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Review

Identifying developability risks for clinical progression of antibodies using high-throughput in vitro and in silico approaches

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Article: 2200540 | Received 13 Feb 2023, Accepted 04 Apr 2023, Published online: 18 Apr 2023

References

  • Ausserwöger H, Schneider MM, Herling TW, Arosio P, Invernizzi G, Knowles TPJ, Lorenzen N. Non-specificity as the sticky problem in therapeutic antibody development. Nat Rev Chem. 2022;6(12):844–20. Available from https://www.nature.com/articles/s41570-022-00438-x.
  • Zhang W, Wang H, Feng N, Li Y, Gu J, Wang Z. Developability assessment at early-stage discovery to enable development of antibody-derived therapeutics. Antib Ther. 2022;6(1):13–29. Available from doi:10.1093/abt/tbac029/6823522.
  • 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.
  • Fernández-Quintero ML, Ljungars A, Waibl F, Greiff V, Andersen JT, Gjølberg TT, Jenkins TP, Voldborg BG, Grav LM, Kumar S, et al. Assessing developability early in the discovery process for novel biologics. MAbs. 2023;15(1):2171248. doi:10.1080/19420862.2023.2171248.
  • 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. Proceedings of the National Academy of Sciences. 2017;114:944–49. doi:10.1073/pnas.1616408114
  • Lecerf M, Lacombe R, Kanyavuz A, Dimitrov JD. Functional changes of therapeutic antibodies upon exposure to pro-oxidative agents. Antibodies. 2022;11(1):11. Available from doi:10.3390/antib11010011.
  • Lecerf M, Kanyavuz A, Rossini S, Dimitrov JD. Interaction of clinical-stage antibodies with heme predicts their physiochemical and binding qualities. Commun Biol. 2021;4(1):391. Available from http://www.ncbi.nlm.nih.gov/pubmed/33758329.
  • Kraft TE, Richter WF, Emrich T, Knaupp A, Schuster M, Wolfert A, Kettenberger H. Heparin chromatography as an in vitro predictor for antibody clearance rate through pinocytosis. MAbs. 2020;12(1):1683432. Available from http://www.ncbi.nlm.nih.gov/pubmed/31769731.
  • Dietlin-Auril V, Lecerf M, Depinay S, Noé R, Dimitrov JD. Interaction with 2,4-dinitrophenol correlates with polyreactivity, self-binding, and stability of clinical-stage therapeutic antibodies. Mol Immunol. 2021;140:233–39. Available from https://linkinghub.elsevier.com/retrieve/pii/S0161589021003096.
  • Kelly RL, Geoghegan JC, Feldman J, Jain T, Kauke M, Le D, Zhao J, Wittrup KD. Chaperone proteins as single component reagents to assess antibody nonspecificity. MAbs. 2017;9(7):1036–40. Available from http://www.ncbi.nlm.nih.gov/pubmed/28745541.
  • Makowski EK, Wu L, Desai AA, Tessier PM. Highly sensitive detection of antibody nonspecific interactions using flow cytometry. MAbs. 2021;13(1):1951426. Available from http://www.ncbi.nlm.nih.gov/pubmed/34313552.
  • Willis LF, Kumar A, Jain T, Caffry I, Xu Y, Radford SE, Kapur N, Vásquez M, Brockwell DJ. The uniqueness of flow in probing the aggregation behavior of clinically relevant antibodies. Eng Rep. 2020;2(5). Available from. doi:10.1002/eng2.12147.
  • Mouquet H, Scheid JF, Zoller MJ, Krogsgaard M, Ott RG, Shukair S, Artyomov MN, Pietzsch J, Connors M, Pereyra F, et al. Polyreactivity increases the apparent affinity of anti-HIV antibodies by heteroligation. Nature. 2010;467(7315):591–95. doi:10.1038/nature09385.
  • Avery LB, Wade J, Wang M, Tam A, King A, Piche-Nicholas N, Kavosi MS, Penn S, Cirelli D, Kurz JC, et al. Establishing in vitro in vivo correlations to screen monoclonal antibodies for physicochemical properties related to favorable human pharmacokinetics. MAbs. 2018;10(2):244–55. doi:10.1080/19420862.2017.1417718.
  • 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(4):1583–91. Available from https://linkinghub.elsevier.com/retrieve/pii/S0022354920307930.
  • Grinshpun B, Thorsteinson N, Pereira JN, Rippmann F, Nannemann D, Sood VD, Fomekong Nanfack Y. Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies. MAbs. 2021;13(1). Available from. doi:10.1080/19420862.2021.1932230.
  • Thorsteinson N, Gunn JR, Kelly K, Long W, Labute P. Structure-based charge calculations for predicting isoelectric point, viscosity, clearance, and profiling antibody therapeutics. MAbs. 2021;13(1):1981805. Available from http://www.ncbi.nlm.nih.gov/pubmed/34632944.
  • Raybould MIJ, Marks C, Krawczyk K, Taddese B, Nowak J, Lewis AP, Bujotzek A, Shi J, Deane CM. Five computational developability guidelines for therapeutic antibody profiling. Proc Natl Acad Sci U S A. 2019;116(10):4025–30. Available from http://www.ncbi.nlm.nih.gov/pubmed/30765520.
  • Ahmed L, Gupta P, Martin KP, Scheer JM, Nixon AE, Kumar S Intrinsic physicochemical profile of marketed antibody-based biotherapeutics. Proceedings of the National Academy of Sciences. 2021;118. doi:10.1073/pnas.2020577118
  • Zhang Y, Wu L, Gupta P, Desai AA, Smith MD, Rabia LA, Ludwig SD, Tessier PM. Physicochemical Rules for Identifying Monoclonal Antibodies with Drug-like Specificity. Mol Pharm. 2020;17(7):2555–69. Available from http://www.ncbi.nlm.nih.gov/pubmed/32453957.
  • Shehata L, Maurer DP, Wec AZ, Lilov A, Champney E, Sun T, Archambault K, Burnina I, Lynaugh H, Zhi X, et al. Affinity maturation enhances antibody specificity but compromises conformational stability. Cell Rep. 2019;28(13):3300–8.e4. doi:10.1016/j.celrep.2019.08.056.
  • Hebditch M, Warwicker J. Charge and hydrophobicity are key features in sequence-trained machine learning models for predicting the biophysical properties of clinical-stage antibodies. PeerJ. 2019;7:e8199. Available from https://peerj.com/articles/8199.
  • Bailly M, Mieczkowski C, Juan V, Metwally E, Tomazela D, Baker J, Uchida M, Kofman E, Raoufi F, Motlagh S, et al. Predicting antibody developability profiles through early stage discovery screening. MAbs. 2020;12(1): Available from. doi: 10.1080/19420862.2020.1743053.
  • Negron C, Fang J, McPherson MJ, Stine WB, McCluskey AJ. Separating clinical antibodies from repertoire antibodies, a path to in silico developability assessment. MAbs. 2022;14(1). Available from. doi:10.1080/19420862.2022.2080628.
  • Waibl F, Fernández-Quintero ML, Kamenik AS, Kraml J, Hofer F, Kettenberger H, Georges G, Liedl KR. Conformational ensembles of antibodies determine their Hydrophobicity. Biophys J. 2021;120(1):143–57. doi:10.1016/j.bpj.2020.11.010.
  • Waibl F, Fernández-Quintero ML, Wedl FS, Kettenberger H, Georges G, Liedl KR. Comparison of hydrophobicity scales for predicting biophysical properties of antibodies. Front Mol Biosci. 2022;9. 10.3389/fmolb.2022.960194
  • Zhou Y, Xie S, Yang Y, Jiang L, Liu S, Li W, Abagna HB, Ning L, Huang J. SSH2.0: a better tool for predicting the Hydrophobic interaction risk of monoclonal Antibody. Front Genet. 2022;13. doi:10.3389/fgene.2022.842127.
  • Jain T, Boland T, Lilov A, Burnina I, Brown M, Xu Y, Vásquez M, Valencia A. Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning. Bioinformatics. 2017;33(23):1–9. Available from http://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btx519/4083264/Prediction-of-delayed-retention-of-antibodies-in.
  • Kelly RL, Le D, Zhao J, Wittrup KD. Reduction of nonspecificity motifs in synthetic antibody libraries. J Mol Biol. 2018;430(1):119–30. doi:10.1016/j.jmb.2017.11.008.
  • Rabia LA, Zhang Y, Ludwig SD, Julian MC, Tessier PM. Net charge of antibody complementarity-determining regions is a key predictor of specificity. Protein Eng Des Sel. 2019:1–10. Available from. doi:10.1093/protein/gzz002/5321218.
  • Gupta P, Makowski EK, Kumar S, Zhang Y, Scheer JM, Tessier PM. Antibodies with weakly basic isoelectric points minimize trade-offs between formulation and physiological colloidal properties. Mol Pharm. 2022;19(3):775–87. doi:10.1021/acs.molpharmaceut.1c00373.
  • Leem J, Dunbar J, Georges G, Shi J, CM D. ABodyBuilder: automated antibody structure prediction with data–driven accuracy estimation. MAbs. 2016;8(7):1259–68. doi:10.1080/19420862.2016.1205773.
  • Labute P. Protonate3d: assignment of ionization states and hydrogen coordinates to macromolecular structures. Proteins Struct Funct Bioinf. 2009;75(1):187–205. doi:10.1002/prot.22234.
  • Olsson MHM, Søndergaard CR, Rostkowski M, Jensen JH. PROPKA3: consistent treatment of internal and surface residues in empirical p K a predictions. J Chem Theory Comput. 2011;7(2):525–37. doi:10.1021/ct100578z.
  • Xu Y, Roach W, Sun T, Jain T, Prinz B, Yu T-Y, Torrey J, Thomas J, Bobrowicz P, Vasquez M, et al. 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. 2013;26(10):663–70. Available from. doi:10.1093/protein/gzt047.
  • Fekete S, Veuthey J-L, Beck A, Guillarme D. Hydrophobic interaction chromatography for the characterization of monoclonal antibodies and related products. J Pharm Biomed Anal. 2016;130:3–18. Available from http://www.ncbi.nlm.nih.gov/pubmed/27084526.
  • Hu S, Datta-Mannan A, D’Argenio DZ. Physiologically based modeling to predict monoclonal antibody pharmacokinetics in humans from in vitro physiochemical properties. MAbs. 2022;14(1):2056944. Available from http://www.ncbi.nlm.nih.gov/pubmed/35491902.
  • Chung S, Nguyen V, Lin YL, Lafrance-Vanasse J, Scales SJ, Lin K, Deng R, Williams K, Sperinde G, Li JJ, et al. An in vitro FcRn- dependent transcytosis assay as a screening tool for predictive assessment of nonspecific clearance of antibody therapeutics in humans. MAbs. 2019;11(5):942–55. Available from. doi:10.1080/19420862.2019.1605270.
  • Warne NW. Development of high concentration protein biopharmaceuticals: the use of platform approaches in formulation development. Eur J Pharm Biopharm. 2011;78(2):208–12. Available from https://linkinghub.elsevier.com/retrieve/pii/S0939641111000889.
  • Yadav S, Sreedhara A, Kanai S, Liu J, Lien S, Lowman H, Kalonia DS, Shire SJ. Establishing a link between amino acid sequences and self-associating and viscoelastic behavior of two closely related monoclonal antibodies. Pharm Res. 2011 [[cited 2013 Nov 5]];28(7):1750–64. doi:10.1007/s11095-011-0410-0.
  • Hotzel I, Theil F-P, Bernstein LJ, Prabhu S, Deng R, Quintana L, Lutman J, Sibia R, Chan P, Bumbaca D, et al. A strategy for risk mitigation of antibodies with fast clearance. MAbs 2012 [cited 2013 Feb 28]; 4:753–60. Available from: http://www.landesbioscience.com/journals/mabs/article/22189/
  • Hu S, Datta-Mannan A, Argenio DZD, Hu S. Physiologically based modeling to predict monoclonal antibody pharmacokinetics in humans from in vitro physiochemical properties. MAbs. 2022;14(1). Available from. doi:10.1080/19420862.2022.2056944.
  • 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 Pharm. 2021;18(7):2744–53. Available from doi:10.1021/acs.molpharmaceut.1c00280.
  • 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. Available from doi:https://doi.org/10.1080/19420862.2019.1589851.
  • 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). Available from. doi:10.1080/19420862.2022.2094750.
  • Kingsbury JS, Saini A, Auclair SM, Fu L, Lantz MM, Halloran KT, Calero-Rubio C, Schwenger W, Airiau CY, Zhang J, et al. 2020. A single molecular descriptor to predict solution behavior of therapeutic antibodies. Sci Adv. 6(32). doi:10.1126/sciadv.abb0372.
  • Wu S-J, Luo J, O’Neil KT, Kang J, Lacy ER, Canziani G, Baker A, Huang M, Tang QM, Raju TS, et al. Structure-based engineering of a monoclonal antibody for improved solubility. Protein Eng Des Sel. 2010;23(8):643–51. Available from. doi:10.1093/protein/gzq037.
  • Dobson CL, Devine PWA, Phillips JJ, Higazi DR, Lloyd C, Popovic B, Arnold J, Buchanan A, Lewis A, Goodman J, et al. 2016. Engineering the surface properties of a human monoclonal antibody prevents self-association and rapid clearance in vivo. Sci Rep. 6(1). doi:10.1038/srep38644.
  • Datta-Mannan A, Thangaraju A, Leung D, Tang Y, Witcher DR, Lu J, Wroblewski VJ. Balancing charge in the complementarity-determining regions of humanized mAbs without affecting pI reduces non-specific binding and improves the pharmacokinetics. MAbs. 2015;7(3):483–93. doi:10.1080/19420862.2015.1016696.
  • Chennamsetty N, Voynov V, Kayser V, Helk B, Trout BL. Prediction of aggregation prone regions of therapeutic proteins. J Phys Chem B. 2010;114(19):6614–24. Available from http://www.ncbi.nlm.nih.gov/pubmed/20411962.
  • Dyson MR, Masters E, Pazeraitis D, Perera RL, Syrjanen JL, Surade S, Thorsteinson N, Parthiban K, Jones PC, Sattar M, et al. 2020. Beyond affinity: selection of antibody variants with optimal biophysical properties and reduced immunogenicity from mammalian display libraries. MAbs. 12(1). doi:10.1080/19420862.2020.1829335.
  • Schoch A, Kettenberger H, Mundigl O, Winter G, Engert J, Heinrich J, Emrich T Charge-mediated influence of the antibody variable domain on FcRn-dependent pharmacokinetics. Proceedings of the National Academy of Sciences. 2015; 112:5997–6002. Available from: 10.1073/pnas.1408766112
  • 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 Pharm. 2012;9(4):791–802. Available from http://pubs.acs.org/doi/abs/10.1021/mp200566k.
  • Han X, Shih J, Lin Y, Chai Q, Cramer SM. Development of QSAR models for in silico screening of antibody solubility. MAbs. 2022;14(1). Available from. doi:https://doi.org/10.1080/19420862.2022.2062807.
  • Sormanni P, Vendruscolo M. Protein solubility predictions using the camsol method in the study of protein homeostasis. Cold Spring Harb Perspect Biol. 2019;11(12):11. doi:10.1101/cshperspect.a033845.
  • Feng J, Jiang M, Shih J, Chai Q. Antibody apparent solubility prediction from sequence by transfer learning. iSci. 2022;25(10):105173. Available from https://linkinghub.elsevier.com/retrieve/pii/S2589004222014456.
  • 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 Pharm. 2021;18(3):1167–75. Available from doi:10.1021/acs.molpharmaceut.0c01073.
  • 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. Proceedings of the National Academy of Sciences. 2014; 111:18601–06. Available from: 10.1073/pnas.1421779112
  • 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. Available from doi:10.1080/19420862.2015.1099773.
  • 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. Pharm Res. 2018;35(10). doi:10.1007/s11095-018-2466-6.
  • 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(3):476–89. Available from doi:10.1080/19420862.2017.1285479.
  • 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 [[cited 2013 Jan 29]];101(1):102–15. doi:10.1002/jps.22758.
  • van Durme J, de Baets G, van der Kant R, Ramakers M, Ganesan A, Wilkinson H, Gallardo R, Rousseau F, Schymkowitz J. Solubis: a webserver to reduce protein aggregation through mutation. Protein Eng Des Sel. 2016;29(8):285–89. doi:10.1093/protein/gzw019.
  • Heads JT, Kelm S, Tyson K, Lawson ADG. A computational method for predicting the aggregation propensity of IgG1 and IgG4(P) mAbs in common storage buffers. MAbs. 2022;14(1). doi:10.1080/19420862.2022.2138092.
  • Sydow JF, Lipsmeier F, Larraillet V, Hilger M, Mautz B, Mølhøj M, Kuentzer J, Klostermann S, Schoch J, Voelger HR, et al. Structure-based prediction of Asparagine and Aspartate degradation sites in antibody variable regions. PLoS One. 2014;9(6):e100736. Available from. doi:10.1371/journal.pone.0100736.
  • Yang R, Jain T, Lynaugh H, Nobrega RP, Lu X, Boland T, Burnina I, Sun T, Caffry I, Brown M, et al. Rapid assessment of oxidation via middle-down LCMS correlates with methionine side-chain solvent-accessible surface area for 121 clinical stage monoclonal antibodies. MAbs. 2017;9(4):646–53. doi:10.1080/19420862.2017.1290753.
  • Delmar JA, Buehler E, Chetty AK, Das A, Quesada GM, Wang J, Chen X. Machine learning prediction of methionine and tryptophan photooxidation susceptibility. Mol Ther Methods Clin Dev. 2021;21:466–77. doi:10.1016/j.omtm.2021.03.023.
  • Sankar K, Hoi KH, Yin Y, Ramachandran P, Andersen N, Hilderbrand A, McDonald P, Spiess C, Zhang Q. Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method. MAbs. 2018;10(8):1281–90. doi:10.1080/19420862.2018.1518887.
  • 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. MAbs2022. 2022;14(1):14. doi:10.1080/19420862.2021.2020082.
  • Bujotzek A, Fuchs A, Qu C, Benz JO, Klostermann S, Antes I, Georges G. MoFvAb: modeling the Fv region of antibodies. MAbs. 2015;7(5):838–52. doi:10.1080/19420862.2015.1068492.
  • Ruffolo JA, Chu L-S, Pooja Mahajan S, Gray JJ. Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies. bioRxiv. 2022;1 Available from. doi:10.1101/2022.04.20.488972.
  • Abanades B, Georges G, Bujotzek A, Deane CM, Xu J. Ablooper: fast accurate antibody CDR loop structure prediction with accuracy estimation. Bioinformatics. 2022;38(7):1877–80. doi:10.1093/bioinformatics/btac016.
  • Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–89. doi:10.1038/s41586-021-03819-2.
  • Raybould MIJ, Marks C, Lewis AP, Shi J, Bujotzek A, Taddese B, Deane CM. Thera-SAbDab: the therapeutic structural antibody database. Nucleic Acids Res. 2020;48(D1):D383–8. doi:10.1093/nar/gkz827.