1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Medicine
  4. Volume 75, 2024
  5. Article

Annual Review of Medicine

Volume 75, 2024

Precision Approaches to Chronic Obstructive Pulmonary Disease Management

Abstract

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. COPD heterogeneity has hampered progress in developing pharmacotherapies that affect disease progression. This issue can be addressed by precision medicine approaches, which focus on understanding an individual's disease risk, and tailoring management based on pathobiology, environmental exposures, and psychosocial issues. There is an urgent need to identify COPD patients at high risk for poor outcomes and to understand at a mechanistic level why certain individuals are at high risk. Genetics, omics, and network analytic techniques have started to dissect COPD heterogeneity and identify patients with specific pathobiology. Drug repurposing approaches based on biomarkers of specific inflammatory processes (i.e., type 2 inflammation) are promising. As larger data sets, additional omics, and new analytical approaches become available, there will be enormous opportunities to identify high-risk individuals and treat COPD patients based on their specific pathophysiological derangements. These approaches show great promise for risk stratification, early intervention, drug repurposing, and developing novel therapeutic approaches for COPD.

Keyword(s): COPD, genetic epidemiology, multiple omics, network medicine, precision medicine, subtyping
Loading

Article metrics loading...

/content/journals/10.1146/annurev-med-060622-101239
2024-01-29
2024-05-07
Loading full text...

Full text loading...

/deliver/fulltext/med/75/1/annurev-med-060622-101239.html?itemId=/content/journals/10.1146/annurev-med-060622-101239&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Vos T, Lim SS, Abbafati C et al. 2020. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396:102581204–22
     [Google Scholar]
  2. 2.
    Global Initiative for Chronic Obstructive Lung Disease 2023. 2023 GOLD Report Accessed May 25. https://goldcopd.org/2023-gold-report-2/
  3. 3.
    Agusti A, Calverley PMA, Celli B et al. 2010. Characterisation of COPD heterogeneity in the ECLIPSE cohort. Respir. Res. 11:122
     [Google Scholar]
  4. 4.
    Sidhaye VK, Nishida K, Martinez FJ. 2018. Precision medicine in COPD: Where are we and where do we need to go?. Eur. Respir. Rev. 27:149180022
     [Google Scholar]
  5. 5.
    Leung JM, Obeidat M, Sadatsafavi M, Sin DD. 2019. Introduction to precision medicine in COPD. Eur. Respir. J. 53:41802460
     [Google Scholar]
  6. 6.
    Stolz D, Mkorombindo T, Schumann DM et al. 2022. Towards the elimination of chronic obstructive pulmonary disease: a Lancet Commission. Lancet 400:10356921–72
     [Google Scholar]
  7. 7.
    Lowe KE, Regan EA, Anzueto A et al. 2019. COPDGene® 2019: Redefining the diagnosis of chronic obstructive pulmonary disease. Chronic Obstr. Pulm. Dis. 6:5384–99
     [Google Scholar]
  8. 8.
    Wan ES, Castaldi PJ, Cho MH et al. 2014. Epidemiology, genetics, and subtyping of preserved ratio impaired spirometry (PRISm) in COPDGene. Respir. Res. 15:89
     [Google Scholar]
  9. 9.
    Stanojevic S, Kaminsky DA, Miller MR et al. 2022. ERS/ATS technical standard on interpretive strategies for routine lung function tests. Eur. Respir. J. 60:12101499
     [Google Scholar]
  10. 10.
    Young KA, Regan EA, Han MK et al. 2019. Subtypes of COPD have unique distributions and differential risk of mortality. Chronic Obstr. Pulm. Dis. 6:5400–13
     [Google Scholar]
  11. 11.
    Castaldi PJ, Dy J, Ross J et al. 2014. Cluster analysis in the COPDGene study identifies subtypes of smokers with distinct patterns of airway disease and emphysema. Thorax 69:5415–22
     [Google Scholar]
  12. 12.
    Pistenmaa CL, Nardelli P, Ash SY et al. 2021. Pulmonary arterial pruning and longitudinal change in percent emphysema and lung function: the Genetic Epidemiology of COPD Study. Chest 160:2470–80
     [Google Scholar]
  13. 13.
    Diaz AA, Orejas JL, Grumley S et al. 2023. Airway-occluding mucus plugs and mortality in patients with chronic obstructive pulmonary disease. JAMA 329:211832–39
     [Google Scholar]
  14. 14.
    Hardin M, Silverman EK, Barr RG et al. 2011. The clinical features of the overlap between COPD and asthma. Respir. Res. 12:127
     [Google Scholar]
  15. 15.
    Christenson SA, Steiling K, van den Berge M et al. 2015. Asthma-COPD overlap. Clinical relevance of genomic signatures of type 2 inflammation in chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 191:7758–66
     [Google Scholar]
  16. 16.
    John C, Guyatt AL, Shrine N et al. 2022. Genetic associations and architecture of asthma-COPD overlap. Chest 161:51155–66
     [Google Scholar]
  17. 17.
    Yun JH, Chase R, Parker MM et al. 2019. Peripheral blood gene expression signatures of eosinophilic chronic obstructive pulmonary disease. Am. J. Respir. Cell Mol. Biol. 61:3398–401
     [Google Scholar]
  18. 18.
    Zhou JJ, Cho MH, Castaldi PJ et al. 2013. Heritability of chronic obstructive pulmonary disease and related phenotypes in smokers. Am. J. Respir. Crit. Care Med. 188:8941–47
     [Google Scholar]
  19. 19.
    Wilk JB, Chen TH, Gottlieb DJ et al. 2009. A genome-wide association study of pulmonary function measures in the Framingham Heart Study. PLOS Genet. 5:3e1000429
     [Google Scholar]
  20. 20.
    Silverman EK, Sandhaus RA. 2009. Clinical practice. Alpha1-antitrypsin deficiency. N. Engl. J. Med. 360:262749–57
     [Google Scholar]
  21. 21.
    Fairbanks KD, Tavill AS. 2008. Liver disease in alpha 1-antitrypsin deficiency: a review. Am. J. Gastroenterol. 103:82136–41
     [Google Scholar]
  22. 22.
    Silverman EK, Miletich JP, Pierce JA et al. 1989. Alpha-1-antitrypsin deficiency. High prevalence in the St. Louis area determined by direct population screening. Am. Rev. Respir. Dis. 140:4961–66
     [Google Scholar]
  23. 23.
    O'Brien ML, Buist NR, Murphey WH. 1978. Neonatal screening for alpha1-antitrypsin deficiency. J. Pediatr. 92:61006–10
     [Google Scholar]
  24. 24.
    Am. Thorac. Soc., Eur. Respir. Soc 2003. American Thoracic Society/European Respiratory Society statement: standards for the diagnosis and management of individuals with alpha-1 antitrypsin deficiency. Am. J. Respir. Crit. Care Med. 168:7818–900
     [Google Scholar]
  25. 25.
    Brantly M. 2006. Efficient and accurate approaches to the laboratory diagnosis of alpha1-antitrypsin deficiency: the promise of early diagnosis and intervention. Clin. Chem. 52:122180–81
     [Google Scholar]
  26. 26.
    Ghosh AJ, Hobbs BD, Moll M et al. 2022. Alpha-1 antitrypsin MZ heterozygosity is an endotype of chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 205:3313–23
     [Google Scholar]
  27. 27.
    Seersholm N, Wencker M, Banik N et al. 1997. Does alpha1-antitrypsin augmentation therapy slow the annual decline in FEV1 in patients with severe hereditary alpha1-antitrypsin deficiency? Wissenschaftliche Arbeitsgemeinschaft zur Therapie von Lungenerkrankungen (WATL) alpha1-AT study group. Eur. Respir. J. 10:102260–63
     [Google Scholar]
  28. 28.
    Dirksen A, Piitulainen E, Parr DG et al. 2009. Exploring the role of CT densitometry: a randomised study of augmentation therapy in alpha1-antitrypsin deficiency. Eur. Respir. J. 33:61345–53
     [Google Scholar]
  29. 29.
    Wencker M, Fuhrmann B, Banik N et al. 2001. Longitudinal follow-up of patients with α1-protease inhibitor deficiency before and during therapy with IV α1-protease inhibitor. Chest 119:3737–44
     [Google Scholar]
  30. 30.
    Greider CW, Blackburn EH. 1989. A telomeric sequence in the RNA of tetrahymena telomerase required for telomere repeat synthesis. Nature 337:6205331–37
     [Google Scholar]
  31. 31.
    Armanios M. 2009. Syndromes of telomere shortening. Annu. Rev. Genom. Hum. Genet. 10:45–61
     [Google Scholar]
  32. 32.
    Alder JK, Chen JJL, Lancaster L et al. 2008. Short telomeres are a risk factor for idiopathic pulmonary fibrosis. PNAS 105:3513051–56
     [Google Scholar]
  33. 33.
    Stanley SE, Merck SJ, Armanios M. 2016. Telomerase and the genetics of emphysema susceptibility. Implications for pathogenesis paradigms and patient care. Ann. Am. Thorac. Soc. 13:Suppl. 5S447–51
     [Google Scholar]
  34. 34.
    Stanley SE, Chen JJL, Podlevsky JD et al. 2015. Telomerase mutations in smokers with severe emphysema. J. Clin. Investig. 125:2563–70
     [Google Scholar]
  35. 35.
    Graul-Neumann LM, Hausser I, Essayie M et al. 2008. Highly variable cutis laxa resulting from a dominant splicing mutation of the elastin gene. Am. J. Med. Genet. A 146A:8977–83
     [Google Scholar]
  36. 36.
    Corbett E, Glaisyer H, Chan C et al. 1994. Congenital cutis laxa with a dominant inheritance and early onset emphysema. Thorax 49:8836–37
     [Google Scholar]
  37. 37.
    Wendel DP, Taylor DG, Albertine KH et al. 2000. Impaired distal airway development in mice lacking elastin. Am. J. Respir. Cell Mol. Biol. 23:3320–26
     [Google Scholar]
  38. 38.
    Deslee G, Woods JC, Moore CM et al. 2009. Elastin expression in very severe human COPD. Eur. Respir. J. 34:2324–31
     [Google Scholar]
  39. 39.
    Rønnow SR, Langholm LL, Sand JMB et al. 2019. Specific elastin degradation products are associated with poor outcome in the ECLIPSE COPD cohort. Sci. Rep. 9:14064
     [Google Scholar]
  40. 40.
    Kelleher CM, Silverman EK, Broekelmann T et al. 2005. A functional mutation in the terminal exon of elastin in severe, early-onset chronic obstructive pulmonary disease. Am. J. Respir. Cell Mol. Biol. 33:4355–62
     [Google Scholar]
  41. 41.
    Cho MH, Ciulla DM, Klanderman BJ et al. 2009. Analysis of exonic elastin variants in severe, early-onset chronic obstructive pulmonary disease. Am. J. Respir. Cell Mol. Biol. 40:6751–55
     [Google Scholar]
  42. 42.
    Judge DP, Dietz HC. 2005. Marfan's syndrome. Lancet 366:95011965–76
     [Google Scholar]
  43. 43.
    Tun MH, Borg B, Godfrey M et al. 2021. Respiratory manifestations of Marfan syndrome: a narrative review. J. Thorac. Dis. 13:106012–25
     [Google Scholar]
  44. 44.
    Sakornsakolpat P, Prokopenko D, Lamontagne M et al. 2019. Genetic landscape of chronic obstructive pulmonary disease identifies heterogeneous cell-type and phenotype associations. Nat. Genet. 51:3494–505
     [Google Scholar]
  45. 45.
    Shrine N, Izquierdo AG, Chen J et al. 2023. Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk. Nat. Genet. 55:3410–22
     [Google Scholar]
  46. 46.
    Pratte KA, Curtis JL, Kechris K et al. 2021. Soluble receptor for advanced glycation end products (sRAGE) as a biomarker of COPD. Respir. Res. 22:1127
     [Google Scholar]
  47. 47.
    Lao T, Glass K, Qiu W et al. 2015. Haploinsufficiency of Hedgehog interacting protein causes increased emphysema induced by cigarette smoke through network rewiring. Genome Med. 7:112
     [Google Scholar]
  48. 48.
    Li Y, Zhang L, Polverino F et al. 2021. Hedgehog interacting protein (HHIP) represses airway remodeling and metabolic reprogramming in COPD-derived airway smooth muscle cells. Sci. Rep. 11:19074
     [Google Scholar]
  49. 49.
    Jiang Z, Lao T, Qiu W et al. 2016. A chronic obstructive pulmonary disease susceptibility gene, FAM13A, regulates protein stability of β-catenin. Am. J. Respir. Crit. Care Med. 194:2185–97
     [Google Scholar]
  50. 50.
    Castaldi PJ, Guo F, Qiao D et al. 2019. Identification of functional variants in the FAM13A chronic obstructive pulmonary disease genome-wide association study locus by massively parallel reporter assays. Am. J. Respir. Crit. Care Med. 199:152–61
     [Google Scholar]
  51. 51.
    Benway CJ, Liu J, Guo F et al. 2021. Chromatin landscapes of human lung cells predict potentially functional chronic obstructive pulmonary disease genome-wide association study variants. Am. J. Respir. Cell Mol. Biol. 65:192–102
     [Google Scholar]
  52. 52.
    Hindorff LA, Sethupathy P, Junkins HA et al. 2009. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. PNAS 106:239362–67
     [Google Scholar]
  53. 53.
    Moll M, Jackson VE, Yu B et al. 2021. A systematic analysis of protein-altering exonic variants in chronic obstructive pulmonary disease. Am. J. Physiol. Lung Cell Mol. Physiol. 321:1L130–43
     [Google Scholar]
  54. 54.
    Zhou X, Baron RM, Hardin M et al. 2012. Identification of a chronic obstructive pulmonary disease genetic determinant that regulates HHIP. Hum. Mol. Genet. 21:61325–35
     [Google Scholar]
  55. 55.
    Hersh CP. 2019. Pharmacogenomics of chronic obstructive pulmonary disease. Expert Rev. Respir. Med. 13:5459–70
     [Google Scholar]
  56. 56.
    Hardin M, Cho MH, McDonald ML et al. 2016. A genome-wide analysis of the response to inhaled β2-agonists in chronic obstructive pulmonary disease. Pharmacogenom. J. 16:4326–35
     [Google Scholar]
  57. 57.
    Hawcutt DB, Francis B, Carr DF et al. 2018. Susceptibility to corticosteroid-induced adrenal suppression: a genome-wide association study. Lancet Respir. Med. 6:6442–50
     [Google Scholar]
  58. 58.
    Seo M, Qiu W, Bailey W et al. 2018. Genomics and response to long-term oxygen therapy in chronic obstructive pulmonary disease. J. Mol. Med. 96:121375–85
     [Google Scholar]
  59. 59.
    Shrine N, Guyatt AL, Erzurumluoglu AM et al. 2019. New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries. Nat. Genet. 51:3481–93
     [Google Scholar]
  60. 60.
    Moll M, Sakornsakolpat P, Shrine N et al. 2020. Chronic obstructive pulmonary disease and related phenotypes: polygenic risk scores in population-based and case-control cohorts. Lancet Respir. Med. 8:7696–708
     [Google Scholar]
  61. 61.
    Buist AS, McBurnie MA, Vollmer WM et al. 2007. International variation in the prevalence of COPD (the BzOLD Study): a population-based prevalence study. Lancet 370:9589741–50
     [Google Scholar]
  62. 62.
    Kim W, Moll M, Qiao D et al. 2021. Interaction of cigarette smoking and polygenic risk score on reduced lung function. JAMA Netw. Open 4:12e2139525
     [Google Scholar]
  63. 63.
    Moll M, Lutz SM, Ghosh AJ et al. 2020. Relative contributions of family history and a polygenic risk score on COPD and related outcomes: COPDGene and ECLIPSE studies. BMJ Open Respir. Res. 7:1e000755
     [Google Scholar]
  64. 64.
    Zhang J, Xu H, Qiao D et al. 2022. A polygenic risk score and age of diagnosis of chronic obstructive pulmonary disease. Eur. Respir. J. 60:32101954
     [Google Scholar]
  65. 65.
    Natarajan P, Young R, Stitziel NO et al. 2017. Polygenic risk score identifies subgroup with higher burden of atherosclerosis and greater relative benefit from statin therapy in the primary prevention setting. Circulation 135:222091–101
     [Google Scholar]
  66. 66.
    Carpenter MJ, Strange C, Jones Y et al. 2007. Does genetic testing result in behavioral health change? Changes in smoking behavior following testing for alpha-1 antitrypsin deficiency. Ann. Behav. Med. 33:122–28
     [Google Scholar]
  67. 67.
    McDonald VM, Osadnik CR, Gibson PG. 2019. Treatable traits in acute exacerbations of chronic airway diseases. Chronic Respir. Dis. 16:1479973119867954
     [Google Scholar]
  68. 68.
    Carr TF, Zeki AA, Kraft M. 2018. Eosinophilic and noneosinophilic asthma. Am. J. Respir. Crit. Care Med. 197:122–37
     [Google Scholar]
  69. 69.
    Oh YM, Lee KS, Hong Y et al. 2018. Blood eosinophil count as a prognostic biomarker in COPD. Int. J. Chronic Obstr. Pulm. Dis. 13:3589–96
     [Google Scholar]
  70. 70.
    Mkorombindo T, Dransfield MT. 2019. Mepolizumab in the treatment of eosinophilic chronic obstructive pulmonary disease. Int. J. Chronic Obstr. Pulm. Dis. 14:1779–87
     [Google Scholar]
  71. 71.
    Pavord ID, Chanez P, Criner GJ et al. 2017. Mepolizumab for eosinophilic chronic obstructive pulmonary disease. N. Engl. J. Med. 377:171613–29
     [Google Scholar]
  72. 72.
    Criner GJ, Celli BR, Brightling CE et al. 2019. Benralizumab for the prevention of COPD exacerbations. N. Engl. J. Med. 381:111023–34
     [Google Scholar]
  73. 73.
    Bhatt SP, Rabe KF, Hanania NA et al. 2023. Dupilumab for COPD with type 2 inflammation indicated by eosinophil counts. N. Engl. J. Med. 389:205–14
     [Google Scholar]
  74. 74.
    Butler CC, Gillespie D, White P et al. 2019. C-reactive protein testing to guide antibiotic prescribing for COPD exacerbations. N. Engl. J. Med. 381:2111–20
     [Google Scholar]
  75. 75.
    Kim TH, Oh DK, Oh YM et al. 2018. Fibrinogen as a potential biomarker for clinical phenotype in patients with chronic obstructive pulmonary disease. J. Thorac. Dis. 10:95260–68
     [Google Scholar]
  76. 76.
    Morrow JD, Cho MH, Platig J et al. 2018. Ensemble genomic analysis in human lung tissue identifies novel genes for chronic obstructive pulmonary disease. Hum. Genom. 12:11
     [Google Scholar]
  77. 77.
    Hobbs BD, Morrow JD, Wang XW et al. 2023. Identifying chronic obstructive pulmonary disease from integrative omics and clustering in lung tissue. BMC Pulm. Med. 23:1115
     [Google Scholar]
  78. 78.
    Zhang YH, Hoopmann MR, Castaldi PJ et al. 2021. Lung proteomic biomarkers associated with chronic obstructive pulmonary disease. Am. J. Physiol. Lung Cell Mol. Physiol. 321:6L1119–30
     [Google Scholar]
  79. 79.
    Sauler M, McDonough JE, Adams TS et al. 2022. Characterization of the COPD alveolar niche using single-cell RNA sequencing. Nat. Commun. 13:1494
     [Google Scholar]
  80. 80.
    Schumacher D, Kramann R. 2023. Multiomic spatial mapping of myocardial infarction and implications for personalized therapy. Arterioscler. Thromb. Vasc. Biol. 43:2192–202
     [Google Scholar]
  81. 81.
    Ghosh A, Coakley RD, Ghio AJ et al. 2019. Chronic E-cigarette use increases neutrophil elastase and matrix metalloprotease levels in the lung. Am. J. Respir. Crit. Care Med. 200:111392–401
     [Google Scholar]
  82. 82.
    Singh D. 2015. Chronic obstructive pulmonary disease, neutrophils and bacterial infection: a complex web involving IL-17 and IL-22 unravels. EBioMedicine 2:111580–81
     [Google Scholar]
  83. 83.
    Moll M, Boueiz A, Ghosh AJ et al. 2022. Development of a blood-based transcriptional risk score for chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 205:2161–70
     [Google Scholar]
  84. 84.
    Enikeev O, Enikeeva SA, Bikmetova NR. 2014. PPAR-alpha agonist (fenofibrate) therapy in exacerbation control of chronic obstructive pulmonary disease of severe stage. Eur. Respir. J. 44:P1514
     [Google Scholar]
  85. 85.
    Belvisi MG, Mitchell JA. 2009. Targeting PPAR receptors in the airway for the treatment of inflammatory lung disease. Br. J. Pharmacol. 158:4994–1003
     [Google Scholar]
  86. 86.
    Yun JH, Lee S, Srinivasa P et al. 2022. An interferon-inducible signature of airway disease from blood gene expression profiling. Eur. Respir. J. 59:52100569
     [Google Scholar]
  87. 87.
    Xu Y, Ritchie SC, Liang Y et al. 2023. An atlas of genetic scores to predict multi-omic traits. Nature 616:7955123–31
     [Google Scholar]
  88. 88.
    Barbeira AN, Dickinson SP, Bonazzola R et al. 2018. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat. Commun. 9:11825
     [Google Scholar]
  89. 89.
    Bhat TA, Panzica L, Kalathil SG, Thanavala Y. 2015. Immune dysfunction in patients with chronic obstructive pulmonary disease. Ann. Am. Thorac. Soc. 12:Suppl. 2S169–75
     [Google Scholar]
  90. 90.
    Boyle EA, Li YI, Pritchard JK. 2017. An expanded view of complex traits: from polygenic to omnigenic. Cell 169:71177–86
     [Google Scholar]
  91. 91.
    Morrow JD, Zhou X, Lao T et al. 2017. Functional interactors of three genome-wide association study genes are differentially expressed in severe chronic obstructive pulmonary disease lung tissue. Sci. Rep. 7:144232
     [Google Scholar]
  92. 92.
    Silverman EK. 2020. Genetics of COPD. Annu. Rev. Physiol. 82:413–31
     [Google Scholar]
  93. 93.
    Buschur KL, Riley C, Saferali A et al. 2023. Distinct COPD subtypes in former smokers revealed by gene network perturbation analysis. Respir. Res. 24:130
     [Google Scholar]
  94. 94.
    Sharma A, Kitsak M, Cho MH et al. 2018. Integration of molecular interactome and targeted interaction analysis to identify a COPD disease network module. Sci. Rep. 8:114439
     [Google Scholar]
  95. 95.
    Glass K, Quackenbush J, Silverman EK et al. 2014. Sexually-dimorphic targeting of functionally-related genes in COPD. BMC Syst. Biol. 8:118
     [Google Scholar]
  96. 96.
    Nelson MR, Tipney H, Painter JL et al. 2015. The support of human genetic evidence for approved drug indications. Nat. Genet. 47:8856–60
     [Google Scholar]
  97. 97.
    Albert RK, Connett J, Bailey WC et al. 2011. Azithromycin for prevention of exacerbations of COPD. N. Engl. J. Med. 365:8689–98
     [Google Scholar]
  98. 98.
    Criner GJ, Connett JE, Aaron SD et al. 2014. Simvastatin for the prevention of exacerbations in moderate-to-severe COPD. N. Engl. J. Med. 370:232201–10
     [Google Scholar]
  99. 99.
    Woodruff PG, Albert RK, Bailey WC et al. 2011. Randomized trial of zileuton for treatment of COPD exacerbations requiring hospitalization. COPD 8:121–29
     [Google Scholar]
  100. 100.
    Curtis KJ, Meyrick VM, Mehta B et al. 2016. Angiotensin-converting enzyme inhibition as an adjunct to pulmonary rehabilitation in chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 194:111349–57
     [Google Scholar]
  101. 101.
    Alagkiozidis I, Facciabene A, Tsiatas M et al. 2011. Time-dependent cytotoxic drugs selectively cooperate with IL-18 for cancer chemo-immunotherapy. J. Transl. Med. 9:77
     [Google Scholar]
  102. 102.
    Zhao S, Iyengar R. 2012. Systems pharmacology: network analysis to identify multiscale mechanisms of drug action. Annu. Rev. Pharmacol. Toxicol. 52:505–21
     [Google Scholar]
  103. 103.
    Cheng F, Desai RJ, Handy DE et al. 2018. Network-based approach to prediction and population-based validation of in silico drug repurposing. Nat. Commun. 9:12691
     [Google Scholar]
  104. 104.
    Islam MdM, Wang Y, Hu P 2021. A maximum flow-based approach to prioritize drugs for drug repurposing of chronic diseases. Life 11:111115
     [Google Scholar]
/content/journals/10.1146/annurev-med-060622-101239
Loading
Precision Approaches to Chronic Obstructive Pulmonary Disease Management
Annual Review of Medicine 75, 247 (2024); https://doi.org/10.1146/annurev-med-060622-101239
/content/journals/10.1146/annurev-med-060622-101239
/content/journals/10.1146/annurev-med-060622-101239
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error