Biopharmaceutical Process Research Articles

Qualification of Mechanistic Models in Biopharmaceutical Process Development

Mechanistic process models play an increasingly important role in biopharmaceutical process development and manufacturing in supporting process design, characterization, and informing process control strategies. Despite the potential of mechanistic models, there is currently no clear consensus or regulatory guideline on their qualification, i.e. the processes of determining whether a model is suitable to support decision making in process development. In this work, a systematic and risk-based qualification framework for mechanistic models in biopharmaceutical process development is introduced. The framework integrates key concepts from other modeling frameworks and guidelines such as the ASME V&V 40 published by the American Society of Mechanical Engineers (ASME) and preliminary considerations in process models published by Quality Innovation Group (QIG) of the European Medicines Agency (EMA). Key concepts of the proposed framework are discussed using two case studies, including a model-informed optimization of a biopharmaceutical ultrafiltration and diafiltration process and a model-informed control strategy of a chromatography polishing step. The suggested framework can act as a foundation for dialogue and guide for other modelers in biopharmaceutical process development. It holds the capability to harmonize modeling procedures throughout the industry and establish an agreement on the qualification of mechanistic models in biopharmaceutical process development.

Optimisation of Additively Manufactured Coiled Flow Inverters for Continuous Viral Inactivation Processes

This article reports the development and utilisation of an adaptive design workflow methodology for use as a platform technology for the printing, testing, and optimisation of biopharmaceutical processing reactors. This design strategy was developed by application to the complex structure of the coiled flow inverter (CFI). In this way, the many possible physical parameters of the reactor were optimised, via a combination of experimental results, computational fluid dynamics and machine learning approaches, to find the CFI setups that provide the optimal flow properties for a particular application.Additively manufactured reactors are seeing increasing interest in the field of biopharmaceutical production. This is because the desired output volumes are typically small and there is an increasing move towards adopting continuous production, to replace traditional batch production. This approach allows for the tailoring of reactors for a specific reaction, i.e. attempting to maximise the desired aspects of the reaction through refinement of the physical parameters of the reactor, so creating a large possible parameter space to explore.Consequently, the holistic optimisation of CFI reactors and 3D printing is established as providing better plug flow mixing relative to traditional tube coiled reactors. In addition, a trained metamodel in combination with multilayer equations is demonstrated to predict reactor performance quickly and accurately.

Monitoring small-scale bioreactor studies for media development using polarized total synchronous fluorescence spectroscopy (pTSFS) and synchronous light scattering (SyLS)

Biopharmaceutical process development often involves the use of small-scale bioreactors (SSBR) for optimizing media formulations and process conditions during scale up to commercial scale production. Two key process parameters (CPP) used in SSBR studies are protein titre and viable cell density (VCD). Here, we explore the efficacy of parallel polarized total synchronous fluorescence spectroscopy (TSFS||) and Synchronous Light Scattering (SyLS||) to qualitatively monitor these CPPs and quantitatively predict titre and VCD for a large-scale cell culture media optimization SSBR study. The study involved 71 different media formulations (50+ components each), and the bioprocess was run for 13 days or more. Samples were extracted at set times (Day 0, 3, 9, and 13) and clarified by centrifugation. TSFS|| spectra showed significant emission changes along with increased light scatter over the course of the bioprocess. SyLS|| measurements strongly correlated with particle size data obtained from Dynamic Light Scattering but did not correlate well with VCD probably because of the centrifugation-based sample preparation. Statistical and principal component analysis (PCA) of the pTSFS data showed that spectral variation was greater between media formulations than due to the evolving bioprocess. This prevented the development of accurate global prediction models for media performance (e.g., predicting product titre at day 9 from media spectra measured at day 0). However, classification methods were successfully used to select media subsets with better quantitative prediction accuracy based on spectral similarities. A practical binary (high/low performance) classification model based on Support Vector Machines was generated for media formulation screening. Combining emission and scatter measurements with multivariate data analysis provides a more holistic, multi-attribute bioprocess monitoring method that minimizes the need to use different offline analytical methods. This methodology can be used to monitor process trajectories and deviations, and ultimately be used to predict bioprocess CPPs when implemented on production scale processes where there is much less compositional variation in the media. We believe this SSBR-pTSFS/SyLS approach will provide a valuable resource to develop the design/parameter space for in-process monitoring at production scale from early-stage process/media development studies.

Explainable Machine Learning Models to Predict Gibbs-Donnan Effect During Ultrafiltration and Diafiltration of High-Concentration Monoclonal Antibody Formulations.

Evaluating the Gibbs-Donnan and volume exclusion effects during protein ultrafiltration and diafiltration (UF/DF) is crucial in biopharmaceutical process development to precisely control the concentration of the drug substance in the final formulation. Understanding the interactions between formulation excipients and proteins under these conditions requires a domain-specific knowledge of molecular-level phenomena. This study developed gradient boosted tree models to predict the Gibbs-Donnan and volume exclusion effects for amino acids and therapeutic monoclonal antibodies using simple molecular descriptors. The models' predictions were interpreted by information gain and Shapley additive explanation (SHAP) values to understand the modes of action of the antibodies and excipients and to validate the models. The results translated feature effects in machine learning to real-world molecular interactions, which were cross-referenced with existing scientific literature for verification. The models were validated in pilot-scale manufacturing runs of two antibody products requiring high levels of concentration. By only requiring a molecule's biophysicochemical descriptors and process conditions, the proposed models provide an in silico alternative to conventional UF/DF experiments to accelerate process development and boost process understanding of the underlying molecular mechanisms through rational interpretation of the models.

Natural Binary Herbal Small Molecules Self-Assembled Nanogel for Synergistic Inhibition of Respiratory Syncytial Virus.

Respiratory syncytial virus (RSV) is one of the most significant pathogenic infections in childhood, associated with high morbidity and mortality rates. Currently, there is no effective and safe drug or vaccine available for RSV. Glycyrrhizic acid (GA), an active compound derived from the natural herb licorice, has been reported to provide protection against influenza and coronaviruses, exhibiting notable antiviral and anti-inflammatory properties. Ephedrine (EPH) is a commonly prescribed medication for the treatment of cough and asthma, and it also demonstrates certain antiviral effects. In this study, EPH and GA were combined to form an efficient nanomaterial (EPH-GA nanogel). The self-assembly of this nanogel is driven by hydrogen bonding and hydrophobic interactions, allowing it to serve as an antiviral nanomedicine without the need for a dual-component carrier, achieving a 100% drug loading efficiency. Oral administration of the EPH-GA nanogel significantly reduced viral load in the lungs of mice and improved lung lesions and tissue infiltration caused by RSV. Notably, we discovered that the assembled drug may create a "physical barrier" that prevents RSV from adsorbing to host cells, while free GA and EPH may compete with RSV for protein binding sites, thereby enhancing cellular uptake of EPH. Consequently, this prevents RSV infection and proliferation within host cells. Furthermore, the EC50 values changed from 310.83 μM for EPH and 262.88 μM for GA to 68.25 μM for the EPH-GA combination, with a combination index of 0.458. In addition, the in vivo biopharmaceutic process of GA and EPH was investigated, revealing that the oral administration of EPH-GA significantly increased the bioavailability of EPH while maintaining its plasma concentration at a relatively stable level. This enhancement may contribute to a synergistic antiviral effect when combined with GA. Furthermore, the in vivo process of EPH-GA demonstrates the advantage of delivering the drug to the lesion at elevated levels, thereby facilitating its antiviral mechanism at the cellular level. In this study, we identified an effective nanomedicine, EPH-GA nanogel, which can inhibit the proliferation of RSV and mitigate lung lesions resulting from viral infection by influencing the biopharmaceutical process in vivo. This research not only offers a novel strategy for the nanomedicine treatment of RSV but also elucidates, to some extent, the compatibility mechanisms of the multicomponents of traditional Chinese medicine.

Clear insight into complex multimodal resins and impurities to overcome recombinant protein purification challenges: A review.

Increasing attention has been paid to the purity of therapeutic proteins imposing extensive costs and challenges to the downstream processing of biopharmaceuticals. One of the efforts, that has been exerted to overcome such limitations, was developing multimodal or mixed-mode chromatography (MMC) resins for launching selective, orthogonal, non-affinity purification platforms. Despite relatively extensive usage of MMC resins, their real potential and fulfillment have not been extensively reviewed yet. In this work, the explanation of practical and key aspects of downstream processing of recombinant proteins with or without MMC resins was debated, as being useful for further purification process development. This review has been written as a step-by-step guide to deconvolute both inherent protein purification and MMC complexities. Here, after complete elucidation of the potential of MMC resins, the effects of frequently used additives (mobile phase modifiers) and their possible interactions during the purification process, the critical characteristics of common product-related impurities (e.g., aggregates, charge variants, fragments), host-related impurities (e.g., host cell protein and DNA) and process related impurities (e.g., endotoxin, and viruses) with solved or unsolved challenges of traditional and MMC resins have been discussed. Such collective experiences which are reported in this study could be considered as an applied guide for developing successful downstream processing in challenging conditions by providing a clear insight into complex MMC resins and impurities.

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.

DiaPASEF Enables High-Throughput Proteomic Analysis of Host Cell Proteins for Biopharmaceutical Process Development.

Monitoring and quantifying host cell proteins (HCPs) in biotherapeutic production processes is crucial to ensure product quality, stability, and safety. Liquid chromatography-mass spectrometry (LC-MS) analysis has emerged as an important tool for identifying and quantifying individual HCPs. However, LC-MS-based approaches face challenges due to the wide dynamic range between HCPs and the therapeutic protein as well as laborious sample preparation and long instrument time. To address these limitations, we evaluated the application of parallel accumulation-serial fragmentation combined with data-independent acquisition (diaPASEF) to HCP analysis for biopharmaceutical process development applications. We evaluated different library generation strategies and LC methods, demonstrating the suitability of these workflows for various HCP analysis needs, such as in-depth characterization and high-throughput analysis of process intermediates. Remarkably, the diaPASEF approach enabled the quantification of hundreds of HCPs that were undetectable by a standard data-dependent acquisition mode while considerably improving sample requirement, throughput, coverage, quantitative precision, and data completeness.

Overcoming the inconsistent cell culture performance caused by single-use bioreactors during scaling-up the manufacturing process for a therapeutic bispecific antibody

Single use technology has been widely used in modern biopharmaceutical process development and manufacturing. However, there are concerns about the adverse effects of disposable materials on bioprocess, product quality and patient safety. Recently, we observed slow cell growth, increased lactate accumulation, and decreased production titer during scaling up a bispecific antibody (BsAb) upstream process from 3 L to 500 L bioreactor. To investigate the phenomenon, we conducted medium incubation and leachable spike-in experiments. We found that cell culture medium incubated in CX5–14 bag caused poor cell culture performance, which was correlated with the concentration of bis(2,4-di-tert-butylphenyl)-phosphate (bDtBPP), a breakdown product of the antioxidant Irgafos®168 in polyethylene film. In addition, when the media were spiked with 0.15 μg/ml or a greater concentration of bDtBPP, slow cell growth and reduced BsAb expression level were observed. In contrast, Aegis5–14 film produced bDtBPP at a level of below detection during medium incubation. Thus, the bispecific-expressing CHO cell line restores normal cell culture performance and production titer when Aegis5–14 single-use bioreactor was employed in large-scale manufacturing. Our results reveal the importance of evaluating leachable profiles from disposable materials during process development of bispecifics and other novel therapeutic modalities.

Real-time multivariate statistical monitoring of biopharmaceutical processes with no prior product-specific history

Biologics drug substance manufacturing normally comprises several processing steps including cell culture, purification, etc. Real-time Multivariate Statistical Process Monitoring (RT-MSPM) is an integral need in order to achieve early fault detection and prevention to guarantee robust and efficient process performance. As a part of RT-MSPM framework, Batch Evolution Models (BEMs), which take into account the transient phase of a batch prior to reaching full maturity (steady state), are the standard methods for real-time early fault detection in batch processes. Prior product-specific knowledge and training dataset are critical in order to model time (batch maturity) dependencies. Unlike traditional settings, modern biopharmaceutical manufacturing and clinical facilities use modular designs to enable simultaneous production of multiple medicines and quickly pivot from producing one medicine to another where no prior product-specific historical data might be available. In this article, first, a framework is proposed to detect the steady state operation of a manufacturing scale cell culture bioreactor. This is critical to achieve the ultimate goal to demonstrate feasibility of developing a continuous statistical process monitoring framework to monitor the steady state operation of a batch with no prior product specific history. Steady state time series data from previous step are processed following a systematic approach. The requirements for statistical properties of the processed time series in order to leverage data from different products to monitor a new product are outlined. The new framework establishes a guideline to develop models using datasets from other products with different recipes to monitor new products with no prior product-specific history. A novel software product is developed to test the framework. The framework is successfully applied for real-time monitoring of fast-rate sensors of a manufacturing cell culture bioreactor.

Improved scheduling model for a multistage biopharmaceutical process in a multiproduct facility

This study critically evaluates deterministic and stochastic/evolutionary strategies for long-term scheduling of multiproduct facilities in batch and continuous biopharmaceutical manufacturing, as reported in existing literature using discrete-time and continuous-time models. It identifies key limitations in literature model/algorithm, including (i) real-time storage violation, (ii) real-time violation of shelf-life due to its calculation over a single event, (iii) minimum campaign length calculated over single events leading to suboptimal use of resources, and (iv) early product deliveries. To overcome these limitations, a deterministic mathematical model is proposed extending the existing unit-specific event-based model(s) with additional features including (i) modified material balances, (ii) new minimum campaign length constraints captured over multiple events, (iii) new shelf-life constraints captured over multiple events, (iv) new setup constraints based on product sequencing, (v) updated tightening constraints, (vi) updated sales and penalty constraints for late deliveries, (vii) updated bounds, and (viii) updated objective function. These improved features lead to better modeling in terms of handling intermediate and final product storage without real-time violations of storage and shelf-life, handling of initial setup in production tasks, and timely delivery of final products. The proposed improved model yields up to 36 % increase in profit when applied on four literature examples.

Dynamic optimization of an integrated cultivation-aggregation model for mAb production.

Due to their proteinaceous structure, monoclonal antibodies (mAbs) are susceptible to irreversible aggregation, with harmful consequences on drug efficacy and patient safety. To mitigate this risk in modern biopharmaceutical processes, it is critical to comply with current good manufacturing practices (cGMP) and pursue operating strategies minimizing irreversible aggregation whilst also maximizing mAb throughput. These conflicting objectives are targeted in this study by formulating and analyzing an integrated dynamic model accounting for both cultivation and aggregation of mAbs from a Chinese Hamster Ovary (CHO) cell line. Two manipulated dynamic variables are considered here in simulation studies: firstly temperature manipulation within a batch reactor, and secondly feed flow manipulation within a series of isothermal fed-batch reactors. Following this, dynamic optimization investigations have been conducted, firstly with the single objective of maximizing mAb throughput and secondly with multiple (two) objectives of maximizing mAb throughput while also minimizing irreversible aggregate content, simultaneously. The study provides key insight into tradeoffs of how simultaneous temperature and feed flowrate manipulation affects mAb throughput and aggregation inside bioreactors.

Induction of heat shock protein expression in SP2/0 transgenic cells and its effect on the production of monoclonal antibodies.

The objective of the current investigation was to evaluate the induction of heat shock proteins (HSPs) in SP2/0 transgenic cells and the effect of these proteins on the production of monoclonal antibodies (mAbs). The SP2/0 cell line expressing the PSG-026 antibody, a biosimilar candidate of golimumab, the culture parameters, and the target protein expression were not justified for industrial production and were used for the experiments. Paracetamol and heat shock were used as chemical and physical inducers of HSPs, respectively. The results showed that paracetamol and heat shock increased the expression of HSP70 and HSP27 at the mRNA and protein levels. The expression of HSPs was greater in paracetamol-treated cells than in heat shock-treated cells. Paracetamol treatment at concentrations above 0.5 mM significantly reduced cell viability and mAb expression. However, treatment with 0.25 mM paracetamol results in delayed cell death and increased mAb production. Heat shock treatment at 45°C for 30 minutes after enhanced mAb expression was applied after pre-treatment with paracetamol. In bioreactor cultures, pretreatment of cells with paracetamol improved cell viability and shortened the lag phase, resulting in increased cell density. The production of mAbs in paracetamol-treated cultures was markedly greater than that in the control. Analysis of protein quality and charge variants revealed no significant differences between paracetamol-treated and control cultures, indicating that the induction of HSPs did not affect protein aggregation or charge variants. These findings suggest that inducing and manipulating HSP expression can be a valuable strategy for improving recombinant protein production in biopharmaceutical processes.

Expediting online liquid chromatography for real-time monitoring of product attributes to advance process analytical technology in downstream processing of biopharmaceuticals

The application of Process Analytical Technology (PAT) principles for manufacturing of biotherapeutics proffers the prospect of ensuring consistent product quality along with increased productivity as well as substantial cost and time savings. Although this paradigm shift from a traditional, rather rigid manufacturing model to a more scientific, risk-based approach has been advocated by health authorities for almost two decades, the practical implementation of PAT in the biopharmaceutical industry is still limited by the lack of fit-for-purpose analytical methods. In this regard, most of the proposed spectroscopic techniques are sufficiently fast but exhibit deficiencies in terms of selectivity and sensitivity, while well-established offline methods, such as (ultra-)high-performance liquid chromatography, are generally considered as too slow for this task. To address these reservations, we introduce here a novel online Liquid Chromatography (LC) setup that was specifically designed to enable real-time monitoring of critical product quality attributes during time-sensitive purification operations in downstream processing. Using this online LC solution in combination with fast, purpose-built analytical methods, sampling cycle times between 1.30 and 2.35 min were achieved, without compromising on the ability to resolve and quantify the product variants of interest. The capabilities of our approach are ultimately assessed in three case studies, involving various biotherapeutic modalities, downstream processes and analytical chromatographic separation modes. Altogether, our results highlight the expansive opportunities of online LC based applications to serve as a PAT tool for biopharmaceutical manufacturing.

Hollow anion-Janus-modified poly(vinylidene fluoride) (PVDF) fiber membranes for nanosized virus removal

In this study, a hollow polyvinylidene fluoride (PVDF) fiber was developed and modified into a Janus membrane (Janus-VM) and an entirely hydrophobic membrane (Hydrophobic-VM) for removing 20-nm-scale viruses during biopharmaceutical processing. The Janus-VM has anionic properties on the outer and feed hydrophilic surfaces, and the inner pore surface (I.P.S) maintains the anionic-hydrophobic properties of the PVDF. Theoretically, an electrostatic repulsion force might be formed between the surface of the strong anionic membrane and anionic bacteriophages, preventing bacteriophages from adsorbing to the membrane surface. As a result, water flux can be increased through the reduction of membrane fouling. The PVDF membrane was modified through UV-irradiation, which causes –OH radicals to form on the membrane surface, and chemical grafting with anionic-hydrophobic/anionic-hydrophilic modifiers, which enhances the membrane’s anion characteristics (−27, −48 mV). Additionally, the average pore size was reduced from 34 nm to less than 20 nm through chemical surface modification, after which the Janus membrane showed an excellent initial water flux, with the protein recovery ratio (PRR) approaching 98.9 %. UV absorbance analysis revealed 100 % removal efficiency for 20- and 40-nm gold particles on the membranes. Furthermore, the initial log reduction value (LRV) was observed through flux performance using MS2 and PP7 bacterial phages, for which the Janus membrane showed an excellent virus removal performance (LRV > 6.8 and 6.5).

AI-based optimal control of fed-batch biopharmaceutical process leveraging deep reinforcement learning

Optimal control of the fed-batch biopharmaceutical process remains an open research and industrial challenge. The fed-batch process is characterized by non-steady operation, partially observable states, and batch uncertainties. Recently, deep reinforcement learning (DRL) has emerged as a powerful tool for chemical process systems. However, directly applying DRL to the simulation-based biopharmaceutical process has been proven a failure due to the mismatch between reinforcement learning algorithms and simulation-based environments. To fill this research gap, we proposed a novel DRL-based control framework that innovatively incorporates human knowledge. A case study of open-source simulator (IndPenSim), is used to verify the effectiveness of the proposed framework. The DRL controller demonstrates advantages in batch yields, computational loads, and measurement requirements compared to the existing control methods. An improvement of 14 % average penicillin yield is achieved with only 0.01∼0.03 s online computation time per step, showing its potential in the control applications for fed-batch biopharmaceutical process.

Generative data augmentation and automated optimization of convolutional neural networks for process monitoring.

Chemometric modeling for spectral data is considered a key technology in biopharmaceutical processing to realize real-time process control and release testing. Machine learning (ML) models have been shown to increase the accuracy of various spectral regression and classification tasks, remove challenging preprocessing steps for spectral data, and promise to improve the transferability of models when compared to commonly applied, linear methods. The training and optimization of ML models require large data sets which are not available in the context of biopharmaceutical processing. Generative methods to extend data sets with realistic in silico samples, so-called data augmentation, may provide the means to alleviate this challenge. In this study, we develop and implement a novel data augmentation method for generating in silico spectral data based on local estimation of pure component profiles for training convolutional neural network (CNN) models using four data sets. We simultaneously tune hyperparameters associated with data augmentation and the neural network architecture using Bayesian optimization. Finally, we compare the optimized CNN models with partial least-squares regression models (PLS) in terms of accuracy, robustness, and interpretability. The proposed data augmentation method is shown to produce highly realistic spectral data by adapting the estimates of the pure component profiles to the sampled concentration regimes. Augmenting CNNs with the in silico spectral data is shown to improve the prediction accuracy for the quantification of monoclonal antibody (mAb) size variants by up to 50% in comparison to single-response PLS models. Bayesian structure optimization suggests that multiple convolutional blocks are beneficial for model accuracy and enable transfer across different data sets. Model-agnostic feature importance methods and synthetic noise perturbation are used to directly compare the optimized CNNs with PLS models. This enables the identification of wavelength regions critical for model performance and suggests increased robustness against Gaussian white noise and wavelength shifts of the CNNs compared to the PLS models.

Parameter-by-parameter estimation method for adsorption isotherm in hydrophobic interaction chromatography

Hydrophobic interaction chromatography (HIC) is used as a critical polishing step in the downstream processing of biopharmaceuticals. Normally the process development of HIC is a cumbersome and time-consuming task, and the mechanical models can provide a powerful tool to characterize the process, assist process design and accelerate process development. However, the current estimation of model parameters relies on the inverse method, which lacks an efficient and logical parameter estimation strategy. In this study, a parameter-by-parameter (PbP) method based on the theoretical derivation and simplifying assumptions was proposed to estimate the Mollerup isotherm parameters for HIC. The method involves three key steps: (1) linear regression (LR) to estimate the salt-protein interaction parameter and the equilibrium constant; (2) linear approximation (LA) to estimate the stoichiometric parameter and the maximum binding capacity; and (3) inverse method to estimate the protein-protein interaction parameter and the kinetic coefficient. The results indicated that the LR step should be used for dilution condition (loading factor below 5%), while the LA step should be conducted when the isotherm is in the transition or nonlinear regions. Six numerical experiments were conducted to implement the PbP method. The results demonstrated that the PbP method developed allows for the systematic estimation of HIC parameters one-by-one, effectively reducing the number of parameters required for inverse method estimation from six to two. This helps prevent non-identifiability of structural parameters. The feasibility of the PbP-HIC method was further validated by real-world experiments. Moreover, the PbP method enhances the mechanistic understanding of adsorption behavior of HIC and shows a promising application to other stoichiometric displacement model-derived isotherms.

Analytical figures of merit of a dual-wavelength absorbance approach for real-time broad protein content monitoring for biomanufacturing

Real-time in-line broad protein content monitoring in biomanufacturing downstream unit operations enables the ability to optimize and afford consistent protein recovery. Protein determination from 2 to 400 mg/mL is demonstrated herein via real-time dual-wavelength LED photometric sensor configured at 280 and 310 nm. The figures of merit of this approach include measurement accuracy within the common acceptance criteria of 100 % ± 5 with negligible bias across the linear dynamic ranges. This work expands the utility of an LED based photometric sensor for biopharmaceutical process analytical technology (PAT) applications. It is also congruent with process digitalization and automation industry 4.0 concepts underpinned by Quality by Design (QbD) principles.

Course Construction of Local Universities in the Era of "Internet + Education": A Case Study of Biopharmaceutical Process

Sichuan University of Science & Engineering (SUSE) is one of the first universities in China to set up biopharmaceutical major, and is also a university in western China. "Internet + education" is an educational development form with the internet as the infrastructure and innovation elements, and is the new normal of education with the deep integration of educational innovation and technological progress. The content and form of traditional offline course education in local universities face the challenge and opportunity of "Internet + education". With "Internet + education" as the background, Sichuan University of Science & Engineering has carried out a series of innovations in course content and teaching mode around professional certification of biopharmaceutical major, made full use of information technology, and flexibly integrated ideological and political elements and network materials into the teaching content. Sichuan University of Science & Engineering has explored a teaching model that breaks through the limitations of time and space, which is efficient and convenient, also has various forms, combining "online + offline". Finally, the paper puts forward the optimization path of professional curriculum education in local universities from the perspective of "Internet + education", hoping to provide references for the reform and innovation of curriculum education in local universities.