Introduction to digital transformation in pharma

Digital transformation in pharma is the adoption of computer software programs and associated IT infrastructure which aims to digitize, connect and automate different aspects of the industry. Companies that successfully implement digital transformation are better equipped to adapt and innovate in response to changing business outcomes and goals.

There are a few key enabling technologies for digital transformation in pharma which include digital data capture, network connectivity, automation, machine learning, artificial Intelligence and advanced analytics. An example of automation in a lab would be replacing a manual process such as recording sample data with pen and paper. This process can easily be automated and digitized using a barcode scanner to scan the sample label and automatically enter the data in a software program.

Another example of digitization in a laboratory involving machine learning and artificial intelligence is a process involving a robotic arm that sorts out the test tubes or samples in a laboratory. This is done by scanning the samples individually and using machine learning algorithms to identify samples for manual verification or validation.

What are the benefits of digital transformation in pharma?

Digital transformation can provide significant benefits at all stages of the biopharmaceutical lifecycle from the laboratory bench to point of care. Streamlining processes and improving the accessibility of quality data can provide integral data insights, increase productivity and uptime and ultimately get drugs to market faster. These benefits will be discussed further below.

  • Digital transformation in pharma can help increase productivity and uptime.

    Digital transformation in pharma can help increase productivity by increasing the volume of samples and/or data that can be processed at any one time. Samples with digital data can be processed quicker and in larger quantities than manual processes. In addition, digitization in pharma can also help increase uptime in the lab, because an automated lab can continue processing data 24 hours a day seven days a week, thus, increasing productivity and uptime.
  • Digital transformation in pharma can help streamline laboratory processes.Digitization in pharma can help streamline processes in the laboratory by eliminating manual tasks and improving the accessibility of critical data required for decision-making. By enabling an “audit by exception” approach, digitization can reduce quality assurance (QA) effort and improve data quality. Digitization is also a prerequisite for lab automation processes such as using robots for sample handling, assay preparation and analysis.
  • Digital transformation in pharma can help reduce manual errors.Digital transformation in pharma can help to reduce manual errors in the laboratory by eliminating manual transcription and increasing standardization and automated data validation. Manual errors are therefore reduced because there is less human input during the laboratory processes. All the while it is essential that data accuracy is maintained.
  • Digital transformation in pharma can help with lab sample recall.If a sample in the laboratory needs to be recalled for any reason, such as recalling a defective sample, or if there is an issue regarding incorrect labeling on test tubes, digital transformation in pharma can easily help with this. By using laboratory software, the exact location and source of the defective sample(s) can be quickly and efficiently identified, and the sample can easily be selected then removed from the laboratory and disposed of, or it can be corrected accordingly.
  • Digital transformation in pharma can help improve reporting methods.One of the main advantages of digital transformation in pharma is the ability to enable interactive reporting tools for greater insight and more informed decision-making. Reports that historically required time-consuming and error-prone data collection and alignment can now be automated. These reports can quickly simplify often large and complex data scenarios and present them in a user-friendly manner to enhance data-driven insights and gain better process understanding to speed up process development. In addition, charts and graphs can be used to display complex data and information in a way that is easy to understand and digest for non-data scientists.
  • A digital workflow with a data backbone can improve pharma lifecycle efficiency.Increasingly, biopharmaceutical development and manufacturing involve a complex network of partner organizations who need to share data securely and transfer learnings from one group to another. In early discovery, for example, digitization and machine learning algorithms can help predict the behavior and interactions of different drug candidates and possible formulation challenges to focus on the most promising candidates. By “failing early”, valuable R&D resources can be focused on projects with a higher likelihood of success, shorter project timelines and greater efficiency.

    “Deploying a digital workflow with a common data backbone to a well-defined pharmaceutical lifecycle management plan enhances understanding of data across the product lifecycle and can help clear the way for faster regulatory approval.”
    Ken Forman, IDBS
  • Challenges of digital transformation in pharmaThere are a variety of challenges that lab technicians face with digital transformation in pharma. Some of these challenges include the lack of software user expertise or lack of software training or support. Data governance and the implementation of data standards is an important consideration to ensure digital data is F.A.I.R. (Findable, Accessible, Interoperable, Reusable). Also, there are challenges around the privacy and security of data and in addition, it is important to ensure that the data is protected from ransomware and virus attacks. Finally, there can be budget constraints related to the software packages and infrastructure that can be implemented.

Regulatory challenges and solutions for digital transformation in pharma

The pharmaceutical industry is heavily regulated to ensure the safety, quality and efficacy of medicines. Specific regulations such as US FDA 21 CFR Part 11 define requirements for electronic records and electronic signatures to be considered trustworthy, reliable and generally equivalent to paper records. Digitization presents both a challenge and an opportunity for adhering to regulatory requirements. Current methods to ensure accuracy and reliability of digital data include audit trails, e-signatures and system validation. Yet approaches that simply digitize manual paper-based workflows can miss opportunities to improve data integrity and reduce errors without creating additional overhead. Taking a more holistic view of digital capabilities and a risk-based approach to system design and validation can improve both quality and efficiency.

In process development, for example, machine learning and predictive analytics can help find optimal conditions for cell culture and purification, enabling robust processes to be developed with fewer lab-based experiments. This requires a solid process data backbone combining data from a variety of sources such as instruments, equipment and historians. IDBS Polar BioProcess provides digital workflows with an embedded integration layer that eliminate repetitive manual tasks and curate a rich data backbone that powers advanced data analytics such as digital twins.

Similarly, IDBS PIMS helps manufacturers create a single source of manufacturing data to speed up Continued Process Verification (CPV) and Annual Product Quality Review (APQR) reporting and help identify potential issues quickly to avoid lost batches.

Conclusion

In conclusion, this article has looked at the benefits that digital transformation in pharma can bring, which include an increase in productivity and uptime while streamlining laboratory processes and reducing manual errors. The main benefit of digital transformation in pharma is the ability to use enhanced data-driven insights to deliver better drugs to market faster and more accessibly. Artificial intelligence and advanced analytics have the potential to transform the industry but require investment in appropriate infrastructure, training and data governance to provide a solid foundation for data science. A data-centric approach to digitization is needed to support business strategy and ensure data accessibility, quality, interoperability and reusability across the biopharmaceutical lifecycle.