A New Approach to Determining Drug Schedules Helen Moore 1, Cherry Lei 2, and Nelson ‘Shasha’ Jumbe 1 1 Modeling & Simulation; 2 Non-clinical Biostatistics; Genentech, Inc., South San Francisco, CA Analysis of the simulated datasets yielded distinctive signature PD profiles for drug effects correlating with the various PK efficacy drivers considered. There are distinct patterns in plots of E for different molecules, but patterns in plots of E’ make the distinctions even clearer. −The t > threshold driver can be distinguished from AUC and Cmax by the characteristic spikes in the plot of E’. The spikes show rapid loss of efficacy, suggesting drug concentration has dropped below an efficacious level. −The AUC driver can be distinguished from Cmax by the change in direction in the plot of E’. Cmax loses effect most rapidly at the beginning, while accumulation of AUC delays the most rapid loss of effect in the case of an AUC driver of efficacy. Each column in the figure above shows simulated data from a dose group (10 mg/kg) of 20 animals for one molecule. This dose group was in the dynamic range of response for each dataset. 1. Tumor volume growth curves similar to those shown on the top row can be obtained from single-dose, dose-ranging experiments. 2. Drug effect (E) was defined as –growth rate, and growth rate was estimated by the slope between values of ln(TV): 3. The key idea is to also examine E’, the derivative of E. E’ was approximated by computing the slope between values of E: A simulated two-compartment PK profile for a hypothetical therapeutic antibody was used as a forcing function for all datasets. Pharmacodynamic (PD) datasets were simulated for which drug effect was correlated with AUC, Cmax, or t > threshold. Each simulated dataset represented results of a single-dose, dose-ranging 21-day experiment for one molecule in a mouse model of cancer. Doses of 0, 1, 5, 10, 25, and 50 mg/kg, with 20 animals per group, were used. The datasets were blinded before being handed over for the analysis. SAS® and Spotfire® were used to perform the analysis. This method should be validated using real tumor volume data for molecules and tumor models for which PK drivers of efficacy have been previously determined. If successfully validated, this method could support dose schedule decisions, potentially eliminating additional studies. THESISDATA AND ANALYSIS SUMMARY Drug dose schedules can affect drug efficacy. Pharmacokinetic (PK) parameters that correlate with drug efficacy, “PK drivers of efficacy”, are used to determine dose schedules that achieve best efficacy. Key parameters considered here are the maximum concentration (Cmax), area under the concentration curve (AUC), and the amount of time the concentration is above a given threshold level (t > threshold). The following guidelines give schedule recommendations for best efficacy, if the indicated PK parameter is the one with highest correlation with efficacy: Cmax: large, infrequent doses; t > threshold: small, frequent doses; AUC: there is flexibility in selecting the schedule. Infusion or dose-fractionation experiments (after single-dose, dose-ranging studies) have been performed pre-clinically in the past to determine schedules that maximize efficacy. Using simulated data, we explore a new approach that may instead allow determination of PK drivers of efficacy from single-dose, dose-ranging studies, thus reducing the number of studies necessary to determine dosing schedules. RESULTS METHODS We propose a new method that uses data from single-dose, dose-ranging studies to distinguish between these PK drivers of efficacy: AUC, Cmax, and t > threshold. The method consists of using derivatives of efficacy, and is demonstrated here applied to simulated tumor volume data. FUTURE DIRECTIONS