Advancing respiratory research with patient-specific data-driven lung models
The Humane Research Trust is funding a research project to improve treatments for people with respiratory diseases. Using in vitro experiments and computer modelling, the University of Reading scientists will simulate lung airflow and generate predictions for tailored treatments. By using patient-specific data, the researchers will reveal insights to yield more effective, targeted inhalation therapies.
In the UK, one in five people are affected by respiratory diseases, such as chronic obstructive pulmonary disease (COPD) and asthma, which cause symptoms such as breathing difficulties. Inhalation therapies, delivered through devices such as inhalers, are key in treating these kinds of illnesses.
These therapies are usually tested on animals, who are exposed to allergens and toxic substances until they develop respiratory symptoms. As well as causing extensive suffering, many scientists have found these kinds of experiments on animals are poorly predictive of the human condition.
Additionally, existing human models of lungs have limitations that restrict their accuracy and applicability. Simple models overlook the complexity of the respiratory system, while many advanced models do not reflect dynamic changes or accurately represent diseased states. As a result, scientists are looking for new models that will result in better patient outcomes.
“These limitations highlight the need for more refined, representative, and adaptable lung models,” says Dr Hisham Al-Obaidi, Lecturer in Pharmacy and Pharmaceutical Sciences at the University of Reading, “Respiratory models should capture the details of healthy lungs and be versatile enough to represent various diseased states. This will lead to accurate drug development, better therapeutic outcomes, and a deeper understanding of physiology and pathology.”
The Humane Research Trust is funding a research project at the University of Reading to advance personalised treatment delivery for respiratory patients. The researchers will simulate patient-specific lung conditions in the lab. They will supplement this approach with innovative computer modelling techniques, using machine learning to generate highly targeted treatments.
The research team, led by Dr Al-Obaidi, will be working closely with the Royal Berkshire Hospital to gather data directly from patients via pulmonary function tests. Using a device called an Andersen Cascade Impactor (ACI), the scientists will recreate the specific airflow limitations of the patients. They can then conduct various experiments to create a detailed dataset of the different ways that inhaled treatment particles deposit in the respiratory tract.
They will then model this data digitally to predict inhaled therapy behaviour in human lungs. Once the scientists have developed a comprehensive analysis, they will then categorise a range of therapeutics based on how well they predict them to work for different levels of lung function. This means that doctors can match patients with the most effective treatment for their particular condition.
“Instead of relying on general models that assume everyone's lungs work the same way, we're gathering detailed information from patients to customise our models. This means treatments can be more tailored to each person's specific needs, making them more effective,” said Dr Al-Obaidi. “This approach is not only more personalised, but also reduces reliance on animal studies in developing treatments for lung diseases.”
Dr Hisham Al-Obaidi
Principal investigator
Dr Hisham Al-Obaidi is a Lecturer in Pharmacy and Pharmaceutical Sciences at the University of Reading, as well as a prescribing pharmacist. His laboratory focuses on designing new models to enhance methods of drug delivery. From fungal nail to respiratory diseases like asthma, Dr Al-Obaidi's work is improving treatments for a wide and varied range of conditions.
Liam Escott
Postdoctoral researcher
Liam is a Postdoctoral Researcher at the University of Reading. He holds a PhD in Applied Mathematics from University College London. During his PhD and his later work as a Research Fellow, he developed his skills in using computational mathematics towards industry-relevant research goals. In particular, he used a cell model to study how liquids such as blood, formed of multiple phases, flow and behave.
