title: In silico ordinary differential equation/partial differential equation hemodialysis model estimates methadone removal during dialysis.
abstract: 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.
  
  
  OBJECTIVE
  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).
  
  
  METHODS
  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%.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Translational Genomic Medicine Lab, Plymouth Pharmacokinetic Modeling Study Group, Plymouth, MI, USA.
authors.forename: Oscar A
authors.initials: OA
authors.lastname: Linares
authors.affiliation: Department of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, PA, USA.
authors.forename: William E
authors.initials: WE
authors.lastname: Schiesser
authors.affiliation: University of Connecticut School of Pharmacy, Storrs, CT, USA ; Western New England College of Pharmacy, Springfield, MA, USA ; Albany College of Pharmacy and Health Sciences, Albany, NY, USA ; Stratton VA Medical Center, Albany, NY, USA.
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Fudin
authors.affiliation: Stratton VA Medical Center, Albany, NY, USA.
authors.forename: Thien C
authors.initials: TC
authors.lastname: Pham
authors.affiliation: Stratton VA Medical Center, Albany, NY, USA.
authors.forename: Jeffrey J
authors.initials: JJ
authors.lastname: Bettinger
authors.affiliation: Stratton VA Medical Center, Albany, NY, USA.
authors.forename: Roy O
authors.initials: RO
authors.lastname: Mathew
authors.affiliation: Grace Hospice of Ann Arbor, Ann Arbor, MI, USA.
authors.forename: Annemarie L
authors.initials: AL
authors.lastname: Daly
keywords: hemodialysis; methadone; modeling; renal failure