Correlation Engine 2.0
Clear Search sequence regions


Sizes of these terms reflect their relevance to your search.

Literature data on the clinical pharmacokinetics of various VEGFR-2 inhibitors along with in vitro potency data were correlated and a linear relationship was established in spite of limited data set. In this work, a model set comprised of axitinib, recentin, sunitinib, pazopanib, and sorafenib were used. The in vitro potencies of the model set compounds were correlated with the published unbound plasma concentrations (Cmax, Cavg, Ctrough). The established linear regression (r2>0.90) equation was used to predict Cmax, Cavg, Ctrough of the 'prediction set' (motesanib, telatinib, CP547632, vatalanib, vandetanib) using in vitro potency and unbound protein free fraction. Cavg and Ctrough of prediction set were closely matched (0.2-1.8 fold of reported), demonstrating the usefulness of such predictions for tracking the target related modulation and/or efficacy signals within the clinically optimized population average. In case of Cmax where correlation was least anticipated, the predicted values were within 0.1-1.1 fold of those reported. Such predictions of appropriate parameters would provide rough estimates of whether or not therapeutically relevant dose(s) have been administered when clinical investigations of novel agents of this class are being performed. Therefore, it may aid in increasing clinical doses to a desired level if safety of the compound does not compromise such dose increases. In conclusion, the proposed model may prospectively guide the dosing strategies and would greatly aid the development of novel compounds in this class. © Georg Thieme Verlag KG Stuttgart · New York.

Citation

B Benjamin, M Sahu, U Bhatnagar, D Abhyankar, N R Srinivas. The observed correlation between in vivo clinical pharmacokinetic parameters and in vitro potency of VEGFR-2 inhibitors. Can this be used as a prospective guide for the development of novel compounds? Arzneimittel-Forschung. 2012 Apr;62(4):194-201

Expand section icon Mesh Tags

Expand section icon Substances


PMID: 22290114

View Full Text