OBJECTIVE: To develop a framework for integrating pharmacogenetics with clinical pharmacokinetics for personalized oxycodone dosing based on a patient's CYP2D6 phenotype. DESIGN: Randomized, crossover, double-blind, placebo-controlled. Subjects were genotyped as CYP2D6 ultra-rapid metabolizer, extensive metabolizer, or poor metabolizer phenotypes. Five subjects from each phenotype were randomly selected for inclusion in our study. SETTING: Studies were performed in silico. SUBJECTS: The subjects were male, age 26 years, height 181.2 cm, and weight 76.3 kg. They were healthy without comorbidities, and their medical examinations were normal. METHODS: The trajectories of phenotype-specific plasma oxycodone concentration-time profiles were analyzed using weighted nonlinear least-squares regression with WinSAAM software. A global two-stage population-based model data analysis procedure was used to analyze the studies. Clinical pharmacokinetics were calculated using the R package cpk, eliminating the need to perform hand-calculations. RESULTS: Our study shows how clinicians can reduce risk and increase effectiveness for oxycodone dosing by (1) determining the patient's likely metabolic response through testing a patient's CYP2D6 phenotype, and (2) calculating clinical pharmacokinetics specific to the patient's CYP2D6 phenotype to design a personalized oxycodone dosing regimen. CONCLUSIONS:Personalized oxycodone dosing is a new tool for a clinician treating chronic pain patients requiring oxycodone. By expressing a patient's CYP2D6 phenotype pharmacokinetically, a clinician (at least theoretically) can improve the safety and efficacy of oxycodone and decrease the risk for iatrogenically induced overdose or death. Pharmacokinomics provides a general framework for the integration of pharmacogenetics with clinical pharmacokinetics into clinical practice for gene-based prescribing. Wiley Periodicals, Inc.
RCT Entities:
OBJECTIVE: To develop a framework for integrating pharmacogenetics with clinical pharmacokinetics for personalized oxycodone dosing based on a patient's CYP2D6 phenotype. DESIGN: Randomized, crossover, double-blind, placebo-controlled. Subjects were genotyped as CYP2D6 ultra-rapid metabolizer, extensive metabolizer, or poor metabolizer phenotypes. Five subjects from each phenotype were randomly selected for inclusion in our study. SETTING: Studies were performed in silico. SUBJECTS: The subjects were male, age 26 years, height 181.2 cm, and weight 76.3 kg. They were healthy without comorbidities, and their medical examinations were normal. METHODS: The trajectories of phenotype-specific plasma oxycodone concentration-time profiles were analyzed using weighted nonlinear least-squares regression with WinSAAM software. A global two-stage population-based model data analysis procedure was used to analyze the studies. Clinical pharmacokinetics were calculated using the R package cpk, eliminating the need to perform hand-calculations. RESULTS: Our study shows how clinicians can reduce risk and increase effectiveness for oxycodone dosing by (1) determining the patient's likely metabolic response through testing a patient's CYP2D6 phenotype, and (2) calculating clinical pharmacokinetics specific to the patient's CYP2D6 phenotype to design a personalized oxycodone dosing regimen. CONCLUSIONS: Personalized oxycodone dosing is a new tool for a clinician treating chronic painpatients requiring oxycodone. By expressing a patient's CYP2D6 phenotype pharmacokinetically, a clinician (at least theoretically) can improve the safety and efficacy of oxycodone and decrease the risk for iatrogenically induced overdose or death. Pharmacokinomics provides a general framework for the integration of pharmacogenetics with clinical pharmacokinetics into clinical practice for gene-based prescribing. Wiley Periodicals, Inc.
Authors: Jenna L McCauley; J Madison Hyer; V Ramesh Ramakrishnan; Renata Leite; Cathy L Melvin; Roger B Fillingim; Christie Frick; Kathleen T Brady Journal: J Am Dent Assoc Date: 2016-04-05 Impact factor: 3.634
Authors: Kelly E Dunn; Elise M Weerts; Andrew S Huhn; Jennifer R Schroeder; David Andrew Tompkins; George E Bigelow; Eric C Strain Journal: Addict Biol Date: 2018-10-08 Impact factor: 4.280
Authors: Concetta Dagostino; Massimo Allegri; Valerio Napolioni; Simona D'Agnelli; Elena Bignami; Antonio Mutti; Ron Hn van Schaik Journal: Pharmgenomics Pers Med Date: 2018-10-24