FDA and CluePoints extend collaboration to enhance clinical trial integrity and safety

By Liza Laws

- Last updated on GMT

© Getty Images
© Getty Images

Related tags Fda Pharmaceutical drug CluePoints Clinical trial Pharmaceutical industry

Yesterday (June 5) CluePoints, providers of established statistical and AI-driven software solutions, and the Food and Drug Administration (FDA) announced an extension to their long-standing collaboration.

FDA and its stakeholders have a vested interest in ensuring the integrity of clinical trial data and the safety of participants while clinical research is being conducted.

People taking part in clinical trials can be put at significant risk of harm if clinical research misconduct such as fabrication or omission of data in reporting study results. Additionally, fraud and other forms of misconduct impairs FDA's goal to safeguard and promote public health by jeopardizing the accuracy of data provided to the agency.

To discover signals of suspected misbehavior, the FDA and other authorities have to rely on site inspections and whistleblowers. Yet due to the large number of product filings, the FDA can only inspect a small percentage of clinical trial sites.

The determination of which sites to inspect can involve FDA inspectors’ judgement and experiences, suggestions by clinical and statistical reviewers, and the Center for Drug Evaluation and Research (CDER)’s risk-based site selection tool. Under the original Contract Research and Development Agreement (CRADA) between FDA and CluePoints​, software was developed, and CluePoints’ existing software was enhanced to produce a ranked list of anomalous sites to help prioritize site inspection(s) for FDA inspectors.

As a result of the original CRADA, the CluePoints​ software was deployed in the FDA high performance computing environment, new statistical tests were developed to detect anomalous sites, the site ranking algorithm was improved, as well as the user interface for use by reviewers and others at FDA was also improved. Additionally, significant progress was made in the detection of modulators of treatment effect, i.e., factors such as center, region, or country that have a statistically significant impact on the magnitude of treatment effect.

Under this new proposed CRADA, FDA and CluePoints, Inc. will focus on two primary objectives:

The first objective is to develop and enhance CluePoints​ SMART software to address a broader range of regulatory issues and concerns. The CRADA will leverage date/time data which takes on greater significance with decentralized trials (DCT) and the increasing use of electronic clinical outcome assessment (eCOA) and electronic patient reported outcomes (ePRO) technologies. Further developments will be added to improve the detection of duplicate patients. The proposed CRADA will explore how Artificial Intelligence and Machine Learning (AI/ML) algorithms can further support anomaly detection. Finally, further research will be carried out to explore the moderators of treatment effect and develop a software solution that can be deployed within the FDA environment.

The second objective is to improve and enhance how SMART software and the CluePoints​ monitoring platform may be adapted to better support FDA processes related to anomaly detection, review and follow-up, as well as site selection for FDA inspections.

Anticipated benefits of the CRADA to the FDA include improved detection of anomalous sites, the ability to explore the interaction of various factors with data quality and their potential impact on treatment effect, and the ability to streamline the processes of data review and site selection for inspection at FDA.

Related news

Show more

Related products

show more

Saama accelerates data review processes

Saama accelerates data review processes

Content provided by Saama | 25-Mar-2024 | Infographic

In this new infographic, learn how Saama accelerates data review processes. Only Saama has AI/ML models trained for life sciences on over 300 million data...

More Data, More Insights, More Progress

More Data, More Insights, More Progress

Content provided by Saama | 04-Mar-2024 | Case Study

The sponsor’s clinical development team needed a flexible solution to quickly visualize patient and site data in a single location

Using Define-XML to build more efficient studies

Using Define-XML to build more efficient studies

Content provided by Formedix | 14-Nov-2023 | White Paper

It is commonly thought that Define-XML is simply a dataset descriptor: a way to document what datasets look like, including the names and labels of datasets...

Related suppliers

Follow us

Webinars

Headlines