ArticleMolecular pharmaceutics2020
Computational Characterization of Antibody-Excipient Interactions for Rational Excipient Selection Using the Site Identification by Ligand Competitive Saturation-Biologics Approach.
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 27 papers.
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Who cites it
27 citing papers in PubMed, 38 citations in OpenAlex.
- Article
- Screening of Unexplored FDA-approved Inactive Ingredients as Stabilizers for Monoclonal Antibody Products.Pharmaceutical research · 2026Article
- Uncovering hidden druggable sites: computational approaches to allosteric and cryptic pocket discovery-from molecular dynamics to AI.Journal of computer-aided molecular design · 2026Review
- Antibody-drug conjugate engineering: from design to efficacy and safety.Signal transduction and targeted therapy · 2026Review
- Emerging Technologies and Integrated Interdisciplinary Strategies for Mitigating Protein Aggregation in Therapeutic Formulations.Pharmaceutical research · 2026Review
- Article
- Investigating the Interaction between Excipients and Monoclonal Antibodies PGT121 and N49P9.6-FR-LS: A Comprehensive Analysis.Molecular pharmaceutics · 2025Article
- Machine learning driven acceleration of biopharmaceutical formulation development using Excipient Prediction Software (ExPreSo).Computational and structural biotechnology journal · 2025Article
- Protein and Peptide Therapeutics: Stability Challenges, Regulatory Demands, and Innovative Formulation Solutions for Enhanced Clinical Effectiveness.Protein and peptide letters · 2025Review
- Modeling Chromatography Binding through Molecular Dynamics Simulations with Resin Fragments.The journal of physical chemistry. B · 2024Article
- Computational Procedure for Predicting Excipient Effects on Protein-Protein Affinities.Journal of chemical theory and computation · 2024Article
- Role of Domain-Domain Interactions on the Self-Association and Physical Stability of Monoclonal Antibodies: Effect of pH and Salt.The journal of physical chemistry. B · 2023Article
- Effects of arginine in therapeutic protein formulations: a decade review and perspectives.Antibody therapeutics · 2023Review
- GPU-specific algorithms for improved solute sampling in grand canonical Monte Carlo simulations.Journal of computational chemistry · 2023Article
- Site Identification by Ligand Competitive Saturation-Biologics Approach for Structure-Based Protein Charge Prediction.Molecular pharmaceutics · 2023Article
- Embedding Dynamics in Intrinsic Physicochemical Profiles of Market-Stage Antibody-Based Biotherapeutics.Molecular pharmaceutics · 2023Article
- Computer-Aided Drug Design: An Update.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Accelerating therapeutic protein design with computational approaches toward the clinical stage.Computational and structural biotechnology journal · 2023Review
- hERG Blockade Prediction by Combining Site Identification by Ligand Competitive Saturation and Physicochemical Properties.Chemistry (Basel, Switzerland) · 2022Article
- Effects of Monovalent Salt on Protein-Protein Interactions of Dilute and Concentrated Monoclonal Antibody Formulations.Antibodies (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
Protein therapeutics typically require a concentrated protein formulation, which can lead to self-association and/or high viscosity due to protein-protein interaction (PPI). Excipients are often added to improve stability, bioavailability, and manufacturability of the protein therapeutics, but the selection of excipients often relies on trial and error. Therefore, understanding the excipient-protein interaction and its effect on non-specific PPI is important for rational selection of formulation development. In this study, we validate a general workflow based on the site identification by ligand competitive saturation (SILCS) technology, termed SILCS-Biologics, that can be applied to protein therapeutics for rational excipient selection. The National Institute of Standards and Technology monoclonal antibody (NISTmAb) reference along with the CNTO607 mAb is used as model antibody proteins to examine PPIs, and NISTmAb was used to further examine excipient-protein interactions, in silico. Metrics from SILCS include the distribution and predicted affinity of excipients, buffer interactions with the NISTmAb Fab, and the relation of the interactions to predicted PPI. Comparison with a range of experimental data showed multiple SILCS metrics to be predictive. Specifically, the number of favorable sites to which an excipient binds and the number of sites to which an excipient binds that are involved in predicted PPIs correlate with the experimentally determined viscosity. In addition, a combination of the number of binding sites and the predicted binding affinity is indicated to be predictive of relative protein stability. Comparison of arginine, trehalose, and sucrose, all of which give the highest viscosity in combination with analysis of
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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.