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
Cited by
Sorted by citations
Items 501–600 of 630
He, C., Huang, W., Wu, X., & Xia, H. (2025) · Biomedicines · doi:10.3390/biomedicines13092055 · Cited by 1
Directed evolution of drug-like Aβ conformation-specific antibodies
Desai, A. A., Smith, M. D., Zupancic, J. M., Makowski, E. K., Zhang, Y., Gerson, J. E., Moore, S. J., Sutter, A. B., Ferris, S. P., Ivanova, M. I., et al. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1655893 · Cited by 1
Germline-aware deep learning models and benchmarks for predicting antibody VH–VL pairing
Joubbi, S., D’Arco, E., Maccari, G., Milazzo, P., & Micheli, A. (2025) · mAbs · doi:10.1080/19420862.2025.2570749 · Cited by 1
“We used data from Jain et al., 40 which includes 137 antibodies with Fab melting temperatures measured by differential scanning fluorimetry and HEK expression titers (mg/L).”
HuAbDiffusion: a discrete language diffusion model used for antibody humanization
Liu, D., Hao, X., & Fan, L. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf658 · Cited by 3
“So far, most therapeutic antibodies were developed from animal immunity [7], as antibodies evolved by the immune system typically have higher potential for development compared to in vitro evolution [8,9].”
Hale, G., Kaufman, Y., Hunt, K., Ramesh, P., Dorin, R. M., & Gerion, D. (2025) · Separation and Purification Technology · doi:10.1016/j.seppur.2025.136564 · Cited by 1
Machine Learning Approaches for Interpretable Antibody Property Prediction Using Structural Data
Michalewicz, K., Barahona, M., & Bravi, B. (2026) · Methods in molecular biology · doi:10.1007/978-1-0716-5408-8_7 · Cited by 1
Aggregation Issues in Radioactive Copper-Labeled Antibodies
Zeeshan, M., Nguyen, H. B., & Yoo, J. (2025) · Nuclear Medicine and Molecular Imaging · doi:10.1007/s13139-025-00974-6 · Cited by 1
“Additionally, modifications to disulfide patterns or glycosylation sites may improve con - formational stability under labeling or storage conditions [56].”
Analysis of CDR3 region diversity with different lengths in bovine immunoglobulin heavy chain genes
Zhao, H., Tang, X., Zhang, Y., & Wu, M. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1722984 · Cited by 1
Steinke, J., Destruel, P.-L., Weiche, B., Dengl, S., Schwach, G., Pohlmann, G., & Hinner, M. J. (2026) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2026.107446 · Cited by 1
Spanke, V. A., Hoerschinger, V. J., Seidler, C. A., Kroell, K. B., Wieser, V., Imhof-Jung, S., Weiche, B., Bujotzek, A., Georges, G. J., & Liedl, K. R. (2026) · mAbs · doi:10.1080/19420862.2026.2627669 · Cited by 1
“29 However, performing these assays requires antibody expression and purification in sufficient amounts, which is time-consuming and costly.”
InfluentialZhu, X., Zhang, T., Zhao, H., Hou, B., Fu, M., Zhao, Q., Xu, T., Zhao, J., Xie, J., Zhou, Y., et al. (2026) · PLoS ONE · doi:10.1371/journal.pone.0345402 · Cited by 1
“This stark functional divergence underscores a fundamental principle in antibody development: high-affinity binding to a catalytic domain does not guarantee inhibiion—epitope localization is decisive [31,32].”
AI-Driven De Novo Binder Design: From Structure Prediction to Closed-Loop Optimization
Li, X., Fan, Z., Yang, J., Jiang, R., Cao, F., Li, J., Xie, D., Meng, X., Sun, M., & Wang, L. (2026) · Computational and Structural Biotechnology Journal · doi:10.34133/csbj.0070 · Cited by 1
“Industrial experience increasingly emphasizes that optimizing affinity alone is insufficient [ 22 ].”
Impact of heme on the therapeutic efficacy of anti-CD20 antibodies
Jarossay, A., Tavares, A., Lacombe, R. V., Delcourt, H., Lecerf, M., Cruz-Aguirre, A. S., Lassouani, T. H., Gilardin, L., Tareste, D. C., Kaveri, S. V., et al. (2026) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2026.111485 · Cited by 1
“%) of them manifest polyreactivity (14).”
Tsunoda, Y., Somatani, A., Yoshiike, C., Mitoma, S., Yamano, N., & Ōmasa, T. (2026) · New Biotechnology · doi:10.1016/j.nbt.2026.05.010 · Cited by 1
Jin, M., Patel-Hett, S. R., Jasuja, R., Rakhe, S., Narula, J., Tam, A., Apgar, J. R., Juo, Z. S., Mosyak, L., Holsti, M., et al. (2026) · mAbs · doi:10.1080/19420862.2026.2685362 · Cited by 1
“16 The resulting antibody, marstacimab, has subsequently been tested in vivo in animals and humans 9–11,”
Efficient inference of non-polyreactive antibody variants dependent on local fine-tuning
Saunders, M., Comyn, S., Medjo, B., Anderson, Z., LaCombe, C., Ripley, J., Chapman, K., Ma, T., Ventura, A. B., Ruelos, A., et al. (2026) · mAbs · doi:10.1080/19420862.2026.2692763 · Cited by 1
“10 are measured with different reagents than those used in this study – specifically versus six recombinant proteins (reported there as “PSR” and here as “Jain PSR”) or BVP – we used the average Jain PSR and BVP values for benchmarking, considering this most representative of a heterogenous protein…”
InfluentialAhmed, S., Devalle, F., Leisen, L., Pham, T., Amofah, B., Lee, A. N., Hutchinson, M., Chakiath, C., DiChiara, J., Farzandh, S., et al. (2026) · mAbs · doi:10.1080/19420862.2026.2694124 · Cited by 1
“1,5,24 We therefore treat CSI-BLI not only as an experimental benchmark but also as a meaningful modeling target to reduce the wet-lab burden through in silico pre-screening and prioritization.”
Hong, T., Chen, N., Yang, Y., Wang, Y., Yao, Y., & Zheng, C. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27167124 · Cited by 1
“Developability assessment should therefore begin during selection and include monovalent affinity, specificity panels, expression yield, thermal and colloidal stability, aggregation and self-interaction, sequence liabilities, formulation stress, and relevant functional assays [59,60].”
Dores-Silva, P. R., Pascarella, T. W., Newman, J. M., & Borges, J. C. (2026) · Biophysical Reviews · doi:10.1007/s12551-026-01452-9 · Cited by 1
Meslamani, A. Z. A., Jarab, A. S., & Marshoodi, K. A. A. (2026) · Expert Opinion on Biological Therapy · doi:10.1080/14712598.2026.2727673 · Cited by 1
“For example, Jain and colleagues' work [106], which showed that successful molecules tend to occupy a constrained, although not uniform, region of biophysical property space.”
Targeted extracellular degradation of LRP8 promotes ferroptosis in cancer cells
Zhao, F., Inague, A., Peters-Clarke, T. M., Chen, Y., Ganjave, S. D., Zhang, Y., Miao, K., Yao, Z., Wu, Y., Seto, M. K. C., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2616933123
“All Fabs exhibited minimal polyreac tivity against nonspecific antigen panels (33) ( SI Appendix , Fig.”
ИНЖЕНЕРИЯ ТЕРАПЕВТИЧЕСКИХ АНТИТЕЛ: ТЕОРИЯ И ПРАКТИКА#, "Молекулярная биология"
Луговской, А. А. (2017) · Molecular Biology · doi:10.7868/s0026898417060027 · Cited by 2
Drug discovery processes: When and where the rubber meets the road
Miller, S. M., Moos, W. H., Munk, B. H., Munk, S. A., Hart, C. P., & Spellmeyer, D. C. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-824304-6.00025-0 · Cited by 1
Szkodny, A. C., & Lee, K. H. (2024) · Biotechnology Progress · doi:10.1002/btpr.3466 · Cited by 1
“5 Preliminary attempts to predict mAb developability have been inspired by previous work with small molecule drugs that identified a set of general guidelines (Lipinski's “ rule of five ” ) that help predict the therapeutic success of new candidates based on molecular structure.”
Kasana, H., Chander, H., & Mathur, A. (2024) · Journal of Applied Biology & Biotechnology · doi:10.7324/jabb.2024.172190 · Cited by 1
“Regarding mAb lead candidates, additional quality attributes, including solubility, viscosity, expression yield, heat, and long-term stability, are crucial [39,40].”
BackgroundPais, D. A. M., Mayer, J.-P. A., Felderer, K., Batalha, M. B., Eichner, T., Santos, S. T., Kumar, R., da Silva, S. R. A., & Kaufmann, H. (2024) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-024-00569-x · Cited by 1
“Developing such algorithms is based on the existence of large publicly available datasets linking analytical characterization (as exemplified in [36]), to antibody sequence, structure, and antigen binding information (compiled in [23]).”
Floyd, J. A., & Shaver, J. M. (2024) · Analytical Biochemistry · doi:10.1016/j.ab.2024.115729 · Cited by 1
Therapeutic antibody development—Remington chapter
Geist, B. J., Zheng, S., & Xu, Y. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-820007-0.00024-6 · Cited by 1
Self-citationLehti, T. A., Pajunen, M. I., Jokilammi, A., Korja, M., Lilie, H., Vettenranta, K. K., & Finne, J. (2021) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-20-1031 · Cited by 1
“The development of antibodies for therapeutic applications is timeconsuming and costly, and traditionally dependent on animal immunization (19, 20).”
BackgroundMonoclonal Antibody-Directed Therapy
Kayser, V., & Datta‐Mannan, A. (2022) · MDPI eBooks · doi:10.3390/books978-3-0365-2872-4 · Cited by 1
Model-Based Risk Assessment of mAb Developability
Karlberg, M., Kizhedath, A., & Glassey, J. (2022) · Springer optimization and its applications · doi:10.1007/978-3-030-90924-6_14 · Cited by 1
Ferrara, F., D’Angelo, S., Erasmus, M. F., Teixeira, A. A. R., Leal‐Lopes, C., Spector, L. P., Pohl, T., Fanni, A. M., Cocklin, S., & Bradbury, A. M. (2022) · mAbs · doi:10.1080/19420862.2022.2133666 · Cited by 2
“While significant numbers of therapeutic antibodies are derived from such libraries, the majority still come from immune sources since antibodies from naïve libraries generally tend to have lower affinities and can have inferior biophysical characteristics (known in the field as poor “developability”), complicating th…”
BackgroundProcess Development of Protein Therapeutics
Thomas, J. N., Guhan, S., & Pettit, D. K. (2010) · Burger's Medicinal Chemistry and Drug Discovery · doi:10.1002/0471266949.bmc160 · Cited by 1
Rahman, N., Islam, M. M., Unzai, S., Miura, S., & Kuroda, Y. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/793687 · Cited by 1
Process Development of Protein Therapeutics
Huang, Y., Bowes, B. D., Yang, T., Thomas, J. N., Guhan, S., & Pettit, D. K. (2021) · Burger's Medicinal Chemistry and Drug Discovery · doi:10.1002/0471266949.bmc160.pub2 · Cited by 1
Ultra-high-diversity factorizable libraries for efficient therapeutic discoveryPreprint
Dai, Z., Saksena, S. D., Horny, G., Banholzer, C., Ewert, S., & Gifford, D. K. (2022) · Genome Research · doi:10.1101/gr.276593.122 · Cited by 1
Structural Survey of Antigen Recognition by Synthetic Human AntibodiesPreprint
Gorelik, M., Miersch, S., & Sidhu, S. S. (2024) · Cold Spring Harbor Protocols · doi:10.1101/pdb.over107759 · Cited by 1
Ophiuchus-Ab: A Versatile Generative Foundation Model for Advanced Antibody-Based ImmunotherapyPreprint
Zhu, Y., Ma, J., Yin, M., Wu, J., Tang, L., Zhang, Z., Li, Q., Feng, S., Liu, H., Qin, T., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.02.703197 · Cited by 1
“…both in vitro properties, such as high-titer expression in heterologous systems, high solubility, thermostability, and minimal self-association and viscosity, as well as in vivo characteristics, including low immunogenicity, reduced non-specific "sticky" binding, and a prolonged half-life [80, 81].”
Modelling antibody structures at the speed of languagePreprint
Ellmen, I., Errington, D., Raybould, M. I. J., & Deane, C. M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.03.729879 · Cited by 1
“We ran FlashABB and ProteinMPNN on all antibodies in the set from Widatalla et al. [27] which is comprised of human antibodies from Shehata et al. [28] and therapeutic antibodies from Jain et al. [29].”
Lecerf, M., Kanyavuz, A., Rossini, S., & Dimitrov, J. D. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.04.236992
Furquim, F. C., Okuyama, C. E., Rostelato‐Ferreira, S., Rivero-Wendt, C. L. G., Oliveira, H., & Diniz, S. N. (2020) · Ensaios e Ciência C Biológicas Agrárias e da Saúde · doi:10.17921/1415-6938.2020v24n3p202-210
Predictive QSAR tools to aid in early process development of monoclonal antibodies
Karlberg, J. M. A. (2019)
Atangcho, L. (2020) · Deep Blue (University of Michigan)
Ban, D., Rice, C., & McCoy, M. A. (2021) · mAbs · doi:10.1080/19420862.2021.1978132
Directed Evolution of Antibodies Against Complex Targets
Desai, A. A. (2021) · Deep Blue (University of Michigan) · doi:10.7302/3079
PBPK MODELING OF BIOTHERAPEUTICS
Peters, S. A. (2021) · Wiley eBooks · doi:10.1002/9781119497813.ch8
Ultra high diversity factorizable libraries for efficient therapeutic discoveryPreprint
Dai, Z., Saksena, S. D., Horny, G., Banholzer, C., Ewert, S., & Gifford, D. K. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.17.476670
Nash, M. A. (2020) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.737739899.793576291
Sidhu, S. S., & Miersch, S. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.734616073.793562968
Zou, P. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.15.524112
Leach, M. W., & Hammerman, K. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-821047-5.00024-5
Therapeutics and Diagnostics Discovery
Mochly‐Rosen, D., Grimes, K., Hannoush, R. N., Koch, B., Ehrenkaufer, G. M., Erlanson, D. A., Saiki, J., Wilson, J. L., Aldred, S. F., Garcia, A. A., et al. (2023) · Springer eBooks · doi:10.1007/978-3-031-34724-5_2
The Employment of Polymerase Chain Reaction in Building Phage Displayed Antibody Libraries
Zemene, A. (2024) · Biochemistry · doi:10.5772/intechopen.110729
Bauer, J., Kube, S., Gupta, P. K., & Kumar, S. (2024) · Springer eBooks · doi:10.1007/978-3-031-62007-2_14
Abeer, A. N. M. N., Boroumand, M., Sermadiras, I., Caldwell, J. G., Stanev, V., Mody, N., Kaplan, G., Savery, J., Croasdale-Wood, R., & Pouryahya, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.02.636157
“5,33 To normalize retention times of monomer content across multiple studies utilized in this paper, ΔRT is considered, which compares the retention time of the monomer protein to a reference protein, NIP228, with a known SEC retention time of approximately 8.47 minutes.”
InfluentialSherpabodies—A Highly Versatile and Modular Scaffold for Biomedical Targeting
Mäkelä, A. R., & Saksela, K. (2025) · Biologics · doi:10.3390/biologics5020013
“…showed a remarkable absence of polyreactivity This was assessed by measuring the tendency of the CAA-targeted sherpabodies to interact with a panel of “sticky” macro-molecules known for promiscuous binding and used by therapeutic antibody developers to predict problematic antibody behavior [90,91].”
Joubbi, S., Maccari, G., Cardamone, D., Ciano, G., Micheli, A., Milazzo, P., & Medini, D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-90714-2_18
Behavioural Profiling of Therapeutic Antibodies via Non-equilibrium Interfacial Wave DynamicsPreprint
Thomas, A. A., John, A. N. S., Crames, M., Caramazza, P., Meghdadi, A., Bailey, J., Hermans, R. I., Lee, S., Lam, E., Krishnan, V., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.09.653084
“…for which five industry-standard developability methods had been performed: purity after Protein A capture, measured by SEC (% purity), Fab melting temperature (Fab T m ), analytical hydrophobic interaction chromatography (aHIC), self-interaction by SINS, and non-specific binding (NSB) [3, 21, 22].”
InfluentialPharmacokinetics of Therapeutic Antibodies and Derivatives
Chen, E. P. (2025) · Wiley eBooks · doi:10.1002/9781119650263.ch08
High‐Throughput Biophysical Assays for Developability Assessment
Pelegri-O’Day, E. M., & Srinivasan, N. (2025) · Wiley eBooks · doi:10.1002/9781394236145.ch12 · Cited by 1
Profiling Hinge Plasticity in Intact Monoclonal Antibodies for Antigen Recognition
Bhattacharya, A., Sinha, S., Dash, R., Rathore, A. S., & Majumder, S. (2025) · Biochemistry · doi:10.1021/acs.biochem.5c00274
Magnetic Nanoworm-Based Screening of Yeast Library for Anti-PEG Nanobodies
Ray, S., Jacques, S., Ettah, U., Dai, S., Gaikwad, H. K., Scheinman, R. I., Mallela, K. M. G., & Simberg, D. (2025) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.5c00540
Property Enhancer – a data efficient multi-objective approach for functional antibody optimizationPreprint
Tagasovska, N., Ludwiczak, J., Loukas, A., Makowski, E. K., MohammadiPeyhani, H., Mahajan, S. P., Zadorozhny, K., Lee, D., Goodman, A., Lin, J. Y.-Y., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.08.681101 · Cited by 2
“We evaluate PropEn’s optimization capabilities using in silico experiments [17, 18], benchmarking its performance against state-of-the-art methods across multiple sequence-and structure-based properties of antibodies (see Methods, Datasets , for details).”
Pharmacophore-driven antibody discovery on the yeast surfacePreprint
Huang, M., Williams, S. J., Trivedi, V. D., Nair, N. U., & Deventer, J. A. V. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.16.682880
Blackman, S. A., Qerqez, A. N., Lee, A. G., Johnson, N. V., Owens, J. M., Lai, G. P., Aldrich, E. C., Sprenger, K. G., Lee, J., Verrier, S., et al. (2026) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2026.111476
“Poly-specificity ELI-SAs were performed as described (22, 23) using the following coat antigens: hemocyanin from Megathura crentulata (KLH) at 5 μ g/ml (Sigma), single stranded DNA at 1 μ g/ml (Sigma), double stranded DNA at 1 μ g/ml (Sigma), insulin at 5 μ g/ml (Sigma), each in PBS, and…”
InfluentialMenzen, T., Vay, K. K. L., Arsiccio, A., Helbig, C., Hausmann, K., Pabstmann, T., & Hawe, A. (2025) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2025.104054
HyperBind2: Multi-Shot Learning Enables Progressive Improvement in Computational Antibody DiscoveryPreprint
Dell'uomo, D., Satz, A., & Averso, B. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.06.687005
“(Jain 2017)”
VHH engineering and utilization for blood-brain barrier transport and anti-infectious diseases
Pérez, J. F., Asano, R., Uto, Y., Tsumoto, K., & Nakakido, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00069-8
Additional file 1 of Latozinemab, a novel progranulin-elevating therapy for frontotemporal dementia
Kurnellas, M., Mitra, A., Schwabe, T., Paul, R., Arrant, A. E., Roberson, E. D., Ward, M., Yeh, F. L., Long, H., & Rosenthal, A. (2024) · Figshare · doi:10.6084/m9.figshare.26597423
Sourcing and screening human antibody repertoires for discovering biotherapeutics
Abdiche, Y. N., & Hoet, R. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00062-5
Sequence-Only Prediction of Antibody Fab Thermostability Using Protein Language Model EmbeddingsPreprint
Sennett, M. A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.30.697053
Ramasubramanian, A. M., Deveney, B. T., Clark, S. A., Boutin, P., Keepseagle, K., Riahi, S., Lao, T., Salemi, M., Kathuria, S. V., Amengual-Rigo, P., et al. (2026) · Scientific Reports · doi:10.1038/s41598-025-33040-4
“Since Jain et al.’s comprehensive effort to characterize acceptable metrics of ‘developability’ among clinical-stage antibodies 23 , there is a strong consensus that antibodies with the most optimal drug-like behavior not only display high affinity and specificity but also well-defined functional…”
Molecularly specific solubilization of therapeutic antibodiesPreprint
Han, Z., Erkamp, N. A., Scrutton, R. M., Licari, G., Predeina, O., Evers, A., Sormanni, P., & Knowles, T. P. J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.29.702671
Deng, J., Huang, Q., Lv, J., Yang, Y. L., Ye, Z., Chu, Y., Wu, Y., Zhao, Q., Fang, W., & Ouyang, D. (2026) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2026.126703
Effect of electric charge on biotherapeutic transport, binding and absorption: A computational study
de Lucio, M., Vlachos, P. P., & Gomez, H. (2026) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2026.126741
A tetravalent PD-L1/VEGF bispecific antibody blocks tumor growth In Vivo
Adams, J., Blazer, L. L., Chen, G., Chung, J., Mallette, E., Singer, A. U., Mendiratta, S. K., Bandyopadhyay, S., Singh, A. K., Kalita, P., et al. (2026) · Disease and Therapeutics · doi:10.1016/j.dist.2026.100006
Antibody discovery technology: innovation and outlook from classic to leading edge
Tang, J., & PengFei, W. A. N. G. (2026) · Scientia Sinica Vitae · doi:10.1360/ssv-2025-0311
Wang, R. W., Jin, K., & Pan, L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.26.714619
“AINN-P1 (cid:136) Antibody engineering: Early developability screens emphasizing stability and expression, complementing binding-focused methods [Jain et al., 2017, Raybould et al., 2019, Zhang et al., 2022, Bailly et al., 2020].”
Kister, A. E., Leshchiner, D. R., & Kister, I. (2026) · PLoS ONE · doi:10.1371/journal.pone.0341970
“For example, according to the Kyte and Doolittle scale [31], the peptide in the 8CMB structure is very hydrophobic (the sum of hydrophobicity indices is 12.03), whereas according to the Jain et al. scale [32], this peptide is essentially non-hydrophobic (the sum of hydrophobicity indices is 0.29).”
InfluentialChun, C., Sohn, B.-K., Ki, H., Jo, J. H., An, H.-T., Park, J., Lee, J., Choi, S., Choi, J. H., Cho, H., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.21.719857 · Cited by 2
“Beyond binding affinity, therapeutic success hinges on the parallel optimization of developability traits, including cellular productivity and thermal stability 18-20 .”
Predicting Agitation Stability of Monoclonal Antibodies during Developability Assessment
Cohrs, M., Pagureva, N., Özbulak, U., Neve, W. D., Braeckmans, K., Smedt, S. C. D., Tcholakova, S. S., Vinarov, Z., & Svilenov, H. L. (2026) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.6c00092
Vanzolini, T., Fiori, V., Mambro, T. D., & Magnani, M. (2026) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2026.152185
Kwon, N.-Y., Brown, C. N., Chen, H.-T., Cottle, S. R., Kelly, R. M., Jones, B. E., Weiss, W. F., & Tessier, P. M. (2026) · mAbs · doi:10.1080/19420862.2026.2663641
John, A. N. S., Holland, J., Lam, E. S., Lee, S., Caramazza, P., Thomas, A. A., & Shrivastava, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.06.723251
“…chromatography, self-interaction nanoparticle spectroscopy, differential scanning calorimetry, polyreactivity assays) each capture a single biophysical axis [13, 14, 15] and do not by themselves identify which combinations of property values constitute a liability under composite stress.”
Sequence-dominant protein therapeutics: mechanistic foundations for efficient biologics development
Niazi, S. K. (2026) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2026.1781560
Explicit representation of germline and non-germline residues improves antibody language modelingPreprint
Kim, J., Blalock, N., Kulkarni, A., Nakamura, K., & Romero, P. A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.06.723387 · Cited by 1
“We score binding affinity via NGL probability and developabil-ity via GL probability (Jain et al., 2017), sign-adjusting all Spearman correlations ( ⇢ ) so positive values denote favorable outcomes.”
Ultra-Dilute Developability Analysis of Antibody Self-Association and Non-Specific Binding
Kwon, N.-Y., Karadag, M., & Tessier, P. M. (2026) · Methods in molecular biology · doi:10.1007/978-1-0716-5041-7_14
Rauschmayer, M., Abdellatif, M., Chubanova, A., Leonardi, N., Preinsberger, A. B., Schwach, J., & Stengl, A. (2026) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2026.113155
“Four isolated residues (alignment positions 5, 11, 93, 115 (Fig.”
Zhang, W., Ye, H., Zhang, X., & Liao, C. (2026) · Antibody Therapeutics · doi:10.1093/abt/tbag024
“Bevacizumab, which exhibits negligible polyspecif icity [ 28], was employed as a negative control, whereas Lenzilumab, which displays robust polyspecif ic reactivity, served as the positive control [ 29].”
Pastrana‐Ríos, B., Nieves, S., & Canto, E. I. (2026) · Applied Spectroscopy · doi:10.1177/00037028261451288
Ching, K. H., Keating, S., Zeng, B., Espinosa, D. A., Stack, E., Wall, S., Beyer, S., Wade, J., Tam, A., Cunningham, O., et al. (2026) · mAbs · doi:10.1080/19420862.2026.2672771
“As a comparison, the T20 scores for the heavy and light chain sequences of a panel of 124 approved or advanced clinical stage antibodies 33 were also calculated.”
Multi-objective antibody design and optimization using machine learning
Kuo, Y., Brown, C. N., Akin, E., Xie, Y., & Tessier, P. M. (2026) · Nature Reviews Bioengineering · doi:10.1038/s44222-026-00444-4
“Although many such datasets are invaluable to the field 88–92 , efforts to promote widespread data sharing and collaboration are essential to grow the computational antibody design space and to democratize the antibody engineering field.”
Bai, Z., Deng, J., Fang, L., Liu, N., Dong, Y., & Zhao, X. (2026) · Journal of Immunotherapy · doi:10.1097/cji.0000000000000608
Lippold, S., Chang, K., Dürkoop, M., Andersen, J. T., Yang, F., & Schlothauer, T. (2026) · mAbs · doi:10.1080/19420862.2026.2687922
“22 Finally, while the selected conditions were determined to be suitable for scC1q AC–MS, additional parameters may be considered for future adaptations of weak–affinity interactions.”
Helbig, C., Arsiccio, A., Schuster, G., Hawe, A., & Menzen, T. (2026) · European Journal of Pharmaceutics and Biopharmaceutics · doi:10.1016/j.ejpb.2026.115160
BoltzProt-1: Towards Efficient De Novo Binder Design with Good DevelopabilityPreprint
Uçar, T., Bates, J., Fu, Y., Shi, W., Stark, H., Nava, D., Cavalleri, L., Wohlwend, J., Corso, G., & Passaro, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.23.733997
“C.6 Affinity-capture Self-interaction Nanoparticle Spectroscopy (AC-SINS) To evaluate the self-interaction potential of protein candidates, the AC-SINS assay was carried out following established protocols [Jain et al., 2017].”
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
