SemanticBits is pleased to announce that we have been awarded the Computerized Labeling Assessment Tool (CLAT) Services contract from the U.S. Food and Drug Administration (FDA).
“We are extremely excited to apply modern artificial intelligence, machine learning, and natural language processing methods to help the FDA streamline drug label review and support the safe use of medications.”
– Ram Chilukuri, SemanticBits CEO
This contract will support the Division of Mitigation and Medication Error Surveillance (DMAMES), Office of Generic Drugs (OGD), and Center for Drug Evaluation and Research (CDER) premarket (pre-approval) and post-market review of drug product labeling (e.g., container labels, carton labeling, Prescribing Information, Drug Facts, Instructions for Use) to minimize the risk of medication errors.
Biopharmaceutical companies electronically submit drug product labeling to the FDA using various file formats. The Division of Medication Error Prevention and Analysis (DMEPA) and other offices within the FDA review the labeling to ensure the drug product conforms with applicable statutes, regulations, standards, FDA guidance for industry, best practices for patient safety, and experience gained through the FDA’s post-marketing surveillance program. If the FDA’s review of the submitted labeling identifies potential deficiencies or inconsistencies, the agency requests the company to revise the labeling and resubmit it for additional inspection. Labeling reviews are highly manual, often subjective, and labor- and time-intensive.
CLAT is a greenfield development project to implement a prototype that automates and streamlines the drug label review process. The system will help ensure the safe use of drug products by minimizing medication errors related to the product’s name, labeling, packaging, and design. We will apply machine learning and natural language processing methodologies to automatically analyze and characterize the drug labels. Automating all or portions of manual review activities will create operational efficiencies and help standardize the review process to ensure consistency across different products and FDA review teams.
SemanticBits has partnered with SAS to bring enhanced automation to the drug label review process. The team will leverage the SAS Platform, powered by SAS Viya—a cloud-based AI and machine learning platform. The usage of SAS Viya will enable computer vision and text analytics algorithms to verify and compare drug labels, which will reduce manual intervention from FDA regulatory staff. SAS Viya’s flexibility and scalability will help ensure the prototype can successfully continue to production. The development of this prototype is the continuation of SAS Viya usage throughout CDER.
The task order will use the Enterprise Performance Lifecycle (EPLC) agile methodology to build the prototype and iterate new enhancements, functionality, integrations, and improvements. SemanticBits will provide program and product management, agile support, usability testing, data and prototype engineering and testing, and data science expertise.
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