July 2, 2020
by Sanji Bhal, Director, Marketing & Communications, ACD/Labs
The manner by which products are manufactured is currently undergoing significant transformation. Certainly, a significant contributor to this transformation—some stakeholders may ascribe this effort as a revolution—is a focus on digitalization.
The first industrial revolution involved mechanization through water and steam power, the second used electricity for mass production and assembly lines, and the third adopted computers and automation. Now, we’re experiencing the fourth industrial revolution, otherwise known as Industry 4.0, where data and artificial intelligence (AI) are powering autonomous systems.
The pharmaceutical industry is no stranger to implementing technologies and processes to better and more efficiently research, develop, and distribute drugs. Andrew Anderson, Vice President of Innovation and Informatics Strategy at ACD/Labs, emphasizes taking a quality-first approach throughout the automation, digitalization, and analysis processes to truly realize the benefits of Industry 4.0. He elaborated on these insights for an article published in Pharma Manufacturing—here are some of the highlights:
Workflow Automation
Drawing from Andrew’s insights, there is a trend towards automating workflows through two processes—distributed manufacturing and continuous manufacturing. While distributed manufacturing is useful for scientists and researchers physically separated but connected by information technology, it is disconnected in nature. Due to this, more organizations are taking advantage of continuous manufacturing, which encourages ongoing operations along the supply chain to optimize processes and fill requirements in a timely fashion.
Workflow automation is especially helpful in reducing the length of time it takes for a new medicine to complete the journey from discovery to manufacturing to the hands of a consumer. This process currently takes at least ten years, hence the pharmaceutical industry’s focus on reducing product development timelines and ensuring quality operations and products.
Data Digitalization
Along the same vein, finding a lead drug candidate generally takes three to five years and involves the synthesis and analysis of more than 2000 molecules. Andrew shed light on the benefits of using AI within these drug programs, sharing that with their implementation, “scientists have been able to focus on a lead candidate from just 400 compounds.”
When thinking about the overarching goal of digitalized data, Andrew writes, “ultimately, the goal of digitalizing data is for the data to be consumable so trial and error efforts in the R&D process can be eliminated or drastically reduced, further allowing hardware and software to work in tandem to gather the insights needed.”
Speaking of this data, we are reminded of the laboratory of the future that is collaborative, automated, and digital. Within a future digitalized laboratory, scientists and researchers can access large analytical datasets, provide data provenance, and associate digital representations of analysis, both in an automated and human-initiated manner. There is software that can aid in these processes as well, ensuring data integrity, enhancing regulatory compliance, and driving innovation. The next step, according to Andrew, is to analyze this standardized data.
Data Analysis
In expanding on this step, Andrew shares that, “AI and ML are a key part of reaching the full picture view of data to associate experimental data with chemical provenance.” With digital representations of data, scientists are able to compare batch information to the digital specification, and with analysis, scientists can then get to the bottom of why a certain correlation is occurring. This analysis step is key in drug development, as it is how impurities can be detected, helping organizations not only avoid drug recalls, but also deliver a quality drug product to the marketplace.
Looking at the big picture, Andrew writes, “Digitalization is a major way Industry 4.0 is being realized by companies across industries. By connecting all different pieces of hardware with software, and coupling that with data, machines can draw conclusions to advance the experiment, and in real-time.”
While I could reiterate the many reasons why organizations should embrace Industry 4.0, Andrew puts it best: “Industry 4.0 is no longer just an idea. There are endless tools and resources available for organizations to realize the ecosystem’s benefits to achieve the efficiency and productivity it offers.”
For more information, read Andrew’s article in Pharma Manufacturing here.
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