Modeling of hydrophobic interaction chromatography for the separation of antibody-drug conjugates and its application towards quality by design

Modeling multi-component separation in hydrophobic interaction chromatography with improved parameter-by-parameter estimation method

Mechanistic models are powerful tools for chromatographic process development and optimization. However, hydrophobic interaction chromatography (HIC) mechanistic models lack an effective and logical parameter estimation method, especially for multi-component system. In this study, a parameter-by-parameter method for multi-component system (called as mPbP-HIC) was derived based on the retention mechanism to estimate the six parameters of the Mollerup isotherm for HIC. The linear parameters (ks,i and keq,i) and nonlinear parameters (ni and qmax,i) of the isotherm can be estimated by the linear regression (LR) and the linear approximation (LA) steps, respectively. The remaining two parameters (kp,i and kkin,i) are obtained by the inverse method (IM). The proposed method was verified with a two-component model system. The results showed that the model could accurately predict the protein elution at a loading of 10 g/L. However, the elution curve fitting was unsatisfactory for high loadings (12 g/L and 14 g/L), which is mainly attributed to the demanding experimental conditions of the LA step and the potential large estimation error of the parameter qmax. Therefore, the inverse method was introduced to further calibrate the parameter qmax, thereby reducing the estimation error and improving the curve fitting. Moreover, the simplified linear approximation (SLA) was proposed by reasonable assumption, which provides the initial guess of qmax without solving any complex matrix and avoids the problem of matrix unsolvable. In the improved mPbP-HIC method, qmax would be initialized by the SLA and finally determined by the inverse method, and this strategy was named as SLA+IM. The experimental validation showed that the improved mPbP-HIC method has a better curve fitting, and the use of SLA+IM reduces the error accumulation effect. In process optimization, the parameters estimated by the improved mPbP-HIC method provided the model with excellent predictive ability and reasonable extrapolation. In conclusion, the SLA+IM strategy makes the improved mPbP-HIC method more rational and can be easily applied to the practical separation of protein mixture, which would accelerate the process development for HIC in downstream of biopharmaceuticals.

Enhancing thermodynamic consistency: Clarification on the application of asymmetric activity model in multi-component chromatographic separation

The single-component Mollerup model, with over 40 direct applications and 442 citations, is the most widely used activity model for chromatographic mechanistic modeling. Many researchers have extended this formula to multi-component systems by directly adding subscripts, a modification deemed thermodynamically inconsistent (referred to as the reference model). In this work, we rederived the asymmetric activity model for multi-component systems, using the van der Waals equation of state, and termed it the multi-component Mollerup model. In contrast to the reference model, our proposed model accounts for the contributions of all components to the activity. Three numerical experiments were performed to investigate the impact of the three different activity models on the chromatographic modeling. The results indicate that our proposed model represents a thermodynamically consistent generalization of the single-component Mollerup model to multi-component systems. This communication advocates adopting of the multi-component Mollerup model for activity modeling in multi-component chromatographic separation to enhance thermodynamic consistency.

Spotlight on ideal target antigens and resistance in antibody-drug conjugates: Strategies for competitive advancement

Antibody-drug conjugates (ADCs) represent a novel and promising approach in targeted therapy, uniting the specificity of antibodies that recognize specific antigens with payloads, all connected by the stable linker. These conjugates combine the best targeted and cytotoxic therapies, offering the killing effect of precisely targeting specific antigens and the potent cell-killing power of small molecule drugs. The targeted approach minimizes the off-target toxicities associated with the payloads and broadens the therapeutic window, enhancing the efficacy and safety profile of cancer treatments. Within precision oncology, ADCs have garnered significant attention as a cutting-edge research area and have been approved to treat a range of malignant tumors. Correspondingly, the issue of resistance to ADCs has gradually come to the fore. Any dysfunction in the steps leading to the ADCs' action within tumor cells can lead to the development of resistance. A deeper understanding of resistance mechanisms may be crucial for developing novel ADCs and exploring combination therapy strategies, which could further enhance the clinical efficacy of ADCs in cancer treatment. This review outlines the brief historical development and mechanism of ADCs and discusses the impact of their key components on the activity of ADCs. Furthermore, it provides a detailed account of the application of ADCs with various target antigens in cancer therapy, the categorization of potential resistance mechanisms, and the current state of combination therapies. Looking forward, breakthroughs in overcoming technical barriers, selecting differentiated target antigens, and enhancing resistance management and combination therapy strategies will broaden the therapeutic indications for ADCs. These progresses are anticipated to advance cancer treatment and yield benefits for patients.

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.

Sequential purification of cannabidiol by two-dimensional liquid chromatography combined with modeling and simulation of elution profiles

Cannabidiol (CBD) has garnered significant attention for its neuroprotective properties, and research on its therapeutic effects has increased dramatically in recent years. However, the systematic purification of CBD through scalable processes has remained bottleneck due to the structural similarities of the cannabinoids. Although preparative chromatography is considered as a potential solution, it is usually time-consuming and expensive. Therefore, the development of scalable strategy via fast and accurate optimization approach is crucial. The present study aimed to develop a sequential process for the scalable purification of CBD through an eco-friendly ethanolic extraction using ultrasonic assisted extraction, decarboxylation of cannabidiolic acid optimized by response surface methodology, followed by the development of off-line two-dimensional semi-preparative chromatography, boosted with stacked injection overloading. In the first dimension, a column packed with macroporous resin allows to enrich the target substance and then, the behavior of resin column for scale-up procedure were predicted and optimized by developed mathematical model. A C18 column was used in the second dimension. The CBD purity and recovery obtained were 94.3 and 82.1 %, respectively. A robust and reliable method was employed for CBD enrichment/purification, which can be generalized to other bioactive compounds in complex matrices.

Parameter-by-parameter estimation method for adsorption isotherm in hydrophobic interaction chromatography

Hydrophobic interaction chromatography (HIC) is used as a critical polishing step in the downstream processing of biopharmaceuticals. Normally the process development of HIC is a cumbersome and time-consuming task, and the mechanical models can provide a powerful tool to characterize the process, assist process design and accelerate process development. However, the current estimation of model parameters relies on the inverse method, which lacks an efficient and logical parameter estimation strategy. In this study, a parameter-by-parameter (PbP) method based on the theoretical derivation and simplifying assumptions was proposed to estimate the Mollerup isotherm parameters for HIC. The method involves three key steps: (1) linear regression (LR) to estimate the salt-protein interaction parameter and the equilibrium constant; (2) linear approximation (LA) to estimate the stoichiometric parameter and the maximum binding capacity; and (3) inverse method to estimate the protein-protein interaction parameter and the kinetic coefficient. The results indicated that the LR step should be used for dilution condition (loading factor below 5%), while the LA step should be conducted when the isotherm is in the transition or nonlinear regions. Six numerical experiments were conducted to implement the PbP method. The results demonstrated that the PbP method developed allows for the systematic estimation of HIC parameters one-by-one, effectively reducing the number of parameters required for inverse method estimation from six to two. This helps prevent non-identifiability of structural parameters. The feasibility of the PbP-HIC method was further validated by real-world experiments. Moreover, the PbP method enhances the mechanistic understanding of adsorption behavior of HIC and shows a promising application to other stoichiometric displacement model-derived isotherms.

A comprehensive overview on antibody-drug conjugates: from the conceptualization to cancer therapy

Antibody-Drug Conjugates (ADCs) represent an innovative class of potent anti-cancer compounds that are widely used in the treatment of hematologic malignancies and solid tumors. Unlike conventional chemotherapeutic drug-based therapies, that are mainly associated with modest specificity and therapeutic benefit, the three key components that form an ADC (a monoclonal antibody bound to a cytotoxic drug via a chemical linker moiety) achieve remarkable improvement in terms of targeted killing of cancer cells and, while sparing healthy tissues, a reduction in systemic side effects caused by off-tumor toxicity. Based on their beneficial mechanism of action, 15 ADCs have been approved to date by the market approval by the Food and Drug Administration (FDA), the European Medicines Agency (EMA) and/or other international governmental agencies for use in clinical oncology, and hundreds are undergoing evaluation in the preclinical and clinical phases. Here, our aim is to provide a comprehensive overview of the key features revolving around ADC therapeutic strategy including their structural and targeting properties, mechanism of action, the role of the tumor microenvironment and review the approved ADCs in clinical oncology, providing discussion regarding their toxicity profile, clinical manifestations and use in novel combination therapies. Finally, we briefly review ADCs in other pathological contexts and provide key information regarding ADC manufacturing and analytical characterization.

Hybrid model development for parameter estimation and process optimization of hydrophobic interaction chromatography

Hydrophobic Interaction Chromatography (HIC) is often employed as a polishing step to remove aggregates for the purification of therapeutic proteins in the biopharmaceutical industry. To accelerate the process development and save the costs of performing time- and resource-intensive experiments, advanced model-based process design and optimization are necessary. Due to the unclear adsorption mechanism of the salt-dependent interaction between the protein and resin, the development of an accurate mechanistic model to describe the complex HIC behavior is challenging. In this work, an isotherm derived from Wang et al. is modified by adding three extra parameters together with an equilibrium dispersive model to represent the HIC process. To reduce the development effort of isotherm equations and extract missing information from the available data, a hybrid model is constructed by combining a simple and well-known multi-component Langmuir isotherm (MCL) with a neural network (NN). It is observed that the structure of the hybrid model is of critical importance to the accuracy of the developed model. During parameter estimation, a regularization strategy is incorporated to prevent overfitting. Furthermore, the impact of NN structures and regularization rates are comprehensively investigated. One of the interesting findings was that a simple NN with one hidden layer with two nodes and sigmoid as the activation function, significantly outperforms the mechanistic model, with a 62% improvement in accuracy in calibration and 31.4% in validation. To ensure the generalizability of the developed hybrid model, an in-silico dataset is generated using the mechanistic model to test the extrapolation capability of the hybrid model. Process optimization is also carried out to find the optimal operating conditions under product quality constraints using the developed hybrid model.

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.

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.

Switching positions: Assessing the dynamics of conjugational heterogeneity in antibody-drug conjugates using CE-SDS

Antibody-drug conjugates (ADCs) are a prospective class of new oncology therapeutics with the ability to deliver a cytotoxic drug to a targeted location. The concept appears simple, but ADCs are highly complex due to their intrinsic heterogeneity. Randomly conjugated ADCs, for instance, are composed of conjugated species carrying between 0 and 8 linker-drug molecules, with several positional isomers that vary in drug distribution across the antibody. The drug load, expressed as drug-to-antibody ratio (DAR), is a critical quality attribute and should be well controlled, together with the distribution of drug molecules. Here, the impact of the duration of disulfide bond reduction on the DAR was investigated by quantitating the (isomeric) DAR species in ADCs produced with varying reduction times. Although hydrophobic interaction chromatography showed a constant DAR value as a function of reduction time, data obtained by non-reducing CE-SDS revealed an unexpected dynamic in the positional conjugated isomers. The insights obtained have improved our understanding of the correlation between the disulfide bond reduction, an important step in the manufacturing of a cysteine-conjugated ADC, and the conjugational heterogeneity.

The use of predictive models to develop chromatography-based purification processes

Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions in silico reduces the number of empirical tests required and provides in-depth process understanding, which is recommended by regulatory authorities. In this article, we discuss the benefits and specific challenges of chromatography modeling. We describe the experimental characterization of chromatography devices and settings prior to modeling, such as the determination of column porosity. We also consider the challenges that must be overcome when models are set up and calibrated, including the cross-validation and verification of data-driven and hybrid (combined data-driven and mechanistic) models. This review will therefore support researchers intending to establish a chromatography modeling workflow in their laboratory.

New Technologies Bloom Together for Bettering Cancer Drug Conjugates

Drug conjugates, including antibody-drug conjugates, are a step toward realizing Paul Ehrlich's idea from over 100 years ago of a "magic bullet" for cancer treatment. Through balancing selective targeting molecules with highly potent payloads, drug conjugates can target specific tumor microenvironments and kill tumor cells. A drug conjugate consists of three parts: a targeting agent, a linker, and a payload. In some conjugates, monoclonal antibodies act as the targeting agent, but new strategies for targeting include antibody derivatives, peptides, and even small molecules. Linkers are responsible for connecting the payload to the targeting agent. Payloads impact vital cellular processes to kill tumor cells. At present, there are 12 antibody-drug conjugates on the market for different types of cancers. Research on drug conjugates is increasing year by year to solve problems encountered in conjugate design, such as tumor heterogeneity, poor circulation, low drug loading, low tumor uptake, and heterogenous expression of target antigens. This review highlights some important preclinical research on drug conjugates in recent years. We focus on three significant areas: improvement of antibody-drug conjugates, identification of new conjugate targets, and development of new types of drug conjugates, including nanotechnology. We close by highlighting the critical barriers to clinical translation and the open questions going forward. SIGNIFICANCE STATEMENT: The development of anticancer drug conjugates is now focused in three broad areas: improvements to existing antibody drug conjugates, identification of new targets, and development of new conjugate forms. This article focuses on the exciting preclinical studies in these three areas and advances in the technology that improves preclinical development.

In silico process characterization for biopharmaceutical development following the quality by design concept

With the quality by design (QbD) initiative, regulatory authorities demand a consistent drug quality originating from a well-understood manufacturing process. This study demonstrates the application of a previously published mechanistic chromatography model to the in silico process characterization (PCS) of a monoclonal antibody polishing step. The proposed modeling workflow covered the main tasks of traditional PCS studies following the QbD principles, including criticality assessment of 11 process parameters and establishment of their proven acceptable ranges of operation. Analyzing effects of multi-variate sampling of process parameters on the purification outcome allowed identification of the edge-of-failure. Experimental validation of in silico results demanded approximately 75% less experiments compared to a purely wet-lab based PCS study. Stochastic simulation, considering the measured variances of process parameters and loading material composition, was used to estimate the capability of the process to meet the acceptance criteria for critical quality attributes and key performance indicators. The proposed workflow enables the implementation of digital process twins as QbD tool for improved development of biopharmaceutical manufacturing processes.

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

In this paper, a methodology for the development of a multimodal chromatography process is presented that is aimed at removal of under-conjugated antibody-drug conjugate (ADC) species. Two ADCs are used as case studies: One ADC results from site-directed conjugation to inserted cysteine residues and has a drug-to-antibody ratio (DAR) of two, the other is the product of conjugation to interchain disulfide bonds with a DAR of eight. First, filter plate screening studies are designed for the unconjugated antibody and the ADCs. Different metrics for the analysis of these data sets are presented and discussed. From this analysis, the selected process conditions are then carried out using a benchtop chromatography system to confirm the separations observed in the filter plate studies while simultaneously generating data to estimate steric mass-action isotherm and mass transport parameters for process simulation. This column model is then employed to develop separation processes in-silico for the removal of the unconjugated parent antibody and under-conjugated product variants. The optimized process conditions identified using the model are then verified experimentally. The methodology presented in this work utilizes multimodal chromatography for ADC purification and provides the framework for a streamlined systematic approach to process development.