Dataset Information

Digitizing Medicines for Remote Capture of Oral Medication Adherence Using Co-encapsulation.

ABSTRACT: High-resolution measurement of medication adherence is essential to personalized drug therapy. A US Food and Drug Administration (FDA)-cleared device, using an edible ingestion sensor (IS), external wearable patch, and paired mobile device can detect and record ingestion events. Oral medications must be combined with an IS to generate precise "digitized-medication" ingestion records. We developed a Good Manufacturing Practice protocol to repackage oral medications with the IS within certified Capsugel capsules, termed co-encapsulation (CoE). A randomized bioequivalence study of CoE-IS-Rifamate (Isoniazid/Rifampin 150/300 mg) vs. native-Rifamate was conducted in 12 patients with active Mycobacterium tuberculosis and demonstrated bioequivalence using the population method ratio test (95% confidence interval). Subsequently, CoE-IS-medications across all biopharmaceutical classes underwent in vitro dissolution testing utilizing USP and FDA guidelines. CoE-IS medications tested met USP dissolution specifications and were equivalent to their native formulations. CoE combines oral medications with the IS without altering the quality of the native formulation, generating "digitized" medications for remote capture of dosing histories.

SUBMITTER: Browne SH

PROVIDER: S-EPMC5836848 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Digitizing Medicines for Remote Capture of Oral Medication Adherence Using Co-encapsulation.

Browne S H SH Peloquin C C Santillo F F Haubrich R R Muttera L L Moser K K Savage G M GM Benson C A CA Blaschke T F TF

Clinical pharmacology and therapeutics 20170919 3

High-resolution measurement of medication adherence is essential to personalized drug therapy. A US Food and Drug Administration (FDA)-cleared device, using an edible ingestion sensor (IS), external wearable patch, and paired mobile device can detect and record ingestion events. Oral medications must be combined with an IS to generate precise "digitized-medication" ingestion records. We developed a Good Manufacturing Practice protocol to repackage oral medications with the IS within certified Ca ...[more]

PMID: 28597911

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Project description:Poor adherence to medication regimens and medical record inconsistencies result in incomplete knowledge of medication therapy in polypharmacy patients. By quantitatively identifying medications in the blood of patients and reconciling detected medications with the medical record, we have defined the severity of this knowledge gap and created a path toward optimizing medication therapy.We validated a liquid chromatography-tandem mass spectrometry assay to detect and/or quantify 38 medications across a broad range of chronic diseases to obtain a comprehensive survey of patient adherence, medical record accuracy, and exposure variability in two patient populations. In a retrospectively tested 821-patient cohort representing U.S. adults, we found that 46% of medications assessed were detected in patients as prescribed in the medical record. Of the remaining medications, 23% were detected, but not listed in the medical record while 30% were prescribed to patients, but not detected in blood. To determine how often each detected medication fell within literature-derived reference ranges when taken as prescribed, we prospectively enrolled a cohort of 151 treatment-regimen adherent patients. In this cohort, we found that 53% of medications that were taken as prescribed, as determined using patient self-reporting, were not within the blood reference range. Of the medications not in range, 83% were below and 17% above the lower and upper range limits, respectively. Only 32% of out-of-range medications could be attributed to short oral half-lives, leaving extensive exposure variability to result from patient behavior, undefined drug interactions, genetics, and other characteristics that can affect medication exposure.This is the first study to assess compliance, medical record accuracy, and exposure as determinants of real-world treatment and response. Variation in medication detection and exposure is greater than previously demonstrated, illustrating the scope of current therapy issues and opening avenues that warrant further investigation to optimize medication therapy.

Project description:BackgroundThis study assesses the feasibility of using machine learning methods such as Random Forests (RF), Artificial Neural Networks (ANN), Support Vector Regression (SVR) and Self-Organizing Feature Maps (SOM) to identify and determine factors associated with hypertensive patients' adherence levels. Hypertension is the medical term for systolic and diastolic blood pressure higher than 140/90 mmHg. A conventional medication adherence scale was used to identify patients' adherence to their prescribed medication. Using machine learning applications to predict precise numeric adherence scores in hypertensive patients has not yet been reported in the literature.MethodsData from 160 hypertensive patients from a tertiary hospital in Kuala Lumpur, Malaysia, were used in this study. Variables were ranked based on their significance to adherence levels using the RF variable importance method. The backward elimination method was then performed using RF to obtain the variables significantly associated with the patients' adherence levels. RF, SVR and ANN models were developed to predict adherence using the identified significant variables. Visualizations of the relationships between hypertensive patients' adherence levels and variables were generated using SOM.ResultMachine learning models constructed using the selected variables reported RMSE values of 1.42 for ANN, 1.53 for RF, and 1.55 for SVR. The accuracy of the dichotomised scores, calculated based on a percentage of correctly identified adherence values, was used as an additional model performance measure, resulting in accuracies of 65% (ANN), 78% (RF) and 79% (SVR), respectively. The Wilcoxon signed ranked test reported that there was no significant difference between the predictions of the machine learning models and the actual scores. The significant variables identified from the RF variable importance method were educational level, marital status, General Overuse, monthly income, and Specific Concern.ConclusionThis study suggests an effective alternative to conventional methods in identifying the key variables to understand hypertensive patients' adherence levels. This can be used as a tool to educate patients on the importance of medication in managing hypertension.

Project description:Many studies have investigated racial/ethnic disparities in medication non-adherence in patients with type 2 diabetes using common measures such as medication possession ratio (MPR) or gaps between refills. All these measures including MPR are quasi-continuous and bounded and their distribution is usually skewed. Analysis of such measures using traditional regression methods that model mean changes in the dependent variable may fail to provide a full picture about differential patterns in non-adherence between groups.A retrospective cohort of 11,272 veterans with type 2 diabetes was assembled from Veterans Administration datasets from April 1996 to May 2006. The main outcome measure was MPR with quantile cutoffs Q1-Q4 taking values of 0.4, 0.6, 0.8 and 0.9. Quantile-regression (QReg) was used to model the association between MPR and race/ethnicity after adjusting for covariates. Comparison was made with commonly used ordinary-least-squares (OLS) and generalized linear mixed models (GLMM).Quantile-regression showed that Non-Hispanic-Black (NHB) had statistically significantly lower MPR compared to Non-Hispanic-White (NHW) holding all other variables constant across all quantiles with estimates and p-values given as -3.4% (p = 0.11), -5.4% (p = 0.01), -3.1% (p = 0.001), and -2.00% (p = 0.001) for Q1 to Q4, respectively. Other racial/ethnic groups had lower adherence than NHW only in the lowest quantile (Q1) of about -6.3% (p = 0.003). In contrast, OLS and GLMM only showed differences in mean MPR between NHB and NHW while the mean MPR difference between other racial groups and NHW was not significant.Quantile regression is recommended for analysis of data that are heterogeneous such that the tails and the central location of the conditional distributions vary differently with the covariates. QReg provides a comprehensive view of the relationships between independent and dependent variables (i.e. not just centrally but also in the tails of the conditional distribution of the dependent variable). Indeed, without performing QReg at different quantiles, an investigator would have no way of assessing whether a difference in these relationships might exist.

Dataset Information

Digitizing Medicines for Remote Capture of Oral Medication Adherence Using Co-encapsulation.

Publications

Digitizing Medicines for Remote Capture of Oral Medication Adherence Using Co-encapsulation.

Similar Datasets

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