Thursday, September 27, 2012

Predicting drug effect by 'putting the patient in a tube'


Basically, the system puts the patient's most relevant characteristics in a tube; doctors want to heal (or kill) the diseased cells, while keeping the healthy cells alive. This is especially important for anti-cancer therapy. By selecting both the healthy and diseased cells and putting them in a tube, scientists can directly measure the effect a drug has on the two cell types. The system was tested for a patient suffering from a very specific form of recurrent respiratory papillomatosis (RRP), a form of cancer.

The patient
For their study, the researchers needed a patient that is not easy to treat. Obviously, when the best treatment option is straightforward, there is no need to test drug efficacy in a test tube. The patient suffering from RRP was no ordinary one, as he already had about 350 surgeries in his life in order to remove the tumours, which is the standard treatment option in RRP. Because the patient was suffering from a rare form that includes spreading of the tumours to the lungs, which is often fatal, the scientists sought to test their system by finding the optimal cancer-killing drug for this patient. They also did a follow-up to see whether treatment in their lab system correlated with an actual effect on the patient's health.

A variety of drugs was tested in the culture system set up with healthy cells and tumour cells. Some drugs were not very specific, meaning they killed healthy cells as well. Other drugs were just not very effective at killing anything, but one of them was deemed suitable based on its tumour-killing abilities while being gentle to healthy cells. This drug is called vorinostat, and is normally used for a certain form of lymphoma. Therefore, the drug was prescribed off-label to the patient, and it was found to have a beneficial effect. No new tumours were found after treatment was started and existing tumours shrunk, which certainly is a promising sign.

By doing their case study, the scientists have shown that their method holds promise for predicting treatment effects and guiding clinical decisions. However, they only tested it on one patient, which means a lot of validation is still necessary in order to establish whether this method can be used in daily practice. Because they had to find a way to get the cells out of the body and keep them alive in a test tube in order to do all the necessary assays, it is likely we will see some optimization before this predictive tool finds its way to the clinic. The scientists note it will probably take a few years.

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