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The AAPS Journal

, 21:16 | Cite as

Survival Prolongation Index as a Novel Metric to Assess Anti-Tumor Activity in Xenograft Models

  • Fiona ChandraEmail author
  • Lihi Zaks
  • Andy Zhu
Research Article
  • 196 Downloads

Abstract

A single efficacy metric quantifying anti-tumor activity in xenograft models is useful in evaluating different tumors’ drug sensitivity and dose-response of an anti-tumor agent. Commonly used metrics include the ratio of tumor volume in treated vs. control mice (T/C), tumor growth inhibition (TGI), ratio of area under the curve (AUC), and growth rate inhibition (GRI). However, these metrics have some limitations. In particular, for biologics with long half-lives, tumor volume (TV) of treated xenografts displays a delay in volume reduction (and in some cases, complete regression) followed by a growth rebound. These observed data cannot be described by exponential functions, which is the underlying assumption of TGI and GRI, and the fit depends on how long the tumor volumes are monitored. On the other hand, T/C and TGI only utilizes information from one chosen time point. Here, we propose a new metric called Survival Prolongation Index (SPI), calculated as the time for drug-treated TV to reach a certain size (e.g., 600 mm3) divided by the time for control TV to reach 600mm3 and therefore not dependent on the chosen final time point tf. Simulations were conducted under different scenarios (i.e., exponential vs. saturable growth, linear vs. nonlinear kill function). For all cases, SPI is the most linear and growth-rate independent metric. Subsequently, a literature analysis was conducted using 11 drugs to evaluate the correlation between pre-clinically obtained SPI and clinical overall response. This retrospective analysis of approved drugs suggests that a predicted SPI of 2 is necessary for clinical response.

KEY WORDS

antitumor activity tumor growth inhibition xenograft 

Notes

Supplementary material

12248_2018_284_MOESM1_ESM.docx (94 kb)
ESM 1 (DOCX 93 kb)

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Copyright information

© American Association of Pharmaceutical Scientists 2019

Authors and Affiliations

  1. 1.Translation Modeling and Simulation, DMPKTakeda PharmaceuticalsCambridgeUSA
  2. 2.Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaUSA

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