Before the FDA start reviewing a submission, your submission must first overcome the new hurdle of ensuring that it conforms to the standards required in the FDA Data Standards Catalog.
“The FDA may refuse to file for New Drug Applications (NDAs) and Biologics License Applications (BLAs) or refuse to receive for Abbreviated NDAs (ANDAs) any electronic submission whose study data do not conform to the required standards specified in the FDA Data Standards Catalog.” - FDA
In a recent analysis conducted by the FDA, 32% of submissions with study data had critical data conformance issues.
When a submission is rejected for lack of study data conformance, the submission sequence is not even transferred from the FDA Electronic Submission Gateway into the FDA electronic document rooms where the FDA Review process officially starts!
Then only 50% of new NME/IND applications that passed the screening process for study data conformance are approved on first submissions by the FDA. There is a median delay of 435 days to approval following the first unsuccessful submission and 24% of the first-time submission failures include inconsistent study results either across endpoints or sites and studies.
Re-submission of failed applications is costly, delaying marketing approval as the process needs to start again with increased timelines, and delaying the availability of vital new drugs to patients.
We review your submission content to:
Quanticate offers CDISC Compliance services and CDISC mapping expertise across multiple situations. When reviewing submission data our team will ensure that CDISC standards are met before the submission is sent with support on:
Quanticate provide a Data Quality Oversight (DQO) service to examine the quality and fitness of the data package associated with the submission. Quanticate has selected the industry leading FDA endorsed software of CluePoints to perform its DQO analysis. We use CluePoints’ machine learning functionality to detect spurious data patterns both within and across studies. If appropriate, we also use software such SAS JMP for further statistical investigation. DQO enables the quick identification of outliers and inconsistencies across studies which can then be addressed to improve data quality.
Our biostatisticians review the impact of anomalies and missing data on the results of the submission in an attempt to pre-empt the regulatory reviewer’s likely comments so that they can be addressed in the initial submission.
In addition, we can broaden the review to: