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Monoclonal antibodies (mAbs) have become a cornerstone in therapeutic development, increasingly administered via subcutaneous (SC) injection due to its convenience and patient adherence benefits. However, accurately predicting SC bioavailability in humans remains a challenge, largely due to the limitations of traditional animal models that fail to provide reliable predictions for clinical outcomes, creating a significant gap in preclinical evaluations. To address this, we have developed an integrated in-vitro/in-silico approach that employs functional principal component analysis (FPCA) to summarize the release and transmission profiles information generated by the Subcutaneous Injection Site Simulator (SCISSOR) platform. The FPCA method extracted main shape functions from SCISSOR profiles, representing the most significant variations, and the resulting FPC scores were used as predictors in the modelling process. We employed self-validated ensemble modelling (SVEM) to predict the SC human bioavailability of mAbs based on the transmission and release features. SVEM is an ensemble modelling technique allowing the use of all observations for both training and validation making it a suitable method for small sample sizes. The model was further tested on new four commercial mAbs, demonstrating a good agreement between the predicted and actual bioavailability, and outperforming monkey data. We then elucidated how SCISSOR release and transmission profile are correlated with different mAbs and formulation parameters. This approach represents valuable addition to the toolkit for predicting the SC human bioavailability of mAbs. By combining in-vitro and in-silico methods, we offer a reliable approach that can outperform preclinical animal models., Competing Interests: Declaration of competing interest All authors were at the time of work employees of AstraZeneca., (Copyright © 2025 Elsevier B.V. All rights reserved.)

To combat the COVID-19 pandemic, potential therapies have been developed and moved into clinical trials at an unprecedented pace. Some of the most promising therapies are neutralizing antibodies against SARS-CoV-2. In order to maximize the therapeutic effectiveness of such neutralizing antibodies, Fc engineering to modulate effector functions and to extend half-life is desirable. However, it is critical that Fc engineering does not negatively impact the developability properties of the antibodies, as these properties play a key role in ensuring rapid development, successful manufacturing, and improved overall chances of clinical success. In this study, we describe the biophysical characterization of a panel of Fc engineered ("TM-YTE") SARS-CoV-2 neutralizing antibodies, the same Fc modifications as those found in AstraZeneca's Evusheld (AZD7442; tixagevimab and cilgavimab), in which the TM modification (L234F/L235E/P331S) reduce binding to FcγR and C1q and the YTE modification (M252Y/S254T/T256E) extends serum half-life. We have previously shown that combining both the TM and YTE Fc modifications can reduce the thermal stability of the CH2 domain and possibly lead to developability challenges. Here we show, using a diverse panel of TM-YTE SARS-CoV-2 neutralizing antibodies, that despite lowering the thermal stability of the Fc CH2 domain, the TM-YTE platform does not have any inherent developability liabilities and shows an in vivo pharmacokinetic profile in human FcRn transgenic mice similar to the well-characterized YTE platform. The TM-YTE is therefore a developable, effector function reduced, half-life extended antibody platform.

Development of biotherapeutics require pharmacokinetic/pharmacodynamic (PK/PD) and immunogenicity assays that are frequently in a ligand-binding assay (LBA) format. Conjugated critical reagents for LBAs are generated conjugation of the biotherapeutic drug or anti-drug molecule with a label. Since conjugated critical reagent quality impacts LBA performance, control of the generation process is essential. Our perspective is that process development methodologies should be integrated into critical reagent production to understand the impact of conjugation reactions, purification techniques and formulation conditions on the quality of the reagent. In this article, case studies highlight our approach to developing process conditions for different molecular classes of critical reagents including antibodies and a peptide. This development approach can be applied to the generation of future conjugated critical reagents.

Machine learning has been recently used to predict therapeutic antibody aggregation rates and viscosity at high concentrations (150 mg/ml). These works focused on commercially available antibodies, which may have been optimized for stability. In this study, we measured accelerated aggregation rates at 45°C and viscosity at 150 mg/ml for 20 preclinical and clinical-stage antibodies. Features obtained from molecular dynamics simulations of the full-length antibody and sequences were used for machine learning model construction. We found a k-nearest neighbors regression model with two features, spatial positive charge map on the CDRH2 and solvent-accessible surface area of hydrophobic residues on the variable fragment, gives the best performance for predicting antibody aggregation rates (r = 0.89). For the viscosity classification model, the model with the highest accuracy is a logistic regression model with two features, spatial negative charge map on the heavy chain variable region and spatial negative charge map on the light chain variable region. The accuracy and the area under precision recall curve of the classification model from validation tests are 0.86 and 0.70, respectively. In addition, we combined data from another 27 commercial mAbs to develop a viscosity predictive model. The best model is a logistic regression model with two features, number of hydrophobic residues on the light chain variable region and net charges on the light chain variable region. The accuracy and the area under precision recall curve of the classification model are 0.85 and 0.6, respectively. The aggregation rates and viscosity models can be used to predict antibody stability to facilitate pharmaceutical development.

Preferential interactions of formulation excipients govern their impact on the stability properties of proteins in solution. The ability to predict these interactions without the need to perform experiments would enable formulation design to begin early in the development of a new antibody therapeutic. With that in mind, we developed a feature set to numerically describe local regions of an antibody's surface for use in machine learning applications. Then, we used these features to train machine learning models for local antibody-excipient preferential interactions for the excipients sorbitol, sucrose, trehalose, proline, arginine·HCl, and NaCl. Our models had accuracies of up to about 85%. We also used linear (elastic net) models to quantify the contribution of antibody surface features to the preferential interaction coefficients, finding that the carbohydrates and proline tend to have similar important features, while the interactions of arginine·HCl and NaCl are governed by charge features. We present several case studies demonstrating how these machine learning models could be used to predict experimental aggregation and viscosity behavior in solution. Finally, we propose an approach to computational formulation design wherein a panel of excipients may be considered while designing an antibody sequence.

Biopharmaceutical product and process development do not yet take advantage of predictive computational modeling to nearly the degree seen in industries based on smaller molecules. To assess and advance progress in this area, spirited coopetition (mutually beneficial collaboration between competitors) was successfully used to motivate industrial scientists to develop, share, and compare data and methods which would normally have remained confidential. The first "Highland Games" competition was held in conjunction with the October 2018 Recovery of Biological Products Conference in Ashville, NC, with the goal of benchmarking and assessment of the ability to predict development-related properties of six antibodies from their amino acid sequences alone. Predictions included purification-influencing properties such as isoelectric point and protein A elution pH, and biophysical properties such as stability and viscosity at very high concentrations. Essential contributions were made by a large variety of individuals, including companies which consented to provide antibody amino acid sequences and test materials, volunteers who undertook the preparation and experimental characterization of these materials, and prediction teams who attempted to predict antibody properties from sequence alone. Best practices were identified and shared, and areas in which the community excels at making predictions were identified, as well as areas presenting opportunities for considerable improvement. Predictions of isoelectric point and protein A elution pH were especially good with all-prediction average errors of 0.2 and 1.6 pH unit, respectively, while predictions of some other properties were notably less good. This manuscript presents the events, methods, and results of the competition, and can serve as a tutorial and as a reference for in-house benchmarking by others. Organizations vary in their policies concerning disclosure of methods, but most managements were very cooperative with the Highland Games exercise, and considerable insight into common and best practices is available from the contributed methods. The accumulated data set will serve as a benchmarking tool for further development of in silico prediction tools., (© 2020 Wiley Periodicals LLC.)

Preferential interactions of excipients with the antibody surface govern their effect on the stability of antibodies in solution. We probed the preferential interactions of proline, arginine.HCl (Arg.HCl), and NaCl with three therapeutically relevant IgG1 antibodies via experiment and simulation. With simulations, we examined how excipients interacted with different types of surface patches in the variable region (Fv). For example, proline interacted most strongly with aromatic surfaces, Arg.HCl was included near negative residues, and NaCl was excluded from negative residues and certain hydrophobic regions. The differences in interaction of different excipients with the same surface patch on an antibody may be responsible for variations in the antibody's aggregation, viscosity, and self-association behaviors in each excipient. Proline reduced self-association for all three antibodies and reduced aggregation for the antibody with an association-limited aggregation mechanism. The effects of Arg.HCl and NaCl on aggregation and viscosity were highly dependent on the surface charge distribution and the extent of exclusion from highly hydrophobic patches. At pH 5.5, both tended to increase the aggregation of an antibody with a strongly positive charge on the Fv, while only NaCl reduced the aggregation of the antibody with a large negative charge patch on the Fv. Arg.HCl reduced the viscosities of antibodies with either a hydrophobicity-driven mechanism or a charge-driven mechanism. Analysis of this data presents a framework for understanding how amino acid and ionic excipients interact with different protein surfaces, and how these interactions translate to the observed stability behavior.

Preferential interactions of formulation excipients govern their overall interactions with protein molecules, and molecular dynamics simulations allow for the examination of the interactions at the molecular level. We used molecular dynamics simulations to examine the interactions of sorbitol, sucrose, and trehalose with three different IgG1 antibodies to gain insight into how these excipients impact aggregation and viscosity. We found that sucrose and trehalose reduce aggregation more than sorbitol because of their larger size and their stronger interactions with high-spatial aggregation propensity residues compared to sorbitol. Two of the antibodies had high viscosity in sodium acetate buffer, and for these, we found that sucrose and trehalose tended to have opposite effects on viscosity. The data presented here provide further insight into the mechanisms of interactions of these three carbohydrate excipients with the antibody surface and thus their impact on excipient stabilization of antibody formulations.

Purpose: To investigate differences in the preferential exclusion of trehalose, sucrose, sorbitol and mannitol from the surface of three IgG1 monoclonal antibodies (mAbs) and understand its effect on the aggregation and reversible self-association of mAbs at high-concentrations., Methods: Preferential exclusion was measured using vapor pressure osmometry. Effect of excipient addition on accelerated aggregation kinetics was quantified using size exclusion chromatography and on reversible self-association was quantified using dynamic light scattering., Results: The doubling of excipient concentration in the 0 to 0.5 m range resulted in a doubling of the mAb transfer free energy for all excipients and antibodies tested in this study. Solution pH and choice of buffering agent did not significantly affect the magnitude of preferential exclusion. We find that aggregation suppression for trehalose, sucrose and sorbitol (but not mannitol) correlates with the magnitude of their preferential exclusion from the native state of the three IgG1 mAbs. We also find that addition of sugars and polyols reduced the tendency for reversible self-association in two mAbs that had weakly repulsive or neutral self-interactions in the presence of buffer alone., Conclusions: The magnitude of preferential exclusion for trehalose, sucrose and sorbitol correlates well with their partial molar volumes in solution. Mannitol is excluded to a greater extent than that expected from its partial molar volume, suggesting specific interactions of mannitol that might be different than the other sugars and polyols tested in this study. Local interactions play a role in the effect of excipient addition on the reversible self-association of mAbs. These results provide further insights into the stabilization of high-concentration mAb formulations by sugars and polyols.

Monoclonal antibodies (mAbs) are complex molecular structures. They are often prone to development challenges particularly at high concentrations due to undesired solution properties such as reversible self-association, high viscosity, and liquid-liquid phase separation. In addition to formulation optimization, applying protein engineering can provide an alternative mitigation strategy. Protein engineering during the discovery phase can provide great benefits to optimize molecular properties, resulting in improved developability profiles. Here, we present a case study utilizing complementary analytical and predictive in silico methods. We have systematically identified and reengineered problematic residues responsible for the self-association of a model mAb, driven by a complex combination of hydrophobic and electrostatic interactions. Noteworthy findings include a more dominant contribution of hydrophobic interactions to self-association and potential feasibility of mutations in the CDR regions to mitigate self-association. The engineered mutation panel enabled us to assess potential correlations among commonly utilized developability screening assays, including affinity capture self-interaction nanospectroscopy (AC-SINS), dynamic light scattering (DLS), and apparent solubility by PEG-precipitation. In addition, we evaluated the correlations between experimental measurements and computational predictions. CamSol, an in silico computational tool that accounts for complex molecular interactions and neighboring hotspots, was found to be an effective screening tool. Our work led to reengineered mAb variants, better suited for high-concentration liquid formulation development. The engineered mAbs exhibited enhanced in vitro and simulated in vivo solubility and reduced self-association propensity, while maintaining binding affinity and thermal stability.

Selecting optimal formulation conditions for monoclonal antibodies for first time in human clinical trials is challenging due to short timelines and reliance on predictive assays to ensure product quality and adequate long-term stability. Accelerated stability studies are considered to be the gold standard for excipient screening, but they are relatively low throughput and time consuming. High throughput screening (HTS) techniques allow for large amounts of data to be collected quickly and easily, and can be used to screen solution conditions for early formulation development. The utility of using accelerated stability compared to HTS techniques (differential scanning light scattering and differential scanning fluorescence) for early formulation screening was evaluated along with the impact of excipients of various types on aggregation of monoclonal antibodies from multiple IgG subtypes. The excipient rank order using quantitative HTS measures was found to correlate with accelerated stability aggregation rate ranking for only 33% (by differential scanning fluorescence) to 42% (by differential scanning light scattering) of the antibodies tested, due to the high intrinsic stability and minimal impact of excipients on aggregation rates and HTS data. Also explored was a case study of employing a platform formulation instead of broader formulation screening for early formulation development., (Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.)

Multiple mutation combinations in the IgG Fc have been characterized to tailor immune effector function or IgG serum persistence to fit desired biological outcomes for monoclonal antibody therapeutics. An unintended consequence of introducing mutations in the Fc (particularly the C H 2 domain) can be a reduction in biophysical stability which can correlate with increased aggregation propensity, poor manufacturability, and lower solubility. Herein, we characterize the changes in IgG conformational and colloidal stability when 2 sets of C H 2 mutations "TM" (L234F/L235E/P331S) and "YTE" (M252Y/S254T/T256E) are combined to generate an antibody format lacking immune receptor binding and exhibiting extended half-life. In addition to significantly lowered thermostability, we observe greater conformational flexibility for TM-YTE in C H 2, increased self-association, and poorer solubility and aggregation profiles. To improve these properties, we dissected the contributions of individual mutations within TM-YTE on thermostability and substituted destabilizing mutations with new mutations that raise thermostability. One novel combination, FQQ-YTE (L234F/L235Q/K322Q/M252Y/S254T/T256E), had significantly improved conformational and colloidal stability, and was found to retain the same biological activities as TM-YTE (extended half-life and lack of antibody-dependent cell-mediated cytotoxicity and complement-dependent cytotoxicity activity). Our engineering approach offers a way to improve the developability of antibodies containing Fc mutations while retaining tailored biological activity., (Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.)

Highly concentrated antibody solutions often exhibit high viscosities, which present a number of challenges for antibody-drug development, manufacturing and administration. The antibody sequence is a key determinant for high viscosity of highly concentrated solutions; therefore, a sequence- or structure-based tool that can identify highly viscous antibodies from their sequence would be effective in ensuring that only antibodies with low viscosity progress to the development phase. Here, we present a spatial charge map (SCM) tool that can accurately identify highly viscous antibodies from their sequence alone (using homology modeling to determine the 3-dimensional structures). The SCM tool has been extensively validated at 3 different organizations, and has proved successful in correctly identifying highly viscous antibodies. As a quantitative tool, SCM is amenable to high-throughput automated analysis, and can be effectively implemented during the antibody screening or engineering phase for the selection of low-viscosity antibodies.