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How Pharmaceutical Companies Can Manage Data-Integrity Challenges

Human health is a fundamental need of the society. The pharmaceutical industry has played a pivotal role in providing mankind with drugs for preventive and curative care. COVID-19 has further underscored the immense significance of the pharmaceutical industry in our lives. The global pharmaceutical market has experienced significant growth in recent years and is expected to grow at a compounded annual growth rate (CAGR) of 11.34% by 2028.

This massive growth witnessed by the industry can be attributed to a slew of factors like emergence of new diseases, emphasis on research and development, technological advancements, new drug discovery techniques, increased prevalence of lifestyle diseases, growing geriatric population, etc. Furthermore, the advent of robotics, big data analytics, artificial intelligence, automation, and integrated paper-less operations has fueled the transformation of the industry landscape by making manufacturing and quality control processes more precise and efficient. 

Despite the adoption of digital technologies, the industry continues to receive significant number of observations from regulators on various accounts including data integrity compliance. While the companies have the framework for reporting data integrity gaps in place, but the nature of the framework is such that it does not monitor and analyze data to identify the deviations on a real-time basis making it a reactive instead of a proactive approach. Preventing Data Integrity issues is a worthwhile investment in the company, because recovering from them, when discovered, is far worse than the costs associated with prevention. Hence, an automated Early warning system (EWS) can be leveraged to detect and plug any data integrity gaps and serve as a mainstay process to identify any compliance deviations.

Role of Data Integrity and Compliance in the Pharmaceutical industry

While marching towards Industry 4.0 by embracing advanced digital technologies like automation and robotics, pharmaceutical manufacturing is becoming increasingly digital with massive data generated on the factory floor from the drug manufacturing and quality testing equipment leading to petabytes of data collected and stored during a drug's lifecycle. Digitalization results not only in reduced human error and variability but also in more efficient manufacturing processes, better compliance checks, improved product quality, real-time monitoring, and hence, faster time to market. However, it has also led to a greater focus and impetus on data integrity for complying with the current good manufacturing practice (CGMP) guidelines laid down by regulators such as the United States Food and Drug Administration (U.S. FDA), World Health Organization (WHO), Indian Food and Drug Administration (FDA), the United Kingdom Medicines and Healthcare Products Regulatory Agency (MHRA) and other regulatory agencies. Though data integrity is important for any industry, it is of critical importance for the pharmaceutical industry as the consequences of error can be a serious and costly affair. Data Integrity failures have led to companies losing their manufacturing licenses, consent decrees, warning letters, import alerts, invocation of the application integrity policy, bad publicity when issues become newsworthy, and more.

Regulatory Focus – Data Integrity Gaps

Data Integrity compliance has always been a focus area for the regulators as evidenced by various actions taken by the regulator in the form of ‘Form 483’ ‘Warning Letter’, ‘Import Alert’, and ‘Consent decree’. Also, it has been noticed that there has been an increase in data breaches and misuse of GMP guideline which can be observed from the Warning Letter such as:

  • Sampling or retesting or use of duplicate injections to achieve the desired result
  • Testing multiple injections/sequences but reporting only the best result
  • Use of trial or test injections
  • Unaccountable project folders
  • Data alteration/ falsifying test results
  • Transcription errors
  • Incomplete or missing data records
  • Overwriting data after multiple processing attempts
  • Omitting data to support the desired result
  • Backdating record entries
  • Changing the setpoints of testing equipment that would not likely cause an out-of-specification result
  • Date or time modification in the data or meta-data
  • Raw data file deleted/replaced/moved

Although a compliance governance program as part of quality processes is implemented, individuals can still choose to defraud or falsify data intentionally or unintentionally. Also, it is important to know that regulators may not make any distinction between human error and falsification/ fraud. 

Early Warning System - Adopting digital technology to monitor data integrity gaps and maintain compliance

A manual approach to reviewing each data point is incredibly resource intensive and is less adaptable in response to risks. Furthermore, the consequences and costs of being non-compliant are relatively well known. An effective Early Warning System (EWS) can, hence, help plug the integrity gaps and quickly identify fraudulent/falsification activities for any corrective action through the identification of human errors and data/procedural gaps in real-time.

Additionally, EWS helps solve the challenge of integrating multiple Quality systems (i.e CDS, NON CDS, LIMS ) into one platform enabling the quality control department to take advantage of the integrated monitoring capabilities to show alerts for any non-compliant activities from a regulatory compliance perspective. EWS will use information from the quality control procedures followed and data logs of equipment (i.e. CDS, Non CDS, and LIMS and its applications) to map out the integrity risks. The risk measures are then used as a part of rules/scenarios to identify the degree of data integrity (DI) issues for further investigation and possible disclosure.

How EWS works?

A complete and effective early warning system consists of five main elements:

  • Process Understanding and Risk Mapping involves determining the equipment to be connected to the EWS system and identifying / mapping DI risk based on the process procedures followed and data generated in the lab and any past event of DI deviations. Subsequently, the risk algorithms are configured in the EWS system. It is only necessary to do this once during the implementation of the EWS system.
  • Data Collection and Risk Analysis - involves automatically collecting data from all the equipment (i.e. CDS, NON-CDS, LIMS etc.)  integrated into a single platform and consolidating the data into a database for analysis.
  • Monitoring and DI alerts - involves executing the algorithms on the data to identify activities that indicate DI issues. Also, if necessary, occasional tuning of the parameters is done as per the data to generate an accurate warning/alert.
  • Reporting and communicating involves communicating the risk and warnings information to the respective quality assurance/ quality control team for further investigation. Depending upon the risk, alert is assigned based on the responsibility of the individuals within the organization.
  • Investigation and Response involves performing a detailed investigation for the alert and filing a response or taking action to mitigate the risk/issue identified.  Also generating reports on the performance of the operations in the quality control lab and status (i.e Closed/open) of the alerts identified. Provides a consolidated view of the multiple facilities connected to the EWS system.

   Figure 1: Early Warning System workflow

How an EWS benefits Pharmaceuticals companies

An early warning system ensures high standards of quality checks and data integrity practices that can be maintained over the course of your day-to-day operations in the manufacturing units and quality labs. The EWS system can be used by the C-suite, leadership team, quality assurance and quality control team to monitor the performance of the resources and achieve maximum output. It can provide benefits such as:

Repercussions of Being Non-compliant With Data Integrity Practices

The regulators have been issuing warning letters consistently over the past several years to Pharmaceutical companies found to be non-compliant. This trend is concerning as there has been no real dip in the number of warning letters where the regulator could have resolved the issue with minor feedback. A warning letter is a serious cause for concern for the company and the industry at large.

Drugmaker to pay $50mn in fine for destroying records.


Global manufacturer received warning letters and import bans, resulting in exports dropping $48 million from previous year, after growing 39% over previous 4 years. EBIT dropped $41 million.

The FDA sent over 2,700 Form 483 observation letters in 2020, compared with more than 4,700 in 2019, a 42% decrease. The trend is essentially the result of fewer inspections by the regulators due to COVID-19 related challenges but as inspection resumes, Data Integrity Compliance is likely to be the focus area of the regulators to ensure reliability and accuracy of the data.



With the ever-evolving regulatory landscape, the pharmaceutical industry needs to embrace new digital innovations that enable them to keep pace with regulatory compliance and technological changes as they strive to stay committed to the needs of the healthcare ecosystem. As pharmaceutical companies expand their portfolio of drugs and geographies, they will have to go away with manual means of managing compliance and implement the latest technologies that protect them in a near real-time manner. A central management dashboard also gives top management in the corporate office peace of mind that their operations across the world are either compliant against regulatory requirements or are equipped to flag any anomaly as soon as detected and take corrective measures. It is time for the pharma industry to bolster their compliance procedures to be proactive in identifying any deviations and resolve them at the earliest to strengthen the trust of the regulators, rather than being reactive. This is where Ankura’s Pharma Data Integrity Early Warning System can address these challenges.

To learn more, download our Pharma Data Integrity solution overview here.

© Copyright 2022. The views expressed herein are those of the author(s) and not necessarily the views of Ankura Consulting Group, LLC., its management, its subsidiaries, its affiliates, or its other professionals. Ankura is not a law firm and cannot provide legal advice.

“Data Integrity refers to the completeness, consistency, and accuracy of data, which should be attributable, legible, contemporaneously recorded, original or a true copy, and accurate” (FDA)


f-risk, report, healthcare & life sciences, data & technology

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