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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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1 retracted
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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
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A strategy for risk mitigation of antibodies with fast clearance
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Developability Assessment During the Selection of Novel Therapeutic Antibodies
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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 301–400 of 630
Applying artificial intelligence to accelerate and de-risk antibody discovery
Musnier, A., Dumet, C., Mitra, S., Verdier, A., Keskes, R., Chassine, A., Jullian, Y., Cortès, M., Corde, Y., Omahdi, Z., et al. (2024) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2024.1339697 · Cited by 14
DOTAD: A Database of Therapeutic Antibody Developability
Li, W., Lin, H., Huang, Z., Xie, S., Zhou, Y., Gong, R., Jiang, Q., Xiang, C., & Huang, J. H. (2024) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-024-00613-2 · Cited by 12
“In their seminal work, Jain et al. compared the biophysical and physicochemical attributes of antibodies undergoing Phase II or III clinical trials with those of FDA-approved antibody drugs [7].”
InfluentialMei, A. H.-C., Letscher, K. P., & Reddy, S. T. (2024) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2024.103223 · Cited by 12
Humatch - fast, gene-specific joint humanisation of antibody heavy and light chains
Chinery, L., Jeliazkov, J. R., & Deane, C. M. (2024) · mAbs · doi:10.1080/19420862.2024.2434121 · Cited by 13
“To demonstrate the utility of including a paired model we examined the relationship between high CNN-P predictions and thermal stability using 137 therapeutics with melting temperature data from Jain et al. [21] (see SI).”
MethodEngineered protein-small molecule conjugates empower selective enzyme inhibition
Lewis, A. K., Harthorn, A., Johnson, S. M., Lobb, R. R., & Hackel, B. J. (2021) · Cell chemical biology · doi:10.1016/j.chembiol.2021.07.013 · Cited by 12
Hingankar, N. K., Deshpande, S., Das, P., Rizvi, Z. A., Wibmer, C. K., Mashilo, P., Ansari, M. Y., Burns, A., Barman, S., Zhao, F., et al. (2022) · PLoS Pathogens · doi:10.1371/journal.ppat.1010465 · Cited by 14
“The antibody polyreactivity was assessed as described earlier [20,49].”
MethodConformational Entropy as a Potential Liability of Computationally Designed Antibodies
Löhr, T., Sormanni, P., & Vendruscolo, M. (2022) · Biomolecules · doi:10.3390/biom12050718 · Cited by 11
Heads, J. T., Kelm, S., Tyson, K. L., & Lawson, A. D. G. (2022) · mAbs · doi:10.1080/19420862.2022.2138092 · Cited by 12
“* Experimental aggregation propensity sourced from Jain et al.16 (maximum AC SINS scores = 29.6), † Experimental aggregation propensity sourced from by Jain et al.16 (low AC SINS scores ≤2.1) and determined in this work by the PEG aggregation assay, ‡ Experimental aggregation propensity sourced from Bethea et al.37 (s…”
BackgroundMethodInfluentialGopal, R., Fitzpatrick, E., Pentakota, N., Jayaraman, A., Tharakaraman, K., & Capila, I. (2022) · Viruses · doi:10.3390/v14122694 · Cited by 12
Park, S.-W., Jun, S.-Y., Kim, J., & Kim, Y. (2021) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2021.08.021 · Cited by 12
Protein engineering via sequence-performance mapping
McConnell, A., & Hackel, B. J. (2023) · Cell Systems · doi:10.1016/j.cels.2023.06.009 · Cited by 13
Prediction of polyspecificity from antibody sequence data by machine learning
Éliás, S., Wrzodek, C., Deane, C. M., Tissot, A. C., Klostermann, S., & Ros, F. (2024) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1286883 · Cited by 11
“…of assay components that are used to determine polyspeci fi city might provide an explanation for the discordance between different readouts (such as the weak inverse correlation between our model ’ s prediction of polyspeci fi city and the PSR assay from (Jain et al., 2017; Shehata et al., 2019)).”
MethodHeisler, J. C., Kovner, D., Izadi, S., Zarzar, J., & Carter, P. J. (2024) · mAbs · doi:10.1080/19420862.2024.2379560 · Cited by 11
“7 Efforts to establish high-throughput low material methods to predict viscosity experimentally, 8–13 or through sequence-based computational methods, 14–20 have shown moderate correlations, but have yet to yield strong predictive power.”
BackgroundDeep learning-based design and experimental validation of a medicine-like human antibody library
Rajagopal, N., Choudhary, U., Tsang, K., Martin, K., Karadag, M., Chen, H.-T., Kwon, N.-Y., Mozdzierz, J., Horspool, A. M., Li, L., et al. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf023 · Cited by 12
Barlow, K. A., Battles, M. B., Brown, M. E., Canfield, K., Lu, X., Lynaugh, H., Morrill, M. B., Rappazzo, C. G., Reyes, S. P., Sandberg, C., et al. (2025) · mAbs · doi:10.1080/19420862.2025.2479531 · Cited by 11
“Measured HIC retention time remained unaffected after the introduction of constant domain mutations, with no discernible change compared to WT, and all values were within the previously defined low hydrophobicity range of retention times (≤10.5 min) 43 (Supplementary Figure S8a).”
InfluentialSelf-citationDeveloping drug-like single-domain antibodies (VHH) from in vitro libraries
Erasmus, M. F., Teixeira, A. A. R., Molina, E., Carnero, L. A. R., Li, J., Knight, D., Niro, R. D., Leal‐Lopes, C., Fanni, A. M., Troell, H. A., et al. (2025) · mAbs · doi:10.1080/19420862.2025.2516676 · Cited by 11
Datta‐Mannan, A., Brown, R. M., Key, S., Cain, P., & Feng, Y. (2021) · Antibodies · doi:10.3390/antib11010002 · Cited by 11
“, our BsAbs were not considered as undesirable from a PK developability perspective, although there were no measures of evaluation of the pH-dependent dissociation from the FcRn reported [10].”
BackgroundEvers, A., Krah, S., Demir, D., Gaa, R., Elter, D., Schroeter, C., Zielonka, S., Rasche, N., Dotterweich, J., Knuehl, C., et al. (2024) · mAbs · doi:10.1080/19420862.2024.2302386 · Cited by 11
“36 In agreement with the engineering strategy and the experimental HIC data, comparison of the AC-SINS data for the different sequence pairs (Table 1) reveals reduced self-interaction of the CS06 VH6.”
ResultSatława, T., Tarkowski, M., Wróbel, S., Dudzic, P., Gawłowski, T., Klaus, T., Orłowski, M., Kostyn, A., Kumar, S., Buchanan, A. G., et al. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011881 · Cited by 11
PULSE SPR: A High Throughput Method to Evaluate the Domain Stability of Antibodies
Zhou, L., Zhang, J., DiGiammarino, E. L., Kavishwar, A., Yan, B., Chumsae, C. M., Ihnat, P. M., Powers, D., Harlan, J. E., & Stine, W. B. (2018) · Analytical Chemistry · doi:10.1021/acs.analchem.8b03452 · Cited by 11
Kizhedath, A., Karlberg, M., & Glassey, J. (2019) · Biotechnology Journal · doi:10.1002/biot.201800696 · Cited by 11
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Karlberg, M., de Souza, J. V., Fan, L., Kizhedath, A., Bronowska, A. K., & Glassey, J. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21218037 · Cited by 12
“During recent years, multiple high-throughput experimental assays have been designed in order to increase the product knowledge and identify potential problems with the antibody candidates in early development phases [8,9].”
BackgroundMethodMutational and biophysical robustness in a prestabilized monobody
Chandler, P. G., Tan, L. L., Porebski, B. T., Green, J. S., Riley, B. T., Broendum, S. S., Hoke, D. E., Falconer, R. J., Munro, T. P., Buckle, M., et al. (2021) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2021.100447 · Cited by 11
Screening for protein–protein interactions with asymmetrical flow field-flow fractionation
Wahlund, P., Lorenzen, N., & Rischel, C. (2021) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2021.02.026 · Cited by 11
A bispecific IgG format containing four independent antigen binding sites
Ljungars, A., Schiött, T., Mattson, U., Steppa, J., Hambe, B., Semmrich, M., Ohlin, M., Tornberg, U., & Mattsson, M. (2020) · Scientific Reports · doi:10.1038/s41598-020-58150-z · Cited by 11
Burman, L., Chong, Y. E., Duncan, S., Klaus, A., Rauch, K., Hamel, K., Hervé, K., Pfaffen, S., Collins, D. W., Heyries, K. A., et al. (2020) · mAbs · doi:10.1080/19420862.2020.1836718 · Cited by 11
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. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/759498 · Cited by 11
Arsiwala, A., Bhatt, R., van Niekerk, L., Quintero-Cadena, P., Ao, X., Rosenbaum, A., Bhatt, A., Smith, A., Yang, Y., Anderson, K., et al. (2025) · mAbs · doi:10.1080/19420862.2025.2593055 · Cited by 16
“The weak correlation we observe with polyreactivity data of Jain et al. 1 and Shehata et al. 17 is likely due to significant differences in methodology and assay formats.”
InfluentialHyperstable Synthetic Mini-Proteins as Effective Ligand Scaffolds
Blanchard, P. L., Knick, B. J., Whelan, S. A., & Hackel, B. J. (2023) · ACS Synthetic Biology · doi:10.1021/acssynbio.3c00409 · Cited by 10
Case, M., Smith, M., Vinh, J., & Thurber, G. M. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2311726121 · Cited by 13
AbLEF: antibody language ensemble fusion for thermodynamically empowered property predictions
Rollins, Z. A., Widatalla, T., Waight, A. B., Cheng, A. C., & Metwally, E. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae268 · Cited by 9
“…with high antigen affinity is possible via antibody display technologies (Valldorf et al. 2022), the optimization on only antigen binding affinity often compromises other developability properties (Jain et al. 2017; Shehata et al. 2019; Bailly et al. 2020; Fern � andez-Quintero et al. 2023).”
BackgroundAbMelt: Learning antibody thermostability from molecular dynamics
Rollins, Z. A., Widatalla, T., Cheng, A. C., & Metwally, E. (2024) · Biophysical Journal · doi:10.1016/j.bpj.2024.06.003 · Cited by 9
Dudzic, P., Chomicz, D., Kończak, J., Satława, T., Janusz, B., Wrobel, S., Gawłowski, T., Jaszczyszyn, I., Bielska, W., Demharter, S., et al. (2024) · mAbs · doi:10.1080/19420862.2024.2361928 · Cited by 10
“19 A workaround these methods are using is in fact constraining the initial libraries (or search space) to sequences that are more developable, e.g., without any liabilities.”
BackgroundMethodVuong, C. N., Reynolds, K. M., Rivera, G. S. M., Zeng, B., karimpourkalou, Z., Norng, M., Zhang, Y., Chowdhury, R., Pedersen, D., Pantoja, M. A. A., et al. (2024) · mAbs · doi:10.1080/19420862.2024.2435476 · Cited by 10
Siegmund, V., Klewinghaus, D., Teroerde, J., Pekar, L., Harwardt, J., Zielonka, S., Militano, F., Licari, G., & Evers, A. (2025) · mAbs · doi:10.1080/19420862.2025.2553622 · Cited by 10
Improving antibody drug development using bionanotechnology
Makowski, E. K., Schardt, J. S., & Tessier, P. M. (2021) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2021.10.027 · Cited by 10
Smith, M. D., Case, M., Makowski, E. K., & Tessier, P. M. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad446 · Cited by 10
“In particular, antibodies are attractive therapeutic agents due to their high target affinity, low off-target binding, ease of production, and favorable biophysical properties (Tiller and Tessier 2015, Jain et al. 2017).”
BackgroundThe uniqueness of flow in probing the aggregation behavior of clinically relevant antibodies
Willis, L. F., Kumar, A., Jain, T. S., Caffry, I., Xu, Y., Radford, S. E., Kapur, N., Vásquez, M., & Brockwell, D. J. (2020) · Engineering Reports · doi:10.1002/eng2.12147 · Cited by 8
Self-citationHarnessing the Therapeutic Potential of ‘Rogue’ Antibodies
Dimitrov, J. D. (2020) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2020.03.005 · Cited by 10
SSH: A Tool for Predicting Hydrophobic Interaction of Monoclonal Antibodies Using Sequences
Dzisoo, A. M., Kang, J., Yao, P., Klugah‐Brown, B., Mengesha, B. A., & Huang, J. H. (2020) · BioMed Research International · doi:10.1155/2020/3508107 · Cited by 10
“Table 2: Threshold values of 3 assays [30].”
BackgroundAnimal immunization merges with innovative technologies: A new paradigm shift in antibody discovery
Prabakaran, P., Rao, S. P., & Wendt, M. (2021) · mAbs · doi:10.1080/19420862.2021.1924347 · Cited by 11
“approved animal-derived antibodies are proven to have these properties as compared to in vitro-derived antibodies.(14) Gray et al.”
BackgroundZiegengeist, T., Orth, J., Kroll, K., Schneider, M., Spindler, N., Dimova, D., Handschuh, S., Brandenburg, A., Ossola, R., Furtmann, N., et al. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.3c00742 · Cited by 9
Wang, Y., Williams, H. D., Dikicioǧlu, D., & Dalby, P. A. (2024) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.4c00859 · Cited by 9
Thermal and pH stress dictate distinct mechanisms of monoclonal antibody aggregation
Meng, H. K., Pang, K. T., Wan, C., Zheng, Z. Y., Qiu, B., Yang, Y., Zhang, W., Ho, Y. S., Walsh, I., & Chia, S. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.136601 · Cited by 9
Chen, L., Zhu, Y., Feng, M., Zuo, D., Chen, G., & Ji, K. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1599764 · Cited by 9
“Immunization protocols were as follows: BALB/c female mice (6 – 8 weeks old, n=5) received a primary immunization with 50 m g CD16A-His protein mixed with a water-soluble adjuvant (BoaoLong), followed by boosters every two weeks (three total) (28).”
Functional Changes of Therapeutic Antibodies upon Exposure to Pro-Oxidative Agents
Lecerf, M., Lacombe, R. V., Kanyavuz, A., & Dimitrov, J. D. (2022) · Antibodies · doi:10.3390/antib11010011 · Cited by 9
Wollacott, A. M., Robinson, L. N., Ramakrishnan, B., Tissire, H., Viswanathan, K., Shriver, Z. H., & Babcock, G. J. (2019) · Journal of Molecular Recognition · doi:10.1002/jmr.2778 · Cited by 9
“Multiple properties of therapeutic antibodies may require optimization, including affinity, specificity, human sequence identity (humanization), and biophysical and “developability” attributes.(6) Computational structure‐ based design can be applied when a structure or high‐quality model is available, and these approa…”
BackgroundMedina‐Cucurella, A. V., Mizrahi, R. A., Asensio, M. A., Edgar, R. C., Leong, J., Leong, R., Lim, Y. W., Nelson, A. L., Niedecken, A. R., Simons, J. F., et al. (2019) · Antibodies · doi:10.3390/antib8010017 · Cited by 9
“However, most therapeutic antibodies have been discovered in mice [5], perhaps due to difficulties with developability of artificial antibodies, such as low solubility binders discovered in phage display [6,7].”
BackgroundDzisoo, A. M., He, B., Karikari, R., Agoalikum, E., & Huang, J. H. (2019) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-019-00330-1 · Cited by 9
Douillard, P., Freissmuth, M., Antoine, G., Thiele, M., Fleischanderl, D., Matthiessen, P., Voelkel, D., Kerschbaumer, R. J., Scheiflinger, F., & Sabarth, N. (2019) · Antibodies · doi:10.3390/antib8030046 · Cited by 9
Physicochemical determinants of antibody-protein interactions
Karadag, M., Arslan, M., Kaleli, N. E., & Kalyoncu, S. (2019) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2019.08.011 · Cited by 9
Conformational changes in a Vernier zone region: Implications for antibody dual specificity
Arslan, M., Karadağ, D., & Kalyoncu, S. (2020) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25964 · Cited by 9
Peng, X., Gibbs, E., Silverman, J. M., Cashman, N. R., & Plotkin, S. S. (2021) · Statistical Methods in Medical Research · doi:10.1177/09622802211002861 · Cited by 9
Estimation of the Viscosity of an Antibody Solution from the Diffusion Interaction Parameter
Shimomura, T., Sekiguchi, M., Honda, R., Yamazaki, M., Yokoyama, M., & Uchiyama, S. (2022) · Biological and Pharmaceutical Bulletin · doi:10.1248/bpb.b22-00263 · Cited by 9
SUMO: In Silico Sequence Assessment Using Multiple Optimization Parameters
Evers, A., Malhotra, S., Bolick, W.-G., Najafian, A., Borisovska, M., Warszawski, S., Fomekong‐Nanfack, Y., Kühn, D., Rippmann, F., Crespo, A., et al. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3279-6_22 · Cited by 9
Biophysical Characterization of Binary Therapeutic Monoclonal Antibody Mixtures
Krieg, D., Berner, C., Winter, G., & Svilenov, H. L. (2020) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.0c00370 · Cited by 9
Generative Diffusion Models for Antibody Design, Docking, and OptimizationPreprint
Peng, Z., Han, C., Wang, X., Li, D., & Yuan, F. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.25.559190 · Cited by 10
“Furthermore, when improved binding is achieved, there may arise the need for subsequent modifications to enhance other properties, such as hydrophobicity [4].”
BackgroundNyesiga, B., Levin, M., Säll, A., Rosén, A., Jansson, K., Fritzell, S., Hägerbrand, K., Weilguny, D., & von Schantz, L. (2024) · mAbs · doi:10.1080/19420862.2024.2330113 · Cited by 8
“35 Instead, a holistic approach that balances multiple biophysical and physicochemical properties is necessary to select candidate drugs with higher chance to make it to the market and patients.”
BackgroundIntrinsic Differential Scanning Fluorimetry for Protein Stability Assessment in Microwell Plates
Cohrs, M., Davy, A. D., Ackere, M. V., Smedt, S. C. D., Braeckmans, K., Epe, M., & Svilenov, H. L. (2025) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.4c01496 · Cited by 8
Click biology highlights the opportunities from reliable biological reactions
Howarth, M. R. (2025) · Nature Chemical Biology · doi:10.1038/s41589-025-01944-x · Cited by 8
“High charge-density promotes nonspecific interactions: high positive charge favors binding to nucleic acids or phospholipid bilayers 95 .”
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Golinski, A. W., Schmitz, Z. D., Nielsen, G. H., Johnson, B., Saha, D., Appiah, S., Hackel, B. J., & Martiniani, S. (2023) · ACS Synthetic Biology · doi:10.1021/acssynbio.3c00196 · Cited by 7
Determinants of Developability and Evolvability of Synthetic Miniproteins as Ligand Scaffolds
McConnell, A., Batten, S. L., & Hackel, B. J. (2023) · Journal of Molecular Biology · doi:10.1016/j.jmb.2023.168339 · Cited by 8
Bryniarski, M. A., Tuhin, M. T. H., Acker, T. M., Wakefield, D. L., Sethaputra, P., Cook, K. D., Soto, M., Ponce, M., Primack, R., Jagarapu, A., et al. (2024) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2024.06.013 · Cited by 8
Anticalin®-based therapeutics: Expanding new frontiers in drug development
Morales‐Kastresana, A., Siegemund, M., Haak, S., Peper-Gabriel, J. K., Neiens, V., & Rothe, C. (2022) · International review of cytology · doi:10.1016/bs.ircmb.2022.03.009 · Cited by 8
Gaa, R., Kumari, K., Mayer, H. M., Yanakieva, D., Tsai, S., Joshi, S., Guenther, R., & Doerner, A. (2023) · Artificial Cells Nanomedicine and Biotechnology · doi:10.1080/21691401.2023.2173219 · Cited by 8
A mammalian cell display platform based on scFab transposition
Jing, C., Rader, C. M., & Peng, H. (2023) · Antibody Therapeutics · doi:10.1093/abt/tbad009 · Cited by 7
Banks, D. D., & Cordia, J. F. (2021) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.0c01138 · Cited by 8
Human Antibody Bispecifics through Phage Display Selection
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solPredict: Antibody Apparent Solubility Prediction from Sequence by Transfer LearningPreprint
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“However, these assays typically correlate poorly with each other, and have only been shown to somewhat correlate with selected in vivo properties in limited specific examples (17,24,27).”
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“42 – 44 The corresponding antibodies were produced by transient transfection of HC and light chain-containing plasmids into suspension-adapted HEK-293 cells (ATUM.bio).”
A Rapid and Modular Nanobody Assay for Plug-and-Play Antigen Detection
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Generation of bispecific antibodies by structure-guided redesign of IgG constant regions
Iwasaki, Y. W., Tharakaraman, K., Subramanian, V., Khongmanee, A., Hatas, A., Fleischer, E., Rurak, T. T., Ngok-ngam, P., Tit-oon, P., Ruchirawat, M., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1063002 · Cited by 7
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“Therapeutic monoclonal antibodies (mAbs) have become a staple in the current pharmaceutical industry by providing clinical solutions for many highly abundant diseases and as alternative therapies to conventional small molecular drugs [1–4].”
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“It is relatively common for antibodies to display nonspecific interactions at such high concentrations (Jain et al., 2017; Rabia et al., 2018), and the lack of such nonspecific interactions likely explains (at least in part) the excellent performance of our antibodies.”
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Isolating Anti-amyloid Antibodies from Yeast-Displayed Libraries
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“In vitro assays looking at different physicochemical behaviour/properties including non-specific binding, self-interaction polyspecificity and the efficiency of transport across MDCK cells expressing human FcRn have been developed (Kelly et al. 2015; Jain et al. 2017; Chung et al. 2019).”
BackgroundRodriguez‐Aponte, S. A., Dalvie, N. C., Wong, T. Y., Johnston, R. S., Naranjo, C. A., Bajoria, S., Kumru, O. S., Kaur, K., Russ, B. P., Lee, K. S., et al. (2022) · Vaccine · doi:10.1016/j.vaccine.2022.12.062 · Cited by 6
“hydrophobicity during development, which has been shown to be a critical factor associated with clinical success[55,56].”
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“Thermal stability, absence of post-translational modifications and propensity to form aggregates are also very important parameters to consider when developing monoclonal antibodies (51).”
BackgroundMachine learning‐driven protein engineering: a case study in computational drug discovery
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Discussions on the quality of antibodies are no reason to ban animal immunization
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“It also helps to select for important properties that go beyond mere molecular recognition (Jain et al, 2017).”
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Antibody affinity engineering using antibody repertoire data and machine learningPreprint
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Topology Determines DNA Origami Diffusion in Intestinal Mucus
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Antibody Polyreactivity: A Challenger of Immune Paradigms
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“13 Although we did not perform a stringent hierarchical clustering in this case, similar yet slightly different groupings of properties were observed in PCA, including charge-based binding (heparin chromatography, poly-D-lysine assay, PEI ELISA, BVP ELISA, membrane prep assay), hydrophobicity…”
BackgroundMethodAutomated optimization of the solubility of a hyper-stable α-amylase
Ali, M., Greenig, M., Oeller, M., Atkinson, M., Xu, X., & Sormanni, P. (2024) · Open Biology · doi:10.1098/rsob.240014 · Cited by 6
“…material, table S1) are coloured in cyan, and sites mutated in the selected models are in magenta (numbered as in the table). the biophysical landscape of clinical-stage antibodies, adalimumab did not show any self-association or cross-reactivity flags, while golimumab had several [19].”
BackgroundFerrara, F., Fanni, A. M., Teixeira, A. A. R., Molina, E., Leal‐Lopes, C., DeAguero, A., D’Angelo, S., Erasmus, M. F., Spector, L. P., Carnero, L. A. R., et al. (2024) · mAbs · doi:10.1080/19420862.2024.2394230 · Cited by 6
“The 64°C threshold is based on the upper threshold of the 10% worse 137 antibodies from Jain et al. 48 We observed 100% of the antibodies exhibiting a T onset of above 50°C (Figure 10e), and all but one (98.5”
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Systematic Engineering of Optimized Autonomous Heavy-Chain Variable Domains
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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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