A novel application of t-statistics to objectively assess the quality of IC(50) fits for P-glycoprotein and other transporters

Current USFDA and EMA guidance for drug transporter interactions is dependent on IC(50) measurements as these are utilized in determining whether a clinical interaction study is warranted. It is therefore important not only to standardize transport inhibition assay systems but also to develop uniform statistical criteria with associated probability statements for generation of robust IC(50) values, which can be easily adopted across the industry. The current work provides a quantitative examination of critical factors affecting the quality of IC(50) fits for P-gp inhibition through simulations of perfect data with randomly added error as commonly observed in the large data set collected by the P-gp IC(50) initiative. The types of errors simulated were (1) variability in replicate measures of transport activity; (2) transformations of error-contaminated transport activity data prior to IC(50) fitting (such as performed when determining an IC(50) for inhibition of P-gp based on efflux ratio); and (3) the lack of well defined “no inhibition” and “complete inhibition” plateaus. The effect of the algorithm used in fitting the inhibition curve (e.g., two or three parameter fits) was also investigated. These simulations provide strong quantitative support for the recommendations provided in Bentz et al. (2013) for the determination of IC(50) values for P-gp and demonstrate the adverse effect of data transformation prior to fitting. Furthermore, the simulations validate uniform statistical criteria for robust IC(50) fits in general, which can be easily implemented across the industry. A calibration of the t-statistic is provided through calculation of confidence intervals associated with the t-statistic.