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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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“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]
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Rapid identification of neutralizing antibodies against SARS-CoV-2 variants by mRNA display
Tanaka, S., Olson, C. A., Barnes, C. O., Higashide, W., Gonzalez, M. R., Taft, J. D., Richardson, A., Martín-Fernández, M., Bogunovic, D., Gnanapragasam, P. N. P., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110348 · Cited by 20
“In addition, multiple biophysical assays were carried out to determine the developability of all 10 antibodies (Table S4) (Jain et al., 2017).”
Negron, C., Fang, J., McPherson, M. J., Stine, W. B., & McCluskey, A. J. (2022) · mAbs · doi:10.1080/19420862.2022.2080628 · Cited by 24
“9 The evaluation of antibody developability early in drug discovery is preferable, as it may mitigate financial investment in a molecule unable to pass the rigors of the therapeutic approval process.”
BackgroundMethodInfluentialPotent monoclonal antibodies neutralize Omicron sublineages and other SARS-CoV-2 variants
Chen, Z., Zhang, P., Matsuoka, Y., Tsybovsky, Y., West, K. A., Santos, C. P., Boyd, L. F., Nguyen, H., Pomerenke, A., Stephens, T., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111528 · Cited by 23
“However, based on their in vitro neutralization activity, we could infer that our antibodies would be effective against these variants in the preclinical model, although physicochemical properties of our antibodies that were not addressed in this study can have a major influence on in vivo efficacy (Jain et al., 2017).”
Best practices for machine learning in antibody discovery and development
Wossnig, L., Furtmann, N., Buchanan, A. G., Kumar, S., & Greiff, V. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.104025 · Cited by 25
Using extensional flow to reveal diverse aggregation landscapes for three IgG1 molecules
Willis, L. F., Kumar, A., Dobson, J., Bond, N. J., Lowe, D., Turner, R., Radford, S. E., Kapur, N., & Brockwell, D. J. (2018) · Biotechnology and Bioengineering · doi:10.1002/bit.26543 · Cited by 24
“These developing in silico approaches complement established accelerated stress studies that are performed in vitro to predict the shelf-life and stability of biologics (Jain et al., 2017; Yang et al., 2013).”
BackgroundInfluentialSchmitt, M. G., White, R. N., & Barnard, G. C. (2020) · Biotechnology Progress · doi:10.1002/btpr.3047 · Cited by 23
“The corresponding amino acid sequences of the 20 mAbs were obtained from Jain et al.54 and the Therapeutic Structural Antibody Database (Thera-SAbDab; http://opig.stats.ox.ac.uk/webapps/therasabdab).”
MethodSchardt, J. S., Pornnoppadol, G., Desai, A. A., Park, K. S., Zupancic, J. M., Makowski, E. K., Smith, M. D., Chen, H., Barbosa, M. G. D. M., Cascalho, M., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-99401-x · Cited by 23
Hu, S., Datta‐Mannan, A., & D’Argenio, D. Z. (2022) · mAbs · doi:10.1080/19420862.2022.2056944 · Cited by 23
“and Jain et al.(10) for the 22 antibodies used in this work (see below) are provided in the Supplemental Material.”
MethodInfluentialSimultaneous affinity maturation and developability enhancement using natural liability-free CDRs
Teixeira, A. A. R., D’Angelo, S., Erasmus, M. F., Leal‐Lopes, C., Ferrara, F., Spector, L. P., Naranjo, L. A., Molina, E., Max, T., DeAguero, A., et al. (2022) · mAbs · doi:10.1080/19420862.2022.2115200 · Cited by 23
“57 In addition, increased immunogenicity risk (e.g., glycosylation) and loss of potency (e.g., asparagine deamidation) are critical quality attributes that require tracking and risk management strategies to be put in place, increasing the resource burden on the development process and on the…”
BackgroundFLAb: Benchmarking deep learning methods for antibody fitness predictionPreprint
Chungyoun, M., Ruffolo, J. A., & Gray, J. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.13.575504 · Cited by 27
“…characteristics that comprise antibody developability, which includes (1) high-level of expression, (2) high conformational and colloidal stability, (3) low immunogenicity, (4) high binding affinity towards the target antigen, (5) a low propensity for aggregation, and (6) low polyreactivity [16].”
BackgroundGolinski, A. W., Mischler, K. M., Laxminarayan, S., Neurock, N., Fossing, M., Pichman, H., Martiniani, S., & Hackel, B. J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2026658118 · Cited by 22
Nucleation in Protein Aggregation in Biotherapeutic Development: A look into the Heart of the Event
Das, T. K., Chou, D. K., Jiskoot, W., & Arosio, P. (2022) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2022.01.017 · Cited by 22
Madsen, A. V., Kristensen, P. K., Buell, A. K., & Goletz, S. (2023) · mAbs · doi:10.1080/19420862.2023.2189432 · Cited by 24
“27 We tested the conformational and colloidal stability of our antibody panel to assess any potential effects from the engineering.”
MethodZhang, Y., Li, Q., Luo, L., Duan, C., Shen, J., & Wang, Z. (2023) · Biotechnology Advances · doi:10.1016/j.biotechadv.2023.108143 · Cited by 23
High-throughput retrieval of physical DNA for NGS-identifiable clones in phage display library
Noh, J., Kim, O., Jung, Y., Han, H., Kim, J., Kim, S., Lee, S., Park, J., Jung, R. H., Kim, S. I., et al. (2019) · mAbs · doi:10.1080/19420862.2019.1571878 · Cited by 22
Engineering an anti-CD52 antibody for enhanced deamidation stability
Qiu, H., Wei, R. R., Jaworski, J. A., Boudanova, E., Hughes, H., VanPatten, S., Lund, A. L., Day, J., Zhou, Y., McSherry, T., et al. (2019) · mAbs · doi:10.1080/19420862.2019.1631117 · Cited by 22
Voynov, V., Adam, P. J., Nixon, A. E., & Scheer, J. M. (2020) · Antibodies · doi:10.3390/antib9040065 · Cited by 22
Evers, A., Guarnera, E., Pekar, L., & Zielonka, S. (2025) · mAbs · doi:10.1080/19420862.2025.2583210 · Cited by 23
Developability profiling of a panel of Fc engineered SARS-CoV-2 neutralizing antibodies
Dippel, A. B., Gallegos, A. S., Aleti, V., Barnes, A. S., Chen, X., Christian, E. A., Delmar, J. A., Du, Q., Esfandiary, R., Farmer, E., et al. (2022) · mAbs · doi:10.1080/19420862.2022.2152526 · Cited by 21
“Determining which biophysical characteristics make an ideal antibody therapeutic has been the focus of intense effort in the antibody engineering field, with several works analyzing which characteristics and assays best differentiate antibodies approved for therapeutic use from those at earlier stages.(16,18) Similarl…”
BackgroundStructure-based engineering of a novel CD3ε-targeting antibody for reduced polyreactivity
Liu, C. Y., Ahonen, C. L., Brown, M. E., Zhou, L. H., Welin, M., Krauland, E. M., Pejchal, R., Widboom, P., & Battles, M. B. (2023) · mAbs · doi:10.1080/19420862.2023.2189974 · Cited by 23
“These findings are also harmonious with literature assessing the utility of such developability properties in predicting the PK and safety profiles of therapeutics in the preclinical stage.(14,16) With the solution of the ADI-26906 Fab:N13 CD3ε peptide co-complex structure, we confirmed that this lineage binds to an e…”
BackgroundResultSelf-citationAlam, M. E., Geng, S. B., Bender, C., Ludwig, S. D., Lindén, L., Hoet, R., & Tessier, P. M. (2017) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b00779 · Cited by 21
Toward a Combinatorial Approach for the Prediction of IgG Half-Life and Clearance
Goulet, D. R., Watson, M. J., Tam, S. H., Zwolak, A., Chiu, M. L., Atkins, W. M., & Nath, A. (2018) · Drug Metabolism and Disposition · doi:10.1124/dmd.118.081893 · Cited by 21
“Although these and other biophysical parameters, such as nonspecific binding or electrostatic interactions, may be predictively important (Datta-Mannan et al., 2015b; Tibbitts et al., 2016; Jain et al., 2017), their relative contributions and the underlying mechanistic relationships remain unclear.”
BackgroundInfluentialModels for Antibody Behavior in Hydrophobic Interaction Chromatography and in Self-Association
Hebditch, M., Roche, A., Curtis, R., & Warwicker, J. (2018) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2018.11.035 · Cited by 21
Dynamics of heavy chain junctional length biases in antibody repertoires
Sankar, K., Hoi, K. H., & Hötzel, I. (2020) · Communications Biology · doi:10.1038/s42003-020-0931-3 · Cited by 20
Recent advances in generative biology for biotherapeutic discovery
Mock, M. L., LANGMEAD, C. J., Grandsard, P. J., Edavettal, S. C., & Russell, A. J. (2024) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2024.01.003 · Cited by 20
Wu, I.-E., Kalejaye, L. A., & Lai, P. (2024) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.4c00804 · Cited by 20
Measuring Protein Aggregation and Stability Using High-Throughput Biophysical Approaches
Kwan, T. O. C., Kolek, S., Danson, A. E., Reis, R. I., Camacho, I. S., Stewart, P. D. S., & Moraes, I. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.890862 · Cited by 26
Genetic and structural basis of the human anti-α-galactosyl antibody response
Langley, D. B., Schofield, P., Névoltris, D., Jackson, J., Jackson, K., Peters, T. J., Burk, M., Matthews, J. M., Basten, A., Goodnow, C. C., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2123212119 · Cited by 20
“Humira, Avastin, and others) (55), our findings may open up strategies toward antibody therapeutics development for α-gal”
BackgroundSvilenov, H. L., Bester, R., Sacherl, J., Absmeier, R. M., Peters, C., Protzer, U., Brockmeyer, C., & Büchner, J. (2022) · Communications Biology · doi:10.1038/s42003-022-04193-z · Cited by 20
p-IgGen: a paired antibody generative language model
Turnbull, O. M., Oglić, D., Croasdale-Wood, R., & Deane, C. M. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae659 · Cited by 27
“However, such libraries often contain sequences with developability issues (Jain et al. 2017) (i.e. propensity for aggregation, polyspecificity, poor expression, or low solubility), which can result in potential therapeutics being discarded or requiring engineering later in the pipeline (Raybould…”
Hutchinson, M., Ruffolo, J. A., Haskins, N., Iannotti, M., Vozza, G., Pham, T., Mehzabeen, N., Shandilya, H., Rickert, K., Croasdale-Wood, R., et al. (2024) · mAbs · doi:10.1080/19420862.2024.2362775 · Cited by 22
“3,4 Affinity, thermal stability, aggregation propensity, and solubility are among the properties typically subject to optimization.”
BackgroundShin, O. S., Monticelli, S. R., Hjorth, C. K., Hornet, V., Doyle, M., Abelson, D. M., Kuehne, A. I., Wang, A., Bakken, R. R., Mishra, A. K., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114502 · Cited by 19
Norden, D. M., Navia, C. T., Sullivan, J. T., & Doranz, B. J. (2024) · mAbs · doi:10.1080/19420862.2024.2393785 · Cited by 19
“To test this, we produced biosimilars of clinical-stage, FDA-approved, and withdrawn mAbs 34 and screened them on the MPA.”
Chen, H.-T., Zhang, Y., Huang, J., Sawant, M. S., Smith, M. D., Rajagopal, N., Desai, A. A., Makowski, E. K., Licari, G., Xie, Y., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114801 · Cited by 19
Lecerf, M., Kanyavuz, A., Rossini, S., & Dimitrov, J. D. (2021) · Communications Biology · doi:10.1038/s42003-021-01931-7 · Cited by 19
Antibody apparent solubility prediction from sequence by transfer learning
Feng, J., Jiang, M., Shih, J., & Chai, Q. (2022) · iScience · doi:10.1016/j.isci.2022.105173 · Cited by 23
“While the current dataset of 260 mAbs is already larger than previous mAbs developability studies (Bailly et al., 2020; Coffman et al., 2020; Han et al., 2022; Jain et al., 2017; Kingsbury et al., 2020; Lai et al., 2021a, 2021b; Raybould et al., 2019; Zhang et al., 2020), it is still smaller than most machine learning…”
BackgroundZou, P. (2023) · The AAPS Journal · doi:10.1208/s12248-023-00798-2 · Cited by 19
“In addition to the FDA- or EMA-approved or reviewed mAbs and fusion proteins, the author collected mAbs and fusion proteins at clinical development stage from the Antibodies to Watch published by mAbs Journal between 2010 and 2022 (16–20) and three research articles (15, 21, 22).”
BackgroundLiu, S., & Shah, D. K. (2023) · The AAPS Journal · doi:10.1208/s12248-023-00812-7 · Cited by 17
“In contrast to the general consensus on rules that define drug-like behaviors of mAbs [4, 9-11], what models are truly predictive of mAb PK characteristics based on molecular properties remain debatable.”
BackgroundResultArras, P., Yoo, H. B., Pekar, L., Schröter, C. J., Clarke, T., Krah, S., Klewinghaus, D., Siegmund, V., Evers, A., & Zielonka, S. (2023) · mAbs · doi:10.1080/19420862.2023.2261149 · Cited by 19
“These frameworks were selected from a diverse panel of well-behaved antibodies known for their favorable biophysical characteristics 29 and at the same time cover different germline families, thereby assuring structural and sequence diversity in the library to improve the ability to select binders…”
BackgroundPEP-Patch: Electrostatics in Protein–Protein Recognition, Specificity, and Antibody Developability
Hoerschinger, V. J., Waibl, F., Pomarici, N. D., Loeffler, J. R., Deane, C. M., Georges, G. J., Kettenberger, H., Fernández‐Quintero, M. L., & Liedl, K. R. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01490 · Cited by 19
Jain, T. S., Prinz, B., Marker, A., Michel, A., Reichel, K., Czepczor, V., Klieber, S., Sun, W., Kathuria, S. V., Bruederle, S. O., et al. (2024) · mAbs · doi:10.1080/19420862.2024.2384104 · Cited by 19
“In agreement with analyses on clinical molecules, 2,21,22 self-inter-action measured using AC-SINS and FcRn column RT cluster with the non-specificity measurements as well.”
BackgroundMethodSelf-citationLi, B., Luo, S., Wang, W., Xu, J., Liu, D., Shameem, M., Mattila, J., Franklin, M. C., Hawkins, P. G., & Atwal, G. S. (2025) · mAbs · doi:10.1080/19420862.2025.2474521 · Cited by 20
“46 To demonstrate the utility of PROPERMAB, we computed all 35 features for 135 mAbs for which the HIC RTs have been consistently measured 47 and first analyzed the correlations of individual features with HIC RT (Figure 3(a)).”
InfluentialRoot, A. R., Guntas, G., Katragadda, M., Apgar, J. R., Narula, J., Chang, C. S., Hanscom, S. R., McKenna, M., Wade, J. M., Meade, C., et al. (2021) · mAbs · doi:10.1080/19420862.2020.1850395 · Cited by 18
“These ideal “developability” characteristics include high conformational stability, high solubility/low self-association potential, low polyreactivity, low risk of post-translational modifications and low immunogenicity.(8) Combining the requirements for good developability with a complex format can lead to a need for…”
BackgroundPhan, S., Walmer, A., Shaw, E. W., & Chai, Q. (2022) · mAbs · doi:10.1080/19420862.2022.2094750 · Cited by 18
Screnci, B., Stafford, L. J., Barnes, T., Shema, K. E., Gilman, S., Wright, R., Absi, S. A., Phillips, T., Azuelos, C., Slovik, K. J., et al. (2022) · iScience · doi:10.1016/j.isci.2022.105665 · Cited by 19
Determining the affinities of high-affinity antibodies using KinExA and surface plasmon resonance
Erasmus, M. F., Dovner, M., Ferrara, F., D’Angelo, S., Teixeira, A. A. R., Leal‐Lopes, C., Spector, L. P., Hopkins, E., & Bradbury, A. M. (2023) · mAbs · doi:10.1080/19420862.2023.2291209 · Cited by 18
Wittkopp, F., Welsh, J. P., Todd, R., Staby, A., Roush, D. J., Lyall, J. Y., Karkov, H. S., Hunt, S., Griesbach, J., Bertran, M., et al. (2024) · Biotechnology and Bioengineering · doi:10.1002/bit.28768 · Cited by 18
“…aggregation (Weiss et al., 2009), hydrophobic retention (Karlberg et al., 2020), nonspecific interactions (Kizhedath et al., 2019) as well as more holistic whole molecule assessments (Bailly et al., 2020; Coffman et al., 2020; Jain et al., 2017; Karlberg et al., 2018, 2020; Lauer et al., 2012).”
Discovery of Therapeutic Antibodies Targeting Complex Multi-Spanning Membrane Proteins
Stephens, A. D., & Wilkinson, T. C. I. (2024) · BioDrugs · doi:10.1007/s40259-024-00682-1 · Cited by 20
“Antibody generation in mice also exploits the natural immune system and mechanisms (including clonal selection and somatic hypermutation), which produce mAbs with high affinity and a good developability profile [157, 158].”
BackgroundKoenig, P. S., Sanowar, S., Lee, C. V., & Fuh, G. (2017) · mAbs · doi:10.1080/19420862.2017.1337618 · Cited by 18
“We therefore measured some of the identified mutants’ biophysical properties, which are linked to the “developability” of antibodies.(33) A high melting temperature of the Fab portion correlates with an increase in antibody expression titers.”
BackgroundBiophysical Characterization Platform Informs Protein Scaffold Evolvability
Golinski, A. W., Holec, P. V., Mischler, K. M., & Hackel, B. J. (2019) · ACS Combinatorial Science · doi:10.1021/acscombsci.8b00182 · Cited by 18
Rapid Selection of High-Affinity Antibody scFv Fragments Using Ribosome Display
Dreier, B., & Plückthun, A. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8648-4_13 · Cited by 18
Bayesian Optimization for Efficient Multiobjective Formulation Development of Biologics
Waibel, I., Schneider, T. N., Fischer, F. J., Dumnoenchanvanit, P., Kulakova, A., Nguyen, T. D., Egebjerg, T., Bertelsen, S., Lorenzen, N., & Arosio, P. (2025) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.5c00591 · Cited by 19
Improving the Developability of an Antigen Binding Fragment by Aspartate Substitutions
Sakhnini, L. I., Greisen, P. J., Wiberg, C., Bozóky, Z., Lund, S., Pérez, A.-M. W., Karkov, H. S., Huus, K., Jens-Jacob, H., Bülow, L., et al. (2019) · Biochemistry · doi:10.1021/acs.biochem.9b00251 · Cited by 18
Petersen, B. M., Ulmer, S. A., Rhodes, E. R., Gutiérrez-González, M., DeKosky, B. J., Sprenger, K. G., & Whitehead, T. A. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.728694 · Cited by 18
“mAbs are engineered not only to have potent and specific binding to a given target but also to have favorable drug properties, including in vivo stability, manufacturability, immunogenicity, solubility, and polyspecificity (5).”
BackgroundPrinciples of antibodies with ultralong complementarity-determining regions and picobodies
Passon, M., Smedt, S. C. D., & Svilenov, H. L. (2023) · Biotechnology Advances · doi:10.1016/j.biotechadv.2023.108120 · Cited by 17
Banik, S. S. R., Kushnir, N., Doranz, B. J., & Chambers, R. (2023) · mAbs · doi:10.1080/19420862.2023.2273018 · Cited by 16
“Our analysis of 137 approved and late-stage therapeutic antibodies 5 found that only 16% of these antibodies have HCDR3 sequences longer than 13 amino acids (Figure 4).”
BackgroundMethodThe medicinal chemistry evolution of antibody–drug conjugates
Hobson, A. D. (2024) · RSC Medicinal Chemistry · doi:10.1039/d3md00674c · Cited by 17
Neutralizing antibody against GDF15 for treatment of cancer-associated cachexia
Xiong, J., Wu, G., Ning, J., Yan, J., Yang, J., & Kang, J. (2024) · PLoS ONE · doi:10.1371/journal.pone.0309394 · Cited by 17
Ball, K., Bruin, G. J. M., Escandón, E., Funk, C., Pereira, J. N. S., Yang, T., & Yu, H. (2022) · Drug Metabolism and Disposition · doi:10.1124/dmd.121.000463 · Cited by 17
“Within industry, these intrinsic properties are typically evaluated in a variety of early-stage developability assays including specific or nonspecific binding to cell-membrane proteins, FcRn interaction assessments, self-and cross-interactions and polyspecificity (Jain et al., 2017).”
BackgroundEFab domain substitution as a solution to the light-chain pairing problem of bispecific antibodies
Cooke, H. A., Arndt, J. W., Quan, C., Shapiro, R. I., Wen, D., Foley, S. F., Vecchi, M. M., & Preyer, M. (2018) · mAbs · doi:10.1080/19420862.2018.1519631 · Cited by 17
“To evaluate the effect of EFab substitution on antibody stability, we chose to engineer trastuzumab, which has a higher than average thermal stability compared to 137 clinical-stage and FDA-approved antibody therapeutics.(21) Trastuzumab was engineered as a heterodimer with one Fab converted to an”
MethodPandya, A., Zhang, C., Barata, T. S., Brocchini, S., Howard, M. J., Zloh, M., & Dalby, P. A. (2024) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.4c00332 · Cited by 16
Trends and challenges in bispecific antibody production
Karbyshev, M. S., Kalashnikova, I. V., Dubrovskaya, V. V., Baskakova, K. O., Kuzmichev, P. K., & Sandig, V. (2025) · Journal of Chromatography A · doi:10.1016/j.chroma.2025.465722 · Cited by 16
AlphaBind, a domain-specific model to predict and optimize antibody–antigen binding affinity
Agarwal, A. A., Harrang, J., Noble, D., McGowan, K. L., Lange, A. W., Engelhart, E., Lahman, M. C., Adamo, J., Yu, X., Serang, O., et al. (2025) · mAbs · doi:10.1080/19420862.2025.2534626 · Cited by 21
Licari, G., Martin, K., Crames, M., Mozdzierz, J., Marlow, M. S., Karow‐Zwick, A. R., Kumar, S., & Bauer, J. (2022) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.2c00838 · Cited by 15
AB-Amy: machine learning aided amyloidogenic risk prediction of therapeutic antibody light chains
Zhou, Y., Huang, Z., Gou, Y., Liu, S., Yang, W., Zhang, H., Dzisoo, A. M., & Huang, J. H. (2023) · Antibody Therapeutics · doi:10.1093/abt/tbad007 · Cited by 16
“However, over 85% of human or humanised mAbs failed in preclinical development, with many cases due to unfavorable physicochemical properties leading to increased aggregation tendency and high viscosity and thus, poor manufacturability [7, 8].”
Profiling the Biophysical Developability Properties of Common IgG1 Fc Effector Silencing Variants
Pejchal, R., Cooper, A. B., Brown, M. E., Vásquez, M., & Krauland, E. M. (2023) · Antibodies · doi:10.3390/antib12030054 · Cited by 16
“In brief, polyspecificity reagent (PSR) binding was characterized by a FACS-based assay and reported as a normalized binding score [3,45].”
BackgroundMethodSelf-citationApplications of Flow Cytometry in Drug Discovery and Translational Research
Ullas, S., & Sinclair, C. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25073851 · Cited by 17
“Lower levels of poly-specificity are correlated with improved antibody developability [26].”
BackgroundGera, N., Fitzgerald, K. M., Ramesh, V., Patel, P., Kanojia, D., Colombo, F., Kien, L., Aoyama, S., Xu, L., Jean, J., et al. (2024) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-23-0784 · Cited by 16
“Extensive developability characterization including aggregation, hydrophobicity, and non-specific binding assessment was conducted as previously described (23).”
MethodAntibody engineering to improve manufacturability
Wijesuriya, S. D., Pongo, E., Tomic, M. T., Zhang, F., Garcia-Rodriquez, C., Conrad, F., Farr-Jones, S., Marks, J. D., & Horwitz, A. H. (2018) · Protein Expression and Purification · doi:10.1016/j.pep.2018.04.003 · Cited by 16
Majumder, S., Saati, A., Philip, S., Liu, L. L., Stephens, E., Rouse, J. C., & Ignatius, A. A. (2019) · Pharmaceutical Research · doi:10.1007/s11095-019-2652-1 · Cited by 16
Antibody heavy chain CDR3 length-dependent usage of human IGHJ4 and IGHJ6 germline genes
Wang, H., Yan, K., Wang, R., Yang, Y., Shen, Y., Yu, C., & Chen, L. (2021) · Antibody Therapeutics · doi:10.1093/abt/tbab010 · Cited by 16
“This limits the translational success of therapeutic antibody candidates derived from in vitro selection of phage or yeast libraries [11].”
BackgroundSSH2.0: A Better Tool for Predicting the Hydrophobic Interaction Risk of Monoclonal Antibody
Zhou, Y., Xie, S., Yang, Y., Jiang, L., Liu, S., Li, W., Abagna, H. B., Lin, N., & Huang, J. H. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.842127 · Cited by 16
Labriola, J. M., Miersch, S., Chen, G., Chen, C., Pavlenco, A., Saberianfar, R., Caccuri, F., Zani, A., Sharma, N., Feng, A., et al. (2022) · ACS Chemical Biology · doi:10.1021/acschembio.2c00411 · Cited by 16
Kilgore, R., Chu, W., Bhandari, D., Fischler, D., Carbonell, R. G., Crapanzano, M. S., & Menegatti, S. (2022) · Journal of Chromatography A · doi:10.1016/j.chroma.2022.463701 · Cited by 17
An Engineered Antibody with Broad Protective Efficacy in Murine Models of SARS and COVID-19Preprint
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. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.17.385500 · Cited by 16
Self-citationReal‐Time Observation of Protein Aggregation at Liquid‐Liquid Interfaces in a Microfluidic Device
Zürcher, D., Wuchner, K., & Arosio, P. (2024) · Small · doi:10.1002/smll.202401085 · Cited by 15
Novel therapies for diabetes mellitus in pregnancy
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Biochemical and biophysical characterization of natural polyreactivity in antibodies
Borowska, M. T., Boughter, C. T., Bunker, J. J., Guthmiller, J. J., Wilson, P. C., Roux, B., Bendelac, A. S., & Adams, E. J. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113190 · Cited by 16
“23– 28–30 In particular, one large study by Harvey et al. as well as our previous study by Boughter et al. show that amino acid contributions to polyreactivity are highly position dependent and more nuanced than previously described.”
BackgroundResultIn Vitro Evolution of Antibodies Inspired by In Vivo Evolution
Persson, H., Kirik, U., Thörnqvist, L., Greiff, L., Levander, F., & Ohlin, M. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.01391 · Cited by 17
“Importantly, processes for identification and development of antibodies with appropriate properties in terms of “Chemistry, Manufacturing, and Control” have also been developed (13, 14) to ensure that a potential drug candidate can actually be turned into a viable commercial product.”
BackgroundRahman, N., Islam, M. M., Unzai, S., Miura, S., & Kuroda, Y. (2020) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.0c00071 · Cited by 15
Berner, C., Menzen, T., Winter, G., & Svilenov, H. L. (2021) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.1c00017 · Cited by 15
Bielska, W., Jaszczyszyn, I., Dudzic, P., Janusz, B., Chomicz, D., Wrobel, S., Greiff, V., Feehan, R., Adolf‐Bryfogle, J., & Krawczyk, K. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1571371 · Cited by 17
Krieg, D., Winter, G., & Svilenov, H. L. (2022) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2022.05.014 · Cited by 14
Huhtinen, O., Salbo, R., Lamminmäki, U., & Prince, S. (2023) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2023.1170081 · Cited by 14
“(Jain et al., 2017) and their average values were calculated to determine cut-off values for each parameter.”
BackgroundMethodInfluentialWei, Y., Qi, W., Maglalang, E., Pelegri-O’Day, E. M., Luong, M., Razinkov, V. I., & Sloey, C. J. (2023) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.3c00797 · Cited by 14
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Skomski, D., Ji, A. J., Kuzman, D., Clénet, D., Hieb, A. R., Roberts, S. W., Berry, J., Lentes, C., Weusten, J. J. A. M., MacArthur, K., et al. (2025) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2025.103873 · Cited by 14
van Kampen, M. D., de Wilt, L. H. A. M., van Egmond, M. L., Reinders-Blankert, P., van den Bremer, E. T. J., Wang, G., Heck, A. J. R., Parren, P. W. H. I., Beurskens, F. J. M., Schuurman, J., et al. (2022) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2022.02.016 · Cited by 14
Chungyoun, M., & Gray, J. R. (2026) · Patterns · doi:10.64898/2025.12.27.696706 · Cited by 17
Next generation Fc scaffold for multispecific antibodies
Estes, B., Sudom, A. M., Gong, D., Whittington, D. A., Li, V., Mohr, C., Li, D., Riley, T. P., Shi, S. D. ‐., Zhang, J., et al. (2021) · iScience · doi:10.1016/j.isci.2021.103447 · Cited by 13
Campuzano, I. D. G., Pelegri-O’Day, E. M., Srinivasan, N., Lippens, J. L., Egea, P. F., Umeda, A., Aral, J., Zhang, T., Laganowsky, A. D., & Netirojjanakul, C. (2022) · Journal of the American Society for Mass Spectrometry · doi:10.1021/jasms.2c00138 · Cited by 13
Zupancic, J. M., Smith, M. D., Trzeciakiewicz, H., Skinner, M. E., Ferris, S. P., Makowski, E. K., Lucas, M. J., McArthur, N., Kane, R. S., Paulson, H. L., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1164080 · Cited by 13
“This is also notable given that other well-known amyloid-specific antibodies that were evaluated in clinical trials, such as gantenerumab and aducanumab, also display high levels of nonspecific binding (34, 37), revealing that amyloid-specific antibodies have an increased risk for non-specific binding.”
BackgroundInfluentialLecerf, M., Lacombe, R. V., & Dimitrov, J. D. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1266668 · Cited by 14
“However, previous analyses demonstrated that PSR and ELISA methods signi fi cantly correlate in their capacity to identify polyreactive Abs (21, 22).”
BackgroundNonspecificity fingerprints for clinical-stage antibodies in solution
Herling, T. W., Invernizzi, G., Ausserwӧger, H., Bjelke, J. R., Egebjerg, T., Lund, S., Lorenzen, N., & Knowles, T. P. J. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2306700120 · Cited by 13
Wang, S., Wang, Y., Li, Z., Ye, H., Wang, Z., Fan, J., Wang, Q., Ge, Y., Zhao, X., Cheng, G., et al. (2024) · mAbs · doi:10.1080/19420862.2024.2374607 · Cited by 13
“8,9 15 The interactions between antibody and antigen, as well as IgG Fc and Fc receptors, are considered as the primary mechanism of actions (MOAs) for antagonistic and cytotoxic antibodies, respectively.”
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) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000475562 · Cited by 16
Mieczkowski, C., Cheng, A. C., Fischmann, T. O., Hsieh, M., Baker, J., Uchida, M., Raghunathan, G., Strickland, C., & Fayadat‐Dilman, L. (2021) · Antibodies · doi:10.3390/antib10010008 · Cited by 13
Schmitz, S., Soto, C. S., Crowe, J. E., & Meiler, J. (2020) · mAbs · doi:10.1080/19420862.2020.1758291 · Cited by 12
“FDA or are in clinical trials.(47,48) All biologics were either annotated with the INN designations(49) HU, ZU, XI, and XIZU as reported by Jain et al.”
BackgroundMethodInfluentialAnimal- versus in vitro-derived antibodies: avoiding the extremes
Bradbury, A. R. M., Dübel, S., Knappik, A., & Plückthun, A. (2021) · mAbs · doi:10.1080/19420862.2021.1950265 · Cited by 13
Ollier, R., Fuchs, A., Gauye, F., Piorkowska, K., Menant, S., Ratnam, M., Montanari, P., Guilhot, F., Phillipe, D., Audrain, M., et al. (2023) · mAbs · doi:10.1080/19420862.2023.2232087 · Cited by 11
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
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