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Process understanding and characterization forms the foundation, ensuring consistent and robust biologics manufacturing process. Using appropriate modeling tools and machine learning approaches, the process data can be monitored in real time to avoid manufacturing risks. In this article, we have outlined an approach toward implementation of chemometrics and machine learning tools (neural network analysis) to model and predict the behavior of a mixed-mode chromatography step for a biosimilar (Teriparatide) as a case study. The process development data and process knowledge was assimilated into a prior process knowledge assessment using chemometrics tools to derive important parameters critical to performance indicators (i.e., potential quality and process attributes) and to establish the severity ranking for the FMEA analysis. The characterization data of the chromatographic operation are presented alongwith the determination of the critical, key and non- key process parameters, set points, operating, process acceptance and characterized ranges. The scale-down model establishment was assessed using traditional approaches and novel approaches like batch evolution model and neural network analysis. The batch evolution model was further used to demonstrate batch monitoring through direct chromatographic data, thus demonstrating its application for continuos process verification. Assimilation of process knowledge through a structured data acquisition approach, built-in from process development to continuous process verification was demonstrated to result in a data analytics driven model that can be coupled with machine learning tools for real time process monitoring. We recommend application of these approaches with the FDA guidance on stage wise process development and validation to reduce manufacturing risks., (© 2022 American Institute of Chemical Engineers.)

Terminal deletions of the chromosome 6q27 region are rare genomic abnormalities, linked to specific brain malformations and other neurological phenotypes. Reported cases have variable sized genomic deletions that harbor several genes including the DLL1 and TBP. We report on an inherited 0.38 Mb terminal deletion of chromosome 6q27 in a 22-week fetus with isolated bilateral ventriculomegaly and her affected mother using microarray-based comparative genomic hybridization and fluorescent in situ hybridization (FISH). The deleted region harbors at least seven genes including DLL1 and TBP. The affected mother had a history of hydrocephalus, developmental delay, and seizures commonly associated with DLL1 and TBP 6q27 deletions. This deletion is one of the smallest reported isolated 6q27 terminal deletions. Our data provides additional evidence that haploinsufficiency of the DLL1 and TBP genes may be sufficient to cause the ventriculomegaly, seizures, and developmental delays associated with terminal 6q27 deletions, indicating a plausible role in the abnormal development of the central nervous system., (© 2018 Wiley Periodicals, Inc.)

Adsorbent lifetime during protein A chromatography is not readily predicted or understood, representing a key challenge to be addressed for biopharmaceutical manufacturers. This article focuses on the impact of feed composition on the performance of a typical agarose-based protein A resin across a lifetime of 50 cycles. Cycling studies were performed using three different feed materials with varying levels of feed components including proteases, histones, DNA, and nonhistone proteins. Changes in the process and quality attributes were measured. The DBCs were not seen to vary between conditions although there was a reduction in particle porosity in all cases. Fluorescence spectroscopy and LC-MS/MS were used to identify the contribution and extent of fouling to the observed capacity loss. Residual protein A ligand density and deposition of foulants (HCP, residual mAb, and DNA) varied between the three feed materials. Resins cycled in feed materials containing high concentrations of HCP and histones were seen to have greater extents of capacity loss. The mode of performance loss, capacity loss, or impact on product quality was seen to vary depending on the feed material. The results indicate that feed material composition may be correlated to the rate and mode of resin aging as a basis for improved process understanding. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:412-419, 2018., (© 2018 American Institute of Chemical Engineers.)

Biotherapeutics have become the focus of the pharmaceutical industry due to their proven effectiveness in managing complex diseases. Downstream processes of these molecules consist of several orthogonal, high resolution unit operations designed so as to be able to separate variants having very similar physicochemical properties. Typical process development involves optimization of the individual unit operations based on Quality by Design principles in order to define the design space within which the process can deliver product that meets the predefined specifications. However, limited efforts are dedicated to understanding the interactions between the unit operations. This paper aims to showcase the importance of understanding these interactions and thereby arrive at operating conditions that are optimal for the overall process. It is demonstrated that these are not necessarily same as those obtained from optimization of the individual unit operations. Purification of Granulocyte Colony Stimulating Factor (G-CSF), a biotherapeutic expressed in E. coli., has been used as a case study. It is evident that the suggested approach results in not only higher yield (91.5 vs. 86.4) but also improved product quality (% RP-HPLC purity of 98.3 vs. 97.5) and process robustness. We think that this paper is very relevant to the present times when the biotech industry is in the midst of implementing Quality by Design towards process development. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 32:355-362, 2016., (© 2015 American Institute of Chemical Engineers.)

Fermentanomics is an emerging field of research and involves understanding the underlying controlled process variables and their effect on process yield and product quality. Although major advancements have occurred in process analytics over the past two decades, accurate real-time measurement of significant quality attributes for a biotech product during production culture is still not feasible. Researchers have used an amalgam of process models and analytical measurements for monitoring and process control during production. This article focuses on using multivariate data analysis as a tool for monitoring the internal bioreactor dynamics, the metabolic state of the cell, and interactions among them during culture. Quality attributes of the monoclonal antibody product that were monitored include glycosylation profile of the final product along with process attributes, such as viable cell density and level of antibody expression. These were related to process variables, raw materials components of the chemically defined hybridoma media, concentration of metabolites formed during the course of the culture, aeration-related parameters, and supplemented raw materials such as glucose, methionine, threonine, tryptophan, and tyrosine. This article demonstrates the utility of multivariate data analysis for correlating the product quality attributes (especially glycosylation) to process variables and raw materials (especially amino acid supplements in cell culture media). The proposed approach can be applied for process optimization to increase product expression, improve consistency of product quality, and target the desired quality attribute profile., (© 2015 American Institute of Chemical Engineers.)

Biotech unit operations are often characterized by a large number of inputs (operational parameters) and outputs (performance parameters) along with complex correlations among them. A typical biotech process starts with the vial of the cell bank, ends with the final product, and has anywhere from 15 to 30 such unit operations in series. Besides the above-mentioned operational parameters, raw material attributes can also impact process performance and product quality as well as interact among each other. Multivariate data analysis (MVDA) offers an effective approach to gather process understanding from such complex datasets. Review of literature suggests that the use of MVDA is rapidly increasing, fuelled by the gradual acceptance of quality by design (QbD) and process analytical technology (PAT) among the regulators and the biotech industry. Implementation of QbD and PAT requires enhanced process and product understanding. In this article, we first discuss the most critical issues that a practitioner needs to be aware of while performing MVDA of bioprocessing data. Next, we present a step by step procedure for performing such analysis. Industrial case studies are used to elucidate the various underlying concepts. With the increasing usage of MVDA, we hope that this article would be a useful resource for present and future practitioners of MVDA., (© 2014 American Institute of Chemical Engineers.)

The malarial parasite Plasmodium falciparum synthesizes fatty acids by the type II mechanism. In this cycle, the dehydration of the beta-hydroxyacyl acyl carrier protein is catalyzed by FabZ. Purified FabZ has been crystallized using the hanging-drop vapour-diffusion and microbatch techniques. The crystals are orthorhombic, with space group I222 or I2(1)2(1)2(1) and unit-cell parameters a = 71.78, b = 81.99, c = 97.49 A. A complete data set to a resolution of 2.5 A has been collected under cryoconditions (100 K) using a MAR imaging-plate detector system mounted on a rotating-anode X-ray generator.