Simultaneous prediction of 16 quality attributes during protein A chromatography using machine learning based Raman spectroscopy models.

Several key technologies for advancing biopharmaceutical manufacturing depend on the successful implementation of process analytical technologies that can monitor multiple product quality attributes in a continuous in-line setting. Raman spectroscopy is an emerging technology in the biopharma industry that promises to fit this strategic need, yet its application is not widespread due to limited success for predicting a meaningful number of quality attributes. In this study, we addressed this very problem by demonstrating new capabilities for preprocessing Raman spectra using a series of Butterworth filters. The resulting increase in the number of spectral features is paired with a machine learning algorithm and laboratory automation hardware to drive the automated collection and training of a calibration model that allows for the prediction of 16 different product quality attributes in an in-line mode. The demonstrated ability to generate these Raman-based models for in-process product quality monitoring is the breakthrough to increase process understanding by delivering product quality data in a continuous manner. The implementation of this multiattribute in-line technology will create new workflows within process development, characterization, validation, and control.

Tailoring polishing steps for effective removal of polysorbate-degrading host cell proteins in antibody purification.

Ensuring the quality and safety of biopharmaceutical products requires the effective separation of monoclonal antibodies (mAbs) from host cell proteins (HCPs). A major challenge in this field is the enzymatic hydrolysis of polysorbates (PS) in drug products. This study addresses this issue by investigating the removal of polysorbate-degrading HCPs during the polishing steps of downstream purification, an area where knowledge about individual HCP behavior is still limited. We investigated the separation of different mAb formats from four individual polysorbate degrading hydrolases (CES1F, CES2C, LPLA2, and PAF-AH) using cation exchange (CEX) and mixed-mode chromatography (MMC) polishing steps. Our research identified a key challenge: The similar elution behavior of mAbs and HCPs during chromatographic separation. To investigate this phenomenon, we performed high-throughput binding screenings for recombinant polysorbate degrading hydrolases and representative mAb candidates on CEX and MMC chromatography resins. We then employed a three-step strategy that also served as a scale-up process, optimizing separation conditions and leading to the successful removal of specific HCPs while maintaining high mAb recovery rates (>96%). This strategy involved the use of surface response models and miniature columns for screening, followed by validation on larger columns using a chromatography system. Our results highlight the critical role of the inherent properties of mAbs for successful separation from HCPs. These results underscore the need to tailor the purification process to leverage the slight differences in binding behavior and elution profiles between mAbs and specific HCPs. This approach lays the foundation for developing more effective strategies for overcoming the challenge of enzymatic polysorbate degradation, paving the way for improved quality and safety in biopharmaceutical products.

Automated calibration and in-line measurement of product quality during therapeutic monoclonal antibody purification using Raman spectroscopy.

Current manufacturing and development processes for therapeutic monoclonal antibodies demand increasing volumes of analytical testing for both real-time process controls and high-throughput process development. The feasibility of using Raman spectroscopy as an in-line product quality measuring tool has been recently demonstrated and promises to relieve this analytical bottleneck. Here, we resolve time-consuming calibration process that requires fractionation and preparative experiments covering variations of product quality attributes (PQAs) by engineering an automation system capable of collecting Raman spectra on the order of hundreds of calibration points from two to three stock seed solutions differing in protein concentration and aggregate level using controlled mixing. We used this automated system to calibrate multi-PQA models that accurately measured product concentration and aggregation every 9.3 s using an in-line flow-cell. We demonstrate the application of a nonlinear calibration model for monitoring product quality in real-time during a biopharmaceutical purification process intended for clinical and commercial manufacturing. These results demonstrate potential feasibility to implement quality monitoring during GGMP manufacturing as well as to increase chemistry, manufacturing, and controls understanding during process development, ultimately leading to more robust and controlled manufacturing processes.

A multiscale modeling method for therapeutic antibodies in ion exchange chromatography.

The development of biopharmaceutical downstream processes relies on exhaustive experimental studies. The root cause is the poorly understood relationship between the protein structure of monoclonal antibodies (mAbs) and their macroscopic process behavior. Especially the development of preparative chromatography processes is challenged by the increasing structural complexity of novel antibody formats and accelerated development timelines. This study introduces a multiscale in silico model consisting of homology modeling, quantitative structure-property relationships (QSPR), and mechanistic chromatography modeling leading from the amino acid sequence of a mAb to the digital representation of its cation exchange chromatography (CEX) process. The model leverages the mAbs' structural characteristics and experimental data of a diverse set of 21 therapeutic antibodies to predict elution profiles of two mAbs that were removed from the training data set. QSPR modeling identified mAb-specific protein descriptors relevant for the prediction of the thermodynamic equilibrium and the stoichiometric coefficient of the adsorption reaction. The consideration of two discrete conformational states of IgG4 mAbs enabled prediction of split-peak elution profiles. Starting from the sequence, the presented multiscale model allows in silico development of chromatography processes before protein material is available for experimental studies.

Modeling the impact of amino acid substitution in a monoclonal antibody on cation exchange chromatography.

A vital part of biopharmaceutical research is decision making around which lead candidate should be progressed in early-phase development. When multiple antibody candidates show similar biological activity, developability aspects are taken into account to ease the challenges of manufacturing the potential drug candidate. While current strategies for developability assessment mainly focus on drug product stability, only limited information is available on how antibody candidates with minimal differences in their primary structure behave during downstream processing. With increasing time-to-market pressure and an abundance of monoclonal antibodies (mAbs) in development pipelines, developability assessments should also consider the ability of mAbs to integrate into the downstream platform. This study investigates the influence of amino acid substitutions in the complementarity-determining region (CDR) of a full-length IgG1 mAb on the elution behavior in preparative cation exchange chromatography. Single amino acid substitutions within the investigated mAb resulted in an additional positive charge in the light chain (L) and heavy chain (H) CDR, respectively. The mAb variants showed an increased retention volume in linear gradient elution compared with the wild-type antibody. Furthermore, the substitution of tryptophan with lysine in the H-CDR3 increased charge heterogeneity of the product. A multiscale in silico analysis, consisting of homology modeling, protein surface analysis, and mechanistic chromatography modeling increased understanding of the adsorption mechanism. The results reveal the potential effects of lead optimization during antibody drug discovery on downstream processing.

Microchip zone electrophoresis for high-throughput analysis of monoclonal antibody charge variants.

A high-throughput screening assay on a microfluidic chip was developed for the determination of charge variants of monocolonal antibodies (mAbs) in pI range of 7-10. This method utilizes microchip zone electrophoresis for rapid separation (<90 s) of mAb charge variants that are labeled fluorescently without altering the overall charge. The microfluidic assay achieves between 8- and 90-fold times faster separation time over conventional methods while maintaining comparable resolution and profiles of charge variant distributions. We further characterized the assay with respect to (i) the effect of pH on resolution, (ii) the effect of excipients and buffering agents, (iii) the performance of the assay compared to conventional methods, and (vi) the reproducibility of charge variant profiles. Finally, we explored the utility of the assay with four case studies: (i) monitoring C-terminal lysine modification of a mAb, (ii) quantifying the extent of deamidation of a mAb, (iii) providing charge variant information on which to base clone selection, and (iv) making process parameter-related decisions from a "design of experiment" (DoE) study. The results of these case studies demonstrate the applicability of the microfluidic assay for high-throughput monitoring of mAb quality in process development of biopharmaceuticals.

Application of Raman spectroscopy during pharmaceutical process development for determination of critical quality attributes in Protein A chromatography.

Raman spectroscopy is considered a Process Analytical Technology (PAT) tool in biopharmaceutical downstream processes. In the past decade, researchers have shown Raman spectroscopy's feasibility in determining Critical Quality Attributes (CQAs) in bioprocessing. This study verifies the feasibility of implementing a Raman-based PAT tool in Protein A chromatography as a CQA monitoring technique, for the purpose of accelerating process development and achieving real-time release in manufacturing. A system connecting Raman to a Tecan liquid handling station enables high-throughput model calibration. One calibration experiment collects Raman spectra of 183 samples with 8 CQAs within 25 h. After applying Butterworth high-pass filters and k-nearest neighbor (KNN) regression for model training, the model showed high predictive accuracy for fragments (Q2 = 0.965) and strong predictability for target protein concentration, aggregates, as well as charge variants (Q2≥ 0.922). The model's robustness was confirmed by varying the elution pH, load density, and residence time using 19 external validation preparative Protein A chromatography runs. The model can deliver elution profiles of multiple CQAs within a set point ± 0.3 pH range. The CQA readouts were presented as continuous chromatograms with a resolution of every 28 s for enhanced process understanding. In external validation datasets, the model maintained strong predictability especially for target protein concentration (Q2 = 0.956) and basic charge variants (Q2 = 0.943), except for overpredicted HCP (Q2 = 0.539). This study demonstrates a rapid, effective method for implementing Raman spectroscopy for in-line CQA monitoring in process development and biomanufacturing, eliminating the need for labor-intensive sample pooling and handling.

Illuminating a biologics development challenge: systematic characterization of CHO cell-derived hydrolases identified in monoclonal antibody formulations.

Monoclonal antibodies (mAb) and other biological drugs are affected by enzymatic polysorbate (PS) degradation that reduces product stability and jeopardizes the supply of innovative medicines. PS represents a critical surfactant stabilizing the active pharmaceutical ingredients, which are produced by recombinant Chinese hamster ovary (CHO) cell lines. While the list of potential PS-degrading CHO host cell proteins (HCPs) has grown over the years, tangible data on industrially relevant HCPs are still scarce. By means of a highly sensitive liquid chromatography-tandem mass spectrometry method, we investigated seven different mAb products, resulting in the identification of 12 potentially PS-degrading hydrolases, including the strongly PS-degrading lipoprotein lipase (LPL). Using an LPL knockout CHO host cell line, we were able to stably overexpress and purify the remaining candidate hydrolases through orthogonal affinity chromatography methods, enabling their detailed functional characterization. Applying a PS degradation assay, we found nine mostly secreted, PS-active hydrolases with varying hydrolytic activity. All active hydrolases showed a serine-histidine-aspartate/glutamate catalytical triad. Further, we subjected the active hydrolases to pH-screenings and revealed a diverse range of activity optima, which can facilitate the identification of residual hydrolases during bioprocess development. Ultimately, we compiled our dataset in a risk matrix identifying PAF-AH, LIPA, PPT1, and LPLA2 as highly critical hydrolases based on their cellular expression, detection in purified antibodies, active secretion, and PS degradation activity. With this work, we pave the way toward a comprehensive functional characterization of PS-degrading hydrolases and provide a basis for a future reduction of PS degradation in biopharmaceutical drug products.

In-line product quality monitoring during biopharmaceutical manufacturing using computational Raman spectroscopy.

The implementation of process analytical technologies is positioned to play a critical role in advancing biopharmaceutical manufacturing by simultaneously resolving clinical, regulatory, and cost challenges. Raman spectroscopy is emerging as a key technology enabling in-line product quality monitoring, but laborious calibration and computational modeling efforts limit the widespread application of this promising technology. In this study, we demonstrate new capabilities for measuring product aggregation and fragmentation in real-time during a bioprocess intended for clinical manufacturing by applying hardware automation and machine learning data analysis methods. We reduced the effort needed to calibrate and validate multiple critical quality attribute models by integrating existing workflows into one robotic system. The increased data throughput resulting from this system allowed us to train calibration models that demonstrate accurate product quality measurements every 38 s. In-process analytics enable advanced process understanding in the short-term and will lead ultimately to controlled bioprocesses that can both safeguard and take necessary actions that guarantee consistent product quality.

The impact of endotoxin masking on the removal of endotoxin during manufacturing of a biopharmaceutical drug product.

Endotoxins are a highly pyrogenic and immunogenic contaminant of bacterial origin that must be avoided during the manufacturing of biopharmaceutical products to ensure safety and efficacy. Low endotoxin recovery, also known as a masking effect, is defined as the ability to detect <50% [21] of the expected endotoxin in an endotoxin assay. Masking can be caused by the ability of endotoxins to build aggregates, bind to the protein or organise in micelles or vesicles that in turn inhibit detection of the endotoxin in the solution being tested. Therefore, a masking effect can result from physical parameters of the molecule being tested or from the buffer/environmental conditions of the solution the molecule is in. This can subsequently lead to the underestimation of endotoxin contaminations and lead to a potential false negative test. Tight control over the effectiveness of the downstream process and the use of well-characterised endotoxin testing assays are needed to ensure optimal endotoxin removal. This manuscript demonstrates the capacity to remove the endotoxins within a proven acceptable range by also controlling and evaluating the potential masking effects during downstream process at ambient temperature and also during sample storage condition until the analyse was performed. The endotoxin removal study (ERS) is divided in the initial part to evaluate the process buffers and the conditions of the molecule to avoid the underestimation of endotoxins in process samples in advance. This pre-study is a necessary prerequisite to evaluate the results after the endotoxin spiked downstream unit operations. With those aspects, the removal capacity can be demonstrated. A study was carried out to characterise the endotoxin removal capability of the purification process including controlling of masking effects. The endotoxin removal capacity on ion exchange chromatography and during ultrafiltration/diafiltration unit operations of the downstream processing of an immunoglobulin G1 antibody was conducted using various process parameters to understand their impact on endotoxin removal. In the small-scale study, the processing steps from each tested unit operation were spiked with Escherichia coli endotoxins. The potential masking effect during purification was addressed by controlling the hold time by spiking studies of the different generated pools at ambient temperature. By conducting a masking study, all generated protein pools (flow-through/wash, eluate and regeneration pools) had no masking effect caused through sample handling prior to analysis. Overall, this study showed that endotoxins could be successfully removed by anion exchange chromatography. A partial removal could be achieved by cation exchange chromatography and endotoxins could not be removed with ultrafiltration/diafiltration.

Specification-driven acceptance criteria for validation of biopharmaceutical processes.

Intermediate acceptance criteria are the foundation for developing control strategies in process validation stage 1 in the pharmaceutical industry. At drug substance or product level such intermediate acceptance criteria for quality are available and referred to as specification limits. However, it often remains a challenge to define acceptance criteria for intermediate process steps. Available guidelines underpin the importance of intermediate acceptance criteria, because they are an integral part for setting up a control strategy for the manufacturing process. The guidelines recommend to base the definition of acceptance criteria on the entirety of process knowledge. Nevertheless, the guidelines remain unclear on how to derive such limits. Within this contribution we aim to present a sound data science methodology for the definition of intermediate acceptance criteria by putting the guidelines recommendations into practice (ICH Q6B, 1999). By using an integrated process model approach, we leverage manufacturing data and experimental data from small scale to derive intermediate acceptance criteria. The novelty of this approach is that the acceptance criteria are based on pre-defined out-of-specification probabilities, while also considering manufacturing variability in process parameters. In a case study we compare this methodology to a conventional +/- 3 standard deviations (3SD) approach and demonstrate that the presented methodology is superior to conventional approaches and provides a solid line of reasoning for justifying them in audits and regulatory submission.

Integrated process model for the prediction of biopharmaceutical manufacturing chromatography and adjustment steps.

A fundamental process understanding of an entire downstream process is essential for achieving and maintaining the high-quality standards demanded for biopharmaceutical drugs. A holistic process model based on mechanistic insights could support process development by identifying dependencies between process parameters and critical quality attributes across unit operations to design a holistic control strategy. In this study, state-of-the-art mechanistic models were calibrated and validated as digital representations of a biopharmaceutical manufacturing process. The polishing ion exchange chromatography steps (Q Sepharose FF, Poros 50 HS) were described by a transport-dispersive model combined with a colloidal particle adsorption model. The elution behavior of four size variants was analyzed and included in the model. Titration curves of pH adjustments were simulated using a mean-field approach considering interactions between the protein of interest and other ions in solution. By including adjustment steps the important process control inputs ionic strength, dilution, and pH were integrated. The final process model was capable to predict online and offline data at manufacturing scale. Process variations at manufacturing scale of 94 runs were adequately reproduced by the model. Furthermore, the process robustness against a 20% input variation of concentration, size variant and ion composition, volume, and pH could be confirmed with the model. The presented model demonstrates the potential of the integrated approach for predicting manufacturing process performance across scales and operating units.

Introduction and clearance of beta-glucan in the downstream processing of monoclonal antibodies.

ß-Glucan process-related impurities can be introduced into biopharmaceutical products via upstream or downstream processing or via excipients. This study obtained a comprehensive process-mapping dataset for five monoclonal antibodies to assess ß-glucan introduction and clearance during development and production runs at various scales. Overall, 198 data points were available for analysis. The greatest ß-glucan concentrations were found in the depth-filtration filtrate (37-2,745 pg/ml). Load volume correlated with ß-glucan concentration in the filtrate, whereas flush volume was of secondary importance. Cation-exchange chromatography significantly cleared ß-glucans. Furthermore, ß-glucan leaching from the Planova 20N virus removal filter was reduced by increasing the flush volume (1 vs. 10 L/m2 ). ß-glucan concentrations after filter flush with 10 L/m2 were consistently <10 pg/ml. No or only limited ß-glucan clearance was attained via ultrafiltration/diafiltration (UF/DF). However, during the first run with monoclonal antibody (mAb) 4, ß-glucan concentration in the UF/DF retentate was 10.8 pg/mg, potentially due to ß-glucan leaching from the first run with a regenerated cellulose membrane. Overall, ß-glucan levels in the final mAb drug substance were 1-12 pg/mg. Assuming high doses of 1,000-5,000 mg, a ß-glucan contamination at 20 pg/mg would translate to 20-100 ng/dose, which is below the previously suggested threshold for product safety (≤500 ng/dose).

Cross-scale quality assessment of a mechanistic cation exchange chromatography model.

Cation exchange chromatography (CEX) is an essential part of most monoclonal antibody (mAb) purification platforms. Process characterization and root cause investigation of chromatographic unit operations are performed using scale down models (SDM). SDM chromatography columns typically have the identical bed height as the respective manufacturing-scale, but a significantly reduced inner diameter. While SDMs enable process development demanding less material and time, their comparability to manufacturing-scale can be affected by variability in feed composition, mobile phase and resin properties, or dispersion effects depending on the chromatography system at hand. Mechanistic models can help to close gaps between scales and reduce experimental efforts compared to experimental SDM applications. In this study, a multicomponent steric mass-action (SMA) adsorption model was applied to the scale-up of a CEX polishing step. Based on chromatograms and elution pool data ranging from laboratory- to manufacturing-scale, the proposed modeling workflow enabled early identification of differences between scales, for example, system dispersion effects or ionic capacity variability. A multistage model qualification approach was introduced to measure the model quality and to understand the model's limitations across scales. The experimental SDM and the in silico model were qualified against large-scale data using the identical state of the art equivalence testing procedure. The mechanistic chromatography model avoided limitations of the SDM by capturing effects of bed height, loading density, feed composition, and mobile phase properties. The results demonstrate the applicability of mechanistic chromatography models as a possible alternative to conventional SDM approaches.

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.

Integrated Process Model Applications Linking Bioprocess Development to Quality by Design Milestones.

Maximizing the value of each available data point in bioprocess development is essential in order to reduce the time-to-market, lower the number of expensive wet-lab experiments, and maximize process understanding. Advanced in silico methods are increasingly being investigated to accomplish these goals. Within this contribution, we propose a novel integrated process model procedure to maximize the use of development data to optimize the Stage 1 process validation work flow. We generate an integrated process model based on available data and apply two innovative Monte Carlo simulation-based parameter sensitivity analysis linearization techniques to automate two quality by design activities: determining risk assessment severity rankings and establishing preliminary control strategies for critical process parameters. These procedures are assessed in a case study for proof of concept on a candidate monoclonal antibody bioprocess after process development, but prior to process characterization. The evaluation was successful in returning results that were used to support Stage I process validation milestones and demonstrated the potential to reduce the investigated parameters by up to 24% in process characterization, while simultaneously setting up a strategy for iterative updates of risk assessments and process controls throughout the process life-cycle to ensure a robust and efficient drug supply.

Analysis of complex protein elution behavior in preparative ion exchange processes using a colloidal particle adsorption model.

A fundamental understanding of the protein retention mechanism in preparative ion exchange (IEX) chromatography columns is essential for a model-based process development approach. For the past three decades, the mechanistic description of protein retention has been based predominantly on the steric mass action (SMA) model. In recent years, however, retention profiles of proteins have been reported more frequently for preparative processes that are not consistent with the mechanistic understanding relying on the SMA model. In this work, complex elution behavior of proteins in preparative IEX processes is analyzed using a colloidal particle adsorption (CPA) model. The CPA model is found to be capable of reproducing elution profiles that cannot be described by the traditional SMA model. According to the CPA model, the reported complex behavior can be ascribed to a strong compression and concentration of the elution front in the lower unsaturated part of the chromatography column. As the unsaturated part of the column decreases with increasing protein load density, exceeding a critical load density can lead to the formation of a shoulder in the peak front. The general applicability of the model in describing preparative IEX processes is demonstrated using several industrial case studies including multiple monoclonal antibodies on different IEX adsorber systems. In this context, the work covers both salt controlled and pH-controlled protein elution.

Straightforward method for calibration of mechanistic cation exchange chromatography models for industrial applications.

Mechanistic modeling of chromatography processes is one of the most promising techniques for the digitalization of biopharmaceutical process development. Possible applications of chromatography models range from in silico process optimization in early phase development to in silico root cause investigation during manufacturing. Nonetheless, the cumbersome and complex model calibration still decelerates the implementation of mechanistic modeling in industry. Therefore, the industry demands model calibration strategies that ensure adequate model certainty in a limited amount of time. This study introduces a directed and straightforward approach for the calibration of pH-dependent, multicomponent steric mass action (SMA) isotherm models for industrial applications. In the case investigated, the method was applied to a monoclonal antibody (mAb) polishing step including four protein species. The developed strategy combined well-established theories of preparative chromatography (e.g. Yamamoto method) and allowed a systematic reduction of unknown model parameters to 7 from initially 32. Model uncertainty was reduced by designing two representative calibration experiments for the inverse estimation of remaining model parameters. Dedicated experiments with aggregate-enriched load material led to a significant reduction of model uncertainty for the estimates of this low-concentrated product-related impurity. The model was validated beyond the operating ranges of the final unit operation, enabling its application to late-stage downstream process development. With the proposed model calibration strategy, a systematic experimental design is provided, calibration effort is strongly reduced, and local minima are avoided.

Mass spectrometric evaluation of upstream and downstream process influences on host cell protein patterns in biopharmaceutical products.

For production of different monoclonal antibodies (mAbs), biopharmaceutical companies often use related upstream and downstream manufacturing processes. Such platforms are typically characterized regarding influence of upstream and downstream process (DSP) parameters on critical quality attributes (CQAs). CQAs must be monitored strictly by an adequate control strategy. One such process-related CQA is the content of host cell protein (HCP) which is typically analyzed by immunoassay methods (e.g., HCP-ELISA). The capacity of the immunoassay to detect a broad range of HCPs, relevant for the individual mAb-production process should be proven by orthogonal proteomic methods such as 2D gel electrophoresis or mass spectrometry (MS). In particular MS has become a valuable tool to identify and quantify HCP in complex mixtures. We evaluate up- and DSP parameters of four different biopharmaceutical products, two different process variants, and one mock fermentation on the HCP pattern by shotgun MS analysis and ELISA. We obtained a similar HCP pattern in different cell culture fluid harvests compared to the starting material from the downstream process. During the downstream purification process of the mAbs, the HCP level and the number of HCP species significantly decreased, accompanied by an increase in diversity of the residual HCP pattern. Based on this knowledge, we suggest a control strategy that combines multi product ELISA for in-process control and release analytics, and MS testing for orthogonal HCP characterization, to attain knowledge on the HCP level, clusters and species. This combination supports a control strategy for HCPs addressing safety and efficacy of biopharmaceutical products.

Prediction and Reduction of the Aggregation of Monoclonal Antibodies.

Protein aggregation remains a major area of focus in the production of monoclonal antibodies. Improving the intrinsic properties of antibodies can improve manufacturability, attrition rates, safety, formulation, titers, immunogenicity, and solubility. Here, we explore the potential of predicting and reducing the aggregation propensity of monoclonal antibodies, based on the identification of aggregation-prone regions and their contribution to the thermodynamic stability of the protein. Although aggregation-prone regions are thought to occur in the antigen binding region to drive hydrophobic binding with antigen, we were able to rationally design variants that display a marked decrease in aggregation propensity while retaining antigen binding through the introduction of artificial aggregation gatekeeper residues. The reduction in aggregation propensity was accompanied by an increase in expression titer, showing that reducing protein aggregation is beneficial throughout the development process. The data presented show that this approach can significantly reduce liabilities in novel therapeutic antibodies and proteins, leading to a more efficient path to clinical studies.