A Recent Regulatory Update on Consequences of Data Integrity Issues and its Management in Pharmaceutical Scenario

Abstract

Purpose: Pharmaceutical industry ensures that data entered for various steps of drug development is accurate, which gives us confidence that the drugs produced by the industry are within specified parameters. Data integrity indicates sustaining and assuring the accuracy and reliability of data throughout the life cycle of the product. Over the years, numerous leading regulatory authorities have communicated their expectations in the form of regulations and guidance documents from the US FDA, MHRA, EMA, PIC/S and WHO, which address data management and data integrity issues. However, with an increase in digitalisation and the role of global markets, data integrity failures have increased. This results in recalls of products, warning letters, seizures, legal action and ultimately the potential for patient harm. Materials and Methods: Over the last few years, several regulatory agencies have acted against data integrity deficiencies in the pharmaceutical industry. In 2016, more than 50% of MHRA warning letters involved data integrity lapses for computerized systems were observed compared to year 2015. Broadly, the U.S. approximately has received 15 percent of the warning letters, European countries have received approximately 8 percent and the rest of the world claims approximately 15 percent from FDA in the years 2008-2018. MHRA published a guidance document on data integrity in the March 2015 and its revised draft copy was published in March 2018 which applies to GxP systems. Results: From a quality standpoint, data integrity plays a pivotal role in a company's quality system. Data management and data governance should be efficiently integrated into the quality management system. Conclusion: This article represents the evaluation of warning letters from the last ten years regarding data integrity deficiencies.

Ensuring data integrity in the pharmaceutical sector

“…Instrumentcollected experimental data is used to identify target chemicals initially, refine prospective new medications, and demonstrate efficacy, which is required to start preclinical studies. Poor data integrity and records management in research records can lead to a number of problems, including troublesome collaboration and project handoffs between research teams, unsuccessful or delayed patent filing processes, poor decisions about future investments in pre-clinical studies, and even the regulatory agencies' rejection of an Investigational New Drug (IND), New Drug Agreement (NDA), or Biologics Licensing Agreement (BLA) [1][2][3][4][5][6]…”