Literature DB >> 33137372

Toward Biotherapeutics Formulation Composition Engineering using Site-Identification by Ligand Competitive Saturation (SILCS).

Sandeep Somani1, Sunhwan Jo2, Renuka Thirumangalathu3, Danika Rodrigues3, Laura M Tanenbaum3, Ketan Amin3, Alexander D MacKerell4, Santosh V Thakkar5.   

Abstract

Formulation of protein-based therapeutics employ advanced formulation and analytical technologies for screening various parameters such as buffer, pH, and excipients. At a molecular level, physico-chemical properties of a protein formulation depend on self-interaction between protein molecules, protein-solvent and protein-excipient interactions. This work describes a novel in silico approach, SILCS-Biologics, for structure-based modeling of protein formulations. SILCS Biologics is based on the Site-Identification by Ligand Competitive Saturation (SILCS) technology and enables modeling of interactions among different components of a formulation at an atomistic level while accounting for protein flexibility. It predicts potential hotspot regions on the protein surface for protein-protein and protein-excipient interactions. Here we apply SILCS-Biologics on a Fab domain of a monoclonal antibody (mAbN) to model Fab-Fab interactions and interactions with three amino acid excipients, namely, arginine HCl, proline and lysine HCl. Experiments on 100 mg/ml formulations of mAbN showed that arginine increased, lysine reduced, and proline did not impact viscosity. We use SILCS-Biologics modeling to explore a structure-based hypothesis for the viscosity modulating effect of these excipients. Current efforts are aimed at further validation of this novel computational framework and expanding the scope to model full mAb and other protein therapeutics.
Copyright © 2020 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Arginine; Excipient; Flexibility; Formulation; FragMaps; High-concentration; In silico modeling; Interaction; Lysine; Molecular dynamics; Proline; Protein; Screening; Viscosity

Mesh:

Substances:

Year:  2020        PMID: 33137372      PMCID: PMC7897284          DOI: 10.1016/j.xphs.2020.10.051

Source DB:  PubMed          Journal:  J Pharm Sci        ISSN: 0022-3549            Impact factor:   3.784


  70 in total

1.  The influence of charge distribution on self-association and viscosity behavior of monoclonal antibody solutions.

Authors:  Sandeep Yadav; Thomas M Laue; Devendra S Kalonia; Shubhadra N Singh; Steven J Shire
Journal:  Mol Pharm       Date:  2012-03-19       Impact factor: 4.939

2.  Theory of protein solubility.

Authors:  T Arakawa; S N Timasheff
Journal:  Methods Enzymol       Date:  1985       Impact factor: 1.600

3.  Reproducing crystal binding modes of ligand functional groups using Site-Identification by Ligand Competitive Saturation (SILCS) simulations.

Authors:  E Prabhu Raman; Wenbo Yu; Olgun Guvench; Alexander D Mackerell
Journal:  J Chem Inf Model       Date:  2011-04-01       Impact factor: 4.956

4.  Using empirical phase diagrams to understand the role of intramolecular dynamics in immunoglobulin G stability.

Authors:  Joshua D Ramsey; Michelle L Gill; Tim J Kamerzell; E Shane Price; Sangeeta B Joshi; Steven M Bishop; Cynthia N Oliver; C Russell Middaugh
Journal:  J Pharm Sci       Date:  2009-07       Impact factor: 3.534

5.  Local dynamics and their alteration by excipients modulate the global conformational stability of an lgG1 monoclonal antibody.

Authors:  Santosh V Thakkar; Jae Hyun Kim; Hardeep S Samra; Hasige A Sathish; Steven M Bishop; Sangeeta B Joshi; David B Volkin; C Russell Middaugh
Journal:  J Pharm Sci       Date:  2012-10-11       Impact factor: 3.534

6.  Partial molar volumes and adiabatic compressibilities of unfolded protein states.

Authors:  Soyoung Lee; Anna Tikhomirova; Napol Shalvardjian; Tigran V Chalikian
Journal:  Biophys Chem       Date:  2008-03-04       Impact factor: 2.352

7.  Preferential interactions determine protein solubility in three-component solutions: the MgCl2 system.

Authors:  T Arakawa; R Bhat; S N Timasheff
Journal:  Biochemistry       Date:  1990-02-20       Impact factor: 3.162

8.  Excipients differentially influence the conformational stability and pretransition dynamics of two IgG1 monoclonal antibodies.

Authors:  Santosh V Thakkar; Sangeeta B Joshi; Matthew E Jones; Hasige A Sathish; Steven M Bishop; David B Volkin; C Russell Middaugh
Journal:  J Pharm Sci       Date:  2012-05-11       Impact factor: 3.534

Review 9.  Viscosity Control of Protein Solution by Small Solutes: A Review.

Authors:  Taehun Hong; Kazuki Iwashita; Kentaro Shiraki
Journal:  Curr Protein Pept Sci       Date:  2018       Impact factor: 3.272

10.  The novel BH3 α-helix mimetic JY-1-106 induces apoptosis in a subset of cancer cells (lung cancer, colon cancer and mesothelioma) by disrupting Bcl-xL and Mcl-1 protein-protein interactions with Bak.

Authors:  Xiaobo Cao; Jeremy L Yap; M Karen Newell-Rogers; Chander Peddaboina; Weihua Jiang; Harry T Papaconstantinou; Dan Jupitor; Arun Rai; Kwan-Young Jung; Richard P Tubin; Wenbo Yu; Kenno Vanommeslaeghe; Paul T Wilder; Alexander D MacKerell; Steven Fletcher; Roy W Smythe
Journal:  Mol Cancer       Date:  2013-05-16       Impact factor: 27.401

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  2 in total

1.  Application of Site-Identification by Ligand Competitive Saturation in Computer-Aided Drug Design.

Authors:  Himanshu Goel; Anthony Hazel; Wenbo Yu; Sunhwan Jo; Alexander D MacKerell
Journal:  New J Chem       Date:  2021-11-29       Impact factor: 3.591

Review 2.  Computational models for studying physical instabilities in high concentration biotherapeutic formulations.

Authors:  Marco A Blanco
Journal:  MAbs       Date:  2022 Jan-Dec       Impact factor: 5.857

  2 in total

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