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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 (P...

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Autores principales: Linares, Oscar A, Schiesser, William E, Fudin, Jeffrey, Pham, Thien C, Bettinger, Jeffrey J, Mathew, Roy O, Daly, Annemarie L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove Medical Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4516209/
https://www.ncbi.nlm.nih.gov/pubmed/26229501
http://dx.doi.org/10.2147/JPR.S84615
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author Linares, Oscar A
Schiesser, William E
Fudin, Jeffrey
Pham, Thien C
Bettinger, Jeffrey J
Mathew, Roy O
Daly, Annemarie L
author_facet Linares, Oscar A
Schiesser, William E
Fudin, Jeffrey
Pham, Thien C
Bettinger, Jeffrey J
Mathew, Roy O
Daly, Annemarie L
author_sort Linares, Oscar A
collection PubMed
description 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, k(m); 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, %Δk(m)=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.
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spelling pubmed-45162092015-07-30 In silico ordinary differential equation/partial differential equation hemodialysis model estimates methadone removal during dialysis Linares, Oscar A Schiesser, William E Fudin, Jeffrey Pham, Thien C Bettinger, Jeffrey J Mathew, Roy O Daly, Annemarie L J Pain Res Original Research 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, k(m); 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, %Δk(m)=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. Dove Medical Press 2015-07-22 /pmc/articles/PMC4516209/ /pubmed/26229501 http://dx.doi.org/10.2147/JPR.S84615 Text en © 2015 Linares et al. This work is published by Dove Medical Press Limited, and licensed under Creative Commons Attribution – Non Commercial (unported, v3.0) License The full terms of the License are available at http://creativecommons.org/licenses/by-nc/3.0/. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.
spellingShingle Original Research
Linares, Oscar A
Schiesser, William E
Fudin, Jeffrey
Pham, Thien C
Bettinger, Jeffrey J
Mathew, Roy O
Daly, Annemarie L
In silico ordinary differential equation/partial differential equation hemodialysis model estimates methadone removal during dialysis
title In silico ordinary differential equation/partial differential equation hemodialysis model estimates methadone removal during dialysis
title_full In silico ordinary differential equation/partial differential equation hemodialysis model estimates methadone removal during dialysis
title_fullStr In silico ordinary differential equation/partial differential equation hemodialysis model estimates methadone removal during dialysis
title_full_unstemmed In silico ordinary differential equation/partial differential equation hemodialysis model estimates methadone removal during dialysis
title_short In silico ordinary differential equation/partial differential equation hemodialysis model estimates methadone removal during dialysis
title_sort in silico ordinary differential equation/partial differential equation hemodialysis model estimates methadone removal during dialysis
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4516209/
https://www.ncbi.nlm.nih.gov/pubmed/26229501
http://dx.doi.org/10.2147/JPR.S84615
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