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Jun 24

Partex and Singapore’s Experimental Drug Development Centre collaborate to bring forward an innovative approach for early drug discovery and development

featured, Press release 2024
Frankfurt, Germany, 3rd June 2024, 9am CET Partex, a leading provider of AI-driven solutions in the pharmaceutical industry, is thrilled...
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23

Apr 24

Partex Partners with Lupin to Revolutionize Drug Discovery through AI-Driven Asset Search and Evaluation

featured, Press release 2024
Frankfurt, Germany, 23 April 2024 – Partex, a leading provider of AI-driven solutions in the pharmaceutical industry, is thrilled to...
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Innoplexus wins Horizon Interactive Gold Award for Curia App

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Leveraging AI to Optimize Clinical Trial Design and Site Enrollment

Life Science / By

The request

The client wanted help with clinical trial design and assistance with site selection, patient enrollment, and identification of suitable investigators.

Background and challenges

The client, a mid-sized pharmaceutical company, was entering phase II clinical trials for a drug targeting diffuse large B-cell lymphoma (DLBCL). However, due to limited prior experience clinically developing an asset in the oncology space and given the large number of competitors pursuing DLBCL indications, the client required help with a number of aspects, from designing the trial (number of patients, endpoints, trial duration, inclusion/exclusion criteria) to identify all potential sites in the US and Europe, and finding the most suitable investigators. The client also wanted to stratify sites based on patient subtypes in DLBCL and prioritize them based on recruitable populations and competition from nearby sites.

Solutions provided by Innoplexus

Innoplexus leveraged its CAAVTM technology to help the client design the clinical trial by offering the Clinical Trial Comparator (CTC) Dashboard. The dashboard presents past and ongoing clinical trials in DLBCL (along with identification of successful and failed trials) and the drugs’ regulatory approvals. In addition to the CTC Dashboard, the Site Optimization & Enrollment (SOE) Dashboard was created to enable informed selection of clinical trial sites. This tool identifies and prioritizes sites based on a multitude of parameters, including participation in pivotal trials, successful trials, the number of grants and publications in the relevant disease area, and presence of reputable researchers and untapped patient populations.

Benefits to the client

Clinical Trial Comparator Dashboard

The CTC Dashboard enabled the client to compare currently running and completed (successful and failed) clinical trials in DLBCL and similar indications across a number of parameters. Our team continually leveraged our proprietary technologies to crawl the web, identifying and updating dashboard information in real time, which ensures that clinical trial design decisions are based on the latest information. By connecting these data points, Innoplexus determined an optimal trial design to help ensure that the trial’s endpoints are clinically significant. Moreover, our technology automated meta analysis and provided actionable insights, thereby greatly reducing time and unnecessary complexity.

Site Selection & Enrollment Dashboard

Innoplexus designed a dashboard to enable the client to identify and prioritize clinical sites based on their past involvement in DLBCL with similar patient segmentation, geography, proximity to competitor clinical trials, and recruitable patient populations. Our team developed customizable visualizations to facilitate rapid evaluation of sites and filters to allow for client-specific weighting of metrics for success. Innoplexus also enabled integration of client enterprise and third-party data in order to provide more robust insights. We leveraged artificial intelligence to generate heat maps based on “realizable” potential, taking into account a number of parameters that helped in quickly triangulating optimal sites.

Ontosight® Influence

Innoplexus created Ontosight® Influence to help assess, segment, and prioritize key opinion leaders (KOLs), including principal investigators for clinical trials. Filters such as experience and connections can be used to discover suitable therapeutic area experts and principal investigators for clinical trials. It leverages artificial intelligence technologies such as network analysis for faster access to connections among optimal researchers and investigators. It also divides the relevant experience of each KOL of interest into four segments, showing whether they are more active as thought leaders, speakers, or researchers, or are more business oriented.

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