Lokavant, the clinical trial intelligence company, has officially announced the launch of Spectrum v15, which is understood to be the most sophisticated, AI-powered clinical trial forecasting solution till date.
According to certain reports, the stated solution arrives bearing the means to leverage machine learning, generative AI, and causal AI models, powered by its historical data set from 500,000 trials, all for strengthening the probability of achieving key last-patient-in milestones. More on the same would reveal how users banking upon this solution can dynamically model study feasibility based on different site and country combinations, along with other inputs. This it can do prior to study start, as well as continuously for iterative analyses and mid-study course correction.
Spectrum also cuts down on the five-week timeline needed to set up forecasting comparators, with the entire mechanism now only asking for a few minutes. This reduction in legwork comes packaged alongside 80+% confidence.
To understand the significance of such a development, we must take into account how the growing industry volatility and trial complexity has spurred the need for way to predict key success factors and understand how changes, such as different site activations, could impact timelines and budgets. Now, the traditional approach here would involve weeks-long process of painstakingly analyzing study feasibility data, manually searching for past trials that simulate their own.
Even after all this hassle, results can often be inaccurate and devoid of considerations to accommodate unique interdependencies between data. This older methodology can also miss out on any changes that may happen during a clinical trial, such as protocol amendments or participant discontinuation.
Fortunately enough, Spectrum accounts for these fluctuating dynamics and constantly re-adjusts its forecasts accordingly.
“In an industry grappling with increasing complexity and volatility, we must move beyond static feasibility assumptions toward dynamic, data-driven adaptability. Solutions like Spectrum exemplify the next generation of intelligent trial design,” said Jonathan Crowther, Pfizer Head of Predictive Analytics. “This isn’t just operational efficiency, it’s strategic foresight. The ability to continuously model feasibility and scenario-plan with real-time responsiveness enables R&D organizations to make confident, early-stage decisions that de-risk timelines, optimize budget allocations, and accelerate portfolio value realization. It brings us closer to a future where clinical development is not reactive, but anticipatory and resilient.”
Talk about the whole value proposition on a slightly deeper level, we begin from its promise to provide granular forecasting at all levels. You see, in pre-study and mid-study analysis, study teams can enter site, country, region, and study-level forecasting inputs across enrollment, discontinuation, and screen failure. As they do that, these teams can simultaneously account for non-enrolling sites.
On top of it, users can also avail detailed projections for each site, country, and region.
Next up, there is an adaptive trial design forecasting facility. This involves configuring forecasts based on adaptive trial designs with the flexibility to define multiple enrollment-driven interim analyses and other study pauses.
Another detail worth a mention is rooted in the prospect of defining and achieving screening goals. In essence, users can model participant screening volumes at site, country, region, or study level, required to achieve enrollment goals, based on screen failure, discontinuation, and enrollment rates at each of these geographic levels.
Rounding up highlights would be the potential for real-time continuous forecasting. The idea here is to help teams continuously track ongoing study startup and enrollment, while generating alternative study scenarios to ensure enrollment success.
Among other things, we ought to mention how Spectrum leverages Lokavant’s Clinical Intelligence Platform, which is built using deep, historical trial data with integrated timelines for country approval, site startup, and enrollment rates, derived from more than 500,000 historical studies.
Thanks to that, the platform should be able to help sponsors and CROs forecast clinical trial enrollment success and accurately see real-time trial performance for decision optimization.
“As an industry experiencing unprecedented volatility, there is a great need to quantify uncertainty while identifying reliable paths to study enrollment success,” said Rohit Nambisan, CEO and Founder at Lokavant. “Spectrum analyzes country approval timelines, site activations, indication, and enrollment rates, leveraging the most advanced models to quantify levels of uncertainty with each forecast and each new variable.”
