High concentration formulation developability approaches and considerations

References

• Kaplon H, Chenoweth A, Crescioli S, Reichert JDSM. Antibodies to watch in 2022. MAbs. 2022;14(1):2014296. doi:10.1080/19420862.2021.2014296.
   PubMed Web of Science ®Google Scholar
• The antibody society. Therapeutic monoclonal antibodies approved or in regulatory review. [cited 2023 May 1]; Available from: https://www.antibodysociety.org/antibody-therapeutics-product-data/
   Google Scholar
• Shire SJ, Shahrokh Z, Liu J. Challenges in the development of high protein concentration formulations. J Pharm Sci. 2004;93(6):1390–13. doi:10.1002/jps.20079.
   PubMed Web of Science ®Google Scholar
• Sharma VK, Biobetters KR. Molecular Assessment of Monoclonal Antibody-Based Therapeutics Enabling Lead Selection for Clinical Development. In: Rosenberg A, Demeule B, editors. Biobetters: Protein Engineering to Approach the Curative. AAPS Advances in the Pharmaceutical Sciences Series. NY: Springer New York; 2015. p. 153–80.
   Google Scholar
• Jarasch A, Koll H, Regula JT, Bader M, Papadimitriou A, Kettenberger H. Developability assessment during the selection of novel therapeutic antibodies. J Pharm Sci. 2015;104(6):1885–98. doi:10.1002/jps.24430.
   PubMed Web of Science ®Google Scholar
• Xu Y, Wang D, Mason B, Rossomando T, Li N, Liu D, Cheung JK, Xu W, Raghava S, Katiyar A, et al. Structure, heterogeneity and developability assessment of therapeutic antibodies. MAbs. 2019;11(2):239–64. doi:10.1080/19420862.2018.1553476.
   PubMed Web of Science ®Google Scholar
• Chennamsetty N, Voynov V, Kayser V, Helk B, Trout BL. Design of therapeutic proteins with enhanced stability. Proc National Acad Sci. 2009;106(29):11937–42. doi:10.1073/pnas.0904191106.
   PubMed Web of Science ®Google Scholar
• Bessa J, Boeckle S, Beck H, Buckel T, Schlicht S, Ebeling M, Kiialainen A, Koulov A, Boll B, Weiser T, et al. The immunogenicity of antibody aggregates in a novel transgenic mouse model. Pharmaceut Res. 2015;32(7):2344–59. doi:10.1007/s11095-015-1627-0.
   PubMed Web of Science ®Google Scholar
• Roberts CJ. Protein aggregation and its impact on product quality. Curr Opin Biotech. 2014;30:211–17. doi:10.1016/j.copbio.2014.08.001.
   PubMed Web of Science ®Google Scholar
• Roberts CJ. Therapeutic protein aggregation: mechanisms, design, and control. Trends Biotechnol. 2014;32(7):372–80. doi:10.1016/j.tibtech.2014.05.005.
   PubMed Web of Science ®Google Scholar
• Mahler H, Friess W, Grauschopf U, Kiese S. Protein aggregation: pathways, induction factors and analysis. J Pharm Sci. 2009;98(9):2909–34. doi:10.1002/jps.21566.
   PubMed Web of Science ®Google Scholar
• Bunc M, Hadzi S, Graf C, Boncina M, Lah J. Aggregation time machine: a platform for the prediction and optimization of long-term antibody stability using short-term kinetic analysis. J Med Chem. 2022;65(3):2623–32. doi:10.1021/acs.jmedchem.1c02010.
   PubMed Web of Science ®Google Scholar
• Das TK, Narhi LO, Sreedhara A, Menzen T, Grapentin C, Chou DK, Antochshuk V, Filipe V. Stress factors in mab drug substance production processes: critical assessment of impact on product quality and control strategy. J Pharm Sci. 2020;109(1):116–33. doi:10.1016/j.xphs.2019.09.023.
   PubMed Web of Science ®Google Scholar
• Lazar KL, Patapoff TW, Sharma VK. Cold denaturation of monoclonal antibodies. MAbs. 2010;2(1):42–52. doi:10.4161/mabs.2.1.10787.
   PubMed Web of Science ®Google Scholar
• Singh SK, Kolhe P, Mehta AP, Chico SC, Lary AL, Huang M. Frozen state storage instability of a monoclonal antibody: aggregation as a consequence of trehalose crystallization and protein unfolding. Pharmaceut Res. 2011;28(4):873–85. doi:10.1007/s11095-010-0343-z.
   PubMed Web of Science ®Google Scholar
• Connolly BD, Le L, Patapoff TW, Cromwell MEM, Moore JMR, Lam P. Protein aggregation in frozen trehalose formulations: effects of composition, cooling rate, and storage temperature. J Pharm Sci. 2015;104(12):4170–84. doi:10.1002/jps.24646.
   PubMed Web of Science ®Google Scholar
• Bluemel O, Anuschek M, Buecheler JW, Hoelzl G, Bechtold-Peters K, Friess W. The effect of mAb and excipient cryoconcentration on long-term frozen storage stability – Part 1: higher molecular weight species and subvisible particle formation. Int J Pharm X. 2022;4:100108. doi:10.1016/j.ijpx.2021.100108.
   PubMedGoogle Scholar
• Bluemel O, Buecheler JW, Hauptmann A, Hoelzl G, Bechtold-Peters K, Friess W. The effect of mAb and excipient cryoconcentration on long-term frozen storage stability – part 2: aggregate formation and oxidation. Int J Pharm X. 2022;4:100109. doi:10.1016/j.ijpx.2021.100109.
   PubMedGoogle Scholar
• Bluemel O, Pavlišič A, Likozar B, Rodrigues MA, Geraldes V, Bechtold-Peters K, Friess W. Computational fluid dynamic simulations of temperature, cryoconcentration, and stress time during large-scale freezing and thawing of monoclonal antibody solutions. Eur J Pharm Biopharm. 2022;177:107–12. doi:10.1016/j.ejpb.2022.06.007.
   PubMed Web of Science ®Google Scholar
• Salnikova M, Varshney D, Shalaev E. Heterogeneity of Protein Environments in Frozen Solutions and in the Dried State. In: Varshney D, Singh M, editors. Lyophilized Biologics and Vaccines. NY: Springer New York; 2015. p. 11–24.
   Google Scholar
• Authelin J-R, Rodrigues MA, Tchessalov S, Singh SK, McCoy T, Wang S, Shalaev E. Freezing of biologicals revisited: scale, stability, excipients, and degradation stresses. J Pharm Sci. 2020;109(1):44–61. doi:10.1016/j.xphs.2019.10.062.
   PubMed Web of Science ®Google Scholar
• Borwankar AU, Dear BJ, Twu A, Hung JJ, Dinin AK, Wilson BK, Yue J, Maynard JA, Truskett TM, Johnston KP. Viscosity reduction of a concentrated monoclonal antibody with arginine·HCl and arginine·Glutamate. Ind Eng Chem Res. 2016;55(43):11225–34. doi:10.1021/acs.iecr.6b02042.
   Google Scholar
• Lilyestrom WG, Yadav S, Shire SJ, Scherer TM. Monoclonal antibody self-association, cluster formation, and rheology at high concentrations. J Phys Chem B. 2013;117(21):6373–84. doi:10.1021/jp4008152.
   PubMed Web of Science ®Google Scholar
• Zeng Y, Tran T, Wuthrich P, Naik S, Davagnino J, Greene DG, Mahoney RP, Soane DS. Caffeine as a viscosity reducer for highly concentrated monoclonal antibody solutions. J Pharm Sci. 2021;110(11):3594–604. doi:10.1016/j.xphs.2021.06.030.
   PubMed Web of Science ®Google Scholar
• Banik N, Braun S, Brandenburg JG, Fricker G, Kalonia DS, Rosenkranz T. Technology development to evaluate the effectiveness of viscosity reducing excipients. Int J Pharmaceut. 2022;626:122204. doi:10.1016/j.ijpharm.2022.122204.
   PubMed Web of Science ®Google Scholar
• Hung JJ, Dear BJ, Dinin AK, Borwankar AU, Mehta SK, Truskett TT, Johnston KP. Improving viscosity and stability of a highly concentrated monoclonal antibody solution with concentrated proline. Pharmaceut Res. 2018;35(7):133. doi:10.1007/s11095-018-2398-1.
   PubMed Web of Science ®Google Scholar
• Dear BJ, Hung JJ, Truskett TM, Johnston KP. Contrasting the influence of cationic amino acids on the viscosity and stability of a highly concentrated monoclonal antibody. Pharmaceut Res. 2017;34(1):193–207. doi:10.1007/s11095-016-2055-5.
   PubMed Web of Science ®Google Scholar
• Dear BJ, Hung JJ, Laber JR, Wilks LR, Sharma A, Truskett TM, Johnston KP. Enhancing stability and reducing viscosity of a monoclonal antibody with cosolutes by weakening protein-protein interactions. J Pharm Sci. 2019;108(8):2517–26. doi:10.1016/j.xphs.2019.03.008.
   PubMed Web of Science ®Google Scholar
• Johnston KP, Maynard JA, Truskett TM, Borwankar AU, Miller MA, Wilson BK, Dinin AK, Khan TA, Kaczorowski KJ. Concentrated dispersions of equilibrium protein nanoclusters that reversibly dissociate into active monomers. Acs Nano. 2012;6(2):1357–69. doi:10.1021/nn204166z.
   PubMed Web of Science ®Google Scholar
• Miller MA, Khan TA, Kaczorowski KJ, Wilson BK, Dinin AK, Borwankar AU, Rodrigues MA, Truskett TM, Johnston KP, Maynard JA. Antibody nanoparticle dispersions formed with mixtures of crowding molecules retain activity and in vivo bioavailability. J Pharm Sci. 2012;101(10):3763–78. doi:10.1002/jps.23256.
   PubMed Web of Science ®Google Scholar
• Laue T. Proximity energies: a framework for understanding concentrated solutions. J Mol Recognit. 2012;25(3):165–73. doi:10.1002/jmr.2179.
   PubMed Web of Science ®Google Scholar
• Saito S, Hasegawa J, Kobayashi N, Kishi N, Uchiyama S, Fukui K. Behavior of monoclonal antibodies: relation between the second virial coefficient (B2) at low concentrations and aggregation propensity and viscosity at high concentrations. Pharmaceut Res. 2012;29(2):397–410. doi:10.1007/s11095-011-0563-x.
   PubMed Web of Science ®Google Scholar
• Connolly BD, Petry C, Yadav S, Demeule B, Ciaccio N, Moore JMR, Shire SJ, Gokarn YR. Weak interactions govern the viscosity of concentrated antibody solutions: high-throughput analysis using the diffusion interaction parameter. Biophys J. 2012;103(1):69–78. doi:10.1016/j.bpj.2012.04.047.
   PubMed Web of Science ®Google Scholar
• Kingsbury JS, Saini A, Auclair SM, Fu L, Lantz MM, Halloran KT, Calero-Rubio C, Schwenger W, Airiau CY, Zhang J, et al. A single molecular descriptor to predict solution behavior of therapeutic antibodies. Sci Adv. 2020;6(32):eabb0372. doi:10.1126/sciadv.abb0372.
   PubMed Web of Science ®Google Scholar
• Ghosh SK, Pal T. Interparticle coupling effect on the surface plasmon resonance of gold nanoparticles: from theory to applications. Chem Rev. 2007;107(11):4797–862. doi:10.1021/cr0680282.
   PubMed Web of Science ®Google Scholar
• Sule SV, Dickinson CD, Lu J, Chow C-K, Tessier PM. Rapid analysis of antibody self-association in complex mixtures using immunogold conjugates. Mol Pharmaceut. 2013;10(4):1322–31. doi:10.1021/mp300524x.
   PubMed Web of Science ®Google Scholar
• Liu Y, Caffry I, Wu J, Geng SB, Jain T, Sun T, Reid F, Cao Y, Estep P, Yu Y, et al. High-throughput screening for developability during early-stage antibody discovery using self-interaction nanoparticle spectroscopy. MAbs. 2013;6(2):483–92. doi:10.4161/mabs.27431.
   PubMed Web of Science ®Google Scholar
• Phan S, Walmer A, Shaw EW, Chai Q. High-throughput profiling of antibody self-association in multiple formulation conditions by PEG stabilized self-interaction nanoparticle spectroscopy. MAbs. 2022;14(1):2094750. doi:10.1080/19420862.2022.2094750.
   PubMed Web of Science ®Google Scholar
• Starr CG, Makowski EK, Wu L, Berg B, Kingsbury JS, Gokarn YR, Tessier PM. Ultradilute measurements of self-association for the identification of antibodies with favorable high-concentration solution properties. Mol Pharmaceut. 2021;18(7):2744–53. doi:10.1021/acs.molpharmaceut.1c00280.
   PubMed Web of Science ®Google Scholar
• Sun T, Reid F, Liu Y, Cao Y, Estep P, Nauman C, Xu Y. High throughput detection of antibody self-interaction by bio-layer interferometry. MAbs. 2013;5(6):838–41. doi:10.4161/mabs.26186.
   PubMed Web of Science ®Google Scholar
• Domnowski M, Jaehrling J, Frieß W. Assessment of antibody self-interaction by bio-layer-interferometry as a tool for early stage formulation development. Pharmaceut Res. 2020;37(2):29. doi:10.1007/s11095-019-2722-4.
   PubMed Web of Science ®Google Scholar
• Quigley A, Williams DR. The second virial coefficient as a predictor of protein aggregation propensity: a self-interaction chromatography study. Eur J Pharm Biopharm. 2015;96:282–90. doi:10.1016/j.ejpb.2015.07.025.
   PubMed Web of Science ®Google Scholar
• Tessier PM, Vandrey SD, Berger BW, Pazhianur R, Sandler SI, Lenhoff AM. Self‐interaction chromatography: a novel screening method for rational protein crystallization. Acta Crystallogr Sect D. 2002;58(10):1531–35. doi:10.1107/S0907444902012775.
   PubMedGoogle Scholar
• Hedberg SHM, Rapley J, Haigh JM, Williams DR. Cross-interaction chromatography as a rapid screening technique to identify the stability of new antibody therapeutics. Eur J Pharm Biopharm. 2018;133:131–37. doi:10.1016/j.ejpb.2018.10.009.
   PubMed Web of Science ®Google Scholar
• Jacobs SA, Wu S-J, Feng Y, Bethea D, O’Neil KT. Cross-interaction chromatography: a rapid method to identify highly soluble monoclonal antibody candidates. Pharmaceut Res. 2009;27(1):65. doi:10.1007/s11095-009-0007-z.
   PubMed Web of Science ®Google Scholar
• Curtis RA, Ulrich J, Montaser A, Prausnitz JM, Blanch HW. Protein–protein interactions in concentrated electrolyte solutions. Biotechnol Bioeng. 2002;79(4):367–80. doi:10.1002/bit.10342.
   PubMed Web of Science ®Google Scholar
• Toprani VM, Joshi SB, Kueltzo LA, Schwartz RM, Middaugh CR, Volkin DB. A micro–polyethylene glycol precipitation assay as a relative solubility screening tool for monoclonal antibody design and formulation development. J Pharm Sci. 2016;105(8):2319–27. doi:10.1016/j.xphs.2016.05.021.
   PubMed Web of Science ®Google Scholar
• Scannell MJ, Hyatt MW, Budyak IL, Woldeyes MA, Wang Y. Revisit PEG-Induced precipitation assay for protein solubility assessment of monoclonal antibody formulations. Pharmaceut Res. 2021;38(11):1947–60. doi:10.1007/s11095-021-03119-4.
   PubMed Web of Science ®Google Scholar
• Chai Q, Shih J, Weldon C, Phan S, Jones BE. Development of a high-throughput solubility screening assay for use in antibody discovery. MAbs. 2019;11(4):747–56. doi:10.1080/19420862.2019.1589851.
   PubMed Web of Science ®Google Scholar
• Jain T, Sun T, Durand S, Hall A, Houston NR, Nett JH, Sharkey B, Bobrowicz B, Caffry I, Yu Y, et al. Biophysical properties of the clinical-stage antibody landscape. Proc National Acad Sci. 2017;114(5):944–49. doi:10.1073/pnas.1616408114.
   PubMed Web of Science ®Google Scholar
• Schuster J, Koulov A, Mahler H-C, Detampel P, Huwyler J, Singh S, Mathaes R. In Vivo stability of therapeutic proteins. Pharmaceut Res. 2020;37(2):23. doi:10.1007/s11095-019-2689-1.
   PubMed Web of Science ®Google Scholar
• den BM, Lai S-H, Xue X, van KM, Bleijlevens B, Heck AJR. Comparative analysis of antibodies and heavily glycosylated macromolecular immune complexes by size-exclusion chromatography multi-angle light scattering, native charge detection mass spectrometry, and mass photometry. Anal Chem. 2022;94(2):892–900. doi:10.1021/acs.analchem.1c03656.
   PubMed Web of Science ®Google Scholar
• Füssl F, Strasser L, Cari S, Bones J. Native LC–MS for capturing quality attributes of biopharmaceuticals on the intact protein level. Curr Opin Biotech. 2021;71:32–40. doi:10.1016/j.copbio.2021.05.008.
   PubMed Web of Science ®Google Scholar
• Asor R, Kukura P. Characterising biomolecular interactions and dynamics with mass photometry. Curr Opin Chem Biol. 2022;68:102132. doi:10.1016/j.cbpa.2022.102132.
   PubMed Web of Science ®Google Scholar
• den EJ, Garidel P, Smulders R, Koll H, Smith B, Bassarab S, Seidl A, Hainzl O, Jiskoot W. Strategies for the assessment of protein aggregates in pharmaceutical biotech product development. Pharmaceut Res. 2011;28(4):920–33. doi:10.1007/s11095-010-0297-1.
   PubMed Web of Science ®Google Scholar
• Beckmann R, Jensen K, Fenn S, Speck J, Krause K, Meier A, Röth M, Fauser S, Kimbung R, Logan DT, et al. DutaFabs are engineered therapeutic fab fragments that can bind two targets simultaneously. Nat Commun. 2021;12(1):708. doi:10.1038/s41467-021-20949-3.
   PubMed Web of Science ®Google Scholar
• Georges GJ, Dengl S, Bujotzek A, Hesse F, Fischer JAA, Gärtner A, Benz J, Lauer ME, Ringler P, Stahlberg H, et al. The contorsbody, an antibody format for agonism: design, structure, and function. Comput Struct Biotechnology J. 2020;18:1210–20. doi:10.1016/j.csbj.2020.05.007.
   PubMed Web of Science ®Google Scholar
• Du W, Klibanov AM. Hydrophobic salts markedly diminish viscosity of concentrated protein solutions. Biotechnol Bioeng. 2011;108(3):632–36. doi:10.1002/bit.22983.
   PubMed Web of Science ®Google Scholar
• Yadav S, Liu J, Shire SJ, Kalonia DS. Specific interactions in high concentration antibody solutions resulting in high viscosity. J Pharm Sci. 2010;99(3):1152–68. doi:10.1002/jps.21898.
   PubMed Web of Science ®Google Scholar
• Yadav S, Laue TM, Kalonia DS, Singh SN, Shire SJ. The influence of charge distribution on self-association and viscosity behavior of monoclonal antibody solutions. Mol Pharmaceut. 2012;9(4):791–802. doi:10.1021/mp200566k.
   PubMed Web of Science ®Google Scholar
• Tomar DS, Singh SK, Li L, Broulidakis MP, Kumar S. In Silico prediction of diffusion interaction parameter (kD), a key indicator of antibody solution behaviors. Pharmaceut Res. 2018;35(10):193. doi:10.1007/s11095-018-2466-6.
   PubMed Web of Science ®Google Scholar
• Sharma VK, Patapoff TW, Kabakoff B, Pai S, Hilario E, Zhang B, Li C, Borisov O, Kelley RF, Chorny I, et al. In silico selection of therapeutic antibodies for development: viscosity, clearance, and chemical stability. Proc National Acad Sci. 2014;111(52):18601–06. doi:10.1073/pnas.1421779112.
   PubMed Web of Science ®Google Scholar
• Pallarés I, Ventura S. Advances in the prediction of protein aggregation propensity. Curr Med Chem. 2019;26(21):3911–20. doi:10.2174/0929867324666170705121754.
   PubMed Web of Science ®Google Scholar
• Conchillo-Solé O, de GN, Avilés FX, Vendrell J, Daura X, Ventura S. AGGRESCAN: a server for the prediction and evaluation of “hot spots” of aggregation in polypeptides. BMC Bioinform. 2007;8(1):65. doi:10.1186/1471-2105-8-65.
   PubMed Web of Science ®Google Scholar
• Trovato A, Seno F, Tosatto SCE. The PASTA server for protein aggregation prediction. Protein Eng Des Sel. 2007;20(10):521–23. doi:10.1093/protein/gzm042.
   PubMed Web of Science ®Google Scholar
• Fernandez-Escamilla A-M, Rousseau F, Schymkowitz J, Serrano L. Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins. Nat Biotechnol. 2004;22(10):1302–06. doi:10.1038/nbt1012.
   PubMed Web of Science ®Google Scholar
• Tartaglia GG, Vendruscolo M. The zyggregator method for predicting protein aggregation propensities. Chem Soc Rev. 2008;37(7):1395–401. doi:10.1039/b706784b.
   PubMed Web of Science ®Google Scholar
• 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(W1):W306–13. doi:10.1093/nar/gkv359.
   PubMed Web of Science ®Google Scholar
• Kuriata A, Iglesias V, Pujols J, Kurcinski M, Kmiecik S, Ventura S. Aggrescan3d (A3D) 2.0: prediction and engineering of protein solubility. Nucleic Acids Res. 2019;47(W1):W300–7. doi:10.1093/nar/gkz321.
   PubMed Web of Science ®Google Scholar
• Agrawal NJ, Helk B, Kumar S, Mody N, Sathish HA, Samra HS, Buck PM, Li L, Trout BL. Computational tool for the early screening of monoclonal antibodies for their viscosities. MAbs. 2016;8(1):43–48. doi:10.1080/19420862.2015.1099773.
   PubMed Web of Science ®Google Scholar
• Voynov V, Chennamsetty N, Kayser V, Helk B, Trout BL. Predictive tools for stabilization of therapeutic proteins. MAbs. 2009;1(6):580–82. doi:10.4161/mabs.1.6.9773.
   PubMed Web of Science ®Google Scholar
• Sankar K, Krystek SR, Carl SM, Day T, JKX M. AggScore: prediction of aggregation‐prone regions in proteins based on the distribution of surface patches. Proteins Struct Funct Bioinform. 2018;86(11):1147–56. doi:10.1002/prot.25594.
   PubMed Web of Science ®Google Scholar
• Lauer TM, Agrawal NJ, Chennamsetty N, Egodage K, Helk B, Trout BL. Developability index: a rapid in silico tool for the screening of antibody aggregation propensity. J Pharm Sci. 2012;101(1):102–15. doi:10.1002/jps.22758.
   PubMed Web of Science ®Google Scholar
• 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 National Acad Sci. 2019;116(10):4025–30. doi:10.1073/pnas.1810576116.
   PubMed Web of Science ®Google Scholar
• Li L, Kumar S, Buck PM, Burns C, Lavoie J, Singh SK, Warne NW, Nichols P, Luksha N, Boardman D. Concentration dependent viscosity of monoclonal antibody solutions: explaining experimental behavior in terms of molecular properties. Pharmaceut Res. 2014;31(11):3161–78. doi:10.1007/s11095-014-1409-0.
   PubMed Web of Science ®Google Scholar
• Buck PM, Chaudhri A, Kumar S, Singh SK. Highly viscous antibody solutions are a consequence of network formation caused by domain–domain electrostatic complementarities: insights from coarse-grained simulations. Mol Pharmaceut. 2015;12(1):127–39. doi:10.1021/mp500485w.
   PubMed Web of Science ®Google Scholar
• Chaudhri A, Zarraga IE, Kamerzell TJ, Brandt JP, Patapoff TW, Shire SJ, Voth GA. Coarse-grained modeling of the self-association of therapeutic monoclonal antibodies. J Phys Chem B. 2012;116:8045–57.
   PubMed Web of Science ®Google Scholar
• Majumdar R, Esfandiary R, Bishop SM, Samra HS, Middaugh CR, Volkin DB, Weis DD. Correlations between changes in conformational dynamics and physical stability in a mutant IgG1 mAb engineered for extended serum half-life. MAbs. 2015;7:84–95.
   PubMed Web of Science ®Google Scholar
• Tomar DS, Li L, Broulidakis MP, Luksha NG, Burns CT, Singh SK, Kumar S. In-silico prediction of concentration-dependent viscosity curves for monoclonal antibody solutions. MAbs. 2017;9:476–89.
   PubMed Web of Science ®Google Scholar
• Tilegenova C, Izadi S, Yin J, Huang CS, Wu J, Ellerman D, Hymowitz SG, Walters B, Salisbury C, Carter PJ. Dissecting the molecular basis of high viscosity of monospecific and bispecific IgG antibodies. MAbs. 2020;12:1692764.
   PubMed Web of Science ®Google Scholar
• Chowdhury A, Bollinger JA, Dear BJ, Cheung JK, Johnston KP, Truskett TM. Coarse-grained molecular dynamics simulations for understanding the impact of short-range anisotropic attractions on structure and viscosity of concentrated monoclonal antibody solutions. Mol Pharmaceut. 2020;17:1748–56.
   PubMed Web of Science ®Google Scholar
• Chaudhri A, Zarraga IE, Yadav S, Patapoff TW, Shire SJ, Voth GA. The role of amino acid sequence in the self-association of therapeutic monoclonal antibodies: insights from coarse-grained modeling. J Phys Chem B. 2013;117:1269–79.
   PubMed Web of Science ®Google Scholar
• Izadi S, Patapoff TW, Walters BT. Multiscale coarse-grained approach to investigate self-association of antibodies. Biophys J. 2020;118:2741–54.
   PubMed Web of Science ®Google Scholar
• Kamerzell TJ, Middaugh CR. Prediction machines: applied machine learning for therapeutic protein design and development. J Pharm Sci. 2021;110:665–81.
   PubMed Web of Science ®Google Scholar
• Lai P-K, Fernando A, Cloutier TK, Gokarn Y, Zhang J, Schwenger W, Chari R, Calero-Rubio C, Trout BL. Machine learning applied to determine the molecular descriptors responsible for the viscosity behavior of concentrated therapeutic antibodies. Mol Pharmaceut. 2021;18:1167–75.
   PubMed Web of Science ®Google Scholar
• Lou H, Hageman MJ. Machine learning attempts for predicting human subcutaneous bioavailability of monoclonal antibodies. Pharmaceut Res. 2021;38:451–60.
   PubMed Web of Science ®Google Scholar
• Lai P-K, Fernando A, Cloutier TK, Kingsbury JS, Gokarn Y, Halloran KT, Calero-Rubio C, Trout BL. Machine learning feature selection for predicting high concentration therapeutic antibody aggregation. J Pharm Sci. 2021;110:1583–91.
   PubMed Web of Science ®Google Scholar
• Lai P-K, Gallegos A, Mody N, Sathish HA, Trout BL. Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics. MAbs. 2022;14:2026208.
   PubMed Web of Science ®Google Scholar
• Jain T, Boland T, Lilov A, Burnina I, Brown M, Xu Y, Vásquez M. Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning. Bioinformatics. 2017;33:3758–66.
   PubMed Web of Science ®Google Scholar
• Lai P-K, Swan JW, Trout BL. Calculation of therapeutic antibody viscosity with coarse-grained models, hydrodynamic calculations and machine learning-based parameters. MAbs. 2021;13:1907882.
   PubMed Web of Science ®Google Scholar
• Cloutier TK, Sudrik C, Mody N, Sathish HA, Trout BL. Machine learning models of antibody–excipient preferential interactions for use in computational formulation design. Mol Pharmaceut. 2020;17:3589–99.
   PubMed Web of Science ®Google Scholar
• Khetan R, Curtis R, Deane CM, Hadsund JT, Kar U, Krawczyk K, Kuroda D, Robinson SA, Sormanni P, Tsumoto K, et al. Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics. MAbs. 2022;14:2020082.
   PubMed Web of Science ®Google Scholar
• Kuroda D, Tsumoto K. Engineering stability, viscosity, and immunogenicity of antibodies by computational design. J Pharm Sci. 2020;109:1631–51.
   PubMed Web of Science ®Google Scholar
• Spiess C, Zhai Q, Carter PJ. Alternative molecular formats and therapeutic applications for bispecific antibodies. Mol Immunol. 2015;67:95–106.
   PubMed Web of Science ®Google Scholar
• Guan X, Eris T, Zhang L, Ren D, Ricci MS, Thiel T, Goudar CT. A high-resolution multi-attribute method for product characterization, process characterization, and quality control of therapeutic proteins. Anal Biochem. 2022;643:114575.
   PubMed Web of Science ®Google Scholar
• Carvalho SB, Gomes RA, Pfenninger A, Fischer M, Strotbek M, Isidro IA, Tugçu N, Gomes-Alves P. Multi attribute method implementation using a high resolution mass spectrometry platform: from sample preparation to batch analysis. PLos One. 2022;17:e0262711.
   PubMed Web of Science ®Google Scholar
• Hao Z, Moore B, Ren C, Sadek M, Macchi F, Yang L, Harris J, Yee L, Liu E, Tran V, et al. Multi-attribute method performance profile for quality control of monoclonal antibody therapeutics. J Pharmaceut Biomed. 2021;205:114330.
   PubMed Web of Science ®Google Scholar
• Woldeyes MA, Josephson LL, Leiske DL, Galush WJ, Roberts CJ, Furst EM. Viscosities and protein interactions of bispecific antibodies and their monospecific mixtures. Mol Pharmaceut. 2018;15:4745–55.
   PubMed Web of Science ®Google Scholar
• Hedberg SHM, Heng JYY, Williams DR, Liddell JM. Micro scale self-interaction chromatography of proteins: a mAb case-study. J Chromatogr A. 2016;1434:57–63.
   PubMed Web of Science ®Google Scholar
• Kinnunen HM, Sharma V, Contreras-Rojas LR, Yu Y, Alleman C, Sreedhara A, Fischer S, Khawli L, Yohe ST, Bumbaca D, et al. A novel in vitro method to model the fate of subcutaneously administered biopharmaceuticals and associated formulation components. J Control Release. 2015;214:94–102.
   PubMed Web of Science ®Google Scholar
• Bown HK, Bonn C, Yohe S, Yadav DB, Patapoff TW, Daugherty A, Mrsny RJ. In vitro model for predicting bioavailability of subcutaneously injected monoclonal antibodies. J Control Release. 2018;273:13–20.
   PubMed Web of Science ®Google Scholar