12/13 October 2023
Artificial intelligence (AI) is revolutionising industry and the pharmaceutical sector is no exception. AI is impacting various aspects of drug discovery, development, manufacturing, and patient care. At present, AI is beginning to be used primarily in drug discovery and development. For example, AI can accelerate the process of virtual screening, reducing the time and cost associated with traditional drug discovery methods. Furthermore, AI algorithms can analyse vast amounts of biomedical data, including genomic data, scientific literature, and non-clinical and clinical trial results, to identify potential drug targets and predict drug efficacy. In assisting in reviewing documentation and identifying gaps or non-compliance issues AI can streamline regulatory submission process and ensure adherence to complex regulatory requirements.
But AI applications are also finding their way more and more into pharmaceutical manufacturing. AI-powered systems can optimize processes by analysing real-time data from sensors and devices. Predictive maintenance algorithms could be able to detect equipment failures in advance, minimizing downtime and improving overall productivity. But AI could also support quality control and batch release processes by automating data analysis and anomaly detection. As technology continues to advance at an unprecedented pace, it is transforming the way tasks are performed, leading to increased efficiency and accuracy.
Few industries are as heavily regulated as the pharmaceutical industry, especially in terms of manufacturing, quality control and quality management. One might think that this is where AI reaches its limits. But even in relation to the tasks outlined in EU-GMP Guidelines Annex 16 "Certification by a Qualified Person and Batch Release", there are possibilities. Here are a few, deliberately provocative, thoughts:
Review of Manufacturing and Quality Control Records:
AI systems can analyse vast amounts of manufacturing and quality control data in a fraction of the time it would take a human. By leveraging machine learning algorithms, AI can identify patterns, detect anomalies, and generate insights to aid the QP in their review process. AI-powered systems can also assist in the review of deviations and investigations by automatically categorizing and prioritizing cases based on predefined rules or historical data. This accelerates the identification of root causes and helps determining appropriate corrective and preventive actions. This ensures a thorough and efficient analysis of records, enabling faster decision-making.
Assessment of Critical Steps and Controls:
AI algorithms can evaluate critical manufacturing steps and controls by analysing data collected from various sources including identifying deviations, and providing continuous monitoring of critical parameters. This can be done in real-time and could enable manufacturers to make informed decisions regarding process adjustments and quality control interventions to support real-time release decisions.
Review of Change Control Records:
AI can streamline the change control review process by automatically comparing proposed changes against predefined rules and historical data. This allows the QP to focus on assessing the impact of significant changes and ensuring compliance with regulatory requirements.
Review of Quality Risk Management (QRM) Activities:
AI can support QRM activities by analysing data from various sources to identify potential risks and predict their impact on product quality. By leveraging AI's capabilities, the quality functions and QPs can make data-driven decisions, prioritize risks, and allocate resources efficiently.
Review of Validation Activities:
AI can assist in the review of validation activities by comparing data against predefined acceptance criteria considering statistical models. This enables the QP to identify potential gaps and ensure that the validation process meets regulatory requirements.
Evaluation of Suppliers and Service Providers:
AI can create supplier performance records by analysing compliance data, to evaluate the suitability and reliability of suppliers and service providers. This helps the QP in making informed decisions regarding supplier selection and qualification.
Review of Contracts:
AI-powered contract analysis systems can assist the QP in reviewing contractual agreements by automatically extracting relevant information and highlighting critical terms and conditions. This saves time and ensures comprehensive contract assessments.
There are certainly a lot more possibilities, up to and including the autonomous production of reports such as the PQR. It remains exciting.