Anurag S. Rathore

High-throughput process development for biopharmaceutical drug substances

Quality by Design (QbD) is gaining industry acceptance as an approach towards development and commercialization of biotechnology therapeutic products that are expressed via microbial or mammalian cell lines. In QbD, the process is designed and controlled to deliver specified quality attributes consistently. To acquire the enhanced understanding that is necessary to achieve the above, however, requires more extensive experimentation to establish the design space for the process and the product. With biotechnology companies operating under ever-increasing pressure towards lowering the cost of manufacturing, the use of high-throughput tools has emerged as a necessary enabler of QbD in a time- and resource-constrained environment. We review this topic for those in academia and industry that are engaged in drug substance process development.

Application of near-infrared (NIR) spectroscopy for screening of raw materials used in the cell culture medium for the production of a recombinant therapeutic protein

Control of raw materials based on an understanding of their impact on product attributes has been identified as a key aspect of developing a control strategy in the Quality by Design (QbD) paradigm. This article presents a case study involving use of a combined approach of Near‐infrared (NIR) spectroscopy and Multivariate Data Analysis (MVDA) for screening of lots of basal medium powders based on their impact on process performance and product attributes. These lots had identical composition as per the supplier and were manufactured at different scales using an identical process. The NIR/MVDA analysis, combined with further investigation at the supplier site, concluded that grouping of medium components during the milling and blending process varied with the scale of production and media type. As a result, uniformity of blending, impurity levels, chemical compatibility, and/or heat sensitivity during the milling process for batches of large‐scale media powder were deemed to be the source of variation as detected by NIR spectra. This variability in the raw materials was enough to cause unacceptably large variability in the performance of the cell culture step and impact the attributes of the resulting product. A combined NIR/MVDA approach made it possible to finger print the raw materials and distinguish between good and poor performing media lots.

Chemometrics applications in biotech processes: a review.

Biotech unit operations are often characterized by a large number of inputs (operational parameters) and outputs (performance parameters) along with complex correlations amongst 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. The aforementioned parameters can impact process performance and product quality and also interact amongst each other. Chemometrics presents one effective approach to gather process understanding from such complex data sets. The increasing use of chemometrics is fuelled by the gradual acceptance of quality by design and process analytical technology amongst the regulators and the biotech industry, which require enhanced process and product understanding. In this article, we review the topic of chemometrics applications in biotech processes with a special focus on recent major developments. Case studies have been used to highlight some of the significant applications.

Optimization of a refolding step for a therapeutic fusion protein in the quality by design (QbD) paradigm.

Production of biotech therapeutics in Escherichia coli involves protein expression as insoluble inclusion bodies that need to be denatured and the resulting protein refolded into the native structure. In this paper, we apply a Quality by Design approach using Design of Experiments for optimization of the refolding process for a recombinant biotech therapeutic, granulocyte colony stimulating factor. First, risk analysis was performed to identify process parameters that require experimental examination. Next, the chosen parameters were examined using a fractional factorial screening design. Based on the results of this study, parameters that have significant effect on refold yield and product quality were identified and examined using a full factorial Design of Experiments for their interactions. The final model was statistically significant and delivered a refolding yield of 77%. Further, kinetics of refolding was evaluated under optimal conditions and was found to be of first order with a rate constant of 0.132/min. Design space was established for the three parameters for a given permissible range of yield, protein concentration, and purity. The primary objective of this paper is to provide a roadmap for implementing Quality by Design for development of a protein refolding step.

Large scale demonstration of a process analytical technology application in bioprocessing: Use of on-line high performance liquid chromatography for making real time pooling decisions for process chromatography

Process Analytical Technology (PAT) has been gaining a lot of momentum in the biopharmaceutical community because of the potential for continuous real time quality assurance resulting in improved operational control and compliance. In previous publications, we have demonstrated feasibility of applications involving use of high performance liquid chromatography (HPLC) and ultra performance liquid chromatography (UPLC) for real‐time pooling of process chromatography column. In this article we follow a similar approach to perform lab studies and create a model for a chromatography step of a different modality (hydrophobic interaction chromatography). It is seen that the predictions of the model compare well to actual experimental data, demonstrating the usefulness of the approach across the different modes of chromatography. Also, use of online HPLC when the step is scaled up to pilot scale (a 2294 fold scale‐up from a 3.4 mL column in the lab to a 7.8 L column in the pilot plant) and eventually to manufacturing scale (a 45930 fold scale‐up from a 3.4 mL column in the lab to a 158 L column in the manufacturing plant) is examined. Overall, the results confirm that for the application under consideration, online‐HPLC offers a feasible approach for analysis that can facilitate real‐time decisions for column pooling based on product quality attributes. The observations demonstrate that the proposed analytical scheme allows us to meet two of the key goals that have been outlined for PAT, i.e., “variability is managed by the process” and “product quality attributes can be accurately and reliably predicted over the design space established for materials used, process parameters, manufacturing, environmental, and other conditions”. The application presented here can be extended to other modes of process chromatography and/or HPLC analysis.

Avoiding antibody aggregation during processing: establishing hold times.

Aggregation of biotech products used therapeutically, such as antibodies, can contribute to potential immunogenicity of the product. Charge‐based heterogeneities may also impact the safety and/or efficacy of a therapeutic. In this study, an approach based on empirical modeling and least squares regression is suggested for establishing hold times for process intermediates during production of monoclonal antibody (Mab) therapeutics. Two immunoglobulins were analyzed with respect to aggregation and charge heterogeneity in buffer conditions that are typically used during downstream processing of Mab products. Size exclusion chromatography, ion exchange chromatography (IEC), and circular dichroism were used. We found that aggregation primarily occurs at pH 3 (buffers used in affinity chromatography) and is higher in citrate buffer compared to acetate and glycine buffers. Aggregation is minimal in buffers used in anion exchange chromatography (Tris–HCl buffer at pH 7.2 and 8) and in cation exchange chromatography (citrate buffer at pH 6, acetate buffer at pH 6, and phosphate buffer at pH 6.5 and 7.5). The behavior is opposite in the case of charged heterogeneities (basic and acidic variants) as measured by IEC. The product is more susceptible to degradation at high pH than at low pH. The data presented here demonstrate that product stability can be a significant issue within the routinely used manufacturing conditions. We suggest that the approach presented needs to be adopted by all manufacturers to ensure product stability during processing.

Multi-period scheduling of a multi-stage multi-product bio-pharmaceutical process

Abstract There have been several works in the literature for scheduling of multi-product continuous processes with significant attention laid on short-term scheduling. This work presents a continuous-time model for multi-period scheduling of a multi-stage multi-product process from bio-pharmaceutical industry. The overall model is a mixed-integer linear programming (MILP) formulation based on state-task-network (STN) representation of the process using unit-specific event-based continuous-time representation. The proposed model is an extension of model by Shaik and Floudas (2007, Industrial & Engineering Chemistry Research, 46, 1764) with several new constraints to deal with additional features such as unit and sequence dependent changeovers, multiple intermediate due dates, handling of shelf-life and waste disposal, and penalties on backlogs and late deliveries. Improved tightening and sequencing constraints have been presented. The validity of the proposed model has been illustrated through an example from the literature.

CFD of mixing of multi‐phase flow in a bioreactor using population balance model

Mixing in bioreactors is known to be crucial for achieving efficient mass and heat transfer, both of which thereby impact not only growth of cells but also product quality. In a typical bioreactor, the rate of transport of oxygen from air is the limiting factor. While higher impeller speeds can enhance mixing, they can also cause severe cell damage. Hence, it is crucial to understand the hydrodynamics in a bioreactor to achieve optimal performance. This article presents a novel approach involving use of computational fluid dynamics (CFD) to model the hydrodynamics of an aerated stirred bioreactor for production of a monoclonal antibody therapeutic via mammalian cell culture. This is achieved by estimating the volume averaged mass transfer coefficient (kLa) under varying conditions of the process parameters. The process parameters that have been examined include the impeller rotational speed and the flow rate of the incoming gas through the sparger inlet. To undermine the two‐phase flow and turbulence, an Eulerian‐Eulerian multiphase model and k‐ε turbulence model have been used, respectively. These have further been coupled with population balance model to incorporate the various interphase interactions that lead to coalescence and breakage of bubbles. We have successfully demonstrated the utility of CFD as a tool to predict size distribution of bubbles as a function of process parameters and an efficient approach for obtaining optimized mixing conditions in the reactor. The proposed approach is significantly time and resource efficient when compared to the hit and trial, all experimental approach that is presently used.

Chemometrics applications in biotechnology processes: Predicting column integrity and impurity clearance during reuse of chromatography resin

Separation media, in particular chromatography media, is typically one of the major contributors to the cost of goods for production of a biotechnology therapeutic. To be cost‐effective, it is industry practice that media be reused over several cycles before being discarded. The traditional approach for estimating the number of cycles a particular media can be reused for involves performing laboratory scale experiments that monitor column performance and carryover. This dataset is then used to predict the number of cycles the media can be used at manufacturing scale (concurrent validation). Although, well accepted and widely practiced, there are challenges associated with extrapolating the laboratory scale data to manufacturing scale due to differences that may exist across scales. Factors that may be different include: level of impurities in the feed material, lot to lot variability in feedstock impurities, design of the column housing unit with respect to cleanability, and homogeneity of the column packing. In view of these challenges, there is a need for approaches that may be able to predict column underperformance at the manufacturing scale over the product lifecycle. In case such an underperformance is predicted, the operators can unpack and repack the chromatography column beforehand and thus avoid batch loss. Chemometrics offers one such solution. In this article, we present an application of chemometrics toward the analysis of a set of chromatography profiles with the intention of predicting the various events of column underperformance including the backpressure buildup and inefficient deoxyribonucleic acid clearance.

Chemometrics applications in biotech processes: Assessing process comparability

A typical biotech process starts with the vial of the cell bank, ends with the final product and has anywhere from 15 to 30 unit operations in series. The total number of process variables (input and output parameters) and other variables (raw materials) can add up to several hundred variables. As the manufacturing process is widely accepted to have significant impact on the quality of the product, the regulatory agencies require an assessment of process comparability across different phases of manufacturing (Phase I vs. Phase II vs. Phase III vs. Commercial) as well as other key activities during product commercialization (process scale‐up, technology transfer, and process improvement). However, assessing comparability for a process with such a large number of variables is nontrivial and often companies resort to qualitative comparisons. In this article, we present a quantitative approach for assessing process comparability via use of chemometrics. To our knowledge this is the first time that such an approach has been published for biotech processing. The approach has been applied to an industrial case study involving evaluation of two processes that are being used for commercial manufacturing of a major biosimilar product. It has been demonstrated that the proposed approach is able to successfully identify the unit operations in the two processes that are operating differently. We expect this approach, which can also be applied toward assessing product comparability, to be of great use to both the regulators and the industry which otherwise struggle to assess comparability.