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

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          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.

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          Most cited references14

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          An ingestible sensor for measuring medication adherence.

          In this paper, we describe the design and performance of the first integrated-circuit microsensor developed for daily ingestion by patients. The ingestible sensor is a device that allows patients, families, and physicians to measure medication ingestion and adherence patterns in real time, relate pharmaceutical compliance to important physiologic metrics, and take appropriate action in response to a patient's adherence pattern and specific health metrics. The design and theory of operation of the device are presented, along with key in-vitro and in-vivo performance results. The chemical, toxicological, mechanical, and electrical safety tests performed to establish the device's safety profile are described in detail. Finally, aggregate results from multiple clinical trials involving 412 patients and 5656 days of system usage are presented to demonstrate the device's reliability and performance as part of an overall digital health feedback system.
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            Drug-related morbidity and mortality: updating the cost-of-illness model.

            To update the 1995 estimate of $76.6 billion for the annual cost of drug-related morbidity and mortality resulting from drug-related problems (DRPs) in the ambulatory setting in the United States to reflect current treatment patterns and costs. For this study, we employed the decision-analytic model developed by Johnson and Bootman. We used the model's original design and probability data, but used updated cost estimates derived from the current medical and pharmaceutical literature. Sensitivity analyses were performed on cost data and on probability estimates. Ambulatory care environment in the United States in the year 2000. A hypothetical cohort of ambulatory patients. Average cost of health care resources needed to manage DRPs. As estimated using the decision-tree model, the mean cost for a treatment failure was $977. For a new medical problem, the mean cost was $1,105, and the cost of a combined treatment failure and resulting new medical problem was $1,488. Overall, the cost of drug-related morbidity and mortality exceeded $177.4 billion in 2000. Hospital admissions accounted for nearly 70% ($121.5 billion) of total costs, followed by long-term-care admissions, which accounted for 18% ($32.8 billion). Since 1995, the costs associated with DRPs have more than doubled. Given the economic and medical burdens associated with DRPs, strategies for preventing drug-related morbidity and mortality are urgently needed.
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              Pharmacogenetics/genomics and personalized medicine.

              Despite the marked advances in drug therapy, some patients do not respond favorably or suffer severe adverse drug effects. Pharmacogenetic studies have shown that polymorphisms of drug metabolizing enzymes, transporters and receptors contribute to variable drug response. Owing to the complexity of drug actions, a broader genomics approach aims at finding new drug targets and optimizing therapy for the individual patient. However, pharmacogenomics has made only a few inroads into clinical practice to date. This review evaluates obstacles that need to be overcome. These include the complexity of mechanisms underlying drug response, given singly or in combination, uncertainty about the genetic underpinnings of complex diseases, such as cancer, diabetes, cardiovascular and mental disorders and a lack of quantitative understanding of the scope of genetic variations, even for well-studied genes. By resolving these hurdles, pharmacogenomics will yield significant, but incremental, therapeutic advances paving the way towards personalized health care.
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                Author and article information

                Contributors
                shbrowne@ucsd.edu
                Journal
                Clin Pharmacol Ther
                Clin. Pharmacol. Ther
                10.1002/(ISSN)1532-6535
                CPT
                Clinical Pharmacology and Therapeutics
                John Wiley and Sons Inc. (Hoboken )
                0009-9236
                1532-6535
                19 September 2017
                March 2018
                19 September 2017
                : 103
                : 3 , Breaking Down Barriers to Effective Patient Care ( doiID: 10.1002/cpt.v103.3 )
                : 502-510
                Affiliations
                [ 1 ] University of California, San Diego School of Medicine, Division of Infectious Diseases San Diego California USA
                [ 2 ] University of Florida, College of Pharmacy and Emerging Pathogens Institute Gainesville Florida USA
                [ 3 ] Research and Analytic Services Sharp Clinical Services Pennsylvania USA
                [ 4 ] Gilead Sciences Gilead Sciences, Inc. Foster City California USA
                [ 5 ] University of California, San Diego Department of Medicine, Research Pharmacy San Diego California USA
                [ 6 ] San Diego County Health and Human Services Agency San Diego California USA
                [ 7 ] Centers for Disease Control National Center for Emerging and Zoonotic Infectious Diseases Atlanta Georgia USA
                [ 8 ] Proteus Digital Health, Inc. Redwood City California USA
                [ 9 ] Stanford University Stanford California USA
                Author notes
                [*] [* ]Correspondence: SH Browne ( shbrowne@ 123456ucsd.edu )
                Article
                CPT760
                10.1002/cpt.760
                5836848
                28597911
                80c618ec-f769-4843-b2a7-9a21bd492c85
                © 2017 The Authors Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics

                This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 19 December 2016
                : 08 May 2017
                : 29 May 2017
                Page count
                Figures: 5, Tables: 2, Pages: 9, Words: 5570
                Funding
                Funded by: Alliance Healthcare Foundation (AHF), San Diego, CA
                Funded by: Specialists in Global Health (SIGH), Encinitas, CA
                Award ID: NIH R43 AI100479‐01A1
                Award ID: NIH R01 MH110057
                Funded by: Proteus Digital Health Inc., Redwood City, CA
                Categories
                Articles
                Research
                Articles
                Custom metadata
                2.0
                cpt760
                March 2018
                Converter:WILEY_ML3GV2_TO_NLMPMC version:version=5.3.2.2 mode:remove_FC converted:05.03.2018

                Pharmacology & Pharmaceutical medicine
                Pharmacology & Pharmaceutical medicine

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