MIT robot may accelerate trials for stroke medications

Robot protocol able to cut time and cost of Phase III drug trials by 70%

Scientists at Massachusetts Institute of Technology in the US have developed a robot that may help speed up the development of drugs to treat acute stroke or aid in stroke recovery.

Hermano Igo Krebs, a principal research scientist in MIT’s Department of Mechanical Engineering, says the robot could let pharmaceutical companies know much earlier in the development process whether a drug will ultimately work in stroke patients.

The team found that by using a robot’s measurements to gauge patient performance, companies might only have to test 240 patients to determine whether a drug works — a reduction of 70% that Krebs says would translate to a similar reduction in time and cost.

While pharmaceutical companies would still have to adhere to the FDA’s established guidelines and outcome measurements to receive final drug approval, Krebs says they could use the robot measurements to guide early decisions on whether to pursue or abandon a certain drug.

In their study, the scientists explored the robot MIT-Manus as a tool for evaluating patient improvement over time. The robot, developed by the team at MIT’s Newman Laboratory for Biomechanics and Human Rehabilitation, has mainly been used as a rehabilitation tool – patients play a video game by manoeuvring the robot’s arm, with the robot assisting as needed.

They could use the robot measurements to guide early decisions on whether to pursue or abandon a certain drug

While the robot has mainly been used as a form of physical therapy, Krebs says it can also be employed as a measurement tool. As a patient moves the robot’s arm, the robot collects motion data, including the patient’s arm speed, movement smoothness, and aim. For the current study, the researchers collected such data from 208 patients who worked with the robot seven days after suffering a stroke, and continued to do so for three months.

The researchers created an artificial neural network map that relates a patient’s motion data to a score that correlates with a standard clinical outcome measurement.

The authors 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 patient performance from the beginning to 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 look to 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 patient recovery.

The study’s authors went one step further and performed a power analysis that determines the optimal sample size for a given technique, finding that the robot scale would require only 240 patients to determine a drug’s effectiveness.

Currently, only a few stroke drugs are in the late stages of development. However, once a company reaches a Phase III clinical trial, Krebs says 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.

The researchers have published their results in the journal Stroke.