Using multimodal chromatography for post-conjugation antibody-drug conjugate purification: A methodology from high throughput screening to in-silico process development

Impact of drug-linker on method selection for analytical characterization and purification of antibody-drug conjugates

In addition to traditional characterisation methods of hydrophobic interaction (HIC) and reverse phase (RP) chromatography, an anion exchange chromatography (AIEX) was developed to analyse and purify antibody drug conjugates (ADCs). Since different drug antibody ratio (DAR) species may impact biological activity, therapeutic index, PK parameters or even potential immunogenicity, homogenous ADC DAR demands have been significantly increasing. To accelerate linker designs, drug screening and ADC DAR purification for in vitro and in vivo studies, we built the analytical toolbox including HIC, RP, AIEX, icIEF, SEC, and MS for downstream ADC DAR purification using HIC and AIEX. The established analytical methods can quickly assess the quality of ADC DAR profiles and provide important information to select the proper ADC DAR purification method. Since drug-linker structures can significantly affect ADC physicochemical properties, and highly impact on selections of analytical methods, we applied both HIC and AIEX characterisation and purification platforms to achieve ADC DAR homogenous. Our experiments also implied that unlike HIC, AIEX could be used to separate DAR4 positional isomers.

Rational downstream development for adeno-associated virus full/empty capsid separation - A streamlined methodology based on high-throughput screening and mechanistic modeling

Recombinant adeno-associated virus (AAV) has emerged as one of the most promising systems for therapeutic gene delivery and has demonstrated clinical success in a wide range of genetic disorders. However, manufacturing of high-quality AAV in large amounts still remains a challenge. A significant difficulty for downstream processing is the need to remove empty capsids that are generated in all currently utilized expression systems and that represent product-related impurities that adversely affect safety and efficacy of AAV vectors. Empty and full capsids exhibit only subtle differences in surface charge and size, making chromatography-based separations highly challenging. Here, we present a rapid methodology for the systematic process development of the crucial AAV full/empty capsid separation on ion-exchange media based on high-throughput screening and mechanistic modeling. Two of the most commonly employed serotypes, AAV8 and AAV9, are used as case studies. First, high-throughput studies in filter-plate format are performed that allow the rapid and comprehensive study of binding and elution behavior of AAV on different resins, using different buffer systems, pH, salt conditions, and solution additives. Small amounts of separated empty and full AAV capsids are generated by iodixanol gradient centrifugation that allow studying the binding and elution behavior of the two vector species separately in miniaturized format. Process conditions that result in maximum differences in elution behavior between empty and full capsids are then transferred to benchtop chromatography systems that are used to generate calibration data for the estimation of steric mass-action isotherm and mass transport parameters for process simulation. The resulting column models are employed for in-silico process development that serves to enhance understanding of separation constraints and to identify optimized conditions for the removal of empty particles. Finally, optimized separation conditions are verified experimentally. The methodology presented in this work provides a systematic framework that affords mechanistic understanding of the crucial empty/full capsid separation and accelerates the development of a scalable AAV downstream process.

An adaptive soft-sensor for advanced real-time monitoring of an antibody-drug conjugation reaction

In the production of antibody-drug conjugates (ADCs), the conjugation reaction is a central step defining the final product composition and, hence, directly affecting product safety and efficacy. To enable real-time monitoring, spectroscopic sensors in combination with multivariate regression models have gained popularity in recent years. The extended Kalman filter (EKF) can be used as so-called soft-sensor to fuse sensor predictions with long-horizon forecasts by process models. This enables the dynamic update of the current state and provides increased robustness against experimental noise or model errors. Due to the uncertainty associated with sensor and process models in biopharmaceutical applications, the deployment of such soft-sensors is challenging. In this study, we demonstrate the combination of an uncertainty-aware sensor model with a kinetic reaction model using an EKF to monitor a site-directed ADC conjugation reaction. As the sensor model, a Gaussian process regression model is presented to realize a time-variant determination of the sensor uncertainty. The EKF fuses the time-discrete predictions of the amount of conjugated drug from the sensor model with the time-continuous predictions from the kinetic model. While the ADC species are not distinguishable by on-line recorded UV/Vis spectra, the developed soft-sensor is able to dynamically update all relevant reaction species. It could be shown that the use of time-variant process and sensor noise computation approaches improved the performance of the EKF and achieved a reduction of the prediction error of up to 23% compared with the kinetic model. The developed framework proved to enhance robustness against noisy sensor measurements or wrong model initialization and was successfully transferred from batch to fed-batch mode. In future, this framework could be implemented for model-based process control and be adopted for other ADC conjugation reaction types.

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.

Standardized method for mechanistic modeling of multimodal anion exchange chromatography in flow through operation

Multimodal chromatography offers an increased selectivity compared to unimodal chromatographic methods and is often employed for challenging separation tasks in industrial downstream processing (DSP). Unfortunately, the implementation of multimodal polishing into a generic downstream platform can be hampered by non-robust platform conditions leading to a time and cost intensive process development. Mechanistic modeling can assist experimental process development but readily applicable and easy to calibrate multimodal chromatography models are lacking. In this work, we present a mechanistic modeling aided approach that paves the way for an accelerated development of anionic mixed-mode chromatography (MMC) for biopharmaceutical purification. A modified multimodal isotherm model was calibrated using only three chromatographic experiments and was employed in the retention prediction of four antibody formats including a Fab, a bispecific, as well as an IgG1 and IgG4 antibody subtype at pH 5.0 and 6.0. The chromatographic experiments were conducted using the anionic mixed-mode resin Capto adhere at industrial relevant process conditions to enable flow through purification. An existing multimodal isotherm model was reduced to hydrophobic interactions in the linear range of the adsorption isotherm and successfully employed in the simulation of six chromatographic experiments per molecule in concert with the transport dispersive model (TDM). The model reduction to only three parameters did prevent structural parameter non-identifiability and enabled an analytical isotherm parameter determination that was further refined by incorporation of size exclusion effects of the selected multimodal resin. During the model calibration, three linear salt gradient elution experiments were performed for each molecule followed by an isotherm parameter uncertainty assessment. Lastly, each model was validated with a set of step and isocratic elution experiments. This standardized modeling approach facilitates the implementation of multimodal chromatography as a key unit operation for the biopharmaceutical downstream platform, while increasing the mechanistic insight to the multimodal adsorption behavior of complex biologics.