May 10, 2023, 11:12 AM EDT
A data-agnostic integration platform for optimizing trial planning and conduct hit the streets three years ago, bringing near-real-time insights about every participant, site, country, region, and study to companies struggling to reduce the cost and speed the pace of clinical research. The technology, powered by machine learning, is currently underpinned by data from more than 2,000 trials and over 400,000 healthcare providers and is in the process of integrating other real-world data as well, according to Rohit Nambisan, CEO and cofounder of Lokavant.
Lokavant is one of many subsidiary biotech companies (so-called “vants”) that have spun out from Roivant Sciences, where Nambisan previously served as head of digital product. A neuroscientist by training, he has a broad view of the landscape with experience in academia, healthcare IT, and the startup sphere as well as in assay development at Novartis.
With the shift in focus from blockbuster drugs to more targeted therapeutics requiring many specialized and disconnected data sources, sponsors and CROs needed a nimble innovation engine to bring all that information together in one place for easy viewing and interpretation, Nambisan says. Lokavant was built for execution inside Roivant to fill that niche but emerged from it to make the technology more broadly available.
The company’s first contract was with Parexel in January 2020 and Lokavant has since tripled its customer base year over year, he continues, a list that includes Japan’s largest CRO (CMIC Group) as well as other biotech companies. Whatever data sources and vendors sponsors opt to use for a trial, Lokavant can connect them and create a normalized view of that study.
“The whole thing is highly permissionable and configurable [based on role]... for the kind of cross-functional dynamic needed to enable smarter trials,” says Nambisan. Data is updated directly from sources up to six times daily so users can see when a study is derailing and make the necessary adjustments.
Lokavant’s clinical trial intelligence platform is unlike any other analytics platforms used across the healthcare industry, he says. It leverages a growing proprietary database of anonymized clinical research data—including metrics such as enrollment rates, protocol deviations, data management, and quality issues—to create a repository of information for developing predictive and diagnostic models for all deployments and customers. That is, it can tell study sponsors and CROs why an event, good or bad, has occurred in a trial as well as when it might happen.
The platform has been validated both retrospectively and prospectively, notes Nambisan. Its predictive analytics has been found to result in a 70-fold improvement in enrollment forecast accuracy, over $1 million in cost savings from patient retention, and time savings of six months from detecting site noncompliance issues on a per-trial basis.
The hub-and-spoke business model of Roivant Sciences, formed in 2014, is responsible for scaling up a string of biotech companies. Lokavant’s maturation speed, Nambisan says, is tied to the fact that they were iterating on challenges directly faced by study teams in an industry that is notoriously risk-averse. At launch, Lokavant’s technology had already been “battle tested” in clinical research and able to generate the evidence to prove its worth.
Several white papers have been produced where Lokavant has validated its claims of time and dollars saved based either on an actual live deployment of the predictive analytics platform in a study or a simulation where time-stamped historical data is run through the clinical data hub. The focus here has been on better forecasting participant enrollment and retention, one of the biggest issues facing trials of every size the world over.
“It’s very hard to predict when a study will complete, as well as the likelihood of achieving the target accrual of participants,” Nambisan says. The company’s predictive model looks at historical data using clustering, a machine learning technique that identifies similar studies to the one about to be deployed and co-registers them based on certain common features—same therapeutic area or trial phase, for example, or conducted in the same country—to, say, learn about the impact of specific geographies and sites on enrollment rates.
Based on that repository of data, the platform builds a model predicting the success of the new study in terms of the metrics of consequence. Many customers therefore use the software for study planning as well as keeping a trial on track once it starts, says Nambisan.
At that point, data get pulled from source systems (e.g., clinical trial management, study startup, and interactive voice response) multiple times a day and get factored into the forecast, he continues. The model adapts and learns based on the incoming information, further improving prediction accuracy as the trial progresses.
In a clinical trial for a rare disease, the platform was able to foresee early on that the target enrollment of about 200 patients would be impossible to hit even with arbitrary changes made to the enrollment window by the study team, Nambisan says, citing one use case. Two years out, the prediction came within one month of when the last patient actually enrolled.