Snakebite Management: The Need of Reassessment, International Relations, and Effective Economic Measures to Reduce the Considerable SBE Burden.

The sole treatment for snakebite envenomation (SBE), the anti-snake venom (ASV), suffers from considerable drawbacks, including side effects and limited species specificity. Additionally, despite its existence for more than a century, uniform availability of good quality ASV does not yet exist. The present review describes the journey of a SBE victim and highlights the global crisis of SBE management. A detailed analysis of the current ASV market has also been presented along with the worldwide snake distribution. The current production of country specific licensed ASV throughout the globe along with their manufacturers has been examined at the snake species level. Furthermore, a detailed analysis of on-ground situation of SBE management in antivenom manufacturing countries has been done using the most recent literature. Additionally, the export and import of different ASVs have been discussed in terms of procurement policies of individual countries, their shortcomings, along with the possible solution at the species level. It is interesting to note that in most countries, the existence of ASV is really either neglected or overstated, implying that it is there but unsuitable for use, or that it is not present but can be obtained from other countries. This highlights the urgent need of significant reassessment and international collaborations not just for development and production, but also for procurement, distribution, availability, and awareness. A PROMISE (Practical ROutes for Managing Indigenous Snakebite Envenoming) approach has also been introduced, offering simple, economical, and easy to adopt steps to efficiently alleviate the worldwide SBE burden.

MADR-Net: multi-level attention dilated residual neural network for segmentation of medical images.

Medical image segmentation has made a significant contribution towards delivering affordable healthcare by facilitating the automatic identification of anatomical structures and other regions of interest. Although convolution neural networks have become prominent in the field of medical image segmentation, they suffer from certain limitations. In this study, we present a reliable framework for producing performant outcomes for the segmentation of pathological structures of 2D medical images. Our framework consists of a novel deep learning architecture, called deep multi-level attention dilated residual neural network (MADR-Net), designed to improve the performance of medical image segmentation. MADR-Net uses a U-Net encoder/decoder backbone in combination with multi-level residual blocks and atrous pyramid scene parsing pooling. To improve the segmentation results, channel-spatial attention blocks were added in the skip connection to capture both the global and local features and superseded the bottleneck layer with an ASPP block. Furthermore, we introduce a hybrid loss function that has an excellent convergence property and enhances the performance of the medical image segmentation task. We extensively validated the proposed MADR-Net on four typical yet challenging medical image segmentation tasks: (1) Left ventricle, left atrium, and myocardial wall segmentation from Echocardiogram images in the CAMUS dataset, (2) Skin cancer segmentation from dermoscopy images in ISIC 2017 dataset, (3) Electron microscopy in FIB-SEM dataset, and (4) Fluid attenuated inversion recovery abnormality from MR images in LGG segmentation dataset. The proposed algorithm yielded significant results when compared to state-of-the-art architectures such as U-Net, Residual U-Net, and Attention U-Net. The proposed MADR-Net consistently outperformed the classical U-Net by 5.43%, 3.43%, and 3.92% relative improvement in terms of dice coefficient, respectively, for electron microscopy, dermoscopy, and MRI. The experimental results demonstrate superior performance on single and multi-class datasets and that the proposed MADR-Net can be utilized as a baseline for the assessment of cross-dataset and segmentation tasks.

Current status and future prospective of breast cancer immunotherapy.

The immune system is complicated, interconnected, and offers a powerful defense system that protects its host from foreign pathogens. Immunotherapy involves boosting the immune system to kill cancer cells, and nowadays, is a major emerging treatment for cancer. With the advances in our understanding of the immunology of cancer, there has been an explosion of studies to develop and evaluate therapies that engage the immune system in the fight against cancer. Nevertheless, conventional therapies have been effective in reducing tumor burden and prolonging patient life, but the overall efficacy of these treatment regimens has been somewhat mixed and often with severe side effects. A common reason for this is the activation of molecular mechanisms that lead to apoptosis of anti-tumor effector cells. The competency to block tumor escape entirely depends on our understanding of the cellular and molecular pathways which operate in the tumor microenvironment. Numerous strategies have been developed for activating the immune system to kill tumor cells. Breast cancer is one of the major causes of cancer death in women, and is characterized by complex molecular and cellular events that closely intertwine with the host immune system. In this regard, predictive biomarkers of immunotherapy, use of nanotechnology, personalized cancer vaccines, antibodies to checkpoint inhibitors, engineered chimeric antigen receptor-T cells, and the combination with other therapeutic modalities have transformed cancer therapy and optimized the therapeutic effect. In this chapter, we will offer a holistic view of the different therapeutic modalities and recent advances in immunotherapy. Additionally, we will summarize the recent advances and future prospective of breast cancer immunotherapies, as a case study.

Deep learning framework for peak detection at the intact level of therapeutic proteins.

While automated peak detection functionalities are available in commercially accessible software, achieving optimal true positive rates frequently necessitates visual inspection and manual adjustments. In the initial phase of this study, hetero-variants (glycoforms) of a monoclonal antibody were distinguished using liquid chromatography-mass spectrometry, revealing discernible peaks at the intact level. To comprehensively identify each peak (hetero-variant) in the intact-level analysis, a deep learning approach utilizing convolutional neural networks (CNNs) was employed in the subsequent phase of the study. In the current case study, utilizing conventional software for peak identification, five peaks were detected using a 0.5 threshold, whereas seven peaks were identified using the CNN model. The model exhibited strong performance with a probability area under the curve (AUC) of 0.9949, surpassing that of partial least squares discriminant analysis (PLS-DA) (probability AUC of 0.8041), and locally weighted regression (LWR) (probability AUC of 0.6885) on the data acquired during experimentation in real-time. The AUC of the receiver operating characteristic curve also illustrated the superior performance of the CNN over PLS-DA and LWR.

Machine learning tool as an enabler for rapid quantification of monoclonal antibodies N-glycans using fluorescence detector.

Liquid chromatography-mass spectrometry (LC-MS) is widely used for identification and quantification of N-glycans of monoclonal antibodies (mAbs), owing to its high sensitivity and accuracy. However, its resource-intensive nature necessitates the development of rapid and cost-effective orthogonal analysis approaches. This study aims to develop an online method utilizing the Extreme Gradient Boosting (XGBoost) machine learning (ML) algorithm for real time quantification of InstantPC labelled N-glycans by Liquid Chromatography (LC) - fluorescence detector (FLD). The LC-FLD profile is pre-processed for baseline correction and noise reduction prior to fed to the machine learning (ML) algorithm. The algorithm has been successfully tested for commercial and inhouse developed mAbs and validated using LC-MS quantification as reference. The LC-FLD-ML model predicted values were at par with the LC-MS values with root mean square error of <0.5 and R2 of >0.95. The average errors using ML model (1.80 %) was reduced by a minimum of 28 % and 40 % for origin (1.5 %) and manual (1.07 %) based integration, respectively. The approach reduces the data analysis time per sample by ~70 % (from ~5 min to ~1.5 min), thereby offering a time and resource efficient orthogonality with LC-MS for quantification of N-glycans in mAbs.

Titer and charge-based heterogeneity multiattribute monitoring of mAbs in cell culture harvest using 2D ProA CEX MS.

Robust monitoring of heterogeneity in biopharmaceutical development is crucial for producing safe and efficacious biotherapeutic products. Multiattribute monitoring (MAM) has emerged as an efficient tool for monitoring of mAb heterogeneities like deamidation, sialylation, glycosylation, and oxidation. Conventional biopharma analysis during mAb development relies on use of one-dimensional methods for monitoring titer and charge-based heterogeneity using non-volatile solvents without direct coupling with mass spectrometry (MS). This approach requires analysis of mAb harvest by ProA for titer estimation followed by separate cation exchange chromatography (CEX) analysis of the purified sample for estimating charge-based heterogeneity. This can take up to 60-90 min due to the required fraction collection and buffer exchange steps. In this work, a native two-dimensional liquid chromatography (2DLC) mass spectrometry method has been developed with Protein A chromatography in the first dimension for titer estimation and cation exchange chromatography (CEX) in the second dimension for charge variant analysis. The method uses volatile salts for both dimensions and enables easy coupling to MS. The proposed 2DLC method exhibits a charge variant profile that is similar to that observed via the traditional methods and takes only 15 min for mass identification of each variant. A total of six charge variants were separated by the CEX analysis after titer estimation, including linearity assessment from 5 µg to 160 µg of injected mAb sample. The proposed method successfully estimated charge variants for the mAb innovator and 4 of its biosimilars, showcasing its applicability for biosimilarity exercises. Hence, the 2D ProA CEX MS method allows direct titer and charge variant estimation of mAbs in a single workflow.

Assessment of change in the basic variants composition of trastuzumab during dilution in saline for administration.

Postproduction handling of drug products during preparation or clinical use may affect the structure and efficacy of the drug and perhaps remain unnoticed. Since chemical modifications can impact the product's structure, stability, and biological activity, this study investigates the impact of elevated temperature and subtle shift in pH on the drug product post-dilution in saline. The mAb sample diluted in saline for administration was stressed at elevated temperature and slightly acidic pH condition. Extended stability studies were performed and monitored for size and charge heterogeneity. Size heterogeneity shows no significant changes, whereas charge heterogeneity shows an increase in basic variants and a reduction in main species. Further, basic variants were isolated and characterized to identify the type and site of chemical modification. Intact mass analysis and peptide mapping identify that the basic variants were attributed mainly to the isomerization of HC Asp102 into iso-Asp or its succinimide intermediate. Four basic variants were found to exhibit similar structural properties as the main and control samples. However, basic variants showed reduced binding affinity to HER2 receptor, while there was no significant difference in FcRn binding. The results indicate that modification in the HC Asp102, which is present in the CDR, affects antigen binding and thus can influence the potency of the drug product. Hence, with the conventional stability studies required to license the drug product, including in-use or extended stability studies to mimic the postproduction handling would be desirable.

LC-MS Characterization and Stability Assessment Elucidate Correlation Between Charge Variant Composition and Degradation of Monoclonal Antibody Therapeutics.

Aggregation stability of monoclonal antibody (mAb) therapeutics is influenced by many critical quality attributes (CQA) such as charge and hydrophobic variants in addition to environmental factors. In this study, correlation between charge heterogeneity and stability of mAbs for bevacizumab and trastuzumab has been investigated under a variety of stresses including thermal stress at 40 °C, thermal stress at 55 °C, shaking (mechanical), and low pH. Size- and charge-based heterogeneities were monitored using analytical size exclusion chromatography (SEC) and cation exchange chromatography (CEX), respectively, while dynamic light scattering was used to assess changes in hydrodynamic size. CEX analysis revealed an increase in cumulative acidic content for all variants of both mAbs post-stress treatment attributed to increased deamidation. Higher charge heterogeneity was observed in variants eluting close to the main peak than the ones eluting further away (25-fold and 42-fold increase in acidic content for main and B1 of bevacizumab and 19-fold for main of trastuzumab, respectively, under thermal stress; 50-fold increase in acidic for main and B1 of bevacizumab and 10% rise in basic content of main of trastuzumab under pH stress). Conversely, variants eluting far away from main exhibit greater aggregation as compared to close-eluting ones. Aggregation kinetics of variants followed different order for the different stresses for both mAbs (2nd order for thermal and pH stresses and 0th order for shaking stress). Half-life of terminal charge variants of both mAbs was 2- to 8-fold less than main indicating increased degradation propensity.

Explainable AI for CHO cell culture media optimization and prediction of critical quality attribute.

Cell culture media play a critical role in cell growth and propagation by providing a substrate; media components can also modulate the critical quality attributes (CQAs). However, the inherent complexity of the cell culture media makes unraveling the impact of the various media components on cell growth and CQAs non-trivial. In this study, we demonstrate an end-to-end machine learning framework for media component selection and prediction of CQAs. The preliminary dataset for feature selection was generated by performing CHO-GS (-/-) cell culture in media formulations with varying metal ion concentrations. Acidic and basic charge variant composition of the innovator product (24.97 ± 0.54% acidic and 11.41 ± 1.44% basic) was chosen as the target variable to evaluate the media formulations. Pearson's correlation coefficient and random forest-based techniques were used for feature ranking and feature selection for the prediction of acidic and basic charge variants. Furthermore, a global interpretation analysis using SHapley Additive exPlanations was utilized to select optimal features by evaluating the contributions of each feature in the extracted vectors. Finally, the medium combinations were predicted by employing fifteen different regression models and utilizing a grid search and random search cross-validation for hyperparameter optimization. Experimental results demonstrate that Fe and Zn significantly impact the charge variant profile. This study aims to offer insights that are pertinent to both innovators seeking to establish a complete pipeline for media development and optimization and biosimilar-based manufacturers who strive to demonstrate the analytical and functional biosimilarity of their products to the innovator. KEY POINTS: • Developed a framework for optimizing media components and prediction of CQA. • SHAP enhances global interpretability, aiding informed decision-making. • Fifteen regression models were employed to predict medium combinations.

Novel purification platform based on multimodal preparative scale separation of mAb fragments and aggregates.

Monoclonal antibodies (mAbs) continue to dominate the biopharmaceutical industry. Certain mAbs are prone to fragmentation and clipping and in these cases, adequate removal of these species is critical during manufacturing. Fragments can be generated during fermentation, purification, storage, formulation, and administration. Their addition to the acidic charge-variant of the purified mAb has been reported to decrease stability and potency of the final product. However, contrary to mAb aggregation, manufacturers have not given much attention to removal of fragments and clipped species and as a result most conventional mAb platforms offer at best limited capabilities for their removal. In this study, we propose a novel purification platform that uses multimodal chromatography and achieves complete removal of a range of mAb fragments and clipped products (25-120 kDa). The utility of the platform has been successfully demonstrated for 2 IgG1s and 2 IgG4s. Further, adequate removal of the various host cell impurities such as host cell proteins (<10 ppm) and host cell DNA (<5 ppb) has been achieved. Finally, the platform was able to deliver adequate removal of high molecular weight impurities (<1 %) and a 30 % clearance of the acidic charge variant. The proposed single step has been shown to deliver what the polishing chromatography and intermediate purification chromatography steps deliver in a traditional mAb platform.

Convolutional Neural Networks Guided Raman Spectroscopy as a Process Analytical Technology (PAT) Tool for Monitoring and Simultaneous Prediction of Monoclonal Antibody Charge Variants.

BACKGROUND: Charge related heterogeneities of monoclonal antibody (mAb) based therapeutic products are increasingly being considered as a critical quality attribute (CQA). They are typically estimated using analytical cation exchange chromatography (CEX), which is time consuming and not suitable for real time control. Raman spectroscopy coupled with artificial intelligence (AI) tools offers an opportunity for real time monitoring and control of charge variants. OBJECTIVE: We present a process analytical technology (PAT) tool for on-line and real-time charge variant determination during process scale CEX based on Raman spectroscopy employing machine learning techniques. METHOD: Raman spectra are collected from a reference library of samples with distribution of acidic, main, and basic species from 0-100% in a mAb concentration range of 0-20 g/L generated from process-scale CEX. The performance of different machine learning techniques for spectral processing is compared for predicting different charge variant species. RESULT: A convolutional neural network (CNN) based model was successfully calibrated for quantification of acidic species, main species, basic species, and total protein concentration with R2 values of 0.94, 0.99, 0.96 and 0.99, respectively, and the Root Mean Squared Error (RMSE) of 0.1846, 0.1627, and 0.1029 g/L, respectively, and 0.2483 g/L for the total protein concentration. CONCLUSION: We demonstrate that Raman spectroscopy combined with AI-ML frameworks can deliver rapid and accurate determination of product related impurities. This approach can be used for real time CEX pooling decisions in mAb production processes, thus enabling consistent charge variant profiles to be achieved.

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

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

Taurine, a Naturally Occurring Amino Acid, as a Physical Stability Enhancer of Different Monoclonal Antibodies.

Degradation of therapeutic monoclonal antibodies (mAbs) is a major concern as it affects efficacy, shelf-life, and safety of the product. Taurine, a naturally occurring amino acid, is investigated in this study as a potential mAb stabilizer with an extensive analytical characterization to monitor product degradation. Forced degradation of trastuzumab biosimilar (mAb1)-containing samples by thermal stress for 30 min resulted in high-molecular-weight species by more than 65% in sample without taurine compared to the sample with taurine. Samples containing mAb1 without taurine also resulted in higher Z-average diameter, altered protein structure, higher hydrophobicity, and lower melting temperature compared to samples with taurine. The stabilizing effect of taurine was retained at different mAb and taurine concentrations, time, temperatures, and buffers, and at the presence of polysorbate 80 (PS80). Even the lowest taurine concentration (10 mM) considered in this study, which is in the range of taurine levels in amino acid injections, resulted in enhanced mAb stability. Taurine-containing samples resulted in 90% less hemolysis than samples containing PS80. Additionally, mAb in the presence of taurine showed enhanced stability upon subjecting to stress with light of 365 nm wavelength, combination of light and H2O2, and combination of Fe2+ and H2O2, as samples containing mAb without taurine resulted in increased degradation products by more than 50% compared to samples with taurine upon subjecting to these stresses for 60 min. In conclusion, the presence of taurine enhanced physical stability of mAb by preventing aggregate formation, and the industry can consider it as a new mAb stabilizer.

Impact of Excipient Extraction and Buffer Exchange on Recombinant Monoclonal Antibody Stability.

The foundation of a biosimilar manufacturer's regulatory filing is the demonstration of analytical and functional similarity between the biosimilar product and the pertinent originator product. The excipients in the formulation may interfere with characterization using typical analytical and functional techniques during this biosimilarity exercise. Consequently, the producers of biosimilar products resort to buffer exchange to isolate the biotherapeutic protein from the drug product formulation. However, the impact that this isolation has on the product stability is not completely known. This study aims to elucidate the extent to which mAb isolation via ultrafiltration-diafiltration-based buffer exchange impacts mAb stability. It has been demonstrated that repeated extraction cycles do result in significant changes in higher-order structure (red-shift of 5.0 nm in fluorescence maxima of buffer exchanged samples) of the mAb and also an increase in formation of basic variants from 19.1 to 26.7% and from 32.3 to 36.9% in extracted innovator and biosimilar Tmab samples, respectively. It was also observed that under certain conditions of tertiary structure disruptions, Tmab could be restabilized depending on formulation composition. Thus, mAb isolation through extraction with buffer exchange impacts the product stability. Based on the observations reported in this paper, we recommend that biosimilar manufacturers take into consideration these effects of excipients on protein stability when performing biosimilarity assessments.

On-line PAT based monitoring and control of resin aging in protein A chromatography for COGs reduction.

Resin aging is a common occurrence in chromatographic processes and generally influenced by factors such as cleaning procedure and composition of the feed stream. Two major events occur along with protein fouling, one is the loss of protein A ligand and the other is non-specific, irreversible interactions of foulants with resin particles. Both these are responsible for resin aging. As a result, the performance of the resin suffers a fall, and this can be quantified through indicators like reduction in dynamic binding capacity, increased column pressure, or peak broadening. The number of reuse cycles of a resin has a major influence on the cost per batch. This is even more significant in the case of protein A resin, which is the primary cost driver for downstream processing. In this work, we first identify chromatogram characteristics that correlate to resin aging. Next, we propose a data monitoring-based tool for prediction of resin aging. Principal component analysis of the UV data of Mab 1 showed a deviation at 120th cycle and an out of specification at around 149th cycle, corroborating with yield decline. Batch level modelling could deliver a predictable trend for resin aging and was demonstrated for two different Mabs (Mab1 and Mab2). The results demonstrate that significant resin aging can be detected 20-25 cycles prior to observable yield decline. A control strategy has been suggested such that once the deviation has been detected, additional resin cleaning is triggered. Overall, a 50-100 Protein A cycle enhancement in resin lifespan could be achieved.

Does Aggregation of Therapeutic IgGs in PBS Offer a True Picture of What Happens in Models Derived from Human Body Fluids?

Therapeutic proteins such as monoclonal antibodies (mAb) are known to form aggregates due to various factors. Phosphate buffered saline (PBS), human serum, and human serum filtrate (HSF) are some of the models used to analyze mAb stability in physiologically relevant in-vitro conditions. In this study, aggregation of mAb in PBS and models derived from body fluids seeded with mAb samples subjected to various stresses were compared. Samples containing mAb subjected to pH, temperature, UV light, stirring, and interfacial agitation stress were seeded into different models for 2 case studies. In the first case study, %HMW (high molecular weight species) of mAb in PBS and HSF were compared using size exclusion chromatography. It was found that change in %HMW was higher in PBS compared to HSF. For example, PBS containing mAb that was subjected to UV light stress showed change in HMW by >10 % over 72 h, but the change was <5 % in HSF. In second case study, aggregates particles of FITC tagged mAb were monitored in PBS and serum using fluorescence microscope image processing. It was found that PBS and serum containing mAb subjected to stirring and interfacial agitation resulted in aggregates of >2 µm size, and average size and percentage number of particles having >10 µm size was higher in serum compared to PBS at all analysis time point. Overall, it was found that aggregation of mAb in PBS was different from that in human body fluids. Second case study also showed the importance of advanced strategies for further characterization of mAb in serum.

What should next-generation analytical platforms for biopharmaceutical production look like?

Biotherapeutic products, particularly complex products such as monoclonal antibodies (mAbs), have as many as 20-30 critical quality attributes (CQAs), thereby requiring a collection of orthogonal, high-resolution analytical tools for characterization and making characterization a resource-intensive task. As discussed in this Opinion, the need to reduce the cost of developing biotherapeutic products and the need to adopt Industry 4.0 and eventually Industry 5.0 paradigms are driving a reappraisal of existing analytical platforms. Next-generation platforms will have reduced offline testing, renewed focus on online testing and real-time monitoring, multiattribute monitoring, and extensive use of advanced data analytics and automation. They will be more complex, more sensitive, resource lean, and more responsive compared with existing platforms.

A novel method for continuous chromatographic separation of monoclonal antibody charge variants by combining displacement mode chromatography and step elution.

Charge heterogeneity of monoclonal antibodies is considered a critical quality attribute and hence needs to be monitored and controlled by the manufacturer. Typically, this is accomplished via separation of charge variants on cation exchange chromatography (CEX) using a pH or conductivity based linear gradient elution. Although an effective approach, this is challenging particularly during continuous processing as creation of linear gradient during continuous processing adds to process complexity and can lead to deviations in product quality upon slightest changes in gradient formation. Moreover, the long length of elution gradient along with the required peak fractionation makes process integration difficult. In this study, we propose a novel approach for separation of charge variants during continuous CEX chromatography by utilizing a combination of displacement mode chromatography and salt-based step elution. It has been demonstrated that while the displacement mode of chromatography enables control of acidic variants ≤26% in the CEX eluate, salt-based step gradient elution manages basic charge variant ≤25% in the CEX eluate. The proposed approach has been successfully demonstrated using feed materials with varying compositions. On comparing the designed strategy with 2-column concurrent (CC) chromatography, the resin specific productivity increased by 95% and resin utilization increased by 183% with recovery of main species >99%. Further, in order to showcase the amenability of the designed CEX method in continuous operation, the method was examined in our in-house continuous mAb platform.

Automated method for quantification of 20 amino acids in cell culture media during biopharmaceutical development.

Herein, a step-by-step protocol for simultaneous detection of 20 amino acids commonly present in cell culture media is described. The protocol facilitates detection of both primary and secondary amino acids through a two-step precolumn derivatization strategy using ortho-phthalaldehyde and 9-fluorenylmethyl chloroformate as derivatizing agents. The separation of derivatized amino acids with varying hydrophobicity is achieved through reverse-phase chromatography. The amino acids are simultaneously detected in a single workflow through the use of Variable Wavelength Detector at 338 and 262 nm. The protocol is applicable for both mammalian and bacterial cell culture matrices with an option for automation of precolumn derivatization.

Development of continuous processing platform utilizing aqueous two-phase extraction for purification of monoclonal antibodies.

Monoclonal antibody downstream processing typically entails chromatography-based purification processes beginning with Protein A chromatography, accounting for 50 % of the total manufacturing expense. Alternatives to protein A chromatography have been explored by several researchers. In this paper, aqueous two-phase extraction (ATPE) has been proposed for continuous processing of monoclonal antibodies (mAbs) as an alternative to the traditional protein A chromatography. The PEG-sulfate system has been employed for phase formation in ATPE, and the mAb is separated in the salt phase, while impurities like high molecular weight (HMW) and host cell proteins (HCPs) are separated in the PEG phase. Following ATPE of clarified cell culture harvest, yield of ≥ 80 % and purity of ≥ 97 % were achieved in the salt phase. Considerable (28 %) reduction in consumable cost has been estimated when comparing the proposed platform to the traditional protein A based platform. The outcomes demonstrate that ATPE can be a potentially effective substitute for the traditional Protein A chromatography for purification of mAbs. The proposed platform offers easy implementation, delivers comparative results, and offers significantly better economics for manufacturing mAb-based biotherapeutics.