Lung tissue destruction in COPD and LAM is driven by a poorly understood interplay of injury, faulty repair, and mechanical forces—and this project will build mathematical and imaging tools to map that process from genes to lung function. Why this matters: COPD alone costs the NHS £1.9 billion annually and is the third leading cause of death worldwide. Current tools cannot detect active tissue destruction early enough, so diagnosis is delayed, clinical trials often fail, and treatments arrive too late. The gap is that no one has integrated biological, imaging, and computational data to track how cellular damage escalates into airspace enlargement and loss of lung function. If successful, the project will deliver validated computational models that predict disease progression and serve as a virtual testing platform for new therapies. This could eventually underpin a "digital twin" of the lung—a personalised simulation that helps clinicians choose the right treatment at the right time, before damage becomes irreversible. The work is primarily fundamental science, but it directly addresses an urgent clinical need for early biomarkers and faster drug evaluation.
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Destructive lung diseases are the third largest cause of death worldwide, with chronic obstructive pulmonary disease (COPD) alone costing the NHS £1.9bn annually. COPD is characterised by destruction of the tissue that make up the air sacs (alveoli) within the lung, leading to airspace enlargement, which reduces the ability of the lung to exchange carbon dioxide and oxygen (i.e. loss of lung function). The damage or injury to the lung tissue is driven by environmental exposures, genetics and ageing. The underlying mechanisms by which injury, abnormal repairprocesses and tissue mechanics interact to cause this destruction are not well understood. This gap in understanding makes estimates of disease activity inaccurate and delays diagnosis and timely treatment. In turn, this makes clinical trials prone to failure, delaying the translation of scientific discovery to personalised care. There is therefore an urgent need for tools that help us better understand the underlying mechanisms and for new sensitive measures(biomarkers) to detect active disease, enabling early interventions to prevent lung damage and death, and facilitating rapid evaluation of new treatments. Lung tissue destruction in COPD is complex, with multiple mechanisms causing lung tissue damage. In contrast, lymphangioleiomyomatosis (LAM), another lung disease associated with tissue destruction that can cause respiratory failure and death, involves a single gene and has a well-understood progression. LAM is rare, primarily affecting younger females; however, the simpler nature of LAM allows a more straightforward starting point for interdisciplinary study of lung tissue destruction and makes it an effective model system for understanding destructive lung disease more widely. We will exploit this to develop and validate mathematical, imaging, and biological approaches for subsequent application to COPD and with broader implications for destructive lung diseases. We will use quantitative imaging, biological and clinical data of tissue injury and repair from LAM and COPD patient lungs alongside the development of mathematical and computational models to predict changes in lung structure and function. The tools developed will enable us to address the following questions: Q1. How do gene-level and cellular mechanisms underpin tissue degradation and repair, and how are they disrupted in disease? Q2. How do (sub)cellular, biochemical, and mechanical processes interact to cause airspace enlargement? Q3. How does lung tissue loss evolve over time and how is it related to lung function? Outcomes, applications, benefits: Addressing these questions through novel combinations of biological and imaging data with computational models will help establish the relationship between injury/repair processes at the genetic/cellular level and lung tissue loss, leading to eventual clinical outcomes. In particular we will understand how the balance between injury and repair is maintained in a healthy lung. We will then identify which mechanisms cause changes in lung structure and function in disease and their early biomarkers (and how they change with time). These are essential tools for detecting LAM or COPD before the damage is severe or irreversible, addressing a significant unmet need for patients suffering from these debilitating conditions. Our validated computational models will enable prediction of disease progression and a virtual platform for testing novel therapies, ultimately contributing to a digital twin of the lung that will enable clinicians to provide the appropriate treatment, to the right patient, at the right time. The complex challenges posed necessitate an integrated interdisciplinary approach, but reciprocally will also drive innovation in each discipline individually.
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