AIMC Topic: Antibodies, Monoclonal

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Physics-based surface patch analysis for prediction of hydrophobic contribution to viscosity of mAbs.

mAbs
The viscosity of monoclonal antibody solutions is critical in their biopharmaceutical application, as it directly influences the ease of subcutaneous injection. Although many descriptors have been developed to enable the prediction of viscosity, the...

Deconvoluting Biophysical Factors that Influence Long-Term Aggregation Rates of High-Concentration Monoclonal Antibody Formulations.

Molecular pharmaceutics
Efficient determination of developable protein drug candidates and stable solution conditions is a key challenge in industrial drug development. Protein aggregation is difficult to predict and can lead to challenges in manufacturing, storage, and pat...

Machine learning enables de novo multiepitope design of circumsporozoite protein to target trimeric L9 antibody.

Proceedings of the National Academy of Sciences of the United States of America
Currently approved vaccines for the prevention of malaria provide only partial protection against disease due to high variability in the quality of induced antibodies. These vaccines present the unstructured central repeat region, as well as the C-te...

AbAgym: a well-curated dataset for the mutational analysis of antibody-antigen complexes.

mAbs
With monoclonal antibodies becoming one of the largest classes of biopharmaceuticals, it is important to have curated data to train computational models that can accelerate their design. Despite the massive amount of mutagenesis data generated on ant...

Selectivity Approaches in Therapeutic Antibody Design.

Journal of medicinal chemistry
Protein therapeutics, particularly antibody-based therapies, have emerged as a cornerstone in modern disease treatment, offering key advantages over small molecules, including superior target specificity, longer half-life, and expanded target accessi...

Deep learning-guided rational engineering of synergistic PD-1 and LAG-3 blockade for enhanced tumor immunomodulation.

Journal of computer-aided molecular design
Evolution has optimized proteins over time by the incorporation of precise and context-specific amino acid substitutions adapted to structural and functional demands. We have reconceptualized this principle using deep learning to engineer monoclonal ...

Monoclonal antibodies production in microbial systems: Current status, challenges and perspectives.

New biotechnology
Monoclonal antibodies (mAbs) serve as indispensable tools in diagnostics, clinical therapeutics, and biomedical research. However, their large-scale production faces significant challenges due to the high costs and lengthy timelines associated with c...

Accelerating antibody development: sequence and structure-based models for predicting developability properties via size exclusion chromatography.

mAbs
Experimental screening for biopharmaceutical developability properties typically relies on resource-intensive, and time-consuming assays such as size exclusion chromatography (SEC). This study highlights the potential of in silico models to accelerat...

Bayesian Optimization for Efficient Multiobjective Formulation Development of Biologics.

Molecular pharmaceutics
Biologics, including emerging engineered formats, can often exhibit poor developability profiles, complicating their translation into successful therapeutics. While formulation design can substantially mitigate some developability issues, it represen...

Predicting Subcutaneous Antibody Bioavailability Using Ensemble Protein Language Models.

Molecular pharmaceutics
Monoclonal antibodies are pivotal in modern therapeutics, yet predicting their subcutaneous bioavailability remains challenging due to the intricacies of the SC environment and the limitations of traditional experimental models. In this study, we int...