Algorithm to Detect the Placebo Effect

By March 23, 2015
Un algorithme pour accélérer la recherche médicale.

A new protocol for an imaging technique displaying brain activity could speed up drug research by detecting ineffective remedies at an early stage.

A team of US and British researchers have developed a protocol for an imaging technique that looks at brain activity, an approach which could be a boon for pharmaceutical research. By using functional magnetic resonance imaging  (fMRI)  shortly after a painkiller drug has been taken, doctors can see whether a person has ingested a real analgesic or a simple placebo. A placebo is neutral, from a medical viewpoint, in the sense that it does not contain any active therapeutic agent to improve the patient’s condition. However there are still neurological mechanisms that enable a patient who has taken a placebo but believes she has taken an active medicine, to enjoy some relief from his/her pain or other medical condition. Placebos are widely used as a control substance during the development phase of a new drug. Comparisons are made on volunteer patients to see how effective the new medication is compared with the placebo. The new molecule must prove more effective than the placebo if it is to be worthwhile developing.

Placebos are used in medical research to test how effective new medicines are

Therapeutic effect or placebo effect?

The placebo effect may in fact be very powerful when it comes to alleviating pain, so that a patient may be absolutely convinced that s/he has taken a real analgesic. This is actually what happens during the trial phases for a new painkiller. Patients who have volunteered to test the substance may insist that it is effective, in the sense that the pain s/he is suffering is reduced, even though this improvement can in fact only be attributed to the natural analgesic provided by his/her own metabolism, triggered by the expectation of therapeutic results. In such cases, the researchers might be led to believe for quite some time that the analgesic being tested is effective, when in fact it is only a traditional placebo. In the longer term, tests carried out on large numbers of people eventually reveal the truth, but time will nevertheless have been wasted working on an unpromising substance. With the new identification algorithm, researchers can save precious time.


The protocol uses machine learning methods and data from multiple published studies to identify reliable associations between drug-related activity modulations and actual drug efficacy

Encouraging test results

The difference between the MRI scan of a patient who has taken real pain medication and that of another who has been given a placebo is very difficult to detect with the naked eye. The researchers looked at scans from eight studies on different analgesics, which had all been tested in the laboratory. Each trial included volunteers who had taken a real analgesic and others who had unknowingly taken a placebo. In each study, the MRIs of the two groups of patients were compared, and the data extracted and used to build the new programme. This was then tested on some patients, with conclusive results overall. In the majority of cases, the new protocol was able to detect when the analgesic was no more effective than a simple placebo. The success rate varied from 57 to 83% depending on the medication. This figure may seem somewhat low, but it should be remembered that the new protocol is still at the prototype stage and results are therefore expected to improve over time. Moreover, although the protocol is not yet 100% reliable it nevertheless allows pharmaceutical researchers to eliminate lots of ineffective substances which they would otherwise have continued to work on had there been no evidence to the contrary. It should moreover be noted that the programme has never mistaken a placebo for an effective drug. The only error it might be guilty of is letting some relatively ineffective medicines pass through the net, whose weaknesses will be flushed out sooner or later in the development process. Meanwhile other research in the field of brain activity has been making progress recently. Last year a technique using 3D imaging to model brain activity in real time opened up possible new ways of treating brain conditions. Last autumn L’Atelier reported on a headset designed to alter the mood of the wearer by sending light electrical pulses that affect neuron activity.


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