AI-driven toolset for IPF and aging research associates lung fibrosis with accelerated aging.

Journal: Aging
Published Date:

Abstract

Idiopathic pulmonary fibrosis (IPF) is a condition predominantly affecting the elderly and leading to a decline in lung function. Our study investigates the aging-related mechanisms in IPF using artificial intelligence (AI) approaches. We developed a pathway-aware proteomic aging clock using UK Biobank data and applied it alongside a specialized version of Precious3GPT (ipf-P3GPT) to demonstrate an AI-driven mode of IPF research. The aging clock shows great performance in cross-validation (R=0.84) and its utility is validated in an independent dataset to show that severe cases of COVID-19 are associated with an increased aging rate. Computational analysis using ipf-P3GPT revealed distinct but overlapping molecular signatures between aging and IPF, suggesting that IPF represents a dysregulation rather than mere acceleration of normal aging processes. Our findings establish novel connections between aging biology and IPF pathogenesis while demonstrating the potential of AI-guided approaches in therapeutic development for age-related diseases.

Authors

  • Fedor Galkin
    InSilico Medicine, Science Park, Hong Kong; Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, UK.
  • Shan Chen
    National Academy of Economic Security, Beijing Jiaotong University, Beijing 100044, China.
  • Alex Aliper
    Pharma.AI Department , Insilico Medicine, Inc. , Baltimore , Maryland 21218 , United States.
  • Alex Zhavoronkov
    Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA.
  • Feng Ren
    Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China.

Keywords

No keywords available for this article.