By Emily Newton, Contributing Writer03.02.22
Artificial intelligence (AI) is increasingly becoming part of pharmaceutical manufacturing. Applying the technology to existing workflows takes time and money. However, it can often pay off. Here are some of the advantages that AI can bring to pharmaceutical drugmakers.
Accelerating the Creation of New Drugs
Research and development efforts must give strong evidence that a new drug would be beneficial for patients. However, that phase can take years. Recent research indicates AI could aid in both the drug discovery and manufacturing efforts of new pharmaceutical products.
A new platform called Pharma.AI enabled a timeframe of only 18 months from the target discovery to the preclinical candidate nomination. Researchers hope the new drug could be a key part of treating idiopathic pulmonary fibrosis, a chronic lung disease.
Succeeding in this area could mean pharmaceutical companies spend more time manufacturing the drugs that are most likely to help their bottom lines. Research shows that every dollar invested into research and development brings a return of less than $.02. AI could change that by reducing the unfortunate failures associated with new-drug efficacy.
Increasing the Production Output of Small-Molecule Drugs
Many efforts to improve pharmaceutical manufacturing center on enhancing factory production levels. Creating more doses of an in-demand drug supports a company’s profits and could mean that the patients who need the product receive it faster.
An agreement between Quartic.ai and Bright Path Labs should cause progress via an AI platform that supports the continuous manufacturing of active pharmaceutical ingredients (APIs) and other small-molecule drugs. One of the goals of this project is to reduce how much the U.S. depends on offshore manufacturing for these products. The parties involved also believe their methods will cause significant efficiency boosts.
AI typically excels at repeated processes. That should mean, if researchers and manufacturers can optimize the associated workflows, they improve their likelihood of letting the AI take care of processes that initially needed manual interventions.
Enhancing Supply Chain Resilience
Failing to plan for supply chain challenges could have costly consequences. One study of medical technology companies found that a decade of supply chain shocks could cause the equivalent loss of 38% of annual earnings. Decision-makers can’t predict every future obstacle. However, AI can help spot and fix supply chain shortcomings. Maintaining a strong supply chain directly supports pharmaceutical manufacturing.
Ram Krishnan is the chief marketing officer at Aera, which offers an AI platform for supply chain planning. While discussing the benefits Aera could bring to pharmaceutical clients, he said, “Aera automatically identifies risks and opportunities and automatically generates prescriptive recommendations for specific decision-makers and users about what should be done. End users can choose to follow recommendations or to modify or augment recommendations.”
Getting more details about supply chain events that could happen months from now enables better pharmaceutical preparedness. Warnings of an ingredient shortage might spur manufacturers to order larger-than-usual quantities from their suppliers soon, for example.
Automating Issue Detection and Resolution
Unexpected production stoppages can be costly and inconvenient in the pharmaceutical manufacturing sector. Fortunately, AI can reduce those instances and provide people with useful data to prevent future issues.
One example is a high-performance liquid chromatography (HPLC) system that uses AI to automatically spot and address issues. People can also use that product for better resource allocation and to quickly review instrument statuses. AI also comes into play for predictive maintenance of critical equipment. Aizon is a company that recently released such a product for the pharmaceutical sector.
John Vitalie, Aizon’s CEO, explained, “Our Asset Health application enables our customers to harness the power of their equipment and environmental data to slash maintenance costs, optimize equipment performance, enhance product output, and improve product quality.” The product gives people warnings about adverse asset conditions, giving them time to correct the problem before it causes operational shutdowns.
Reducing Unnecessary Inventory
Using AI in pharmaceutical manufacturing can also limit the excess inventory a factory has. That benefit could prove critical since many drugs have expiration dates and become useless after those times.
Sanofi is one example of a major pharmaceutical brand that has invested in AI to optimize inventory. Representatives indicated that using AI and process digitization should cause a 20-day reduction in inventory levels. Sanofi decision-makers also expect a 20% decrease in baseline costs with its contract suppliers.
AI could also assist in other ways. It might show that certain regions of the world have a particularly high demand for certain drugs. In such cases, a company could redistribute its product shipments there and away from places with lower marketplace needs.
AI Can Improve Pharma Manufacturing
These examples show why pharmaceutical leaders should strongly consider implementing AI into manufacturing processes. However, they should define clear goals before investing in any technological platform. Knowing what people want to achieve at the outset is crucial for measuring whether AI is getting the intended results.
It’s also useful to identify any pain points and investigate whether AI could ease them. Finding case studies from customers who have been in similar positions and benefited from AI could help pharmaceutical leaders feel more confident about moving ahead with artificial intelligence.
About the Author: Emily Newton is the Editor-in-Chief of Revolutionized. She’s always excited to learn how the latest industry trends will improve the world. She has over four years of experience covering stories in the science and tech sectors.
Accelerating the Creation of New Drugs
Research and development efforts must give strong evidence that a new drug would be beneficial for patients. However, that phase can take years. Recent research indicates AI could aid in both the drug discovery and manufacturing efforts of new pharmaceutical products.
A new platform called Pharma.AI enabled a timeframe of only 18 months from the target discovery to the preclinical candidate nomination. Researchers hope the new drug could be a key part of treating idiopathic pulmonary fibrosis, a chronic lung disease.
Succeeding in this area could mean pharmaceutical companies spend more time manufacturing the drugs that are most likely to help their bottom lines. Research shows that every dollar invested into research and development brings a return of less than $.02. AI could change that by reducing the unfortunate failures associated with new-drug efficacy.
Increasing the Production Output of Small-Molecule Drugs
Many efforts to improve pharmaceutical manufacturing center on enhancing factory production levels. Creating more doses of an in-demand drug supports a company’s profits and could mean that the patients who need the product receive it faster.
An agreement between Quartic.ai and Bright Path Labs should cause progress via an AI platform that supports the continuous manufacturing of active pharmaceutical ingredients (APIs) and other small-molecule drugs. One of the goals of this project is to reduce how much the U.S. depends on offshore manufacturing for these products. The parties involved also believe their methods will cause significant efficiency boosts.
AI typically excels at repeated processes. That should mean, if researchers and manufacturers can optimize the associated workflows, they improve their likelihood of letting the AI take care of processes that initially needed manual interventions.
Enhancing Supply Chain Resilience
Failing to plan for supply chain challenges could have costly consequences. One study of medical technology companies found that a decade of supply chain shocks could cause the equivalent loss of 38% of annual earnings. Decision-makers can’t predict every future obstacle. However, AI can help spot and fix supply chain shortcomings. Maintaining a strong supply chain directly supports pharmaceutical manufacturing.
Ram Krishnan is the chief marketing officer at Aera, which offers an AI platform for supply chain planning. While discussing the benefits Aera could bring to pharmaceutical clients, he said, “Aera automatically identifies risks and opportunities and automatically generates prescriptive recommendations for specific decision-makers and users about what should be done. End users can choose to follow recommendations or to modify or augment recommendations.”
Getting more details about supply chain events that could happen months from now enables better pharmaceutical preparedness. Warnings of an ingredient shortage might spur manufacturers to order larger-than-usual quantities from their suppliers soon, for example.
Automating Issue Detection and Resolution
Unexpected production stoppages can be costly and inconvenient in the pharmaceutical manufacturing sector. Fortunately, AI can reduce those instances and provide people with useful data to prevent future issues.
One example is a high-performance liquid chromatography (HPLC) system that uses AI to automatically spot and address issues. People can also use that product for better resource allocation and to quickly review instrument statuses. AI also comes into play for predictive maintenance of critical equipment. Aizon is a company that recently released such a product for the pharmaceutical sector.
John Vitalie, Aizon’s CEO, explained, “Our Asset Health application enables our customers to harness the power of their equipment and environmental data to slash maintenance costs, optimize equipment performance, enhance product output, and improve product quality.” The product gives people warnings about adverse asset conditions, giving them time to correct the problem before it causes operational shutdowns.
Reducing Unnecessary Inventory
Using AI in pharmaceutical manufacturing can also limit the excess inventory a factory has. That benefit could prove critical since many drugs have expiration dates and become useless after those times.
Sanofi is one example of a major pharmaceutical brand that has invested in AI to optimize inventory. Representatives indicated that using AI and process digitization should cause a 20-day reduction in inventory levels. Sanofi decision-makers also expect a 20% decrease in baseline costs with its contract suppliers.
AI could also assist in other ways. It might show that certain regions of the world have a particularly high demand for certain drugs. In such cases, a company could redistribute its product shipments there and away from places with lower marketplace needs.
AI Can Improve Pharma Manufacturing
These examples show why pharmaceutical leaders should strongly consider implementing AI into manufacturing processes. However, they should define clear goals before investing in any technological platform. Knowing what people want to achieve at the outset is crucial for measuring whether AI is getting the intended results.
It’s also useful to identify any pain points and investigate whether AI could ease them. Finding case studies from customers who have been in similar positions and benefited from AI could help pharmaceutical leaders feel more confident about moving ahead with artificial intelligence.
About the Author: Emily Newton is the Editor-in-Chief of Revolutionized. She’s always excited to learn how the latest industry trends will improve the world. She has over four years of experience covering stories in the science and tech sectors.
