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. 2017 Jan 31;114(5):944-949.
doi: 10.1073/pnas.1616408114. Epub 2017 Jan 17.

Biophysical Properties of the Clinical-Stage Antibody Landscape

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Free PMC article

Biophysical Properties of the Clinical-Stage Antibody Landscape

Tushar Jain et al. Proc Natl Acad Sci U S A. .
Free PMC article

Abstract

Antibodies are a highly successful class of biological drugs, with over 50 such molecules approved for therapeutic use and hundreds more currently in clinical development. Improvements in technology for the discovery and optimization of high-potency antibodies have greatly increased the chances for finding binding molecules with desired biological properties; however, achieving drug-like properties at the same time is an additional requirement that is receiving increased attention. In this work, we attempt to quantify the historical limits of acceptability for multiple biophysical metrics of "developability." Amino acid sequences from 137 antibodies in advanced clinical stages, including 48 approved for therapeutic use, were collected and used to construct isotype-matched IgG1 antibodies, which were then expressed in mammalian cells. The resulting material for each source antibody was evaluated in a dozen biophysical property assays. The distributions of the observed metrics are used to empirically define boundaries of drug-like behavior that can represent practical guidelines for future antibody drug candidates.

Keywords: biophysical properties; developability; manufacturability; monoclonal antibody; nonspecificity.

Conflict of interest statement

All of the authors are employed by Adimab, LLC, whose business is the discovery of antibody drugs.

Figures

Fig. 1.
Fig. 1.
Histograms of 12 different biophysical assay values for 137 monoclonal antibodies in commercial clinical development. An arrow above each panel indicates the direction of unfavorable values (e.g., higher PSR is unfavorable, because it indicates greater nonspecific binding activity by the antibody assayed.) Most of the distributions are asymmetrically long-tailed in the unfavorable direction.
Fig. 2.
Fig. 2.
(A) Hierarchical clustering of biophysical properties. (B) Matrix and clustering representation of biophysical properties. The lower triangle shows Spearman correlation coefficients, and the upper triangle shows a graphic representation of the same correlation values. The values for SGAC100 were negated before calculating the clustering and correlation coefficients, because its direction of favorability is opposite to HIC and SMAC. The eccentricity of the ellipses is proportional to the magnitude of the correlation coefficient. The slope of the major axis has the same sign as the correlation coefficient.
Fig. 3.
Fig. 3.
(A) Histogram showing number of flags as a function of antibody status in the clinic. For each antibody, a cluster of biophysical properties contributes a value of one to the number of flags if any constituent assay exceeds the thresholds listed in Table 1. (B) Statistical significance analysis of flags per cluster of biophysical properties as a function of clinical progression. (C) Histogram showing number of flags as a function of origin of antibody; +Phage indicates antibodies discovered directly or assisted by phage selection or screening.
Fig. S1.
Fig. S1.
Histogram showing number of flags as a function of antibody status in the clinic for the entire set of 137 and for the 77 IgG1 subset.
Fig. 4.
Fig. 4.
Clustering of antibodies based on biophysical properties. The rectangles are ordered by decreasing cluster size. The approved, phase-3, and phase-2 antibodies are shown in red, brown, and green, respectively. The colors in the clustering matrix follow the same scale as in Fig. 2B.
Fig. S2.
Fig. S2.
Histogram of number of flags as a function of antibody clusters.
Fig. S3.
Fig. S3.
(A) Comparison of HIC and SMAC retention time (minutes) for antibody clusters defined in Fig. 4. (B) Comparison of AS, ELISA, and BVP for antibody clusters. (C) Comparison of PSR and CIC retention times for antibody clusters. (D) Comparison of CSI-BLI responses and AC-SINS wavelength shifts for antibody clusters.
Fig. S4.
Fig. S4.
Clustering of antibodies using an alternative choice of parameters.
Fig. S5.
Fig. S5.
Example of (A) 90% threshold, or (B) 80% threshold difference between approved vs. phase-2 and phase-3 set.

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