AI-ML applications in bioprocessing: ML as an enabler of real time quality prediction in continuous manufacturing of mAbs

Antibody glycan quality predicted from CHO cell culture media markers and machine learning

N-glycosylation can have a profound effect on the quality of mAb therapeutics. In biomanufacturing, one of the ways to influence N-glycosylation patterns is by altering the media used to grow mAb cell expression systems. Here, we explore the potential of machine learning (ML) to forecast the abundances of N-glycan types based on variables related to the growth media. The ML models exploit a dataset consisting of detailed glycomic characterisation of Anti-HER fed-batch bioreactor cell cultures measured daily under 12 different culture conditions, such as changes in levels of dissolved oxygen, pH, temperature, and the use of two different commercially available media. By performing spent media quantitation and subsequent calculation of pseudo cell consumption rates (termed media markers) as inputs to the ML model, we were able to demonstrate a small subset of media markers (18 selected out of 167 mass spectrometry peaks) in a Chinese Hamster Ovary (CHO) cell cultures are important to model N-glycan relative abundances (Regression - correlations between 0.80-0.92; Classification - AUC between 75.0-97.2). The performances suggest the ML models can infer N-glycan critical quality attributes from extracellular media as a proxy. Given its accuracy, we envisage its potential applications in biomaufactucuring, especially in areas of process development, downstream and upstream bioprocessing.

Advances and opportunities in process analytical technologies for viral vector manufacturing

Viral vectors are an emerging, exciting class of biologics whose application in vaccines, oncology, and gene therapy has grown exponentially in recent years. Following first regulatory approval, this class of therapeutics has been vigorously pursued to treat monogenic disorders including orphan diseases, entering hundreds of new products into pipelines. Viral vector manufacturing supporting clinical efforts has spurred the introduction of a broad swath of analytical techniques dedicated to assessing the diverse and evolving panel of Critical Quality Attributes (CQAs) of these products. Herein, we provide an overview of the current state of analytics enabling measurement of CQAs such as capsid and vector identities, product titer, transduction efficiency, impurity clearance etc. We highlight orthogonal methods and discuss the advantages and limitations of these techniques while evaluating their adaptation as process analytical technologies. Finally, we identify gaps and propose opportunities in enabling existing technologies for real-time monitoring from hardware, software, and data analysis viewpoints for technology development within viral vector biomanufacturing.

Sensors and chemometrics in downstream processing

The biopharmaceutical industry is still running in batch mode, mostly because it is highly regulated. In the past, sensors were not readily available and in-process control was mainly executed offline. The most important product parameters are quantity, purity, and potency, in addition to adventitious agents and bioburden. New concepts using disposable single-use technologies and integrated bioprocessing for manufacturing will dominate the future of bioprocessing. To ensure the quality of pharmaceuticals, initiatives such as Process Analytical Technologies, Quality by Design, and Continuous Integrated Manufacturing have been established. The aim is that these initiatives, together with technology development, will pave the way for process automation and autonomous bioprocessing without any human intervention. Then, real-time release would be realized, leading to a highly predictive and robust biomanufacturing system. The steps toward such automated and autonomous bioprocessing are reviewed in the context of monitoring and control. It is possible to integrate real-time monitoring gradually, and it should be considered from a soft sensor perspective. This concept has already been successfully implemented in other industries and requires relatively simple model training and the use of established statistical tools, such as multivariate statistics or neural networks. This review describes a scenario for integrating soft sensors and predictive chemometrics into modern process control. This is exemplified by selective downstream processing steps, such as chromatography and membrane filtration, the most common unit operations for separation of biopharmaceuticals.

Rapid total sialic acid monitoring during cell culture process using a machine learning model based soft sensor

Total sialic acid content (TSA) in biotherapeutic proteins is often a critical quality attribute as it impacts the drug efficacy. Traditional wet chemical assays to quantify TSA in biotherapeutic proteins during cell culture typically takes several hours or longer due to the complexity of the assay which involves isolation of sialic acid from the protein of interest, followed by sample preparation and chromatographic based separation for analysis. Here, we developed a machine learning model-based technology to rapidly predict TSA during cell culture by using typically measured process parameters. The technology features a user interface, where the users only have to upload cell culture process parameters as input variables and TSA values are instantly displayed on a dashboard platform based on the model predictions. In this study, multiple machine learning algorithms were assessed on our dataset, with the Random Forest model being identified as the most promising model. Feature importance analysis from the Random Forest model revealed that attributes like viable cell density (VCD), glutamate, ammonium, phosphate, and basal medium type are critical for predictions. Notably, while the model demonstrated strong predictability by Day 14 of observation, challenges remain in forecasting TSA values at the edges of the calibration range. This research not only emphasizes the transformative power of machine learning and soft sensors in bioprocessing but also introduces a rapid and efficient tool for sialic acid prediction, signaling significant advancements in bioprocessing. Future endeavors may focus on data augmentation to further enhance model precision and exploration of process control capabilities.

NIR spectroscopy-CNN-enabled chemometrics for multianalyte monitoring in microbial fermentation

As the biopharmaceutical industry looks to implement Industry 4.0, the need for rapid and robust analytical characterization of analytes has become a pressing priority. Spectroscopic tools, like near-infrared (NIR) spectroscopy, are finding increasing use for real-time quantitative analysis. Yet detection of multiple low-concentration analytes in microbial and mammalian cell cultures remains an ongoing challenge, requiring the selection of carefully calibrated, resilient chemometrics for each analyte. The convolutional neural network (CNN) is a puissant tool for processing complex data and making it a potential approach for automatic multivariate spectral processing. This work proposes an inception module-based two-dimensional (2D) CNN approach (I-CNN) for calibrating multiple analytes using NIR spectral data. The I-CNN model, coupled with orthogonal partial least squares (PLS) preprocessing, converts the NIR spectral data into a 2D data matrix, after which the critical features are extracted, leading to model development for multiple analytes. Escherichia coli fermentation broth was taken as a case study, where calibration models were developed for 23 analytes, including 20 amino acids, glucose, lactose, and acetate. The I-CNN model result statistics depicted an average R2 values of prediction 0.90, external validation data set 0.86 and significantly lower root mean square error of prediction values ∼0.52 compared to conventional regression models like PLS. Preprocessing steps were applied to I-CNN models to evaluate any augmentation in prediction performance. Finally, the model reliability was assessed via real-time process monitoring and comparison with offline analytics. The proposed I-CNN method is systematic and novel in extracting distinctive spectral features from a multianalyte bioprocess data set and could be adapted to other complex cell culture systems requiring rapid quantification using spectroscopy.

Advancements in microneedle fabrication techniques: artificial intelligence assisted 3D-printing technology

Microneedles (MNs) are micron-scale needles that are a painless alternative to injections for delivering drugs through the skin. MNs find applications as biosensing devices and could serve as real-time diagnosis tools. There have been numerous fabrication techniques employed for producing quality MN-based systems, prominent among them is the three-dimensional (3D) printing. 3D printing enables the production of quality MNs of tuneable characteristics using a variety of materials. Further, the possible integration of artificial intelligence (AI) tools such as machine learning (ML) and deep learning (DL) with 3D printing makes it an indispensable tool for fabricating microneedles. Provided that these AI tools can be trained and act with minimal human intervention to control the quality of products produced, there is also a possibility of mass production of MNs using these tools in the future. This work reviews the specific role of AI in the 3D printing of MN-based devices discussing the use of AI in predicting drug release patterns, its role as a quality control tool, and in predicting the biomarker levels. Additionally, the autonomous 3D printing of microneedles using an integrated system of the internet of things (IoT) and machine learning (ML) is discussed in brief. Different categories of machine learning including supervised learning, semi-supervised learning, unsupervised learning, and reinforced learning have been discussed in brief. Lastly, a brief section is dedicated to the biosensing applications of MN-based devices.

Toward microfluidic continuous-flow and intelligent downstream processing of biopharmaceuticals

Biopharmaceuticals have emerged as powerful therapeutic agents, revolutionizing the treatment landscape for various diseases, including cancer, infectious diseases, autoimmune and genetic disorders. These biotherapeutics pave the way for precision medicine with their unique and targeted capabilities. The production of high-quality biologics entails intricate manufacturing processes, including cell culture, fermentation, purification, and formulation, necessitating specialized facilities and expertise. These complex processes are subject to rigorous regulatory oversight to evaluate the safety, efficacy, and quality of biotherapeutics prior to clinical approval. Consequently, these drugs undergo extensive purification unit operations to achieve high purity by effectively removing impurities and contaminants. The field of personalized precision medicine necessitates the development of novel and highly efficient technologies. Microfluidic technology addresses unmet needs by enabling precise and compact separation, allowing rapid, integrated and continuous purification modules. Moreover, the integration of intelligent biomanufacturing systems with miniaturized devices presents an opportunity to significantly enhance the robustness of complex downstream processing of biopharmaceuticals, with the benefits of automation and advanced control. This allows seamless data exchange, real-time monitoring, and synchronization of purification steps, leading to improved process efficiency, data management, and decision-making. Integrating autonomous systems into biopharmaceutical purification ensures adherence to regulatory standards, such as good manufacturing practice (GMP), positioning the industry to effectively address emerging market demands for personalized precision nano-medicines. This perspective review will emphasize on the significance, challenges, and prospects associated with the adoption of continuous, integrated, and intelligent methodologies in small-scale downstream processing for various types of biologics. By utilizing microfluidic technology and intelligent systems, purification processes can be enhanced for increased efficiency, cost-effectiveness, and regulatory compliance, shaping the future of biopharmaceutical production and enabling the development of personalized and targeted therapies.

Digitalization concepts in academic bioprocess development

Digitalization with integrated devices, digital and physical assistants, automation, and simulation is setting a new direction for laboratory work. Even with complex research workflows, high staff turnover, and a limited budget some laboratories have already shown that digitalization is indeed possible. However, academic bioprocess laboratories often struggle to follow the trend of digitalization. Due to their diverse research circumstances, high variety of team composition, goals, and limitations the concepts are substantially different. Here, we will provide an overview on different aspects of digitalization and describe how academic laboratories successfully digitalized their working environment. The key aspect is the collaboration and communication between IT-experts and scientific staff. The developed digital infrastructure is only useful if it supports the laboratory worker and does not complicate their work. Thereby, laboratory researchers have to collaborate closely with IT-experts in order for a well-developed and maintainable digitalization concept that fits their individual needs and level of complexity. This review may serve as a starting point or a collection of ideas for the transformation toward a digitalized laboratory.

Explainable deep learning enhances robust and reliable real-time monitoring of a chromatographic protein A capture step

The application of model-based real-time monitoring in biopharmaceutical production is a major step toward quality-by-design and the fundament for model predictive control. Data-driven models have proven to be a viable option to model bioprocesses. In the high stakes setting of biopharmaceutical manufacturing it is essential to ensure high model accuracy, robustness, and reliability. That is only possible when (i) the data used for modeling is of high quality and sufficient size, (ii) state-of-the-art modeling algorithms are employed, and (iii) the input-output mapping of the model has been characterized. In this study, we evaluate the accuracy of multiple data-driven models in predicting the monoclonal antibody (mAb) concentration, double stranded DNA concentration, host cell protein concentration, and high molecular weight impurity content during elution from a protein A chromatography capture step. The models achieved high-quality predictions with a normalized root mean squared error of <4% for the mAb concentration and of ≈10% for the other process variables. Furthermore, we demonstrate how permutation/occlusion-based methods can be used to gain an understanding of dependencies learned by one of the most complex data-driven models, convolutional neural network ensembles. We observed that the models generally exhibited dependencies on correlations that agreed with first principles knowledge, thereby bolstering confidence in model reliability. Finally, we present a workflow to assess the model behavior in case of systematic measurement errors that may result from sensor fouling or failure. This study represents a major step toward improved viability of data-driven models in biopharmaceutical manufacturing.

Soft sensor based rapid detection of trace chlorine dioxide (ClO2) concentration in water

Chlorine dioxide (ClO2) is a widely used sterilizer and a disinfectant across a multitude of industries. When using ClO2, it is imperative to measure the ClO2 concentration to abide by the safety regulations. This study presents a novel, soft sensor method based on Fourier transform infrared spectroscopy (FTIR) spectroscopy for measurement of ClO2 concentration in different water samples varying from milli Q to wastewater. Six distinct artificial neural network models were constructed and evaluated based on three overarching statistical standards to select the optimal model. The OPLS-RF model outperformed all other models with R2, RMSE, and NRMSE values of 0.945, 0.24, and 0.063, respectively. The developed model demonstrated limit of detection and limit of quantification values of 0.1 and 0.25 ppm, respectively, for water. Furthermore, the model also exhibited good reproducibility and precision as measured by the BCMSEP (0.064). The soft sensor-based method presented in the study offers significant advantages in terms of simplicity and speedy detection. In summary, the study presents development of a soft sensor that is capable of predicting the trace content of chlorine dioxide ranging between 0.1 to 5 ppm in a water sample by connecting FTIR with an OPLS-RF model.

Review on machine learning-based bioprocess optimization, monitoring, and control systems

Machine Learning is quickly becoming an impending game changer for transforming big data thrust from the bioprocessing industry into actionable output. However, the complex data set from bioprocess, lagging cyber-integrated sensor system, and issues with storage scalability limit machine learning real-time application. Hence, it is imperative to know the state of technology to address prevailing issues. This review first gives an insight into the basic understanding of the machine learning domain and discusses its complexities for more comprehensive applications. Followed by an outline of how relevant machine learning models are for statistical and logical analysis of the enormous datasets generated to control bioprocess operations. Then this review critically discusses the current knowledge, its limitations, and future aspects in different subfields of the bioprocessing industry. Further, this review discusses the prospects of adopting a hybrid method to dovetail different modeling strategies, cyber-networking, and integrated sensors to develop new digital biotechnologies.

Process Automation and Control Strategy by Quality-by-Design in Total Continuous mRNA Manufacturing Platforms

Vaccine supply has a bottleneck in manufacturing capacity due to operation personnel and chemicals needed. Assessment of existing mRNA (messenger ribonucleic acid) vaccine processing show needs for continuous manufacturing processes. This is enabled by strict application of the regulatory demanded quality by design process based on digital twins, process analytical technology, and control automation strategies in order to improve process transfer for manufacturing capacity, reduction out-of-specification batch failures, qualified personnel training and number, optimal utilization of buffers and chemicals as well as speed-up of product release. In this work, process control concepts, which are necessary for achieving autonomous, continuous manufacturing, for mRNA manufacturing are explained and proven to be ready for industrialization. The application of the process control strategies developed in this work enable the previously pointed out benefits. By switching from batch-wise to continuous mRNA production as was shown in previous work, which was the base for this study, a potential cost reduction by a factor 5 (i.e., from EUR 0.380 per dose to EUR 0.085 per dose) is achievable. Mainly, based on reduction of personnel (factor 30) and consumable (factor 7.5) per campaign due to the significant share of raw materials in the manufacturing costs (74–97). Future research focus following this work may be on model-based predictive control to gain further optimization potential of potential batch failure and out of specification (OOS) number reduction.