Individualized Hydrocodone Therapy Based on Phenotype, Pharmacogenetics, and Pharmacokinetic Dosing.

OBJECTIVES: (1) To quantify hydrocodone (HC) and hydromorphone (HM) metabolite pharmacokinetics with pharmacogenetics in CYP2D6 ultra-rapid metabolizer (UM), extensive metabolizer (EM), and poor metabolizer (PM) metabolizer phenotypes. (2) To develop an HC phenotype-specific dosing strategy for HC that accounts for HM production using clinical pharmacokinetics integrated with pharmacogenetics for patient safety. SETTING: In silico clinical trial simulation. PARTICIPANTS: Healthy white men and women without comorbidities or history of opioid, or any other drug or nutraceutical use, age 26.3±5.7 years (mean±SD; range, 19 to 36 y) and weight 71.9±16.8 kg (range, 50 to 108 kg). MAIN OUTCOME MEASURES: CYP2D6 phenotype-specific HC clinical pharmacokinetic parameter estimates and phenotype-specific percentages of HM formed from HC. RESULTS: PMs had lower indices of HC disposition compared with UMs and EMs. Clearance was reduced by nearly 60% and the t1/2 was increased by about 68% compared with EMs. The canonical order for HC clearance was UM>EM>PM. HC elimination mainly by the liver, represented by ke, was reduced about 70% in PM. However, HC's apparent Vd was not significantly different among UMs, EMs, and PM. The canonical order of predicted plasma HM concentrations was UM>EM>PM. For each of the CYP2D6 phenotypes, the mean predicted HM levels were within HM's therapeutic range, which indicates HC has significant phenotype-dependent pro-drug effects. CONCLUSIONS: Our results demonstrate that pharmacogenetics afford clinicians an opportunity to individualize HC dosing, while adding enhanced opportunity to account for its conversion to HM in the body.

In silico ordinary differential equation/partial differential equation hemodialysis model estimates methadone removal during dialysis.

BACKGROUND: There is a need to have a model to study methadone's losses during hemodialysis to provide informed methadone dose recommendations for the practitioner. AIM: To build a one-dimensional (1-D), hollow-fiber geometry, ordinary differential equation (ODE) and partial differential equation (PDE) countercurrent hemodialyzer model (ODE/PDE model). METHODOLOGY: We conducted a cross-sectional study in silico that evaluated eleven hemodialysis patients. Patients received a ceiling dose of methadone hydrochloride 30 mg/day. Outcome measures included: the total amount of methadone removed during dialysis; methadone's overall intradialytic mass transfer rate coefficient, km ; and, methadone's removal rate, j ME. Each metric was measured at dialysate flow rates of 250 mL/min and 800 mL/min. RESULTS: The ODE/PDE model revealed a significant increase in the change of methadone's mass transfer with increased dialysate flow rate, %Δkm =18.56, P=0.02, N=11. The total amount of methadone mass transferred across the dialyzer membrane with high dialysate flow rate significantly increased (0.042±0.016 versus 0.052±0.019 mg/kg, P=0.02, N=11). This was accompanied by a small significant increase in methadone's mass transfer rate (0.113±0.002 versus 0.014±0.002 mg/kg/h, P=0.02, N=11). The ODE/PDE model accurately predicted methadone's removal during dialysis. The absolute value of the prediction errors for methadone's extraction and throughput were less than 2%. CONCLUSION: ODE/PDE modeling of methadone's hemodialysis is a new approach to study methadone's removal, in particular, and opioid removal, in general, in patients with end-stage renal disease on hemodialysis. ODE/PDE modeling accurately quantified the fundamental phenomena of methadone's mass transfer during hemodialysis. This methodology may lead to development of optimally designed intradialytic opioid treatment protocols, and allow dynamic monitoring of outflow plasma opioid concentrations for model predictive control during dialysis in humans.