Machine Learning and Chemical Imaging to Elucidate Enzyme Immobilization for Biocatalysis

Machine Learning for Deconvolution and Segmentation of Hyperspectral Imaging Data from Biopharmaceutical Resins

Biopharmaceutical resins are pivotal inert matrices used across industry and academia, playing crucial roles in a myriad of applications. For biopharmaceutical process research and development applications, a deep understanding of the physical and chemical properties of the resin itself is frequently required, including for drug purification, drug delivery, and immobilized biocatalysis. Nevertheless, the prevailing methodologies currently employed for elucidating these important aspects of biopharmaceutical resins are often lacking, frequently require significant sample alteration, are destructive or ionizing in nature, and may not adequately provide representative information. In this work, we propose the use of unsupervised machine learning technologies, in the form of both non-negative matrix factorization (NMF) and k-means segmentation, in conjugation with Raman hyperspectral imaging to rapidly elucidate the molecular and spatial properties of biopharmaceutical resins. Leveraging our proposed technology, we offer a new approach to comprehensively understanding important resin-based systems for application across biopharmaceuticals and beyond. Specifically, focusing herein on a representative resin widely utilized across the industry (i.e., Immobead 150P), our findings showcase the ability of our machine learning-based technology to molecularly identify and spatially resolve all chemical species present. Further, we offer a comprehensive evaluation of optimal excitation for hyperspectral imaging data collection, demonstrating results across 532, 638, and 785 nm excitation. In all cases, our proposed technology deconvoluted, both spatially and spectrally, resin and glass substrates via NMF. After NMF deconvolution, image segmentation was also successfully accomplished in all data sets via k-means clustering. To the best of our knowledge, this is the first report utilizing the combination of two unsupervised machine learning methodologies, combining NMF and k-means, for the rapid deconvolution and segmentation of biopharmaceutical resins. As such, we offer a powerful new data-rich experimentation tool for application across multidisciplinary fields for a deeper understanding of resins.

Machine Learning and Hyperspectral Imaging for Analysis of Human Papillomaviruses (HPV) Vaccine Self-Healing Particles

Human papillomaviruses (HPV) are known to cause a variety of diseases, including cervical cancer and genital warts. HPV is a highly prevalent virus and is considered the most common sexually transmitted disease. Because of the risks associated with HPV, Gardasil, a quadrivalent recombinant vaccine, was developed by Merck & Co., Inc., Rahway, NJ, USA, and approved by the Food and Drug Administration (FDA) in 2006. The second generation of the vaccine, Gardasil9, was subsequently approved by the FDA in 2014, providing significant protection against HPV. The HPV vaccine may be given as 2 or 3 doses; however, vaccine administration as a single dose with a sustained release mechanism may potentially offer benefits to meet emerging health needs. To explore this, HPV vaccines were formulated within microporous self-healing particles (SHPs) to enable potential controlled release of HPV virus-like particle (VLP) antigen. Machine learning, in the form of multivariate curve resolution-alternating least-squares (MCR-ALS), with Raman hyperspectral imaging was used to determine the molecular identity and spatial distribution of all relevant species within this HPV vaccine formulation. The results indicate that machine learning with Raman hyperspectral imaging was able to spatially resolve HPV VLP antigens within SHP vaccines for the first time, providing crucial information necessary for vaccine development.

When nanozymes meet enzyme: Unlocking the dual-activity potential of integrated biocomposites

Integrating enzymes and nanozymes in various applications is a topic of significant interest. The researchers have explored the encapsulation of enzymes using diverse nanostructures to create nanomaterial-enzyme hybrids. These nanomaterials introduce unique properties that contribute to the additional activity along with the stabilization of enzymes in immobilized form, enabling a cascade of second-order reactions. This review centers on dual-activity nanozymes, providing insights into their applications in biosensors and biocatalysis. These applications leverage the enhanced catalytic activity and stability offered by dual-activity nanozymes. These nanozymes find promising applications in fields like bioremediation, offering eco-friendly solutions for mitigating environmental pollution while showing potential in medical diagnostics. The review delves into various techniques for creating enzyme-nanozyme hybrid catalysts, including adsorption, encapsulation, and incorporation methods. The review also addresses the challenges that must be overcome, such as overlapping catalytic surfaces and disparities in reaction rates in multi-enzyme cascade reactions. It concludes by presenting strategies to tackle these issues and offers insights into the field's promising future, suggesting that machine learning may drive further advancements in enzyme-nanozyme integration. This comprehensive exploration illuminates the present and charts a promising course for future innovations in the seamless integration of enzymes and nanozymes, heralding a new era of catalytic possibilities.

In situ process analytical technology for real time viable cell density and cell viability during live-virus vaccine production

Viable cell density (VCD) and cell viability (CV) are key performance indicators of cell culture processes in biopharmaceutical production of biologics and vaccines. Traditional methods for monitoring VCD and CV involve offline cell counting assays that are both labor intensive and prone to high variability, resulting in sparse sampling and uncertainty in the obtained data. Process analytical technology (PAT) approaches offer a means to address these challenges. Specifically, in situ probe-based measurements of dielectric spectroscopy (also commonly known as capacitance) can characterize VCD and CV continuously in real time throughout an entire process, enabling robust process characterization. In this work, we propose in situ dielectric spectroscopy as a PAT tool for real time analysis of live-virus vaccine (LVV) production. Dielectric spectroscopy was collected across 25 discreet frequencies, offering a thorough evaluation of the proposed technology. Correlation of this PAT methodology to traditional offline cell counting assays was performed, in which VCD and CV were both successfully predicted using dielectric spectroscopy. Both univariate and multivariate data analysis approaches were evaluated for their potential to establish correlation between the in situ dielectric spectroscopy and offline measurements. Univariate analysis strategies are presented for optimal single frequency selection. Multivariate analysis, in the form of partial least squares (PLS) regression, produced significantly higher correlations between dielectric spectroscopy and offline VCD and CV data, as compared to univariate analysis. Specifically, by leveraging multivariate analysis of dielectric information from all 25 spectroscopic frequencies measured, PLS models performed significantly better than univariate models. This is particularly evident during cell death, where tracking VCD and CV have historically presented the greatest challenge. The results of this work demonstrate the potential of both single and multiple frequency dielectric spectroscopy measurements for enabling robust LVV process characterization, suggesting that broader application of in situ dielectric spectroscopy as a PAT tool in LVV processes can provide significantly improved process understanding. To the best of our knowledge, this is the first report of in situ dielectric spectroscopy with multivariate analysis to successfully predict VCD and CV in real time during live virus-based vaccine production.

Modernized Machine Learning Approach to Illuminate Enzyme Immobilization for Biocatalysis

Biocatalysis is an established technology with significant application in the pharmaceutical industry. Immobilization of enzymes offers significant benefits for commercial and practical purposes to enhance the stability and recyclability of biocatalysts. Determination of the spatial and chemical distributions of immobilized enzymes on solid support materials is essential for an optimal catalytic performance. However, current analytical methodologies often fall short of rapidly identifying and characterizing immobilized enzyme systems. Herein, we present a new analytical methodology that combines non-negative matrix factorization (NMF)-an unsupervised machine learning tool-with Raman hyperspectral imaging to simultaneously resolve the spatial and spectral characteristics of all individual species involved in enzyme immobilization. Our novel approach facilitates the determination of the optimal NMF model using new data-driven, quantitative selection criteria that fully resolve all chemical species present, offering a robust methodology for analyzing immobilized enzymes. Specifically, we demonstrate the ability of NMF with Raman hyperspectral imaging to resolve the spatial and spectral profiles of an engineered pantothenate kinase immobilized on two different commercial microporous resins. Our results demonstrate that this approach can accurately identify and spatially resolve all species within this enzyme immobilization process. To the best of our knowledge, this is the first report of NMF within hyperspectral imaging for enzyme immobilization analysis, and as such, our methodology can now provide a new powerful tool to streamline biocatalytic process development within the pharmaceutical industry.

Machine Learning for Prediction, Classification, and Identification of Immobilized Enzymes for Biocatalysis

BACKGROUND

Enzyme immobilization is a beneficial component involved in biocatalytic strategies. Understanding and evaluating the enzyme immobilization system plays an important role in the successful development and implementation of the biocatalysis route. Ensuring the implementation of a successful enzyme immobilization process is vital for realizing a highly functioning and well suited biocatalytic process within pharmaceutical development.

AIM

To develop a method which can accurately and objectively identify and classify differences within enzyme immobilization systems, sample preparation methods, and data collection parameters.

METHODS

Raman hyperspectral imaging was used to obtain a total of eight spectral data sets from enzyme immobilization samples. Partial least squares discriminant analysis (PLS-DA) was used to classify and identify the samples based on their differences.

RESULTS

Several two-class, four-class, and eight-class PLS-DA models were built to classify the different sample data sets. All models reached between 92-100% accuracy after cross-validation and external validation, illustrating great success of the models for identifying differences between the samples.

CONCLUSION

Raman hyperspectral imaging with machine learning can be used to investigate, interpret, and classify different data collection parameters, sample preparation methods, and enzyme immobilization supports, providing crucial insight into enzyme immobilization process development.

Research progress of engineering microbial cell factories for pigment production

Pigments are widely used in people's daily life, such as food additives, cosmetics, pharmaceuticals, textiles, etc. In recent years, the natural pigments produced by microorganisms have attracted increased attention because these processes cannot be affected by seasons like the plant extraction methods, and can also avoid the environmental pollution problems caused by chemical synthesis. Synthetic biology and metabolic engineering have been used to construct and optimize metabolic pathways for production of natural pigments in cellular factories. Building microbial cell factories for synthesis of natural pigments has many advantages, including well-defined genetic background of the strains, high-density and rapid culture of cells, etc. Until now, the technical means about engineering microbial cell factories for pigment production and metabolic regulation processes have not been systematically analyzed and summarized. Therefore, the studies about construction, modification and regulation of synthetic pathways for microbial synthesis of pigments in recent years have been reviewed, aiming to provide an up-to-date summary of engineering strategies for microbial synthesis of natural pigments including carotenoids, melanins, riboflavins, azomycetes and quinones. This review should provide new ideas for further improving microbial production of natural pigments in the future.

Putting precision and elegance in enzyme immobilisation with bio-orthogonal chemistry

The covalent immobilisation of enzymes generally involves the use of highly reactive crosslinkers, such as glutaraldehyde, to couple enzyme molecules to each other or to carriers through, for example, the free amino groups of lysine residues, on the enzyme surface. Unfortunately, such methods suffer from a lack of precision. Random formation of covalent linkages with reactive functional groups in the enzyme leads to disruption of the three dimensional structure and accompanying activity losses. This review focuses on recent advances in the use of bio-orthogonal chemistry in conjunction with rec-DNA to affect highly precise immobilisation of enzymes. In this way, cost-effective combination of production, purification and immobilisation of an enzyme is achieved, in a single unit operation with a high degree of precision. Various bio-orthogonal techniques for putting this precision and elegance into enzyme immobilisation are elaborated. These include, for example, fusing (grafting) peptide or protein tags to the target enzyme that enable its immobilisation in cell lysate or incorporating non-standard amino acids that enable the application of bio-orthogonal chemistry.

In situ Raman spectroscopy for real time detection of cysteine

Cysteine serves a wide range of important biological and chemical functions and may have an association to neurodegenerative disease and cancer. Rapid, accurate analytical methods for cysteine detection are thus highly desirable. In this work, we report an investigation into the utility of in situ Raman spectroscopy as a Process Analytical Technology (PAT) for real time monitoring of cysteine. Cysteine concentrations are tracked in real time using Raman spectroscopy across a range of pharmaceutically-relevant concentrations, demonstrating the capability of Raman spectroscopy detection for in situ cysteine monitoring. The concentration range over which this analytical methodology can be applied is successfully established. As such, the results herein serve as a proof-of-principle investigation to demonstrate and evaluate the capabilities of a real time Raman spectroscopic approach for in situ cysteine detection, thus informing the range of important chemical and biological processes to which this approach can be applied. To the best of our knowledge, this is the first report of in situ Raman spectroscopy for real time monitoring of dynamically changing cysteine process concentrations.

Bioprospecting of microbial enzymes: current trends in industry and healthcare

Microbial enzymes have an indispensable role in producing foods, pharmaceuticals, and other commercial goods. Many novel enzymes have been reported from all domains of life, such as plants, microbes, and animals. Nonetheless, industrially desirable enzymes of microbial origin are limited. This review article discusses the classifications, applications, sources, and challenges of most demanded industrial enzymes such as pectinases, cellulase, lipase, and protease. In addition, the production of novel enzymes through protein engineering technologies such as directed evolution, rational, and de novo design, for the improvement of existing industrial enzymes is also explored. We have also explored the role of metagenomics, nanotechnology, OMICs, and machine learning approaches in the bioprospecting of novel enzymes. Overall, this review covers the basics of biocatalysts in industrial and healthcare applications and provides an overview of existing microbial enzyme optimization tools. KEY POINTS: • Microbial bioactive molecules are vital for therapeutic and industrial applications. • High-throughput OMIC is the most proficient approach for novel enzyme discovery. • Comprehensive databases and efficient machine learning models are the need of the hour to fast forward de novo enzyme design and discovery.

Process monitoring of polysaccharide deketalization for vaccine bioconjugation development using in situ analytical methodology

Pneumococcal conjugate vaccines (PCVs) are formed by bioconjugation of a carrier protein to the purified capsular polysaccharide (Ps) from multiple serological strains of Streptococcus pneumoniae. The associated bioconjugation chemistry relies on initial selective modifications to the Ps backbone structure. Among these modifications, removal of a ketal functional group, termed deketalization, is one that is important for pharmaceutical PCV production. Herein, we report a process monitoring investigation into the deketalization of a polysaccharide relevant to PCV process development. We have applied process analytical technology (PAT) for in situ process monitoring to study the deketalization reaction in real time. We find that in situ FTIR spectroscopy elucidates multiple classes of reaction kinetics, one of which correlates strongly with the deketalization reaction of interest. This PAT approach to real time reaction monitoring offers the possibility of improved process monitoring in the pharmaceutical production of PCVs. To our knowledge, this report represents the first PAT investigation into Ps deketalization. Our findings suggest that broader application of PAT to the chemical modifications associated with PCV bioconjugation, as well as other pharmaceutically relevant bioconjugation processes, carries the power to enhance process understanding, control, and efficiency through real time process monitoring.