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Chi factor (bispecific), Cis avidity chi factor, Trans avidity chi factor

Avidity describes the enhanced strength of interaction resulting from multiple affinities of single non-covalent binding interactions. For antibodies binding to multiple target receptors on a cell surface, through bivalent or bispecific interactions, avidity models describe the enhanced on-rate due to higher local receptor concentrations at the membrane surface (Kaufman and Jain 1992). For T-cell engagers (TCE's), avidity models describe the enhanced on-rate of crosslinking interactions due to localization in the immune synapse.

The classic model of avidity is the "chi factor" model, where the chi factor is a unitless multiplicative factor quantifying the enhancement of the second (and all subsequent) binding steps' on-rate relative to the first step (Harms et al. 2012). In Assess, the avidity model captures the two-dimensional geometry of the second binding step. For cis-avidity, these interactions are confined to the cell membrane. For trans-avidity, these interactions are confined to the 2-D environment of the immune synapse. This is parameterized via a standardized chi factor, which is based on the chi factor observed under a standard set of conditions.

Avidity is drug-specific and difficult to predict a priori. However, scanning across a range of standardized chi factors can help inform the importance of characterizing avidity during candidate screening/selection.

Typical Values and Interpretation

\(\chi^{\circ}\) Interpretation
0 Single-arm binding
1 Identical and independent binding at the standard condition
300-300,000 Typically observed values for mAbs
28,800 Measured value for CD2-CD58 interaction (Tolentino et al. 2008)


  • Kaufman, E. N., and R. K. Jain. 1992. "Effect of Bivalent Interaction Upon Apparent Antibody-Affinity - Experimental Confirmation of Theory Using Fluorescence Photobleaching and Implications for Antibody-Binding Assays." Cancer Research 52 (15): 4157–67.

  • Harms, Brian D., Jeffrey D. Kearns, Stephen V. Su, Neeraj Kohli, Ulrik B. Nielsen, and Birgit Schoeberl. 2012. "Optimizing Properties of Antireceptor Antibodies Using Kinetic Computational Models and Experiments." Methods in Enzymology 502: 67–87.

  • Tolentino, Timothy P., Jianhua Wu, Veronika I. Zarnitsyna, Ying Fang, Michael L. Dustin, and Cheng Zhu. 2008. "Measuring Diffusion and Binding Kinetics by Contact Area FRAP." Biophysical Journal 95 (2): 920–30.