A Systematic Review of the Efficacy of Preclinical Models of Lung Cancer Drugs

• Main Authors: Elizabeth Pan, David Bogumil, Victoria Cortessis, Sherrie Yu, Jorge Nieva
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-04-01
• Series: Frontiers in Oncology
• Subjects: lung cancer; preclinical studies; mouse models; lung cancer therapies; cell models
• Online Access: https://www.frontiersin.org/article/10.3389/fonc.2020.00591/full

Background: Preclinical cell models are the mainstay in the early stages of drug development. We sought to explore the preclinical data that differentiated successful from failed therapeutic agents in lung cancer.Methods: One hundred thirty-four failed lung cancer drugs and twenty seven successful lung cancer drugs were identified. Preclinical data were evaluated. The independent variable for cell model experiments was the half maximal inhibitory concentration (IC50), and for murine model experiments was tumor growth inhibition (TGI). A logistic regression was performed on quartiles (Q) of IC50s and TGIs.Results: We compared odds of approval among drugs defined by IC50 and TGI quartile. Compared to drugs with preclinical cell experiments in highest IC50 quartile (Q4, IC50 345.01–100,000 nM), those in Q3 differed little, but those in the lower two quartiles had better odds of being approved. However, there was no significant monotonic trend identified (P-trend 0.4). For preclinical murine models, TGI values ranged from −0.3119 to 1.0000, with a tendency for approved drugs to demonstrate poorer inhibition than failed drugs. Analyses comparing success of drugs according to TGI quartile produced interval estimates too wide to be statistically meaningful, although all point estimates accord with drugs in Q2-Q4 (TGI 0.5576–0.7600, 0.7601–0.9364, 0.9365–1.0000) having lower odds of success than those in Q1 (−0.3119–0.5575).Conclusion: There does not appear to be a significant linear trend between preclinical success and drug approval, and therefore published preclinical data does not predict success of therapeutics in lung cancer. Newer models with predictive power would be beneficial to drug development efforts.