Optimize CMC data management for better process efficiency and regulatory compliance

CMC data management in the pharmaceutical industry is complex and challenging

In the highly regulated world of pharmaceuticals, managing chemistry, manufacturing, and controls (CMC) data is crucial. Strategic management of this data not only ensures drug consistency and safety, along with compliance with regulatory requirements, but also improves the overall efficiency of the drug development and manufacturing process.

By nature, data from CMC processes are complex and include a wide range of sources which makes them very difficult to query and retrieve, especially for regulatory approvals. The types of data can include:

• Chemical: details about the chemical structure and purity of the active pharmaceutical ingredient, information about excipients, formulation development, and analytical methods.
• Manufacturing: descriptions of the manufacturing process and materials, including validation processes and batch records.
• Controls: quality control and quality assurance records, stability testing data, and regulatory compliance documentation.

Knowledge graphs for streamlining CMC data integration and retrieval

Data used for CMC purposes are often siloed in different systems, including laboratory information management systems (LIMS), enterprise resource planning (ERP) systems, and quality management systems (QMS). This fragmentation poses challenges for data integration, retrieval, and compliance with regulatory requirements.

Knowledge graphs offer seamless integration of heterogeneous and complex data sources in a way that can be scaled easily with evolving business and regulatory landscapes. They have a unified, flexible structure that can encompass all the various types of CMC data and their interrelationships. This creates a coherent framework and breaks down silos to provide a comprehensive view of the manufacturing lifecycle.

The benefits of implementing knowledge graphs for CMC

Enhance data retrieval and utilization

Knowledge graph technology can enhance CMC processes and drive innovation. A well-structured knowledge graph allows scientists and regulatory professionals to perform complex searches using query languages like SPARQL. This facilitates easier and more intuitive data retrieval through queries based on context. For example, a search for stability data could also return related information such as the testing conditions, or a query about a specific drug’s effectiveness could also return the current status of any relevant clinical trials. The results are then visualized in an intuitive manner that allows users to see connections and patterns that may not be obvious through traditional database queries.

Cross-departmental collaborations, for example between R&D, manufacturing, and quality control, can also be fostered through integrating data from these different sources into a single knowledge graph. This enables a shared understanding of CMC data. Such knowledge graphs can provide a comprehensive knowledge base for scientists to support their decision making through aggregating knowledge from various sources, such as scientific literature, patents, and internal research.

Knowledge graphs can significantly reduce the time spent searching through multiple disconnected systems for information. Among other things, they can be used to explore new formulations, understand the impact of changing manufacturing conditions, or optimize quality control measures to enhance decision making, save costs, and offer solutions for continuous improvement.

Improve regulatory compliance

The way that a knowledge graph is built allows it to maintain detailed records of the origin of data (their provenance) and their lineage, which is how the data have been transformed or moved. This information is essential for audit trails, which regulatory bodies often require to demonstrate compliance. Knowledge graphs can enhance compliance efforts by providing robust data governance and traceability, and even proactive quality control when combined with artificial intelligence (AI) and machine learning (ML). Organized and easily accessible data streamlines regulatory submission and ensures compliance with global standards.

The regulatory benefits don’t end there. Knowledge graphs allow CMC data to be mapped to regulatory requirements, which helps to keep all necessary documentation and data complete and up to date. This reduces the risk of non-compliance and facilitates smoother regulatory transitions. Going one step further and embedding regulatory rules and guidelines into the knowledge graph allows companies to automate compliance checks, identifying gaps or discrepancies in real time, and substantially reducing the manual burden on compliance teams.

Insights into future trends and innovations in CMC data management

Knowledge graphs allow scalable data management

Since knowledge graphs can be built on adaptive architecture capable of handling large volumes of data, they can scale with pharmaceutical data as they grow in complexity. Using specific ontologies and semantic relationships to integrate data from various sources ensures that the data are consistently represented and interconnected, regardless of source. Maintaining and updating the relationships between different data entities means that scientists have easy access to the most up to date information, thereby supporting drug discovery, process optimization, and regulatory affairs.

Integrating AI and Machine Learning with knowledge graphs

Standardizing and consolidating CMC data into a knowledge graph acts as a foundation for integrating further technology, such as AI and ML models. This can significantly enhance a range of processes, including:

Integration and data enrichment: AI technologies, such as natural language processing (NLP) can be used in conjunction with knowledge graphs to automatically extract and organize information from unstructured data sources, such as research articles, patents, and regulatory documents, further reducing the administrative burden on pharmaceutical companies.

• Predictive analytics and anomaly detection: ML models, like regression or classification models, are trained on historical data to identify patterns and correlations between process variables and product quality outcomes. These models can be integrated into the knowledge graph to identify or predict potential quality issues based on real-time data inputs.
• Root cause analysis: Together, knowledge graphs and AI systems can perform a root cause analysis on any detected anomalies or quality deviations. The semantic capabilities of knowledge graphs can identify relevant data points and the relationships between them, while AI systems can analyze the relationships to pinpoint the root cause of any issues.
• Process optimization: Insights gained from predictive models and anomaly detection can be used to optimize manufacturing processes. ML algorithms can propose adjustments to process parameters, such as temperature, mixing times, or concentrations, to enhance the consistency and quality of the product.

Knowledge graph technology is a valuable investment

Optimizing CMC data management through the use of knowledge graphs represents a significant advancement in the pharmaceutical industry. Seamlessly integrating diverse datasets and providing a comprehensive, interconnected view of the entire drug development and manufacturing process, means knowledge graphs enable more efficient data retrieval, better decision-making, and enhanced compliance with regulatory standards. This approach not only streamlines operations and reduces the risk of errors but also fosters innovation by making it easier to explore complex data relationships and derive actionable insights.

As the life sciences industry continues to evolve, leveraging knowledge graphs in CMC processes will become increasingly necessary, driving both operational efficiency and ensuring that high-quality, compliant products reach the market faster. Furthermore, thanks to continuous improvements in knowledge graph technology, it can evolve in step with AI’s rapid advancement. Implementing knowledge graphs to build a strong foundation for data management now will be essential to remain competitive and benefit from any further evolutions.

Get in touch for advice on how DISQOVER’s knowledge graph technology can assist with your specific CMC data management requirements.