Title: Achieving Precision Medicine in Allergic Disease: Progress and Challenges.

Authors: Proper, Steven P; Azouz, Nurit P; Mersha, Tesfaye B

Published In Front Immunol, (2021)

Abstract: Allergic diseases (atopic dermatitis, food allergy, eosinophilic esophagitis, asthma and allergic rhinitis), perhaps more than many other traditionally grouped disorders, share several overlapping inflammatory pathways and risk factors, though we are still beginning to understand how the relevant patient and environmental factors uniquely shape each disease. Precision medicine is the concept of applying multiple levels of patient-specific data to tailor diagnoses and available treatments to the individual; ideally, a patient receives the right intervention at the right time, in order to maximize effectiveness but minimize morbidity, mortality and cost. While precision medicine in allergy is in its infancy, the recent success of biologics, development of tools focused on large data set integration and improved sampling methods are encouraging and demonstrates the utility of refining our understanding of allergic endotypes to improve therapies. Some of the biggest challenges to achieving precision medicine in allergy are characterizing allergic endotypes, understanding allergic multimorbidity relationships, contextualizing the impact of environmental exposures (the "exposome") and ancestry/genetic risks, achieving actionable multi-omics integration, and using this information to develop adequately powered patient cohorts and refined clinical trials. In this paper, we highlight several recently developed tools and methods showing promise to realize the aspirational potential of precision medicine in allergic disease. We also outline current challenges, including exposome sampling and building the "knowledge network" with multi-omics integration.

PubMed ID: 34484229

MeSH Terms: Allergens/immunology; Animals; Biomarkers; Computational Biology/methods; Diagnosis, Differential; Disease Management; Disease Susceptibility; Genomics/methods; Humans; Hypersensitivity/diagnosis*; Hypersensitivity/etiology; Hypersensitivity/therapy*; Machine Learning; Phenotype; Precision Medicine*/methods; Proteomics/methods