April 19, 2021
Raj Indupuri, eClinical’s CEO and Co-founder, discusses the impact of AI on drug development and its potential to transform clinical research in this article from Datanami. Read the full article here.
AI to Manage Clinical Trial Data
The mRNA-based COVID-19 vaccines created by Pfizer-Biontech and Moderna show how much progress has been made on the science side of the pharmaceutical house. And novel modeling approaches, such as those from CytoReason, are on the cusp of speeding things up even more.
But not as much progress has been made on the underlying data systems that pharmaceutical companies and biotech firms use to manage the actual clinical trials that are necessary to get a drug approved for use, says Raj Indupuri, the CEO of eClinical Solutions.
“You’d be surprised how companies are still depending on Excel to look at the data,” Indupuri says. “There’s a lot of progress that has been made with the science, but unfortunately, in terms of clinical development, I don’t think over the last two decades much has happened.”
Many drug companies rely on legacy IT systems to manage their clinical trials. They hire armies of folks to enter data into the system, to cleanse the data, and to generate the reports that the U.S. Food and Drug Administration (FDA) requires. This takes a lot of time and money, which is why bringing a single drug or therapy to market can take 10 to 15 years and cost $2.5 billion to $4 billion, Indupuri says.
“It’s an industry where we lag because we’re very conservative,” he says. “We deal with clinical patients and data related to that, so it’s very controlled. But that shouldn’t be an excuse in terms of adopting modern technologies. I do believe that technology is a key enabler to help with this transformation and to deal with some of the inefficiencies.”
eClinical Solutions develops a cloud-based data and analytics platform that automates several aspects of running a clinical trial. For starters, it enables drug companies to ingest raw data from multiple sources, and transform and cleanse the data to make it available for analysis. This could be demographic data from clinical trial participants, biomarker data, laboratory data, and data from EMR and insurance claims systems.
Once the data is ingested into the platform, the Massachusetts-based company uses data visualization and machine learning techniques to help biostatisticians and scientists spot anomalies and outliers in the data. This could indicate that different cohorts are responding to a novel treatment in different ways, which could inform a change in the treatment is necessary. Or it could help to spot issues occurring in a specific hospital that’s participating in the trial, which could lead to changes in how the trial is conducted.
Finally, the company’s software helps generate the reports that its clients will submit to the FDA to gain approval for their new drug or treatment. All told, the automation can help increase the efficiency of a phase 1-3 clinical trial by 40% to 50%, Indupuri says.
Machine learning and AI is necessary to gain the scale necessary to automate some of the most difficult aspects of managing data in a clinical trial, Indupuri says. “We have this enormous stream of data from patients, and there’s no way you can [manage that efficiently] without having modern data infrastructure or data pipeline, or taking advantage of advanced data science techniques, like ML models or AI,” he says.
The company is rolling out a new machine learning system that will automatically transform data based on how human curators have manually cleansed raw data in the past.
“What we have done is we’ve used that information to train the ML model,” he says. “It can detect outliers and can detect data issues and provide outputs so that a reviewer can quickly confirm and take action. It eliminates the need to manually look at so many data points. Without an ML model, it wouldn’t be scalable. As the amount of data increases, this would not be feasible.”
Data is the critical element that will enable us to design and test better drugs at a faster cadence. As the volume and variety of data about diseases and drugs reactions builds, machine learning technology will be absolutely essential to making sense of it in a timely manner.
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