PURPOSE: Immunodeficient mice transplanted with subcutaneous tumors (xenograft or allograft) are widely used as a model of preclinical activity for the discovery and development of anticancer drug candidates. Despite their widespread use, there is a widely held view that these models provide minimal predictive value for discerning clinically active versus inactive agents. To improve the predictive nature of these models, we have carried out a retrospective population pharmacokinetic-pharmacodynamic (PK-PD) analysis of relevant xenograft/allograft efficacy data for eight agents (molecularly targeted and cytotoxic) with known clinical outcome. EXPERIMENTAL DESIGN: PK-PD modeling was carried out to first characterize the relationship between drug concentration and antitumor activity for each agent in dose-ranging xenograft or allograft experiments. Next, simulations of tumor growth inhibition (TGI) in xenografts/allografts at clinically relevant doses and schedules were carried out by replacing the murine pharmacokinetics, which were used to build the PK-PD model with human pharmacokinetics obtained from literature to account for species differences in pharmacokinetics. RESULTS: A significant correlation (r = 0.91, P = 0.0008) was observed between simulated xenograft/allograft TGI driven by human pharmacokinetics and clinical response but not when TGI observed at maximum tolerated doses in mice was correlated with clinical response (r = 0.36, P = 0.34). CONCLUSIONS: On the basis of these analyses, agents that led to greater than 60% TGI in preclinical models, at clinically relevant exposures, are more likely to lead to responses in the clinic. A proposed strategy for the use of murine subcutaneous models for compound selection in anticancer drug discovery is discussed.
PURPOSE: Immunodeficient mice transplanted with subcutaneous tumors (xenograft or allograft) are widely used as a model of preclinical activity for the discovery and development of anticancer drug candidates. Despite their widespread use, there is a widely held view that these models provide minimal predictive value for discerning clinically active versus inactive agents. To improve the predictive nature of these models, we have carried out a retrospective population pharmacokinetic-pharmacodynamic (PK-PD) analysis of relevant xenograft/allograft efficacy data for eight agents (molecularly targeted and cytotoxic) with known clinical outcome. EXPERIMENTAL DESIGN: PK-PD modeling was carried out to first characterize the relationship between drug concentration and antitumor activity for each agent in dose-ranging xenograft or allograft experiments. Next, simulations of tumor growth inhibition (TGI) in xenografts/allografts at clinically relevant doses and schedules were carried out by replacing the murine pharmacokinetics, which were used to build the PK-PD model with human pharmacokinetics obtained from literature to account for species differences in pharmacokinetics. RESULTS: A significant correlation (r = 0.91, P = 0.0008) was observed between simulated xenograft/allograft TGI driven by human pharmacokinetics and clinical response but not when TGI observed at maximum tolerated doses in mice was correlated with clinical response (r = 0.36, P = 0.34). CONCLUSIONS: On the basis of these analyses, agents that led to greater than 60% TGI in preclinical models, at clinically relevant exposures, are more likely to lead to responses in the clinic. A proposed strategy for the use of murine subcutaneous models for compound selection in anticancer drug discovery is discussed.
Authors: S Torok; A Vegvari; M Rezeli; T E Fehniger; J Tovari; S Paku; V Laszlo; B Hegedus; A Rozsas; B Dome; G Marko-Varga Journal: Br J Pharmacol Date: 2015-01-12 Impact factor: 8.739
Authors: Dejan Juric; Ian Krop; Ramesh K Ramanathan; Timothy R Wilson; Joseph A Ware; Sandra M Sanabria Bohorquez; Heidi M Savage; Deepak Sampath; Laurent Salphati; Ray S Lin; Huan Jin; Hema Parmar; Jerry Y Hsu; Daniel D Von Hoff; José Baselga Journal: Cancer Discov Date: 2017-03-22 Impact factor: 39.397
Authors: Leanne C Sayles; Marcus R Breese; Amanda L Koehne; Stanley G Leung; Alex G Lee; Heng-Yi Liu; Aviv Spillinger; Avanthi T Shah; Bogdan Tanasa; Krystal Straessler; Florette K Hazard; Sheri L Spunt; Neyssa Marina; Grace E Kim; Soo-Jin Cho; Raffi S Avedian; David G Mohler; Mi-Ok Kim; Steven G DuBois; Douglas S Hawkins; E Alejandro Sweet-Cordero Journal: Cancer Discov Date: 2018-09-28 Impact factor: 39.397
Authors: Alison M Betts; Nahor Haddish-Berhane; John Tolsma; Paul Jasper; Lindsay E King; Yongliang Sun; Subramanyam Chakrapani; Boris Shor; Joseph Boni; Theodore R Johnson Journal: AAPS J Date: 2016-05-19 Impact factor: 4.009