Lokavant has launched the AI-powered clinical trial forecasting solution, Spectrum v15, to dispel uncertainty in the feasibility of studies.
The solution leverages machine learning, causal AI, and generative AI models, utilising a historical dataset from 500,000 studies to predict the likelihood of achieving critical last-patient-in milestones.
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According to Lokavant, previously, setting up forecasting comparators manually took a minimum of five weeks, but with this solution, this process now takes five minutes.
This advancement comes as a response to the industry’s need for more control over increasingly complex and costly trials.
The solution introduces benefits for mid-trial and pre-trial analysis.
Teams can input data for site, region, nation, and study-level forecasting, including enrolment, screen failure rates, discontinuation, and even considering non-enrolling sites.
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By GlobalDataSpectrum v15 also allows for the configuration of forecasts based on study designs, accommodating several enrolment-driven interim analyses and other trial pauses.
Additionally, it sets screening goals to model subject screening volumes needed to meet enrolment targets, factoring in screen failure and discontinuation rates at various geographic levels.
With real-time integration of live trial data, Spectrum v15 enables continuous tracking and (re)forecasting, allowing teams to create alternative trial scenarios for ensuring success in enrolment.
Powered by the company’s Clinical Intelligence Platform, the solution delivers trial feasibility forecasting for biotechnology sponsors globally.
It allows contract research organisations (CROs) and sponsors to predict the success of patient enrolment in trials and monitor the performance in real-time, to enhance timely decision-making.
The solution also allows teams to independently predict timelines of the enrolment and evaluate operational risks, during and prior to the execution of the trial.
Lokavant founder and CEO Rohit Nambisan said: “As an industry experiencing unprecedented volatility, there is a great need to quantify uncertainty while identifying reliable paths to study enrolment success.
“Spectrum analyses country approval timelines, site activations, indication, and enrolment rates, leveraging the most advanced models to quantify levels of uncertainty with each forecast and each new variable.”
