Kyle E Thomson1, H Steve White2. 1. Bioengineering Dept, University of Utah, Salt Lake City, UT, USA. 2. Anticonvulsant Drug Development Program, Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT, USA. Electronic address: steve.white@hsc.utah.edu.
Abstract
BACKGROUND: Nonadherence to a physician-prescribed therapeutic intervention is a costly, dangerous, and sometimes fatal concern in healthcare. To date, the study of nonadherence has been constrained to clinical studies. The novel approach described herein allows for the preclinical study of nonadherence in etiologically relevant disease animal model systems. NEW METHOD: The method herein describes a novel computer-automated pellet delivery system which allows for the study of nonadherence in animals. This system described herein allows for tight experimenter control of treatment using a drug-in-food protocol. Food-restricted animals receive either medicated or unmedicated pellets, designed to mimic either "taking" or "missing" a drug. RESULTS: The system described permits the distribution of medicated or unmedicated food pellets on an experimenter-defined feeding schedule. The flexibility of this system permits the delivery of drug according to the known pharmacokinetics of investigational drugs. COMPARISON WITH OTHER METHODS: Current clinical adherence research relies on medication-event monitoring system (MEMS) tracking caps, which allows clinicians to directly monitor patient adherence. However, correlating the effects of nonadherence to efficacy still relies on the accuracy of patient journals. CONCLUSION: This system allows for the design of studies to address the impact of nonadherence in an etiologically relevant animal model. Given methodological and ethical concerns of designing clinical studies of nonadherence, animal studies are critical to better understand medication adherence. While the system described was designed to measure the impact of nonadherence on seizure control, it is clear that the utility of this system extends beyond epilepsy to include other disease states.
BACKGROUND: Nonadherence to a physician-prescribed therapeutic intervention is a costly, dangerous, and sometimes fatal concern in healthcare. To date, the study of nonadherence has been constrained to clinical studies. The novel approach described herein allows for the preclinical study of nonadherence in etiologically relevant disease animal model systems. NEW METHOD: The method herein describes a novel computer-automated pellet delivery system which allows for the study of nonadherence in animals. This system described herein allows for tight experimenter control of treatment using a drug-in-food protocol. Food-restricted animals receive either medicated or unmedicated pellets, designed to mimic either "taking" or "missing" a drug. RESULTS: The system described permits the distribution of medicated or unmedicated food pellets on an experimenter-defined feeding schedule. The flexibility of this system permits the delivery of drug according to the known pharmacokinetics of investigational drugs. COMPARISON WITH OTHER METHODS: Current clinical adherence research relies on medication-event monitoring system (MEMS) tracking caps, which allows clinicians to directly monitor patient adherence. However, correlating the effects of nonadherence to efficacy still relies on the accuracy of patient journals. CONCLUSION: This system allows for the design of studies to address the impact of nonadherence in an etiologically relevant animal model. Given methodological and ethical concerns of designing clinical studies of nonadherence, animal studies are critical to better understand medication adherence. While the system described was designed to measure the impact of nonadherence on seizure control, it is clear that the utility of this system extends beyond epilepsy to include other disease states.
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Authors: Cheryl Clarkson; Roy M Smeal; Meredith G Hasenoehrl; John A White; Maria E Rubio; Karen S Wilcox Journal: Exp Neurol Date: 2020-01-11 Impact factor: 5.330
Authors: Anthony D Umpierre; Isaiah V Bennett; Lismore D Nebeker; Thomas G Newell; Bruce B Tian; Kyle E Thomson; H Steve White; John A White; Karen S Wilcox Journal: Exp Neurol Date: 2016-02-16 Impact factor: 5.330
Authors: Melissa L Barker-Haliski; Kristina Johnson; Peggy Billingsley; Jennifer Huff; Laura J Handy; Rizvana Khaleel; Zhenmei Lu; Matthew J Mau; Timothy H Pruess; Carlos Rueda; Gerald Saunders; Tristan K Underwood; Fabiola Vanegas; Misty D Smith; Peter J West; Karen S Wilcox Journal: Neurochem Res Date: 2017-03-16 Impact factor: 3.996
Authors: Melissa Barker-Haliski; Kevin Knox; Dannielle Zierath; Zachery Koneval; Cameron Metcalf; Karen S Wilcox; H Steve White Journal: Epilepsia Date: 2021-06-02 Impact factor: 6.740
Authors: Dipan C Patel; Glenna Wallis; E Jill Dahle; Pallavi B McElroy; Kyle E Thomson; Raymond J Tesi; David E Szymkowski; Peter J West; Roy M Smeal; Manisha Patel; Robert S Fujinami; H Steve White; Karen S Wilcox Journal: eNeuro Date: 2017-05-09
Authors: Nicolas F Fumeaux; Senan Ebrahim; Brian F Coughlin; Adesh Kadambi; Aafreen Azmi; Jen X Xu; Maurice Abou Jaoude; Sunil B Nagaraj; Kyle E Thomson; Thomas G Newell; Cameron S Metcalf; Karen S Wilcox; Eyal Y Kimchi; Marcio F D Moraes; Sydney S Cash Journal: Epilepsia Date: 2020-08-06 Impact factor: 6.740
Authors: Francisco Javier Mesas-Carrascosa; Daniel Verdú Santano; Jose Emilio Meroño de Larriva; Rafael Ortíz Cordero; Rafael Enrique Hidalgo Fernández; Alfonso García-Ferrer Journal: Sensors (Basel) Date: 2016-09-29 Impact factor: 3.576