December 15, 2023, 4:19 PM EST
A Reflection of 2023 in Clinical Research
By Rohit Nambisan
In many ways, 2023 was a banner year for patients – the FDA approved 55 new medications, the most in five years – including the first treatment leveraging genome editing. Despite these incredible advancements, 2023 also saw setbacks. The harsh financial environment continues to punish both biopharma and health tech companies, with a four-year low in fundraising. Many companies are shelving promising experimental therapies due to insufficient resources to plan and conduct clinical trials.
What’s worse is the number of new treatments that failed due to problems with clinical trial data collection, as opposed to efficacy or safety issues. Here are several examples: “BioVie blames protocol errors at trial sites for phase 3 Alzheimer’s drug fail as stock craters,” “Acelyrin Flags CRO Errors in Izokibep Program After Late-Stage Failure,” “Pfizer scraps half of participants in Lyme disease drug trial due to quality issues.”
The growing amounts, types, and sources of data collected during clinical trials exacerbate these problems. Trial teams rely on at least six different sources that produce as much as 3.6 million data points per trial, according to Tufts University – triple the data points collected per trial only a decade ago. And those figures are rising.
As data is collected from a growing set of diverse sources, there are more opportunities for errors and miscalculations. The White House Office of Science and Technology Policy has recognized the critical nature of this issue, calling for a “whole-of-government” approach to strengthening clinical trial infrastructure.
Lokavant was created to eliminate these types of problems. Our mission is to redefine clinical trial analytics, with a focus on empowering researchers to make real-time and informed decisions that improve trial performance. Since emerging from Roivant Sciences, we have made much progress.
Let’s take a look back at 2023.
Summit for Clinical Ops Executives (SCOPE) Conference
In February at the 14th Annual SCOPE Conference, we presented the Lokavant platform (and its proprietary data set of 2,000+ clinical trials) approach to helping our customers benchmark studies in real-time. We shared case studies, including:
- Advanced warning of protocol adherence issues in an important COVID-19 study where participants were lost to follow-up.
- Accurately predicting site non-compliance that would have prevented site closure, trial delays, and loss of $500,000 in patient enrollment costs.
- The benefits of combining 30+ source systems for a global phase 3 study, into one comprehensive dashboard, with the ability to scale long-term.
Helping Customers with Diversity, Equity & Inclusion in Clinical Trials
In April, we relaunched Lokavant to the market, driven by rapid growth and new product development. Since our founding, Lokavant increased its customer base 3x year over year; our platform and analytical applications have provided a 70x improvement in enrollment forecast accuracy, and study sponsors have realized six months' time savings from detecting site noncompliance issues, on a per trial basis. We partnered with Craig Lipset, founder of Clinical Trial Innovation Partners, to share how we can harness the value of data intelligence in clinical trials to help pharmaceutical companies create diversity action plans for their trials.
Using Real-World Data to Inform Trial Operations
In June, we had the opportunity to meet with customers and partners at the annual DIA conference. We presented the results of our groundbreaking research study that linked real-world data with actual clinical trial and site performance, providing insights into how Lokavant’s Clinical Trial Intelligence Platform can help customers increase participant enrollment predictability as well as optimize site performance.
Study Planning and Forecasting Solution
In August, we announced our new solution that reduces trial costs and timelines, with an initial case showing it can cut the number of sites needed for a trial by 36 percent. This offering empowers sponsors and CROs to accurately identify the right number, location, and mix of sites, driving key performance and diversity milestones while optimizing study performance.
In September, we joined BMS at the Disruptive Innovations to Advance Clinical Research (DPharm) conference to discuss how we partnered to deliver our solution for computational feasibility. Here, we focused on leveraging real-world data (RWD), clinical trial data, and publicly available data sets to drive transformative feasibility forecast models, offering a highly targeted and patient-centric approach to site placement for an underdiagnosed disease.
Inaugural Lokavant Summit
Also in September, we held our inaugural Lokavant Summit in New York City. This event facilitated provocative and engaging discussions between more than 50 clinical research executives and thought leaders. Key topics included how artificial intelligence (AI) can be used to generate better models for analysis and prediction and the necessity to generate quantifiable value with AI. These challenges that we face as an industry we can not face alone. We are eagerly planning to host this prestigious group again in 2024.
Investment and Expansion in Japan
In October, we announced that global business titan Mitsui & Co. Ltd. provided an $8 million strategic investment to help Lokavant expand its AI-optimized platform across the Asia-Pacific region. This strategic business partnership will enable Lokavant’s platform to reach more sponsors and CROs whose clinical trials can benefit from actionable and intelligent insights.
2023 was a year when artificial intelligence stepped into the limelight, even though it’s been in practice for years - including in Lokavant’s platform. But to generate a valuable AI strategy, we need a data strategy as data fuels AI models. After a year of progress and exciting developments, I’m energized for the future and what 2024 will bring.
Early in January, I’ll share my outlook on the year and how we can work together with different algorithm developers, different data sources, physicians, regulators, and patients to bring together the right data strategy that helps us predict clinical trial outcomes - ultimately accelerating new therapies to the patients in need.