London: Computer simulations of disease processes and detailed digital models of our organs could provide more accurate monitoring and outcome measurements for clinical trials, according to a new research.
The research being presented at the University of Sheffield in the UK on Thursday aims to develop a model of pulmonary arterial hypertension (PAH) using MRI technology.
"Two-thirds of the patients we assessed could be correctly diagnosed with PAH using our model, which meant only those patients, where diagnosis was unclear, would have had to have the catheter test if this was in full clinical use," said Andy Swift, Insigneo Senior Clinical Research fellow.
Currently, the condition is diagnosed by inserting a catheter into the patient, often in the neck or groin to test the pressure in the pulmonary artery. It's an invasive test that can be distressing to the patient.
Delegates at this years Insigneo Showcase at The Octagon Centre in University of Sheffield will deliberate how in silico medicine - computer simulations of the human body and its disease processes can help improve diagnosis andprognosis for conditions like Parkinsons and pulmonary vascular disease.
Although, ultimately destined for the clinic, the technology looks likely to move quickly into use within clinical trials as it can enable more effective monitoring of the impact of new drugs and treatments.
In a study of data from 450 patients, researchers were able to use the model to diagnose PAH accurately.
"Many clinical trials in pulmonary hypertension also use the catheter test as an outcome measure but it could provide a significant advantage to be able to replace it with a less invasive MRI scan as our model also provides more detailed information on physical changes to the heart itself," he added.
Insigneo Senior Clinical Fellow Dr Alisdair McNeill will present his work on the use of gait analysis to develop a model able to assess disease progression in neurological disorders. He is working initially with patients with 22q11 deletion syndrome (a chromosomal defect) who are at high risk of developing Parkinson's disease.
"There are known to be changes to gait that are linked to Parkinson's disease such as shortening of step length," said Dr McNeill.
"Our model will use data including walking speed, step length and rhythm of walking plus other parameters to see if we can pick up changes as the disease progresses or at risk individuals develop Parkinsonism. Although this type of analysis is less likely to be easily translated to the clinic, it could be very effective for clinical trials as so many of the current tests for progression of Parkinson's disease and impact of treatments are very subjective and not sensitive to changes in clinical state."
