A Model-Informed Drug Development Approach to Design a Phase 3 Trial of Teverelix Drug Product in Advanced Prostate Cancer Patients with Increased Cardiovascular Risk.

Teverelix drug product (DP) is a parenteral gonadotropin-releasing hormone (GnRH) antagonist that has been successfully tested in phase 2 trials for hormone-sensitive advanced prostate cancer (APC) and benign prostatic hyperplasia (BPH). In previous APC trials, teverelix DP was administered as intramuscular (IM) and subcutaneous (SC) injections, using a loading dose and (in a single trial) a maintenance dose. Our objective was to derive an optimal dosing regimen for phase 3 clinical development, using a pharmacometrics modeling approach. Data from 9 phase 2 studies (229 patients) was utilized to develop a population pharmacokinetic (PK) model that described the concentration profile accommodating both IM and SC routes of administration. A 2-compartment model with sequential first-order absorption (slow and fast) and lag times best described the PK profiles of teverelix following SC and IM administration. An indirect response model with inhibition of production rate was fit to describe testosterone (T) concentrations based on physiological relevance. The final population PK-pharmacodynamic model was used to conduct simulations of various candidate dosing regimens to select the optimal dosing regimen to achieve clinical castration (T < 0.5 ng/mL by day 28) and to sustain clinical castration for 26 weeks. Model simulation showed that a loading dose of 360 mg SC and 180 mg IM with a maintenance dose of 360 mg SC 6-weekly (Q6W) starting at day 28 can achieve a ≥95% castration rate up to 52 weeks. This dose regimen was selected for phase 3 clinical development, which includes cardiovascular safety assessment in comparison to a GnRH agonist.

Population pharmacokinetics of primaquine and the effect of hepatic and renal dysfunction: An exploratory approach.

OBJECTIVES: We attempted to develop a population pharmacokinetic model for primaquine (PQ) and evaluate the effect of renal and hepatic dysfunction on PQ pharmacokinetics. MATERIALS AND METHODS: The data were collected from a prospective, nonrandomized clinical study in healthy volunteers and patients with mild-moderate hepatic dysfunction and renal dysfunction. Model development was conducted using NONMEM® software, and parameter estimation was conducted using first-order conditional estimation with interaction method. RESULTS: Final data included a total of 53 study participants (13 healthy individuals, 12 with mild hepatic dysfunction, 6 with moderate hepatic dysfunction, and 22 with renal dysfunction) with 458 concentrations records. Absorption rate constant (Ka) was constrained to be higher than elimination rate constant to avoid flip-flop situation. Mild hepatic dysfunction was a significant covariate on volume of distribution, and it is approximately three folds higher compared to other subjects. Fixed effects parameter estimates of the final model - absorption rate constant (Ka), volume of distribution (V), and clearance (CL) - were 0.95/h, 498 L, and 39 L/h, respectively. Between-subject variability estimates (% CV) on Ka, V, and CL were 77, 66, and 65, respectively. Residual error was modeled as combination error model with the parameter estimates for proportion error 12% CV and additive error (standard deviation) 1.5 ng/ml. CONCLUSION: Population pharmacokinetic modeling showed that the volume of distribution of PQ in subjects with moderate hepatic dysfunction increases approximately three folds resulting in a significantly lower plasma concentration.