The pharmaceutical industry has continued to focus on the development of innovative medicines, novel drug delivery systems, and advanced manufacturing technologies to enable timely access to lifesaving medicines. In parallel, digital transformation (e.g., Pharma 4.0), informatics and analytics are providing insights from predictive modeling and artificial intelligence to advance product development, efficient manufacturing technologies, and supply chain management. In this new era, it is essential to acquire substantial, reliable, and fit-for-purpose data across discovery, clinical development, commercialization, and lifecycle management. Timely approval for drug and biological products will be guided by the strength of scientific evidence. As new technologies are progressing, regulators are developing new standards with greater expectations for data quality and knowledge management.
In the context of drug product efficacy and chemical safety, a plethora of empirical data can be gathered to derive insights for knowledge management. Meaningful insights can be gathered from various types of data, but in this new era, there are challenges to address, such as the lack of comprehensive digital data, accessibility of siloed data, and quantifiable uncertainty of data. Evidence for informed decisions is based on credible knowledge and depends on data integrity and quality. Data integrity is a compliance aspect of current good manufacturing practices (cGMP) to ensure records are original, complete, consistent, accurate, attributable, legible, contemporaneous, enduring, and unadulterated. Data quality follows the same criteria with an emphasis on a methodology that is fit for purpose, precise, valid, and timely. Data quality is critical to build a deep understanding of the “why” behind the data to develop appropriate methods fit for the purpose.
Proper data is steered by the analytical procedure, which should be clearly connected to specific product attributes and justifiable endpoints. A robust analytical procedure will define parameters to reduce the risk of poor method performance and incorrect results. Robust data can be acquired using the analytical quality by design (A-QbD) approach as outlined in the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) Q14 guideline, Analytical Procedure Development. This represents a harmonized science-and-risked-based approach based on a predefined analytical target profile for procedure development with established conditions (EC) specific to the expected performance parameters. Related instruments and apparatuses will need to be justified for calculated outputs. The ECs are linked to the method’s critical quality attributes and acceptance criteria to provide purpose-driven protocols for validation and control. Method parameters can be inherently variable; in this case, acceptable reportable ranges would be factored into the control strategy.
Risks to the measurement should be assessed early in A-QbD and mitigated as more information becomes available. Multiple factors, including the materials used in the procedure, processes, environment, instrumentation, analyst training, and experience, can independently or collectively contribute to the uncertainty of the measurement. Preventive processes can be put in place over the A-QbD lifecycle to ensure continual improvement as more knowledge and experience are acquired. Process knowledge and continual improvement will trigger analytical procedure changes, especially as technologies evolve and become more advanced. New regulations, guidelines, standards, and advanced technologies will drive the development of new procedures, platform procedure revisions, or refinement. Implementation of A-QbD will promote quality data to afford the exchange of scientific knowledge.
The new pharma will enable cross-functional networks to overcome siloes and drive evidence-based decisions. External collaborators and strategic partners are also paving the way for efficient product development with new manufacturing technologies and timely distribution processes. A quality culture that prioritizes the integrity and quality of the data will be the bedrock for the pharmaceutical digital transformation. This era will exemplify broad digitalization and analytics with the implementation of advanced, next-generation technologies to enable predictive modeling, simulations, artificial intelligence, machine learning, and smart technologies to holistically improve timely access to innovative and essential medicines.
