Exploring Chemical Space for new Substances to stabilize a therapeutic Monoclonal Antibody

Tosstorff, Andreas ; Menzen, Tim und Winter, Gerhard (Januar 2020): Exploring Chemical Space for new Substances to stabilize a therapeutic Monoclonal Antibody. In: Journal of Pharmaceutical Sciences

Abstract

The physical stability of therapeutic proteins is a major concern in the development of liquid protein formulations. The number of degrees of freedom to tweak a given protein’s stability is limited to pH, ionic strength and type and concentration of excipient. There are only very few, mostly similar excipients currently in use, limited to the short list of substances generally recognized as safe for human use by the FDA. Opposed to the limited number of molecules the formulation scientist has at hand to stabilize a protein, there is the vastness of chemical space which is hypothesized to consist of 1060 compounds. Its potential to stabilize proteins has never been explored systematically in the context of stabilization of therapeutic proteins. Here we present a screening strategy to discover new excipients to further stabilize an already stable formulation of a therapeutic antibody. We use our data to build a predictive model to evaluate the stabilizing potential of small molecules. We argue that prior to worrying about the hurdles of toxicity and approval of novel excipient candidates, it is mandatory to assess the actual potential hidden in the chemical space.

• Dokumententyp: Zeitschriftenartikel
• EU Funded Grant Agreement Number: 675074
• EU-Projekte: Horizon 2020 > Marie Skłodowska Curie Actions > Marie Skłodowska-Curie Innovative Training Networks > 675074: PIPPI - Protein-excipient Interactions and Protein-Protein Interactions in formulation
• Publikationsform: Preprint
• Keywords: mAb; excipient; protein stability; nanoDSF; DSF; chemoinformatics
• Fakultät: Chemie und Pharmazie > Department für Pharmazie - Zentrum für Pharmaforschung
• Themengebiete: 500 Naturwissenschaften und Mathematik > 540 Chemie
• ISSN: 0022-3549
• Sprache: Englisch

Literaturliste

Wang W. Advanced protein formulations. Protein Sci [Internet] 2015 [cited 2018 Jan 18]; 24:1031–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25858529 2. Chi EY. Excipients Used in Biotechnology Products. John Wiley & Sons, Inc.; 2017. 3. Svilenov H, Winter G. Rapid sample-saving biophysical characterisation and long-term storage stability of liquid interferon alpha2a formulations: Is there a correlation? Int J Pharm [Internet] 2019; 562:42–50. Available from: https://doi.org/10.1016/j.ijpharm.2019.03.025 4. Ratanji KD, Derrick JP, Dearman RJ, Kimber I. Immunogenicity of therapeutic proteins: Influence of aggregation. J Immunotoxicol [Internet] 2014 [cited 2018 Feb 28]; 11:99–109. Available from: http://www.tandfonline.com/doi/full/10.3109/1547691X.2013.821564 5. Koo OMY. Pharmaceutical excipients: properties, functionality, and applications in research and industry. Hoboken, New Jersey, USA: John Wiley & Sons, Inc.; 2017. 6. Manning MC, Chou DK, Murphy BM, Payne RW, Katayama DS. Stability of Protein Pharmaceuticals: An Update. 2010 [cited 2018 Jan 28]; Available from: http://static.springer.com/sgw/documents/1345446/application/pdf/PHARMRES_Stability+of+Protein+Pharmaceuticals.pdf 7. Bohacek RS, Mcmartin C, Guida WC. The Art and Practice of Structure-Based Drug Design : A Molecular Modeling Perspective. 1996; 16:3–50. 8. Kamerzell TJ, Esfandiary R, Joshi SB, Middaugh CR, Volkin DB. Protein-excipient interactions: Mechanisms and biophysical characterization applied to protein formulation development. Adv Drug Deliv Rev [Internet] 2011; 63:1118–59. Available from: http://dx.doi.org/10.1016/j.addr.2011.07.006 9. Paulette M. Gaynor, Richard Bonnette, Edmundo Garcia, Jr., Linda S. Kahl, Luis G. Valerio J. FDA’s Approach to the GRAS Provision: A History of Processes [Internet]. 2006; Available from: https://www.fda.gov/Food/IngredientsPackagingLabeling/GRAS/ucm094040.htm 10. Ecker DM, Jones SD, Levine HL. The therapeutic monoclonal antibody market. MAbs [Internet] 2015; 7:9–14. Available from: http://www.tandfonline.com/doi/full/10.4161/19420862.2015.989042 11. Raybould MIJ, Marks C, Krawczyk K, Taddese B, Nowak J, Lewis AP, Bujotzek A, Shi J, Deane CM. Five computational developability guidelines for therapeutic antibody profiling. Proc Natl Acad Sci [Internet] 2019; :201810576. Available from: http://www.pnas.org/lookup/doi/10.1073/pnas.1810576116 12. Seeliger D, Schulz P, Litzenburger T, Spitz J, Hoerer S, Blech M, Enenkel B, Studts JM, Garidel P, Karow AR. Boosting antibody developability through rational sequence optimization. MAbs 2015; 7:505–15. 13. Zambrano R, Jamroz M, Szczasiuk A, Pujols J, Kmiecik S, Ventura S. AGGRESCAN3D (A3D): Server for prediction of aggregation properties of protein structures. Nucleic Acids Res 2015; 43:W306–13. 14. Kranz JK, Schalk-Hihi C. Protein Thermal Shifts to Identify Low Molecular Weight Fragments. Methods Enzymol [Internet] 2011 [cited 2018 Oct 16]; 493:277–98. Available from: https://www-sciencedirect-com.emedien.ub.uni-muenchen.de/science/article/pii/B978012381274200011X?via%3Dihub 15. Svilenov H, Markoja U, Winter G. Isothermal chemical denaturation as a complementary tool to overcome limitations of thermal differential scanning fluorimetry in predicting physical stability of protein formulations. Eur J Pharm Biopharm [Internet] 2018 [cited 2018 Jun 13]; 125:106–13. Available from: https://www.sciencedirect.com/science/article/pii/S093964111731127X 16. Svilenov H, Winter G. The ReFOLD assay for protein formulation studies and prediction of protein aggregation during long-term storage. Eur J Pharm Biopharm [Internet] 2019; 137:131–9. Available from: https://www.sciencedirect.com/science/article/pii/S0939641118315364?dgcid=author 17. Svilenov H, Gentiluomo L, Friess W, Roessner D, Winter G. A New Approach to Study the Physical Stability of Monoclonal Antibody Formulations—Dilution From a Denaturant. J Pharm Sci [Internet] 2018; 107:3007–13. Available from: https://doi.org/10.1016/j.xphs.2018.08.004 18. Leach AR, Gillet VJ. An Introduction to Chemoinformatics [Internet]. [cited 2018 Dec 28]. Available from: http://www.acad.bg/ebook/cheminformatics/Leach_An Introduction to Chemoinformatics Rev Ed.pdf 19. Sakuratani Y, Kasai K, Noguchi Y, Yamada J. Comparison of predictivities of log P calculation models based on experimental data for 134 simple organic compounds. QSAR Comb Sci 2007; 26:109–16. 20. Polton DJ. Installation and operational experiences with MACCS (Molecular Access System). Online Rev [Internet] 1982; 6:235–42. Available from: http://www.emeraldinsight.com/doi/10.1108/eb024099 21. Welford SM, Lynch MF, Barnard JM. Towards simplified access to chemical structure information in the patent literature. J Inf Sci [Internet] 1983; 6:3–10. Available from: http://journals.sagepub.com/doi/10.1177/016555158300600102 22. Myint K-Z, Wang L, Tong Q, Xie XQ. Molecular fingerprint-based artificial neural networks QSAR for ligand biological activity predictions. Mol Pharm 2012; 9:2912–23. 23. Oyetayo OO, Méndez-Lucio O, Bender A, Kiefer H. Diversity selection, screening and quantitative structure–activity relationships of osmolyte-like additive effects on the thermal stability of a monoclonal antibody. Eur J Pharm Sci [Internet] 2017; 97:151–7. Available from: http://dx.doi.org/10.1016/j.ejps.2016.11.016 24. Pantoliano MW, Bone RF, Rhind AW, Salemme FR. Microplate Thermal Shift Assay Apparatus for Ligand Development and multi-variable protein chemistry optimization. 2004; 25. Niesen FH, Berglund H, Vedadi M. The use of differential scanning fluorimetry to detect ligand interactions that promote protein stability. Nat Protoc 2007; 2:2212–21. 26. Borysko P, Moroz YS, Vasylchenko O V., Hurmach V V., Starodubtseva A, Stefanishena N, Nesteruk K, Zozulya S, Kondratov IS, Grygorenko OO. Straightforward hit identification approach in fragment-based discovery of bromodomain-containing protein 4 (BRD4) inhibitors. Bioorganic Med Chem [Internet] 2018; 26:3399–405. Available from: https://doi.org/10.1016/j.bmc.2018.05.010 27. Sule S V., Cheung JK, Antochshuk V, Bhalla AS, Narasimhan C, Blaisdell S, Shameem M, Tessier PM. Solution pH that minimizes self-association of three monoclonal antibodies is strongly dependent on ionic strength. Mol Pharm 2012; 9:744–51. 28. Dunn WJ. QSAR approaches to predicting toxicity. Toxicol Lett 1988; 43:277–83. 29. Jones E, Oliphant E, Peterson P, Others A. SciPy: Open Source Scientific Tools for Python. 2001; 30. Newville M, Stensitzki T, Allen DB, Ingargiola A. LMFIT: Non-Linear Least-Square Minimization and Curve-Fitting for Python. 2014 [cited 2019 Jun 4]; Available from: https://doi.org/10.5281/zenodo.11813#.XPYEU5_8WOs.mendeley