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Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods

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Therapeutic Antibodies

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2313))

Abstract

Although antibodies have become the fastest-growing class of therapeutics on the market, it is still challenging to develop them for therapeutic applications, which often require these molecules to withstand stresses that are not present in vivo. We define developability as the likelihood of an antibody candidate with suitable functionality to be developed into a manufacturable, stable, safe, and effective drug that can be formulated to high concentrations while retaining a long shelf life. The implementation of reliable developability assessments from the early stages of antibody discovery enables flagging and deselection of potentially problematic candidates, while focussing available resources on the development of the most promising ones. Currently, however, thorough developability assessment requires multiple in vitro assays, which makes it labor intensive and time consuming to implement at early stages. Furthermore, accurate in vitro analysis at the early stage is compromised by the high number of potential candidates that are often prepared at low quantities and purity. Recent improvements in the performance of computational predictors of developability potential are beginning to change this scenario. Many computational methods only require the knowledge of the amino acid sequences and can be used to identify possible developability issues or to rank available candidates according to a range of biophysical properties. Here, we describe how the implementation of in silico tools into antibody discovery pipelines is increasingly offering time- and cost-effective alternatives to in vitro experimental screening, thus streamlining the drug development process. We discuss in particular the biophysical and biochemical properties that underpin developability potential and their trade-offs, review various in vitro assays to measure such properties or parameters that are predictive of developability, and give an overview of the growing number of in silico tools available to predict properties important for antibody development, including the CamSol method developed in our laboratory.

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References

  1. Kaplon H, Muralidharan M, Schneider Z, Reichert JM (2020) Antibodies to watch in 2020. mAbs 12:1703531

    Article  PubMed  CAS  Google Scholar 

  2. Hernandez I, Bott SW, Patel AS, Wolf CG, Hospodar AR, Sampathkumar S, Shrank WH (2018) Pricing of monoclonal antibody therapies: higher if used for cancer? Am J Manag Care 24:109–112

    PubMed  Google Scholar 

  3. Jarasch A, Koll H, Regula JT, Bader M, Papadimitriou A, Kettenberger H (2015) Developability assessment during the selection of novel therapeutic antibodies. J Pharm Sci 104:1885–1898

    Article  CAS  PubMed  Google Scholar 

  4. Igawa T, Ishii S, Tachibana T, Maeda A, Higuchi Y, Shimaoka S, Moriyama C, Watanabe T, Takubo R, Doi Y, Wakabayashi T, Hayasaka A, Kadono S, Miyazaki T, Haraya K, Sekimori Y, Kojima T, Nabuchi Y, Aso Y, Kawabe Y, Hattori K (2010) Antibody recycling by engineered pH-dependent antigen binding improves the duration of antigen neutralization. Nat Biotechnol 28:1203–1207

    Article  CAS  PubMed  Google Scholar 

  5. Rabia LA, Desai AA, Jhajj HS, Tessier PM (2018) Understanding and overcoming trade-offs between antibody affinity, specificity, stability and solubility. Biochem Eng J 137:365–374

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Sormanni P, Aprile FA, Vendruscolo M (2018) Third generation antibody discovery methods: in silico rational design. Chem Soc Rev 47:9137–9157

    Article  CAS  PubMed  Google Scholar 

  7. Wolf Pérez A-M, Sormanni P, Andersen JS, Sakhnini LI, Rodriguez-Leon I, Bjelke JR, Gajhede AJ, De Maria L, Otzen DE, Vendruscolo M, Lorenzen N (2018) In vitro and in silico assessment of the developability of a designed monoclonal antibody library. mAbs 11(2):388–400

    Article  Google Scholar 

  8. Jain T, Sun T, Durand S, Hall A, Houston NR, Nett JH, Sharkey B, Bobrowicz B, Caffry I, Yu Y, Cao Y, Lynaugh H, Brown M, Baruah H, Gray LT, Krauland EM, Xu Y, Vásquez M, Wittrup KD (2017) Biophysical properties of the clinical-stage antibody landscape. Proc Natl Acad Sci U S A 114:944–949

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sharma VK, Patapoff TW, Kabakoff B, Pai S, Hilario E, Zhang B, Li C, Borisov O, Kelley RF, Chorny I, Zhou JZ, Dill KA, Swartz TE (2014) In silico selection of therapeutic antibodies for development: viscosity, clearance, and chemical stability. Proc Natl Acad Sci U S A 111:18601–18606

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Raybould MIJ, Marks C, Krawczyk K, Taddese B, Nowak J, Lewis AP, Bujotzek A, Shi J, Deane CM (2019) Five computational developability guidelines for therapeutic antibody profiling. Proc Natl Acad Sci U S A 116:4025–4030

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Pallarès I, Ventura S (2016) Understanding and predicting protein misfolding and aggregation: insights from proteomics. Proteomics 16:2570–2581

    Article  PubMed  CAS  Google Scholar 

  12. Van Durme J, De Baets G, Van Der Kant R, Ramakers M, Ganesan A, Wilkinson H, Gallardo R, Rousseau F, Schymkowitz J (2016) Solubis: a webserver to reduce protein aggregation through mutation. Protein Eng Des Sel 29(8):285–289

    Article  PubMed  CAS  Google Scholar 

  13. Chennamsetty N, Voynov V, Kayser V, Helk B, Trout BL (2009) Design of therapeutic proteins with enhanced stability. Proc Natl Acad Sci U S A 106:11937–11942

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Kuroda D, Shirai H, Jacobson MP, Nakamura H (2012) Computer-aided antibody design. Protein Eng Des Sel 25:507–521

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Seeliger D, Schulz P, Litzenburger T, Spitz J, Hoerer S, Blech M, Enenkel B, Studts JM, Garidel P, Karow AR (2015) Boosting antibody developability through rational sequence optimization. MAbs 7(3):505–515

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Tomar DS, Singh SK, Li L, Broulidakis MP, Kumar S (2018) In silico prediction of diffusion interaction parameter (kD), a key indicator of antibody solution behaviors. Pharm Res 35:193

    Article  PubMed  CAS  Google Scholar 

  17. Norman RA, Ambrosetti F, Bonvin AMJJ, Colwell LJ, Kelm S, Kumar S, Krawczyk K (2019) Computational approaches to therapeutic antibody design: established methods and emerging trends. Brief Bioinform 208:1673–1619

    Google Scholar 

  18. Shire SJ, Shahrokh Z, Liu J (2004) Challenges in the development of high protein concentration formulations. J Pharm Sci 93:1390–1402

    Article  CAS  PubMed  Google Scholar 

  19. Roberts CJ (2014) Protein aggregation and its impact on product quality. Curr Opin Biotechnol 30:211–217

    Article  CAS  PubMed  Google Scholar 

  20. Vázquez-Rey M, Lang DA (2011) Aggregates in monoclonal antibody manufacturing processes. Biotechnol Bioeng 108:1494–1508

    Article  PubMed  CAS  Google Scholar 

  21. Bee JS, Randolph TW, Carpenter JF, Bishop SM, Dimitrova MN (2011) Effects of surfaces and Leachables on the stability of biopharmaceuticals. J Pharm Sci 100:4158–4170

    Article  CAS  PubMed  Google Scholar 

  22. Rajan RS, Illing ME, Bence NF, Kopito RR (2001) Specificity in intracellular protein aggregation and inclusion body formation. Proc Natl Acad Sci U S A 98:13060–13065

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hober S, Nord K, Linhult M (2007) Protein a chromatography for antibody purification. J Chromatogr B Analyt Technol Biomed Life Sci 848:40–47

    Article  CAS  PubMed  Google Scholar 

  24. Roberts PL (2014) Virus elimination during the purification of monoclonal antibodies by column chromatography and additional steps. Biotechnol Prog 30:1341–1347

    Article  CAS  PubMed  Google Scholar 

  25. Yamniuk AP, Ditto N, Patel M, Dai J, Sejwal P, Stetsko P, Doyle ML (2013) Application of a kosmotrope-based solubility assay to multiple protein therapeutic classes indicates broad use as a high-throughput screen for protein therapeutic aggregation propensity. J Pharm Sci 102:2424–2439

    Article  CAS  PubMed  Google Scholar 

  26. Haverick M, Mengisen S, Shameem M, Ambrogelly A (2014) Separation of mAbs molecular variants by analytical hydrophobic interaction chromatography HPLC: overview and applications. mAbs 6:852–858

    Article  PubMed  PubMed Central  Google Scholar 

  27. Pinholt C, Hartvig RA, Medlicott NJ, Jorgensen L (2011) The importance of interfaces in protein drug delivery—why is protein adsorption of interest in pharmaceutical formulations? Expert Opin Drug Deliv 8:949–964

    Article  CAS  PubMed  Google Scholar 

  28. Clarkson JR, Cui ZF, Darton RC (2000) Effect of solution conditions on protein damage in foam. Biochem Eng J 4:107–114

    Article  CAS  Google Scholar 

  29. Smith C, Li Z, Holman R, Pan F, Campbell RA, Campana M, Li P, Webster JRP, Bishop S, Narwal R, Uddin S, van der Walle CF, Lu JR (2017) Antibody adsorption on the surface of water studied by neutron reflection. mAbs 9:466–475

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Mahler H-C, Müller R, Friess W, Delille A, Matheus S (2005) Induction and analysis of aggregates in a liquid IgG1-antibody formulation. Eur J Pharm Biopharm 59:407–417

    Article  CAS  PubMed  Google Scholar 

  31. Liu L, Qi W, Schwartz DK, Randolph TW, Carpenter JF (2013) The effects of excipients on protein aggregation during agitation: an interfacial shear rheology study. J Pharm Sci 102:2460–2470

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Jones LS, Kaufmann A, Middaugh CR (2005) Silicone oil induced aggregation of proteins. J Pharm Sci 94:918–927

    Article  CAS  PubMed  Google Scholar 

  33. Krayukhina E, Tsumoto K, Uchiyama S, Fukui K (2015) Effects of syringe material and silicone oil lubrication on the stability of pharmaceutical proteins. J Pharm Sci 104:527–535

    Article  CAS  PubMed  Google Scholar 

  34. Bee JS, Davis M, Freund E, Carpenter JF, Randolph TW (2010) Aggregation of a monoclonal antibody induced by adsorption to stainless steel. Biotechnol Bioeng 105:121–129

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hoehne M, Samuel F, Dong A, Wurth C, Mahler H-C, Carpenter JF, Randolph TW (2011) Adsorption of monoclonal antibodies to glass microparticles. J Pharm Sci 100:123–132

    Article  CAS  PubMed  Google Scholar 

  36. Frokjaer S, Otzen DE (2005) Protein drug stability: a formulation challenge. Nat Rev Drug Discov 4:298–306

    Article  CAS  PubMed  Google Scholar 

  37. Daugherty AL, Mrsny RJ (2006) Formulation and delivery issues for monoclonal antibody therapeutics. Adv Drug Deliv Rev 58:686–706

    Article  CAS  PubMed  Google Scholar 

  38. Bye JW, Platts L, Falconer RJ (2014) Biopharmaceutical liquid formulation: a review of the science of protein stability and solubility in aqueous environments. Biotechnol Lett 36:869–875

    Article  CAS  PubMed  Google Scholar 

  39. Richter WF, Bhansali SG, Morris ME (2012) Mechanistic determinants of biotherapeutics absorption following SC administration. AAPS J 14:559–570

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Bee JS, Stevenson JL, Mehta B, Svitel J, Pollastrini J, Platz R, Freund E, Carpenter JF, Randolph TW (2009) Response of a concentrated monoclonal antibody formulation to high shear. Biotechnol Bioeng 103:936–943

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Tyagi AK, Randolph TW, Dong A, Maloney KM, Hitscherich C, Carpenter JF (2009) IgG particle formation during filling pump operation: a case study of heterogeneous nucleation on stainless steel nanoparticles. J Pharm Sci 98:94–104

    Article  CAS  PubMed  Google Scholar 

  42. Collins DS, Kourtis LC, Thyagarajapuram NR, Sirkar R, Kapur S, Harrison MW, Bryan DJ, Jones GB, Wright JM (2017) Optimizing the bioavailability of subcutaneously administered biotherapeutics through Mechanochemical drivers. Pharm Res 34:2000–2011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Roberts CJ (2014) Therapeutic protein aggregation: mechanisms, design, and control. Trends Biotechnol 32:372–380

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Sydow JF, Lipsmeier F, Larraillet V, Hilger M, Mautz B, Mølhøj M, Kuentzer J, Klostermann S, Schoch J, Voelger HR, Regula JT, Cramer P, Papadimitriou A, Kettenberger H (2014) Structure-based prediction of asparagine and aspartate degradation sites in antibody variable regions. PLoS One 9:e100736

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Lu X, Nobrega RP, Lynaugh H, Jain T, Barlow K, Boland T, Sivasubramanian A, Vásquez M, Xu Y (2018) Deamidation and isomerization liability analysis of 131 clinical-stage antibodies. mAbs 262:1–13

    Google Scholar 

  46. Adem YT, Molina P, Liu H, Patapoff TW, Sreedhara A, Esue O (2014) Hexyl glucoside and hexyl Maltoside inhibit light-induced oxidation of tryptophan. J Pharm Sci 103:409–416

    Article  CAS  PubMed  Google Scholar 

  47. Lam XM, Lai WG, Chan EK, Ling V, Hsu CC (2011) Site-specific tryptophan oxidation induced by autocatalytic reaction of Polysorbate 20 in protein formulation. Pharm Res 28:2543–2555

    Article  CAS  PubMed  Google Scholar 

  48. Torosantucci R, Mozziconacci O, Sharov V, Schöneich C, Jiskoot W (2012) Chemical modifications in aggregates of recombinant human insulin induced by metal-catalyzed oxidation: covalent cross-linking via Michael addition to tyrosine oxidation products. Pharm Res 29:2276–2293

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Torosantucci R, Schöneich C, Jiskoot W (2014) Oxidation of therapeutic proteins and peptides: structural and biological consequences. Pharm Res 31:541–553

    Article  CAS  PubMed  Google Scholar 

  50. Ji JA, Zhang B, Cheng W, Wang YJ (2009) Methionine, tryptophan, and histidine oxidation in a model protein, PTH: mechanisms and stabilization. J Pharm Sci 98:4485–4500

    Article  CAS  PubMed  Google Scholar 

  51. Anraku M, Tsurusaki Y, Watanabe H, Maruyama T, Kragh-Hansen U, Otagiri M (2004) Stabilizing mechanisms in commercial albumin preparations: octanoate and N-acetyl-l-tryptophanate protect human serum albumin against heat and oxidative stress. Biochim Biophys Acta 1702:9–17

    Article  CAS  PubMed  Google Scholar 

  52. Grebenau RC, Goldenberg DM, Chien-Hsing C, Koch GA, Gold DV, Kunz A, Hansen HJ (1992) Microheterogeneity of a purified IgG1, due to asymmetric fab glycosylation. Mol Immunol 29:751–758

    Article  CAS  PubMed  Google Scholar 

  53. Coloma MJ, Trinh RK, Martinez AR, Morrison SL (1999) Position effects of variable region carbohydrate on the affinity and in vivo behavior of an anti-(1→6) dextran antibody. J Immunol 162:2162–2170

    CAS  PubMed  Google Scholar 

  54. Huang L, Biolsi S, Bales KR, Kuchibhotla U (2006) Impact of variable domain glycosylation on antibody clearance: an LC/MS characterization. Anal Biochem 349:197–207

    Article  CAS  PubMed  Google Scholar 

  55. Wang Y, Lu Q, Wu S-L, Karger BL, Hancock WS (2011) Characterization and comparison of disulfide linkages and scrambling patterns in therapeutic monoclonal antibodies - using LC-MS with electron transfer dissociation. Anal Chem 83:3133–3140

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Dudgeon K, Rouet R, Kokmeijer I, Schofield P, Stolp J, Langley D, Stock D, Christ D (2012) General strategy for the generation of human antibody variable domains with increased aggregation resistance. Proc Natl Acad Sci U S A 109:10879–10884

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Perchiacca JM, Ladiwala ARA, Bhattacharya M, Tessier PM (2012) Aggregation-resistant domain antibodies engineered with charged mutations near the edges of the complementarity-determining regions. Protein Eng Des Sel 25:591–601

    Article  CAS  PubMed  Google Scholar 

  58. Perchiacca JM, Lee CC, Tessier PM (2014) Optimal charged mutations in the complementarity-determining regions that prevent domain antibody aggregation are dependent on the antibody scaffold. Protein Eng Des Sel 27:29–39

    Article  CAS  PubMed  Google Scholar 

  59. Perchiacca JM, Bhattacharya M, Tessier PM (2011) Mutational analysis of domain antibodies reveals aggregation hotspots within and near the complementarity determining regions. Proteins 79:2637–2647

    Article  CAS  PubMed  Google Scholar 

  60. Kayser V, Chennamsetty N, Voynov V, Helk B, Trout BL (2011) Conformational stability and aggregation of therapeutic monoclonal antibodies studied with ANS and thioflavin T binding. mAbs 3:408–411

    Article  PubMed  PubMed Central  Google Scholar 

  61. Kowalski JM, Parekh RN, Mao J, Wittrup KD (1998) Protein folding stability can determine the efficiency of escape from endoplasmic reticulum quality control. J Biol Chem 273:19453–19458

    Article  CAS  PubMed  Google Scholar 

  62. Kowalski JM, Parekh RN, Wittrup KD (1998) Secretion efficiency in Saccharomyces cerevisiae of bovine pancreatic trypsin inhibitor mutants lacking disulfide bonds is correlated with thermodynamic stability. Biochemistry 37:1264–1273

    Article  CAS  PubMed  Google Scholar 

  63. Sormanni P, Vendruscolo M (2019) Protein solubility predictions using the CamSol method in the study of protein homeostasis. Cold Spring Harb Perspect Biol 11(12):a033845

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Moussa EM, Panchal JP, Moorthy BS, Blum JS, Joubert MK, Narhi LO, Topp EM (2016) Immunogenicity of therapeutic protein aggregates. J Pharm Sci 105:417–430. https://doi.org/10.1016/j.xphs.2015.11.002

    Article  CAS  PubMed  Google Scholar 

  65. De Groot AS, Scott DW (2007) Immunogenicity of protein therapeutics. Trends Immunol 28:482–490

    Article  PubMed  CAS  Google Scholar 

  66. Hansel TT, Kropshofer H, Singer T, Mitchell JA, George AJT (2010) The safety and side effects of monoclonal antibodies. Nat Rev Drug Discov 9:325–338

    Article  CAS  PubMed  Google Scholar 

  67. Carpenter JF, Randolph TW, Jiskoot W, Crommelin DJA, Middaugh CR, Winter G, Fan Y-X, Kirshner S, Verthelyi D, Kozlowski S, Clouse KA, Swann PG, Rosenberg A, Cherney B (2009) Overlooking subvisible particles in therapeutic protein products: gaps that may compromise product quality. J Pharm Sci 98:1201–1205

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Singh SK, Afonina N, Awwad M, Bechtold-Peters K, Blue JT, Chou D, Cromwell M, Krause H-J, Mahler H-C, Meyer BK, Narhi L, Nesta DP, Spitznagel T (2010) An industry perspective on the monitoring of subvisible particles as a quality attribute for protein therapeutics. J Pharm Sci 99:3302–3321

    Article  CAS  PubMed  Google Scholar 

  69. Wang W, Singh S, Zeng DL, King K, Nema S (2007) Antibody structure, instability, and formulation. J Pharm Sci 96:1–26

    Article  CAS  PubMed  Google Scholar 

  70. Lazar KL, Patapoff TW, Sharma VK (2010) Cold denaturation of monoclonal antibodies. mAbs 2:42–52

    Article  PubMed  PubMed Central  Google Scholar 

  71. Temel DB, Landsman P, Brader ML (2016) Orthogonal methods for characterizing the unfolding of therapeutic monoclonal antibodies. In: Methods in Enzymology. Elsevier, Amsterdam, pp 359–389

    Google Scholar 

  72. Dobson CM (2003) Protein folding and misfolding. Nature 426:884–890

    Article  CAS  PubMed  Google Scholar 

  73. Kazlauskas R (2018) Engineering more stable proteins. Chem Soc Rev 47:9026–9045

    Article  CAS  PubMed  Google Scholar 

  74. Manning MC, Chou DK, Murphy BM, Payne RW, Katayama DS (2010) Stability of protein pharmaceuticals: an update. Pharm Res 27:544–575

    Article  PubMed  CAS  Google Scholar 

  75. Trevino SR, Scholtz JM, Pace CN (2008) Measuring and increasing protein solubility. J Pharm Sci 97:4155–4166

    Article  CAS  PubMed  Google Scholar 

  76. Pindrus M, Shire SJ, Kelley RF, Demeule B, Wong R, Xu Y, Yadav S (2015) Solubility challenges in high concentration monoclonal antibody formulations: relationship with amino acid sequence and intermolecular interactions. Mol Pharm 12:3896–3907

    Article  CAS  PubMed  Google Scholar 

  77. Kanai S, Liu J, Patapoff TW, Shire SJ (2008) Reversible self-association of a concentrated monoclonal antibody solution mediated by Fab-Fab interaction that impacts solution viscosity. J Pharm Sci 97:4219–4227

    Article  CAS  PubMed  Google Scholar 

  78. Wu S-J, Gilliland GL, Feng Y (2014) Solubility and early assessment of stability for protein therapeutics. In: Biophysical methods for biotherapeutics. John Wiley & Sons Ltd, Hoboken, New Jersey, pp 65–91

    Chapter  Google Scholar 

  79. Gibson TJ, Mccarty K, McFadyen IJ, Cash E, Dalmonte P, Hinds KD, Dinerman AA, Alvarez JC, Volkin DB (2011) Application of a high-throughput screening procedure with PEG-induced precipitation to compare relative protein solubility during formulation development with IgG1 monoclonal antibodies. J Pharm Sci 100:1009–1021

    Article  CAS  PubMed  Google Scholar 

  80. Toprani VM, Joshi SB, Kueltzo LA, Schwartz RM, Middaugh CR, Volkin DB (2016) A micro–polyethylene glycol precipitation assay as a relative solubility screening tool for monoclonal antibody design and formulation development. J Pharm Sci 105:2319–2327

    Article  CAS  PubMed  Google Scholar 

  81. Nicoud L, Owczarz M, Arosio P, Morbidelli M (2015) A multiscale view of therapeutic protein aggregation: a colloid science perspective. Biotechnol J 10:367–378

    Article  CAS  PubMed  Google Scholar 

  82. Mahler H-C, Friess W, Grauschopf U, Kiese S (2009) Protein aggregation: pathways, induction factors and analysis. J Pharm Sci 98:2909–2934

    Article  CAS  PubMed  Google Scholar 

  83. Arora J, Hu Y, Esfandiary R, Sathish HA, Bishop SM, Joshi SB, Middaugh CR, Volkin DB, Weis DD (2016) Charge-mediated fab-fc interactions in an IgG1 antibody induce reversible self-association, cluster formation, and elevated viscosity. mAbs 8:1561–1574

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Kumar V, Dixit N, Zhou LL, Fraunhofer W (2011) Impact of short range hydrophobic interactions and long range electrostatic forces on the aggregation kinetics of a monoclonal antibody and a dual-variable domain immunoglobulin at low and high concentrations. Int J Pharm 421:82–93

    Article  CAS  PubMed  Google Scholar 

  85. Liu J, Nguyen MDH, Andya JD, Shire SJ (2005) Reversible self-association increases the viscosity of a concentrated monoclonal antibody in aqueous solution. J Pharm Sci 94:1928–1940

    Article  CAS  PubMed  Google Scholar 

  86. Alam ME, Geng SB, Bender C, Ludwig SD, Linden L, Hoet R, Tessier PM (2018) Biophysical and sequence-based methods for identifying monovalent and bivalent antibodies with high colloidal stability. Mol Pharm 15:150–163

    Article  CAS  PubMed  Google Scholar 

  87. Tiller KE, Li L, Kumar S, Julian MC, Garde S, Tessier PM (2017) Arginine mutations in antibody complementarity-determining regions display context-dependent affinity/specificity trade-offs. J Biol Chem 292:16638

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Chennamsetty N, Voynov V, Kayser V, Helk B, Trout BL (2010) Prediction of aggregation prone regions of therapeutic proteins. J Phys Chem B 114:6614–6624

    Article  CAS  PubMed  Google Scholar 

  89. Lauer TM, Agrawal NJ, Chennamsetty N, Egodage K, Helk B, Trout BL (2012) Developability index: a rapid in silico tool for the screening of antibody aggregation propensity. J Pharm Sci 101:102–115

    Article  CAS  PubMed  Google Scholar 

  90. Jain T, Boland T, Lilov A, Burnina I, Brown M, Xu Y, Vásquez M (2017) Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning. Bioinformatics 33:3758–3766

    Article  CAS  PubMed  Google Scholar 

  91. Lawrence MS, Phillips KJ, Liu DR (2007) Supercharging proteins can impart unusual resilience. J Am Chem Soc 129:10110–10112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Miklos AE, Kluwe C, Der BS, Pai S, Sircar A, Hughes RA, Berrondo M, Xu J, Codrea V, Buckley PE, Calm AM, Welsh HS, Warner CR, Zacharko MA, Carney JP, Gray JJ, Georgiou G, Kuhlman B, Ellington AD (2012) Structure-based design of supercharged, highly thermoresistant antibodies. Chem Biol 19:449–455

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Trevino SR, Scholtz JM, Pace CN (2007) Amino acid contribution to protein solubility: asp, Glu, and Ser contribute more favorably than the other hydrophilic amino acids in RNase Sa. J Mol Biol 366:449–460

    Article  CAS  PubMed  Google Scholar 

  94. Yadav S, Shire SJ, Kalonia DS (2012) Viscosity behavior of high-concentration monoclonal antibody solutions: correlation with interaction parameter and electroviscous effects. J Pharm Sci 101:998–1011

    Article  CAS  PubMed  Google Scholar 

  95. Jain D, Salunke DM (2019) Antibody specificity and promiscuity. Biochem J 476:433–447

    Article  CAS  PubMed  Google Scholar 

  96. Xu Y, Roach W, Sun T, Jain T, Prinz B, Yu T-Y, Torrey J, Thomas J, Bobrowicz P, Vásquez M, Wittrup KD, Krauland E (2013) Addressing polyspecificity of antibodies selected from an in vitro yeast presentation system: a FACS-based, high-throughput selection and analytical tool. Protein Eng Des Sel 26:663–670

    Article  CAS  PubMed  Google Scholar 

  97. Hötzel I, Theil F-P, Bernstein LJ, Prabhu S, Deng R, Quintana L, Lutman J, Sibia R, Chan P, Bumbaca D, Fielder P, Carter PJ, Kelley RF (2012) A strategy for risk mitigation of antibodies with fast clearance. mAbs 4:753–760

    Article  PubMed  PubMed Central  Google Scholar 

  98. Vugmeyster Y, Guay H, Szklut P, Qian MD, Jin M, Widom A, Spaulding V, Bennett F, Lowe L, Andreyeva T, Lowe D, Lane S, Thom G, Valge-Archer V, Gill D, Young D, Bloom L (2010) In vitro potency, pharmacokinetic profiles and pharmacological activity of optimized anti-IL-21R antibodies in a mouse model of lupus. mAbs 2:335–346

    Article  PubMed  PubMed Central  Google Scholar 

  99. Wu H, Pfarr DS, Johnson S, Brewah YA, Woods RM, Patel NK, White WI, Young JF, Kiener PA (2007) Development of Motavizumab, an ultra-potent antibody for the prevention of respiratory syncytial virus infection in the upper and lower respiratory tract. J Mol Biol 368:652–665

    Article  CAS  PubMed  Google Scholar 

  100. Geng SB, Wittekind M, Vigil A, Tessier PM (2016) Measurements of monoclonal antibody self-association are correlated with complex biophysical properties. Mol Pharm 13:1636–1645

    Article  CAS  PubMed  Google Scholar 

  101. Wardemann H, Yurasov S, Schaefer A, Young JW, Meffre E, Nussenzweig MC (2003) Predominant autoantibody production by early human B cell precursors. Science 301:1374–1377

    Article  CAS  PubMed  Google Scholar 

  102. Warszawski S, Katz AB, Lipsh R, Khmelnitsky L, Nissan GB, Javitt G, Dym O, Unger T, Knop O, Albeck S, Diskin R, Fass D, Sharon M, Fleishman SJ (2019) Optimizing antibody affinity and stability by the automated design of the variable light-heavy chain interfaces. PLoS Comput Biol 15:e1007207

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Spencer S, Bethea D, Raju TS, Giles-Komar J, Feng Y (2012) Solubility evaluation of murine hybridoma antibodies. mAbs 4:319–325

    Article  PubMed  PubMed Central  Google Scholar 

  104. Burkovitz A, Sela-Culang I, Ofran Y (2014) Large-scale analysis of somatic hypermutations in antibodies reveals which structural regions, positions and amino acids are modified to improve affinity. FEBS J 281:306–319

    Article  CAS  PubMed  Google Scholar 

  105. Pepinsky RB, Silvian L, Berkowitz SA, Farrington G, Lugovskoy A, Walus L, Eldredge J, Capili A, Mi S, Graff C, Garber E (2010) Improving the solubility of anti-LINGO-1 monoclonal antibody Li33 by isotype switching and targeted mutagenesis. Protein Sci 19:954–966

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Wu S-J, Luo J, O’Neil KT, Kang J, Lacy ER, Canziani G, Baker A, Huang M, Tang QM, Raju TS, Jacobs SA, Teplyakov A, Gilliland GL, Feng Y (2010) Structure-based engineering of a monoclonal antibody for improved solubility. Protein Eng Des Sel 23:643–651

    Article  CAS  PubMed  Google Scholar 

  107. Avery LB, Wade J, Wang M, Tam A, King A, Piche-Nicholas N, Kavosi MS, Penn S, Cirelli D, Kurz JC, Zhang M, Cunningham O, Jones R, Fennell BJ, McDonnell B, Sakorafas P, Apgar J, Finlay WJ, Lin L, Bloom L, O’Hara DM (2018) Establishing in vitro in vivo correlations to screen monoclonal antibodies for physicochemical properties related to favorable human pharmacokinetics. mAbs 10:244–255

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Finlay WJJ, Coleman JE, Edwards JS, Johnson KS (2019) Anti-PD1 ‘SHR-1210′ aberrantly targets pro-angiogenic receptors and this polyspecificity can be ablated by paratope refinement. mAbs 11:26–44

    Article  CAS  PubMed  Google Scholar 

  109. Ratanji KD, Derrick JP, Dearman RJ, Kimber I (2014) Immunogenicity of therapeutic proteins: influence of aggregation. J Immunotoxicol 11:99–109

    Article  CAS  PubMed  Google Scholar 

  110. Nelson AL, Dhimolea E, Reichert JM (2010) Development trends for human monoclonal antibody therapeutics. Nat Rev Drug Discov 9:767–774

    Article  CAS  PubMed  Google Scholar 

  111. Lonberg N, Taylor LD, Harding FA, Trounstine M, Higgins KM, Schramm SR, Kuo CC, Mashayekh R, Wymore K, McCabe JG (1994) Antigen-specific human antibodies from mice comprising four distinct genetic modifications. Nature 368:856–859

    Article  CAS  PubMed  Google Scholar 

  112. Jakobovits A, Amado RG, Yang X, Roskos L, Schwab G (2007) From XenoMouse technology to panitumumab, the first fully human antibody product from transgenic mice. Nat Biotechnol 25:1134–1143

    Article  CAS  PubMed  Google Scholar 

  113. Ponsel D, Neugebauer J, Ladetzki-Baehs K, Tissot K (2011) High affinity, developability and functional size: the holy grail of combinatorial antibody library generation. Molecules 16:3675–3700

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Winter G, Griffiths AD, Hawkins RE, Hoogenboom HR (1994) Making antibodies by phage display technology. Annu Rev Immunol 12:433–455

    Article  CAS  PubMed  Google Scholar 

  115. Smith GP, Petrenko VA (1997) Phage display. Chem Rev 97:391–410

    Article  CAS  PubMed  Google Scholar 

  116. Bradbury ARM, Sidhu S, Dübel S, McCafferty J (2011) Beyond natural antibodies: the power of in vitro display technologies. Nat Biotechnol 29:245–254

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Macdougall IC (2005) Antibody-mediated pure red cell aplasia (PRCA): epidemiology, immunogenicity and risks. Nephrol Dial Transplant 20(Suppl 4):iv9–iv15

    Article  PubMed  Google Scholar 

  118. Anfinsen CB (1972) The formation and stabilization of protein structure. Biochem J 128:737–749

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Knowles TPJ, Waudby CA, Devlin GL, Cohen SIA, Aguzzi A, Vendruscolo M, Terentjev EM, Welland ME, Dobson CM (2009) An analytical solution to the kinetics of breakable filament assembly. Science 326:1533–1537

    Article  CAS  PubMed  Google Scholar 

  120. Fersht AR (1999) Structure and mechanism in protein science. Macmillan, New York City

    Google Scholar 

  121. Vendruscolo M, Paci E, Dobson CM, Karplus M (2001) Three key residues form a critical contact network in a protein folding transition state. Nature 409:641–645

    Article  CAS  PubMed  Google Scholar 

  122. Broom A, Jacobi Z, Trainor K, Meiering EM (2017) Computational tools help improve protein stability but with a solubility tradeoff. J Biol Chem 292:14349–14361

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Goldenzweig A, Fleishman S (2018) Principles of protein stability and their application in computational design. Annu Rev Biochem 87:annurev-biochem-062917-012102

    Article  CAS  Google Scholar 

  124. Espargarό A, Castillo V, de Groot NS, Ventura S (2008) The in vivo and in vitro aggregation properties of globular proteins correlate with their conformational stability: the SH3 case. J Mol Biol 378:1116–1131

    Article  CAS  Google Scholar 

  125. Thakkar SV, Sahni N, Joshi SB, Kerwin BA, He F, Volkin DB, Middaugh CR (2013) Understanding the relevance of local conformational stability and dynamics to the aggregation propensity of an IgG1 and IgG2 monoclonal antibodies. Protein Sci 22:1295–1305

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Trainor K, Gingras Z, Shillingford C, Malakian H, Gosselin M, Lipovšek D, Meiering EM (2016) Ensemble modeling and intracellular aggregation of an engineered immunoglobulin-like domain. J Mol Biol 428:1365–1374

    Article  CAS  PubMed  Google Scholar 

  127. Stenvang M, Schafer NP, Malmos KG, Pérez A-MW, Niembro O, Sormanni P, Basaiawmoit RV, Christiansen G, Andreasen M, Otzen DE (2018) Corneal dystrophy mutations drive pathogenesis by targeting TGFBIp stability and solubility in a latent amyloid-forming domain. J Mol Biol 430:1116–1140

    Article  CAS  PubMed  Google Scholar 

  128. Sormanni P, Amery L, Ekizoglou S, Vendruscolo M, Popovic B (2017) Rapid and accurate in silico solubility screening of a monoclonal antibody library. Sci Rep 7:8200

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  129. Julian MC, Lee CC, Tiller KE, Rabia LA, Day EK, Schick AJ, Tessier PM (2015) Co-evolution of affinity and stability of grafted amyloid-motif domain antibodies. Protein Eng Des Sel 28:339–350

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Julian MC, Li L, Garde S, Wilen R, Tessier PM (2017) Efficient affinity maturation of antibody variable domains requires co-selection of compensatory mutations to maintain thermodynamic stability. Sci Rep 7:45259

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Nguyen MN, Pradhan MR, Verma C, Zhong P (2017) The interfacial character of antibody paratopes: analysis of antibody-antigen structures. Bioinformatics 33:2971–2976

    Article  CAS  PubMed  Google Scholar 

  132. Kelly RL, Le D, Zhao J, Wittrup KD (2018) Reduction of nonspecificity motifs in synthetic antibody libraries. J Mol Biol 430:119–130

    Article  CAS  PubMed  Google Scholar 

  133. Peng H-P, Lee KH, Jian J-W, Yang A-S (2014) Origins of specificity and affinity in antibody-protein interactions. Proc Natl Acad Sci U S A 111:E2656–E2665

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Xu Y, Wang D, Mason B, Rossomando T, Li N, Liu D, Cheung JK, Xu W, Raghava S, Katiyar A, Nowak C, Xiang T, Dong DD, Sun J, Beck A, Liu H (2019) Structure, heterogeneity and developability assessment of therapeutic antibodies. mAbs 11:239–264

    Article  CAS  PubMed  Google Scholar 

  135. Zurdo J, Arnell A, Obrezanova O, Smith N, Gómez de la Cuesta R, Gallagher TRA, Michael R, Stallwood Y, Ekblad C, Abrahmsén L, Höidén-Guthenberg I (2015) Early Implementation of QbD in Biopharmaceutical Development: A Practical Example. Biomed Res Int 2015:605427

    Article  PubMed  PubMed Central  Google Scholar 

  136. Norman RA, Ambrosetti F, Bonvin AMJJ, Colwell LJ, Kelm S, Kumar S, Krawczyk K (2020) Computational approaches to therapeutic antibody design: established methods and emerging trends. Brief Bioinform 21(5):1549–1567

    Article  PubMed  Google Scholar 

  137. Greenfield NJ (2006) Using circular dichroism spectra to estimate protein secondary structure. Nat Protoc 1:2876–2890

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Baker MJ, Trevisan J, Bassan P, Bhargava R, Butler HJ, Dorling KM, Fielden PR, Fogarty SW, Fullwood NJ, Heys KA, Hughes C, Lasch P, Martin-Hirsch PL, Obinaju B, Sockalingum GD, Sulé-Suso J, Strong RJ, Walsh MJ, Wood BR, Gardner P, Martin FL (2014) Using Fourier transform IR spectroscopy to analyze biological materials. Nat Protoc 9:1771–1791

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Filipe V, Hawe A, Carpenter JF, Jiskoot W (2013) Analytical approaches to assess the degradation of therapeutic proteins. Trac-Trend Anal Chem 49:118–125

    Article  CAS  Google Scholar 

  140. Ranjbar B, Gill P (2009) Circular dichroism techniques: biomolecular and Nanostructural analyses- a review. Chem Biol Drug Des 74:101–120

    Article  CAS  PubMed  Google Scholar 

  141. Fiedler S, Cole L, Keller S (2013) Automated circular dichroism spectroscopy for medium-throughput analysis of protein conformation. Anal Chem 85:1868–1872

    Article  CAS  PubMed  Google Scholar 

  142. Schermeyer M-T, Wöll AK, Kokke B, Eppink M, Hubbuch J (2017) Characterization of highly concentrated antibody solution - a toolbox for the description of protein long-term solution stability. mAbs 9:1169–1185

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Vivian JT, Callis PR (2001) Mechanisms of tryptophan fluorescence shifts in proteins. Biophys J 80:2093–2109

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Saluja A, Kalonia DS (2008) Nature and consequences of protein-protein interactions in high protein concentration solutions. Int J Pharm 358:1–15

    Article  CAS  PubMed  Google Scholar 

  145. Curtis RA, Prausnitz JM, Blanch HW (1998) Protein-protein and protein-salt interactions in aqueous protein solutions containing concentrated electrolytes. Biotechnol Bioeng 57:11–21

    Article  CAS  PubMed  Google Scholar 

  146. Saito S, Hasegawa J, Kobayashi N, Kishi N, Uchiyama S, Fukui K (2012) Behavior of monoclonal antibodies: relation between the second virial coefficient (B2) at low concentrations and aggregation propensity and viscosity at high concentrations. Pharm Res 29:397–410

    Article  CAS  PubMed  Google Scholar 

  147. Blanco MA, Perevozchikova T, Martorana V, Manno M, Roberts CJ (2014) Protein–protein interactions in dilute to concentrated solutions: α-Chymotrypsinogen in acidic conditions. J Phys Chem B 118:5817–5831

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. Ruppert S, Sandler SI, Lenhoff AM (2001) Correlation between the osmotic second virial coefficient and the solubility of proteins. Biotechnol Prog 17:182–187

    Article  CAS  PubMed  Google Scholar 

  149. Le Brun V, Friess W, Bassarab S, Mühlau S, Garidel P (2010) A critical evaluation of self-interaction chromatography as a predictive tool for the assessment of protein–protein interactions in protein formulation development: a case study of a therapeutic monoclonal antibody. Eur J Pharm Biopharm 75:16–25

    Article  PubMed  CAS  Google Scholar 

  150. Haas C, Drenth J, Wilson WW (1999) Relation between the solubility of proteins in aqueous solutions and the second virial coefficient of the solution. J Phys Chem B 103:2808–2811

    Article  CAS  Google Scholar 

  151. Connolly BD, Petry C, Yadav S, Demeule B, Ciaccio N, Moore JMR, Shire SJ, Gokarn YR (2012) Weak interactions govern the viscosity of concentrated antibody solutions: high-throughput analysis using the diffusion interaction parameter. Biophys J 103:69–78

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. Harding SE, Johnson P (1985) The concentration-dependence of macromolecular parameters. Biochem J 231:543–547

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. Saluja A, Badkar AV, Zeng DL, Nema S, Kalonia DS (2007) Ultrasonic storage modulus as a novel parameter for analyzing protein-protein interactions in high protein concentration solutions: correlation with static and dynamic light scattering measurements. Biophys J 92:234–244

    Article  CAS  PubMed  Google Scholar 

  154. He F, Woods CE, Becker GW, Narhi LO, Razinkov VI (2011) High-throughput assessment of thermal and colloidal stability parameters for monoclonal antibody formulations. J Pharm Sci 100:5126–5141

    Article  CAS  PubMed  Google Scholar 

  155. Yadav S, Shire SJ, Kalonia DS (2010) Factors affecting the viscosity in high concentration solutions of different monoclonal antibodies. J Pharm Sci 99:4812–4829

    Article  CAS  PubMed  Google Scholar 

  156. Schein CH (1990) Solubility as a function of protein structure and solvent components. Biotechnology 8:308–317

    CAS  PubMed  Google Scholar 

  157. Tomar DS, Kumar S, Singh SK, Goswami S, Li L (2016) Molecular basis of high viscosity in concentrated antibody solutions: strategies for high concentration drug product development. mAbs 8:216–228

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Wang W, Nema S, Teagarden D (2010) Protein aggregation--pathways and influencing factors. Int J Pharm 390:89–99

    Article  CAS  PubMed  Google Scholar 

  159. Wang W (1999) Instability, stabilization, and formulation of liquid protein pharmaceuticals. Int J Pharm 185:129–188

    Article  CAS  PubMed  Google Scholar 

  160. Chi EY, Krishnan S, Randolph TW, Carpenter JF (2003) Physical stability of proteins in aqueous solution: mechanism and driving forces in nonnative protein aggregation. Pharm Res 20:1325–1336

    Article  CAS  PubMed  Google Scholar 

  161. Hong P, Koza S, Bouvier ESP (2012) A review size-exclusion chromatography for the analysis of protein biotherapeutics and their aggregates. J Liq Chromatogr Relat Technol 35:2923–2950

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. Liu J, Andya JD, Shire SJ (2006) A critical review of analytical ultracentrifugation and field flow fractionation methods for measuring protein aggregation. AAPS J 8:E580–E589

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  163. Hofmann M, Gieseler H (2018) Predictive screening tools used in high-concentration protein formulation development. J Pharm Sci 107:772–777

    Article  CAS  PubMed  Google Scholar 

  164. Weiss WF, Young TM, Roberts CJ (2009) Principles, approaches, and challenges for predicting protein aggregation rates and shelf life. J Pharm Sci 98:1246–1277

    Article  CAS  PubMed  Google Scholar 

  165. Hawe A, Wiggenhorn M, van de Weert M, Garbe JHO, Mahler H-C, Jiskoot W (2012) Forced degradation of therapeutic proteins. J Pharm Sci 101:895–913

    Article  CAS  PubMed  Google Scholar 

  166. Joubert MK, Luo Q, Nashed-Samuel Y, Wypych J, Narhi LO (2011) Classification and characterization of therapeutic antibody aggregates. J Biol Chem 286:25118–25133

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  167. Sahin E, Grillo AO, Perkins MD, Roberts CJ (2010) Comparative effects of pH and ionic strength on protein-protein interactions, unfolding, and aggregation for IgG1 antibodies. J Pharm Sci 99:4830–4848

    Article  CAS  PubMed  Google Scholar 

  168. Wang W, Roberts CJ (2013) Non-Arrhenius Protein Aggregation. AAPS J 15:840–851

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  169. Kameoka D, Masuzaki E, Ueda T, Imoto T (2007) Effect of buffer species on the unfolding and the aggregation of humanized IgG. J Biochem 142:383–391

    Article  CAS  PubMed  Google Scholar 

  170. Kayser V, Chennamsetty N, Voynov V, Helk B, Forrer K, Trout BL (2011) Evaluation of a non-Arrhenius model for therapeutic monoclonal antibody aggregation. J Pharm Sci 100:2526–2542

    Article  CAS  PubMed  Google Scholar 

  171. Goldberg DS, Bishop SM, Shah AU, Sathish HA (2011) Formulation development of therapeutic monoclonal antibodies using high-throughput fluorescence and static light scattering techniques: role of conformational and colloidal stability. J Pharm Sci 100:1306–1315

    Article  CAS  PubMed  Google Scholar 

  172. Sormanni P, Aprile FA, Vendruscolo M (2015) The CamSol method of rational Design of Protein Mutants with enhanced solubility. J Mol Biol 427:478–490

    Article  CAS  PubMed  Google Scholar 

  173. Cromwell MEM, Hilario E, Jacobson F (2006) Protein aggregation and bioprocessing. AAPS J 8:E572–E579

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  174. Li L, Kantor A, Warne N (2013) Application of a PEG precipitation method for solubility screening: a tool for developing high protein concentration formulations. Protein Sci 22:1118–1123

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  175. Chai Q, Shih J, Weldon C, Phan S, Jones BE (2019) Development of a high-throughput solubility screening assay for use in antibody discovery. mAbs 11:747–756

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  176. Laue T (2012) Proximity energies: a framework for understanding concentrated solutions. J Mol Recognit 25:165–173

    Article  CAS  PubMed  Google Scholar 

  177. Minton AP (2006) Macromolecular crowding. Curr Biol 16:R269–R271

    Article  CAS  PubMed  Google Scholar 

  178. Geoghegan JC, Fleming R, Damschroder M, Bishop SM, Sathish HA, Esfandiary R (2016) Mitigation of reversible self-association and viscosity in a human IgG1 monoclonal antibody by rational, structure-guided Fv engineering. mAbs 8:941–950

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  179. Tilegenova C, Izadi S, Yin J, Huang CS, Wu J, Ellerman D, Hymowitz SG, Walters B, Salisbury C, Carter PJ (2019) Dissecting the molecular basis of high viscosity of monospecific and bispecific IgG antibodies. mAbs 12(1):1692764

    Article  PubMed Central  CAS  Google Scholar 

  180. Zhang Z, Liu Y (2017) Recent progresses of understanding the viscosity of concentrated protein solutions. Curr Opin Chem Eng 16:48–55

    Article  Google Scholar 

  181. He F, Becker GW, Litowski JR, Narhi LO, Brems DN, Razinkov VI (2010) High-throughput dynamic light scattering method for measuring viscosity of concentrated protein solutions. Anal Biochem 399:141–143

    Article  CAS  PubMed  Google Scholar 

  182. Arosio P, Hu K, Aprile FA, Müller T, Knowles TPJ (2016) Microfluidic diffusion viscometer for rapid analysis of complex solutions. Anal Chem 88:3488–3493

    Article  CAS  PubMed  Google Scholar 

  183. Kopp MRG, Villois A, Capasso Palmiero U, Arosio P (2018) Microfluidic diffusion analysis of the size distribution and microrheological properties of antibody solutions at high concentrations. Ind Eng Chem Res 57:7112–7120

    Article  CAS  Google Scholar 

  184. Neergaard MS, Kalonia DS, Parshad H, Nielsen AD, Møller EH, van de Weert M (2013) Viscosity of high concentration protein formulations of monoclonal antibodies of the IgG1 and IgG4 subclass—prediction of viscosity through protein-protein interaction measurements. Eur J Pharm Sci 49:400–410

    Article  CAS  PubMed  Google Scholar 

  185. Wright TA, Stewart JM, Page RC, Konkolewicz D (2017) Extraction of thermodynamic parameters of protein unfolding using parallelized differential scanning Fluorimetry. J Phys Chem Lett 8:553–558

    Article  CAS  PubMed  Google Scholar 

  186. Johnson CM (2013) Differential scanning calorimetry as a tool for protein folding and stability. Arch Biochem Biophys 531:100–109

    Article  CAS  PubMed  Google Scholar 

  187. Jacobs SA, Wu S-J, Feng Y, Bethea D, O’Neil KT (2009) Cross-interaction chromatography: a rapid method to identify highly soluble monoclonal antibody candidates. Pharm Res 27:65

    Article  PubMed  CAS  Google Scholar 

  188. Kohli N, Jain N, Geddie ML, Razlog M, Xu L, Lugovskoy AA (2015) A novel screening method to assess developability of antibody-like molecules. mAbs 7:752–758

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  189. Kelly RL, Sun T, Jain T, Caffry I, Yu Y, Cao Y, Lynaugh H, Brown M, Vásquez M, Wittrup KD, Xu Y (2015) High throughput cross-interaction measures for human IgG1 antibodies correlate with clearance rates in mice. mAbs 7:770–777

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  190. Estep P, Caffry I, Yu Y, Sun T, Cao Y, Lynaugh H, Jain T, Vásquez M, Tessier PM, Xu Y (2015) An alternative assay to hydrophobic interaction chromatography for high-throughput characterization of monoclonal antibodies. mAbs 7:553–561

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  191. Liu Y, Caffry I, Wu J, Geng SB, Jain T, Sun T, Reid F, Cao Y, Estep P, Yu Y, Vásquez M, Tessier PM, Xu Y (2014) High-throughput screening for developability during early-stage antibody discovery using self-interaction nanoparticle spectroscopy. mAbs 6:483–492

    Article  PubMed  Google Scholar 

  192. Lueking A, Possling A, Huber O, Beveridge A, Horn M, Eickhoff H, Schuchardt J, Lehrach H, Cahill DJ (2003) A nonredundant human protein Chip for antibody screening and serum profiling. Mol Cell Proteomics 2:1342–1349

    Article  CAS  PubMed  Google Scholar 

  193. Feyen O, Lueking A, Kowald A, Stephan C, Meyer HE, Göbel U, Niehues T (2008) Off-target activity of TNF-α inhibitors characterized by protein biochips. Anal Bioanal Chem 391:1713–1720

    Article  CAS  PubMed  Google Scholar 

  194. Frese K, Eisenmann M, Ostendorp R, Brocks B, Pabst S (2013) An automated immunoassay for early specificity profiling of antibodies. mAbs 5:279–287

    Article  PubMed  PubMed Central  Google Scholar 

  195. Ramos-López P, Irizarry J, Pino I, Blackshaw S (2018) Antibody specificity profiling using protein microarrays. Methods Mol Biol 1785:223–229

    Article  PubMed  CAS  Google Scholar 

  196. Sule SV, Dickinson CD, Lu J, Chow C-K, Tessier PM (2013) Rapid analysis of antibody self-association in complex mixtures using immunogold conjugates. Mol Pharm 10:1322–1331

    Article  CAS  PubMed  Google Scholar 

  197. Yadav S, Laue TM, Kalonia DS, Singh SN, Shire SJ (2012) The influence of charge distribution on self-association and viscosity behavior of monoclonal antibody solutions. Mol Pharm 9:791–802

    Article  CAS  PubMed  Google Scholar 

  198. Dobson CL, Devine PWA, Phillips JJ, Higazi DR, Lloyd C, Popovic B, Arnold J, Buchanan A, Lewis A, Goodman J, van der Walle CF, Thornton P, Vinall L, Lowne D, Aagaard A, Olsson L-L, Wollberg AR, Welsh F, Karamanos TK, Pashley CL, Iadanza MG, Ranson NA, Ashcroft AE, Kippen AD, Vaughan TJ, Radford SE, Lowe DC (2016) Engineering the surface properties of a human monoclonal antibody prevents self-association and rapid clearance in vivo. Sci Rep 6:1–14

    Article  CAS  Google Scholar 

  199. Tessier PM, Vandrey SD, Berger BW, Pazhianur R, Sandler SI, Lenhoff AM, IUCr (2002) Self-interaction chromatography: a novel screening method for rational protein crystallization. Acta Crystallogr D Biol Crystallogr 58:1531–1535

    Article  PubMed  CAS  Google Scholar 

  200. Patro SY, Przybycien TM (1996) Self-interaction chromatography: a tool for the study of protein–protein interactions in bioprocessing environments. Biotechnol Bioeng 52:193–203

    Article  CAS  PubMed  Google Scholar 

  201. Tessier PM, Lenhoff AM, Sandler SI (2002) Rapid measurement of protein osmotic second virial coefficients by self-interaction chromatography. Biophys J 82:1620–1631

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  202. Johnson DH, Parupudi A, Wilson WW, DeLucas LJ (2008) High-throughput self-interaction chromatography: applications in protein formulation prediction. Pharm Res 26:296

    Article  PubMed  CAS  Google Scholar 

  203. Sun T, Reid F, Liu Y, Cao Y, Estep P, Nauman C, Xu Y (2013) High throughput detection of antibody self-interaction by bio-layer interferometry. mAbs 5:838–841

    Article  PubMed  PubMed Central  Google Scholar 

  204. Wu J, Schultz JS, Weldon CL, Sule SV, Chai Q, Geng SB, Dickinson CD, Tessier PM (2015) Discovery of highly soluble antibodies prior to purification using affinity-capture self-interaction nanoparticle spectroscopy. Protein Eng Des Sel 28:403–414

    Article  CAS  PubMed  Google Scholar 

  205. Shan L, Mody N, Sormani P, Rosenthal KL, Damschroder MM, Esfandiary R (2018) Developability assessment of engineered monoclonal antibody variants with a complex self-association behavior using complementary analytical and in silico tools. Mol Pharm 15:5697–5710

    Article  CAS  PubMed  Google Scholar 

  206. Fekete S, Veuthey J-L, Beck A, Guillarme D (2016) Hydrophobic interaction chromatography for the characterization of monoclonal antibodies and related products. J Pharm Biomed Anal 130:3–18

    Article  CAS  PubMed  Google Scholar 

  207. Gentiluomo L, Svilenov HL, Augustijn D, El Bialy I, Greco ML, Kulakova A, Indrakumar S, Mahapatra S, Morales MM, Pohl C, Roche A, Tosstorff A, Curtis R, Derrick JP, Nørgaard A, Khan TA, Peters GHJ, Pluen A, Rinnan Å, Streicher WW, van der Walle CF, Uddin S, Winter G, Roessner D, Harris P, Frieß W (2020) Advancing therapeutic protein discovery and development through comprehensive computational and biophysical characterization. Mol Pharm 17:426–440

    CAS  PubMed  Google Scholar 

  208. Bradbury A, Plückthun A (2015) Reproducibility: standardize antibodies used in research. Nat News 518:27

    Article  CAS  Google Scholar 

  209. Tsumoto K, Ejima D, Senczuk AM, Kita Y, Arakawa T (2007) Effects of salts on protein–surface interactions: applications for column chromatography. J Pharm Sci 96:1677–1690

    Article  CAS  PubMed  Google Scholar 

  210. Charmet J, Arosio P, Knowles TPJ (2018) Microfluidics for protein biophysics. J Mol Biol 430:565–580

    Article  CAS  PubMed  Google Scholar 

  211. Kopp MRG, Arosio P (2018) Microfluidic approaches for the characterization of therapeutic proteins. J Pharm Sci 107:1228–1236

    Article  CAS  PubMed  Google Scholar 

  212. Arosio P, Müller T, Rajah L, Yates EV, Aprile FA, Zhang Y, Cohen SIA, White DA, Herling TW, De Genst EJ, Linse S, Vendruscolo M, Dobson CM, Knowles TPJ (2016) Microfluidic diffusion analysis of the sizes and interactions of proteins under native solution conditions. ACS Nano 10:333–341

    Article  CAS  PubMed  Google Scholar 

  213. Obrezanova O, Arnell A, de la Cuesta RG, Berthelot ME, Gallagher TR, Zurdo J, Stallwood Y (2015) Aggregation risk prediction for antibodies and its application to biotherapeutic development. mAbs 7:352–363

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  214. Meric G, Robinson AS, Roberts CJ (2017) Driving forces for nonnative protein aggregation and approaches to predict aggregation-prone regions. Annu Rev Chem Biomol Eng 8:139–159. https://doi.org/10.1146/annurev-chembioeng-060816-101404

    Article  CAS  PubMed  Google Scholar 

  215. Zambrano R, Jamroz M, Szczasiuk A, Pujols J, Kmiecik S, Ventura S (2015) AGGRESCAN3D (A3D): server for prediction of aggregation properties of protein structures. Nucleic Acids Res 43:W306–W313

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  216. Shan L, Mody N, Sormanni P, Rosenthal KL, Damschroder MM, Esfandiary R (2018) Developability assessment of engineered monoclonal antibody variants with a complex self-association behavior using complementary analytical and in silico tools. Mol Pharm 15(12):5697–5710

    Article  CAS  PubMed  Google Scholar 

  217. Camilloni C, Sala BM, Sormanni P, Porcari R, Corazza A, Rosa MD, Zanini S, Barbiroli A, Esposito G, Bolognesi M, Bellotti V, Vendruscolo M, Ricagno S (2016) Rational design of mutations that change the aggregation rate of a protein while maintaining its native structure and stability. Sci Rep 6:1–11

    Article  CAS  Google Scholar 

  218. Smialowski P, Doose G, Torkler P, Kaufmann S, Frishman D (2012) PROSO II – a new method for protein solubility prediction. FEBS J 279:2192–2200

    Article  CAS  PubMed  Google Scholar 

  219. Agostini F, Vendruscolo M, Tartaglia GG (2012) Sequence-based prediction of protein solubility. J Mol Biol 421:237–241

    Article  CAS  PubMed  Google Scholar 

  220. Magnan CN, Randall A, Baldi P (2009) SOLpro: accurate sequence-based prediction of protein solubility. Bioinformatics 25:2200–2207

    Article  CAS  PubMed  Google Scholar 

  221. Huang H-L, Charoenkwan P, Kao T-F, Lee H-C, Chang F-L, Huang W-L, Ho S-J, Shu L-S, Chen W-L, Ho S-Y (2012) Prediction and analysis of protein solubility using a novel scoring card method with dipeptide composition. BMC Bioinformatics 13:S3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  222. Hirose S, Noguchi T (2013) ESPRESSO: a system for estimating protein expression and solubility in protein expression systems. Proteomics 13:1444–1456

    Article  CAS  PubMed  Google Scholar 

  223. Chan P, Curtis RA, Warwicker J (2013) Soluble expression of proteins correlates with a lack of positively-charged surface. Sci Rep 3:3333

    Article  PubMed  PubMed Central  Google Scholar 

  224. Schaller A, Connors NK, Oelmeier SA, Hubbuch J, Middelberg APJ (2015) Predicting recombinant protein expression experiments using molecular dynamics simulation. Chem Eng Sci 121:340–350

    Article  CAS  Google Scholar 

  225. Yang Y, Niroula A, Shen B, Vihinen M (2016) PON-sol: prediction of effects of amino acid substitutions on protein solubility. Bioinformatics 32:2032–2034

    Article  CAS  PubMed  Google Scholar 

  226. Paladin L, Piovesan D, Tosatto SCE (2017) SODA: prediction of protein solubility from disorder and aggregation propensity. Nucleic Acids Res 45:W236–W240

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  227. Hebditch M, Carballo-Amador MA, Charonis S, Curtis R, Warwicker J (2017) Protein–sol: a web tool for predicting protein solubility from sequence. Bioinformatics 33:3098–3100

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  228. Chiti F, Stefani M, Taddei N, Ramponi G, Dobson CM (2003) Rationalization of the effects of mutations on peptide andprotein aggregation rates. Nature 424:805–808

    Article  CAS  PubMed  Google Scholar 

  229. DuBay KF, Pawar AP, Chiti F, Zurdo J, Dobson CM, Vendruscolo M (2004) Prediction of the absolute aggregation rates of amyloidogenic polypeptide chains. J Mol Biol 341:1317–1326

    Article  CAS  PubMed  Google Scholar 

  230. Fernandez-Escamilla A-M, Rousseau F, Schymkowitz J, Serrano L (2004) Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins. Nat Biotechnol 22:1302–1306

    Article  CAS  PubMed  Google Scholar 

  231. Conchillo-Solé O, de Groot NS, Avilés FX, Vendrell J, Daura X, Ventura S (2007) AGGRESCAN: a server for the prediction and evaluation of “hot spots” of aggregation in polypeptides. BMC Bioinformatics 8:65

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  232. Tartaglia GG, Vendruscolo M (2008) The Zyggregator method for predicting protein aggregation propensities. Chem Soc Rev 37:1395–1401

    Article  CAS  PubMed  Google Scholar 

  233. Garbuzynskiy SO, Lobanov MY, Galzitskaya OV (2010) FoldAmyloid: a method of prediction of amyloidogenic regions from protein sequence. Bioinformatics 26:326–332

    Article  CAS  PubMed  Google Scholar 

  234. Thompson MJ, Sievers SA, Karanicolas J, Ivanova MI, Baker D, Eisenberg D (2006) The 3D profile method for identifying fibril-forming segments of proteins. Proc Natl Acad Sci U S A 103:4074–4078

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  235. Goldschmidt L, Teng PK, Riek R, Eisenberg D (2010) Identifying the amylome, proteins capable of forming amyloid-like fibrils. Proc Natl Acad Sci U S A 107:3487–3492

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  236. Maurer-Stroh S, Debulpaep M, Kuemmerer N, de la Paz ML, Martins IC, Reumers J, Morris KL, Copland A, Serpell L, Serrano L, Schymkowitz JWH, Rousseau F (2010) Exploring the sequence determinants of amyloid structure using position-specific scoring matrices. Nat Methods 7:237–242

    Article  CAS  PubMed  Google Scholar 

  237. O’Donnell CW, Waldispühl J, Lis M, Halfmann R, Devadas S, Lindquist S, Berger B (2011) A method for probing the mutational landscape of amyloid structure. Bioinformatics 27:i34–i42

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  238. Tsolis AC, Papandreou NC, Iconomidou VA, Hamodrakas SJ (2013) A consensus method for the prediction of ‘aggregation-prone’ peptides in globular proteins. PLoS One 8:e54175

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  239. Emily M, Talvas A, Delamarche C (2013) MetAmyl: a METa-predictor for AMYLoid proteins. PLoS One 8:e79722

    Article  PubMed  PubMed Central  Google Scholar 

  240. Trovato A, Seno F, Tosatto SCE (2007) The PASTA server for protein aggregation prediction. Protein Eng Des Sel 20:521–523

    Article  CAS  PubMed  Google Scholar 

  241. Trovato A, Chiti F, Maritan A, Seno F (2006) Insight into the structure of amyloid fibrils from the analysis of globular proteins. PLoS Comput Biol 2:e170

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  242. Walsh I, Seno F, Tosatto SCE, Trovato A (2014) PASTA 2.0: an improved server for protein aggregation prediction. Nucleic Acids Res 42:W301–W307

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  243. Gasior P, Kotulska M (2014) FISH amyloid—a new method for finding amyloidogenic segments in proteins based on site specific co-occurence of aminoacids. BMC Bioinformatics 15:54

    Article  PubMed  PubMed Central  Google Scholar 

  244. Kuriata A, Iglesias V, Pujols J, Kurcinski M, Kmiecik S, Ventura S (2019) Aggrescan3D (A3D) 2.0: prediction and engineering of protein solubility. Nucleic Acids Res 47:W300–W307

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  245. Agrawal NJ, Helk B, Kumar S, Mody N, Sathish HA, Samra HS, Buck PM, Li L, Trout BL (2015) Computational tool for the early screening of monoclonal antibodies for their viscosities. mAbs 8(1):43–48

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  246. Tomar DS, Li L, Broulidakis MP, Luksha NG, Burns CT, Singh SK, Kumar S (2017) In-silico prediction of concentration-dependent viscosity curves for monoclonal antibody solutions. mAbs 9:476–489

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  247. De Groot AS, Sbai H, Aubin CS, McMurry J, Martin W (2002) Immuno-informatics: mining genomes for vaccine components. Immunol Cell Biol 80:255–269

    Article  PubMed  Google Scholar 

  248. Dalkas GA, Rooman M (2017) SEPIa, a knowledge-driven algorithm for predicting conformational B-cell epitopes from the amino acid sequence. BMC Bioinformatics 18:95

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  249. Gil-Garcia M, Bañó-Polo M, Varejão N, Jamroz M, Kuriata A, Díaz-Caballero M, Lascorz J, Morel B, Navarro S, Reverter D, Kmiecik S, Ventura S (2018) Combining structural aggregation propensity and stability predictions to redesign protein solubility. Mol Pharm 15:3846–3859

    Article  CAS  PubMed  Google Scholar 

  250. Hebditch M, Warwicker J (2019) Web-based display of protein surface and pH-dependent properties for assessing the developability of biotherapeutics. Sci Rep 9:1969

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  251. Chiti F, Stefani M, Taddei N, Ramponi G, Dobson CM (2003) Rationalization of the effects of mutations on peptide and protein aggregation rates. Nature 424:805–808

    Article  CAS  PubMed  Google Scholar 

  252. Fowler SB, Poon S, Muff R, Chiti F, Dobson CM, Zurdo J (2005) Rational design of aggregation-resistant bioactive peptides: reengineering human calcitonin. Proc Natl Acad Sci U S A 102:10105–10110

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  253. Trainor K, Broom A, Meiering EM (2017) Exploring the relationships between protein sequence, structure and solubility. Curr Opin Struct Biol 42:136–146

    Article  CAS  PubMed  Google Scholar 

  254. Pastor MT, Esteras-Chopo A, Serrano L (2007) Hacking the code of amyloid formation: the amyloid stretch hypothesis. Prion 1:9–14

    Article  PubMed  PubMed Central  Google Scholar 

  255. Chiti F, Dobson CM (2006) Protein misfolding, functional amyloid, and human disease. Annu Rev Biochem 75:333–366

    Article  CAS  PubMed  Google Scholar 

  256. Dobson CL, Devine PWA, Phillips JJ, Higazi DR, Lloyd C, Popovic B, Arnold J, Buchanan A, Lewis A, Goodman J, van der Walle CF, Thornton P, Vinall L, Lowne D, Aagaard A, Olsson L-L, Ridderstad Wollberg A, Welsh F, Karamanos TK, Pashley CL, Iadanza MG, Ranson NA, Ashcroft AE, Kippen AD, Vaughan TJ, Radford SE, Lowe DC (2016) Engineering the surface properties of a human monoclonal antibody prevents self-association and rapid clearance in vivo. Sci Rep 6:38644

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  257. Dunbar J, Krawczyk K, Leem J, Marks C, Nowak J, Regep C, Georges G, Kelm S, Popovic B, Deane CM (2016) SAbPred: a structure-based antibody prediction server. Nucleic Acids Res 44:W474–W478

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  258. Schymkowitz J, Borg J, Stricher F, Nys R, Rousseau F, Serrano L (2005) The FoldX web server: an online force field. Nucleic Acids Res 33:W382–W388

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  259. Adolf-Bryfogle J, Xu Q, North B, Lehmann A, Dunbrack RL (2015) PyIgClassify: a database of antibody CDR structural classifications. Nucleic Acids Res 43:D432–D438

    Article  CAS  PubMed  Google Scholar 

  260. Di Rienzo L, Milanetti E, Lepore R, Olimpieri PP, Tramontano A (2017) Superposition-free comparison and clustering of antibody binding sites: implications for the prediction of the nature of their antigen. Sci Rep 7:45053

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  261. Al-Lazikani B, Lesk AM, Chothia C (1997) Standard conformations for the canonical structures of immunoglobulins. J Mol Biol 273:927–948

    Article  CAS  PubMed  Google Scholar 

  262. Krawczyk K, Dunbar J, Deane CM (2017) Computational tools for aiding rational antibody design. Methods Mol Biol 1529:399–416

    Article  CAS  PubMed  Google Scholar 

  263. Regep C, Georges G, Shi J, Popovic B, Deane CM (2017) The H3 loop of antibodies shows unique structural characteristics. Proteins 85:1311–1318

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  264. Marks C, Deane CM (2017) Antibody H3 structure prediction. Comput Struct Biotechnol J 15:222–231

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  265. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46:3–26

    Article  CAS  PubMed  Google Scholar 

  266. Pawar AP, Dubay KF, Zurdo J, Chiti F, Vendruscolo M, Dobson CM (2005) Prediction of “aggregation-prone” and “aggregation-susceptible” regions in proteins associated with neurodegenerative diseases. J Mol Biol 350:379–392

    Article  CAS  PubMed  Google Scholar 

  267. Tartaglia GG, Pawar AP, Campioni S, Dobson CM, Chiti F, Vendruscolo M (2008) Prediction of aggregation-prone regions in structured proteins. J Mol Biol 380:425–436

    Article  CAS  PubMed  Google Scholar 

  268. Walther DM, Kasturi P, Zheng M, Pinkert S, Vecchi G, Ciryam P, Morimoto RI, Dobson CM, Vendruscolo M, Mann M, Hartl FU (2015) Widespread proteome remodeling and aggregation in Aging C. elegans. Cell 161:919–932

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  269. Família C, Dennison SR, Quintas A, Phoenix DA (2015) Prediction of peptide and protein propensity for amyloid formation. PLoS One 10:e0134679

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  270. Flower DR (2003) Towards in silico prediction of immunogenic epitopes. Trends Immunol 24:667–674

    Article  CAS  PubMed  Google Scholar 

  271. Fowler DM, Fields S (2014) Deep mutational scanning: a new style of protein science. Nat Methods 11:801–807

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  272. Gray VE, Sitko K, Kameni FZN, Williamson M, Stephany JJ, Hasle N, Fowler DM (2019) Elucidating the molecular determinants of Aβ aggregation with deep mutational scanning. G3 9:3683–3689

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  273. Walle IV, Gansemans Y, Parren PW, Stas P, Lasters I (2007) Immunogenicity screening in protein drug development. Expert Opin Biol Ther 7:405–418

    Article  PubMed  CAS  Google Scholar 

  274. De Groot AS, McMurry J, Moise L (2008) Prediction of immunogenicity: in silico paradigms, ex vivo and in vivo correlates. Curr Opin Pharmacol 8:620–626

    Article  PubMed  CAS  Google Scholar 

  275. Kennedy PJ, Oliveira C, Granja PL, Sarmento B (2018) Monoclonal antibodies: technologies for early discovery and engineering. Crit Rev Biotechnol 38:394–408

    Article  CAS  PubMed  Google Scholar 

  276. Olsson MHM, Søndergaard CR, Rostkowski M, Jensen JH (2011) PROPKA3: consistent treatment of internal and surface residues in empirical pKa predictions. J Chem Theory Comput 7:525–537

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, UK

    Adriana-Michelle Wolf Pérez, Michele Vendruscolo & Pietro Sormanni

  2. Biophysics and Injectable Formulation, Novo Nordisk A/S, Måløv, Denmark

    Nikolai Lorenzen

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  1. Adriana-Michelle Wolf Pérez
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  2. Nikolai Lorenzen
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  3. Michele Vendruscolo
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  4. Pietro Sormanni
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Correspondence to Michele Vendruscolo .

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  1. Department of Neurology, Rigshospitalet, Glostrup, Denmark

    Gunnar Houen

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Wolf Pérez, AM., Lorenzen, N., Vendruscolo, M., Sormanni, P. (2022). Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods. In: Houen, G. (eds) Therapeutic Antibodies. Methods in Molecular Biology, vol 2313. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1450-1_4

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