The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
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
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
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
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
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 432
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
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
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
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 206
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
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
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
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
Self-citationWarne, N. W. (2011) · European Journal of Pharmaceutics and Biopharmaceutics · doi:10.1016/j.ejpb.2011.03.004 · Cited by 112
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
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 107
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
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
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 89
Kowalski, J. M., Parekh, R. N., & Wittrup, K. D. (1998) · Biochemistry · doi:10.1021/bi9722397 · Cited by 75
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
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
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
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
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
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
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
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
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 49
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
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
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 5
Cited by
Sorted by citations
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
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 780
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 711
“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
“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 307
Self-citation
Metadata
- 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
- Life Sciences › Immunology and Microbiology › Immunology › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review