Improve and accelerate your investigator selection

There has been an increasing degree of competition for high-performing sites in both common disorders and orphan disease areas. The number of trials registered at has tripled in the last decade alone. In addition, the protocol designs have become more demanding and burdensome on investigative sites.

Anyone who has worked in the industry knows that identifying and selecting the right site and investigators is of paramount importance to the success of your pipeline and to keep costs down. A weak feasibility and selection process often has a significant impact on enrolment rates and the overall outcome of the trial. However, a rigorous feasibility and selection process requires access to detailed data that is both troublesome and incredibly time-consuming to find and interpret. This makes it difficult to thoroughly assess and compare sites and their affiliated investigators.

To depict this problem as hands-on as possible we thought that we might share a use case derived from reality. It’s revolving around a pharmaceutical firm, let’s call them Company A.

Real-life use case

Company A was planning their study design for a Phase IIa for the treatment of NASH. After thorough screening processes, Company A was left with a shortlist of three potential study sites. All deemed equally attractive in terms of capabilities and patient enrolment. The potential principal investigators at each site all seem to be equally suitable to run the trial as they

  • all had a track record of conducting similar trials and what seemed to be transferable experiences
  • based on their publication history, all candidates seem to have the same level of interest and experience in the research question, as well as the same level of recognized status in the research community
  • all had prior experience with similar patient groups and patient enrolment in the disease area

With the data at hand, it looks as if Company A is equally well off with any of the candidates. But first, let’s look at what kind of insight we can uncover by using grants and financial transaction data in the feasibility process.

Investigator candidate 1

By looking at the readily available financial data in Monocl EGO we can see that Candidate 1 has received a large number of industry payments from a key competitor over a long period of time.

Industry Payments categorized by nature of the payment (e.g. research payment), company and year

By segmenting payments and drilling into detail on individual transactions we can see that Gilead Sciences has been paying Candidate 1 consulting fees, travel expenses and royalties for in-licensing of a patent portfolio on NASH. In addition, the financial data shows that his research has been heavily funded by Gilead in other indications. It becomes evident that this candidate is deeply involved with a key competitor in the same indication, potentially making Candidate 1 unfit as an investigator for the study.

Individual Transactions sourced from CMS Open Payments. All categorized and clickable in Monocl EGO

Investigator candidate 2

By analysing the data visualized in the image below, we can see that Candidate 2 has secured large grants in several grant applications. The grants receiving’s history of Candidate 2 indicates that this individual is a senior expert with high credibility, as the funding bodies make decisions based on a rigorous review process. Examining researchers by their success in receiving grants, in combination with other metrics, is a great way to evaluate the credibility and track-record in achieving research objectives and producing satisfying results. This adds another interesting dimension to the assessment, which could prove to be very important.

Moreover, Candidate 2 has been actively collaborating with industry. But as opposed to Candidate 1, this individual has been working with a range of companies indicating a lack of loyalty to competitors. The amounts received are also smaller.

Investigator candidate 3

Candidate 3 has only received has a few small grants and does not have any industry payments. Although the research profile of this candidate aligns well with the requirements of Company A, the lack of industry experience and major grants receiving’s make him a non-optimal candidate based on the company’s established selection criteria.

When Company A was presented with the information displayed above, they chose to proceed with Candidate 2, who ended up leading the Phase IIa study.

Even though the example is rather simple, the results retrieved from the analysis showcase the power of analysing data from multiple sources simultaneously rather than separately. Having a holistic capability at the tip of your fingers will not only increase your operating speed and efficiency but also enhance the accuracy of your analysis and decision-making. This example illustrates that there is not a single source of information that provides the full picture. Instead, combining and adding multiple data sources will uncover and depict a greater part of the truth and provide you with powerful insights to drive better decision making. If needed, this approach should, of course, be combined with additional secondary market research.

I hope you enjoyed this brief use case in which we try to showcase how sophisticated life science professionals are increasingly looking to work in a more data-driven fashion. Stay tuned and subscribe to our newsletter for more updates on how you can make data work for and enable you to make unbiased, smarter decisions.