Harnessing the potential of machine learning for advancing "Quality by Design" in biomanufacturing. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Walsh, Ian; Myint, Matthew; Nguyen-Khuong, Terry; Ho, Ying Swan; Ng, Say Kong; Lakshmanan, Meiyappan
• Is Part Of: MAbs. Volume 14:Issue 1(2022)
• Publication Date: 2022-12-31
• Subjects: Biomanufacturing -- upstream bioprocess design -- Quality by Design (QbD) -- machine learning (ML) -- Multivariate data analysis (MVDA) -- hybrid modeling; Monoclonal antibodies -- Therapeutic use -- Periodicals; Monoclonal antibodies -- Periodicals; Antibodies, Monoclonal -- Periodicals; 616.0798
• DOI: 10.1080/19420862.2021.2013593
• Languages: English
• ISSNs: 1942-0862

Abstract

ABSTRACT: Ensuring consistent high yields and product quality are key challenges in biomanufacturing. Even minor deviations in critical process parameters (CPPs) such as media and feed compositions can significantly affect product critical quality attributes (CQAs). To identify CPPs and their interdependencies with product yield and CQAs, design of experiments, and multivariate statistical approaches are typically used in industry. Although these models can predict the effect of CPPs on product yield, there is room to improve CQA prediction performance by capturing the complex relationships in high-dimensional data. In this regard, machine learning (ML) approaches offer immense potential in handling non-linear datasets and thus are able to identify new CPPs that could effectively predict the CQAs. ML techniques can also be synergized with mechanistic models as a 'hybrid ML' or 'white box ML' to identify how CPPs affect the product yield and quality mechanistically, thus enabling rational design and control of the bioprocess. In this review, we describe the role of statistical modeling in Quality by Design (QbD) for biomanufacturing, and provide a generic outline on how relevant ML can be used to meaningfully analyze bioprocessing datasets. We then offer our perspectives on how relevant use of ML can accelerate the implementation of systematic QbD within the biopharma 4.0 paradigm.