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.

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.

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 majorly 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

An Online Two-Dimensional Approach to Characterizing the Charge-Based Heterogeneity of Recombinant Monoclonal Antibodies Using a 2D-CEX-AEX-MS Workflow.

Assessment of product quality attributes such as charge heterogeneity is an upmost requisite for the release of a monoclonal antibody (mAb). Analytical techniques, such as cation-exchange chromatography (CEX), accomplish this, causing the mAb to separate into acidic, main species, and basic variants. Here, an online volatile-salt-containing two-dimensional liquid chromatography (2D-LC) method coupled with mass spectrometry (MS) was performed to characterize the charge heterogeneity of mAbs using CEX chromatography in the first dimension (D1) and anion-exchange chromatography (AEX) in the second dimension (D2). The main peak of the CEX profile of D1 was transferred through a 2D heart-cut method to D2 for further analysis by the AEX-MS method. In the CEX method, mAb A showed 10 distinct variants, while the AEX method resulted in eight variants. However, a total of 13 variants were successfully resolved for mAb A in the 2D method. Similarly, mAb B exhibited seven variants in the CEX method and four variants in the AEX method, but the 2D-LC method revealed a total of nine variants for mAb B. Likewise, mAb C displayed seven variants in CEX and seven variants in AEX, whereas the 2D-LC method unveiled a total of 11 variants for mAb C. Additionally, native MS analysis revealed that the resolved charge variants were identified as amidation, oxidation, and isomerization of Asp variants in the main peak, which were not resolved in stand-alone methods. The present study demonstrates how 2D-LC can assist in identifying minor variations in charge distribution or conformation of mAb variants that would otherwise not be picked up by a single analytical method alone.

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.

Assessment of structural and functional similarity of biosimilar products: Bevacizumab as a case study.

The antiangiogenic drug bevacizumab is a blockbuster therapeutic pharmaceutical product that is used to treat many different types of cancer including kidney, colon, rectum, lung, and breast cancer. As a result, multiple biosimilars have been approved across the various regulatory jurisdictions in India (>20 in number till date). The rapidly growing market and acceptance of biosimilars was the motivation to perform comparability study of bevacizumab biosimilars that are presently available in the Indian market. A comprehensive analytical and functional biosimilarity assessment has been performed to examine and compare innovator product of bevacizumab (Avastin-innovator product, Roche Products (India) Pvt Ltd) and six biosimilars that are being marketed in India (Abevmy from Mylan Pharmaceuticals Pvt Ltd, Bevazza from Lupin Ltd, Bryxta from Zydus Cadila, Krabeva from Biocon, Ivzumab from RPG Life Sciences Ltd, and Advamab from Alkem Laboratories Ltd). Physiochemical characterization of drug products was performed with respect to their primary structure (intact mass, reduced mass, peptide mapping by LC-MS), higher order structure (secondary structure by FTIR, Far-UV-CD, and tertiary structure by Near-UV-CD, intrinsic fluorescence spectroscopy), impurity profile (SE-HPLC, SEC-MALS, extrinsic fluorescence: size heterogenicity, degradation, stability; DLS: hydrodynamic radius; WCX-HPLC: charge variants analysis) and post-translational modifications by measuring reduced glycans through fluorescence dye analysis. Functional characterization was performed by SPR and cell proliferation assay. Further, chemometrics based quantitative evaluation of biosimilarity has been performed by combining the data obtained from analytical characterization platform. The analysis of the analytical, functional and chemometric results revealed significant levels of similarity, with biosimilar4 being the sole exception. Despite being within product specifications, Biosimilar4 displayed significant deviations with respect to critical quality attributes, including a lower proportion of monomer content, a larger percentage of basic charge variant species, and a lower proportion of aglycosylated glycoform.

A Domain-Shift Invariant CNN Framework for Cardiac MRI Segmentation Across Unseen Domains.

The emergence of various deep learning approaches in diagnostic medical image segmentation has made machines capable of accomplishing human-level accuracy. However, the generalizability of these architectures across patients from different countries, Magnetic Resonance Imaging (MRI) scans from distinct vendors, and varying imaging conditions remains questionable. In this work, we propose a translatable deep learning framework for diagnostic segmentation of cine MRI scans. This study aims to render the available SOTA (state-of-the-art) architectures domain-shift invariant by utilizing the heterogeneity of multi-sequence cardiac MRI. To develop and test our approach, we curated a diverse group of public datasets and a dataset obtained from private source. We evaluated 3 SOTA CNN (Convolution neural network) architectures i.e., U-Net, Attention-U-Net, and Attention-Res-U-Net. These architectures were first trained on a combination of three different cardiac MRI sequences. Next, we examined the M&M (multi-center & mutli-vendor) challenge dataset to investigate the effect of different training sets on translatability. The U-Net architecture, trained on the multi-sequence dataset, proved to be the most generalizable across multiple datasets during validation on unseen domains. This model attained mean dice scores of 0.81, 0.85, and 0.83 for myocardial wall segmentation after testing on unseen MyoPS (Myocardial Pathology Segmentation) 2020 dataset, AIIMS (All India Institute of Medical Sciences) dataset and M&M dataset, respectively. Our framework achieved Pearson's correlation values of 0.98, 0.99, and 0.95 between the observed and predicted parameters of end diastole volume, end systole volume, and ejection fraction, respectively, on the unseen Indian population dataset.

An efficient computational protocol for template-based design of peptides that inhibit interactions involving SARS-CoV-2 proteins.

The RNA-dependent RNA polymerase (RdRp) complex of SARS-CoV-2 lies at the core of its replication and transcription processes. The interfaces between holo-RdRp subunits are highly conserved, facilitating the design of inhibitors with high affinity for the interaction interface hotspots. We, therefore, take this as a model protein complex for the application of a structural bioinformatics protocol to design peptides that inhibit RdRp complexation by preferential binding at the interface of its core subunit nonstructural protein, nsp12, with accessory factor nsp7. Here, the interaction hotspots of the nsp7-nsp12 subunit of RdRp, determined from a long molecular dynamics trajectory, are used as a template. A large library of peptide sequences constructed from multiple hotspot motifs of nsp12 is screened in-silico to determine sequences with high geometric complementarity and interaction specificity for the binding interface of nsp7 (target) in the complex. Two lead designed peptides are extensively characterized using orthogonal bioanalytical methods to determine their suitability for inhibition of RdRp complexation. Binding affinity of these peptides to accessory factor nsp7, determined using a surface plasmon resonance (SPR) assay, is slightly better than that of nsp12: dissociation constant of 133nM and 167nM, respectively, compared to 473nM for nsp12. A competitive ELISA is used to quantify inhibition of nsp7-nsp12 complexation, with one of the lead peptides giving an IC50 of 25µM . Cell penetrability and cytotoxicity are characterized using a cargo delivery assay and MTT cytotoxicity assay, respectively. Overall, this work presents a proof-of-concept of an approach for rational discovery of peptide inhibitors of SARS-CoV-2 protein-protein interactions.

Soft sensor based rapid detection of trace chlorine dioxide (ClO2) concentration in water.

Chlorine dioxide (ClO2) is a widely used sterilizer and a disinfectant across a multitude of industries. When using ClO2, it is imperative to measure the ClO2 concentration to abide by the safety regulations. This study presents a novel, soft sensor method based on Fourier transform infrared spectroscopy (FTIR) spectroscopy for measurement of ClO2 concentration in different water samples varying from milli Q to wastewater. Six distinct artificial neural network models were constructed and evaluated based on three overarching statistical standards to select the optimal model. The OPLS-RF model outperformed all other models with R2, RMSE, and NRMSE values of 0.945, 0.24, and 0.063, respectively. The developed model demonstrated limit of detection and limit of quantification values of 0.1 and 0.25 ppm, respectively, for water. Furthermore, the model also exhibited good reproducibility and precision as measured by the BCMSEP (0.064). The soft sensor-based method presented in the study offers significant advantages in terms of simplicity and speedy detection. In summary, the study presents development of a soft sensor that is capable of predicting the trace content of chlorine dioxide ranging between 0.1 to 5 ppm in a water sample by connecting FTIR with an OPLS-RF model.

A novel filter-assisted protein precipitation (FAPP) based sample pre-treatment method for LC-MS peptide mapping for biosimilar characterization.

Establishing analytical and functional comparability serves as the foundation of biosimilar development. A critical part of this exercise is sequence similarity search and categorization of post-translational modifications (PTMs), often by peptide mapping using liquid chromatography-mass spectrometry (LC-MS). When performing bottom-up proteomic sample preparation, efficient digestion of the protein and extraction of peptides for subsequent mass spectrometric analysis can be a challenge. Conventional sample preparation strategies face the risk of allowing interference of chemicals which are essential for extraction but are likely to interfere with digestion, resulting in complex chromatographic profiles due to semi-cleavages, insufficient peptide cleavages, and other unwanted reactions. Further, peptide cleanup through commonly used immobilized C-18 pipette tips can cause significant peptide loss as well as variability in individual peptide yields, thereby causing artifacts of various product-related modifications. In this study, we proposed a simple enzymatic digestion technique by incorporating different molecular weight filters and protein precipitation, with the objective to minimize interference of denaturing, reducing, and alkylating agents throughout overnight digestion. As a result, the need for peptide cleanup is significantly reduced and results in higher peptide yield. The proposed FAPP approach outperformed the conventional method across multiple metrics including, 30% more peptides, 8.19% more fully digested peptides, 14% higher sequence coverage rate, and 11.82% more site-specific alterations. Quantitative and qualitative repeatability of the proposed approach have been demonstrated. It can be concluded that the filter-assisted protein precipitation (FAPP) protocol proposed in this study offers an effective substitute for the traditional approach.