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.

The circulatory half-life of recombinant therapeutic proteins is an important pharmacokinetic attribute because it determines the dosing frequency of these drugs, translating directly to treatment cost. Thus, recombinant therapeutic glycoproteins such as monoclonal antibodies have been chemically modified by various means to enhance their circulatory half-life. One approach is to manipulate the N-glycan composition of these agents. Among the many glycan constituents, sialic acid (specifically, N-acetylneuraminic acid) plays a critical role in extending circulatory half-life by masking the terminal galactose that would otherwise be recognised by the hepatic asialoglycoprotein receptor (ASGPR), resulting in clearance of the biotherapeutic from the circulation. This review aims to provide an illustrative overview of various strategies to enhance the pharmacokinetic/pharmacodynamic properties of recombinant therapeutic proteins through manipulation of their sialic acid content.

Stem cells possess the unique ability to differentiate into specialized cell types. These specialized cell types can be used for regenerative medicine purposes such as cell therapy. Myosatellite cells, also known as skeletal muscle stem cells (MuSCs), play important roles in the growth, repair, and regeneration of skeletal muscle tissues. However, despite its therapeutic potential, the successful differentiation, proliferation, and expansion processes of MuSCs remain a significant challenge due to a variety of factors. For example, the growth and differentiation of MuSCs can be greatly influenced by actively replicating the MuSCs microenvironment (known as the niche) using mechanical forces. However, the molecular role of mechanobiology in MuSC growth, proliferation, and differentiation for regenerative medicine is still poorly understood. In this present review, we comprehensively summarize, compare, and critically analyze how different mechanical cues shape stem cell growth, proliferation, differentiation, and their potential role in disease development (Fig. 1). The insights developed from the mechanobiology of stem cells will also contribute to how these applications can be used for regenerative purposes using MuSCs.

In antibody development and manufacturing, protein aggregation is a common challenge that can lead to serious efficacy and safety issues. To mitigate this problem, it is important to investigate its molecular origins. This review discusses (1) our current molecular understanding and theoretical models of antibody aggregation, (2) how various stress conditions related to antibody upstream and downstream bioprocesses can trigger aggregation, and (3) current mitigation strategies employed towards inhibiting aggregation. We discuss the relevance of the aggregation phenomenon in the context of novel antibody modalities and highlight how in silico approaches can be exploited to mitigate it.

It is estimated that food fraud, where meat from different species is deceitfully labelled or contaminated, has cost the global food industry around USD 6.2 to USD 40 billion annually. To overcome this problem, novel and robust quantitative methods are needed to accurately characterise and profile meat samples. In this study, we use a glycomic approach for the profiling of meat from different species. This involves an O-glycan analysis using LC-MS qTOF, and an N-glycan analysis using a high-resolution non-targeted ultra-performance liquid chromatography-fluorescence-mass spectrometry (UPLC-FLR-MS) on chicken, pork, and beef meat samples. Our integrated glycomic approach reveals the distinct glycan profile of chicken, pork, and beef samples; glycosylation attributes such as fucosylation, sialylation, galactosylation, high mannose, α-galactose, Neu5Gc, and Neu5Ac are significantly different between meat from different species. The multi-attribute data consisting of the abundance of each O-glycan and N-glycan structure allows a clear separation between meat from different species through principal component analysis. Altogether, we have successfully demonstrated the use of a glycomics-based workflow to extract multi-attribute data from O-glycan and N-glycan analysis for meat profiling. This established glycoanalytical methodology could be extended to other high-value biotechnology industries for product authentication.