Biophysical properties of the clinical-stage antibody landscape
Open accessAbstract
Significance: In addition to binding to a desired target molecule, all antibody drugs must also meet a set of criteria regarding the feasibility of their manufacture, stability in storage, and absence of off-target stickiness. This suite of characteristics is often termed “developability.” We present here a comprehensive analysis of these properties for essentially the full set of antibody drugs that have been tested in phase-2 or -3 clinical trials, or are approved by the FDA. Surprisingly, many of the drugs or candidates in this set exhibit properties that indicate significant developability risks; however, the number of such red warning flags decreases with advancement toward approval. This reference dataset should help prioritize future drug candidates for development.
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Jain et al. demonstrated the feasibility of using multiple biophysical metrics to characterize mAbs for drug-like behavior.
“and Jain et al. demonstrated the feasibility of using multiple biophysical metrics to characterize mAbs for drug‐like behavior.58”
From Tutorial on monoclonal antibody pharmacokinetics and its considerations in early development (2018) · cites this paper as [58]
The increasing number of structural, sequence and experimental data on antibodies being deposited in the public domain provides the necessary foundation for improving data-driven methods.
“The increasing number of structural [16], sequence [17] and experimental data [18–21] on antibodies being deposited in the public domain provides the necessary foundation for improving such data-driven methods.”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [21]
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1 retracted
Wang, F., Sen, S., Zhang, Y., Ahmad, I., Zhu, X., Wilson, I. A., Smider, V. V., Magliery, T. J., & Schultz, P. G. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1301810110 · Cited by 63 · see citing works
RetractedLipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(00)00129-0 · Cited by 18,139 · see citing works
Asymptotic Theory of Certain "Goodness of Fit" Criteria Based on Stochastic Processes
Anderson, T. W., & Darling, D. A. (1952) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729437 · Cited by 3,542 · see citing works
The influence of drug-like concepts on decision-making in medicinal chemistry
Leeson, P. D., & Springthorpe, B. (2007) · Nature Reviews Drug Discovery · doi:10.1038/nrd2445 · Cited by 2,403 · see citing works
An analysis of the attrition of drug candidates from four major pharmaceutical companies
Waring, M. J., Arrowsmith, J., Leach, A. R., Leeson, P. D., Mandrell, S., Owen, R. M., Pairaudeau, G., Pennie, W. D., Pickett, S. D., Wang, J., et al. (2015) · Nature Reviews Drug Discovery · doi:10.1038/nrd4609 · Cited by 1,450 · see citing works
Polyreactivity increases the apparent affinity of anti-HIV antibodies by heteroligation
Mouquet, H., Scheid, J. F., Zöller, M., Krogsgaard, M., Ott, R. G., Shukair, S. A., Artyomov, M. N., Pietzsch, J., Connors, M., Pereyra, F., et al. (2010) · Nature · doi:10.1038/nature09385 · Cited by 426 · see citing works
Sharma, V. K., Patapoff, T. W., Kabakoff, B., Pai, S., Hilario, E., Zhang, B., Li, C., Borisov, O., Kelley, R. F., Chorny, I., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1421779112 · Cited by 256 · see citing works
A strategy for risk mitigation of antibodies with fast clearance
Hötzel, I., Theil, F.-P., Bernstein, L. J., Prabhu, S., Deng, R., Quintana, L., Lutman, J., Sibia, R., Chan, P., Bumbaca, D., et al. (2012) · mAbs · doi:10.4161/mabs.22189 · Cited by 245 · see citing works
Developability Assessment During the Selection of Novel Therapeutic Antibodies
Jarasch, A., Koll, H., Regula, J. T., Bader, M., Papadimitriou, A., & Kettenberger, H. (2015) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.24430 · Cited by 242 · see citing works
Developability Index: A Rapid In Silico Tool for the Screening of Antibody Aggregation Propensity
Lauer, T. M., Agrawal, N. J., Chennamsetty, N., Egodage, K. L., Helk, B., & Trout, B. L. (2011) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.22758 · Cited by 208 · see citing works
Charge-mediated influence of the antibody variable domain on FcRn-dependent pharmacokinetics
Schoch, A., Kettenberger, H., Mundigl, O., Winter, G., Engert, J., Heinrich, J., & Emrich, T. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1408766112 · Cited by 202 · see citing works
Xu, Y., Roach, W., Sun, T., Jain, T. S., Prinz, B., Yu, T. Y., Torrey, J., Thomas, J., Bobrowicz, P., Vásquez, M., et al. (2013) · Protein Engineering Design and Selection · doi:10.1093/protein/gzt047 · Cited by 178 · see citing works
Self-citationLiu, Y., Caffry, I., Wu, J., Geng, S. B., Jain, T. S., Sun, T., Reid, F., Cao, Y., Estep, P., Yu, Y., et al. (2013) · mAbs · doi:10.4161/mabs.27431 · Cited by 139 · see citing works
Self-citationKowalski, J. M., Parekh, R. N., Mao, J., & Wittrup, K. D. (1998) · Journal of Biological Chemistry · doi:10.1074/jbc.273.31.19453 · Cited by 119 · see citing works
Self-citationWarne, N. W. (2011) · European Journal of Pharmaceutics and Biopharmaceutics · doi:10.1016/j.ejpb.2011.03.004 · Cited by 112 · see citing works
Aggregation risk prediction for antibodies and its application to biotherapeutic development
Obrezanova, O., Arnell, A., de la Cuesta, R. G., Berthelot, M. E., Gallagher, T. R., Zurdo, J., & Stallwood, Y. (2015) · mAbs · doi:10.1080/19420862.2015.1007828 · Cited by 107 · see citing works
He, F., Woods, C. E., Becker, G. W., Narhi, L. O., & Razinkov, V. I. (2011) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.22712 · Cited by 106 · see citing works
Jacobs, S., Wu, S., Feng, Y., Bethea, D., & O’Neil, K. T. (2009) · Pharmaceutical Research · doi:10.1007/s11095-009-0007-z · Cited by 92 · see citing works
Kelly, R. L., Sun, T., Jain, T. S., Caffry, I., Yu, Y., Cao, Y., Lynaugh, H., Brown, M. E., Vásquez, M., Wittrup, K. D., et al. (2015) · mAbs · doi:10.1080/19420862.2015.1043503 · Cited by 90 · see citing works
Self-citationDatta‐Mannan, A., Lu, J., Witcher, D. R., Leung, D., Tang, Y., & Wroblewski, V. J. (2015) · mAbs · doi:10.1080/19420862.2015.1075109 · Cited by 85 · see citing works
Kowalski, J. M., Parekh, R. N., & Wittrup, K. D. (1998) · Biochemistry · doi:10.1021/bi9722397 · Cited by 75 · see citing works
Self-citationA novel screening method to assess developability of antibody-like molecules
Kohli, N., Jain, N., Geddie, M. L., Razlog, M., Xu, L., & Lugovskoy, A. A. (2015) · mAbs · doi:10.1080/19420862.2015.1048410 · Cited by 74 · see citing works
Rapid Analysis of Antibody Self-Association in Complex Mixtures Using Immunogold Conjugates
Sule, S. V., Dickinson, C. D., Lu, J., Chow, C.-K., & Tessier, P. M. (2013) · Molecular Pharmaceutics · doi:10.1021/mp300524x · Cited by 70 · see citing works
Geng, S. B., Cheung, J. K., Narasimhan, C. N., Shameem, M., & Tessier, P. M. (2014) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.24130 · Cited by 65 · see citing works
Estep, P., Caffry, I., Yu, Y., Sun, T., Cao, Y., Lynaugh, H., Jain, T. S., Vásquez, M., Tessier, P. M., & Xu, Y. (2015) · mAbs · doi:10.1080/19420862.2015.1016694 · Cited by 64 · see citing works
Self-citationHalf-life of polyreactive antibodies
Sigounas, G., Harindranath, N., Donadel, G., & Notkins, A. L. (1994) · Journal of Clinical Immunology · doi:10.1007/bf01541346 · Cited by 64 · see citing works
High-Throughput Analysis of Concentration-Dependent Antibody Self-Association
Sule, S. V., Sukumar, M., Weiss, W. F., Marcelino, A. M. C., Sample, T., & Tessier, P. M. (2011) · Biophysical Journal · doi:10.1016/j.bpj.2011.08.036 · Cited by 61 · see citing works
Nishi, H., Miyajima, M., Wakiyama, N., Kubota, K., Hasegawa, J., Uchiyama, S., & Fukui, K. (2011) · Journal of Bioscience and Bioengineering · doi:10.1016/j.jbiosc.2011.06.017 · Cited by 51 · see citing works
High throughput detection of antibody self-interaction by bio-layer interferometry
Sun, T., Reid, F., Liu, Y., Cao, Y., Estep, P., Nauman, C., & Xu, Y. (2013) · mAbs · doi:10.4161/mabs.26186 · Cited by 51 · see citing works
Self-citationTarget-independent variable region mediated effects on antibody clearance can be FcRn independent
Kelly, R. L., Yu, Y., Sun, T., Caffry, I., Lynaugh, H., Brown, M. E., Jain, T. S., Xu, Y., & Wittrup, K. D. (2016) · mAbs · doi:10.1080/19420862.2016.1208330 · Cited by 51 · see citing works
Self-citationSolubility evaluation of murine hybridoma antibodies
Spencer, S. R., Bethea, D., Raju, T., Giles-Komar, J., & Feng, Y. (2012) · mAbs · doi:10.4161/mabs.19869 · Cited by 47 · see citing works
Jayaraman, J., Wu, J., Brunelle, M. C., Cruz, A. M. M., Goldberg, D. S., Lobo, B. A., Shah, A., & Tessier, P. M. (2014) · Biotechnology and Bioengineering · doi:10.1002/bit.25221 · Cited by 34 · see citing works
Best Practices in Assessment of Developability of Biopharmaceutical Candidates
Hartmann, S. F., & Kocher, H. P. (2015) · CRC Press eBooks · doi:10.1201/b19023-10 · Cited by 4 · see citing works
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Rogers, T. F., Zhao, F., Huang, D., Beutler, N., Burns, A., He, W.-T., Limbo, O., Smith, C., Song, G., Woehl, J., et al. (2020) · Science · doi:10.1126/science.abc7520 · Cited by 1,230 · see citing works
Next generation antibody drugs: pursuit of the 'high-hanging fruit'
Carter, P. J., & Lazar, G. A. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.227 · Cited by 760 · see citing works
Antibody Structure and Function: The Basis for Engineering Therapeutics
Chiu, M. L., Goulet, D. R., Teplyakov, A., & Gilliland, G. L. (2019) · Antibodies · doi:10.3390/antib8040055 · Cited by 646 · see citing works
“When applied to the experimentally characterized antibodies in the clinical stage [277], AggScore accurately identified aggregation-prone regions and predicted changes in aggregation behavior upon residue mutation.”
BackgroundMethodFive computational developability guidelines for therapeutic antibody profiling
Raybould, M. I. J., Marks, C., Krawczyk, K., Taddese, B., Nowak, J., Lewis, A. P., Bujotzek, A., Shi, J., & Deane, C. M. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1810576116 · Cited by 357 · see citing works
“As a dataset of mAbs unlikely to suffer with developability issues, we used the variable domain heavy- and light-chain sequences of 137 clinical-stage antibody therapeutics (137 CSTs) (18).”
BackgroundMethodBroad and potent activity against SARS-like viruses by an engineered human monoclonal antibody
Rappazzo, C. G., Tse, L. V., Kaku, C. I., Wrapp, D., Sakharkar, M., Huang, D., Deveau, L. M., Yockachonis, T. J., Herbert, A. S., Battles, M. B., et al. (2021) · Science · doi:10.1126/science.abf4830 · Cited by 304 · see citing works
Self-citationProtein design and variant prediction using autoregressive generative models
Shin, J.-E., Riesselman, A. J., Kollasch, A. W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A. C., & Marks, D. S. (2021) · Nature Communications · doi:10.1038/s41467-021-22732-w · Cited by 320 · see citing works
“The designed library had more length diversity and a longer CDR3 median length (13) than the synthetic library (12) (Supplementary Fig.”
BackgroundMason, D. M., Friedensohn, S., Weber, C. R., Jordi, C., Wagner, B., Meng, S. M., Ehling, R. A., Bonati, L., Dahinden, J., Gainza, P., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00699-9 · Cited by 308 · see citing works
“…plasmids, this last stage of development is done at very low throughput, as elaborate cloning, transfection and purification strategies must be implemented to screen libraries in the maximum range of 1 × 10 3 , meaning that only minor changes (for example, point mutations) are screened 5 .”
BackgroundConsiderations for the Design of Antibody-Based Therapeutics
Goulet, D. R., & Atkins, W. M. (2019) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2019.05.031 · Cited by 305 · see citing works
Tutorial on monoclonal antibody pharmacokinetics and its considerations in early development
Ovacik, M., & Lin, K. (2018) · Clinical and Translational Science · doi:10.1111/cts.12567 · Cited by 299 · see citing works
Phage Display Derived Monoclonal Antibodies: From Bench to Bedside
Alfaleh, M. A., Alsaab, H. O., Mahmoud, A. B., Alkayyal, A. A., Jones, M. L., Mahler, S. M., & Hashem, A. M. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01986 · Cited by 280 · see citing works
“Other antibody quality attributes, such as solubility, viscosity, expression yield, and thermal and long-term stability are vital to ensure the success of mAb lead candidates in biomanufacturing and clinical trials (95, 96).”
BackgroundDesigning antibodies as therapeutics
Carter, P. J., & Rajpal, A. (2022) · Cell · doi:10.1016/j.cell.2022.05.029 · Cited by 275 · see citing works
Structure, heterogeneity and developability assessment of therapeutic antibodies
Xu, Y., Wang, D., Mason, B. D., Rossomando, T., Li, N., Liu, D., Cheung, J. K., Xu, W., Raghava, S., Katiyar, A., et al. (2018) · mAbs · doi:10.1080/19420862.2018.1553476 · Cited by 268 · see citing works
“Besides aggregation, mAbs with lower thermal stability have been shown to have lower expression.(73,207) Thermal stability has been commonly measured by differential scanning calorimetry (DSC).”
BackgroundSelf-citationCancer therapy with antibodies
Paul, S., Konig, M. F., Pardoll, D. M., Bettegowda, C., Papadopoulos, N., Wright, K. M., Gabelli, S. B., Ho, M., van Elsas, A., & Zhou, S. (2024) · Nature reviews. Cancer · doi:10.1038/s41568-024-00690-x · Cited by 271 · see citing works
“It requires the generation of number of antibodies against a target antigen followed by the selection of a few lead candidates with ideal binding and biophysical properties 27 .”
Kovaltsuk, A., Leem, J., Kelm, S., Snowden, J., Deane, C. M., & Krawczyk, K. (2018) · The Journal of Immunology · doi:10.4049/jimmunol.1800708 · Cited by 247 · see citing works
“Furthermore, contrasting the naturally observed Abs with therapeutic ones can offer insight as to the naturally favored biophysical properties of these molecules (4).”
BackgroundPredicting antibody developability profiles through early stage discovery screening
Bailly, M., Mieczkowski, C., Juan, V., Metwally, E., Tomazela, D., Baker, J., Uchida, M., Kofman, E., Raoufi, F., Motlagh, S., et al. (2020) · mAbs · doi:10.1080/19420862.2020.1743053 · Cited by 228 · see citing works
“We report the percentage of monomeric antibody under two conditions (after protein A purification step and buffer exchange and after incubation at low pH (pH 3.5) for 30 minutes), while Jain et al. report data after 30 days at 40°C. Assessment of Tonset, Tagg, pI by cIEF, retention times on UP-SEC column, HP-RP purity…”
MethodInfluentialSelf-citationComputational approaches to therapeutic antibody design: established methods and emerging trends
Norman, R. A., Ambrosetti, F., Bonvin, A. M. J. J., Colwell, L. J., Kelm, S., Kumar, S., & Krawczyk, K. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbz095 · Cited by 219 · see citing works
Akbar, R., Robert, P. A., Pavlović, M., Jeliazkov, J. R., Snapkov, I., Slabodkin, A., Weber, C. R., Scheffer, L., Miho, E., Haff, I. H., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.108856 · Cited by 182 · see citing works
IgLM: Infilling language modeling for antibody sequence design
Shuai, R. W., Ruffolo, J. A., & Gray, J. J. (2023) · Cell Systems · doi:10.1016/j.cels.2023.10.001 · Cited by 165 · see citing works
A single molecular descriptor to predict solution behavior of therapeutic antibodies
Kingsbury, J. S., Saini, A., Auclair, S. M., Fu, L., Lantz, M. M., Halloran, K. T., Calero‐Rubio, C., Schwenger, W. J., Airiau, C. Y., Zhang, J., et al. (2020) · Science Advances · doi:10.1126/sciadv.abb0372 · Cited by 158 · see citing works
Strategies to manipulate the performance of aptamers in SELEX, post-SELEX and microenvironment
Qi, S., Duan, N., Khan, I. M., Dong, X., Zhang, Y., Wu, S., & Wang, Z. (2022) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107902 · Cited by 164 · see citing works
Phage Display Libraries for Antibody Therapeutic Discovery and Development
Almagro, J. C., Pedraza‐Escalona, M., Arrieta-Oliva, H. I., & Pérez‐Tapia, S. M. (2019) · Antibodies · doi:10.3390/antib8030044 · Cited by 163 · see citing works
“Such properties, collectively called developability [18,19], include (but are not limited to) cross-reactivity against the human target and orthologs from relevant tox species and animal models, solubility, expression yield in manufacturing cells, and thermal and long-term stability.”
BackgroundProgress and Challenges in the Design and Clinical Development of Antibodies for Cancer Therapy
Almagro, J. C., Daniels‐Wells, T. R., Pérez‐Tapia, S. M., & Penichet, M. L. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2017.01751 · Cited by 158 · see citing works
“On the experimental side, recently Jain and collaborators (67) have assessed the limits of developability of 137 FDA-approved therapeutic antibodies (including those listed in Table 1) as well as those in advanced stages of clinical development, i.”
BackgroundMethodRabia, L. A., Desai, A. A., Jhajj, H. S., & Tessier, P. M. (2018) · Biochemical Engineering Journal · doi:10.1016/j.bej.2018.06.003 · Cited by 159 · see citing works
“The fact that measurements of antibody specificity (non-specific binding and self-association) are the best biophysical descriptors of the likelihood of antibody success in the clinic [67] highlights the importance of developing computational and bioinformatics methods for predicting antibody specificity.”
BackgroundMakowski, E. K., Kinnunen, P. C., Huang, J., Wu, L., Smith, M. D., Wang, T., Desai, A. A., Streu, C. N., Zhang, Y., Zupancic, J. M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31457-3 · Cited by 155 · see citing works
Advances in antibody phage display technology
Ledsgaard, L., Ljungars, A., Rimbault, C., Sørensen, C. V., Tulika, T., Wade, J., Wouters, Y., McCafferty, J., & Laustsen, A. H. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.05.002 · Cited by 152 · see citing works
Goodwin, E. C., Gilman, M., Wrapp, D., Chen, M., Ngwuta, J. O., Moin, S., Bai, P., Sivasubramanian, A., Connor, R., Wright, P., et al. (2018) · Immunity · doi:10.1016/j.immuni.2018.01.005 · Cited by 146 · see citing works
“We next assessed the polyreactivity of the infant antibodies using a previously described assay (Jain et al., 2017).”
BackgroundMethodInfluentialAnimal immunization, in vitro display technologies, and machine learning for antibody discovery
Laustsen, A. H., Greiff, V., Karatt-Vellatt, A., Muyldermans, S., & Jenkins, T. P. (2021) · Trends in Biotechnology · doi:10.1016/j.tibtech.2021.03.003 · Cited by 157 · see citing works
Principles for computational design of binding antibodies
Baran, D., Pszolla, M. G., Lapidoth, G. D., Norn, C., Dym, O., Unger, T., Albeck, S., Tyka, M. D., & Fleishman, S. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1707171114 · Cited by 136 · see citing works
Narayanan, H., Dingfelder, F., Butté, A., Lorenzen, N., Sokolov, M., & Arosio, P. (2021) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2020.12.004 · Cited by 146 · see citing works
Deamidation and isomerization liability analysis of 131 clinical-stage antibodies
Lu, X., Nobrega, R. P., Lynaugh, H., Jain, T. S., Barlow, K. A., Boland, T., Sivasubramanian, A., Vásquez, M., & Xu, Y. (2018) · mAbs · doi:10.1080/19420862.2018.1548233 · Cited by 141 · see citing works
“All antibodies used in this study were formatted and expressed as IgG1 isotype mAbs, regardless of original drug isotype, with variable regions matching published sequences (see Jain et al.,31 for further details).”
BackgroundMethodInfluentialSelf-citationThird generation antibody discovery methods: in silico rational design
Sormanni, P., Aprile, F. A., & Vendruscolo, M. (2018) · Chemical Society Reviews · doi:10.1039/c8cs00523k · Cited by 136 · see citing works
Sankar, K., Krystek, S. R., Carl, S. M., Day, T., & Maier, J. K. X. (2018) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25594 · Cited by 124 · see citing works
“51 Among those, three are often used to assess the extent of solubility of the antibodies by measuring their retention times in a chromatographic column, in particular hydrophobic interaction chromatography (HIC), 52 cross-interaction chromatography (CIC) 53 and standup mono-layer adsorption…”
BackgroundDesign of Biopharmaceutical Formulations Accelerated by Machine Learning
Narayanan, H., Dingfelder, F., Morales, I. C., Patel, B., Heding, K. E., Bjelke, J. R., Egebjerg, T., Butté, A., Sokolov, M., Lorenzen, N., et al. (2021) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.1c00469 · Cited by 117 · see citing works
Akbar, R., Bashour, H., Rawat, P., Robert, P. A., Smorodina, E., Cotet, T., Karlsen, K. F., Frank, R., Mehta, B. B., Vu, M. H., et al. (2022) · mAbs · doi:10.1080/19420862.2021.2008790 · Cited by 117 · see citing works
“In an attempt to define success limits for developability parameters, Jain et al.(52) assessed 12 assaybased biophysical properties of 137 mAbs that had reached at least phase II clinical trials and observed that decreased protein stability, an increased disposition for protein aggregation, and polyreactivity are link…”
BackgroundMethodInfluentialKulenkampff, K., Perez, A.-M. W., Sormanni, P., Habchi, J., & Vendruscolo, M. (2021) · Nature Reviews Chemistry · doi:10.1038/s41570-021-00254-9 · Cited by 110 · see citing works
Immunogenicity risk assessment and mitigation for engineered antibody and protein therapeutics
Carter, P. J., & Quarmby, V. E. (2024) · Nature Reviews Drug Discovery · doi:10.1038/s41573-024-01051-x · Cited by 114 · see citing works
“Antibodies 110,111 and other protein therapeutic candidates are now routinely assessed preclinically for developability risks, including chemical degradation hotspots and poly-reactivity.”
InfluentialWarszawski, S., Katz, A. B., Lipsh‐Sokolik, R., Khmelnitsky, L., Ben‐Nissan, G., Javitt, G., Dym, O., Unger, T., Knop, O., Albeck, S., et al. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007207 · Cited by 111 · see citing works
Wec, A. Z., Bornholdt, Z. A., He, S., Herbert, A. S., Goodwin, E. C., Wirchnianski, A. S., Gunn, B. M., Zhang, Z., Zhu, W., Liu, G., et al. (2019) · Cell Host & Microbe · doi:10.1016/j.chom.2018.12.004 · Cited by 106 · see citing works
“, 2012), we assessed the polyspecificity of ADI-15946 and ADI-23774 as described (Jain et al., 2017; Xu et al., 2013).”
MethodAffinity Maturation Enhances Antibody Specificity but Compromises Conformational Stability
Shehata, L., Maurer, D. P., Wec, A. Z., Lilov, A., Champney, E. R., Sun, T., Archambault, K., Burnina, I., Lynaugh, H., Zhi, X., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.08.056 · Cited by 104 · see citing works
Self-citationYang, B., Liu, Z., Liu, H., & Nash, M. A. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.00085 · Cited by 103 · see citing works
In vitro and in silico assessment of the developability of a designed monoclonal antibody library
Wolf Pérez, A.-M., Sormanni, P., Andersen, J. S., Sakhnini, L. I., Rodriguez-Leon, I., Bjelke, J. R., Gajhede, A. J., De Maria, L., Otzen, D. E., Vendruscolo, M., et al. (2019) · mAbs · doi:10.1080/19420862.2018.1556082 · Cited by 97 · see citing works
“This aspect makes developability assessment demanding in both time and resources.(13) Early screening is further challenged by the quality and quantity of antibody candidates available at the early stages of development, which are commonly prepared in minute amounts, low concentrations and relatively low purity.”
BackgroundResultInfluentialAvery, L. B., Wade, J. M., Wang, M., Tam, A. S. P., King, A. C., Piche‐Nicholas, N., Kavosi, M. S., Penn, S., Cirelli, D. J., Kurz, J. C., et al. (2017) · mAbs · doi:10.1080/19420862.2017.1417718 · Cited by 97 · see citing works
“Our study here further expanded the utility of this assay, and demonstrated for the first time the correlative value of this assay in terms of in vivo behavior of mAbs related to PK. Interestingly, most mAbs with abovethreshold DNA and insulin binding also had above-threshold AC-SINS scores, although there are excepti…”
BackgroundInfluentialAssessing developability early in the discovery process for novel biologics
Fernández‐Quintero, M. L., Ljungars, A., Waibl, F., Greiff, V., Andersen, J. T., Gjølberg, T. T., Jenkins, T. P., Voldborg, B. G. R., Grav, L. M., Kumar, S., et al. (2023) · mAbs · doi:10.1080/19420862.2023.2171248 · Cited by 99 · see citing works
“While the clinical success of a biologic ultimately depends on its safety and efficacy in human patients, the underlying properties of these endpoints include everything from drug potency, specificity, immunogenicity, and pharmacokinetics to biophysical behavior in vivo and during storage.(2,3) Moreover, with more adv…”
BackgroundInfluentialEvolution of phage display libraries for therapeutic antibody discovery
Zhang, Y. (2023) · mAbs · doi:10.1080/19420862.2023.2213793 · Cited by 99 · see citing works
Li, L., Gupta, E., Spaeth, J., Shing, L., Jaimes, R., Engelhart, E., Lopez, R., Caceres, R. S., Bepler, T., & Walsh, M. E. (2023) · Nature Communications · doi:10.1038/s41467-023-39022-2 · Cited by 96 · see citing works
High concentration formulation developability approaches and considerations
Zarzar, J., Khan, T. A., Bhagawati, M., Weiche, B., Sydow-Andersen, J., & Sreedhara, A. (2023) · mAbs · doi:10.1080/19420862.2023.2211185 · Cited by 94 · see citing works
“For example, the extensive survey conducted by Jain et al. 50 looked at biophysical properties found in clinical stage and commercial antibodies with the assumption that antibodies that reached that stage of clinical trials have characteristics amenable to therapeutic development.”
BackgroundMethodResultInfluentialEngineering Strategies to Overcome the Stability–Function Trade-Off in Proteins
Teufl, M., Zajc, C. U., & Traxlmayr, M. W. (2022) · ACS Synthetic Biology · doi:10.1021/acssynbio.1c00512 · Cited by 89 · see citing works
Lee, C., Kang, T. H., Godon, O., Watanabe, M., Delidakis, G., Gillis, C. M., Sterlin, D., Hardy, D., Cogné, M., Macdonald, L. E., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13108-2 · Cited by 87 · see citing works
Khuat, T. T., Bassett, R., Otte, E. A., Grevis-James, A., & Gabryś, B. (2024) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2024.108585 · Cited by 98 · see citing works
Khetan, R., Curtis, R., Deane, C. M., Hadsund, J. T., Kar, U., Krawczyk, K., Kuroda, D., Robinson, S. A., Sormanni, P., Tsumoto, K., et al. (2022) · mAbs · doi:10.1080/19420862.2021.2020082 · Cited by 86 · see citing works
“6 Therefore, frameworks and procedures are being developed for the design of antibody libraries with improved developability and manufacturability.”
InfluentialSingle-Domain Antibodies and the Promise of Modular Targeting in Cancer Imaging and Treatment
Iezzi, M. E., Policastro, L., Werbajh, S., Podhajcer, O. L., & Canziani, G. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.00273 · Cited by 84 · see citing works
“Immunol Cell Biol (2010) 88(6):667–75.”
InfluentialMason, D. M., Weber, C. R., Parola, C., Meng, S. M., Greiff, V., Kelton, W., & Reddy, S. T. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky550 · Cited by 83 · see citing works
“Therefore, to generate the naïve repertoire optimized (NRO) library, for each position the degenerate codon was selected that produced the minimal MSE value relative to the naïve repertoire, while also using weights to punish degenerate codons that result in cysteines or stop codons (Equation 1).”
InfluentialSelecting and engineering monoclonal antibodies with drug-like specificity
Starr, C. G., & Tessier, P. M. (2019) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2019.01.008 · Cited by 81 · see citing works
Unlocking de novo antibody design with generative artificial intelligencePreprint
Shanehsazzadeh, A., McPartlon, M., Kasun, G. W., Steiger, A. K., Sutton, J. M., Yassine, E., McCloskey, C. M., Haile, R. G., Shuai, R. W., Alverio, J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.08.523187 · Cited by 83 · see citing works
Net charge of antibody complementarity-determining regions is a key predictor of specificity
Rabia, L. A., Zhang, Y., Ludwig, S. D., Julian, M. C., & Tessier, P. M. (2018) · Protein Engineering Design and Selection · doi:10.1093/protein/gzz002 · Cited by 81 · see citing works
Non-specificity as the sticky problem in therapeutic antibody development
Ausserwöger, H., Schneider, M. M., Herling, T. W., Arosio, P., Invernizzi, G., Knowles, T. P. J., & Lorenzen, N. (2022) · Nature Reviews Chemistry · doi:10.1038/s41570-022-00438-x · Cited by 80 · see citing works
Wilman, W., Wróbel, S., Bielska, W., Deszyński, P., Dudzic, P., Jaszczyszyn, I., Kaniewski, J., Młokosiewicz, J., Rouyan, A., Satława, T., et al. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac267 · Cited by 83 · see citing works
“An antibody binder towards a therapeutic target should meet a range of biophysical features, termed collectively as manufacturability/developability [97].”
BackgroundCunningham, O., Scott, M. J., Zhou, Z., & Finlay, W. J. J. (2021) · mAbs · doi:10.1080/19420862.2021.1999195 · Cited by 72 · see citing works
“characterized a broad panel of antibodies from across the clinical landscape in a diverse set of developability assays, including polyreactivity.(28)”
BackgroundDesign and engineering of bispecific antibodies: insights and practical considerations
Madsen, A. V., Pedersen, L. E., Kristensen, P. K., & Goletz, S. (2024) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2024.1352014 · Cited by 76 · see citing works
“These traits are commonly known as the developability pro fi le and screening for developability is typically done early in the drug development process to avoid investing in antibodies that are unlikely to succeed as clinical candidates (Jain et al., 2017; Raybould et al., 2019).”
BackgroundAssessing antibody and nanobody nativeness for hit selection and humanization with AbNatiV
Ramon, A., Ali, M., Atkinson, M., Saturnino, A., Didi, K., Visentin, C., Ricagno, S., Xu, X., Greenig, M., & Sormanni, P. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-023-00778-3 · Cited by 80 · see citing works
“…in vitro directed evolution, antibody selection carried out by immune systems usually yields antibodies with higher developability potential and especially better in vivo properties, including long half-life, low immunogenicity, no toxicity and low cross-reactivity against self-antigens 15,16 .”
Zhang, W., Wang, H., Feng, N., Li, Y., Gu, J., & Wang, Z. (2022) · Antibody Therapeutics · doi:10.1093/abt/tbac029 · Cited by 74 · see citing works
“A previous report showed that most marketed or preclinical antibodies have a melting temperature of Fab greater than 59◦C (ranging from 59◦C to 90◦C) [113] and a Tagg greater than 55◦C [63].”
Toward Drug-Like Multispecific Antibodies by Design
Sawant, M. S., Streu, C. N., Wu, L., & Tessier, P. M. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21207496 · Cited by 74 · see citing works
A Descriptor Set for Quantitative Structure‐property Relationship Prediction in Biologics
Sankar, K., Trainor, K., Blazer, L. L., Adams, J., Sidhu, S. S., Day, T., Meiering, E. M., & Maier, J. K. X. (2022) · Molecular Informatics · doi:10.1002/minf.202100240 · Cited by 72 · see citing works
Ehkirch, A., Goyon, A., Hernandez‐Alba, O., Rouvière, F., D’Atri, V., Dreyfus, C., Haeuw, J., Diemer, H., Beck, A., Heinisch, S., et al. (2018) · Analytical Chemistry · doi:10.1021/acs.analchem.8b03333 · Cited by 72 · see citing works
Finlay, W. J. J., Coleman, J. E., Edwards, J. S., & Johnson, K. S. (2018) · mAbs · doi:10.1080/19420862.2018.1550321 · Cited by 72 · see citing works
“The basic chemistry issues that drive polyreactivity are now better understood and a series of assays have been developed that can identify clones with such issues early in the drug discovery process.(12,17,19) Due to a lack of clear examples, however, the same cannot yet be said of the more complex issue of whether p…”
BackgroundJain, T. S., Boland, T., & Vásquez, M. (2023) · mAbs · doi:10.1080/19420862.2023.2200540 · Cited by 71 · see citing works
“5,20,21 A violation of eight out of 12 proposed rules was recommended as the threshold to flag nonspecific mAbs.”
BackgroundMethodResultInfluentialSelf-citationAlam, M. E., Slaney, T. R., Wu, L., Das, T. K., Kar, S. R., Barnett, G. V., Leone, A. M., & Tessier, P. M. (2019) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2019.10.051 · Cited by 70 · see citing works
Schepens, B., van Schie, L., Nerinckx, W., Roose, K., Breedam, W. V., Fijałkowska, D., Devos, S., Weyts, W., Cae, S. D., Vanmarcke, S., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abi7826 · Cited by 66 · see citing works
The ABCD database: a repository for chemically defined antibodies
de Lima, W. C., Gasteiger, E., Marcatili, P., Duek, P. D., Bairoch, A., & Cosson, P. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz714 · Cited by 70 · see citing works
Heparin chromatography as an in vitro predictor for antibody clearance rate through pinocytosis
Kraft, T. E., Richter, W. F., Emrich, T., Knaupp, A., Schuster, M., Wolfert, A., & Kettenberger, H. (2019) · mAbs · doi:10.1080/19420862.2019.1683432 · Cited by 68 · see citing works
“IgG1 Fc domain, transiently expressed in HEK cell culture and kindly provided by the authors of a previous study of these 131 molecules.(35) This set of molecules is meant to span a sequence variety encountered in currently investigated therapeutic antibodies.”
MethodInfluentialDyson, M. R., Masters, E. W., Pazeraitis, D., Perera, R. L., Syrjänen, J. L., Surade, S. B., Thorsteinson, N., Parthiban, K., Jones, P. C., Sattar, M., et al. (2020) · mAbs · doi:10.1080/19420862.2020.1829335 · Cited by 68 · see citing works
“Two anti-IL12 antibodies with differing polyreactivity profiles were identified in the study by Jain et al.(5) Ustekinumab (which was approved to treat psoriasis and Crohn’s disease) was considered to be well-behaved regarding its biophysical properties, whereas briakinumab (which did not progress beyond Phase 3 clini…”
BackgroundInfluentialMartin, K., Grimaldi, C. M., Grempler, R., Hansel, S. B., & Kumar, S. (2023) · mAbs · doi:10.1080/19420862.2023.2191301 · Cited by 67 · see citing works
“27,28 41 More recent publications on marketed mAbs have looked at deeply specific characteristics, 42,43 such as glycosylation patterns, 44,45 or broad concepts like development trajectories.”
BackgroundReduction of Nonspecificity Motifs in Synthetic Antibody Libraries
Kelly, R. L., Le, D., Zhao, J., & Wittrup, K. D. (2017) · Journal of Molecular Biology · doi:10.1016/j.jmb.2017.11.008 · Cited by 67 · see citing works
Self-citationProtein aggregation: in silico algorithms and applications
Prabakaran, R., Rawat, P., Thangakani, A. M., Kumar, S., & Gromiha, M. M. (2021) · Biophysical Reviews · doi:10.1007/s12551-021-00778-w · Cited by 67 · see citing works
Yang, R., Jain, T. S., Lynaugh, H., Nobrega, R. P., Lu, X., Boland, T., Burnina, I., Sun, T., Caffry, I., Brown, M. E., et al. (2017) · mAbs · doi:10.1080/19420862.2017.1290753 · Cited by 66 · see citing works
“able regions matching published sequences, were produced recombinantly in HEK293 cells.(11) All mAbs were expressed as IgG1”
MethodSelf-citationAutomated optimisation of solubility and conformational stability of antibodies and proteins
Rosace, A., Bennett, A., Oeller, M., Mortensen, M. M., Sakhnini, L. I., Lorenzen, N., Poulsen, C., & Sormanni, P. (2023) · Nature Communications · doi:10.1038/s41467-023-37668-6 · Cited by 65 · see citing works
“Various studies have reported a correlation between conformational stability and recombinant expression yield (24, 35, 36), and high stability translates into enhanced ability to withstand the physical or chemical stresses encountered during manufacturing and formulation (37).”
BackgroundBlueprint for antibody biologics developability
Mieczkowski, C., Zhang, X., Lee, D., Nguyen, K., Lv, W., Wang, Y., Zhang, Y., Way, J., & Gries, J. (2023) · mAbs · doi:10.1080/19420862.2023.2185924 · Cited by 65 · see citing works
“Notably, favorable PSR and BvP properties on clinical-stage antibodies have correlated with clinical stage progression.(19)”
BackgroundMethodInfluentialAmimeur, T., Shaver, J. M., Ketchem, R. R., Taylor, J. A., Clark, R. H., Smith, J., Citters, D. V., Siska, C. C., Smidt, P., Sprague, M., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.12.024844 · Cited by 64 · see citing works
Shin, S., Kim, J., Park, S.-W., Jun, S.-Y., Kweon, H.-J., Choi, D.-K., Lee, D., Cho, Y. B., & Kim, Y. (2020) · Science Advances · doi:10.1126/sciadv.aay2174 · Cited by 63 · see citing works
Bachas, S. T., Rakocevic, G., Spencer, D., Sastry, A. V., Haile, R. G., Sutton, J. M., Kasun, G. W., Stachyra, A. L., Gutierrez, J. M., Yassine, E., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.16.504181 · Cited by 63 · see citing works
Puranik, A., Dandekar, P. P., & Jain, R. D. (2022) · Biotechnology Progress · doi:10.1002/btpr.3291 · Cited by 63 · see citing works
Jain, T., Boland, T., Lilov, A., Burnina, I., Brown, M., Xu, Y., & Vásquez, M. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx519 · Cited by 61 · see citing works
Self-citationFacile Affinity Maturation of Antibody Variable Domains Using Natural Diversity Mutagenesis
Tiller, K. E., Chowdhury, R., Li, T., Ludwig, S. D., Sen, S., Maranas, C. D., & Tessier, P. M. (2017) · Frontiers in Immunology · doi:10.3389/fimmu.2017.00986 · Cited by 61 · see citing works
“Despite these complexities, it will be important in the future to better define how CDR sequence and structure impacts antibody specificity because antibody specificity appears to be a key factor in differentiating approved antibody therapeutics from those in clinical trials (75).”
BackgroundProtein Solubility Predictions Using the CamSol Method in the Study of Protein HomeostasisPreprint
Sormanni, P., & Vendruscolo, M. (2019) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a033845 · Cited by 59 · see citing works
“Defining the solubility of proteins, however, is difficult both theoretically and experimentally (Jain et al. 2017; Wolf Pérez et al. 2019).”
BackgroundIntrinsic physicochemical profile of marketed antibody-based biotherapeutics
Ahmed, L., Gupta, P., Martin, K., Scheer, J. M., Nixon, A. E., & Kumar, S. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2020577118 · Cited by 60 · see citing works
“Throughout the plots of physicochemical descriptors, we have used trastuzumab as a reference because this antibody has been known to possess good physicochemical attributes (14).”
Teixeira, A. A. R., Erasmus, M. F., D’Angelo, S., Naranjo, L. A., Ferrara, F., Leal‐Lopes, C., Durrant, O., Galmiche, C., Morelli, A., Scott-Tucker, A., et al. (2021) · mAbs · doi:10.1080/19420862.2021.1980942 · Cited by 60 · see citing works
“assess relevant properties in a large study of the biophysical characteristics of clinical-stage antibodies.(3) Some act as surrogates for properties desired in an antibody but not directly quantifiable as a meaningful characteristic.”
BackgroundMethodResultInfluentialDevelopment and Differentiation in Monobodies Based on the Fibronectin Type 3 Domain
Chandler, P. G., & Buckle, A. M. (2020) · Cells · doi:10.3390/cells9030610 · Cited by 60 · see citing works
“Domain swapping is usually removed through stabilisation of the monomeric fold, since any tendency for oligomerisation complicates developability [113], for example in the use of the scaffold for therapeutic applications.”
BackgroundBrown, A. J., Snapkov, I., Akbar, R., Pavlović, M., Miho, E., Sandve, G. K., & Greiff, V. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00071b · Cited by 59 · see citing works
Makowski, E. K., Wu, L., Gupta, P., & Tessier, P. M. (2021) · mAbs · doi:10.1080/19420862.2021.1895540 · Cited by 60 · see citing works
“These results may explain, at least in part, why the phage display-derived antibodies have higher average levels of self-association relative to the immunization-derived antibodies.(29) More generally, these and other results suggest that the presence of hydrophobic and charged patches in antigen-binding sites are imp…”
BackgroundResultSequence-based prediction of pH-dependent protein solubility using CamSol
Oeller, M., Kang, R. J. D., Bell, R., Ausserwӧger, H., Sormanni, P., & Vendruscolo, M. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad004 · Cited by 58 · see citing works
“Although several methods have been developed for the experimental measurement of protein solubility [2, 5], these methods are not readily amenable to high-throughput screening campaigns, which are required to assess the large number of candidates typically available the early stages of industrial pipelines.”
MethodLi, T., Li, Y., Zhu, X., He, Y., Wu, Y., Ying, T., & Xie, Z. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.02.007 · Cited by 59 · see citing works
Challenges in antibody structure prediction
Fernández-Quintero, M. L., Kokot, J., Waibl, F., Fischer, A.-L. M., Quoika, P. K., Deane, C. M., & Liedl, K. R. (2023) · mAbs · doi:10.1080/19420862.2023.2175319 · Cited by 55 · see citing works
“Here, we investigated a dataset consisting of 137 antibody sequences published by Jain et al.(34) and used the available antibody structure prediction tools ABlooper,(7) IgFold,(8) DeepAb,(9) Immunebuilder,(29)and the MOE Antibody Modeler(35) to generate structure models for further biophysical characterizations.”
MethodAn in silico method to assess antibody fragment polyreactivity
Harvey, E. P., Shin, J.-E., Skiba, M. A., Nemeth, G. R., Hurley, J. D., Wellner, A., Shaw, A. Y., Miranda, V. G., Min, J., Liu, C. C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-35276-4 · Cited by 57 · see citing works
“Consistent with previous literature 22,30 , we found that hydrophobicity, as described by thehydrophobicity index 45 , is not strongly predictive of polyreactivity (AUC=0.57, Fig.”
BackgroundMethodInfluentialEngineering Stability, Viscosity, and Immunogenicity of Antibodies by Computational Design
Kuroda, D., & Tsumoto, K. (2020) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2020.01.011 · Cited by 58 · see citing works
Lecerf, M., Kanyavuz, A., Lacroix‐Desmazes, S., & Dimitrov, J. D. (2019) · Molecular Immunology · doi:10.1016/j.molimm.2019.06.012 · Cited by 56 · see citing works
“The experimental data presented in the study of Jain (T. Jain et al., 2017) and the publicly available sequence information of the therapeutic Abs offer an opportunity for addressing this essential question.”
BackgroundInfluentialLatozinemab, a novel progranulin-elevating therapy for frontotemporal dementia
Kurnellas, M., Mitra, A., Schwabe, T., Paul, R. H., Arrant, A. E., Roberson, E. D., Ward, M. E., Yeh, F. L., Long, H., & Rosenthal, A. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04251-y · Cited by 55 · see citing works
“Latozinemab was generated and affinity-matured through collaboration between Alector and Adimab (Lebanon, New Hampshire, USA) to optimize in vitro and in vivo activity and to ensure manufacturing development capability [40].”
BackgroundAI can help to speed up drug discovery — but only if we give it the right data
Mock, M. L., Edavettal, S. C., LANGMEAD, C. J., & Russell, A. J. (2023) · Nature · doi:10.1038/d41586-023-02896-9 · Cited by 55 · see citing works
Boughter, C. T., Borowska, M. T., Guthmiller, J. J., Bendelac, A. S., Wilson, P. C., Roux, B., & Adams, E. J. (2020) · eLife · doi:10.7554/elife.61393 · Cited by 56 · see citing works
Zupancic, J. M., Desai, A. A., Schardt, J. S., Pornnoppadol, G., Makowski, E. K., Smith, M. D., Kennedy, A. A., Barbosa, M. G. D. M., Cascalho, M., Lanigan, T. M., et al. (2021) · Cell chemical biology · doi:10.1016/j.chembiol.2021.05.019 · Cited by 54 · see citing works
Antibody variable sequences have a pronounced effect on cellular transport and plasma half-life
Grevys, A., Frick, R., Mester, S., Flem-Karlsen, K., Nilsen, J., Foss, S., Sand, K. M. K., Emrich, T., Fischer, J. A. A., Greiff, V., et al. (2022) · iScience · doi:10.1016/j.isci.2022.103746 · Cited by 51 · see citing works
“However, some monoclonal IgG1 antibodies have surprisingly poor pharmacokinetics (Carter and Lazar, 2018; Jain et al., 2017).”
- Authors
- Jain, T., Sun, T., Durand, S., Hall, A., Houston, N. R., Nett, J. H., Sharkey, B., Bobrowicz, B., Caffry, I., Yu, Y., Cao, Y., Lynaugh, H., Brown, M., Baruah, H., Gray, L. T., Krauland, E. M., Xu, Y., Vásquez, M., & Wittrup, K. D.
- Affiliations
- Adimab (United States) (US)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2017-01-17
- Volume
- 114
- Issue
- 5
- Pages
- 944-949
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC5293111
- OpenAlex
- W2578029714
- Cited by
- 629
- References
- 33
- Citations by year
- 2026: 84 · 2025: 75 · 2024: 75 · 2023: 83 · 2022: 80 · 2021: 71 · 2020: 52 · 2019: 58 · 2018: 35 · 2017: 15
- FWCI
- 33.54
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein purification and stability
- Life Sciences › Immunology and Microbiology › Immunology › Biosimilars and Bioanalytical Methods
- Keywords
- therapeutic antibodies, antibody developability, biophysical properties, IgG1 antibodies, antibody engineering, drug-likeness
- MeSH terms
- Amino Acid Sequence, Antibodies, Monoclonal, Humans, Immunoglobulin G, Drug Approval, Biophysical Phenomena, Drug Discovery, HEK293 Cells
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