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Optimization of therapeutic antibodies for reduced self-association and non-specific binding via interpretable machine learning



Antibody development, delivery, and efficacy are influenced by antibody-antigen affinity interactions, off-target interactions that reduce antibody bioavailability and pharmacokinetics, and repulsive self-interactions that increase the stability of concentrated antibody formulations and reduce their corresponding viscosity. Yet identifying antibody variants with optimal combinations of these three types of interactions is challenging. Here we show that interpretable machine-learning classifiers, leveraging antibody structural features descriptive of their variable regions and trained on experimental data for a panel of 80 clinical-stage monoclonal antibodies, can identify antibodies with optimal combinations of low off-target binding in a common physiological-solution condition and low self-association in a common antibody-formulation condition. For three clinical-stage antibodies with suboptimal combinations of off-target binding and self-association, the classifiers predicted variable-region mutations that optimized non-affinity interactions while maintaining high-affinity antibody-antigen interactions. Interpretable machine-learning models may facilitate the optimization of antibody candidates for therapeutic applications.

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Fig. 1: Approach for predicting non-affinity interactions for clinical-stage antibodies.
Fig. 2: Experimental classification of clinical-stage antibodies with different levels of self-association and non-specific binding.
Fig. 3: Interpretable models for predicting the levels of self-association and non-specific binding for clinical-stage antibodies.
Fig. 4: Prediction of mutations that co-optimize affinity and non-affinity interactions for three clinical-stage antibodies.
Fig. 5: Predicted mutations in clinical-stage antibodies display co-optimized levels of affinity and non-affinity interactions.

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Data availability

The main data supporting the results of this study are available within the paper and its Supplementary Information. Source data for Fig. 5 are provided with this paper.

Code availability

The scripts to generate the main figures and to perform the analysis described in this paper are available at the Tessier lab GitHub repository at


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We thank members of the Tessier lab for helpful suggestions. This work was supported by the Biomolecular Interaction Technology Center, the National Institutes of Health (R35GM136300 to P.M.T., 1T32GM140223-01 to E.K.M., and F32GM137513 to J.S.S.), the National Science Foundation (CBET 1813963, 1605266 and 1804313 to P.M.T.) and the Albert M. Mattocks Chair (to P.M.T.).

Author information

Authors and Affiliations



E.K.M. developed the methodology, with conceptual input from P.M.T. E.K.M., T.W., J.M.Z., L.W., J.H. and J.S.S. performed antibody synthesis and preparation. E.K.M. and J.M.Z. performed antibody characterization. A.D.G., W.D.M. and S.L.E. designed the immunogenicity analysis. E.K.M. performed structural modelling and the computational analyses of antibody sequences. E.K.M. and P.M.T. wrote and revised the manuscript.

Corresponding author

Correspondence to Peter M. Tessier.

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Competing interests

The University of Michigan and Sanofi have filed a patent application for the CS-SINS method (WO 2022/148777 A1 (2022)). P.M.T. is a member of the scientific advisory board for Nabla Bio, and has received honoraria for invited presentations including on this research from GlaxoSmithKline, Bristol Myers Squibb, Janssen and Genentech. The other authors declare no competing interests.

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Nature Biomedical Engineering thanks Charlotte Deane, Sai Reddy and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Correlation analysis between structure-based antibody Fv molecular features and self-association and non-specific binding measurements.

(A) Molecular features from Fv homology models, including hydrophobic (hyd.), positively charged (pos.), negatively charged (neg.), and ionic (positively and negatively charged) features, were found to be correlated to different extents with the self-association (CS-SINS) and non-specific binding (PSP) measurements for the 80 clinical-stage antibodies in this study. (B) Relationship between the CS-SINS and PSP measurements and a subset of key molecular features. In (A), total area features are those that describe the total additive patch area in Å, the largest patch area features are those that describe the area of the single largest patch in Å, the # of patches features are those that describe the additive number of patches larger than 50 Å2, and the % surface area features are those that describe the total patch area relative to the total surface area of the Fv. Moreover, hyd. stands for hydrophobic, hph. stands for hydrophilic, vdw stands for van der Waals, and ASA stands for accessible surface area. In (A-B), independent two-sided t-tests were performed to determine significance and the p-values are < 0.05 (*), < 0.01 (**) and < 0.001 (***).

Extended Data Fig. 2 Application of models to previously reported antibodies with diverse physicochemical properties.

(A) Variants of an antibody, AB-001, with low viscosity (150 mg/mL, 10 mM histidine 5% sucrose buffer, pH 5.8) are well discriminated from variants that display high viscosity using our self-association model (Apgar et al., PLoS One, 2020). (B) Variants of bococizumab with low non-specific binding (as judged by binding to bovine serum albumin) are identified using our non-specific binding model (Dyson et al., mAbs, 2020). (C-D) Variants of emibetuzumab with reduced non-specific binding and modestly increased self-association are correctly described in terms of the direction of the predictions, as the models predict (D) reduced non-specific binding and (C) modest increased in self-association without promoting high self-association (Makowski et al., Nat Commun, 2022). (E) Variants of omalizumab with reduced viscosity (125 mg/mL; 15 mM histidine, pH 6.0) include those that are predicted near the decision boundary for low self-association. In (A), the viscosity measurements are indicated by marker size (larger size indicates higher viscosity). In (C-E), the measurements for the corresponding properties are reported next to the data points, including the (C) PSP scores, (D) CS-SINS scores, and (E) viscosities. Model features are normalized between 0 and 1 for optimized model fitting.

Supplementary information

Main Supplementary Information

Supplementary figures.

Reporting Summary

Supplementary Dataset 1

Summary of the clinical-stage antibodies in this study.

Supplementary Dataset 2

Summary of CS-SINS and PSP data for 80 clinical-stage antibodies.

Supplementary Dataset 3

Description of the 33 features extracted from MOE, with descriptions.

Supplementary Dataset 4

Summary of the calculated features values used in the self-association and non-specific binding models for each antibody.

Supplementary Dataset 5

Summary of model performance for the self-association and non-specific binding models trained using “3*” or “3” features, as explained in Supplementary Fig. 3.

Supplementary Dataset 6

Summary of the impact of single mutations for cinpanemab, gantenerumab and panitumumab on the five antibody features used in the self-association and non-specific binding models.

Source data

Source Data for Fig. 5

CS-SINS, PSP and EC50 measurements for the parental antibodies and variants.

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Makowski, E.K., Wang, T., Zupancic, J.M. et al. Optimization of therapeutic antibodies for reduced self-association and non-specific binding via interpretable machine learning. Nat. Biomed. Eng 8, 45–56 (2024).

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