ArticlemAbs
Approaches to expand the conventional toolbox for discovery and selection of antibodies with drug-like physicochemical properties.
Article in mAbs. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- Article
- Predicting Agitation Stability of Monoclonal Antibodies during Developability Assessment.Molecular pharmaceutics · 2026Article
- Hotspot Interactions between Two Fab Molecules in Molecular Dynamics Simulations Improve Predictive Models of Aggregation Kinetics.Molecular pharmaceutics · 2026Article
- Review
- Article
- Article
- Bayesian Optimization for Efficient Multiobjective Formulation Development of Biologics.Molecular pharmaceutics · 2025Article
- Pharmacophore-driven antibody discovery on the yeast surface.bioRxiv : the preprint server for biology · 2025Article
- Next-Generation Therapeutic Antibodies for Cancer Treatment: Advancements, Applications, and Challenges.Molecular biotechnology · 2025Review
- Understanding IgM Structure and Biology to Engineer New Antibody Therapeutics.BioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy · 2025Review
- Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools.Biomarker research · 2025Review
- Intrinsic Differential Scanning Fluorimetry for Protein Stability Assessment in Microwell Plates.Molecular pharmaceutics · 2025Article
- Rationalizing mAb Candidate Screening Using a Single Holistic Developability Parameter.Molecular pharmaceutics · 2025Article
- Applying computational protein design to therapeutic antibody discovery - current state and perspectives.Frontiers in immunology · 2025Review
- Accurate and transferable drug-target interaction prediction with DrugLAMP.Bioinformatics (Oxford, England) · 2024Article
- Predictive Model Building for Aggregation Kinetics Based on Molecular Dynamics Simulations of an Antibody Fragment.Molecular pharmaceutics · 2024Article
- Molecular properties and diagnostic potential of monoclonal antibodies targeting cytotoxic α-synuclein oligomers.NPJ Parkinson's disease · 2024Article
- Matrixed CDR grafting: A neoclassical framework for antibody humanization and developability.The Journal of biological chemistry · 2024Article
- Biophysical Analysis of Therapeutic Antibodies in the Early Development Pipeline.Biologics : targets & therapy · 2024Review
- Design and engineering of bispecific antibodies: insights and practical considerations.Frontiers in bioengineering and biotechnology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antibody drugs should exhibit not only high-binding affinity for their target antigens but also favorable physicochemical drug-like properties. Such drug-like biophysical properties are essential for the successful development of antibody drug products. The traditional approaches used in antibody drug development require significant experimentation to produce, optimize, and characterize many candidates. Therefore, it is attractive to integrate new methods that can optimize the process of selecting antibodies with both desired target-binding and drug-like biophysical properties. Here, we summarize a selection of techniques that can complement the conventional toolbox used to de-risk antibody drug development. These techniques can be integrated at different stages of the antibody development process to reduce the frequency of physicochemical liabilities in antibody libraries during initial discovery and to co-optimize multiple antibody features during early-stage antibody engineering and affinity maturation. Moreover, we highlight biophysical and computational approaches that can be used to predict physical degradation pathways relevant for long-term storage and in-use stability to reduce the need for extensive experimentation.
Indexed as
Identifiers
What OpenQuestion holds
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.