Airway autoimmunity, asthma exacerbations, and response to biologics.
Carmen Venegas GarridoManali MukherjeeAnurag BhallaParameswaran NairPublished in: Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology (2022)
Biologic therapies in asthma are indicated in severe disease, and they are directed against specific inflammatory modulators that contribute to pathogenesis and severity. Currently approved biologics target T2 cytokines (IgE, IL-5, IL-4/IL-13, and TLSP) and have demonstrated efficacy in clinical outcomes such as exacerbation rate and oral corticosteroid dose reductions, blood and airway eosinophil depletion, and lung function improvement. However, a proportion of these patients may show inadequate responses to biologics, with either initial lack of improvement or clinical and functional worsening after an optimal initial response. Exacerbations while on a biologic may be due to several reasons, including imprecise identification of the dominant effector pathway contributing to severity, additional inflammatory pathways that are not targeted by the biologic, inaccuracies of the biomarker used to guide therapy, inadequate dosing schedules, intercurrent airway infections, anti-drug neutralizing antibodies, and a novel phenomenon of autoimmune responses in the airways interfering with the effectiveness of the monoclonal antibodies. This review, illustrated using case scenarios, describes the underpinnings of airway autoimmune responses in driving exacerbations while patients are being treated with biologics, device a strategy to evaluate such exacerbations, an algorithm to switch between biologics, and perhaps to consider two biologics concurrently.
Keyphrases
- chronic obstructive pulmonary disease
- lung function
- cystic fibrosis
- end stage renal disease
- rheumatoid arthritis
- newly diagnosed
- ejection fraction
- chronic kidney disease
- prognostic factors
- randomized controlled trial
- peritoneal dialysis
- stem cells
- air pollution
- oxidative stress
- machine learning
- climate change
- mesenchymal stem cells
- patient reported outcomes
- neural network
- dengue virus
- regulatory t cells
- cancer therapy
- drug administration