Personalised Healthcare – Simulation of Blood

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Penn State University researchers have hit upon a novel, and very workable idea. If you simulate a patient's blood, you can test exactly how any given drug will react with it, before injecting into the patient. More specifically, you can investigate plaques and clots inside the blood vessels of the body, and determine which drugs – for that specific patient – are likely to dissolve a clot without a piece breaking off, and causing untold damage elsewhere.

This visualization is a simulation of platelets under flow conditions, as a particle swarm, for maximum realism. It depicts a single artery, showing how the platelets bind collagen and release ADP and thromboxane A2, recruiting additional platelets to a growing deposit, and how that deposit affects blood flow.

First reported in the journal Blood, the researcher's work involved both the use of medical virtual reality systems, and several hundred robotic devices for physical tests to verify the results of those simulations. The team of biomedical engineers and hematologists designed the system so that a given patient's blood sample could be analysed, and from there the simulation parameters automatically extracted. In other words, a custom, bespoke simulation for every patient, using just a normal blood test as source data.

“Blood platelets are like computers in that they integrate many signals and make a complex decision of what to do,” said senior author Scott Diamond, professor of chemical and biomolecular engineering in the School of Engineering and Applied Science. “We were interested to learn if we could make enough measurements in the lab to detect the small differences that make each of us unique. It would be impossible to do this with the cells of the liver, heart or brain. But we can easily obtain a tube of blood from each donor and run tests of platelet calcium release.”

When blood platelets are exposed to the conditions of a cut or, in a more dangerous situation, a ruptured atherosclerotic plaque, they respond by elevating their internal calcium, which causes release of two chemicals, thromboxane and ADP. These two activating agents further enhance calcium levels and are the targets of common anti-platelet drugs such as aspirin or clopidogrel, also known as Plavix.

By preventing platelets from increasing their calcium levels, these drugs make them less able to stick together and block blood vessels, decreasing the likelihood of a heart attack. However, if existing calcium is removed, the risk of a polyp forming is high. These polyps are parts of the plaque that break away from the main mass, and become free-floating in the artery. They drift until they lodge somewhere else, and start a new plaque. If this occurs in the heart, it leads to a heart attack. If it occurs in the brain, a stroke occurs. Other organs are similarly disastrously affected. It is critical then, to make absolutely sure the right drug is chosen for the patient, and the dose is exact.

Since blood is a liquid, the liquid-handling robots originally developed for drug screening tests were ideal to test platelet function, and verify that the results the simulations were predicting, indeed matched the reality of each situation. Several hundred trials were carried out to be absolutely sure that the simulations were predicting exactly what would happen to the patient.

“We used a technique developed in our lab called ‘pairwise agonist scanning’ on platelets from three different donors to generate a massive data set of how their cells responded to all different pairs of these activating agents,” Diamond said. “Then we trained neural network models for each donor based on this data to simulate how each and every cell in a blood clot is responding.”

“We even identified one person who was resistant to aspirin,” Diamond said, “and then discovered a novel genetic mutation in their thromboxane receptor gene. The computer simulation for that donor identified the functional defect before we even sequenced the gene.”

The development of equations and algorithms to model reactive blood flow will be very helpful in predicting clinical risks, drug responses and new disease mechanisms and in designing biomedical devices.