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Title: Dosing Strategies of Drugs with Narrow Therapeutic Windows: A simple Approach to Determine Trade-off Between Efficacy and Toxicity Authors: Mohamad-Samer Mouksassi* (1,2), Chunlin Chen (1,2), Jean-François Marier (1) Institutions: (1) Pharsight Corporation, Reporting and Analysis Services, Montreal, Canada (2) Université de Montréal, Faculté de pharmacie, Montreal, Canada Objectives: Dosing strategies of drugs with narrow therapeutic windows are difficult to develop and apply in clinical practice. Population pharmacokinetics offers the possibility of explaining the between subject variability (BSV) with patient-specific covariates. The objective was to develop a quick method to determine trade-offs between efficacy and toxicity in order to design better dosing strategies given a BSV of a drug with narrow therapeutic window. Methods: The methodology will be illustrated using a Drug X, with a pre-defined AUC therapeutic window of 220 and 360 mg.h/L. Assuming that a population pharmacokinetics analyses showed that BSA is a significant covariate on clearance (Power model, CL=TVCL.(BSA/1.1)1.2), and that the unexplained BSV on AUC was 38%, an original dosing regimen of 0.8 and 1.0 mg/m2 was developed for patients with BSA < 1.2 m2 and > 1.2 m2, respectively. This dosing regimen resulted in a median AUC of 200 mg.h/L and a percentage of patients in the sub-therapeutic, therapeutic and toxic range of 53%, 41% and 6%, respectively. The first method (Method I) to estimate an optimal dosing strategy was previously described by Jonsson et al.1 Optimal cutoffs and doses to use were estimated with 2, 3, and 5 BSA cutoffs. The loss function was chosen to be quadratic, symmetrical and centered on the middle of the therapeutic window (i.e: 290 mg.h/L). Each estimated dosing strategy was then tested by simulation (n=200), incorporating parameter uncertainty, and the percentage of patients within subtherapeutic, therapeutic and toxic ranges were computed. The second method (Method II) used the area under the log-normal distribution of AUC. The dosing strategy was assumed to change the location parameter (translation on the x-axis) but not the variance. Percentage of the patient population within sub-therapeutic, therapeutic and toxic ranges were computed and plotted versus the targeted AUC. The optimal AUC was deduced graphically as a trade-off between the population of patients in therapeutic and toxic ranges. Results: For Method I, optimal cutoffs for 2, 3, and 5 dose levels the percentages of patients within the subtherapeutic, therapeutic and toxic ranges are presented in Figure 1. As the number of cutoffs increased, the distribution of AUC was more centered on the target (i.e., the middle of the therapeutic range). However, because of the large variability, the percentage of patients in the toxic range did not necessarily decrease. RESULTS FOR 200 SIMULATIONS FIVE DOSES REGIMEN RESULTS FOR 200 SIMULATIONS ORIGINAL DOSE REGIMEN 46% 48% 6% 56% 19% Density Density 25% 0 100 200 100 UNDERDOSED 300 AUC THERAPEUTIC 400 500 TOXIC 6 19 80 24 60 48 56 20 40 55 46 25 0 21 2 3 Number of Doses 5 0 100 200 300 400 500 AUC Figure 1: Upper left, densities of AUC with the original dosing regimen. Upper right, densities of AUC with 5 dosing levels. Lower Left, barplot summarizing the results of the different regimens tested. Green areas display the therapeutic window or percentage of therapeutic patients. Blue and red areas display subtherapeutic and toxic areas or patient populations within sub-therapeutic or toxic ranges, respectively. Figure 2: The green line (therapeutic patients), the blue line (sub-therapeutic patients) and the red line (toxic patients) versus a range of possible target AUC’s. A red horizontal line was drawn at 10%. The target should be at most 220 mg.h/L in order to keep the percentage of patients with toxicity below 10%. The current targeted AUC would result in 40% of the population in the therapeutic zone (green horizontal line) 0% Percentage of patients 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Method II calculated the percentages of patients within the sub-therapeutic, therapeutic and toxic range as a function of the target AUC (Figure 2). This approach helps to visualize the percentage of patients in the therapeutic and toxic range for any targeted AUC. Furthermore, this helps to optimize the targeted AUC according to pre-defined acceptable percentage of patients within the toxic range. Figure 2 shows that a target AUC of 290 mg.h/L would result in the highest percentage of patients within the therapeutics range while toxicity would increase to more than 20%. This is consistent with the results of Method 1. An alternative to Method II would be to conduct Method I with more complex loss function with a penalty if a patient falls within the toxic range. 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 Target AUC Conclusions: Method I and II are complementary. Before conducting Method I, we suggest to determine graphically the best targeted AUC in order to determine the trade-off between patients in therapeutic and toxic ranges. Once an optimal target AUC is determined, Method I can be used to estimate the optimal cutoffs and doses that will center the distribution of AUC over the optimal target. References: [1] Jonsson S, Karlsson MO. A rational approach for selection of optimal covariate-based dosing strategies. Clin Pharmacol Ther. 2003 Jan;73(1):7-19.