ArticlemAbs2025
Accelerating high-concentration monoclonal antibody development with large-scale viscosity data and ensemble deep learning.
Article in mAbs, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Article
- Article
- Article
- Predicting non-specific binding of VHHs using machine learning models with cluster-aware validation.mAbs · 2026Article
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- Article
- A Review on Applications of Artificial Intelligence (AI) in Monoclonal Antibody (mAb) Manufacturing.Molecules (Basel, Switzerland) · 2026Review
- Modulation of Intravenous Immunoglobulin Aggregation, Subvisible Particle Formation, and Viscosity by Acetylated Amino Acids.Pharmaceutics · 2026Article
- Fab-Fc and Fab-Fab interactions of variable strength and valency contribute to the high concentration viscosity of IgGProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Characterising nanobody developability to improve therapeutic design using the Therapeutic Nanobody Profiler.Communications biology · 2026Article
- Article
- Bayesian Optimization for Efficient Multiobjective Formulation Development of Biologics.Molecular pharmaceutics · 2025Article
- Fifty years of monoclonals: the past, present and future of antibody therapeutics.Nature reviews. Immunology · 2025Article
Corrections and comments
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Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Highly concentrated antibody solutions are necessary for developing subcutaneous injections but often exhibit high viscosities, posing challenges in antibody-drug development, manufacturing, and administration. Previous computational models were only limited to a few dozen data points for training, a bottleneck for generalizability. In this study, we measured the viscosity of a panel of 229 monoclonal antibodies (mAbs) to develop predictive models for high concentration mAb screening. We developed DeepViscosity, consisting of 102 ensemble artificial neural network models to classify low-viscosity (≤20 cP) and high-viscosity (>20 cP) mAbs at 150 mg/mL, using 30 features from a sequence-based DeepSP model. Two independent test sets, comprising 16 and 38 mAbs with known experimental viscosity, were used to assess DeepViscosity's generalizability. The model exhibited an accuracy of 87.5% and 89.5% on both test sets, respectively, surpassing other predictive methods. DeepViscosity will facilitate early-stage antibody development to select low-viscosity antibodies for improved manufacturability and formulation properties, critical for subcutaneous drug delivery. The webserver-based application can be freely accessed via https://devpred.onrender.com/DeepViscosity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.