Chronic respiratory diseases are defined as the collection of conditions resulting from infection and injury to the lungs. It includes diseases such as chronic obstructive pulmonary disease (COPD), asthma, lung cancer, CysticFibrosis, and more (Truemanet al., 2017). While chronic lung diseases disproportionately affect individuals in developing countries, they account for more than 10% of global disability-adjusted life-years (DALYs) (Kassebaum,2016).
Two of the most common chronic respiratory diseases are COPD and asthma.Globally, an estimated 65 million people have moderate to severe COPD, and 3million annual deaths are attributed to the disease (The Global Impact of RespiratoryDisease, 2017). In 2010, COPD was the third most common cause of death in the U.S. and was estimated to have cost the U.S.health system a total of $50 billion, including $20 billion indirect costs and$30 billion direct costs (Guarascioet al., 2013). COPD is most frequently caused by smoking but can also result from environmental exposure, such as to biomass fuels (Millerand Levy, 2015). Symptoms include breathlessness, a persistent chest cough, frequent chest infections, and wheezing (NHS,2019). The only treatment for COPD is symptom relief including bronchodilators, inhaled corticosteroids, and long-acting beta-agonists (Guarascioet al., 2013). Frequent hospital visits and diagnostics remain the most significant cost burden.
Compared to the high prevalence of COPD in developing countries, the rise of asthma is associated with changes to a modern, urban lifestyle (Cruz, 2007). Asthma is an extremely heterogeneous disease that affects about 334 million people globally, including14% of all children, and the number is rising (The Global Impact of RespiratoryDisease, 2017). In westernized countries, including the U.S. and Great Britain, the proportion of the population with asthma is above 10% (Cruz, 2007). While asthma has a significantly lower risk of death than COPD, it can arise early in childhood and incur lifelong treatment costs that are a burden to both individuals and healthcare systems (Szefler,2015). Treatments rely on inhaled corticosteroids and beta-agonists that have been the staple for years, but side effects and serious complications—including lung infections and severe asthma attacks—still result in high costs of disease management (NHS, 2018; Wadsworth and Sandford,2013). The cultural costs of asthma, such as missed school days, social events, and work performance result insignificant additional indirect costs: an estimated £98 million in the U.K.alone (Truemanet al., 2017).
Overall, asthma and COPD present a massive economic and social burden, resulting in decreased productivity and contributing life-years. Innovations in diagnosis and treatment must continue to be investigated to prevent the massive costs of these diseases.
Personalized medicine is a growing trend in which therapies are tailored to a specific patient to better ensure safety and effective response (Vogenberget al., 2010). Approaches to personalized medicine include not only a more complex analysis of patients but also more complex understandings of diseases themselves and how they can present differently between patients (NHS,2016). The mechanisms of predictive tests for diagnosis and prediction of effective treatments vary greatly from observable traits such as sex, age and ethnicity, to complex tests including genetic testing and imaging (Redekopand Mladsi, 2013).
But personalized medicine has applications beyond point-of-care treatment. Drug developers can monitor databases that aggregate information collected through personalized medicine tests and predict which patient groups will respond better to potential therapies (Vogenberget al., 2010). Healthcare systems can conduct predictive analytics on population data to analyze groups at risk and provide better services (NHS,2016). Researchers working to understand complex diseases can also observe patterns in large datasets to identify pathways or mechanisms of interest (Ibid).
Fundamentally, personalized medicine works to improve patient care on both an individual and societal level by providing specific, impactful data about individual cases of disease.
It has already been demonstrated that asthma and COPD are a massive burden on healthcare systems and individual lives. The proportion of these costs that are preventable arise from a combination of misdiagnosis, poor prediction of effective treatment, and hospitalization events. Personalized medicine offers a promising opportunity to disrupt the healthcare system around chronic respiratory diseases in developed countries, saving billions of dollars in healthcare costs and improving patients’ lives.
Asthma and COPD are often difficult to distinguish from one another and are heterogenous themselves, making misdiagnosis a chronic problem. It is estimated that while 12 million Americans are known to have COPD, up to 24million additional individuals likely have the disease but are not diagnosed (Guarascioet al., 2013). Of those that are diagnosed withCOPD, up to 13% are believed to be misdiagnosed, resulting in huge costs to the healthcare system from unnecessary or ineffective prescribed treatments (Millerand Levy, 2015). A study conducted in Canada found that 30% of patients who had been diagnosed with asthma were found to not actually have the disease after follow-up tests (Pakhaleet al., 2011). 71% of those patients were actively taking asthma medication that they did not need—and that the healthcare system did not need to be paying for. Particularly in patients over40, distinguishing between asthma and COPD can be extremely challenging; but while inhaled corticosteroids are observed to be highly effective in asthma, they are significantly less effective in controlling cases of COPD (Izquierdoet al., 2010). This demonstrates how misdiagnosed individuals are receiving and paying for ineffective treatment through misdiagnosis, ultimately resulting in poor health outcomes.
A primary reason for the frequent misdiagnosis of respiratory disease is the lack of reliable diagnostic tests and poor understanding among general practitioners of the complexity of the disease manifestations. It has been demonstrated that many primary physicians are unable to distinguish betweenasthma and COPD and were additionally unaware that women are at a higher risk for COPD than men, resulting in the frequent misdiagnosis of women (Badnjevicet al., 2018). While there are existing tests to distinguish between the two diseases, they are not frequently employed:spirometry tests, which are considered critical in distinguishing between asthma and COPD, are found to have been administered to only 43% of individuals over 40 diagnosed with COPD in the United States (NBCH,2012). Similarly in Canada, fewer than50% of diagnoses of asthma in adults are confirmed with lung function tests (Pakhaleet al., 2011). The lack of cohesion amongst testing and diagnosis is contributing to the poor treatment of respiratory diseases, leaving patients either paying for unnecessary treatments or at higher risk for serious adverse events.
An additional difficulty in effective respiratory treatment is the correlation of effective treatments to specific symptom subtypes rather than actual disease diagnosis. The table below (Figure 1), taken from Agusti et al. (2017),demonstrates the variety of treatments available for different symptoms in respiratory diseases. Notably, these symptoms do no cleanly align with only asthma or COPD; two patients with different diseases could present with similar symptoms and therefore benefit from similar treatments, but solely diagnosing their disease as asthma or COPD will not provide the level of detail necessary to identify that treatment. The real difficulty is understanding the specific profile of each patient and which treatment options align with their profiles.
Personalized medicine could resolve these difficulties through bothimproved diagnostic technology combined with a centralized database to identifypatterns in patient symptoms and correlating effective treatments. A largedatabase of patient phenotypes will elucidate patterns in disease prevalenceand will allow for the identification and utilization of biomarkers to predicteffective treatments. Furthermore, a database of patients’ symptoms, histories,and treatment response will also facilitate new drug development for asthma andCOPD. Centralizing individual patient profiles into one database allows forpattern recognition within the scope of highly personalized results, which canidentify biomarkers and disease pathways as potential targets.
The proposal is to invest in a company that is disrupting diagnosis and treatment of asthma and COPD through two aspects of personalized medicine: a centralized database of biomarkers, symptoms, and associated treatments that will be licensed to customers; and a medical grade eNose technology platform that will analyse breathomics for rapid diagnostic testing. This joint model will not only reduce costs from misdiagnosis and trial-by-error treatment regimens but will also capitalize upon the thousands of centralized patient records to analyse population-level patterns in these heterogenous diseases to better recommend effective treatments and facilitate new drug development.
The company currently leading in this approach in Breathomix. It will be referenced as a model throughout the proposal.
Centralized databases that synthesize patient symptoms and histories to accurately recommend treatment for heterogenous diseases are staple applications of personalized medicine (Yuet al., 2019). A database specifically for asthma and COPD would synthesize the presence of different biomarkers, pulmonary function tests, chest X-rays and CT scans, and patient histories from thousands of patients. Physicians would then be able to input a patient’s symptoms, and an algorithm would compare those symptoms to the broader database to identify which disease (asthma or COPD) the patient most likely has, and the most effective treatments based upon the symptomatic profile. Physicians will engage with this platform for point-of-care diagnostics, while drug developers will be able to analyze the core dataset to identify relevant patterns for therapeutic potential and patient subgroups.
This method was demonstrated as early as 2006, when the Geno2Pheno platform was created to run patients’ HIV viral DNA through a database of known drug resistance profiles to recommend effective drug regimens (Fröhlichet al., 2018). A trial database of this kind for respiratory disease was created by Badnjevic et al. (2018). Badnjevic used an artificial neural network and demonstrated a greater than 96% accuracy in distinguishing between COPD and asthma, resulting in a 49% decreased demand for additional tests.
Breathomix’ version of this platform isBreathBase, a central database that integrates multiple data sources to diagnose patients of respiratory disease, including asthma and COPD (“Breathomix,”n.d.). The database allows for “real-time analysis of the sensor data based on advanced signal processing and artificial intelligence, providing diagnostic feedback to the user within seconds” (“Breathomix,”n.d.).
Breathomics is the study of volatile organic compounds (VOCs) that are exhaled in extremely low concentrations with every breath. These compounds are mostly hydrocarbons and are divided into those derived from the environment and inhaled (exogenous VOCs) and those that are metabolized inside the body (endogenous VOCs). Endogenous VOCs are particularly useful for diagnostics because they are demonstrative of internal conditions of the airways and alveoli (Neerincxet al., 2017). eNoses—non-invasive breath analyzers—can rapidly collect exhaled breath condensates to analyze these VOCs and then sync with a central databases to provide patient data in real time (Owlstone,n.d.).
The sensitivity of eNose technologies in respiratory diseases was demonstrated in 2018 by de Vries et al. Their study, which involved 435 asthma and COPD patients, was able to identify five significant patient clusters that differed regarding ethnicity, systemic eosinophilia, neutrophilia, exhaled nitric oxide fraction, atopy, BMI, and exacerbation rate (deVries et al., 2018).
The specificity of these clusters is particularly promising given the range of treatments presented by Agusti et al.(2017) in Figure 1, the efficacies of which are dependent upon the presence of specific patient characteristics. Clearly, eNose technology is capable of distinguishing between these patient subgroups to readily predict—in combination with the database—which treatment options are most likely to be effective.
Breathomix’ version of the eNose has cross-reactive nonspecific sensory arrays to which VOCs competitively bind, mirroring the mammalian olfactory system and releasing a firing of sensors that identifies the VOC mixture (“Breathomix,”n.d.). Breathomix’ eNose is called the SpiroNose because it combines eNose technology with spirometry—a commonly used test in respiratory disease to measure lung efficiency. The sensor readings are sent to the BreathBase platform, which compares results to existing data via machine learning algorithms and determines a diagnosis. The breath data of each SpiroNose patient is added to the overall data pool to continuously improve accuracy.
Diagnosing and treating these dynamic and highly complex diseases requires a variety of symptomatic data and a large comparison pool to maintain high therapeutic efficiency. The user-friendly model of eNoses will reduce misdiagnosis events, increase access to quality medical diagnostics, and improve patient treatment.
The potential market for this opportunity is confirmed by the growth potential across two separate areas: the respiratory disease drug market and the respiratory disease testing market. The respiratory disease drug market will be entered through partnerships with pharmaceutical companies as well as licensing the full database to different players, whereas the respiratory disease testing market will account for sales of the eNose device and diagnostic platform.
Branded sales of asthma-COPD drugs are likely to pass $24 billion in 2021 (Shahand Fazeli, 2017). Growth is primarily being driven by the development of monoclonal antibodies: as of 2013, seventeen different monoclonal antibodies were in development (Fazeliand Shah, 2013). The first monoclonal antibody for asthma by Roche was priced at $1,500 per month, and sales are expected to reach$5.4 billion by 2021 (Ibid, Shah and Fazeli 2017). Generics entered the market in 2018 for the staple drug combination of long-acting beta-agonists and inhaled corticosteroids, spurring innovation in LABA-LAMA combinations (Shahand Fazeli, 2017). GSK’s triple combination therapy is the leader for COPD treatment and is expected to generate sales of $1billion by 2021 (Ibid). Both Novartis and AstraZeneca have COPD drugs in late stage clinical trials.
Licensing the platform to and/or partnering with pharmaceutical companies to identify novel targets, patient subpopulations, and disease phenotypes could entitle the company to a percentage of drug royalties and annual license fees for the database.
The market for respiratory disease testing/diagnostics was valued at $5.0 billion in 2016 and has an expected CAGR of 3.3% (GrandView Research, 2017). Respiratory measurement devices accounted for about $1 billion of the $5 billion in 2015 (see Figure 3) and are expected grow steadily through 2025. The eNose and diagnostic licensing of the platform will capitalize on this growing market to offer a cheaper, more exact solution to asthma and COPD diagnosis and treatment.
Based upon the evaluation of these markets, the proposed business should adopt a two-prong structure of annual licensing fees for the central database(both full data access and diagnostic licensing) as well as the purchase and servicing of the physical eNose technology. Sales of the eNose device and diagnostic licensing will contribute a relatively lower percentage of the company’s income compared to pharmaceutical licensing and partnerships.
Within the licensed platform, user fees will be scaled based upon using the data for purely diagnostic purposes (but not access to the raw, analysable data) compared to full dataset access. Diagnostic licensing will be available to physicians and medical groups, and the annual payments for the purely diagnostic platform will additionally be scaled based upon the size of the organization and the number of patients it serves. Full dataset access will be used by insurers for identification of high-risk areas and by pharmaceutical companies for the purposes of drug development and patient sub population identification. The ability to input new algorithms and perform data analysis instead of receiving purely diagnostic information (as physicians will have)will be significantly more expensive.
Partnerships will provide a core source of revenue in which the company partners with pharmaceutical firms to develop novel algorithms for the dataset toaid drug development. The company will accept lower licensing fees in exchange for a percentage of royalties on future drugs.
The following players are critical to engage in order to guarantee success of the company.
While the following three groups will be primarily interested in the diagnostic elements of the technology, the majority of revenue will come from licensing the full database to pharmaceutical companies and development partnerships to facilitate novel drug development for chronic respiratory diseases. Pharmaceutical companies would hugely benefit from a diverse, integrated platform that communicates the patterns and variability of patient cases, as was evidenced by the purchase of FlatironHealth by Roche. Flatiron is a Real-World Data company whose data platformRoche believed would greatly facilitate therapeutic research in oncology, so Roche led Flatiron’s $175 million Series C in exchange for 12.6% of the company (Dietsche, 2018). In 2018, Roche announced that it was purchasing the rest of Flatiron’s shares for $2 billion (Murjani and Penfold-Welch, 2018).
The proposed platform could explore a similar partnership to facilitate drug development in respiratory disease. A good potential partner could be GSK, who is currently the market leader in respiratory disease drugs but is facing significant revenue declines after the expiration of its patent on Advair (Shah and Fazeli, 2017). Having access to the data that the platform provides will allow GSK to accelerate its pipeline while simultaneously providing additional data to grow the platform into a more valuable database.If the partnership yields promising results, it could facilitate a strong exit opportunity, as well.
Insurers must be the primary target customer for the diagnostics platform. If insurers are willing to reimburse the platform, then hospitals and clinics can purchase the eNose and license the diagnostics, applying for reimbursement from insurers on a per-use basis (Clarke,2017). In order to ensure that hospitals purchase and are reimbursed for the technology, the cost-savings potential to insurance companies must be sufficiently demonstrated.
Thankfully, there is massive potential for cost-savings to insurers through the application of personalized medicine to asthma and COPD. COPD is estimated to have cost the U.S. health system $20billion in indirect costs and $30 billion in direct health care costs (Guarascio et al., 2013; Thomas,2018). In the U.K., the total cost is a bit less than $4 billion (NHS, n.d.). But it is estimated that over 55%of the costs treating COPD could be avoided through better disease management, meaning that there is an opportunity to reduce costs in the U.S. by $27.5billion and in the UK by $2.2 billion (NBCH, 2012).
10% of the population of the both the U.S. andGreat Britain have asthma, with a total of 48 million people currently diagnosed in the U.S. and EU5 in 2016 (Cruz, 2007; Shah and Fazeli, 2017). Mean costs per patient per year are estimated at $1,900 in Europe and $3,100 in the U.S., with a total annual disease burden of $80 billion in the United States (American Thoracic Society, 2018;Shah and Fazeli, 2017). Based upon research conducted by Pakhale et al. (2011), avoidable costs due to misdiagnosis of asthma per 100individuals is as high as $26,500.
Considering solely the U.S. healthcare system, total preventable costs in asthma/COPD are over $30 billion (see Figure 4).Conservative estimates of potential savings from the broad application of this technology are around $7 billion USD. The vast savings implications suggest that insurers will be eager to partner and implement this cost-reducing technology.
Public insurers—the government—stand to benefit even more from cost-savings of the technology and are similarly likely to reimburse its implementation. The reduced costs may also encourage data-sharing partnerships with government bodies such as the NIH. This would be similar to the Accelerating Medicines Partnership between the NIH and industry groups just announced for Parkinson’s Disease, which launched a data portal to help develop effective therapies (Wonders,2019).
As previously stated, a major reason for them is diagnosis of respiratory diseases is a lack of understanding and adequate testing on the part of primary care physicians (Badnjevicet al., 2018). Having an eNose and access to the diagnostic database in primary doctors’ offices will mean that doctors do not have to independently decipher between asthma and COPD but rather will be able to capitalize on thousands of data points to compare their patient’s personal profile to population-level patterns. Physicians are additionally reported to prefer in-house options for diagnostics rather than having to refer their patients to outdoor labs (GrandView Research, 2017). Patients will receive stronger first diagnoses and effective therapies, which will reduce hospitalization events and trial-and-error periods for beneficial treatments.
Hospitals are anticipated to contribute significant revenue to the growth of the respiratory disease testing market from 2018-2025, likely because the cost-cutting pressure resulting from situations of non-reimbursement (Grajewski,2015; MarketWatch, 2019). This platform will not only reduce costs associated with asthma and COPD, but through rapid and scalable diagnosis, will also free up resources to be allocated to other areas of the hospitals.
The benefit of this platform is that as more patients are uploaded to the database, the increasingly valuable the database will be. For this reason, licensing of the diagnostics and full data platform is expected to have an increasing growth rate as development continues.
The ideal exit scenario is acquisition by one of the established players in the respiratory diagnostics market, such as Philips Healthcare (Netherlands), Becton, Dickinson andCompany (U.S.), or Abbott Laboratories (U.S.) within five to ten years (Markets&Markets, 2016). Alternatively, the company could pursue astrategy of initial partnership with ultimate acquisition similar to that ofRoche and Flatiron Health, which was acquired for almost $2 billion.
510kfda,n.d. FDA 510K Costs [WWW Document]. 510k FDA Consulting : Medical DeviceClearance. URL https://510kfda.com/pages/fda-510k-costs (accessed 11.27.19).
Agustí, A., Bafadhel, M., Beasley, R., Bel, E.H.,Faner, R., Gibson, P.G., Louis, R., McDonald, V.M., Sterk, P.J., Thomas, M.,Vogelmeier, C., Pavord, I.D., 2017. Precision medicine in airway diseases:moving to clinical practice. Eur Respir J 50, 1701655.https://doi.org/10.1183/13993003.01655-2017
American Thoracic Society, 2018. Asthma costs the USeconomy more than $80 billion per year. ScienceDaily.
Badnjevic, A., Gurbeta, L., Custovic, E., 2018. AnExpert Diagnostic System to Automatically Identify Asthma and ChronicObstructive Pulmonary Disease in Clinical Settings. Sci Rep 8.https://doi.org/10.1038/s41598-018-30116-2
Breathomix – Lifesciences @ Work, n.d. URLhttps://www.lifesciencesatwork.nl/profile/breathomix/ (accessed 11.11.19).
Breathomix [WWW Document], n.d. . Breathomix. URLhttps://www.breathomix.com/ (accessed 11.25.19).
Clarke, D.P., 2017. From “Approved” To “Covered” —What Medical Device Companies Need to Know. Med Device Online. URL https://www.meddeviceonline.com/doc/from-approved-to-covered-what-medical-device-companies-need-to-know-0001(accessed 11.27.19).
Cruz, A., 2007. Global surveillance, prevention andcontrol of chronic respiratory diseases: a comprehensive approach. WHO, Geneva.
de Vries, R., Dagelet, Y.W.F., Spoor, P., Snoey, E.,Jak, P.M.C., Brinkman, P., Dijkers, E., Bootsma, S.K., Elskamp, F., de Jongh,F.H.C., Haarman, E.G., in ‘t Veen, J.C.C.M., Maitland-van der Zee, A.-H.,Sterk, P.J., 2018. Clinical and inflammatory phenotyping by breathomics inchronic airway diseases irrespective of the diagnostic label. Eur Respir J 51,1701817. https://doi.org/10.1183/13993003.01817-2017
Dietsche, E., 2018. Roche will acquire Flatiron Healthfor $1.9B. MedCity News.
Fazeli, S., Shah, M., 2013. Product Sales and NewsFlow Asthma-COPD. Bloomberg Intelligence.
Fröhlich, H., Balling, R., Beerenwinkel, N.,Kohlbacher, O., Kumar, S., Lengauer, T., Maathuis, M.H., Moreau, Y., Murphy,S.A., Przytycka, T.M., Rebhan, M., Röst, H., Schuppert, A., Schwab, M., Spang,R., Stekhoven, D., Sun, J., Weber, A., Ziemek, D., Zupan, B., 2018. From hypeto reality: data science enabling personalized medicine. BMC Medicine 16, 150.https://doi.org/10.1186/s12916-018-1122-7
Grajewski, B., 2015. Why are hospitals cutting costs?Becker’s Hospital CFO Report. URLhttps://www.beckershospitalreview.com/finance/why-are-hospitals-cutting-costs.html(accessed 11.27.19).
Grand View Research, 2017. Respiratory DiseaseTesting/Diagnostics Market (Industry Report No. GVR-1-68038-807-7). Grand ViewResearch, San Francisco, CA.
Guarascio, A., Ray, S., Finch, C., Self, T., 2013. Theclinical and economic burden of chronic obstructive pulmonary disease in theUSA. CEOR 235. https://doi.org/10.2147/CEOR.S34321
Izquierdo, J.L., Martín, A., de Lucas, P., Rodríguez,J.M., 2010. Misdiagnosis of patients receiving inhaled therapies in primarycare. International Journal of Chronic Obstructive Pulmonary Disease 9.
Jones Medical, n.d. A good Spirometer is one of thebest physician office investments available. Jones. URLhttps://www.jonesmedical.com/10-tips-for-buying-a-spirometer/ (accessed11.27.19).
Kassebaum, N.J., 2016. Global, regional, and nationaldisability-adjusted life-years (DALYs) for 315 diseases and injuries and healthylife expectancy (HALE), 1990–2015: a systematic analysis for the Global Burdenof Disease Study 2015. The Lancet 388, 1603–1658.https://doi.org/10.1016/S0140-6736(16)31460-X
Markets&Markets, 2016. Respiratory DiagnosticsMarket by Test Type & Product (Market Research Report No. MD 4740).Markets&Markets.
MarketWatch, 2019. Respiratory Disease Testing MarketSize 2019 Outlook, Opportunity and Demand Analysis Report by 2025. MarketWatch.
Miller, M.R., Levy, M.L., 2015. Chronic obstructivepulmonary disease: missed diagnosis versus misdiagnosis. BMJ 351, h3021.https://doi.org/10.1136/bmj.h3021
Murjani, R., Penfold-Welch, K., 2018. Real-World Data:The future of healthcare, pharma and tech. IGNITE Data. URLhttps://www.ignitedata.co.uk/real-world-data-future-of-healthcare-pharma-tech/(accessed 11.27.19).
NBCH, 2012. COPD: A Major Driver of Avoidable HealthCare Costs. National Business Coalition on Health, Washington, D.C.
Neerincx, A.H., Vijverberg, S.J.H., Bos, L.D.J.,Brinkman, P., van der Schee, M.P., de Vries, R., Sterk, P.J., Maitland-van derZee, A.-H., 2017. Breathomics from exhaled volatile organic compounds inpediatric asthma. Pediatr Pulmonol 52, 1616–1627.https://doi.org/10.1002/ppul.23785
NHS, 2019. Chronic obstructive pulmonary disease(COPD) - Treatment [WWW Document]. NHS. URLhttps://www.nhs.uk/conditions/chronic-obstructive-pulmonary-disease-copd/treatment/(accessed 11.24.19).
NHS, 2018. Asthma [WWW Document]. NHS. URLhttps://www.nhs.uk/conditions/asthma/ (accessed 11.24.19).
NHS, 2016. Improving Outcomes Through PersonalisedMedicine. NHS England, Leeds, UK.
NHS, n.d. NHS England » Respiratory disease [WWWDocument]. URLhttps://www.england.nhs.uk/ourwork/clinical-policy/respiratory-disease/(accessed 11.26.19).
Owlstone, n.d. Owlstone Medical - the home of BreathBiopsy® [WWW Document]. Owlstone Medical. URL https://www.owlstonemedical.com/(accessed 11.28.19).
Pakhale, S., Sumner, A., Coyle, D., Vandemheen, K.,Aaron, S., 2011. (Correcting) misdiagnoses of asthma: a cost effectiveness analysis.BMC Pulm Med 11, 27. https://doi.org/10.1186/1471-2466-11-27
Redekop, W.K., Mladsi, D., 2013. The Faces ofPersonalized Medicine: A Framework for Understanding Its Meaning and Scope.Value in Health, Personalized Medicine and the Role of Health Economics andOutcomes Research: Applications, Emerging Trends, and Future Research 16,S4–S9. https://doi.org/10.1016/j.jval.2013.06.005
Shah, M., Fazeli, S., 2017. Asthma-COPD 101. BloombergIntelligence.
Szefler, S.J., 2015. Advances in pediatric asthma in2014: Moving toward a population health perspective. Journal of Allergy andClinical Immunology 135, 644–652. https://doi.org/10.1016/j.jaci.2014.12.1921
The Global Impact of Respiratory Disease, 2017. .Forum of International Respiratory Societies, Sheffield.
Thomas, J., 2018. COPD: Facts, Statistics, and You[WWW Document]. Healthline. URLhttps://www.healthline.com/health/copd/facts-statistics-infographic (accessed11.26.19).
Trueman, D., Woodcock, F., Hancock, E., 2017. TheBattle for Breath. British Lung Foundation, London.
Vogenberg, F.R., Isaacson Barash, C., Pursel, M.,2010. Personalized Medicine. P T 35, 560–576.
Wadsworth, S.J., Sandford, A.J., 2013. PersonalisedMedicine and Asthma Diagnostics/Management. Curr Allergy Asthma Rep 13, 118–129.https://doi.org/10.1007/s11882-012-0325-9
Wonders, C., 2019. Accelerating Medicines Partnershiplaunches data knowledge portal for Parkinson’s disease. National Institutes ofHealth (NIH).
Yu, Y., Wang, Y., Xia, Z., Zhang, X., Jin, K., Yang,J., Ren, L., Zhou, Z., Yu, D., Qing, T., Zhang, C., Jin, L., Zheng, Y., Guo,L., Shi, L., 2019. PreMedKB: an integrated precision medicine knowledgebase forinterpreting relationships between diseases, genes, variants and drugs. NucleicAcids Research 47, D1090–D1101. https://doi.org/10.1093/nar/gky1042
The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.
A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!
Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.
February 17, 2020