Neurologist Peter Chin discusses the evolution of measuring multiple sclerosis.
Over the past decade I’ve worked in clinical development to advance new medicines to treat multiple sclerosis (MS). My job has been to design clinical trials and monitor the data they generate to determine a potential medicine’s safety and efficacy. One question we aim to answer is whether the new medicine is more effective than what’s already available. Answering this question of efficacy requires tools for measuring and comparing both the state and progression of the disease.
Measuring the impact of MS isn’t as simple as unspooling a tape measure – we’re limited both by what a patient can report and what a neurologist can assess. The biology of MS is not fully understood, symptoms of MS vary from person to person, and the biological markers of MS don’t always correlate with a patient’s clinical signs and symptoms. MS usually appears early in life and it is a chronic, lifelong disease, where symptoms typically evolve over time. Furthermore, there are different subtypes of MS.
Most people living with MS have the relapsing form, in which new symptoms abruptly appear or old symptoms worsen for a limited period of time and are followed by either full or partial recovery, in episodes known as relapses or MS attacks. Others have progressive MS, in which the disease steadily worsens over time. Thus, demonstrating the efficacy of treatments for MS is complicated and the science around this continuously evolves.
In designing a therapeutic clinical trial, patients are randomly assigned into two or more treatment groups, those receiving the new medicine and those receiving an existing treatment or placebo (also known as the control group). Then we identify patient outcomes that are important in that disease and use them to define the trial’s endpoints, which are quantified outcome measures used to formally compare results between the two groups. We assess all of the patients over time, record their outcomes during the trial, and compare the two groups at the end of the trial using the pre-defined endpoints.
At present, neurologists use three major types of outcome measures to assess the state of one’s MS disease: frequency of relapses; physical disability status; and biological markers, including brain scans using MRI. In MS clinical trials, these same types of outcomes are used to define the specific endpoints to be statistically analysed.
In the early 1980s, a new assessment tool became available –
There’s no single measure that captures the entire MS experience. As more becomes known about the biology of disease and treatment, the field develops newer ways to integrate relapse records, disability scores and biological markers. One example of progress in MS outcomes measurement is
Our measurements have improved, but there’s more to be done. There’s a symbiotic relationship between our understanding of MS and our tools for measuring it – better understanding leads to better tools, and better tools lead to better understanding. But it’s perhaps overly optimistic to expect an exact measurement tool, as the disease is very complex.
John Tukey, a mathematician who developed an algorithm that is fundamental to investigating MS with MRI, said it well: “Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise.”
We learn as we go, both in science and in measurement of outcomes. At Roche, we’re committed to advancing on both fronts.