Applied BioMath Assess™ can be used to optimize the design of CD3 bispecific T-cell engagers by evaluating the risk of on-target, off-tumor toxicities and to make an early prediction about therapeutic index and projections of effective and tolerable doses for T-cell engagers in solid tumors. In this case study, we will demonstrate the ability to predict the on-target, off-tumor toxicity observed for solitomab, a drug that failed to meet its end points due to dose limiting toxicity.

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.

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.

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Abstract

Applied BioMath Assess™ can be used to evaluate the risk of on-target, off-tumor toxicities for CD3 bispecific T-cell engagers and to make an early prediction about therapeutic index and projections of effective and tolerable doses for T-cell engagers in solid tumors. In this case study, we will demonstrate the ability to predict the on-target, off-tumor toxicity observed for solitomab. We will use this model to identify properties of tumor-associated antigens that could be more successfully targeted with a TCE.

Abstract 

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 i

Abstract

This case study uses Applied BioMath Assess™ to establish the feasibility and dose projections for an mRNA enzyme replacement therapy that produces liver-specific protein glucuronosyltransferase family 1 member A1 (UGT1A1), an enzyme needed to clear bilirubin. Such therapies often need to be chronically administered to maintain efficacious outcome.

Abstract

Model-informed drug discovery and development (MID3) uses mathematical modeling to inform decision-making in drug development programs. Applying MID3 early in development can reduce late-stage risk by determining feasibility of drugging a given target, prioritizing between targets, or defining optimal drug properties for a target product profile. However, the lack of pharmacokinetic (PK) and pharmacodynamic (PD) data available at early stages can make modeling a challenge.