Time for a Change – How KASA Impacts Regulatory CMC Submission Strategy  

Planning, assembling, and editing a Chemistry, Manufacturing, and Controls (CMC) dossier is a critical part of every clinical trial application and marketing authorization submission. Every CMC dossier requires the compilation of copious amounts of data from various departments (i.e., Research and Development, Manufacturing, and Quality). Regulatory and technical authors must then summarize these data according to the respective region-specific requirements and facilitate comprehensive and thorough review processes to optimize all content and verify data presentation against source documents. As a final step, the dossier must be formatted, published and packaged in the Electronic Common Technical Document, eCTD, format. Managing this complex process and the corresponding regulatory requirements for both initial applications and lifecycle maintenance can be challenging for sponsors, particularly those embarking on their first major regulatory submission.  

The review and evaluation of the CMC dossiers can be equally challenging for the U.S. Food and Drug Administration (FDA) assessors who must review complex CMC text-heavy free-style narratives. Further, the FDA repository of product knowledge is not easily accessible to the individual assessors resulting in review of each application in isolation, thereby introducing subjectivity in assessment and regulatory decisions.  

Despite its significant benefits, the eCTD poses challenges for FDA assessors because the submitted content does not follow the development flow, contains unstructured data, and varies in the level of granularity provided. Furthermore, the documents are in PDF format so information cannot be easily searched, making lifecycle management challenging. 

In a recent FDA webinar, the Office of Pharmaceutical Quality (OPQ) noted, per year, they review about 3,000 Investigational New Drug Applications (INDs), 240 New Drug Applications and Biologic License Applications (NDAs/BLAs), 900 Abbreviated New Drug Applications (ANDAs), and 10,000 supplements, all with data submitted in an unstructured PDF format (i.e., various types of data stored in native formats). Additionally, expectations from industry and demands from Congress and the public to speed up the availability of safe and efficacious drugs and emerging advanced drug development technologies are behind FDA’s initiative to develop a system to optimize the efficiency and agility of the CMC assessment process and to effectively manage the corresponding data throughout the lifecycle of a product.  

To this end, the FDA has developed and launched the Knowledge Aided Assessment and Structured Application (KASA) initiative with an eye towards the adoption of a consistent, harmonized, efficient and agile regulatory review and approval process necessary to support the increasing complexity of leading-edge technology of new drugs.  

With an eye towards the adoption of a consistent, harmonized, efficient and agile regulatory review and approval process necessary to support the increasing complexity of leading-edge technology of new drugs, FDA’s OPQ incorporated modern information technology tools including artificial intelligence (AI) and machine learning (ML) to develop KASA.  

The intent of KASA:  

• Assure patient-focused quality standards and objectivity are given to regulatory evaluations through knowledge management  
• Enhance science and risk-based approaches through established rules and algorithms including the use of artificial intelligence (AI) and machine learning (ML) 
• Enrich regulatory oversight through life cycle management of products and facilities  

This content traces the path of the program from its inception to its current state, discussing key features, highlighting potential benefits to industry and regulators, and providing insights on how drug developers may begin to prepare as the KASA initiative further unfolds.

*Note: This article features insights from Jason Birri, Director GRS Strategy, Blueprint Medicines  

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