Artificial neural network (ANN)-based prediction of depth filter loading capacity for filter sizing

Deep learning in preclinical antibody drug discovery and development

Antibody drugs have become a key part of biotherapeutics. Patients suffering from various diseases have benefited from antibody therapies. However, its development process is rather long, expensive and risky. To speed up the process, reduce cost and improve success rate, artificial intelligence, especially deep learning methods, have been widely used in all aspects of preclinical antibody drug development, from library generation to hit identification, developability screening, lead selection and optimization. In this review, we systematically summarize antibody encodings, deep learning architectures and models used in preclinical antibody drug discovery and development. We also critically discuss challenges and opportunities, problems and possible solutions, current applications and future directions of deep learning in antibody drug development.

Artificial intelligence and machine learning applications in biopharmaceutical manufacturing

Artificial intelligence and machine learning (AI-ML) offer vast potential in optimal design, monitoring, and control of biopharmaceutical manufacturing. The driving forces for adoption of AI-ML techniques include the growing global demand for biotherapeutics and the shift toward Industry 4.0, spurring the rise of integrated process platforms and continuous processes that require intelligent, automated supervision. This review summarizes AI-ML applications in biopharmaceutical manufacturing, with a focus on the most used AI-ML algorithms, including multivariate data analysis, artificial neural networks, and reinforcement learning. Perspectives on the future growth of AI-ML applications in the area and the challenges of implementing these techniques at manufacturing scale are also presented.

Implementation of chemometrics, design of experiments and neural network analysis for prior process knowledge assessment (PPKA), failure modes and effect analysis (FMEA), scale-down model development (SDM) and process characterization for a chromatographic purification of Teriparatide

Process understanding and characterization forms the foundation, ensuring consistent and robust biologics manufacturing process. Using appropriate modelling tools and machine learning approaches, the process data can be monitored in real time to avoid manufacturing risks. In this article, we have outlined an approach towards implementation of chemometrics and machine learning tools (neural network analysis) to model and predict the behaviour of a mixed-mode chromatography step for a biosimilar (Teriparatide) as a case study. The process development data and process knowledge was assimilated into a prior process knowledge assessment using chemometrics tools to derive important parameters critical to performance indicators (i.e. potential quality and process attributes) and to establish the severity ranking for the FMEA analysis. The characterization data of the chromatographic operation are presented alongwith the determination of the critical, key and non- key process parameters, set points, operating, process acceptance and characterized ranges. The scale-down model establishment was assessed using traditional approaches and novel approaches like batch evolution model and neural network analysis. The batch evolution model was further used to demonstrate batch monitoring through direct chromatographic data, thus demonstrating its application for continuos process verification. Assimilation of process knowledge through a structured data acquisition approach, built-in from process development to continuous process verification was demonstrated to result in a data analytics driven model that can be coupled with machine learning tools for real time process monitoring. We recommend application of these approaches with the FDA guidance on stage wise process development and validation to reduce manufacturing risks. This article is protected by copyright. All rights reserved.

A decade in review: use of data analytics within the biopharmaceutical sector

There are large amounts of data generated within the biopharmaceutical sector. Traditionally, data analysis methods labelled as multivariate data analysis have been the standard statistical technique applied to interrogate these complex data sets. However, more recently there has been a surge in the utilisation of a broader set of machine learning algorithms to further exploit these data. In this article, the adoption of data analysis techniques within the biopharmaceutical sector is evaluated through a review of journal articles and patents published within the last ten years. The papers objectives are to identify the most dominant algorithms applied across different applications areas within the biopharmaceutical sector and to explore whether there is a trend between the size of the data set and the algorithm adopted.

Automation of Dead End Filtration: An Enabler for Continuous Processing of Biotherapeutics

Dead end filtration is a critical unit operation that is used for primary and secondary clarification during manufacturing of both microbial and mammalian cell based biotherapeutics. Dead end filtration is conventionally done in batch mode and requires filter pre-sizing using extensive scouting studies, along with filter over-sizing before deployment to handle potential variability. However, continuous manufacturing processes require consistent use of dead-end filtration over weeks or months, with potential unpredictable variations in feed stream attributes, which is a challenge currently facing the industry. In this work, a dead-end filtration skid is designed for continuous depth filtration, incorporating multiple small-sized filters along with turbidity, and pressure sensors with immediate switching to a fresh filter whenever turbidity or pressure breakthrough above a pre-determined cut-off is detected in real time. The skid has been successfully tested for manufacturing of granulocyte colony stimulating factor from Escherichia coli, human serum albumin from Pichia pastoris, and a monoclonal antibody therapeutic from CHO cells. The proposed skid can be directly applied for any dead-end filtration application with minimal prior scouting studies or sizing calculations for scale-up. It is a useful solution for continuous processing trains where the nature of the feed, such as its turbidity or host cell proteins content, may change over long continuous campaigns, rendering previous sizing calculations inaccurate. The skid also allows significant cost savings by eliminating the sizing safety factor of 1.5-2x which is generally added before filter deployment at manufacturing scale.