Pharma CDMO Excellence
Operational Excellence and Data-Driven Strategies in Pharma CDMOs
Introduction
In the wake of recent global health crises, pharmaceutical manufacturing is adapting with remarkable innovation and investment. Data, especially unstructured textual data residing in corporate documents, is pivotal for Contract Development and Manufacturing Organizations (CDMOs). It influences every aspect from Investigational New Drug (IND) development to large-scale production, anchoring both safety and quality. Key decision-makers and top-level executives face the complex task of navigating this data to ensure both operational performance and compliance amidst dynamic technological and regulatory landscapes.
Why Unstructured Data Cannot Be Ignored
Textual data, often dismissed as difficult to manage, contains a wealth of information critical for various facets of pharmaceutical operations. Documents critical to the pharmaceutical manufacturing industry, ranging from Master Validation Plans to Batch Manufacturing Records to Deviation Reports, are gold mines of actionable intelligence. Each of these documents contains granular data elements that are fundamental to both operational performance and regulatory compliance. As a consequence, by adopting a fit-for-purpose data analysis approach, pharmaceutical companies can significantly accelerate their performance, enabling enhanced productivity, fewer product recalls, less waste, reduced labor, maintenance, energy, water costs, and several other operational benefits[1].
Extracting Actionable Insights from Textual Data
While technology is pivotal for extracting insights from textual data, the main challenge is still to put the retrieved information into context and derive a strategy from it. Nonetheless, it would be imprudent not to recognize the importance of techniques like Named Entity Recognition (NER) in the context of pharmaceutical manufacturing.
For instance, in Master Validation Plans, data extraction strategies can automate the identification of critical process parameters, critical quality attributes and other key variables affecting the production process. Similarly, in Batch Manufacturing Records, extraction techniques can automatically flag names of raw materials, equipment, and processes, thereby revealing potential bottlenecks or inefficiencies.
In Integrated Development Plans and Audit Reports, applicable technology can facilitate a proactive approach to project management and regulatory compliance by identifying key performance indicators, areas of risk, or non-compliance. When dealing with large documents that are dense with technical jargon, such as New Drug Applications (NDA) or Biologics License Applications (BLA), techniques such as Named Entity Recognition can rapidly streamline the review process.
Lastly, documents such as Product Quality Reviews (PQR) and Quality Technical Agreements (QTA) are vital for maintaining product quality. Here, data extraction technology can flag discrepancies or deviations, track metrics over time, and highlight trends or patterns that need immediate attention.
Considering their excellent efficacy and scalability, various open-source tools with modules can be programmed to recognize biotechnology-specific “named entities”, providing a layer of customization that can significantly enhance data quality and operational insights. Hence, implementing these techniques could be a transformative move for any pharmaceutical manufacturer, especially CDMOs. The application within the biosciences and manufacturing sectors offers numerous benefits, from automating the extraction of key metrics to streamlining the processing of vast, unstructured data repositories.
A constant refinement of data extraction models is crucial and must align with a company’s strategic vision and operational metrics. To ensure this alignment, a multi-disciplinary approach is essential. This should involve not only internal IT staff and technical engineers but also regulatory affairs professionals, quality assurance experts, and process improvement specialists. Each of these stakeholders brings a unique perspective that collectively contributes to tailoring a solution that meets the organization’s specific needs. Given the complexity and critical importance of data in the pharmaceutical sector, such a comprehensive approach, coupled with effective planning and robust governance, is pivotal for a successful data extraction process.
The Human Element in Data-Driven Operational Excellence.
In the realm of data-driven decision-making, particularly within the complex field of pharmaceutical manufacturing, the value of human expertise should not be underestimated. While leveraging state-of-the-art technologies for data extraction and analysis is undoubtedly beneficial, it serves predominantly as a facilitative tool. The core essence of true operational excellence resides in the skill to integrate and optimize a wide range of existing information and knowledge, a capability uniquely human.
It is the seasoned professionals at the helm who bear the responsibility for translating the raw, often unstructured, data into actionable insights. These insights subsequently drive improvements in operational performance and regulatory compliance. Hence, it is not merely the technology but the human element that serves as the cornerstone of both short-term responsiveness and long-term strategic planning. By incorporating the expertise and judgment of qualified individuals, organizations are better equipped to make proactive decisions that address immediate operational challenges while also setting the course for sustainable, long-term successes.
Conclusion
In an industry where data analytics is not just a buzzword but an essential criteria for success, the untapped wealth of textual knowledge in an organization holds the key to the future. The transformative potential of data-centric strategies in pharmaceutical manufacturing will provide tangible results in achieving operational and strategic success.
While specific technologies have their place in mastering data management in pharmaceutical manufacturing, it is still the end result – the delivery of significant return on investment – which ultimately counts. To this, operational insights of humans, and not single tools or methodologies, are mandatory requirements to utilize the outcome of data analytics to develop and execute strategic perspectives.
[1] Ernst & Young LLP by Srihari Rangarajan and Peter Bruns. How digital helps pharma manufacturers drive operational excellence. Dec 2022. Source.