4.6 Article

A two-tiered physiologically based model for dually labeled single-chain Fv-Fc antibody fragments

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MOLECULAR CANCER THERAPEUTICS
卷 5, 期 6, 页码 1550-1558

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AMER ASSOC CANCER RESEARCH
DOI: 10.1158/1535-7163.MCT-06-0072

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  1. NCI NIH HHS [P01 CA43904, 5-T32-CA009120-28] Funding Source: Medline

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Monoclonal antibodies (mAb) are being used at an increasing rate in the treatment of cancer, with current efforts focused on developing engineered antibodies that exhibit optimal biodistribution profiles for imaging and/or radioimmunotherapy. We recently developed the single-chain Fv-Fc (scFv-Fc) mAb, which consists of a single-chain antibody Fv fragment (light-chain and heavy-chain variable domains) coupled to the IgG1 Fc region. Point mutations that attenuate binding affinity to FcRn were introduced into the Fc region of the wild-type scFv-Fc mAb, resulting in several new antibodies, each with a different half-life. Here, we describe the construction of a two-tiered physiologically based pharmacokinetic model capable of simulating the apparent biodistribution of both In-111- and I-125-labeled scFv-Fc mAbs, where In-111-labeled metabolites from degraded In-111-labeled mAbs tend to become trapped within the lysosomal compartment, whereas free 1 251 from degraded I-125-labeled mAbs is quickly eliminated via the urinary pathway. The different concentration-time profiles of In-111- and 125 I-labeled mAbs permits estimation of the degradation capacity of each organ and elucidates the dependence of cumulative degradation in liver, muscle, and skin on FcRn affinity and tumor mass. Liver is estimated to account for similar to 50% of all degraded mAb when tumor is small (similar to 0. 1 g) and drops to about 35% when tumor mass is larger (similar to 0.3 g). mAb degradation in residual carcass (primarily skin and muscle) decreases from similar to 45% to 16% as FcRn affinity of the three mAb variants under consideration increases. In addition, elimination of a small amount of mAb in the kidneys is shown to be required for a successful fit of model to data.

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