ReviewmAbs
Computational models for studying physical instabilities in high concentration biotherapeutic formulations.
Review in mAbs. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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
12 citing papers in PubMed.
- Emerging Technologies and Integrated Interdisciplinary Strategies for Mitigating Protein Aggregation in Therapeutic Formulations.Pharmaceutical research · 2026Review
- Toward Rational Design of Ion-Exchange Nanofiber Membranes: Meso-Scale Computational Approaches.Membranes · 2025Review
- Review
- Neurodegeneration Through the Lens of Bioinformatics Approaches: Computational Mechanisms of Protein Misfolding.International journal of molecular sciences · 2025Review
- A Convenient Strategy for Studying Antibody Aggregation and Inhibition of Aggregation: Characterization and Simulation.Pharmaceutics · 2025Article
- Growth of Clusters toward Liquid-Liquid Phase Separation of Monoclonal Antibodies as Characterized by Small-Angle X-ray Scattering and Molecular Dynamics Simulation.The journal of physical chemistry. B · 2025Article
- Stability of Protein Pharmaceuticals: Recent Advances.Pharmaceutical research · 2024Review
- Viscosity Prediction of High-Concentration Antibody Solutions with Atomistic Simulations.Journal of chemical information and modeling · 2023Article
- IgG1 and IgG4 antibodies sample initial structure dependent local conformational states and exhibit non-identical Fab dynamics.Scientific reports · 2023Article
- How can we discover developable antibody-based biotherapeutics?Frontiers in molecular biosciences · 2023Review
- SUMO: In Silico Sequence Assessment Using Multiple Optimization Parameters.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational prediction of the behavior of concentrated protein solutions is particularly advantageous in early development stages of biotherapeutics when material availability is limited and a large set of formulation conditions needs to be explored. This review provides an overview of the different computational paradigms that have been successfully used in modeling undesirable physical behaviors of protein solutions with a particular emphasis on high-concentration drug formulations. This includes models ranging from all-atom simulations, coarse-grained representations to macro-scale mathematical descriptions used to study physical instability phenomena of protein solutions such as aggregation, elevated viscosity, and phase separation. These models are compared and summarized in the context of the physical processes and their underlying assumptions and limitations. A detailed analysis is also given for identifying protein interaction processes that are explicitly or implicitly considered in the different modeling approaches and particularly their relations to various formulation parameters. Lastly, many of the shortcomings of existing computational models are discussed, providing perspectives and possible directions toward an efficient computational framework for designing effective protein formulations.
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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.