In an era of rapid technological advancement and growing demand for innovative therapies, digital transformation is a strategic necessity to stay competitive, ensure compliance, and meet patient needs effectively. Key technologies in biomanufacturing include artificial intelligence and machine learning (AI/ML), digital twins, Internet of Things (IoT), and predictive modeling, are changing the way companies develop, produce, and deliver biopharmaceuticals. Digital technologies bring enhanced efficiency and flexibility to help manufacturers meet evolving market demands.
The U.S. biologics market is growing rapidly compared to the overall pharmaceutical market. In 2023, it was valued at approximately $419.07 billion USD and is projected to grow at a compound annual growth rate (CAGR) of 7.6 percent (1). In contrast, the U.S. pharmaceutical market was valued at around $574.37 billion, with a CAGR of 5.48 percent expected from 2024 to 2030 (2). Of the 55 novel drugs approved by FDA in 2023, 34 were small molecule drugs, and 21 were biologics (3). As FDA and the U.S. congress have taken steps to simplify the categorization of biosimilars in the US (4) by eliminating the interchangeability testing requirements, biologic drug sponsors may face steep competition as downward pricing pressures increase.
In a recent study seventy-seven percent of respondents said their organization views digital innovation as a competitive differentiator (5). To turn these innovations to biomanufacturing’s advantage it is important to understand the underlying reasons of past failures and the underlying infrastructure required to be successful.
Digital Innovations Driving Change
We see adoption of digital technologies across the entire value chain from drug discovery to patient delivery. The tools to develop these technologies are evolving rapidly, pushing the boundaries and capabilities of innovation technologies such as Large Language Models (LLMs) in AI. A number of key technologies are driving next generation biomanufacturing:
Cloud Computing and Serverless-at-Scale
Both technologies enable real-time analytics and the use of collaborative platforms to optimize equipment performance. Cloud-connected bioreactors and data-driven modeling facilitate biomanufacturing screening. The scalability and flexibility of these services allow biotech companies to adjust computing resources as needed, while the cost-effective pay-as-you-go model reduces IT expenses.
Artificial Intelligence/Machine Learning
Artificial Intelligence (AI) and Machine Learning (ML) for biologic drugs enable smarter, more efficient, and adaptable processes. These technologies enhance process development with predictive modeling and dynamic optimization, ensure real-time monitoring and quality control through advanced analytics, and improve supply chain efficiency with predictive insights and traceability solutions. AI-powered tools facilitate rapid scalability, reduce costs, and maximize resource utilization, while predictive maintenance and anomaly detection minimize downtime and batch failures.
AI and Machine Learning (ML) are transforming the application of Process Analytical Technology (PAT) in biomanufacturing by enabling real-time monitoring, analysis, and control of complex processes. These technologies enhance data processing, anomaly detection, and multivariate analysis, providing deeper insights into critical quality and process parameters. AI-driven PAT facilitates dynamic process control with real-time feedback loops and adaptive learning, ensuring optimal production conditions and consistent product quality.
In terms of Compliance AI is being applied to accelerate root cause analysis as part of Deviation and CAPA programs. AI-integrated PAT improves quality systems efficiency, reduces variability, and ensures regulatory adherence.
Digital Twins
Digital twins are revolutionizing biomanufacturing for biologic drugs by providing dynamic, virtual representations of physical processes. They optimize production by simulating and monitoring conditions in real-time, which improves yield and quality. Predictive maintenance capabilities reduce downtime by forecasting equipment failures. Digital twins also enhance quality control by identifying potential issues before they occur and facilitate scalability by simulating different production scenarios. Additionally, they ensure regulatory compliance through detailed process records providing better support for decision-making in biomanufacturing.
Industrial Internet of Things (IIoT)
Many of the components which comprise digital transformation in bioprocessing utilize IIOT technology. Soft sensors, hybrid modelling, digital twins and machine learning are all directly linked with the concept of IIoT. The capabilities of smart devices within the manufacturing process are not limited to measuring process parameters and are used for tracking and tracing the data collected, detecting abnormalities, and assessing instrument availability in real-time. They combine with AI, blockchain and cloud computing technology approaches.
Wearable Technology
Wearable technology in biomanufacturing enhances workforce efficiency, safety, and process oversight. Devices like smart glasses, wearable sensors, and augmented reality (AR) tools are enabling innovative solutions, Operators use them for access to standard operating procedures (SOPs), manuals, and workflows, while actively performing tasks, resulting in enhanced training environments, improved hazard alerts to operators and technicians of potentially hazardous situations and enabling an efficient solution for the traceability required for GMP manufacturing.
Blockchain
Blockchain technology enhances transparency, traceability, and trust across the biopharmaceutical supply chain and manufacturing processes. Its decentralized and secure nature provides the audit and transactional confidence required for a global manufacturing supply chain with many moving parts.
Critical Components for Success
More than changing a few processes, to effectively leverage such innovations requires a clear understanding of how the digital programs impact the organization, and a plan to manage its introduction.
Strategic Planning
The definition of digital transformation varies across businesses, and a one-size-fits-all approach doesn’t work. Successful transformations progress in phases, giving organizations the time to assimilate new practices, adapt, and integrate technology into functional processes. Your plan will define the milestones to achieve, the framework and methodology to use, how performance will be measured, and what constitutes success.
When rolling out a digital transformation plan it is important to clearly communicate what this means to employees, customers, and key stakeholders. Connecting the “what” of the transformation with the “why” aligns the organization, builds consensus to develop actionable, realistic tactics with measurable outcomes. Transformation initiatives may fail when organizations underestimate the importance of building a digital culture. A clear understanding of what it means to foster this culture will ensure lasting success.
For example, building a digital twin of a manufacturing process to pursue a real time release strategy can potentially reduce the cost of goods and bring more rapid inventory turnover that translates to more working capital for an organization. However, building a model will require gathering historical data that is relevant to the process train, implementing or leveraging PAT data and investing in the infrastructure to build a model and continuously update and maintain it. Beyond the technical expertise to build the digital model, the quality organization will have to gain an understanding of the model’s design and output in order to become comfortable with releasing a product without analytical testing. Correlating in-line tests such as NIR or Raman spectroscopy as a surrogate for HPLC testing will require focused training and education to provide the quality organization with the insight and knowledge for product release. Understanding how process variations translate to a predictive model’s output is not necessarily intuitive.
Data Management Infrastructure
Digital transformation involves interconnected high-quality, reliable, and complete data. In next generation biomanufacturing, the concept of “data as a product” comes into play and helps an organization approach data management as a unified effort. Data management and more importantly data quality requires specific processes which are not part of the current quality paradigm. Data stewardship is one example of this. Data stewards are responsible for identifying and rectifying data quality issues while implementing processes to maintain data accuracy and consistency across systems. Key practices such as data hygiene and data cleansing maintain the functionality and integrity of databases. Eliminating incomplete or truncated data is a critical activity in ensuring the usability of the data required. Data governance processes and metadata management are essential to building defensible audit trails and ensuring 21 CFR Part 11 compliance. In other words, to build and maintain predictive models or digital twins requires structured programs to manage the data that feeds these models.
Conclusion
The innovations powering next generation biomanufacturing are delivering substantial improvements competitive advantage and business performance. To embark on this transformative journey, a clearly defined digital strategy is critical. Equally important is prioritizing a comprehensive digital cultural transformation plan to unlock the full potential of these advancements and achieve measurable business outcomes. Lastly, securing access to digital expertise and dedicated resources—preferably with industry-specific experience—will ensure your program stays on track, minimizing the risk of unexpected delays and disruptions.
Figure 1. Four Outcomes of Digital Transformation.
References and notes
- https://www.towardshealthcare.com/insights/biologics-market-size
- https://www.grandviewresearch.com/industry-analysis/us-pharmaceuticals-market-report
- https://www.fda.gov/drugs/novel-drug-approvals-fda/novel-drug-approvals-2023
- https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-demonstrating-interchangeability-reference-product-update
- https://www2.deloitte.com/us/en/insights/industry/life-sciences/biopharma-digital-transformation.html
