Pharmacokinetic modeling of R and S-Methadone and their metabolites to study the effects of various covariates in post-operative children.

Methadone is a synthetic opioid used as an analgesic and for the treatment of opioid abuse disorder. The analgesic dose in the pediatric population is not well-defined. The pharmacokinetics (PKs) of methadone is highly variable due to the variability in alpha-1 acid glycoprotein (AAG) and genotypic differences in drug-metabolizing enzymes. Additionally, the R and S enantiomers of methadone have unique PK and pharmacodynamic properties. This study aims to describe the PKs of R and S methadone and its metabolite 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (EDDP) in pediatric surgical patients and to identify sources of inter- and intra-individual variability. Children aged 8-17.9 years undergoing orthopedic surgeries received intravenous methadone 0.1 mg/kg intra-operatively followed by oral methadone 0.1 mg/kg postoperatively every 12 h. Pharmacokinetics of R and S methadone and EDDP were determined using liquid chromatography tandem mass spectrometry assays and the data were modeled using nonlinear mixed-effects modeling in NONMEM. R and S methadone PKs were well-described by two-compartment disposition models with first-order absorption and elimination. EDDP metabolites were described by one compartment disposition models with first order elimination. Clearance of both R and S methadone were allometrically scaled by bodyweight. CYP2B6 phenotype was a determinant of the clearance of both the enantiomers in an additive gene model. The intronic CYP3A4 single-nucleotide polymorphism (SNP) rs2246709 was associated with decreased clearance of R and S methadone. Concentrations of AAG and the SNP of AAG rs17650 independently increased the volume of distribution of both the enantiomers. The knowledge of these important covariates will aid in the optimal dosing of methadone in children.

Prediction of prasugrel active metabolite concentrations from 2 downstream inactive metabolite concentrations using multilinear regression analysis.

Prasugrel is a thienopyridine prodrug that is metabolized to an active metabolite (Pras-AM), which inhibits adenosine diphosphate (ADP)-induced platelet aggregation. The study objective was to describe a multilinear regression correlation model that was used to quantitatively predict concentrations of Pras-AM from downstream inactive metabolites, R-119251 and R-106583, for the purpose of estimating Pras-AM exposure in patients in the TRITON-TIMI 38 substudies. The model development included 1462 Pras-AM, 1345 R-119251, and 1456 R-106583 concentration data points from 103 healthy participants with a prasugrel dose range of 15 to 80 mg. The model was shown to provide good correlation between predicted and observed concentrations with only a minor deviation of approximately 6% from the unity line and described the variability within approximately 4.5%. Examination of the data indicated that regardless of ethnicity, age, weight, moderate hepatic impairment, or renal impairment, predictions were reliable. Predicted Pras-AM concentrations in TRITON-TIMI 38 were comparable with historical data.

Clinical Pharmacokinetics of Dulaglutide in Patients with Type 2 Diabetes: Analyses of Data from Clinical Trials.

BACKGROUND AND OBJECTIVE: Dulaglutide is a long-acting glucagon-like peptide-1 receptor agonist administered as once-weekly subcutaneous injections for the treatment of type 2 diabetes (T2D). The clinical pharmacokinetics of dulaglutide were characterized in patients with T2D and healthy subjects. METHODS: The pharmacokinetics of dulaglutide were assessed throughout clinical development, including conventional pharmacokinetic analysis in clinical pharmacology studies and population pharmacokinetic analyses of data from combined phase 2 and phase 3 studies in patients with T2D. The effects of potential covariates on dulaglutide population pharmacokinetics were evaluated using nonlinear mixed-effects models. RESULTS: Dulaglutide gradually reached the maximum concentration in 48 h and had a terminal elimination half-life of 5 days. Steady state was achieved between the second and fourth doses. The accumulation ratio was 1.56 for the 1.5 mg dose. Intra-individual variability estimates for the area under the plasma concentration-time curve and the maximum concentration were both <17% [coefficient of variation (CV)]. There was no difference in pharmacokinetics between injection sites (arm, thigh or abdomen). Dulaglutide pharmacokinetics were well described by a two-compartment model with first-order absorption and elimination. The population clearance was estimated at 0.126 L/h [inter-individual variability (CV) 33.8%]. Age, body weight, sex, race and ethnicity did not influence dulaglutide pharmacokinetics to any clinically relevant degree. CONCLUSION: The pharmacokinetics of dulaglutide support once-weekly administration in patients with T2D. The pharmacokinetic findings suggest that dose adjustment is not necessary on the basis of body weight, sex, age, race or ethnicity or site of injection.

Pharmacokinetics/Pharmacodynamics and the stages of drug development: role of modeling and simulation.

Pharmacokinetic (PK) and pharmacodynamic (PD) modeling and simulation (M&S) are well-recognized powerful tools that enable effective implementation of the learn-and-confirm paradigm in drug development. The impact of PK/PD M&S on decision making and drug development risk management is dependent on the question being asked and on the availability and quality of data accessible at a particular stage of drug development. For instance, M&S methodologies can be used to capture uncertainty and use the expected variability in PK/PD data generated in preclinical species for projection of the plausible range of clinical dose; clinical trial simulation can be used to forecast the probability of achieving a target response in patients based on information obtained in early phases of development. Framing the right question and capturing the key assumptions are critical components of the "learn-and-confirm" paradigm in the drug development process and are essential to delivering high-value PK/PD M&S results. Selected works of PK/PD modeling and simulation from preclinical to phase III are presented as case examples in this article.