded low root mean square errors across the entire dataset and were further validated by removing input data from individual screening campaigns and predicting developability scores for those drug candidates.The adoption of the described workflow will enable significantly streamlined preclinical development of Anticalin drug candidates and could potentially be applied to other therapeutic protein scaffolds.

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Towards Digital Twin for Biopharmaceutical Processes: Concept and Progress

Ding,

Yang,

Ierapetritou

2023

Cell Engineering

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Application of advanced machine learning algorithms for anomaly detection and quantitative prediction in protein A chromatography

Cited by 9 publications

References 35 publications

Toward microfluidic continuous-flow and intelligent downstream processing of biopharmaceuticals

Toward microfluidic continuous-flow and intelligent downstream processing of biopharmaceuticals

Holistic in silico developability assessment of novel classes of small proteins using publicly available sequence-based predictors

Towards Digital Twin for Biopharmaceutical Processes: Concept and Progress

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