Pharmaceutical and life sciences manufacturers are under growing pressure to compress development timelines, from discovery to commercialization, as demographic, technological and geopolitical trends increase the pace of innovation and disruption. In the face of these challenges, many pharmaceutical manufacturers are finding their traditional processes, which are often built on fragmented data and highly manual workflows, are insufficient.
Aging populations in many developed nations are increasing the demand for advanced therapies to treat cancer, chronic conditions and other ailments whose prevalence tracks with age. Simultaneously, patient demand for precision drugs and therapies is rising, increasing development complexity, all while geopolitical tensions threaten global supply chains. These market, demographic, and geopolitical dynamics add to the urgency pharmaceutical companies are facing.
Working from disjointed data sources and relying on highly manual processes will not suffice in the future. Pharmaceutical manufacturers should seek to update processes to increase development speed and prepare for a future in which digital enterprises can leverage Industrial artificial intelligence (AI) to rapidly discover, refine, and produce lifesaving new medicines.
Friction Slows Pharmaceutical Development
Today, the journey of a new therapy from initial discovery to commercial availability takes years, often up to a decade. Thousands of early candidates are methodically evaluated and filtered through development, clinical trials and regulatory approval to produce a viable medicine. Meanwhile, pharmaceutical companies must complete production design, testing, and recipe scaling and organize reliable supply chains.
This highly complex process is fragile, and even small bottlenecks compound across the lifecycle, amounting to significant delays. The sizable investment required to develop a new drug, estimated at $6 billion, and limited windows of patent exclusivity heighten the pressure on companies to optimize every phase of development and rapidly scale production.
Pharmaceutical development is also characterized by intense collaboration as research laboratories, development teams, manufacturers, regulators, and suppliers all must share information. And many of these collaborations involve the transfer of complex and critically important scientific data between teams where errors and mistakes can prove costly.
Yet data silos and manual transfers of information persist. These technology transfers happen through manual exchanges of documents (both physical and digital), large spreadsheets, and other formats. Data structures and storage practices differ between teams, requiring manual re-entry and significant work to ingest and interpret data sets. These manual technology transfers create friction points throughout the development of a pharmaceutical product, contributing significantly to delays and cost.
Building a Strong Data Foundation
Digitalization in the pharmaceutical industry should be undertaken with the goal of accelerating key processes and connecting teams through efficient and automated data flows. Such digitalization helps create a connected Digital Enterprise, supporting accelerated innovation through connected, digital processes, powerful simulations and industrial AI.
At each stage of drug development, digitalization enables faster, more informed decisions by creating digital representations of molecules, processes and production systems that can be analyzed, simulated and optimized before physical implementation.
The digital twin is a particularly powerful tool throughout the pharmaceutical development and production lifecycle. In the lab, researchers can use digital replications of molecules to conduct in silico experiments, simulating the behavior and interactions of molecules to identify promising candidates more quickly as compared to physical experiments. In addition, process and production digital twins support early production engineering and planning, ensuring that the transition from development to full-scale production occurs quickly and smoothly. And during live production, the digital twin of production processes, machines and facilities supports real-time data analysis and optimization.
Digitalization and the digital twin also support the creation of a unified data fabric across the pharmaceutical lifecycle, critical for deploying Industrial AI to accelerate processes throughout development and production. Digital platforms offer structured data storage and transfers, preparing companies for industrial AI and improving tech transfers in the near term.
Such improvements cascade through an organization. Faster isolation and development of candidate molecules, aided by digitalized data transfers, jumpstart regulatory approval and production scaling. This improves the utilization of patent exclusivity windows and, ultimately, delivers potentially life-saving medications to patients more quickly.
How Industrial AI Helps Pharmaceutical Manufacturers Surge Ahead
AI has been used in pharmaceutical research and development for several years. So far, these applications of AI have centered on accelerating experimental processes through generating and predicting the properties of novel molecules, screening candidate compounds, optimizing clinical trials and more. Though impactful, these applications are also limited in scope. Now, pharmaceutical manufacturers should seek to scale Industrial AI, moving from individual processes and pilot programs to enterprise-wide AI implementation through human-in-the-loop frameworks that safeguard quality and safety.
Digitalization provides the foundation by knitting a comprehensive data fabric that captures data from discovery and experimentation through the development of production systems and live production processes. Building on this data fabric, industrial AI can accelerate individual processes and significantly improve the connection and transfer of data between teams and domains throughout the pharmaceutical lifecycle.
In candidate discovery and evaluation, for instance, Industrial AI and the digital twin enable in silico experimentation, replacing hundreds or thousands of slower and more costly physical experiments with high-fidelity simulations. Industrial AI enables the rapid testing of many variations to identify the most promising compounds. Here, the use of AI and simulation helps focus the valuable time of scientists on the compounds most likely to result in commercially viable medicines, lowering development costs and accelerating timelines.
In production, industrial AI enables real-time analysis and optimization of production systems. Live production data can be continuously fed to Industrial AI systems to quickly detect deviations in data from connected machines, quality sensors — including AI-enabled computer vision systems — or the systems that maintain suitable environmental conditions for certain compounds. With access to live data, AI systems can alert teams to potential issues before they manifest, maintaining stringent quality standards, supporting recursive optimization and enabling predictive maintenance. Finally, industrial AI can also analyze complex global supply chains to identify vulnerabilities, predict disruptions, and recommend alternative sourcing or routing strategies.
And throughout the lifecycle, Industrial AI supports faster and more accurate technology transfers, particularly when coordinating with regulatory agencies. Industrial AI can automate much of the tedium involved in compiling documentation that captures experimental protocol, results and the conclusions drawn in structured and portable formats. This significantly simplifies the transfer of knowledge between internal teams and speeds up coordination with regulators.
How to Approach Digital Transformation for the Future
The pharmaceutical industry stands at an inflection point. Growing complexity in drug development, changing patient populations, geopolitical disruptions, and other factors all contribute to a heightening pressure on pharmaceutical manufacturers to accelerate. Today, pharmaceutical manufacturers must find means of accelerating development processes without compromising on the safety or quality of their product.
Fortunately, digital transformation can reduce friction throughout the pharmaceutical lifecycle, reducing errors and speeding up development processes from the lab through delivery to patients in need. These technologies also lay the foundation for more powerful and advanced solutions in the future.
For many companies, the biggest question now is how to start on a digital transformation program that can often feel immense or intractable. Companies can employ two key strategies to remove this hurdle. First, companies should start with basic pain points, digitalizing knowledge transfers, and building a solid data foundation. Key targets for digitalization should include the removal of data silos, digitalization of manual processes, and adopting strong standards for data structure and storage.
Second, pharmaceutical companies should seek out technology partners with solutions and industry experience that can provide needed support. Digital transformation is complex, and even more so given current market dynamics. Seeking partnerships with technology providers, industry groups, and other collaborative endeavors can help surface key insights, streamline tool adoption, and manage risk.
Ultimately, the purpose of digital transformation in pharmaceuticals is to deliver effective, safe treatments to patients faster than ever. Whether it's a cancer patient hoping for an experimental therapy, diabetes patients benefiting from more precisely targeted insulin, or developing nations receiving locally-produced vaccines, the human impact of acceleration in the pharmaceutical and life sciences industries will be profound.
This article was written by Maria Grahm, Global Vice President for Life Sciences at Siemens, where she leads a global organization focused on accelerating digital transformation across the entire pharmaceutical and biotech value chain. For more information, visit here .
