Speaker
Description
Nontuberculous mycobacteria (NTM) lung infection is increasing in prevalence globally
and remains difficult to treat in part due to varied disease presentation and prognosis.
Some patients experience stable symptoms and require observation and bronchial
hygiene treatment; other patients will demonstrate disease progression symptomatically
and radiographically, requiring antibiotic therapy that may cause intolerable side effects.
Predicting patient prognosis to inform therapeutic decisions remains a challenge. To
better understand the biological mechanisms of NTM disease progression and help
identify patient prognostic factors, we developed an agent-based model of a pulmonary
NTM granuloma. These virtual granulomas are initialized to represent existing chronic
infection and are informed by histological studies. Peripheral immune cell data from
NTM patients and lung resection immunohistochemical staining inform model
initialization, parameters and calibration. The model captures multiple trajectories of
granuloma development, recapitulating the heterogeneity in granuloma bacterial burden
and infection progression observed in animal models of mycobacterial infection. The
model demonstrates the feasibility of simulating in vivo NTM granulomas representing
chronic infection. Patient data integration increases model translatability, with the
potential for predicting patient prognosis and simulating patient-specific granulomas.
