Evaluating the Use of Antibody Variable Region (Fv) Charge as a Risk Assessment Tool for Predicting Typical Cynomolgus Monkey Pharmacokinetics

评估抗体可变区 (Fv) 电荷作为预测典型食蟹猴药代动力学风险评估工具的用途

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作者:Daniela Bumbaca Yadav, Vikas K Sharma, Charles Andrew Boswell, Isidro Hotzel, Devin Tesar, Yonglei Shang, Yong Ying, Saloumeh K Fischer, Jane L Grogan, Eugene Y Chiang, Konnie Urban, Sheila Ulufatu, Leslie A Khawli, Saileta Prabhu, Sean Joseph, Robert F Kelley

Abstract

The pharmacokinetic (PK) behavior of monoclonal antibodies in cynomolgus monkeys (cynos) is generally translatable to that in humans. Unfortunately, about 39% of the antibodies evaluated for PKs in cynos have fast nonspecific (or non-target-mediated) clearance (in-house data). An empirical model relating variable region (Fv) charge and hydrophobicity to cyno nonspecific clearance was developed to gauge the risk an antibody would have for fast nonspecific clearance in the monkey. The purpose of this study was to evaluate the predictability of this empirical model on cyno nonspecific clearance with antibodies specifically engineered to have either high or low Fv charge. These amino acid changes were made in the Fv region of two test antibodies, humAb4D5-8 and anti-lymphotoxin α. The humAb4D5-8 has a typical nonspecific clearance in cynos, and by making it more positively charged, the antibody acquires fast nonspecific clearance, and making it less positively charged did not impact its clearance. Anti-lymphotoxin α has fast nonspecific clearance in cynos, and making it more positively charged caused it to clear even faster, whereas making it less positively charged caused it to clear slower and within the typical range. These trends in clearance were also observed in two other preclinical species, mice and rats. The effect of modifying Fv charge on subcutaneous bioavailability was also examined, and in general bioavailability was inversely related to the direction of the Fv charge change. Thus, modifying Fv charge appears to impact antibody PKs, and the changes tended to correlate with those predicted by the empirical model.

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