Continuous innovation is required in various technological aspects to effectively manage the challenges of vast clinical data.
In the pharmaceutical industry, ensuring data integrity is fundamental to maintaining product quality, safety, and efficacy. Data integrity refers to data's precision, completeness, uniformity, and reliability throughout its lifecycle, from generation to retention.
In today's world, data holds a central position in engagement and decision-making processes. Health care and life sciences are no different, in which data play a critical role in public health discussions and driving scientific progress.
The pandemic has accelerated the digitalization of health care, leading to a substantial increase in health-related data availability. This solidified its crucial role in providing vital information and increased the extent of clinical data available.
Continuous innovation is required in various technological aspects to effectively manage the challenges of vast clinical data. These include the adoption of artificial intelligence (AI) and machine learning (ML) technologies, robotic process automation (RPA), and blockchain.
Additionally, novel data collection instruments, such as wearables, sensors, and eSource solutions, are being utilized while maximizing the value of electronic data capture (EDC) and developing intelligent clinical data management systems (CDMS) for real-time, data-driven decision-making.
The volume of data has significantly increased, transitioning from a few data points per case report form (CRF) to thousands or even millions of data points per patient per week. Apart from EDC, data are now sourced from various channels, including real world data (RWD), biomarkers, genomics, imaging, video, sensors, and wearables.
This introduces structured and unstructured data, and the speed at which data are generated is nearly real-time, unlike the traditional eCRF data entered over days, weeks, or months. Ensuring data credibility and reliability is crucial, with a focus on what matters (critical to quality factor), risk-based data strategies, AI-driven automation for issue detection and resolution, and the implementation of fit-for-purpose solutions that are scientifically plausible and strong enough to support the reliability of trial results.
Standards such as RESTful Application Programming Interfaces (APIs) facilitate interoperability between computer systems. Despite the exponential growth in health care data, there are still challenges and opportunities to ensure its effective utilization and impact, with data integrity being the central aspect.
As per the guidelines issued by global regulatory authorities, data integrity refers to the comprehensive quality of data, ensuring it remains complete, consistent, and accurate throughout its lifecycle. This encompasses all original records and their authentic duplicates, comprising source (raw) data, metadata, and any subsequent transformations or reports derived from these data.
Regulatory bodies anticipate both paper-based and electronic data produced during various stages, such as testing, licensing, manufacturing, packaging, distribution, and monitoring of pharmaceuticals, to be gathered and preserved securely. The criteria for data integrity demand that the data should be attributable, legible, contemporaneously recorded, originating from a trustworthy source, and accurate (referred to as ALCOA).
It should be possible to trace a recorded task's origin to the individual or computerized system responsible for its execution. This documentation requirement serves the purpose of demonstrating that qualified personnel performed the task, and it also applies when making alterations to records, including corrections, deletions, and modifications.
All records must possess clarity, as the information must be easily readable to be useful. This criterion applies to all data that should be considered comprehensive, including all initial records or entries. Although electronic data offer dynamic features, such as searchability, querying, and trend analysis, the ability to interact with the data using a suitable application is crucial for ensuring accessibility.
Actions, events, or decisions should be documented as they occur. This documentation should accurately record what transpired and why, providing insight into the factors influencing decisions at that specific moment.
The original record refers to the initial capture of information, whether it is documented on static paper or electronically (typically dynamic, depending on the system's complexity). Information initially captured in a dynamic state should remain available in that state.
Ensuring the precision of results and records can be achieved through various components of a robust pharmaceutical quality system, including:
In 2022, the global market for generic drugs was valued at $439.37 billion. It is projected to reach approximately $670.82 billion by 2030, exhibiting a compound annual growth rate (CAGR) of 5.4% between 2022 and 2030.
This forecast indicates a substantial upswing in the global generic drugs market. The primary driver of this industry's imminent growth is the cost-effectiveness of generics compared to branded drugs. Additionally, the implementation of robotic process automation (RPA) for regulatory compliance and adherence to standards offers lucrative expansion opportunities for key market players.
RPA leverages artificial intelligence (AI) technology to automate routine tasks, enabling industry participants to focus on more advanced value-added endeavors. This adoption of RPA for compliance is expected to gain momentum within the generic drugs market.
Pharmaceutical companies frequently deploy RPA systems for high-volume research and development (R&D) and production operations. The RPA technology encompasses software that executes various tasks such as data entry, measurements, and completion of necessary activities, all with the aim of expediting procedures, reducing costs, and ensuring regulatory compliance.
The Hatch-Waxman Act includes a number of provisions to facilitate the FDA-approval of generic drugs sold in the United States and encourage generic drug entry, as described below:
ANDAs seek authorization to generate generic copies of previously approved drugs (also referred to as reference drugs). Although an ANDA must contain nearly the same information as an NDA, a major advantage over an NDA is that approval does not require preclinical and clinical safety and efficacy testing. ANDA approval is given based on the therapeutic equivalence of the generic drug with the reference drug, primarily similar labeling, the same active ingredient(s), bioequivalence, and the same route of administration, dosage form, and strength.
This clause of the Hatch-Waxman Act is intended to stimulate the generic drug market by providing an incentive to the first generic applicant who challenges a branded manufacturer's listed patents. In particular, the first ANDA-applicant to make a paragraph IV certification is rewarded with a 180-day period of exclusivity over other generic versions of the reference product if a court holds the patent invalid or not infringed.
The use of generics has increased substantially in the years following the passage of the Hatch-Waxman. Act for a number of reasons, namely: a) strengthened mechanisms promoting generic use, such as tiered formularies with lower patient co-payments for generic than for brand-name drugs, and commercial insurance and public coverage plan restrictions limiting formulary coverage to generics in certain therapeutic categories; and b) state laws that permit generic forms to be substituted by pharmacists for trade-name drugs that were prescribed by physicians, for contexts in which physicians do not state that a drug should be "dispensed as written."
The attractiveness of the US generics market has resulted in a rise in the number of ANDA applications received by the FDA, which has further undertaken initiatives to ease out the overall approval process. The FDA introduced Generic Drug User Fee Amendments (GDUFA) to quicken the delivery of efficacious generic drugs to patients and, thereby, reduce costs to the industry. GDUFA was instrumental in improving ANDA approval rates, which have catapulted to ~65 ANDA approvals/month (2019) from ~40 in 2012.
Life science organizations encounter several challenges when implementing good data practices, including:
Many companies recognize the importance of a data quality culture but struggle to implement it effectively. Nurturing a culture in which everyone takes responsibility for data quality through proper training and empowerment fosters transparency and improves data integrity.
A strong data culture relies on providing employees with the necessary resources and training to work efficiently. In high-pressure environments, employees may compromise data quality to meet deadlines, emphasizing the need to educate them about the true costs of poor data quality.
To integrate data quality and data integrity into the culture, organizations must implement quality systems, well-defined processes, and robust controls to optimize the data lifecycle and ensure consistent, timely, and high-quality data generation.
When outsourcing work to third-party vendors, life science companies must ensure the adequacy of data and systems used by these vendors. This responsibility can be challenging when ensuring data quality across an extended enterprise.
The life sciences industry faces exponential growth in data volumes from multiple sources, leading to varying confidence in data quality and reliability. Interpreting, analyzing, and converting data into meaningful insights becomes a struggle, and data integrity risks can emerge in previously considered safe areas, affecting competitiveness and efficiency.
There have been violations and failures in data integrity, leading to various regulatory and non-regulatory consequences for certain companies, such as:
ANDA submissions play a pivotal role in the approval process of generic drugs, as they seek regulatory approval based on the safety and efficacy data of a reference (innovator) drug. Data integrity ensures that the data presented in the ANDA submission are accurate and reliable, as any discrepancies may result in the rejection of the application or serious consequences for public health.
During the "generics scandal" in the 1980s, falsified data were submitted to the FDA to support ANDA approvals. As a result, the FDA shifted its focus during pre-approval inspections (PAI) to evaluate raw laboratory data included in marketing applications and ensure the manufacturing site's capability aligns with the application's description.
Concurrently, recognizing the pharmaceutical industry's increased reliance on computerized systems, the FDA introduced 21 CFR 11 and its preamble in 1997, which established the eventual rule on Electronic Records and Electronic Signatures. In 2003, the FDA issued "Guidance for Industry, Part 11, Electronic Records; Electronic Signatures – Scope and Application" to clarify enforcement priorities related to electronic records and signatures.
Pharmaceutical companies often collaborate with CROs and other partners for various stages of drug development and testing. Maintaining data integrity in these collaborations is vital to fostering trust with clients and ensuring the results' accuracy.
Data integrity is a cornerstone of the pharmaceutical industry, particularly in the context of ANDA submissions, client relations, and regulatory audits. Data accuracy, completeness, and reliability are essential to maintain product quality, safety, and efficacy.
By adopting stringent data integrity measures, pharmaceutical companies can enhance client trust, streamline regulatory approvals, and eventually contribute to delivering safe and effective medicines to patients worldwide. Continuous vigilance toward data integrity remains an ongoing commitment that benefits the industry and public health.
About the Author
Ayaaz Hussain Khan, Global Head – Generics, Navitas Life Sciences, is an authority in BA/BE domain of generic drug industry. He brings with him rich experience of conducting over 1500 Bioavailability/Bioequivalence studies. He has been a gold medalist in his academics and completed his M. Pharm in Pharmaceutics from Birla Institute of Technology, Ranchi. He further pursued and earned a PhD in Pharmaceutical Medicine with specialization in Clinical Pharmacokinetics from Jamia Hamdard University, New Delhi.