Integration Approaches to Model Bioreactor Hydrodynamics and Cellular Kinetics for Advancing Bioprocess Optimisation.

Large-scale bioprocesses are increasing globally to cater to the larger market demands for biological products. As fermenter volumes increase, the efficiency of mixing decreases, and environmental gradients become more pronounced compared to smaller scales. Consequently, the cells experience gradients in process parameters, which in turn affects the efficiency and profitability of the process. Computational fluid dynamics (CFD) simulations are being widely embraced for their ability to simulate bioprocess performance, facilitate bioprocess upscaling, downsizing, and process optimisation. Recently, CFD approaches have been integrated with dynamic Cell reaction kinetic (CRK) modelling to generate valuable information about the cellular response to fluctuating hydrodynamic parameters inside large production processes. Such coupled approaches have the potential to facilitate informed decision-making in intelligent biomanufacturing, aligning with the principles of "Industry 4.0" concerning digitalisation and automation. In this review, we discuss the benefits of utilising integrated CFD-CRK models and the different approaches to integrating CFD-based bioreactor hydrodynamic models with cellular kinetic models. We also highlight the suitability of different coupling approaches for bioprocess modelling in the purview of associated computational loads.

A Human Skin Explant Test as a Novel In Vitro Assay for the Detection of Skin Sensitization to Aggregated Monoclonal Antibodies.

Introduction: Monoclonal antibodies (mAbs) are important therapeutics. However, the enhanced potential for aggregation has become a critical quality parameter during the production of mAbs. Furthermore, mAb aggregation may also present a potential health risk in a clinical setting during the administration of mAb therapeutics to patients. While the extent of immunotoxicity in patient populations is uncertain, reports show it can lead to immune responses via cell activation and cytokine release. In this study, an autologous in vitro skin test designed to predict adverse immune events, including skin sensitization, was used as a novel assay for the assessment of immunotoxicity caused by mAb aggregation. Material and Methods: Aggregation of mAbs was induced by a heat stress protocol, followed by characterization of protein content by analytical ultra-centrifugation and transmission electron microscopy, revealing a 4% aggregation level of total protein content. Immunotoxicity and potential skin sensitization caused by the aggregates, were then tested in a skin explant assay. Results: Aggregated Herceptin and Rituximab caused skin sensitization, as shown by histopathological damage (grade II-III positive response) together with positive staining for Heat Shock Protein 70 (HSP70). Changes in T cell proliferation were not observed. Cytokine analysis revealed a significant increase of IL-10 for the most extreme condition of aggregation (65 °C at pH3) and a trend for an overall increase of IFN-γ, especially in response to Rituximab. Conclusions: The skin explant assay demonstrated that aggregated mAbs showed adverse immune reactions, as demonstrated as skin sensitization, with histopathological grades II-III. The assay may, therefore, be a novel tool for assessing immunotoxicity and skin sensitization caused by mAb aggregation.

Ultrasound and sonochemistry enhance education outcomes: From fundamentals and applied research to entrepreneurial potential.

With this manuscript we aim to initiate a discussion specific to educational actions around ultrasonics sonochemistry. The importance of these actions does not just derive from a mere pedagogical significance, but they can be an exceptional tool for illustrating various concepts in other disciplines, such as process intensification and microfluidics. Sonochemistry is currently a far-reaching discipline extending across different scales of applicability, from the fundamental physics of tiny bubbles and molecules, up to process plants. This review is part of a special issue in Ultrasonics Sonochemistry, where several scholars have shared their experiences and highlighted opportunities regarding ultrasound as an education tool. The main outcome of our work is that teaching and mentorship in sonochemistry are highly needed, with a balanced technical and scientific knowledge to foster skills and implement safe protocols. Applied research typically features the use of ultrasound as ancillary, to merely enhance a given process and often leading to poorly conceived experiments and misunderstanding of the actual effects. Thus, our scientific community must build a consistent culture and monitor reproducible practices to rigorously generate new knowledge on sonochemistry. These practices can be implemented in teaching sonochemistry in classrooms and research laboratories. We highlight ways to collectively provide a potentially better training for scientists, invigorating academic and industry-oriented careers. A salient benefit for education efforts is that sonochemistry-based projects can serve multidisciplinary training, potentially gathering students from different disciplines, such as physics, chemistry and bioengineering. Herein, we discuss challenges, opportunities, and future avenues to assist in designing courses and research programs based on sonochemistry. Additionally, we suggest simple experiments suitable for teaching basic physicochemical principles at the undergraduatelevel. We also provide arguments and recommendations oriented towards graduate and postdoctoral students, in academia or industry to be more entrepreneurial. We have identified that sonochemistry is consistently seen as a 'green' or sustainable tool, which particular appeal to process intensification approaches, including microfluidics and materials science. We conclude that a globally aligned pedagogical initiative and constantly updated educational tools will help to sustain a virtuous cycle in STEM and industrial applications of sonochemistry.

Perceptions and factors affecting the adoption of digital games for engineering education: a mixed-method research.

Digital games are considered relevant in higher education due to their ability to foster authentic, active and experiential learning opportunities that are of importance in engineering education. However, as a relatively new pedagogical tool, there is the need to understand the perceptions of engineering students as well as to identify factors that influence their adoption of games for learning. So far, only a few studies have investigated the perceptions of higher education students towards learning games and even fewer for engineering students. To bridge this research gap, the current study utilises a mixed-method research design to identify factors that influence the adoption of digital learning games by engineering students as well as their overall perceptions of the use of games for engineering education. Results from the analysed quantitative and qualitative data suggest that engineering students value fun and engagement as well as relevance to the curriculum as factors that would influence their intentions to use digital games for engineering education. Students also showed openness to the use of digital games for learning, but resistance to their use for assessment. These findings have implications for the design of games and classroom deployment of games, as these provide insights to game designers and educators on the factors to consider in the design and classroom deployment of games, respectively.

Drivers of immersive virtual reality adoption intention: a multi-group analysis in chemical industry settings.

The present study uses the modified Unified Theory of Acceptance and Use of Technology 2 to examine the effect of factors such as performance expectancy (PE), effort expectancy (EE), social influence (SI), and hedonic motivation (HM) that may motivate operators and employees to adopt IVR-based technology into their training. Results of a multi-group analysis based on nationality, prior IVR experience, and/or length of work experience, to analyse the potential similarities and/or differences in perception and acceptance towards using IVR-based technology are also presented. The quantitative research data were gathered using an online questionnaire from 438 chemical operators and/or employees who either speak German, French, or English. Partial least squares structural equation modelling and multi-group analysis based on SmartPLS™ version 3 were used to carry out the path and multi-group analyses. The results show that the behavioural intention (BI) towards adoption of IVR was influenced by PE, EE, and HM for all abovementioned subpopulation. However, the relationship of SI to BI was not supported for respondents with prior IVR experience and for respondents coming from Western region. Although Henseler's-based multi-group PLS analysis reveals that there was no significant difference between the group comparisons, it is still important to take into account these socio-demographic factors as there are definite group differences in terms of the ranking order of each construct for the IVR adoption intentions among each subpopulation. The implications and future directions were discussed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10055-021-00586-3.

QSAR Implementation for HIC Retention Time Prediction of mAbs Using Fab Structure: A Comparison between Structural Representations.

Monoclonal antibodies (mAbs) constitute a rapidly growing biopharmaceutical sector. However, their growth is impeded by high failure rates originating from failed clinical trials and developability issues in process development. There is, therefore, a growing need for better in silico tools to aid in risk assessment of mAb candidates to promote early-stage screening of potentially problematic mAb candidates. In this study, a quantitative structure-activity relationship (QSAR) modelling workflow was designed for the prediction of hydrophobic interaction chromatography (HIC) retention times of mAbs. Three novel descriptor sets derived from primary sequence, homology modelling, and atomistic molecular dynamics (MD) simulations were developed and assessed to determine the necessary level of structural resolution needed to accurately capture the relationship between mAb structures and HIC retention times. The results showed that descriptors derived from 3D structures obtained after MD simulations were the most suitable for HIC retention time prediction with a R2 = 0.63 in an external test set. It was found that when using homology modelling, the resulting 3D structures became biased towards the used structural template. Performing an MD simulation therefore proved to be a necessary post-processing step for the mAb structures in order to relax the structures and allow them to attain a more natural conformation. Based on the results, the proposed workflow in this paper could therefore potentially contribute to aid in risk assessment of mAb candidates in early development.

Multivariate data analysis in cell gene therapy manufacturing.

The emergence of cell gene therapy (CGT) as a safe and efficacious treatment for numerous severe inherited and acquired human diseases has led to growing interest and investment in new CGT products. The most successful of these have been autologous viral vector-based treatments. The development of viral vector manufacturing processes and ex vivo patient cell processing capabilities is a pressing issue in the advancement of autologous viral vector-based CGT treatments. In viral vector production, scale-up is a critical task due to the limited scalability of traditional laboratory systems and the demand for high volumes of viral vector manufactured in accordance with current good manufacturing practice. Ex vivo cell processing methods require optimisation and automation before they can be scaled out, and several other manufacturing challenges are prevalent such as high levels of raw material and process variability, difficulty characterising complex materials, and a lack of knowledge of critical process parameters and their effect on critical quality attributes of the viral vector and cell drug products. Multivariate data analysis (MVDA) has been leveraged successfully in a variety of applications in the chemical and biochemical industries, including for tasks such as bioprocess monitoring, identification of critical process parameters and assessment of process variability and comparability during process development, scale-up and technology transfer. Henceforth, MVDA is reviewed here as a suitable tool for tackling some of the challenges faced in the development of CGT manufacturing processes. A summary of some key CGT manufacturing challenges is provided along with a review of MVDA applications to mammalian and microbial processes, and an exploration of the potential benefits, requirements and pre-requisites of MVDA applications in the development of CGT manufacturing processes.

Multivariate Data Analysis Methodology to Solve Data Challenges Related to Scale-Up Model Validation and Missing Data on a Micro-Bioreactor System.

Multivariate data analysis (MVDA) is a highly valuable and significantly underutilized resource in biomanufacturing. It offers the opportunity to enhance understanding and leverage useful information from complex high-dimensional data sets, recorded throughout all stages of therapeutic drug manufacture. To help standardize the application and promote this resource within the biopharmaceutical industry, this paper outlines a novel MVDA methodology describing the necessary steps for efficient and effective data analysis. The MVDA methodology is followed to solve two case studies: a "small data" and a "big data" challenge. In the "small data" example, a large-scale data set is compared to data from a scale-down model. This methodology enables a new quantitative metric for equivalence to be established by combining a two one-sided test with principal component analysis. In the "big data" example, this methodology enables accurate predictions of critical missing data essential to a cloning study performed in the ambr15 system. These predictions are generated by exploiting the underlying relationship between the off-line missing values and the on-line measurements through the generation of a partial least squares model. In summary, the proposed MVDA methodology highlights the importance of data pre-processing, restructuring, and visualization during data analytics to solve complex biopharmaceutical challenges.

Cross-Interaction Chromatography-Based QSAR Model for Early-Stage Screening to Facilitate Enhanced Developability of Monoclonal Antibody Therapeutics.

Monoclonal antibodies (mAbs) constitute a rapidly growing biopharmaceutical sector. However, their growth is impeded by developability issues such as polyspecificity and lack of solubility, which leads to attrition as well as manufacturing failures. In this study a multitool hybrid quantitative structure-activity relationship (QSAR) model development framework is described. This framework uses four novel datasets derived from the primary sequences of IgG1-κ-humanized mAbs with varying degrees of resolutions. Unsupervised pattern recognition is first performed on the descriptor sets to visualize any intrinsic property-based clustering, followed by regression of descriptors against cross-interaction chromatography (CIC) retention times. Model optimization is performed via unsupervised variable reduction followed by supervised variable selection. Finally, the models and datasets are benchmarked based on the regression model performance metrics such as R2 , Q2 , and RMSE. The results show that datasets containing localized descriptors rather than averaged value over the entire protein have better predictive performance of CIC retention behavior with R2 > 0.8 and RMSE < 0.3. Furthermore, the results indicate the physicochemical, electronic, and topological properties of hypervariable regions of antibodies that contribute most to the CIC retention times. The results of these studies could contribute to early-stage screening and better design of mAbs.

Toward biotherapeutic product real-time quality monitoring.

Biotherapeutics, such as those derived from monoclonal antibodies (mAbs), are industrially produced in controlled multiunit operation bioprocesses. Each unit operation contributes to the final characteristics of the bioproduct. The complexity of the bioprocesses, the cellular machinery, and the bioproduct molecules, typically leads to inherent heterogeneity and variability of the final critical quality attributes (CQAs). In order to improve process control and increase product quality assurance, online and real-time monitoring of product CQAs is most relevant. In this review, the recent advances in CQAs monitoring of biotherapeutic drugs, with emphasis on mAbs, and throughout, the different bioprocess unit operations are reviewed. Recent analytical techniques used for assessment of product-related CQAs of mAbs are considered in light of the analytical speed and ability to measure different CQAs. Furthermore, the state of art modeling approaches for CQA estimation in real-time are presented as a viable alternative for real-time bioproduct CQA monitoring under the process analytical technology and quality-by-design frameworks in the biopharmaceutical industry, which have recently been demonstrated.

Assessment of hepatotoxicity and dermal toxicity of butyl paraben and methyl paraben using HepG2 and HDFn in vitro models.

Parabens, esters of parahydroxybenzoic acid, are widely used in cosmetic, food and pharmaceutical industries mainly for their antibacterial and fungicidal properties. Methyl paraben has shown very low toxicity in a wide range of in vitro and animal tests. However, butyl paraben and derivatives, such as isobutyl parabens, are classified as allergens and have been shown to induce toxic effects. In the present study the effects of exposure to methyl or butyl paraben (5-1000 µM) on cytotoxicity, oxidative stress, mitochondrial dysfunction and genotoxicity were measured in a hepatocarcinoma cell line (HepG2) and human dermal fibroblasts neonatal (HDFn). Butyl paraben caused a concentration dependent decrease (above 400 µM) in cell viability for both cell lines. Toxicity of butyl paraben observed appeared to be mediated via ATP depletion as seen from luminescence assays. Depletion of glutathione was also observed for higher concentrations of butyl paraben, which may indicate the involvement of oxidative stress. Methyl paraben, however, did not show any significant decrease in cell viability, reduction in ATP or glutathione levels in HepG2 and HDFn cell lines at the concentrations tested. In vitro studies based on human cell lines can provide information in the early stages of multitier paraben toxicity studies and can be combined with in vivo and ex vivo studies to build more comprehensive, scientifically sound strategies for paraben safety testing. The results obtained in this study could supplement existing in vivo toxicity data for defining more robust limits for human exposure.

Scale-Down Model Development in ambr systems: An Industrial Perspective.

High-Throughput (HT) technologies such as miniature bioreactors (MBRs) are increasingly employed within the biopharmaceutical manufacturing industry. Traditionally, these technologies have been utilized for discrete screening approaches during pre-clinical development (e.g., cell line selection and process optimization). However, increasing interest is focused towards their use during late clinical phase process characterization studies as a scale-down model (SDM) of the cGMP manufacturing process. In this review, the authors describe a systematic approach toward SDM development in one of the most widely adopted MBRs, the ambr 15 and 250 mL (Sartorius Stedim Biotech) systems. Recent efforts have shown promise in qualifying ambr systems as SDMs to support more efficient, robust and safe biomanufacturing processes. The authors suggest that combinatorial improvements in process understanding (matching of mass transfer and cellular stress between scales through computational fluid dynamics and in vitro analysis), experimental design (advanced risk assessment and statistical design of experiments), and data analysis (combining uni- and multi-variate techniques) will ultimately yield ambr SDMs applicable for future regulatory submissions.

Exploiting mAb structure characteristics for a directed QbD implementation in early process development.

In today's biopharmaceutical industries, the lead time to develop and produce a new monoclonal antibody takes years before it can be launched commercially. The reasons lie in the complexity of the monoclonal antibodies and the need for high product quality to ensure clinical safety which has a significant impact on the process development time. Frameworks such as quality by design are becoming widely used by the pharmaceutical industries as they introduce a systematic approach for building quality into the product. However, full implementation of quality by design has still not been achieved due to attrition mainly from limited risk assessment of product properties as well as the large number of process factors affecting product quality that needs to be investigated during the process development. This has introduced a need for better methods and tools that can be used for early risk assessment and predictions of critical product properties and process factors to enhance process development and reduce costs. In this review, we investigate how the quantitative structure-activity relationships framework can be applied to an existing process development framework such as quality by design in order to increase product understanding based on the protein structure of monoclonal antibodies. Compared to quality by design, where the effect of process parameters on the drug product are explored, quantitative structure-activity relationships gives a reversed perspective which investigates how the protein structure can affect the performance in different unit operations. This provides valuable information that can be used during the early process development of new drug products where limited process understanding is available. Thus, quantitative structure-activity relationships methodology is explored and explained in detail and we investigate the means of directly linking the structural properties of monoclonal antibodies to process data. The resulting information as a decision tool can help to enhance the risk assessment to better aid process development and thereby overcome some of the limitations and challenges present in QbD implementation today.

Applicability of Traditional In Vitro Toxicity Tests for Assessing Adverse Effects of Monoclonal Antibodies: A Case Study of Rituximab and Trastuzumab.

Monoclonal antibody (mAb) therapeutics have a promising outlook within the pharmaceutical industry having made positive strides in both research and development as well as commercialisation, however this development has been hampered by manufacturing failures and attrition. This study explores the applicability of traditional in vitro toxicity tests for detecting any off-target adverse effect elicited by mAbs on specific organ systems using hepatocarcinoma cell line (HepG2) and human dermal fibroblasts neonatal (HDFn), respectively. The mechanism of antibody dependent cytotoxicity (ADCC), complement dependent cytotoxicity (CDC) via complement activation, and complement dependent cellular cytotoxicity (CDCC) were assessed. Major results: no apparent ADCC, CDCC, or CDC mediated decrease in cell viability was measured for HepG2 cells. For HDFn cells, though ADCC or CDCC mediated decreases in cell viability wasn't detected, a CDC mediated decrease in cell viability was observed. Several considerations have been elucidated for development of in vitro assays better suited to detect off target toxicity of mAbs.

Editorial: The European Symposium on Biochemical Engineering Sciences, Dublin 2016.

The European Symposium on Biochemical Engineering Sciences, Dublin 2016.

Applicability of predictive toxicology methods for monoclonal antibody therapeutics: status Quo and scope.

Biopharmaceuticals, monoclonal antibody (mAb)-based therapeutics in particular, have positively impacted millions of lives. MAbs and related therapeutics are highly desirable from a biopharmaceutical perspective as they are highly target specific and well tolerated within the human system. Nevertheless, several mAbs have been discontinued or withdrawn based either on their inability to demonstrate efficacy and/or due to adverse effects. Approved monoclonal antibodies and derived therapeutics have been associated with adverse effects such as immunogenicity, cytokine release syndrome, progressive multifocal leukoencephalopathy, intravascular haemolysis, cardiac arrhythmias, abnormal liver function, gastrointestinal perforation, bronchospasm, intraocular inflammation, urticaria, nephritis, neuropathy, birth defects, fever and cough to name a few. The advances made in this field are also impeded by a lack of progress in bioprocess development strategies as well as increasing costs owing to attrition, wherein the lack of efficacy and safety accounts for nearly 60 % of all factors contributing to attrition. This reiterates the need for smarter preclinical development using quality by design-based approaches encompassing carefully designed predictive models during early stages of drug development. Different in vitro and in silico methods are extensively used for predicting biological activity as well as toxicity during small molecule drug development; however, their full potential has not been utilized for biological drug development. The scope of in vitro and in silico tools in early developmental stages of monoclonal antibody-based therapeutics production and how it contributes to lower attrition rates leading to faster development of potential drug candidates has been evaluated. The applicability of computational toxicology approaches in this context as well as the pitfalls and promises of extending such techniques to biopharmaceutical development has been highlighted.

Effect of inorganic salts on bacterial omega-3 PUFA production.

The increasing demand of omega-3 in the market and the challenges facing its conventional supplies led to an increasing interest to microbial omega-3 sources. This research concentrates on the statistical role of some metal ions on the biosynthesis and productivity of eicosapentaenoic acid (essential omega-3 element) in bacterial isolate, Shewanella 717. A Plackett-Burman design was applied to screen the main effect of all metal salts entrenched in the artificial sea water medium components. Four salts, in particular, in addition to the interaction among them were highlighted as having a statistically significant effect upon the growth and/or eicosapentaenoic acid production. A subsequent central composite design was performed to determine the exact optimum concentration of each of the chosen variables which was found to be 2.5, 1.8, 1.2, and 23 g/l, for Na2HPO4, MgSO4, KCl, and NaCl, respectively. All the experiments were performed with the minimal amount of carbon and nitrogen to eliminate any potential masking effect. A bioreactor batch run was operated and the ion uptake was monitored, using EDAX® electron microscopy, concluding that the process of microbial omega-3 production could be a phosphate-limited process. Optimizing the concentration of the tested metal ions led to a remarkable increase in the omega-3 productivity resulted in a 30, 9, and 10 times increase in yield, concentration, and percentage to the total fatty acids, respectively, even though the carbon and nitrogen were kept constant all over the research work.

Hybrid modeling for quality by design and PAT-benefits and challenges of applications in biopharmaceutical industry.

This report highlights the drivers, challenges, and enablers of the hybrid modeling applications in biopharmaceutical industry. It is a summary of an expert panel discussion of European academics and industrialists with relevant scientific and engineering backgrounds. Hybrid modeling is viewed in its broader sense, namely as the integration of different knowledge sources in form of parametric and nonparametric models into a hybrid semi-parametric model, for instance the integration of fundamental and data-driven models. A brief description of the current state-of-the-art and industrial uptake of the methodology is provided. The report concludes with a number of recommendations to facilitate further developments and a wider industrial application of this modeling approach. These recommendations are limited to further exploiting the benefits of this methodology within process analytical technology (PAT) applications in biopharmaceutical industry.