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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. [ABSTRACT FROM AUTHOR]

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.)

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.