ArticleMolecular pharmaceutics2020
Physicochemical Rules for Identifying Monoclonal Antibodies with Drug-like Specificity.
Article in Molecular pharmaceutics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed.
- Predicting non-specific binding of VHHs using machine learning models with cluster-aware validation.mAbs · 2026Article
- Directed evolution of drug-like Aβ conformation-specific antibodies.Frontiers in immunology · 2025Article
- Human antibody polyreactivity is governed primarily by the heavy-chain complementarity-determining regions.Cell reports · 2024Article
- Evaluating the chaos game representation of proteins for applications in machine learning models: prediction of antibody affinity and specificity as a case study.Journal of molecular modeling · 2023Article
- Simplifying complex antibody engineering using machine learning.Cell systems · 2023Review
- Physiologically Based Pharmacokinetic Modeling to Characterize the Effect of Molecular Charge on Whole-Body Disposition of Monoclonal Antibodies.The AAPS journal · 2023Article
- Surface patches induce nonspecific binding and phase separation of antibodies.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Automated optimisation of solubility and conformational stability of antibodies and proteins.Nature communications · 2023Article
- How can we discover developable antibody-based biotherapeutics?Frontiers in molecular biosciences · 2023Review
- Quantitative flow cytometric selection of tau conformational nanobodies specific for pathological aggregates.Frontiers in immunology · 2023Article
- An in silico method to assess antibody fragment polyreactivity.Nature communications · 2022Article
- Non-specificity as the sticky problem in therapeutic antibody development.Nature reviews. Chemistry · 2022Review
- Article
- Learning what not to select for in antibody drug discovery.Cell reports methods · 2022Article
- Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space.Nature communications · 2022Article
- Conformational Entropy as a Potential Liability of Computationally Designed Antibodies.Biomolecules · 2022Article
- Antibodies with Weakly Basic Isoelectric Points Minimize Trade-offs between Formulation and Physiological Colloidal Properties.Molecular pharmaceutics · 2022Article
- Functional Changes of Therapeutic Antibodies upon Exposure to Pro-Oxidative Agents.Antibodies (Basel, Switzerland) · 2022Article
- Agonist antibody discovery: Experimental, computational, and rational engineering approaches.Drug discovery today · 2022Review
- Intrinsic physicochemical profile of marketed antibody-based biotherapeutics.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
The ability of antibodies to recognize their target antigens with high specificity is fundamental to their natural function. Nevertheless, therapeutic antibodies display variable and difficult-to-predict levels of nonspecific and self-interactions that can lead to various drug development challenges, including antibody aggregation, abnormally high viscosity, and rapid antibody clearance. Here we report a method for predicting the overall specificity of antibodies in terms of their relative risk for displaying high levels of nonspecific or self-interactions at physiological conditions. We find that individual and combined sets of chemical rules that limit the maximum and minimum numbers of certain solvent-exposed amino acids in antibody variable regions are strong predictors of specificity for large panels of preclinical and clinical-stage antibodies. We also demonstrate how the chemical rules can be used to identify sites that mediate nonspecific interactions in suboptimal antibodies and guide the design of targeted sublibraries that yield variants with high antibody specificity. These findings can be readily used to improve the selection and engineering of antibodies with drug-like specificity.
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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.