TY - JOUR
T1 - Phenotype- and patient-specific modelling in asthma
T2 - Bronchial thermoplasty and uncertainty quantification
AU - Donovan, Graham M.
AU - Langton, David
AU - Noble, Peter B.
PY - 2020/9/21
Y1 - 2020/9/21
N2 - Theoretical models can help to overcome experimental limitations to better our understanding of lung physiology and disease. While such efforts often begin in broad terms by determining the effect of a disease process on a relevant biological output, more narrowly defined simulations may inform clinical practice. Two such examples are phenotype-specific and patient-specific models, the former being specific to a group of patients with common characteristics, and the latter to an individual patient, in view of likely differences (heterogeneity) between patients. However, in order for such models to be useful, they must be sufficiently accurate, given the available data about the specific characteristics of the patient. We show that, for asthma in particular, this approach is promising: phenotype-specific targeting may be an effective way of selecting patients for treatment based on their airway remodelling phenotype, and patient-specific targeting may be viable with the use of a clinically-plausible dataset. Specifically we consider asthma and its treatment by bronchial thermoplasty, in which the airway smooth muscle layer is directly targeted by thermal energy. Patient-specific and phenotype-specific models in this context are considered using a combination of biobank data from ex vivo tissue samples, CT imaging, and optical coherence tomography which allows more detailed resolution of the airway wall structures.
AB - Theoretical models can help to overcome experimental limitations to better our understanding of lung physiology and disease. While such efforts often begin in broad terms by determining the effect of a disease process on a relevant biological output, more narrowly defined simulations may inform clinical practice. Two such examples are phenotype-specific and patient-specific models, the former being specific to a group of patients with common characteristics, and the latter to an individual patient, in view of likely differences (heterogeneity) between patients. However, in order for such models to be useful, they must be sufficiently accurate, given the available data about the specific characteristics of the patient. We show that, for asthma in particular, this approach is promising: phenotype-specific targeting may be an effective way of selecting patients for treatment based on their airway remodelling phenotype, and patient-specific targeting may be viable with the use of a clinically-plausible dataset. Specifically we consider asthma and its treatment by bronchial thermoplasty, in which the airway smooth muscle layer is directly targeted by thermal energy. Patient-specific and phenotype-specific models in this context are considered using a combination of biobank data from ex vivo tissue samples, CT imaging, and optical coherence tomography which allows more detailed resolution of the airway wall structures.
KW - Airway hyper-responsiveness
KW - Clustered ventilation defects
KW - Targeted therapy
KW - Ventilation heterogeneity
UR - http://www.scopus.com/inward/record.url?scp=85086475098&partnerID=8YFLogxK
U2 - 10.1016/j.jtbi.2020.110337
DO - 10.1016/j.jtbi.2020.110337
M3 - Article
C2 - 32511977
AN - SCOPUS:85086475098
SN - 0022-5193
VL - 501
JO - Journal of Theoretical Biology
JF - Journal of Theoretical Biology
M1 - 110337
ER -