Ajoy Velayudhan

Strategic Assay Selection for analytics in high‐throughput process development: Case studies for downstream processing of monoclonal antibodies

During bioprocess development a potentially large number of analytes require measurement. Selection of the best set of analytical methods to deploy can reduce the analytical requirements for process investigation but currently relies on application of heuristics. This paper introduces a generic methodology, Strategic Assay Selection, for screening a large number of analytical methods to produce a subset of analytics that best suit high-throughput studies. The methodology uses a stochastic ranking approach where analytics are ranked based on their holistic performance in a set of criteria. Strategic Assay Selection can be used to help minimizing the impact of analytics in the generation of bottlenecks often encountered during high-throughput process development studies. This is illustrated by using a typical downstream purification process for a monoclonal antibody product. A list of assays is populated for routinely measured analytes across the different units of operation followed by the calculation of their performances in four criteria. The methodology is then applied to select analytics testing for three analytes and the results are analyzed to demonstrate how it can lead to the selection of analytical methods with the most favorable features.

Development and application of an automated, low-volume chromatography system for resin and condition screening.

A small‐volume chromatography system was developed for rapid resin and parameter screening and applied to the purification of a therapeutic monoclonal antibody from a key product‐related impurity. Accounting for constraints in peripheral volume, gradient formation, column integrity, and fraction collection in microtiter plates, the resulting system employed 2‐mL columns and was successfully integrated with plate‐based methods for rapid sample analysis (e. g., use of automated liquid handlers, plate readers, and HPLC). Several cation‐exchange chromatography resins were screened using automated programs and tailored gradients for the combination of a particular resin and a given antibody feedstock produced during Phase 1 development. Results from the tailored gradient runs were used to select a resin, and to arrive at efficient stepwise elution schedules for the chosen resin. By maintaining a constant residence time, final operating parameters were successfully scaled to representative bed heights and column diameters up to 2.6 cm (106 mL). This approach significantly improved throughput while reducing development time and material consumption.

Strategic assay deployment as a method for countering analytical bottlenecks in high throughput process development: Case studies in ion exchange chromatography

High throughput approaches to facilitate the development of chromatographic separations have now been adopted widely in the biopharmaceutical industry, but issues of how to reduce the associated analytical burden remain. For example, acquiring experimental data by high level factorial designs in 96 well plates can place a considerable strain upon assay capabilities, generating a bottleneck that limits significantly the speed of process characterization. This article proposes an approach designed to counter this challenge; Strategic Assay Deployment (SAD). In SAD, a set of available analytical methods is investigated to determine which set of techniques is the most appropriate to use and how best to deploy these to reduce the consumption of analytical resources while still enabling accurate and complete process characterization. The approach is demonstrated by investigating how salt concentration and pH affect the binding of green fluorescent protein from Escherichia coli homogenate to an anion exchange resin presented in a 96‐well filter plate format. Compared with the deployment of routinely used analytical methods alone, the application of SAD reduced both the total assay time and total assay material consumption by at least 40% and 5%, respectively. SAD has significant utility in accelerating bioprocess development activities.

Multi-criteria manufacturability indices for ranking high-concentration monoclonal antibody formulations

The need for high‐concentration formulations for subcutaneous delivery of therapeutic monoclonal antibodies (mAbs) can present manufacturability challenges for the final ultrafiltration/diafiltration (UF/DF) step. Viscosity levels and the propensity to aggregate are key considerations for high‐concentration formulations. This work presents novel frameworks for deriving a set of manufacturability indices related to viscosity and thermostability to rank high‐concentration mAb formulation conditions in terms of their ease of manufacture. This is illustrated by analyzing published high‐throughput biophysical screening data that explores the influence of different formulation conditions (pH, ions, and excipients) on the solution viscosity and product thermostability. A decision tree classification method, CART (Classification and Regression Tree) is used to identify the critical formulation conditions that influence the viscosity and thermostability. In this work, three different multi‐criteria data analysis frameworks were investigated to derive manufacturability indices from analysis of the stress maps and the process conditions experienced in the final UF/DF step. Polynomial regression techniques were used to transform the experimental data into a set of stress maps that show viscosity and thermostability as functions of the formulation conditions. A mathematical filtrate flux model was used to capture the time profiles of protein concentration and flux decay behavior during UF/DF. Multi‐criteria decision‐making analysis was used to identify the optimal formulation conditions that minimize the potential for both viscosity and aggregation issues during UF/DF. Biotechnol. Bioeng. 2017;114: 2043–2056.

Application of simplex‐based experimental optimization to challenging bioprocess development problems: Case studies in downstream processing

The identification of feasible operating conditions during the early stages of bioprocess development is implemented frequently through High Throughput (HT) studies. These typically employ techniques based on regression analysis, such as Design of Experiments. In this work, an alternative approach, based on a previously developed variant of the Simplex algorithm, is compared to the conventional regression‐based method for three experimental systems involving polishing chromatography and protein refolding. This Simplex algorithm variant was found to be more effective in identifying superior operating conditions, and in fact it reached the global optimum in most cases involving multiple optima. By contrast, the regression‐based method often failed to reach the global optimum, and in many cases reached poor operating conditions. The Simplex‐based method is further shown to be robust in dealing with noisy experimental data, and requires fewer experiments than regression‐based methods to reach favorable operating conditions. The Simplex‐variant also lends itself to the use of HT analytical methods, when they are available, which can assist in avoiding analytical bottlenecks. It is suggested that this Simplex‐variant is ideally suited to rapid optimization in early‐phase process development.

Simplex-based optimization of numerical and categorical inputs in early bioprocess development: Case studies in HT chromatography

Bioprocess development studies often involve the investigation of numerical and categorical inputs via the adoption of Design of Experiments (DoE) techniques. An attractive alternative is the deployment of a grid compatible Simplex variant which has been shown to yield optima rapidly and consistently. In this work, the method is combined with dummy variables and it is deployed in three case studies wherein spaces are comprised of both categorical and numerical inputs, a situation intractable by traditional Simplex methods. The first study employs in silico data and lays out the dummy variable methodology. The latter two employ experimental data from chromatography based studies performed with the filter-plate and miniature column High Throughput (HT) techniques. The solute of interest in the former case study was a monoclonal antibody whereas the latter dealt with the separation of a binary system of model proteins. The implemented approach prevented the stranding of the Simplex method at local optima, due to the arbitrary handling of the categorical inputs, and allowed for the concurrent optimization of numerical and categorical, multilevel and/or dichotomous, inputs. The deployment of the Simplex method, combined with dummy variables, was therefore entirely successful in identifying and characterizing global optima in all three case studies. The Simplex-based method was further shown to be of equivalent efficiency to a DoE-based approach, represented here by D-Optimal designs. Such an approach failed, however, to both capture trends and identify optima, and led to poor operating conditions. It is suggested that the Simplex-variant is suited to development activities involving numerical and categorical inputs in early bioprocess development.

Continuous antibody purification using precipitation: An important step forward

Recent advances in cell culture have reduced upstream costs, and made downstream processing the more expensive component of biologics manufacturing. Within downstream processing, the chromatographic steps have usually been the most expensive, on the one hand because chromatographic steps are traditionally run in batch mode and on the other because resins are typically more expensive per unit mass of product than other bioproduct-contact materials [1, 2]. There have been many attempts to consider alternatives to chromatography [3, 4], but the comparatively high resolution of chromatography, combined with the bioprocess industrys wealth of experience in using the various chromatographic modes, have made it hard to replace in a manufacturing setting. In this issue of Biotechnology Journal Hammerschmid et al. [5] make several original contributions to this discussion.

The development of a monolith-based purification process for Orthopoxvirus vaccinia virus Lister strain

The purification of large viruses remains an important field of research and development. The development of efficient purification trains is restricted by limited analytical methods, as well as by the complexity of large viruses, as well as the high variability in starting material from cell culture. Vaccinia virus holds great potential as an oncolytic and immunotherapeutic vaccine against a broad spectrum of cancers. In this work, monolith-based capture and polishing chromatographic steps for vaccinia virus Lister strain has been developed. Virus produced in CV-1 cells was harvested and passed through a 0.8μm pre-filter before loading onto CIEX, AIEX and HIC CIM monoliths. Without the need for nuclease treatment, up to 99% of the total DNA loaded can be removed from the vaccinia feed stream by the CIM OH monolith, which also reduces the total protein concentration in the product pool to LLOQ levels, and achieves infectious virus recoveries of 90%. Binding capacities of greater than 1×109pfu of vaccinia per mL of matrix were obtained on both CIM SO3 and CIM OH monoliths. Multiple orthogonal analytical methods have been used to develop process knowledge and understanding.

High-throughput investigation of single and binary protein adsorption isotherms in anion exchange chromatography employing multivariate analysis

The combination of multi-well plates and automated liquid handling is well suited to the rapid measurement of the adsorption isotherms of proteins. Here, single and binary adsorption isotherms are reported for BSA, ovalbumin and conalbumin on a strong anion exchanger over a range of pH and salt levels. The impact of the main experimental factors at play on the accuracy and precision of the adsorbed protein concentrations is quantified theoretically and experimentally. In addition to the standard measurement of liquid concentrations before and after adsorption, the amounts eluted from the wells are measured directly. This additional measurement corroborates the calculation based on liquid concentration data, and improves precision especially under conditions of weak or moderate interaction strength. The traditional measurement of multicomponent isotherms is limited by the speed of HPLC analysis; this analytical bottleneck is alleviated by careful multivariate analysis of UV spectra.

Manufacturability Indices for High-Concentration Monoclonal Antibody Formulations

Abstract The need for high-concentration formulations for subcutaneous delivery of therapeutic monoclonal antibodies (mAbs) can present manufacturability challenges for the final ultrafiltration/diafiltration (UF/DF) step. Viscosity levels and the propensity to aggregate are key considerations for high-concentration formulations. This work presents a novel framework for deriving a set of manufacturability indices related to viscosity and thermostability to rank high-concentration mAb formulation conditions in terms of their ease of manufacture. This is illustrated by analysing published high-throughput biophysical screening data that explores the influence of different formulation conditions (pH, ions and excipients) on the solution viscosity and product thermostability. A decision tree classification method, CART (Classification and Regression Tree) is used to identify the critical formulation conditions that influence the viscosity and thermostability. Polynomial regression techniques combined with the impact of protein concentration-time profiles and flux decay behaviour during UF/DF are used to transform the experimental data into a set of stress maps which show viscosity and thermostability as functions of the formulation conditions and time profiles during UF/DF. Manufacturability indices are derived from analysis of the stress maps and the process conditions experienced in the final UF/DF step. The indices are used to identify the optimal formulation conditions that minimize the potential for both viscosity and aggregation issues during UF/DF.