Optimizing Processes With AI
How AI-driven process optimization could lower costs and improve success rates in drug development.
| 2 min read | Future
What we asked: “Looking ahead to the next 5–10 years, what will be the key disruptors and/or what can be improved upon in the pharma industry?”
Response from: Chad Telgenhof, Chief Commercial Officer, Sterling Pharma Solutions
“As the pharmaceutical industry strives to create the drugs of the future, it is looking to open up new molecular space, and explore the potential of new modalities. Patient safety and quality remain paramount, and technology plays an important role in both the design of new drugs and their manufacture.
“A significant improvement within the industry could be achieved through the widespread implementation of AI and ML throughout the lifecycle of a molecule. These would allow the design of new drugs to be undertaken much quicker, with the virtual evaluation of numerous parameters to assess physical properties, potential side effects, and efficacy.
“Process optimization could be significantly enhanced, building on information from numerous sources to reduce the time spent undertaking real-time experiments. Screening processes in terms of hazard evaluation, and finding the optimal parameters with which to safely and efficiently manufacture products requires – and generates – vast amounts of data, and being able to concentrate the time spent by scientists and engineers to key steps will accelerate processes towards scale-up manufacturing.
“In manufacturing, these technologies could monitor processes in real time to ensure quality control of products during synthesis, as well as the reactors and equipment being used, reducing human involvement in maintenance schedules. Supply chains and procurement could be automated to avoid stock availability issues for key reagents and intermediates, reducing delays and potential shut down of manufacturing, and maintaining operational and delivery schedules.
“These technologies can dramatically accelerate the process of identifying and optimizing drug candidates, predicting clinical trial outcomes, and personalizing treatments, ultimately reducing costs, time, and improving the success rate of bringing new therapies to market.
“There is application of AI and ML already within some areas of the industry, but the setup of the technologies requires significant capital expenditure, and the models are dependent on the amount and quality of data that are available for them to draw on. We are potentially some distance away from seeing universal adoption, but we are seeing major players in the industry harnessing the potential of existing technology, and building systems to grow as the amount of data that can be accessed increases. While there is no substitute for human interaction and experience, technology must be leveraged where possible to make the design and manufacture of drugs safer and more efficient.”
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