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Abstract

Drug-delivery technology has come a long way since the handmade tinctures, lozenges, and pills of the eighteenth and nineteenth centuries. Today, a new generation of smart pharmaceuticals offers the potential to monitor and improve self-management of chronic conditions, enhance adherence, and tailor treatment more precisely to the patient. Smart pharmaceuticals also offer the opportunity to collect an unprecedented breadth and depth of metadata, such as medication adherence and compliance with technique, and the correlation of these factors with cost-effectiveness and clinical efficacy outcomes. This metadata brings key knowledge at point of care and may facilitate consultations between patients and healthcare professionals (HCPs), improve allocation of increasingly pressurized health resources, and optimize service design. There is also the potential to use these data as a healthcare monitor. Ultimately, the hope is that use of these data will optimize therapy and overall patient care.

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Abbreviations

COPD:

Chronic obstructive pulmonary disease

ECS:

Extensive Care System

FENO:

Fractional exhaled nitric oxide

HCP:

Healthcare professional

MDI:

Metered-dose inhaler

MEMS:

Medication Event Monitoring System

PMI:

Precision Medicine Initiative

References

  1. Trevitt S, Simpson S, Wood A (2015) Artificial pancreas device systems for the closed-loop control of type 1 diabetes: what systems are in development? J Diab Sci Technol 10(3):714–723. doi:10.1177/1932296815617968

    Article  Google Scholar 

  2. Drury RL (2014) Wearable biosensor systems and resilience: a perfect storm in health care? Front Psychol 5:853. doi:10.3389/fpsyg.2014.00853. Accessed Jan 2016, Available from: http://www.frontiersin.org/Journal/Abstract.aspx?s=944&name=psychology_for_clinical_settings&ART_DOI=10.3389/fpsyg.2014.00853

  3. Pew Research Center (2015) The smartphone difference. Available via http://www.pewinternet.org/2015/04/01/us-smartphone-use-in-2015/. Accessed Jan 2016

  4. Pew Research Center (2015) US smartphone use in 2015. Available via http://www.pewinternet.org/2015/04/01/us-smartphone-use-in-2015/pi_2015-04-01_smartphones_03/. Accessed Jan 2016

  5. Ericsson (2015) Mobility report: press release. Available via http://www.ericsson.com/news/1925907. Accesed Jan 2016

  6. Kass M, Zimmerman T, Yablonski M (1977) Compliance to pilocarpine therapy. Invest Ophthalmol 108:Abstract 2

    Google Scholar 

  7. Vrijens B, Urquhart J (2014) Methods for measuring, enhancing, and accounting for medication adherence in clinical trials. Clin Pharmacol Ther 95:617–626. Available via http://www.ncbi.nlm.nih.gov/pubmed/24739446. Cited 11 Feb 2016

  8. iAdherence (n.d.) Available via www.iAdherence.org

  9. Proteus (2015) U.S. FDA accepts first digital medicine new drug application for Otsuka and Proteus Digital Health. Available via http://www.proteus.com/press-releases/u-s-fda-accepts-first-digital-medicine-new-drug-application-for-otsuka-and-proteus-

  10. Cui J, Zheng X, Hou W, Zhuang Y, Pi X, Yang J (2008) The study of a remote-controlled gastrointestinal drug delivery and sampling system. Telemed J e-Health 14(7):715–719. doi:10.1089/tmj.2007.0118

    Article  Google Scholar 

  11. Xitian P, Hongying L, Kang W, Yulin L, Xiaolin Z, Zhiyu W (2009) A novel remote controlled capsule for site-specific drug delivery in human GI tract. Int J Pharm 382:160–164. doi:10.1016/j.ijpharm.2009.08.026

    Article  Google Scholar 

  12. Farr SJ, Rowe AM, Rubsamen R, Taylor G (1995) Aerosol deposition in the human lung following administration from a microprocessor controlled pressurised metered dose inhaler. Thorax 50(6):639–644. Available via http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1021264&tool=pmcentrez&rendertype=abstract. Cited 11 Feb 2016

  13. Julius SM, Sherman JM, Hendeles L (2002) Accuracy of three electronic monitors for metered-dose inhalers. Chest 121(3):871–876. doi:10.1378/chest.121.3.871. Available via http://www.ncbi.nlm.nih.gov/pubmed/11888975. Cited 12 Feb 2016

  14. Wamboldt FS, Bender BG, O’Connor SL, Gavin LA, Wamboldt MZ, Milgrom H et al (1999) Reliability of the model MC-311 MDI chronolog. J Allergy Clin Immunol 104(1):53–57. doi:10.1016/S0091-6749(99)70113-2. Cited 23 Feb 2016

  15. Chan AHY, Stewart AW, Harrison J, Camargo CA Jr, Black PN, Mitchell EA (2015) The effect of an electronic monitoring device with audiovisual reminder function on adherence to inhaled corticosteroids and school attendance in children with asthma: a randomised controlled trial. Lancet Respir Med 3:210–219. doi:10.1016/S2213-2600(15)00008-9

    Article  Google Scholar 

  16. Taylor L (2011) Drug delivery device sales “will hit $197B in 2014” [Internet]. Pharma Times. Available via PharmaTimes Digial. http://www.pharmatimes.com/Article/11-09-12/Drug_delivery_device_sales_will_hit_197B_in_2014.aspx

  17. Holmes MS, Seheult JN, Geraghty C, D’Arcy S, O’Brien U, Crispino O’Connell G, et al (2013) A method of estimating inspiratory flow rate and volume from an inhaler using acoustic measurements. Physiol Meas 34(8):903–914. doi:10.1088/0967-3334/34/8/903. Cited 24 Feb 2016

  18. Holmes MS, D’Arcy S, Costello RW, Reilly RB (2013) An acoustic method of automatically evaluating patient inhaler technique. In: Procedings of the 36th Annual International IEEE EMBS Conference of IEEE Engineering in Medicine and Biology Society, pp 1322–1325. doi:10.1109/EMBC.2013.6609752

  19. LabStyle (n.d.) Available via mydario.com Accessed Jan 2016

  20. Heaney LG, Horne R (2012) Non-adherence in difficult asthma: time to take it seriously. Thorax 67(3):268–270. doi:10.1136/thoraxjnl-2011-200257

    Article  Google Scholar 

  21. Gamble J, Stevenson M, McClean E, Heaney LG (2009) The prevalence of nonadherence in difficult asthma. Am J Respir Crit Care Med 180(9):817–822. doi:10.1164/rccm.200902-0166OC

    Article  Google Scholar 

  22. Patel P, Gupta PKC, White CMJ, Stanley AG, Williams B, Tomaszewski M (2015) Screening for non-adherence to antihypertensive treatment as a part of the diagnostic pathway to renal denervation. J Hum Hypertens 30(6):368–373. doi:10.1038/jhh.2015.103

    Article  Google Scholar 

  23. Vrijens B, De Geest S, Hughes DA, Przemyslaw K, Demonceau J, Ruppar T et al (2012) A new taxonomy for describing and defining adherence to medications. Br J Clin Pharmacol 73(5):691–705. doi:10.1111/j.1365-2125

    Article  Google Scholar 

  24. Blaschke TF, Osterberg L, Vrijens B, Urquhart J (2012) Adherence to medications: insights arising from studies on the unreliable link between prescribed and actual drug dosing histories. Annu Rev Pharmacol Toxicol 52:275–301. doi:10.1146/annurev-pharmtox-011711-113247. Cited 5 Feb 2016

  25. Yeaw J, Benner JS, Walt JG, Sian S, Smith DB (2016) Comparing adherence and persistence across 6 chronic medication classes. J Manag Care Pharm 15(9):728–740. Available via http://www.ncbi.nlm.nih.gov/pubmed/19954264. Cited 12 Feb 2016

  26. Fischer MA, Stedman MR, Lii J, Vogeli C, Shrank WH, Brookhart MA et al (2010) Primary medication non-adherence: analysis of 195,930 electronic prescriptions. J Gen Intern Med 25:284–290. doi:10.1007/s11606-010-1253-9

    Article  Google Scholar 

  27. Granger BB, Bosworth HB (2011) Medication adherence: emerging use of technology. Curr Opin Cardiol 26(4):279–287. doi:10.1097/HCO.0b013e328347c150

    Article  Google Scholar 

  28. Kalichman SC, Amaral CM, Cherry C, Flanagan J, Pope H, Eaton L et al (2008) Monitoring medication adherence by unannounced pill counts conducted by telephone: reliability and criterion-related validity. HIV Clin Trials 9(5):298–308. doi:10.1310/hct0905-298

    Article  Google Scholar 

  29. Iuga AO, McGuire MJ (2014) Adherence and health care costs. Risk Manag Heal Policy 7:35–44. doi:10.2147/RMHP.S19801

    Google Scholar 

  30. CMS (n.d.) National Health Expenditures 2014 Highlights. Available via https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/Downloads/highlights.pdf. Accessed Jan 2016

  31. Shi L, Liu J, Fonseca V, Walker P, Kalsekar A, Pawaskar M (2010) Correlation between adherence rates measured by MEMS and self-reported questionnaires: a meta-analysis. Heal Qual Life Outcomes 8:99. doi:10.1186/1477-7525-8-99

    Article  Google Scholar 

  32. Bender BG, Rand C (2004) Medication non-adherence and asthma treatment cost. Curr Opin Allergy Clin Immunol 4(3):191–195

    Article  Google Scholar 

  33. Williams LK, Pladevall M, Xi H, Peterson EL, Joseph C, Lafata JE et al (2004) Relationship between adherence to inhaled corticosteroids and poor outcomes among adults with asthma. J. Allergy Clin Immunol 114(6):1288–1293. doi:10.1016/j.jaci.2004.09.028

    Article  Google Scholar 

  34. Demonceau J, Ruppar T, Kristanto P, Hughes DA, Fargher E, Kardas P et al (2013) Identification and assessment of adherence-enhancing interventions in studies assessing medication adherence through electronically compiled drug dosing histories: a systematic literature review and meta-analysis. Drugs 73(6):545–562. doi:10.1007/s40265-013-0041-3. Cited 24 Feb 2016

  35. Mosnaim GS, Pappalardo AA, Resnick SE, Codispoti CD, Bandi S, Nackers L et al (2016) Behavioral interventions to improve asthma outcomes for adolescents: a systematic review. J Allergy Clin Immunol Pract 4(1):130–141. doi:10.1016/j.jaip.2015.09.011

    Article  Google Scholar 

  36. Foster JM, Usherwood T, Smith L, Sawyer SM, Xuan W, Rand CS et al (2014) Inhaler reminders improve adherence with controller treatment in primary care patients with asthma. J Allergy Clin Immunol 134(1260–1268):e3. doi:10.1016/j.jaci.2014.05.041

    Google Scholar 

  37. Neville R, Greene A, McLeod J, Tracy A, Surie J (2002) Mobile phone text messaging can help young people manage asthma. Br Med J 325(7364):600

    Article  Google Scholar 

  38. Head KJ, Noar SM, Iannarino NT, Grant Harrington N (2013) Efficacy of text messaging-based interventions for health promotion: a meta-analysis. Soc Sci Med 97:41–48. doi:10.1016/j.socscimed.2013.08.003

    Article  Google Scholar 

  39. Thakkar J, Kurup R, Laba T-L, Santo K, Thiagalingam A, Rodgers A et al (2016) Mobile telephone text messaging for medication adherence in chronic disease. JAMA Intern Med 176(3):340–349. doi:10.1001/jamainternmed.2015.7667. Cited 2 Feb 2016

  40. Lin M, Mahmooth Z, Dedhia N, Frutchey R, Mercado CE, Epstein DH et al (2015) Tailored, interactive text messages for enhancing weight loss among African American adults: the TRIMM randomized controlled trial. Am J Med 128(8):896–904. doi:10.1016/j.amjmed.2015.03.013. Cited 12 Feb 2016

  41. Saeedi OJ, Luzuriaga C, Ellish N, Robin A (2015) Potential limitations of e-mail and text messaging in improving adherence in glaucoma and ocular hypertension. J Glaucoma 24(5):e95–e102. doi:10.1097/IJG.0000000000000150. Cited 12 Feb 2016

  42. Orrell C, Cohen K, Mauff K, Bangsberg DR, Maartens G, Wood R (2015) A randomized controlled trial of real-time electronic adherence monitoring with text message dosing reminders in people starting first-line antiretroviral therapy. J Acquir Immune Defic Syndr 70(5):495–502. doi:10.1097/QAI.0000000000000770. Cited 22 Feb 2016

  43. Nguyen T-M-U, Caze AL, Cottrell N (2014) What are validated self-report adherence scales really measuring?: a systematic review. Br J Clin Pharmacol 77(30):427–445. doi:10.1111/bcp.12194

  44. Jacobs S (2014) What else could smart contact lenses do? Available via MIT Technol Rev. http://www.technologyreview.com/news/529196/what-else-could-smart-contact-lenses-do/

  45. Ricciardolo FL, Sorbello V, Bellezza Fontana R, Schiavetti I, Ciprandi G (2015) Exhaled nitric oxide in relation to asthma control: a real-life survey. Allergol Immunopathol (Madr) 44(3):197–205. doi:10.1016/j.aller.2015.05.012

    Article  Google Scholar 

  46. Crompton G (2006) A brief history of inhaled asthma therapy over the last fifty years. Prim Care Respir J 15:326–331. doi:10.1016/j.pcrj.2006.09.002

    Article  Google Scholar 

  47. Braido F, Baiardini I, Blasi F, Pawankar R, Canonica GW (2015) Adherence to asthma treatments: “we know, we intend, we advocate”. Curr Opin Allergy Clin Immunol 15(1):49–55. doi:10.1097/ACI.0000000000000132

    Article  Google Scholar 

  48. Bender BG (2014) Nonadherence in chronic obstructive pulmonary disease patients: what do we know and what should we do next? Curr Opin Pulm Med 20(2):132–137. doi:10.1097/MCP.0000000000000027

    Article  Google Scholar 

  49. Covvey JR, Mullen AB, Ryan M, Steinke DT, Johnston BF, Wood FT et al (2014) A comparison of medication adherence/persistence for asthma and chronic obstructive pulmonary disease in the United Kingdom. Int J Clin Pract 68(10):1200–1208. doi:10.1111/ijcp.12451

    Article  Google Scholar 

  50. Lavorini F, Magnan A, Christophe Dubus J, Voshaar T, Corbetta L, Broeders M et al (2008) Effect of incorrect use of dry powder inhalers on management of patients with asthma and COPD. Respir Med 102(4):593–604. doi:10.1016/j.rmed.2007.11.003

    Article  Google Scholar 

  51. Federman AD, Wolf MS, Sofianou A, Martynenko M, O’Connor R, Halm EA et al (2014) Self-management behaviors among older adults with asthma: associations with health literacy. J Am Geriatr Soc 62(5):872–879. doi:10.1111/jgs.12797

    Article  Google Scholar 

  52. Taylor TE, Holmes MS, Sulaiman I, D’Arcy S, Costello RW, Reilly RB (2014) An acoustic method to automatically detect pressurized metered dose inhaler actuations. In: Proceeding of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. pp 4611–4614. doi:10.1109/EMBC.2014.6944651

  53. Hertegonne KB, Rombaut B, Houtmeyers P, Van Maele G, Pevernagie DA (2008) Titration efficacy of two auto-adjustable continuous positive airway pressure devices using different flow limitation-based algorithms. Respiration 75(1):48–54. doi:10.1159/000103515

    Article  Google Scholar 

  54. Philips (n.d.) System One CPAP/BiPAP. Available via www.respironicsonline.co.uk/PublishedService?pageID=23&freePage=322. Accessed Jan 2016

  55. Philips (n.d.) I-neb AAD System. Available via http://www.usa.philips.com/healthcare/product/HC85167/i-neb-battery-powered-drug-delivery-system. Accessed Jan 2016

  56. Nadarassan DK, Assi KH, Chrystyn H (2010) Aerodynamic characteristics of a dry powder inhaler at low inhalation flows using a mixing inlet with an Andersen Cascade Impactor. Eur J Pharm Sci 39(5):348–354. doi:10.1016/j.ejps.2010.01.002. Cited 12 Feb 2016

  57. de Matas M, Shao Q, Biddiscombe MF, Meah S, Chrystyn H, Usmani OS (2010) Predicting the clinical effect of a short acting bronchodilator in individual patients using artificial neural networks. Eur J Pharm Sci 41(5):707–715. doi:10.1016/j.ejps.2010.09.018

    Article  Google Scholar 

  58. Suissa S, Blais L, Ernst P (1994) Patterns of increasing beta-agonist use and the risk of fatal or near-fatal asthma. Eur Respir J 7(9):1602–1609. Available via http://erj.ersjournals.com/content/erj/7/9/1602.full.pdf

  59. Alvarez GG, Schulzer M, Jung D, FitzGerald JM (2005) A systematic review of risk factors associated with near-fatal and fatal asthma. Can Respir J 12(5):265–270

    Article  Google Scholar 

  60. Tattersfield A, Postma D, Barnes P, Svensson K, Bauer C-A, O’Byrne P et al (1999) Exacerbations of asthma. Am J Respir Crit Care Med 160(2):594–599. doi:10.1164/ajrccm.160.2.9811100

    Article  Google Scholar 

  61. Bender BG, Krishnan JA, Chambers DA, Cloutier MM, Riekert KA, Rand CS et al (2015) American Thoracic Society and National Heart, Lung, and Blood Institute Implementation research workshop report. Ann Am Thorac Soc 12(12):S213–S221. doi:10.1513/AnnalsATS.201506-367OT. Cited 15 Feb 2016

  62. van Boven JF, Tommelein E, Boussery K, Mehuys E, Vegter S, Brusselle GG et al (2014) Improving inhaler adherence in patients with chronic obstructive pulmonary disease: a cost-effectiveness analysis. Respir Res 15(1):66. doi:10.1186/1465-9921-15-66. Cited 24 Feb 2016

  63. Elliott RA, Boyd MJ, Salema N-E, Davies J, Barber N, Mehta RL et al (2015) Supporting adherence for people starting a new medication for a long-term condition through community pharmacies: a pragmatic randomised controlled trial of the New Medicine Service. BMJ Qual Saf. doi:10.1136/bmjqs-2015-004400. Cited 24 Feb 2016

  64. Mannu P (n.d.) Digital health debate 2015. Available via Cello Health Insight. http://www.cellohealthinsight.com/work/digital-health-debate-2015/. Accessed Jan 2016

  65. Vrijens B, Urquhart J, White D (2014) Electronically monitored dosing histories can be used to develop a medication-taking habit and manage patient adherence. Expert Rev Clin Pharmacol 7(5):633–644. doi:10.1586/17512433.2014.940896

    Article  Google Scholar 

  66. Baptist AP, Thompson M, Grossman KS, Mohammed L, Sy A, Sanders GM (2011) Social media, text messaging, and email-preferences of asthma patients between 12 and 40 years old. J Asthma 48(8):824–830. doi:10.3109/02770903.2011.608460. Cited 15 Feb 2016

  67. Pew Research Center (2015) Racial and ethnic differences in how people use mobile technology. Available via Pew Research Center. http://www.pewresearch.org/fact-tank/2015/04/30/racial-and-ethnic-differences-in-how-people-use-mobile-technology/

  68. Patel B (2010) Mobile medical apps: draft guidance. Available via http://www.fda.gov/downloads/MedicalDevices/NewsEvents/WorkshopsConferences/UCM271893.pdf

  69. National Institute of Health (2016) Precision medicine initiative cohort program—frequently asked questions. https://www.nih.gov/precision-medicine-initiative-cohort-program/precision-medicine-initiative-cohort-program-frequently-asked-questions

  70. Khoury MJ (2015) Planning for the future of epidemiology in the era of big data and precision medicine. Am J Epidemiol 182(12):977–979. doi:10.1093/aje/kwv228

    Google Scholar 

  71. McLaughlin D, Long A (1996) An extended literature review of health professionals’ perceptions of illicit drugs and their clients who use them. J Psychiatr Ment Health Nurs 3(5):283–288. doi:10.1111/j.1365-2850.1996.tb00127.x

    Article  Google Scholar 

  72. Phelan SM, Puhl RM, Burke SE, Hardeman R, Dovidio JF, Nelson DB et al (2015) The mixed impact of medical school on medical students’ implicit and explicit weight bias. Med Educ 49(10):983–992. doi:10.1111/medu.12770

    Article  Google Scholar 

  73. Mullen K, Nicolson M, Cotton P (2010) Improving medical students’ attitudes towards the chronic sick: a role for social science research. BMC Med Educ [Internet]. BioMed Central 10:84. doi:10.1186/1472-6920-10-84

  74. Hillas G, Perlikos F, Tsiligianni I, Tzanakis N (2015) Managing comorbidities in COPD. Int J Chron Obstruct Pulmon Dis 10:95–109. doi:10.2147/COPD.S54473

    Google Scholar 

  75. Ivanova JI, Bergman R, Birnbaum HG, Colice GL, Silverman RA, McLaurin K (2012) Effect of asthma exacerbations on health care costs among asthmatic patients with moderate and severe persistent asthma. J Allergy Clin Immunol 129(5):1229–1235. doi:10.1016/j.jaci.2012.01.039

    Article  Google Scholar 

  76. Dhamane AD, Moretz C, Zhou Y, Burslem K, Saverno K, Jain G et al (2015) COPD exacerbation frequency and its association with health care resource utilization and costs. Int J Chron Obstruct Pulmon Dis 10:2609–2618. doi:10.2147/COPD.S90148

    Article  Google Scholar 

  77. Lieu TA, Quesenberry CP, Sorel ME, Mendoza GR, Leong AB (1998) Computer-based models to identify high-risk children with asthma. Am J Respir Crit Care Med 157(4):1173–1780. doi:10.1164/ajrccm.157.4.9708124

    Article  Google Scholar 

  78. Ehteshami-Afshar S, FitzGerald JM, Doyle-Waters MM, Sadatsafavi M (2016) The global economic burden of asthma and chronic obstructive pulmonary disease. Int J Tuberc Lung Dis 20(1):11–23. doi:10.5588/ijtld.15.0472

    Article  Google Scholar 

  79. Kullgren JT, Williams GC, An LC (2013) Patient-centered financial incentives for health: can employers get change for their dollars? 1(3–4):82–85. doi:10.1016/j.hjdsi.2013.08.001. Cited 3 Feb 2016

  80. Chernew ME, Rosen AB, Fendrick AM (2007) Value-based insurance design. Health Aff (Millwood) 26(2):w195–w203. doi:10.1377/hlthaff.26.2.w19. Cited 15 Feb 2016

  81. Maeng DD, Pitcavage JM, Snyder SR, Davis DE (2016) The value of value-based insurance design: savings from eliminating drug co-payments. Am J Manag Care 22(2):116–121. Cited 24 Feb 2016

    Google Scholar 

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Acknowledgments

The authors would like to acknowledge the editorial support of Mark Greener on behalf of NexGen Healthcare Communications in the preparation of this chapter, supported by Teva Pharmaceuticals. Teva Pharmaceuticals was given an opportunity to review the manuscript for accuracy and suggested changes were incorporated at the discretion of the authors. The views expressed are those of the authors and are not necessarily shared by Teva Pharmaceuticals. All authors contributed fully to the development of the chapter, approved all drafts, and are responsible for its accuracy and integrity.

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Correspondence to Bruce G. Bender , Henry Chrystyn or Bernard Vrijens .

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Bender, B.G., Chrystyn, H., Vrijens, B. (2017). Smart Pharmaceuticals. In: Thuemmler, C., Bai, C. (eds) Health 4.0: How Virtualization and Big Data are Revolutionizing Healthcare. Springer, Cham. https://doi.org/10.1007/978-3-319-47617-9_4

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