‘Gearing’ up to accelerate stroke drug trials

 

But a robot developed by Prof. Hermano Igo Krebs, a principal research scientist in the Massachusetts Institute of Technology’s (MIT) Department of Mechanical Engineering, and his MIT colleagues may cut the time and cost of Phase 3 clinical trials of stroke medications by as much as 70 percent.

 

As MIT noted in a news release about the robot and a paper outlining its benefits—which was published in a recent issue of the journal Stroke—the development of compounds that can treat acute stroke or aid in stroke recovery is, like so many pharma and biotech therapeutic R&D efforts, a multibillion-dollar endeavor that only rarely results in government-approved pharmaceuticals that can be sold to recoup those expenses.

 

In fact, pharmas and biotechs often get close to the finish line, having spent years testing safety and dosage in the clinic, only to find in Phase 3 clinical efficacy trials that target compounds have little or no benefit.

 

As Krebs notes, this lengthy process is costly and often inefficient and discouraging as well.

 

“Most drug studies failed and some companies are getting discouraged,” Krebs says. “Many have recently abandoned the neuro area [because] they have spent so much money on developing drugs that don’t work. They end up focusing somewhere else.”

 

But the MIT-Manus robot Krebs and his team developed may help speed up drug development, letting pharmaceutical companies know much earlier in the process whether a drug will ultimately work in stroke patients.

 

Getting approval from the U.S. Food and Drug Administration (FDA) typically requires that a company enroll some 800 patients to demonstrate that a drug is effective during a Phase 3 clinical trial, MIT notes.

 

In the case of stroke studies, for example, the sample size for the trial is determined in part by the accuracy of standard outcome measurements, which quantify certain abilities of a patient, such as the ability to move a limb or lift it past a certain point. Aside from the time taken to enroll appropriate patients, trials can take several years to finish because of the tests and analyses that need to be run.

 

As MIT notes, Krebs and the other authors of the Stroke article found that by using a robot’s measurement abilities to gauge patient performance, companies might only have to test 240 patients to determine whether a drug works. This is where that potential 70-percent reduction comes in, and that reduction in the number of people translates into reduced time and money spent, as well.

 

The MIT-Manus robot wouldn’t enable stroke researchers to sidestep the FDA requirements, but it would allow them to make determinations earlier that will tell them whether to continue with a trial, and possibly make adjustments to it, or simply end it early. Given the increasingly loud mantra of “fail early” when it comes to drug trials—that is, determine earlier if you aren’t likely to succeed—this could be a boon for clinical trials of stroke therapeutics.

 

The robot, developed by the team at MIT’s Newman Laboratory for Biomechanics and Human Rehabilitation, has until now been seen primarily as a rehabilitation tool. Patients play a video game by maneuvering the robot’s arm, with the robot assisting as needed, and this is a form of physical therapy—but Krebs and his team are confident based on their work that the robot can be used as a measurement tool as well, such as in clinical trials, by evaluating patient improvement over time.

 

“Because robotic devices record the kinematics and kinetics of human movements with high resolution, we hypothesized that robotic measures collected longitudinally in patients after stroke would bear a significant relationship to standard clinical outcome measures and, therefore, might provide superior biomarkers,” the authors wrote in the paper, “Robotic Measurement of Arm Movements After Stroke Establishes Biomarkers of Motor Recovery.”

 

In the case of the MIT-Manus robot, this was achieved by the robot collecting motion data as the patient moves the robot’s arm, including arm speed, movement smoothness and aim. Collecting such data from more than 200 patients who worked with the robot beginning seven days after suffering a stroke and continuing for three months, the researchers created what MIT calls “an artificial neural network map” that turns a patient’s motion data into a score that is then correlated with a standard clinical outcome measurement.

 

The researchers then selected a separate group of nearly 3,000 stroke patients who did not use the robot but underwent standard clinical tests. Using the robot-derived neural network map, the researchers found that the robot scale demonstrated greater sensitivity in measuring patient recovery.

 

The researchers noted in their paper that their results “demonstrate that robotic measures of motor performance will more than adequately capture outcome, and the altered effect size will reduce the required sample size. Reducing sample size will likely improve study efficiency.