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.

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.

Rapid Estimation of Size-Based Heterogeneity in Monoclonal Antibodies by Machine Learning-Enhanced Dynamic Light Scattering.

Aggregation of monoclonal antibody therapeutics is a serious concern that is believed to impact product safety and efficacy. There is a need for analytical approaches that enable rapid estimation of mAb aggregates. Dynamic light scattering (DLS) is a well-established technique for estimating the average size of protein aggregates or for evaluating sample stability. It is usually used to measure the size and size distribution over a wide range of nano- to micro-sized particles using time-dependent fluctuations in the intensity of scattered light arising from the Brownian motion of particles. In this study, we present a novel DLS-based approach that allows us to quantify the relative percentage of multimers (monomer, dimer, trimer, and tetramer) in a monoclonal antibody (mAb) therapeutic product. The proposed approach uses a machine learning (ML) algorithm and regression to model the system and predict the amount of relevant species such as monomer, dimer, trimer, and tetramer of a mAb in the size range of 10-100 nm. The proposed DLS-ML technique compares favorably to all potential alternatives with respect to the key method attributes, including per sample cost of analysis, per sample time of data acquisition along with ML-based aggregate prediction (<2 min), sample requirements (<3 µg), and user-friendliness of analysis. The proposed rapid method can serve as an orthogonal tool to size exclusion chromatography, which is the current industry workhorse for aggregate assessment.

N-Glycosylation of monoclonal antibody therapeutics: A comprehensive review on significance and characterization.

N-glycosylation of therapeutic antibodies starts as a co-translational step followed by a set of post-translational modifications and is considered as one of the critical quality attributes because of its impact on biological functions as well as therapy outcome. In addition to detailed product characterization of these glycans by the manufacturers, their comprehensive analysis is also a regulatory requirement. However, the structural complexity and heterogeneity of these N-linked carbohydrates make their characterization quite challenging. In this review, we give a comprehensive overview of N-glycosylation diversity and its functional importance for monoclonal antibody therapeutic products. A descriptive coverage is also provided for the various strategies and techniques employed for oligosaccharide characterization, that include analysis of intact-glycoproteins, their sub-units, glycopeptides as well as released glycans through chromatographic, electrophoretic and spectroscopic techniques. To assist the readers, relevant examples from the literature are cited and critically discussed for each of the strategies and techniques. To conclude, a discussion on unique challenges associated with the analysis of this important post-translational modification is presented.

Flow behavior analysis of Chlorella Vulgaris microalgal biomass.

The processing volume of bioengineering operations requires flow properties of algal mass for effective processing techniques. Chlorella Vulgaris microalgae cultured at 25 °C in Tap media under continuous illumination was considered. It showed an exponential phase of growth up to 8 days and then a stationary phase of growth from 8 days to 15 days. The rheological properties of microalgae biomass during the growth represented power law model. Microscopic analysis showed the influence of shearing on variation of algal structure from clusters to complete cell separation. The flow properties supported the microscopy analysis showing the shear thickening property at high shear rates and shear thinning nature at low shear regime. Optimal power required for the agitation of biomass based on the variations of non-Newtonian viscosity were predicted by considering the vessel geometry.