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MIT robot may accelerate clinical trials by 70 percent

Scientists have created a robot capable of carrying out extremely precise tests on pharmaceutical products for treating acute strokes or aiding in stroke recovery, thus notably reducing the cost of Phase III drug trials.

September 2014

MIT robot IMALab News

In the United States, for a typical Phase III clinical drug trial the Food and Drug Administration (FDA) requires that the drug be tested on at least 800 patients in order to demonstrate its effectiveness in the majority of cases. That sample size is partially determined by the accuracy of standard outcome measurements, which quantify a patient’s ability to meet specific criteria.

Between finding and enrolling appropriate patients, conducting the necessary tests and analyzing the results, clinical trials can typically take several years to complete, and are consequently highly expensive for the pharmaceutical companies. If at the end of the process the drug turns out not to have performed as well as hoped, the economic loss and the entrepreneurial frustration can be devastating.

One of the fields in need of considerable experimentation is that of acute stroke treatment and stroke recovery aid. These involve multimillion-dollar investments which only rarely pay off in the form of government-approved pharmaceuticals. This is why only a small number of companies are truly interested in creating new stroke medication products.

At MIT, the Newman Laboratory for Biomechanics and Human Rehabilitation has created a robot – the MIT-Manus – for use in physical therapies for post-stroke rehabilitation.

Patients learn to operate the robot in order to improve their mobility through playing a videogame by maneuvering the robot’s arm, with the robot assisting as needed.

Their colleagues in the Department of Mechanical Engineering had the idea of using the same robot as a measuring tool, with the goal of carrying out the tests requested by the FDA.

As a patient moves the robot’s arm, the robot collects motion data, including the patient’s arm speed, movement smoothness, and aim.

The researchers created an artificial neural network map that relates a patient’s motion data to a score which is correlated with standard clinical outcome measurement. Then they started collecting this data from 208 patients who began working with the robot seven days after suffering a stroke, and continued to do so for the following three months.

The scientists then selected a separate group of nearly 3,000 stroke patients who did not use the robot, but who went through standard clinical tests. In particular, the researchers calculated the “effect size” — the difference in a patient’s performance between the beginning and the end of a trial, divided by the standard deviation, or variability, of improvement among these patients. To determine whether a drug works, the FDA will often examine a study’s effect size.

Using the robot-derived neural network map, the group calculated the effect size at twice the rate usually achieved with standard clinical outcome measurements, indicating that the robot scale demonstrated greater sensitivity in measuring a patient’s recovery.

The study’s authors went one step further and performed a power analysis, which determines the optimal sample size for testing a given technique, finding that the robot scale would require only 240 patients to determine a drug’s effectiveness — a reduction in sample size that would save a company up to 70 percent in time and cost.

With this technique, if after 240 patients a drug has no measurable effect the company can pursue other therapeutic avenues. If, however, a drug improves performance in 240 robot-measured patients, the pharmaceutical company can continue investing in the trial with confidence that the drug will ultimately pass muster.

Currently, only a few stroke drugs are in the late stages of development. However, once a company reaches a Phase III clinical trial — explains Hermano Igo Krebs, principal research scientist in the research group — it may use the MIT-Manus robot as a more efficient way to evaluate the drug’s impact by employing the measurement techniques on a smaller group of patients.

This new testing technique may well provide a new stimulus for developing drugs for treating strokes, which are the third cause of death after cardiovascular disease and malignant cancer, being also the first cause of invalidity and the second cause of dementia.

In 2010 there were 16.9 million stroke cases in the world, and it is calculated that the overall rate of disability, illness and premature death caused by strokes will more than double worldwide by 2030.