An organization that seeks to advance research and promote awareness of antimicrobial resistance (AMR) has proposed a strategy to expedite the use of new microbial diagnostics in clinical practice.
The Antibacterial Resistance Leadership Group (ARLG) writing in the journal Clinical Infectious Diseases introduced its Master Protocol for Evaluating Multiple Infection Diagnostics (MASTERMIND) scheme. ARLG plans to bring various industry sectors together to develop MASTERMIND studies on diagnostic tests.
The World Health Organization (WHO) considers AMR as a global health threat, one that is driven by overuse and misuse of antiviral and antibiotic therapies. ARLG’s article describes how rapid tests are in short supply to identify bacteria before patients receive unnecessary or inappropriate antimicrobial therapies.
“Although conventional approaches, such as bacterial culture and routine antimicrobial susceptibility testing, can provide a diagnosis for some infectious syndromes, the turnaround time from specimen collection to final result is often too long to provide actionable results,” ARLG’s article stated.
One remedy is to develop affordable diagnostic tests with the capacity to rapidly identify bacterial pathogens, but this is easier said than done. Diagnostic firms often face a long and difficult road in getting U.S. Food and Drug Administration (FDA) clearance for new tests, and access to clinical specimens that yield strong validation data is hard to come by. There’s also the issue of limited clinical trial sites.
MASTERMIND, whose strategy draws from novel study designs used in oncology trials, promises to eliminate these challenges through a collaborative analysis of multiple diagnostic tests.
Typically, manufacturers set up clinical trials for specimen collection to evaluate diagnostic tests, the article’s lead author, Robin Patel, MD, ARLG’s director of diagnostics and master protocol and a member of its mentoring and steering committees, told CLN Stat. “If multiple companies are looking for the same types of specimens for their assays, they may be competing with one another.”
This is not an efficient process, said Patel, who also chairs the Mayo Clinic’s Division of Clinical Microbiology.
Under a MASTERMIND study, companies could work together, collecting and dividing up a specimen (such as blood) or collecting multiple specimens (via swab) from the same patient, and testing these samples on multiple companies’ platforms.
“This provides efficiencies for clinical trials sites and for industry. So the hurdle removed is logistical and financial,” she said. ARLG would work with FDA and the involved companies on study design—although each company ultimately would be able to decide whether to submit its data to FDA.
The article describes a number of scenarios in which MASTERMIND studies could improve efficiency and workflow over more conventional study methods. As an example, for a diagnostic study on bloodstream infections, an analysis of a direct-from-blood pathogen detection test could use the blood from one test subject.
“Moreover, that positive blood culture could be used to evaluate a new phenotypic or genotypic antimicrobial susceptibility platform. In this way, a single subject is still being used to evaluate multiple diagnostics, although the diagnostics focus on different clinical and microbiological questions,” according to the MASTERMIND developers.
Urine diagnostics, antimicrobial susceptibility in respiratory specimens, and anti-infective clinical trials are other areas of research that lend themselves to a MASTERMIND trial design.
One potential challenge in designing MASTERMIND studies is setting appropriate comparator methods. Statistical considerations and industry commitments might pose other roadblocks, yet, the article lists solutions for all of these hurdles.
In the event a comparator method test has subpar performance characteristics, the developers suggest using tests “with a high level of preclinical validation or include clinical and laboratory components in the comparator.” Using clinical adjudication or an algorithmic approach offers other solutions to this problem.
Developing these studies won’t be easy, ARLG members cautioned: “They will take a large effort, require funding for people who focus exclusively on the trial (eg, operations, statistics, data management), and require standardization and robust informatics infrastructure linking elements together for the participating laboratories.”