We stand with Ukraine

QA in Regulatory Affairs Submissions

Years of discovery and development effort go into creating new interventions before trials and regulatory submissions get started. No matter your company size or phase of product development, regulatory submissions are challenging. Failed applications are costly, slowing approvals as the process needs to start again with longer timelines and delays in new drug availability to patients.

The process of writing submissions and quality audits (IND, INAD, NDA, NADA, etc.) involves numerous steps and requires close coordination by research, non-clinical, or clinical departments with regulatory, safety, quality, marketing, and other stakeholders. Companies can mitigate submission quality and data conformance issues in regulatory reports by automatically identifying discrepancies between information in the report and source materials. Simply put, use expert.ai to accelerate regulatory approval processes with a more accurate and comprehensive submission quality review processes.

Download the brief

Regulatory affairs teams focused on preclinical or clinical submissions to regulatory bodies benefit from:

  • Standard-based life sciences knowledge graph for data normalization
  • Comparative content analytics to identify content similarities and differences between source and target documents
  • Configurable extraction engine to recognize and processes specific content sections
  • Tunable hybrid knowledge models to identify relevant data elements related to toxicology, pharmacokinetics, or clinical reports
  • Customizable process workflows for regulatory submissions and technical documentation
  • Dedicated quality control user interface to review identified variations in the original document context

Expert.ai’s Life Sciences and Pharmaceutical solutions provide medical, research, pre-clinical, clinical, regulatory and knowledge management teams with the highest quality, standards-based and most consistent Life Sciences Knowledge Models available. Teams can now accelerate quality assurance processes for regulatory reporting by using a hybrid AI approach combining natural language processing & understanding (NLP/NLU), machine learning and Large Language Models (LLMs).

Measured Outcomes for Regulatory Teams:

  • Mitigate risk of regulatory submission delays because of data inconsistencies
  • Accelerate the identification of similar content in summary and source documents
  • Identify misalignments more quickly with toxicology, pharmacokinetics, and clinical data
  • Increase the number of identified misalignments
  • Reduce overall quality control review time
  • Equip regulatory affairs and quality control resources with intelligent applications