Precision Medicine in Respiratory Diseases: a Primer for Pharmacists


July 1, 2019


July 31, 2021


Donna M. Lisi, PharmD, BCPS, BCACP, BCGP, BCPP
Adjunct Faculty
Union County College, Plainfield, New Jersey
Medical Writer/Educator
Somerset, New Jersey


Dr. Lisi has no actual or potential conflict of interest in relation to this activity.

Postgraduate Healthcare Education, LLC does not view the existence of relationships as an implication of bias or that the value of the material is decreased. The content of the activity was planned to be balanced, objective, and scientifically rigorous. Occasionally, authors may express opinions that represent their own viewpoint. Conclusions drawn by participants should be derived from objective analysis of scientific data.


acpePostgraduate Healthcare Education, LLC is accredited by the Accreditation Council for Pharmacy Education as a provider of continuing pharmacy education.
UAN: 0430-0000-19-056-H01-P
Credits: 2.0 hours (0.20 ceu)
Type of Activity: Knowledge


This accredited activity is targeted to pharmacists. Estimated time to complete this activity is 120 minutes.

Exam processing and other inquiries to:
CE Customer Service: (800) 825-4696 or


Participants have an implied responsibility to use the newly acquired information to enhance patient outcomes and their own professional development. The information presented in this activity is not meant to serve as a guideline for patient management. Any procedures, medications, or other courses of diagnosis or treatment discussed or suggested in this activity should not be used by clinicians without evaluation of their patients’ conditions and possible contraindications or dangers in use, review of any applicable manufacturer’s product information, and comparison with recommendations of other authorities.


To educate the pharmacist about the emerging field of precision medicine as it pertains to the diagnosis and management of respiratory diseases.


After completing this activity, the participant should be able to:

  1. Identify what is meant by precision or personalized medicine as it applies to the respiratory patient.
  2. Describe the various types of omic studies being used for the diagnosis and management of respiratory diseases.
  3. Recognize the current and future role of precision medicine in the diagnosis and management of respiratory diseases.
  4. Develop a treatment plan for respiratory diseases for which precision medicine is already being applied, such as cystic fibrosis and asthma.

ABSTRACT: Precision medicine (PM) offers the promise of improving prognostication and diagnostic accuracy and enhancing individualization of drug therapy based on a patient’s genetics, environmental exposures, lifestyle choices, concomitant medications, and comorbidities. Respiratory diseases are associated with significant morbidity and mortality. The heterogeneity of their presentations and the variability in response to treatments make respiratory diseases prime targets for the application of PM. Pharmacists need to be familiar with the terminology associated with PM and the latest developments in the diagnosis and treatment of respiratory diseases such as lung cancer, cystic fibrosis, asthma, and chronic obstructive pulmonary disease that have occurred as a result of research in this field.

In January 2015, President Obama launched the Precision Medicine Initiative (PMI). PMI “is a long-term research endeavor, involving the National Institutes of Health (NIH) and multiple other research centers, which aims to understand how a person’s genetics, environment, and lifestyle can help determine the best approach to prevent or treat disease.”1 While the short-range goals of the PMI focus on applying precision medicine (PM) to cancer research, its long-term goals include applying PM on a large scale to all areas of health and healthcare, including respiratory medicine. This is being achieved through the national research program called All of Us, which is currently recruiting at least one million volunteers to help build a database of genetic information, biological samples, and other health data that will be used to predict disease risk, understand how diseases occur, and improve the diagnosis and treatment of medical conditions.2

The potential impact of PM to improve the diagnosis of and alleviate the suffering from respiratory diseases is significant. Data from the 2017 National Health Interview Survey on adults aged 18 and older with selected respiratory diseases can be found in TABLE 1.3


The terms PM, personalized medicine, genomic medicine, stratified medicine, individualized medicine, and P4 medicine (personalized, predictive, preventive, and participatory) are often used interchangeably.4 Although the term PM was first coined in 2008, it wasn’t until 2011 that it became widely used following a report from the U.S. National Research Council (US NRC) entitled Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Diseases.5,6 What all of these terms have in common is that they recognize that a “one-size-fits-all” approach to medicine is not sufficient.

PM incorporates data from a patient’s medical history and physical examination, lifestyle, laboratory tests (including pharmacogenomics), imaging/functional diagnostics, immunology and histology, and omics—the technology used to explore the molecules that make up cells.7 (See TABLE 2 for definitions of basic terminology associated with PM.)5,8-30 A concept introduced under PM is that of treatable traits, i.e., disease subgroups that can be treated in a better way because of more precise and validated phenotypic recognition or a better understanding of the critical causal pathways.7 This concept moves away from labels for respiratory diseases such as asthma or chronic obstructive pulmonary disease (COPD) and focuses instead on identifying the pathogenesis of common symptomatology shared by these conditions.

More than 80 million genetic variants have been identified in the human genome. However, science has not yet clarified the role that these play in health and wellness.31 The Trans-Omics for Precision Medicine (TOPMed) Program is gathering omics data across diverse populations, including those that have been traditionally underrepresented in research.32 It is highly unlikely that one single biomarker would adequately address or capture these phenotypic intricacies.33 Additionally, interlaboratory results differ regarding the interpretation of genomic variants. ClinVar, a database from the National Center for Biotechnology Information, serves as a public portal for the deposition and retrieval of variants and the interpretation of their significance.31


Following the US NRC’s report in 2011,6 in 2012 the Institute of Medicine published a report entitled Evolution of Translational Omics, which identified best practices in the field of omics-based tests.34 Through technological advances such as the use of high-throughput assays that allow for hundreds of thousands of experimental samples to be processed simultaneously, next-generation sequencing, computational biology, clinical bioinformatics, genome-wide assay sequencing, and wearable technology, these advances are coming into reach of clinical application.9,10 Combining multiple omics data sets increases predictive accuracy of disease classification.35


Epigenetic changes involve changes in gene expression secondary to external stimuli that occur without altering the inherent DNA code. The most common markers of epigenetic changes are DNA methylation and histone H4 acetylation, which indicate changes in transcription.29


The FDA has developed a table of medications (TABLE 3) for which pharmacogenomic biomarker drug-labeling information is available.36-38 These pharmacogenomic labels provide recommendations on drug-specific predictive genotyping based on a patient’s germline or somatic DNA.39


The metabolome can serve as a representative of a patient’s overall health status. It reflects biological information encoded by the genome and how this information is modified by diet, aging, the environment, concomitant diseases, medications, and the gut microbiome. At present, medicine captures only a small part of the information contained within the metabolome. No single biomaterial is suitable for all tests.11 Any perturbance in any part of metabolism can have a ripple effect resulting in alteration of enzymes downstream.40 Respiratory conditions for which there is a body of literature on metabolite biomarkers include asthma and lung cancer.10

Technologies used in metabolomics include nuclear magnetic resonance and mass spectrometry.41 The Pharmacometabolomics Research Network is working to integrate the rapidly evolving science of metabolomics with molecular pharmacology and pharmacogenomics in order to move toward the goal of individualized drug therapy and subclassification of disease based on treatment outcomes.42

The NIH is funding six regional comprehensive metabolomics resource cores.43 Resources developed by these centers can be found in the Metabolomics Workbench, which is a data repository that allows researchers to upload, search, and analyze metabolomics data via online interfaces.44 Furthermore, the Human Metabolome Database has categorized over 42,000 metabolites, including about 80% derived from food sources. However, less than 10% of these metabolites are drug metabolites.40


Mutations in the same protein may produce different effects, ranging from no disease to a variety of different diseases.45 Technology used in proteomics includes mass spectrometry.41


A feature of transcriptomics is the ability to identify untargeted compounds and the capacity to analyze more than one million features. Limitations of this technology include difficulty in clinical implementation, concerns over accurate quantitation, and the occurrence of false positives.41 Technology used in transcriptomics includes in situ oligonucleotide RNA sequencing.41


Of these omics tests, breathomics is the least-developed biomarker. However, this field of PM has great potential because it is noninvasive and, therefore, would be beneficial in pediatrics. More than 3,400 individual volatile organic compounds (VOCs) in breath have been identified, but their significance is not completely known. Discoveries in this field are hampered by interference from confounders such as ambient air, diet, exercise, and medications, as well as concomitant diseases. Additionally, in patients with infection, the VOCs obtained would reflect the metabolism of both the infectious agent and its human host. Another drawback is that the technology has not evolved to point-of-care testing. There is also a lack of standardized methods for breath collection.12,46 Recently, a bioelectronic nose, called eNose, was developed, and may serve as a useful tool in precision medicine.12,46


Knowledge gained from the Human Microbiome Project has provided growing evidence that interactions between the host and the microbiome contribute to differences in clinical phenotypes and disease course in conditions such as cystic fibrosis (CF).47 Analyzing the lung microbiome can predict response to therapy, exacerbation risk, and rate of progression of diseases.48 Overuse of antibiotics can adversely affect the lung microbiome, decreasing microbial diversity.48 Use of corticosteroids may also affect the lung microbiome by altering neutrophil function and increasing the amount of potential airway pathogens present, which may promote the development of pneumonia.48


PM has contributed to the diagnosis and/or management of respiratory diseases including lung cancer, CF, asthma, COPD, and overlap or mixed asthma-COPD (ACOS).

Lung Cancer

Multiple gene signatures have been studied as prognostic and predictive biomarkers. However, the most important prognostic factor for non–small-cell lung cancer (NSCLC) is pathological stage. EGFR (epidermal growth factor receptor) mutations, ALK (anaplastic lymphoma kinase) gene rearrangements, and KRAS mutations have been validated as predictive markers and are used clinically when determining pharmacologic regimens.49 In NSCLC, EGFR tyrosine kinase status provides both prognostic information regarding survival and predictive information regarding response to therapy with tyrosine kinase inhibitors such as erlotinib, afatinib, and gefitinib.50 NSCLC patients with ALK gene rearrangement experience dramatic improvement with the use of crizotinib, an inhibitor of ALK.7

Cystic Fibrosis

Unlike other respiratory diseases, the genetic determinant of CF is rather straightforward, as this is a monogenic disease. CF results from mutations in a single gene, the CF transmembrane conductance regulator (CFTR) gene located on chromosome 7, which causes disturbances in chloride and bicarbonate transport in epithelial cells. The CFTR protein is a chloride channel that is expressed on epithelial cells in the lung, gut, and exocrine glands.51,52 These mutations in the CFTR protein result in either the lack of protein at the apical surface or an improperly functioning protein.53

CF manifests as a multiorgan disease involving repeated pulmonary infections; pancreatic insufficiency54; alterations in sweat gland function, metabolism of proteins, fat-soluble vitamins and salt depletion; and infertility in males.55 Despite CF involving a single gene, it is now known that there are more than 2,000 CFTR mutations, which can present as varying phenotypes.51 Expression of these various phenotypes may be influenced by environmental causes, including airway microbiology.48 Furthermore, there are complex alleles that contain more than one CFTR mutation.55

There are six functional classes in CF. These are Class I, which is associated with a lack of CFTR protein resulting in premature termination signals; Class II, which involves mutants that fail to traffic to the cell surface due to misfolding and premature degradation by the endoplasmic reticulum; Class III, which involves no function due to lack of defective channel gating once CFTR protein reaches the cell surface; Class IV, which is associated with decreased function due to reduced flows of ions through the CFTR channel pore; Class V, which is associated with production of very low levels of normal CFTR; and Class VI, which results from less stability of the protein molecule.55 A single mutation, the F580del, is the most common mutation, occurring in about 80% to 85% of CF patients who have at least one allele: About 50% of CF patients are homozygous for F580del.52,55 This deletion results in Class II, III, and VI defects.55

CFTR modulators are small molecules that target specific defects produced by the mutations in the CFTR gene.56 CFTR modulating compounds are classified into five main groups based on their mechanisms of action, which include read-through agents, correctors, potentiators, stabilizers, and amplifiers. Pharmacologic agents that function as potentiators and correctors have been FDA approved.57

In 2012, the CFTR potentiator ivacaftor was approved for use in patients who have one mutation in the CFTR gene that is responsive to ivacaftor based on clinical and/or in vitro assay data. About 10% of CF patients have the gating and other residual function mutations that are addressed by the use of ivacaftor. Ivacaftor corrects the gating impairment in Class III mutations, the conductance problems in Class IV mutations, and protein biosynthesis defects in Class V mutations. Ivacaftor normalizes sweat chloride content, improves lung function as evidenced by a 10% mean increase in forced expiratory volume (FEV1), reduces CF exacerbations, and improves BMI.57

F508del-CFTR is degraded while transitioning through the endoplasmic reticulum to the cell surface with little or no mutant protein reaching the apical membrane of the epithelial cell. Drugs like ivacaftor, whose mode of action is as a potentiator, do not benefit the majority of CF patients with the Class II F508del-CFTR mutation. In order for the F508del-CFTR mutation to reach the cell surface, it needs a chaperone that can repair defective protein folding and rescue trafficking of the protein. Lumacaftor was found to restore F508del-CFTR channel activity but, unfortunately, monotherapy with lumacaftor did not lead to improved lung function.57 Defects in F508del need both a corrector to increase the amount of protein at the cell surface and a potentiator to increase gating of the abnormal CFTR channel.52

In 2015, a combination product containing lumacaftor, a CFTR corrector, and ivacaftor was marketed and indicated for patients who are homozygous for the F508del mutation, which involves approximately 40% to 50% of the CF population. However, response to this combination drug has been less than anticipated, with only modest improvements in lung function despite reductions in pulmonary exacerbations and an increase in BMI. It is not well tolerated by 10% to 20% of patients, with side effects including dyspnea, hepatotoxicity, and bronchospasm and involvement in significant drug-drug interactions.57 A next-generation CFTR corrector, tazacaftor, has also been approved recently for use in combination with ivacaftor. It appears to be more effective for those who are homozygous for the F508del mutation and is better tolerated.58 It has a longer half-life and improved pharmacokinetics. Improvement in lung function was as good as, or better than, the lumacaftor/ivacaftor combination. However, there is much heterogeneity in the response to either of these combinations among CF patients.57 Guidelines for the use of CFTR modulator therapy in patients with CF have been published.59


Asthma is a heterogeneous disease of overlapping symptoms and varying phenotypes that results from interactions between environmental factors (such as allergens and infections) in concert with susceptibility genes.17

Despite advances in asthma pharmacotherapy, approximately 5% to 10% of patients have refractory asthma manifesting as an inadequate response to high-dose corticosteroids.60 These patients may benefit from anti–interleukin 5 (IL-5) therapy. IL-5 is essential for the growth, differentiation, activation, and apoptosis of eosinophils.17

Biomarkers are being used in the selection of drug therapy for asthma. They can be used to classify patients according to phenotype and/or endotype.60 The presence of a high sputum eosinophil count is predictive of response to corticosteroid therapy.61 A recent study categorized 190 potential asthma biomarkers made up of DNA loci, transcripts, proteins, metabolites, epimutations, and noncoding RNAs, representing 13 different types of omics data. These included genomics, epigenomics, transcriptomics, proteomics, interactomics, metabolomics, mRNAomics, glycomics, lipidomics, environmental omics, pharmacogenomics, phenomics, and integrative omics. Ten biomarkers were identified using two or more omics analyses.62

In asthma, phenotypes which profile clinical, physiologic, and hereditary characteristics have evolved into endotypes in which specific biological pathways are identified to explain the observable properties of the phenotype.61 The most common stratification breaks asthmatic patients into two groups—type 2 or non–type 2—based on sputum cell counts, clinical phenotyping, and type 2 biomarkers. Type 2 asthma is further subdivided into allergic eosinophilic (atopic) and nonallergic eosinophilic asthma (nonatopic).61 Type 2 immune response involves T-helper 2 (Th-2) cells.63 Non–type 2 is associated with a neutrophilic/paucigranulocytes and mixed granulocytic asthma pattern.61 Eosinophilic asthma is also referred to as Th-2 high asthma whereas non–type 2 asthma is also called Th-2 low asthma.64

Type 2 atopic eosinophilic asthma is associated with increased levels of markers of inflammation, mainly eosinophils, mast cells, group 2 innate lymphoid cells, and IgE-producing B lymphocytes, as well as with increased serum periostin, dipeptidyl peptidase-4, and fraction of exhaled nitric oxide. Patients with this form of asthma are more likely to respond to corticosteroids or biologic agents.17,65 Most children and about 50% of adults with asthma have type 2 allergic eosinophilic asthma.61

Type 2 nonallergic eosinophilic asthma, often referred to as late-onset asthma, develops later in life and is not associated with IgE reactivity to allergens, nor does it involve adaptive immunity, such as activation of Th-2 cells. This form of asthma is often associated with chronic rhinosinusitis, nasal polyps, and nonresponsiveness to drug therapy, necessitating the use of long-term systemic corticosteroids.61 This endotype is less well characterized.66

Non–type 2 noneosinophilic asthma is a neutrophilpredominant disease with an absence of Th-2 cytokines. Similar to nonatopic type 2 asthmatics, these patients have adult-onset disease. They are less likely to be atopic. Triggers for exacerbations may include obesity, respiratory infections, smoking, and air pollution. This type is characterized by increased asthma severity and remodeling and lower response to bronchodilator and anti-inflammatory treatment. IL-17 appears to have a prognostic value in determining asthma severity in this endotype. IL-17–producing cells are resistant to inhibition by corticosteroids, making these patients difficult to manage. Some patients have a mixed granulocytic asthma with eosinophils and neutrophils or paucigranulocytic asthma, but these inflammatory cells are much less than what is seen in type 2 atopic asthma. This group is poorly defined, heterogenous, and lacks biomarkers to help guide drug therapy.61,67

The U-BIOPRED (Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes) consortium is a pan-European public-private collaboration that is subphenotyping patients with severe refractory asthma in order to determine the mechanisms and biological pathways associated with this condition.68 Four clusters of adults with asthma were identified in this study: Phenotype 1 consisted of adults with moderate-to-severe asthma who were well controlled with mild-to-no airflow obstruction and medium-to-high inhaled corticosteroid (ICS) use; phenotype 2 was made of up patients with late-onset (adult onset) disease who were either smokers or ex-smokers, had high blood eosinophil counts, and experienced severe disease and airflow obstruction; phenotype 3 was composed of adults with severe disease manifesting as moderate-to-severe airflow obstruction and who were dependent on oral corticosteroid therapy; and phenotype 4 included adult females with severe asthma associated with mild-to-no airflow obstruction but frequent exacerbations.69

Another way to stratify patients is based on early (childhood-onset) versus late (adult-onset) asthma. Early childhood asthma is more often associated with atopy and is responsive to glucocorticoids. Adult-onset asthma occurs more often in females, is generally not associated with atopy, and portends a poor prognosis.70

Aspirin-exacerbated respiratory disease (AERD) is a distinct asthma phenotype that is characterized by severe symptomatology, chronic hyperplastic eosinophilic sinusitis with nasal polyps, eosinophilia, and increased concentrations of urinary leukotriene E4. Multiple subphenotypes have been identified within the AERD phenotype.71

Type-2 targeted biologics have been used to treat severe eosinophilic asthma and hypereosinophilic-associated disorders. Targeted therapies require biomarkers with high diagnostic and prognostic capacities.61 These agents target IgE, IL-5, IL-5R, and IL-4R.

Omalizumab is a monoclonal antibody against IgE. It is administered by subcutaneous injection every 2 to 4 weeks with the dose based on weight and serum IgE levels. It binds to the Fc part of free IgE, thereby preventing binding of IgE to FccR1 receptors. This reduces free IgE levels and downregulates receptor expression. However, despite dosing based on IgE levels, baseline IgE levels do not predict the likelihood of response. Mepolizumab and reslizumab are human monoclonal antibodies against IL-5. Mepolizumab and reslizumab are administered SC every 4 weeks. Both drugs act by binding to circulating IL-5. Benralizumab is a humanized monoclonal antibody targeting the alpha-subunit of IL-5R, which is present on eosinophils and basophils, resulting in the death of the eosinophil. It is administered every 4 weeks for three doses then every 8 weeks there-after. Dupilumab is a monoclonal antibody that binds to IL-4 receptor alpha, blocking both IL-4 and IL-13 signaling. It is administered via SC injection every 2 weeks. It is also approved for use in moderate-to-severe atopic dermatitis.72

The use of biologics in selected patients with evidence of elevated eosinophilic biomarkers has been somewhat successful. On the other hand, agents that target neutrophilic inflammation have failed in clinical trials.73

The latest Global Initiative for Asthma (GINA) guidelines on the management and prevention of asthma in adults and children older than age 5 years, released in April 2019, recommend anti-IgE therapy with omalizumab as an add-on option for patients aged 6 years or older with severe allergic asthma uncontrolled on high-dose ICS–long-acting beta-agonist (LABA) combinations; anti-I-5 (mepolizumab or reslizumab) or anti-I-5R (benralizumab) therapy as add-on options for patients with severe eosinophilic asthma uncontrolled on high-dose ICS-LABA combinations; and anti-I-4R therapy (dupilumab) as an add-on for patients aged 12 years or older with severe eosinophilic or type 2 asthma uncontrolled on high doses of ICS-LABA combinations or requiring maintenance oral corticosteroids. Both mepolizumab and benralizumab are recommended in patients aged 12 years or older, whereas reslizumab’s use is restricted to those aged 18 years or older.74

Additionally, in April 2019, GINA also published guidelines on difficult-to-treat and severe asthma in adolescents and adults. In these guidelines, GINA advocates for the use of biomarkers in selecting and monitoring therapy. Step 6b in the guidelines recommends considering add-on type 2–targeted biologics for patients with exacerbations or poorly controlled symptoms, or those taking high doses of ICS-LABA combinations who have eosinophilic or allergic biomarkers or need maintenance oral corticosteroids. It further outlines which biological agent is appropriate for initial treatment. It recommends anti-IgE therapy for patients with sensitization on skin-prick testing or specific IgE biomarkers/total serum IgE and history of exacerbations in the past year. Patients for whom a good response to anti-IgE therapy is predicted include those with blood eosinophil levels of >260 microliters, fraction of exhaled nitric oxide of >20 parts per billion, allergen-driven symptoms, and childhood-onset asthma. Omalizumab is associated with a significant decrease (34% to 65%) in exacerbations, a 40% to 50% reduction in oral corticosteroid doses, and mixed effect on quality of life.72

Alternatively, patients may be started on anti–IL-5/ anti–IL-5R therapy based on the occurrence of exacerbations in the past year and on blood eosinophil counts of >300 microliters. Factors that predict a good response to anti–IL-5/anti–IL-5R therapy include higher blood eosinophil counts, more exacerbations in the previous year, adult-onset asthma, and nasal polyps. Collectively, the agents in this class are associated with about a 55% decrease in severe exacerbations, improvement in quality of life, and reduction in oral corticosteroid doses.72

Those meeting eligibility for anti–IL-4R treatment include patients with severe eosinophilic type 2 asthma with exacerbations in the previous year, blood eosinophil counts of >150 microliters, fraction of exhaled nitric oxide >25 parts per billion, or the need for maintenance oral corticosteroids. Patients most likely to respond to this regimen include those with higher blood eosinophil counts and higher fraction of exhaled nitric oxide concentrations. A 50% reduction in both severe exacerbations and oral corticosteroid doses, as well as significant improvements in quality of life, have been associated with dupilumab use.72

Once a drug regimen is chosen, a 4-month trial should be conducted to determine adequate response. If response is equivocal, the trial can be extended to 6 to 12 months. If response is inadequate, consider switching to a different type 2–targeted therapy. If the patient experiences a good response to type 2–targeted therapy, which is not well defined, the need for continued treatment should be reevaluated every 3 to 6 months. Adjustments to other anti-asthma medications should be made accordingly.72

These latter guidelines clearly state that biological agents should be reserved for patients whose symptoms or exacerbations and type 2 biomarkers do not respond to properly administered ICS.72 In unselected patient populations, these drugs are of limited utility.61 They warn that biomarkers of type 2 inflammation such as blood eosinophils, sputum eosinophils, and fraction of exhaled nitric oxide are often suppressed due to oral corticosteroid use and, therefore, would not serve as a good biomarker in these patients. Testing should be conducted prior to the initiation of oral corticosteroids if possible. If not, testing should be done while the patient is on the lowest effective oral dose.72

Chronic Obstructive Pulmonary Disease

COPD, including emphysema, is a heterogeneous disease, which some have preferred to call a syndrome instead of a disease, owing to the complexity of its presentation. This complexity is thought to be related to the interaction between environmental exposures such as smoking and susceptibility genes.75 There is a lack of well-studied and defined biomarkers and patient phenotypes for COPD.76 Emphysema is also a heterogeneous disease, as there are variations even among commonly used histologic categories such as centrilobular, panlobular, and paraseptal.77 When traits have been identified in COPD patients, such as airflow limitations, they appear in varying degrees within the same individual, making exclusive phenotyping difficult. This finding supports the argument for the concept of treatable traits instead of more rigid classifications.78,79 Much of our knowledge about COPD is based on results from radiological as opposed to omics data.77,80,81 Recently, 127 genes whose expression levels were significantly related to regional emphysema severity within the same lung were identified.82

In addition to the heterogeneity in disease presentation and etiology, there are differences in response to treatment. About 50% of patients treated with long-acting muscarinic agents do not experience benefit, and about 25% actually have worsening of their disease.41 Blood eosinophil counts may be predictive of beneficial effects from ICS.75

A proposed classification of patients with COPD includes COPD with persistent systemic inflammation (eosinophilic or Th-2 high COPD) who may respond to corticosteroids; COPD with persistent pathogenic bacterial colonization who may benefit from azithromycin due to its antibacterial and anti-inflammatory effects; and COPD with alpha-1-antitrypsin (AAT)deficiency.83,84 Another phenotype classification for COPD includes those with severe airflow limitation, low BMI, and poor health status, and those with moderate airflow limitation, high BMI, and cardiovascular comorbidities.78 A third phenotype schema includes those with emphysema; COPD with chronic bronchitis; COPD combined with asthma; and COPD with frequent exacerbations.85 The drug roflumilast is indicated for the COPD with chronic bronchitis phenotype.18 A fourth phenotypic grouping divides COPD into those who are more symptomatic, with breathlessness and exertional limitations being cardinal signs; those with frequent (2 or more yearly) exacerbations and characterized by neutrophilic inflammation, lower airway bacterial colonization, obesity, and comorbidities; those with chronic bronchitis; and those who have both asthma and COPD.29,86

One form of COPD is due to a severe deficiency of AAT, a major inhibitor of neutrophil elastase that is encoded on chromosome 14. This deficiency most often occurs in persons of European descent. A severe AAT deficiency increases the risk of emphysema, especially in smokers. It is also associated with hepatic impairment, cirrhosis, hepatocellular carcinoma, bronchiectasis, necrotizing panniculitis, and granulomatosis with poly angiitis. This disease is due to autosomal recessive inheritance of the PI*Z allele of the gene SERPINA1. Misfolded Z protein polymerizes in the liver, causing toxicity. It is unclear whether the increased risk of COPD in patients with severe AAT deficiency is due to low AAT levels or damage from the misfolded Z protein. Guidelines suggest testing all COPD patients for AAT deficiency as well as genotyping of the S and Z alleles.87

The search for biomarkers for COPD continues.88 Most recently, plasma fibrinogen levels of >350 mg/dL were associated with an increased risk of hospitalization over a 12-month period as well as an increased risk of all-cause mortality over a 36-month period. Rather than pointing to a pulmonary etiology for morbidity and mortality, this elevated biomarker may actually be reflecting elevated cardiovascular risk. This is not surprising given that COPD and cardiovascular disease share similar risk factors such as smoking.76 Cellular biomarkers (sputum neutrophils, circulating white blood cells), blood protein biomarkers (fibrinogen, CC16, SP-D, CCL18, sRAGE, inflamasome, adipokines, vitamin D), gene studies (evaluating smoking history, emphysema, COPD susceptibility, cachexia, blood biomarkers), sputum transcriptomics (evaluating airflow limitations), serum metabolomics and exhaled breath condensate (examining pH and the adenosine/ purine content) have been the focus of investigation in COPD.30,89 IL-16 has been explored as a biomarker for emphysema, because loss of IL-16–secreting T cells might underlie the pathogenesis of the condition.90 It may increase the predictive value for death in COPD.75

Other biomarkers that may also portend impending mortality include white blood cell and neutrophil counts, chemokine ligand 18, C-reactive protein, surfactant protein D, club cell protein 16, serum amyloid A, IL-8, and tumor necrosis factor–alpha.18,75 More research is needed to determine the significance of these biomarkers, which may have varied roles, from diagnosis to assessment of treatment. Ongoing investigation in this field supports one of the goals of the COPD National Action Plan, which is to increase and sustain research to better understand the prevention, pathogenesis, diagnosis, treatment, and management of COPD.91

Changes in the upper bronchial tract microbiome in patients with COPD, including the abundance of Streptococcus species and increased capacity of bacterial growth, may play a role in acute exacerbations.92 Dysbiosis may contribute to the etiology of the frequent exacerbator COPD phenotype.29

Failure to achieve normal lung function by early adulthood may be an important risk factor for the development of COPD in later life.29 This is an urgent public health matter, as continued efforts need to be made to prevent smoking and/or vaping by youth.


In 2017, the American Thoracic Society and the National Heart, Lung, and Blood Institute published a workshop report on the overlap between asthma and COPD.93 About 25% to 30% of patients have a combination of diseases with features of both asthma and COPD. These patients present with a history of asthma, marked reversibility to bronchodilators, sputum eosinophilia, and increased total serum IgE.86 Three biological clusters within this phenotype have been described and include a type-2 inflammatory group, which is asthma-predominant; a proinflammatory group driven mainly by IL-1beta and tumor necrosis factor–alpha; and a COPD-predominant group.93 Breathomics is being used to further help define this phenotype.94


Despite its great promise, the emerging field of omics has limitations. Often biomarker studies are fraught with methodological issues.41 It is now clear that simply inheriting the polymorphic susceptibility gene may not predict who will acquire the disease, because there are often other undetermined factors at play in order for disease expression to occur.95 Yet what those factors are still needs to be determined. Other challenges faced by omics include publication bias, because negative results are less likely to appear in print; missing a true signal that is obscured by all of the noise due to data overload; lack of longitudinal follow-up as subjects age, alter their diet, develop comorbidities, etc., which may prevent the development of a complete genomic picture; and assessments limited to one type of biological tissue may miss downstream effects.96 A list of website links and resource databases and studies in PM can be found in TABLE 4.


As medicine abandons the one-size-fits-all mentality, PM is poised to target pharmacotherapy so that the right drug is given to the right patient. However, while healthcare providers see the value of PM, they often feel ill-prepared to apply these principles in their practice. For the community-based pharmacist, PM is perfectly aligned with the goals of medication management services. As more PM tests are validated and become available for point-of-care, pharmacists can serve as champions for individualizing medication regimens based on underlying phenotypic pathophysiologic disease mechanisms and drugs’ mechanisms of action.97 Recently, the American Society of Health-System Pharmacists published a practice research report which calls upon pharmacists to take advantage of new opportunities in the field of pharmacogenomics. As the potential and value of PM becomes realized, hospitals and health systems are also being asked to make pharmacogenomics and PM a priority.98


1. National Institutes of Health. U.S. National Library of Medicine. Genetics Home Reference. What is the precision medicine initiative? Accessed April 6, 2019.

2. U.S. Department of Health and Human Services. National Institute on Health. All of Us Research Program. Accessed April 6, 2019.

3. Blackwell DL, Villarroel MA. Tables of Summary health statistics for U.S. adults: 2017 National Health Interview Survey. National Center for Health Statistics. 2018. tables.htm. Accessed April 6, 2019.

4. Roden DM, Tyndale RF. Genomic medicine, precision medicine, personalized medicine: what’s in a name? Clin Pharmacol Ther. 2013;94(2):169-172.

5. Zhang XD. Precision medicine, personalized medicine, omics and big data: concepts and relationships. J Pharmacogenomics Pharmacoproteomics. 2015;6:2. Accessed April 6, 2019.

6. National Research Council. 2011. Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease. Washington, DC: The National Academies Press. Accessed April 6, 2019.

7. Konig IR, Fuchs O, Hansen G, et al. What is precision medicine? Eur Respir J. 2017;50(4).

8. Weinshilboum RM, Wang L. Pharmacogenomics: precision medicine and drug response. Mayo Clin Proc. 2017;92:1711-1722.

9. Chen C, He M, Zhu Y, et al. Five critical elements to ensure the precision medicine. Cancer Metastasis Rev. 2015;34(2):313-318.

10. Trivedi DK, Hollywood KA, Goodacre R. Metabolomics for the masses: the future of metabolomics in a personalized world. New Horiz Transl Med. 2017;3(6):294-305.

11. Beger RD, Dunn W, Schmidt MA, et al, for the Precision Medicine and Pharmacometabolomics Task Group-Metabolomics Society Initiative. Metabolomics enables precision medicine: “A White Paper, Community Perspective.” Metabolomics. 2016;12(10):149.

12. Rattray NJW, Hamrang Z, Trivedi DK, et al. Taking your breath away: metabolomics breathes life in to personalized medicine. Trends Biotechnol. 2014;32(10):538-548.

13. Ogino S, Nishihara R, VanderWeele TJ, et al. The role of molecular pathological epidemiology in the study of neoplastic and nonneo-plastic diseases in the era of precision medicine. Epidemiology. 2016;27(4):602-611.

14. Steiling K, Christenson SA. Targeting ‘types’: precision medicine in pulmonary disease. Am J Respir Crit Care Med. 2015;191:1093-1094.

15. Kan M, Shumyatcher M, Himes BE. Using omics approaches to understand pulmonary diseases. Respir Res. 2017;18(1):149.

16. Sanchez-de-la-Torre M, Khalyfa A, Sanchez-de-la-Torre A, et al, for the Spanish Sleep Network. Precision medicine in patients with resistant hypertension and obstructive sleep apnea: blood pressure response to continuous positive airway pressure treatment. J Am Coll Cardiol. 2015;66:1023-1032.

17. Varricchio G, Senna G, Loffredo S, et al. Reslizumab and eosinophilic asthma: one step closer to precision medicine? Front Immunol. 2017;8:242.

18. Agusti A, Sobradillo P, Celli B. Addressing the complexity of chronic obstructive pulmonary disease: from phenotype and bio-markers to scale-free networks, systems biology, and P4 medicine. Am J Respir Crit Care Med. 2011;183:1129-1137.

19. National Human Genome Research Institute. Epigenomics Fact Sheet. Accessed April 6, 2019.

20. Genos. What is an exome? html. Accessed April 6, 2019.

21. U.S. Library of Medicine. Genetics Home Reference. What are genome-wide association studies? genomicresearch/gwastudies. Accessed April 6, 2019.

22. Science Direct. Immunomics. immunology-and-microbiology/immunomics. Accessed April 6, 2019.

23. Science Direct. Metagenomics. Accessed April 6, 2019.

24. University of Washington. Fast facts about the human microbiome. pdf. Accessed April 6, 2019.

25. Omics, bioinformatics, computational biology. http:// Accessed April 6, 2019.

26. Mestrovic T. What is proteomics? News Medical. Accessed April 6, 2019.

27. U.S. Library of Medicine. Genetics Home Reference. What are single nucleotide polymorphisms (SNPs)? primer/genomicresearch/snp. Accessed April 6, 2019.

28. PHG Foundation. What is transciptomics? www.phgfoundation. org/blog/what-is-transcriptomics. Accessed April 6, 2019.

29. Sidhaye VK, Nishida K, Martinez FJ. Precision medicine in COPD: where are we and where do we need to go? Eur Respir Rev. 2018;27(149).

30. Agusti A, Gea J, Faner R. Biomarkers, the control panel and personalized COPD medicine. Respirology. 2016;21:24-33.

31. Rehm HL, Berg JS, Brooks LD, et al. ClinGen—The Clinical Genome Resource. N Engl J Med. 2015;372:2235-2242.

32. National Heart, Lung and Blood Institute. Trans-Omics for Precision Medicine (TOPMed) Program. trans-omics-precision-medicine-topmed-program. Accessed April 6, 2019.

33. Nussinov R, Jang H, Tsai CJ, Cheng F. Precision medicine review: rare driver mutations and their biophysical classification. Biophysical Rev. 2019;11:5-19.

34. Institute of Medicine. 2012. Evolution of translational omics: lessons learned and the path forward. Washington, DC: The National Academies Press. April 6, 2019.

35. Li CX, Wheelock CE, Skold CM, Wheelock AM. Integration of multi-omics datasets enables molecular classification of COPD. Eur Respir J. 2018;51(5).

36. FDA. Table of pharmacogenomics biomarkers in drug labeling. Accessed April 6, 2019.

37. FDA. Paving the way for personalized medicine. FDA’s role in a new era of medical product development. resources/files/10/10-28-13-Personalized-Medicine.pdf. Accessed April 6, 2019.

38. Drozda K, Pacanowski MA, Grimstein C, Zineh I. Pharmacogenetic labeling of FDA-approved drugs: a regulatory retrospective. JACC Basic Transl Sci. 2018;3(4):545-549.

39. Ingelman-Sundberg M. Personalized medicine into the next generation. J Intern Med. 2015;277:152-154.

40. Nielsen J. Systems biology of metabolism: a driver for developing personalized and precision medicine. Cell Metab. 2017;25:572-579.

41. Sin DD, Hollander Z, DeMarco ML, et al. Biomarker development for chronic obstructive pulmonary disease: from discovery to clinical implementation. Am J Respir Crit Care Med. 2015;192;1162-1170.

42. Duke University School of Medicine. Pharmacometabolomics Research Network. home. Accessed April 6, 2019.

43. National Institutes of Health. Metabolomics. www.commonfund. Accessed April 6, 2019.

44. Metabolomics Workbench. The Metabolomics Consortium Data Repository and Coordinating Center (DRCC). April 6, 2019.

45. Agusti A, Anto JM, Auffray C, et al. Personalized respiratory medicine: exploring the horizon, addressing the issues. Summary of a BRN-AJRCCM Workshop. Am J Respir Crit Care Med.


46. Neerincx AH, Vijverberg SJH, Bos LDJ, et al. Breathomics from exhaled volatile organic compounds in pediatric asthma. Pediatr Pulmonol. 2017;52(12):1616-1627.

47. National Institutes of Health. Human Microbiome Project. Accessed April 6, 2019.

48. Rogers GB, Wesselingh S. Precision respiratory medicine and the microbiome. Lancet Respir Med. 2016;4(1):73-82.

49. Shibata M, Hoque MO. Development of biomarkers for real precision medicine. Transl Lung Cancer Res. 2018;7(Suppl


50. Brownell R, Kaminski N, Woodruff PG, et al. Precision medicine: the new frontier in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 2016;193:1213-1218.

51. Harutyunyan M, Huang Y, Mun KS, et al. Personalized medicine in CF: from modulator development to therapy for cystic fibrosis patients with rare CFTR mutations. Am J Physiol Lung Cell Mol Physiol. 2018;314(4):L529-L543.

52. Martiniano SL, Sagel SD, Zemanick ET. Cystic fibrosis: a model system for precision medicine. Cur Opin Pediatr. 2016;28(3):312-317.

53. Corvol H, Thompson KE, Tabary O, et al. Translating the genetics of cystic fibrosis to personalized medicine. Transl Res. 2016;168:40-49.

54. Skov M, Hansen CR, Pressler T. Cystic fibrosis—an example of personalized and precision medicine. APMIS. 2019;127(5):352-360.

55. Amaral MD. Novel personalized therapies for cystic fibrosis: treating the basic defect in all patients. J Intern Med. 2015;277:155-166.

56. Lopes-Pacheco M. CFTR modulators: shedding light on precision medicine for cystic fibrosis. Front Pharmacol. 2016;7:275.

57. Awatade NT, Wong SL, Hewson CK, et al. Human primary epithelial cell models: promising tools in the era of cystic fibrosis personalized medicine. Front Pharmacol. 2018;9:1429.

58. Burgener EB, Moss RB. Cystic fibrosis transmembrane conductance regulator modulators: precision medicine in cystic fibrosis. Curr Opin Pediatr. 2018;30:372-377.

59. Ren CL, Morgan RL, Oermann C, et al. Cystic Fibrosis Foundation Pulmonary Guidelines. Use of cystic fibrosis transmembrane conductance regulator modulator therapy in patients with cystic fibrosis. Ann Am Thorac Soc. 2018;15(3):271-280.

60. Heffler E, Canonica GW, Diamant Z, et al. Personalized approach to severe asthma. Biomed Res Int. 2018;2018:2465172.

61. Godar M, Blanchetot C, de Haard H, et al. Personalized medicine with biologics for severe type 2 asthma: current status and future prospects. MAbs. 2018;10:34-45.

62. Pecak M, Korosec P, Kunej T. Multiomics data triangulation for asthma candidate biomarkers and precision medicine. OMICS. 2018;22:392-409.

63. Agache I, Akdis CA. Endotypes of allergic diseases and asthma: an important step in building blocks for the future of precision medicine. Allergol Int. 2016;65:243-252.

64. Samitas K, Zervas E, Gaga M. T2-low asthma: current approach to diagnosis and therapy. Curr Opin Pulm Med. 2017;23:48-55.

65. Scelfo C, Galeone C, Bertolini F, et al. Towards precision medicine: The application of omics technologies in asthma management. F1000Res. 2018;7:423.

66. Durack J, Boushey HA, Huang YJ. Incorporating the airway microbiome into asthma phenotyping: moving toward personalized medicine for noneosinophilic asthma. J Allergy Clin Immunol. 2018;141:82-83.

67. Agache I, Rogozea L. Asthma biomarkers: do they bring precision medicine closer to the clinic? Allergy Asthma Immunol Res. 2017;9:466-476.

68. BIOMED. Unbiased biomarkers in prediction of respiratory diseases outcomes. projects/u-biopred/home. Accessed April 6, 2019.

69. Lefaudeux D, De Meulder B, Loza MJ, et al. U-BIOPRED clinical adult asthma clusters linked to a subset of sputum –omics. J Allergy Clin Immunol. 2017;139:1797-1807.

70. Poynter ME, Irvin CG. Interleukin-6 as a biomarker for asthma: hype or is there something else? Eur Respir J. 2016;48:979-981.

71. Bochenek G, Kuschill-Dziurda J, Szafraniec K, et al. Certain sub-phenotypes of aspirin-exacerbated respiratory disease distinguished by latent class analysis. J Allergy Clin Immunol. 2014;133:98-103.

72. Global Initiative for Asthma. Difficult-to-treat and severe asthma in adolescents and adult patients: diagnosis and management. A GINA pocket guide for health professionals. April 2019. Accessed May 1, 2019.

73. Oberle AJ, Mathur P. Precision medicine in asthma: the role of bronchial thermoplasty. Curr Opin Pulm Med. 2017;23:254-260.

74. Global Initiative for Asthma. Pocket guide for asthma management and prevention for adults and children older than 5 years. April 2019. GINA-2019-main-Pocket-Guide-wms.pdf. Accessed May 1, 2019.

75. Roche N. Adding biological markers to COPD categorisation schemes: a way towards more personalised care? Eur Respir J. 2016;47:1601-1605.

76. Hurst JR. Precision medicine in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2016;193:593-606.

77. Wu BG, Segal LN. The road to precision medicine in chronic obstructive pulmonary disease: squeezing more out of chest computed tomography scans. Ann Am Thorac Soc. 2018;15:428-429.

78. Castaldi PJ, Benet M, Petersen H, et al. Do “COPD subtypes” really exist? Thorax. 2017;72:998-1006.

79. Agusti A, Bel E, Thomas M, et al. Treatable traits: toward precision medicine of chronic airway diseases. Eur Respir J. 2016;47:410-419.

80. Washko GR. Chest computed tomography for phenotyping chronic obstructive pulmonary disease: a pathway and a challenge for personalized medicine. Ann Am Thorac Soc. 2015;12:966-967.

81. de Jong P, Hoesein FM. Precision medicine in COPD: are we making it too difficult? Respirology. 2017;22:211-212.

82. Campbell JD, McDonough JE, Zeskind JE, et al. A gene expression signature of emphysema-related lung destruction and its reversal by the tripeptide GHK. Genome Med. 2012;4(8):67.

83. Soriano JB. An epidemiological overview of chronic obstructive pulmonary disease: what can real-life data tell us about disease management? COPD. 2017;14(S1):S3-S7.

84. Woodruff PG, Agusti A, Roche N, et al. Current concepts in targeting COPD pharmacotherapy: making progress towards personalised management. Lancet. 2015;385: 1789-1798.

85. Hoogendoorn M, Feenstra TL, Asukai Y, et al. Patient heterogeneity in health economic decision models for chronic obstructive pulmonary disease: are current models suitable to evaluate personalized medicine? Value Health. 2016;19:800-810.

86. Rooney C, Sethi T. Biomarkers for precision medicine in airways disease. Ann NY Acad Sci. 2015;1346:18-32.

87. Hersh CP. Diagnosing alpha-1 antitrypsin deficiency: the first step in precision medicine. F1000Res. 2017;6:2049. 88. Wain LV, Shrine N, Artigas MS, et al. Genome-wide association analyses for lung function and chronic obstructive pulmonary disease identify new loci and potential druggable targets. Nat Gent. 2017;49:416-425.

89. Faner R, Tal-Singer R, Riley JH, et al. Lessons from ECLIPSE: a review of COPD biomarkers. Thorax. 2014;69:666-672.

90. Bowler RP, Bahr TM, Hughes G, et al. Integrative omics approach identifies interleukin-16 as a biomarker of emphysema. OMICS. 2013;17(12):619-626.

91. National Heart, Blood and Lung Institute. COPD national action plan. COPD-national-action-plan. Accessed April 6, 2019.

92. Cameron SJ, Lewis KE, Huws SA, et al. Metagenomic sequencing of the chronic obstructive pulmonary disease upper bronchial tract microbiome reveals functional changes associated with disease severity. PLoS One. 2016;11(2):e0149095.

93. Woodruff PG, van den Berge M, Boucher RC, et al. American Thoracic Society. National Heart, Lung, and Blood Institute asthma-chronic obstructive pulmonary disease overlap workshop report. Am J Respir Crit Care Med. 2017;196:375-381.

94. Bos LD, Sterk PJ, Fowler SJ. Breathomics in the setting of asthma and chronic obstructive pulmonary disease. J Allergy Clin Immunol. 2016;138:970-976.

95. Pare PD. Genetic testing for respiratory disease: are we there yet? Can Respir J. 2012;19: 246-248.

96. Michelakis ED. PVDOMICS drive the pulmonary hypertension field into the precision medicine era. Circ Res. 2017;121:1106-1108.

97. Johnson JA, Weitzel KW. Advancing pharmacogenomics as a component of precision medicine: how, where and who? Clin Pharmacol Ther. 2016;99(2):154-156.

98. Valgus J, Weitzel KW, Peterson JF, et al. Current practices in the delivery of pharmacogenomics: impact of the recommendations of the Pharmacy Practice Model Summit. Am J Health-Syst Pharm. 2019;76:521-529.