Built-in data integrity is a bare minimum requirement in today’s BioPharmaceutical manufacturing. Good Manufacturing Practices (GMP) regulations of the Food and Drug Administration (FDA) highlight the importance of the accuracy and reliability of data across supply chains. Yet, the difficulty of achieving this milestone can still be underestimated by drug sponsors, especially when working with manufacturing partners. 

Although organizations are expected to demonstrate full governance of their data across the product lifecycle, their access to process data remains limited, which hinders early risk detection as well as the scalability of manufacturing. With agile manufacturing networks, novel therapeutic modalities such as mRNA vaccines and cell and gene therapies, the single-source manufacturing model using on-premise/siloed infrastructure can have an adverse effect on data integrity and prevent the goals of getting the next generation therapies to patients faster, at lower cost. 

Importance of data contextualization 

The value of meaningful data that can be proven to be accurate, consistent, attributable, and containing the appropriate metadata for attaining data integrity cannot be overstated. When entering into contractual research and manufacturing partnerships, all parties must aim for full visibility of process and product data as it relates to their functional objectives. Context and transparency are the two must-have qualities for all data being exchanged. 

Data transparency provides all parties the confidence to monitor processes, mitigate risks, and intervene as necessary. Contextualized data streamline the scaling of complex processes and product information, as well as provide the foundation for applying analytics and models to gain insights from both product and business requirements, such as product quality and cost of goods. As the product portfolio and digital capabilities of drug sponsors expand, the ability to trace, repurpose and utilize existing contextualized data assets becomes paramount for accelerated innovation.  

Challenges in data utilization due to lack of metadata and contextualization 

Data sharing across the BioPharma industry is hindered by time-consuming verification and approval processes. Despite a growing interest in automated and near real-time data sharing, organizations frequently employ manual, paper-based processes instead of automated systems due to cost, complexity, deployment timelines and manufacturing needs of a product portfolio. 

While data sharing at a high level of Digital Plant Maturity (Level 3, 4) could work effortlessly in single-source manufacturing, large data input from multiple sources outside of this digital ecosystem convolutes data traceability and utilization. 

A popular approach undertaken by BioPharma companies is the creation of data lakes to enable seamless transfer with teams internally and external partners. Here, all data is gathered and grouped in a large intermediate repository. Even without any human errors and security risks, data recorded require a lot of effort to cleanse, aggregate and compact. Organizations might miss the mark, lacking comprehensive contextualization or essential metadata as necessitated by the current or future needs of the relevant party. 

These issues can be potentially disruptive, especially for advanced personalized medicine, where an inability to understand safety and efficacy endpoints can have serious repercussions for both patients and drug sponsors. More specifically, users will still struggle to extract appropriate data needed for analysis and compliance in very large data repositories.

Cloud-based digital integration to overcome data utilization issues

The better approach entails a smart and integrative platform that can receive data from various resources, store them securely and help users structure information according to its target purpose. The system mitigates human errors caused by manual data exchange by providing the bare minimum level of digitization. Cloud-based digital solutions offer several advantages over legacy on-premise software that sponsors may offer individually to their partners. 

First, they acknowledge the fact that sponsors or contract organizations may have varying levels of digital maturity and may be accustomed to their own data-recording strategies. Instead of forcing all parties to adhere to an entirely new technology, various data types, from paper-sourced data and MS Excel files to near-real-time high-throughput experimental inputs are easily accommodated. 

Next, the right tool will not only house the data, but also contextualize all data types depending on why they are being recorded, where they are being submitted to and how they will be used by the relevant parties. This also confers the versatility needed to apply insights across the process to optimize both product quality and cost of goods. From this perspective, standardized data-sharing platforms accelerate the integration of new partnerships in large-scale BioPharma projects. 

Establishing a cloud-based, user-friendly software platform for BioPharma partners to exchange various types of data across the product lifecycle, is imperative to fulfilling this objective and ensures all parties in the product lifecycle are involved equally without digital plant maturity alignment. 

How the BioPharma industry must prepare for the era of digital transformation

Cloud-based integrated data management solutions can only reach their full potential if implementing follows an appropriate plan of action between sponsors and partners. Alignment of partner contracts with the upgrades in digital data management, such as deploying software designed specifically for digital integration in their product lifecycle is a critical first step. 

Sponsors must clearly and concisely layout their expectations for the type and format of data needed to ensure data integrity across the product lifecycle. More importantly, the contract must highlight the versatile nature of the project so that data structure and content can be adjusted on-the-fly, relying on the drug sponsor’s knowledge. 

Critical process parameters (CPP) and critical quality attributes (CQA) are an essential component of BioPharma data structures, and partners often identify novel CPPs during manufacturing that cause deviations in the final product quality. This may be due to challenges in Tech Transfer, or facility fit, that could be addressed with appropriate data contextualization. The contract must establish a plan of action and timeline to address situations where adjustments to CPP and other dynamic data input are to be made in order to provide sufficient control of the process. 

By covering all aspects of data management from the beginning, sponsors and partners can establish strong relationships based on trust, communication and credibility.

Mindset + technology = innovation

Data exchange lies at the heart of innovation. With the right data available, BioPharma companies can best implement advanced analytics tools like AI/ML for timely interventions in manufacturing deficiencies and optimize processes. 

Standardized data transparency and integrity through cloud-based platforms is a surefire way to achieve governance in all aspects of the product lifecycle and create functional communication channels between partners, with a view to bring life-saving therapeutics to the clinic faster, with improved quality and lower cost.

Photo: mathisworks, Getty Images

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