High-throughput in silico workflow for optimization and characterization of multimodal chromatographic processes.

While high-throughput (HT) experimentation and mechanistic modeling have long been employed in chromatographic process development, it remains unclear how these techniques should be used in concert within development workflows. In this work, a process development workflow based on HT experiments and mechanistic modeling was constructed. The integration of HT and modeling approaches offers improved workflow efficiency and speed. This high-throughput in silico (HT-IS) workflow was employed to develop a Capto MMC polishing step for mAb aggregate removal. High-throughput batch isotherm data was first generated over a range of mobile phase conditions and a suite of analytics were employed. Parameters for the extended steric mass action (SMA) isotherm were regressed for the multicomponent system. Model validation was performed using the extended SMA isotherm in concert with the general rate model of chromatography using the CADET modeling software. Here, step elution profiles were predicted for eight RoboColumn runs across a range of ionic strength, pH, and load density. Optimized processes were generated through minimization of a complex objective function based on key process metrics. Processes were evaluated at lab-scale using two feedstocks, differing in composition. The results confirmed that both processes obtained high monomer yield (>85%) and removed ∼ 50 % $$ \sim 50\% $$ of aggregate species. Column simulations were then carried out to determine sensitivity to a wide range of process inputs. Elution buffer pH was found to be the most critical process parameter, followed by resin ionic capacity. Overall, this study demonstrated the utility of the HT-IS workflow for rapid process development and characterization.

Predictive mechanistic modeling of loading and elution in protein A chromatography.

Protein A chromatography is an enabling technology in current manufacturing processes of monoclonal antibodies (mAbs) and mAb derivatives, largely due to its ability to reduce the levels of process-related impurities by several orders of magnitude. Despite its widespread application, the use of mathematical modeling capable of accurately predicting the full protein A chromatographic process, including loading, post-loading wash and elution stages, has been limited. This work describes a mechanistic modeling approach utilizing the general rate model (GRM), the capabilities of which are explored and optimized using two isotherm models. Isotherm parameters were estimated by inverse-fitting simulated breakthrough curves to experimental data at various pH values. The parameter values so obtained were interpolated across the relevant pH range using a best-fit curve, thus enabling their use in predictive modeling, including of elution over a range of pH. The model provides accurate predictions (< 3% mean error in 10% dynamic binding capacity predictions and ∼ 5% mean error in elution mass and pool volume predictions, both on scale-up) for various residence times, buffer conditions and elution schemes and its effectiveness for use in scale-up and process development is shown by applying the same parameters to larger columns and a wider range of residence times.

Adeno-associated viral capsid stability on anion exchange chromatography column and its impact on empty and full capsid separation.

Recombinant adeno-associated viral vector (rAAV) mediated gene therapy is gaining traction in treating genetic disorders. Current rAAV production systems yield a mixture of capsids largely devoid of the transgene (empty capsid) compared with the desired therapeutic product (full capsid). Anion exchange chromatography (AEX) is an attractive method for separating empty and full AAV capsids because of its scalability. Resin types and buffer composition are key considerations for AEX and must support capsid stability to be suitable for downstream processing. We examined the impact of binding durations (0-8 h) using various binding ionic strengths (15-75 mM), pH (7.5-9.0), resin chemistry (POROS XQ, POROS HQ, POROS I, and BIA QA monolith), and proprietary Q resins with different ligand densities for effects on capsid stability. Empty capsids were altered upon extended binding, leading to retention time shifts and loss of resolution between empty and full capsids. Viral capsid protein analysis reveals that full capsids have more viral capsid protein 3 (VP3) proteins than empty capsids. Analytical hydrophilic liquid chromatography showed that empty capsid retention time shift is accompanied by changes to the empty capsid's native VP3 protein. Among the potential stabilizing additives considered, magnesium chloride was the most effective at reducing negative impacts caused by extended binding.

Isotherm model discrimination for multimodal chromatography using mechanistic models derived from high-throughput batch isotherm data.

In this work, we have examined an array of isotherm formalisms and characterized them based on their relative complexities and predictive abilities with multimodal chromatography. The set of isotherm models studied were all based on the stoichiometric displacement framework, with considerations for electrostatic interactions, hydrophobic interactions, and thermodynamic activities. Isotherm parameters for each model were first determined through twenty repeated fits to a set of mAb - Capto MMC batch isotherm data spanning a range of loading, ionic strength, and pH as well as a set of mAb - Capto Adhere batch data at constant pH. The batch isotherm data were used in two ways-spanning the full range of loading or consisting of only the high concentration data points. Predictive ability was defined through the model's capacity to capture prominent changes in salt gradient elution behavior with respect to pH for Capto MMC or unique elution patterns and yield losses with respect to gradient slope for Capto Adhere. In both cases, model performance was quantified using a scoring metric based on agreement in peak characteristics for column predictions and accuracy of fit for the batch data. These scores were evaluated for all twenty isotherm fits and their corresponding column predictions, thereby producing a statistical distribution of model performances. Model complexity (number of isotherm parameters) was then considered through use of the Akaike information criterion (AIC) calculated from the score distributions. While model performance for Capto MMC benefitted substantially from removal of low protein concentration data, this was not the case for Capto Adhere; this difference was likely due to the qualitatively different shapes of the isotherms between the two resins. Surprisingly, the top-performing (high accuracy with minimal number of parameters) isotherm model was the same for both resins. The extended steric mass action (SMA) isotherm (containing both protein-salt and protein-protein activity terms) accurately captured both the pH-dependent elution behavior for Capto MMC as well as loss in protein recovery with increasing gradient slope for Capto Adhere. In addition, this isotherm model achieved the highest median score in both resin systems, despite it lacking any explicit hydrophobic stoichiometric terms. The more complex isotherm models, which explicitly accounted for both electrostatic and hydrophobic interaction stoichiometries, were ill-suited for Capto MMC and had lower AIC model likelihoods for Capto Adhere due to their increased complexity. Interestingly, the ability of the extended SMA isotherm to predict the Capto Adhere results was largely due to the protein-salt activity coefficient, as determined via isotherm parameter sensitivity analyses. Further, parametric studies on this parameter demonstrated that it had a major impact on both binding affinity and elution behavior, therein fully capturing the impact of hydrophobic interactions. In summary, we were able to determine the isotherm formalisms most capable of consistently predicting a wide range of column behavior for both a multimodal cation-exchange and multimodal anion-exchange resin with high accuracy, while containing a minimized set of model parameters.

Robust mechanistic modeling of protein ion-exchange chromatography.

Mechanistic models for ion-exchange chromatography of proteins are well-established and a broad consensus exists on most aspects of the detailed mathematical and physical description. A variety of specializations of these models can typically capture the general locations of elution peaks, but discrepancies are often observed in peak position and shape, especially if the column load level is in the non-linear range. These discrepancies may prevent the use of models for high-fidelity predictive applications such as process characterization and development of high-purity and -productivity process steps. Our objective is to develop a sufficiently robust mechanistic framework to make both conventional and anomalous phenomena more readily predictable using model parameters that can be evaluated based on independent measurements or well-accepted correlations. This work demonstrates the implementation of this approach for industry-relevant case studies using both a model protein, lysozyme, and biopharmaceutical product monoclonal antibodies, using cation-exchange resins with a variety of architectures (SP Sepharose FF, Fractogel EMD SO3-, Capto S and Toyopearl SP650M). The modeling employs the general rate model with the extension of the surface diffusivity to be variable, as a function of ionic strength or binding affinity. A colloidal isotherm that accounts for protein-surface and protein-protein interactions independently was used, with each characterized by a parameter determined as a function of ionic strength and pH. Both of these isotherm parameters, along with the variable surface diffusivity, were successfully estimated using breakthrough data at different ionic strengths and pH. The model developed was used to predict overloads and elution curves with high accuracy for a wide variety of gradients and different flow rates and protein loads. The in-silico methodology used in this work for parameter estimation, along with a minimal amount of experimental data, can help the industry adopt model-based optimization and control of preparative ion-exchange chromatography with high accuracy.

Displacement to separate host-cell proteins and aggregates in cation-exchange chromatography of monoclonal antibodies.

An efficient and consistent method of monoclonal antibody (mAb) purification can improve process productivity and product consistency. Although protein A chromatography removes most host-cell proteins (HCPs), mAb aggregates and the remaining HCPs are challenging to remove in a typical bind-and-elute cation-exchange chromatography (CEX) polishing step. A variant of the bind-and-elute mode is the displacement mode, which allows strongly binding impurities to be preferentially retained and significantly improves resin utilization. Improved resin utilization renders displacement chromatography particularly suitable in continuous chromatography operations. In this study we demonstrate and exploit sample displacement between a mAb and impurities present at low prevalence (0.002%-1.4%) using different multicolumn designs and recycling. Aggregate displacement depends on the residence time, sample concentration, and solution environment, the latter by enhancing the differences between the binding affinities of the product and the impurities. Displacement among the mAb and low-prevalence HCPs resulted in an effectively bimodal-like distribution of HCPs along the length of a multi-column system, with the mAb separating the relatively more basic group of HCPs from those that are more acidic. Our findings demonstrate that displacement of low-prevalence impurities along multiple CEX columns allows for selective separation of mAb aggregates and HCPs that persist through protein A chromatography.

Mechanistic Modeling of Preparative Column Chromatography for Biotherapeutics.

Chromatography has long been, and remains, the workhorse of downstream processing in the production of biopharmaceuticals. As bioprocessing has matured, there has been a growing trend toward seeking a detailed fundamental understanding of the relevant unit operations, which for some operations include the use of mechanistic modeling in a way similar to its use in the conventional chemical process industries. Mechanistic models of chromatography have been developed for almost a century, but although the essential features are generally understood, the specialization of such models to biopharmaceutical processing includes several areas that require further elucidation. This review outlines the overall approaches used in such modeling and emphasizes current needs, specifically in the context of typical uses of such models; these include selection and improvement of isotherm models and methods to estimate isotherm and transport parameters independently. Further insights are likely to be aided by molecular-level modeling, as well as by the copious amounts of empirical data available for existing processes.

Estimating and leveraging protein diffusion on ion-exchange resin surfaces.

Protein mobility at solid-liquid interfaces can affect the performance of applications such as bioseparations and biosensors by facilitating reorganization of adsorbed protein, accelerating molecular recognition, and informing the fundamentals of adsorption. In the case of ion-exchange chromatographic beads with small, tortuous pores, where the existence of surface diffusion is often not recognized, slow mass transfer can result in lower resin capacity utilization. We demonstrate that accounting for and exploiting protein surface diffusion can alleviate the mass-transfer limitations on multiple significant length scales. Although the surface diffusivity has previously been shown to correlate with ionic strength (IS) and binding affinity, we show that the dependence is solely on the binding affinity, irrespective of pH, IS, and resin ligand density. Different surface diffusivities give rise to different protein distributions within the resin, as characterized using confocal microscopy and small-angle neutron scattering (length scales of micrometer and nanometer, respectively). The binding dependence of surface diffusion inspired a protein-loading approach in which the binding affinity, and hence the surface diffusivity, is modulated by varying IS. Such gradient loading increased the protein uptake efficiency by up to 43%, corroborating the importance of protein surface diffusion in protein transport in ion-exchange chromatography.

Design of experiments applications in bioprocessing: Chromatography process development using split design of experiments.

Development of a chromatographic step in a time and resource efficient manner remains a serious bottleneck in protein purification. Chromatographic performance typically depends on raw material attributes, feed material attributes, process factors, and their interactions. Design of experiments (DOE) based process development is often chosen for this purpose. A challenge is, however, in performing a DOE with such a large number of process factors. A split DOE approach based on process knowledge in order to reduce the number of experiments is proposed. The first DOE targets optimizing factors that are likely to significantly impact the process and their effect on process performance is unknown. The second DOE aims to fine-tune another set of interacting process factors, impact of whom on process performance is known from process understanding. Furthermore, modeling of a large set of output response variables has been achieved by fitting the output responses to an empirical equation and then using the parametric constants of the equation as output response variables for regression modeling. Two case studies involving hydrophobic interaction chromatography for removal of aggregates and cation exchange chromatography for separation of charge variants and aggregates have been utilized to illustrate the proposed approach. Proposed methodology reduced total number of experiments by 25% and 72% compared to a single DOE based on central composite design and full factorial design, respectively. The proposed approach is likely to result in a significant reduction in resources required as well as time taken during process development. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2730, 2019.

Multi-column displacement chromatography for separation of charge variants of monoclonal antibodies.

Native forms of therapeutic monoclonal antibodies (mAbs) coexist with various acidic and basic charge variants throughout process development and into drug product formulation. During downstream purification, a product's charge variant composition is controlled, as necessary, primarily through peak fractionation and pooling of elution fractions using cation-exchange chromatography (CEX). This can be a cumbersome process with poor resolution and it may result in a significant reduction in product yield. In the present work, separation and enrichment of the native form of a mAb and of basic and acidic variants is achieved using self-displacement chromatography in a multi-column continuous chromatography set-up. Basic mAb variants are more strongly retained in CEX owing to their higher charge, and can displace the native and the acidic variants. Similarly, the native variant can displace the acidic variants if the amount loaded exceeds the total resin capacity. To this end, we utilized a three-column continuous system to consecutively displace acidic, native and basic charge variants of a therapeutic mAb in the order of increasing binding strength during product loading. Using our optimized operating parameters, we were able to enrich the native variant from 65% to 90% while loading above the capacity of the column, with a process yield of above 90%. This method and approach will help to control and reduce in particular the charged variant heterogeneity, and, in general, aid in the separation of charged proteins at preparative scale.

Clinical Spectrum of Chikungunya in Pakistan.

Background Chikungunya fever is a pandemic disease caused by an arthropod-borne chikungunya virus (CHIKV). The virus spreads through mosquitoes. This mosquito induced viral illness is clinically suspected on symptoms from fever and severe polyarthralgia. The recent outbreak of chikungunya was reported in November 2016 in the metropolitan city Karachi, Pakistan. We emphasis on the awareness of the etiology and vector control to prevent serious consequences. Method A total number of 1275 patients were included in this cross-sectional study. These patients were enrolled based on clinical findings described by Centers for Disease Control and Prevention (CDC). Our exclusion criteria were patients with missing data or having co-infection with dengue or malaria. The patients were tested for chikungunya antibodies, malaria, and dengue. The patients were followed for three months. Results Out of 1275 consenting patients from the emergency department, 564 tested positive for chikungunya antibodies and out of these 564 patients 365 had co-infection of dengue and malaria. So based on exclusion criteria, 199 patients had isolated chikungunya infection and were studied for the frequency of clinical symptoms. The most common finding was joint pain and fever on presentation and joint pain was the only chronic finding which persisted. Conclusion Our study demonstrated the frequency of clinical findings in chikungunya infection. It also signifies the importance of testing for antibodies because it helped in excluding patients with false positive clinical findings and differentiating co-infection with malaria and dengue. It also gauged patient's view about the cause of this disease.

Integrated Chromatographic Platform for Simultaneous Separation of Charge Variants and Aggregates from Monoclonal Antibody Therapeutic Products.

Achieving consistent product quality of a biotherapeutic is a major target for any biopharmaceutical manufacturer, even more for a biosimilar producer as comparability with the innovator product is a regulatory expectation. The complexity of biotherapeutic products and their tedious manufacturing processes, however, make this a non-trivial exercise. The primary motivation of this work is to develop an integrated chromatographic platform for purification of monoclonal antibody (mAb) therapeutics that can deliver the desired separation of both charge variants and aggregates, in addition to the process related impurities like host cell proteins (HCP) and host cell DNA. To achieve the same, an integrated two-stage chromatographic process platform consisting of cation exchange chromatography and multimodal chromatography is being proposed. The versatility of the proposed platform has been successfully demonstrated for three different mAbs. It have been shown that in each case charge variant separation is achieved with the required clearance of aggregates (<1%), HCP (<10 ppm), and DNA (<5 ppb). Moreover, the proposed platform is conducive to use for development of a continuous process and offers smaller process time, lower buffer utilization, and decreased operational costs when compared to the conventional purification platforms.

Mechanistic Modeling Based PAT Implementation for Ion-Exchange Process Chromatography of Charge Variants of Monoclonal Antibody Products.

Process chromatography is typically used to remove product related impurities and variants that have very similar physicochemical properties to the product. Baseline separation may not be achieved in most cases due to high protein loading and thus, pooling of the elution peak can be challenging for maximizing yield and achieving consistency in product quality. Batch-to-batch variability in quality of the feed material also occurs in commercial manufacturing. Mechanistic modeling of process chromatography, though non-trivial, can be an enabler for implementation of Process Analytical Technology. This paper presents one such application involving prediction of the impact of variability in feed quality and in gradient shape on separation of charge variants by cation exchange process chromatography and thereby facilitating feed forward control. Five batches having different compositions of charge variants have been used to demonstrate the proposed pooling strategy based on simulated chromatograms and the outcome has been compared to offline pooling based on fractionation. For all the conditions examined and for the desired target of main product (67%), the proposed approach resulted in remarkable consistency in product quality (67 ± 2%) while delivering a yield of greater than 90%.

Optimization of ion exchange sigmoidal gradients using hybrid models: Implementation of quality by design in analytical method development.

Thorough product understanding is one of the basic tenets for successful implementation of Quality by Design (QbD). Complexity encountered in analytical characterization of biotech therapeutics such as monoclonal antibodies (mAbs) requires novel, simpler, and generic approaches towards product characterization. This paper presents a methodology for implementation of QbD for analytical method development. Optimization of an analytical cation exchange high performance liquid chromatography (CEX-HPLC) method utilizing a sigmoidal gradient has been performed using a hybrid mechanistic model that is based on Design of experiment (DOE) based studies. Since sigmodal gradients are much more complex than the traditional linear gradients and have a large number of input parameters (five) for optimization, the number of DOE experiments required for a full factorial design to estimate all the main effects as well as the interactions would be too large (243). To address this problem, a mechanistic model was used to simulate the analytical separation for the DOE and then the results were used to build an empirical model. The mechanistic model used in this work is a more versatile general rate model in combination of modified Langmuir binding kinetics. The modified Langmuir model is capable of modelling the impact of nonlinear changes in the concentration of the salt modifier. Further, to get the input and output profiles of mAb and salts/buffers, the HPLC system, consisting of the mixer, detectors, and tubing was modelled as a sequence of dispersed plug flow reactors and continuous stirred tank reactors (CSTR). The experimental work was limited to calibration of the HPLC system and finding the model parameters through three linear gradients. To simplify the optimization process, only three peaks in the centre of the profile (main product and the adjacent acidic and basic variants) were chosen to determine the final operating condition. The regression model made from the DoE data yielded a R2>0.97 which made it possible to predict and choose the design space where the optimal resolution between the acidic/main peaks and the basic/main peaks could be achieved (>1.2 and >2.5, respectively). The optimal operating condition was validated using experimental runs and was found to give the same resolution as what was predicted by the simulation. The proposed approach aims to significantly reduce the time required for method optimization as well as the extent of experimentation.

Mechanistic modeling of ion-exchange process chromatography of charge variants of monoclonal antibody products.

Ion-exchange chromatography (IEX) is universally accepted as the optimal method for achieving process scale separation of charge variants of a monoclonal antibody (mAb) therapeutic. These variants are closely related to the product and a baseline separation is rarely achieved. The general practice is to fractionate the eluate from the IEX column, analyze the fractions and then pool the desired fractions to obtain the targeted composition of variants. This is, however, a very cumbersome and time consuming exercise. A mechanistic model that is capable of simulating the peak profile will be a much more elegant and effective way to make a decision on the pooling strategy. This paper proposes a mechanistic model, based on the general rate model, to predict elution peak profile for separation of the main product from its variants. The proposed approach uses inverse fit of process scale chromatogram for estimation of model parameters using the initial values that are obtained from theoretical correlations. The packed bed column has been modeled along with the chromatographic system consisting of the mixer, tubing and detectors as a series of dispersed plug flow and continuous stirred tank reactors. The model uses loading ranges starting at 25% to a maximum of 70% of the loading capacity and hence is applicable to process scale separations. Langmuir model has been extended to include the effects of salt concentration and temperature on the model parameters. The extended Langmuir model that has been proposed uses one less parameter than the SMA model and this results in a significant ease of estimating the model parameters from inverse fitting. The proposed model has been validated with experimental data and has been shown to successfully predict peak profile for a range of load capacities (15-28mg/mL), gradient lengths (10-30CV), bed heights (6-20cm), and for three different resins with good accuracy (as measured by estimation of residuals). The model has been also validated for a two component mixture consisting of the main mAb product and one of its basic charge variants. The proposed model can be used for optimization and control of preparative scale chromatography for separation of charge variants.

Rapid analysis of charge variants of monoclonal antibodies using non-linear salt gradient in cation-exchange high performance liquid chromatography.

A method is proposed for rapid development of a short, analytical cation exchange high performance liquid chromatography method for analysis of charge heterogeneity in monoclonal antibody products. The parameters investigated and optimized include pH, shape of elution gradient and length of the column. It is found that the most important parameter for development of a shorter method is the choice of the shape of elution gradient. In this paper, we propose a step by step approach to develop a non-linear sigmoidal shape gradient for analysis of charge heterogeneity for two different monoclonal antibody products. The use of this gradient not only decreases the run time of the method to 4min against the conventional method that takes more than 40min but also the resolution is retained. Superiority of the phosphate gradient over sodium chloride gradient for elution of mAbs is also observed. The method has been successfully evaluated for specificity, sensitivity, linearity, limit of detection, and limit of quantification. Application of this method as a potential at-line process analytical technology tool has been suggested.

Role of organic modifier and gradient shape in RP-HPLC separation: analysis of GCSF variants.

Reversed-phase high-performance liquid chromatography (RP-HPLC) of therapeutic proteins continues to play a significant role in product characterization. This study focuses on two key aspects of HPLC method development, namely the selection of organic modifier and the gradient shape. Separation of granulocyte colony-stimulating factor variants is being used as a case study to illustrate these concepts. The results demonstrate that careful selection of a binary or ternary mixture of solvents with water is an important factor to be considered for achieving the desired resolution of closely related impurities. The resolution of different types of impurities has been shown to be selective toward the choice of eluent along with the ratio in which they are mixed. In addition, this study also presents a systematic approach for selection of gradient shape based on center point solvent composition, initial solvent composition and the steepness of the gradient. The approach proposed in this study was successfully used to reduce the time of analysis from 70 min for the pertinent European Pharmacopeia method to 15 min by using a solvent system with two organic modifiers (acetonitrile and methanol) along with a sigmoidal-shaped gradient.

Two-stage chromatographic separation of aggregates for monoclonal antibody therapeutics.

Aggregates of monoclonal antibody (mAb) therapeutics, due to their perceived impact on immunogenicity, are recognized as a critical quality attribute by the regulatory authorities as well as the industry. Hence, removal of aggregates is a key objective of bioprocessing. At present, this is achieved by a combination of two or more orthogonal chromatographic steps with possible modalities of ion exchange, hydrophobic interaction and mixed mode. A two-stage chromatographic purification process consisting of ion-exchange and hydrophobic interaction modes is proposed in this paper for effective and efficient control of aggregates for a mAb therapeutic. The proposed scheme does not require any intermediate processing of the process stream. Further, baseline separation is achieved for monomer and aggregates resulting in robust performance. This was possible because the proposed operational scheme allowed for an addition of selectivities of the two chromatography modes vs. the traditional two column scheme where part of the separation of aggregates achieved by the first column is lost upon pooling. The proposed process scheme yielded improved separation of aggregates (0% vs. 1-2%) at >95% recovery and reduced overall process time (6h vs. 14 h) for a typical application. Further, clearance of host cell proteins was also shown to have improved with the suggested process scheme. Successful implementation of the proposed scheme has been demonstrated for two different monoclonal antibody therapeutic products.

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.

Design of experiments applications in bioprocessing: concepts and approach.

Most biotechnology unit operations are complex in nature with numerous process variables, feed material attributes, and raw material attributes that can have significant impact on the performance of the process. Design of experiments (DOE)-based approach offers a solution to this conundrum and allows for an efficient estimation of the main effects and the interactions with minimal number of experiments. Numerous publications illustrate application of DOE towards development of different bioprocessing unit operations. However, a systematic approach for evaluation of the different DOE designs and for choosing the optimal design for a given application has not been published yet. Through this work we have compared the I-optimal and D-optimal designs to the commonly used central composite and Box-Behnken designs for bioprocess applications. A systematic methodology is proposed for construction of the model and for precise prediction of the responses for the three case studies involving some of the commonly used unit operations in downstream processing. Use of Akaike information criterion for model selection has been examined and found to be suitable for the applications under consideration.