Digital Twins in Biomanufacturing

Digital Twin for Continuous Production of Virus-like Particles toward Autonomous Operation

Lentiviral vector and virus-like particle (VLP) manufacturing have been published in fed-batch upstream and batch downstream modes before. Batch downstream and continuous upstream in perfusion mode were reported as well. This study exemplifies development and validation steps for a digital twin combining a physical-chemical-based mechanistic model for all unit operations with a process analytical technology strategy in order to show the efforts and benefits of autonomous operation approaches for manufacturing scale. As the general models are available from various other biologic manufacturing studies, the main step is model calibration for the human embryo kidney cell-based VLPs with experimental quantitative validation within the Quality-by-Design (QbD) approach, including risk assessment to define design and control space. For continuous operation in perfusion mode, the main challenge is the efficient separation of large particle manifolds for VLPs and cells, including cell debris, which is of similar size. Here, innovative tangential flow filtration operations are needed to avoid fast blocking with low mechanical stress pumps. A twofold increase of productivity was achieved using simulation case studies. This increase is similar to improvements previously described for other entities like plasmid DNAs, monoclonal antibodies (mAbs), and single-chain fragments of variability (scFv) fragments. The advantages of applying a digital twin for an advanced process control strategy have proven additional productivity gains of 20% at 99.9% reliability.

Pressurized Hot Water Extraction as Green Technology for Natural Products as Key Technology with Regard to Hydrodistillation and Solid-Liquid Extraction

Hydrodistillation and solid-liquid extraction with organic solvents or supercritical CO2 are standard technologies for natural product manufacturing. Within this technology, portfolio pressurized hot water technology is ranked as a green, sustainable, resilient, kosher and halal manufacturing process. Essential for sustainability is energy integration for heating and cooling the auxiliary water as well as product concentration without evaporation but with the aid of low energy consuming ultra- and nanofiltration membrane technology. The incorporation of modern unit operations, such as pressurized hot water extraction, along with inline measurement devices for Process Analytical Technology approaches, showcases a shift in traditional extraction processes. Traditional equipment and processes still dominate the manufacturing of plant extracts, yet leveraging innovative chemical process engineering methods offers promising avenues for the economic and ecological advancement of botanicals. Techniques such as modeling and process intensification with green technology hold potential in this regard. Digitalization and Industry 4.0 methodologies, including machine learning and artificial intelligence, play pivotal roles in sustaining natural product extraction manufacturing and can profoundly impact the future of human health.

Bayesian cell therapy process optimization

Optimizing complex bioprocesses poses a significant challenge in several fields, particularly in cell therapy manufacturing. The development of customized, closed, and automated processes is crucial for their industrial translation and for addressing large patient populations at a sustainable price. Limited understanding of the underlying biological mechanisms, coupled with highly resource-intensive experimentation, are two contributing factors that make the development of these next-generation processes challenging. Bayesian optimization (BO) is an iterative experimental design methodology that addresses these challenges, but has not been extensively tested in situations that require parallel experimentation with significant experimental variability. In this study, we present an evaluation of noisy, parallel BO for increasing noise levels and parallel batch sizes on two in silico bioprocesses, and compare it to the industry state-of-the-art. As an in vitro showcase, we apply the method to the optimization of a monocyte purification unit operation. The in silico results show that BO significantly outperforms the state-of-the-art, requiring approximately 50% fewer experiments on average. This study highlights the potential of noisy, parallel BO as valuable tool for cell therapy process development and optimization.

Digital Twin Fundamentals of mRNA In Vitro Transcription in Variable Scale Toward Autonomous Operation

The COVID-19 pandemic caused the rapid development of mRNA (messenger ribonucleic acid) vaccines and new RNA-based therapeutic methods. However, the approval rate for candidates has the potential to be increased, with a significant number failing so far due to efficacy, safety, and manufacturing deficiencies, hindering equitable vaccine distribution during pandemics. This study focuses on optimizing the production of mRNA, a critical component of mRNA-based vaccines, using a scalable machine by investigating the key mechanisms of mRNA in vitro transcription. First, kinetic parameters for the mRNA production process were determined. The validity of the determination and the robustness of the model are demonstrated by predicting different reactions with and without substrate limitations as well as different transcripts. The optimized reaction conditions, including temperature, urea concentration, and concentration of reaction-enhancing additives, resulted in a 55% increase in mRNA yield with a 33% reduction in truncated mRNA. Additionally, the feasibility of a segmented flow approach allowed for high-throughput screening (HTS), enabling the production of 20 vaccine candidates within a short time frame, representing a 10-fold increase in productivity, compared to nonsegmented reactions limited by the residence time in the plug flow reactor. The findings presented for the first time here contribute to the development of a fully continuous and efficient manufacturing process for mRNA and other cell and gene therapy drugs/vaccine candidates as presented in our previous work, which discussed the integration of process analytical technologies and predictive process models in a Biopharma 4.0 facility to enable the production of clinical and large-scale doses, ensuring a rapid and resilient supply of critical therapeutics. The results in this study especially highlight that the same machine and equipment can be used for screening and manufacturing different drug candidates in continuous operation. By streamlining production and adhering to quality standards, this approach enhances the industry's ability to respond swiftly to pandemics and public health emergencies, addressing the urgent need for accessible and effective vaccines.

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.

India's Opportunities and Challenges in Establishing a Twin Registry: An Unexplored Human Resource for the World's Second-Most Populous Nation

Nature and nurture have always been a prerogative of evolutionary biologists. The environment's role in shaping an organism's phenotype has always intrigued us. Since the inception of humankind, twinning has existed with an unsettled parley on the contribution of nature (i.e. genetics) versus nurture (i.e. environment), which can influence the phenotypes. The study of twins measures the genetic contribution and that of the environmental influence for a particular trait, acting as a catalyst, fine-tuning the phenotypic trajectories. This is further evident because a number of human diseases show a spectrum of clinical manifestations with the same underlying molecular aberration. As of now, there is no definite way to conclude just from the genomic data the severity of a disease or even to predict who will get affected. This greatly justifies initiating a twin registry for a country as diverse and populated as India. There is an unmet need to set up a nationwide database to carefully curate the information on twins, serving as a valuable biorepository to study their overall susceptibility to disease. Establishing a twin registry is of paramount importance to harness the wealth of human information related to the biomedical, anthropological, cultural, social and economic significance.

Towards Autonomous Process Control—Digital Twin for CHO Cell-Based Antibody Manufacturing Using a Dynamic Metabolic Model

The development of new biologics is becoming more challenging due to global competition and increased requirements for process understanding and assured quality in regulatory approval. As a result, there is a need for predictive, mechanistic process models. These reduce the resources and time required in process development, generating understanding, expanding the possible operating space, and providing the basis for a digital twin for automated process control. Monoclonal antibodies are an important representative of industrially produced biologics that can be used for a wide range of applications. In this work, the validation of a mechanistic process model with respect to sensitivity, as well as accuracy and precision, is presented. For the investigated process conditions, the concentration of glycine, phenylalanine, tyrosine, and glutamine have been identified as significant influencing factors for product formation via statistical evaluation. Cell growth is, under the investigated process conditions, significantly dependent on the concentration of glucose within the investigated design space. Other significant amino acids were identified. A Monte Carlo simulation was used to simulate the cultivation run with an optimized medium resulting from the sensitivity analysis. The precision of the model was shown to have a 95% confidence interval. The model shown here includes the implementation of cell death in addition to models described in the literature.

Development of a General PAT Strategy for Online Monitoring of Complex Mixtures—On the Example of Natural Product Extracts from Bearberry Leaf (Arctostaphylos uva-ursi)

For the first time, a universally applicable and methodical approach from characterization to a PAT concept for complex mixtures is conducted—exemplified on natural products extraction processes. Bearberry leaf (Arctostaphylos uva-ursi) extract is chosen as an example of a typical complex mixture of natural plant origin and generalizable in its composition. Within the quality by design (QbD) based process development the development and implementation of a concept for process analytical technology (PAT), a key enabling technology, is the next necessary step in risk and quality-based process development and operation. To obtain and provide an overview of the broad field of PAT, the development process is shown on the example of a complex multi-component plant extract. This study researches the potential of different process analytical technologies for online monitoring of different component groups and classifies their possible applications within the framework of a QbD-based process. Offline and online analytics are established on the basis of two extraction runs. Based on this data set, PLS models are created for the spectral data, and correlations are conducted for univariate data. In a third run, the prediction potential is researched. Conclusively, the results of this study are arranged in the concept of a holistic quality and risk-based process design and operation concept.

Digital Twins for Continuous mRNA Production

The global coronavirus pandemic continues to restrict public life worldwide. An effective means of limiting the pandemic is vaccination. Messenger ribonucleic acid (mRNA) vaccines currently available on the market have proven to be a well-tolerated and effective class of vaccine against coronavirus type 2 (CoV2). Accordingly, demand is presently outstripping mRNA vaccine production. One way to increase productivity is to switch from the currently performed batch to continuous in vitro transcription, which has proven to be a crucial material-consuming step. In this article, a physico-chemical model of in vitro mRNA transcription in a tubular reactor is presented and compared to classical batch and continuous in vitro transcription in a stirred tank. The three models are validated based on a distinct and quantitative validation workflow. Statistically significant parameters are identified as part of the parameter determination concept. Monte Carlo simulations showed that the model is precise, with a deviation of less than 1%. The advantages of continuous production are pointed out compared to batchwise in vitro transcription by optimization of the space–time yield. Improvements of a factor of 56 (0.011 µM/min) in the case of the continuously stirred tank reactor (CSTR) and 68 (0.013 µM/min) in the case of the plug flow reactor (PFR) were found.