Using Avidity to Optimize the Therapeutic Index of Bispecific Drugs


An advantage of monoclonal antibodies (mAbs) as drugs is their high potency and specificity for their target. This greatly reduces the chance of off-target toxicity common to small molecules, where toxicity is often caused by interacting similar targets. As a result toxicity for large molecules is often driven by on-target off-tissue toxicology where the target of the drug is expressed not just on the target cell type, but in other tissues where the same pharmacology is undesirable. Because of their high potency even low expression of the target in other tissues can lead to dose limiting toxicities. 


Bispecific drugs have great potential to improve tissue selectivity through avid binding interactions, but introduce non-trivial drug design parameters that must be considered as part of target selection and lead identification. Applied BioMath Assess™ Avidity model pack supports early feasibility assessment for bispecific modalities. This case study demonstrates how to use Applied BioMath Assess™ to identify the level of avidity required for a drug to have a favorable efficacy and therapeutic index. It also illustrates how drug design decisions can benefit from modeling and simulation due to non-trivial impacts on drug behavior.

Design Challenges with Using Avidity to Drive Tissue Selectivity

Taking advantage of this strategy is a difficult balance

Designing a drug to take advantage requires balancing competing properties. Many of the drug parameters such as affinity (Kd) have a similar impact on on-target and off-target tissues. Increasing the affinity for the efficacy target will increase activity in both target and the disease compartment. However, reducing the Kd too low will spare the tox compartment but will also eliminate the activity in the disease compartment.

Avidity to the rescue

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, avidity models describe the enhanced on-rate of binding due to higher local receptor concentrations at the membrane surface.

Using Applied BioMath Assess™ to determine the feasibility of achieving the desired efficacy at an acceptable TI

Because the optimal drug properties depend on many factors such as target and drug distribution, affinity, avidity, and pharmacokinetics it is not practical to screen large libraries of bsAbs to find ones with optimal properties. Instead we perform a virtual screen using Applied BioMath Assess™ to establish the feasibility of using this targeting approach to develop a bsAb.

Simulations and Analysis

Simulations and analyses



The requirements of efficacy and specificity (TI) greatly limit the feasible drug target space, as it’s rare for targets to have strong target to disease linkage, and be specific only to the desired tissues and cells. The use of bsAb provides an opportunity to combine pairs targets that can get efficacy from one target and specificity from the other. Designing a drug to take advantage requires balancing competing properties. Many of the drug parameters such as affinity (Kd) have a similar impact on on-target and off target tissues. Applied BioMath Assess™ provides provides models and a computational workflows to support this challenging design problem. In particular, we showed that we could identify the Avidity requirements for a bsAb with sufficient TI.


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