AI and pharma manufacturing
Elliott Charbonneau Elliott Charbonneau
July 30 5 min

AI and pharma manufacturing

From research to manufacturing, pharmaceutical companies are looking to make improvements across their operations, with a particularly strong focus on those impacting quality control. Pharma manufacturing is no exception, and change has been a long time coming — as far back as 2004, a US Food and Drug Administration (FDA) report called to increase “product quality and performance achieved and assured by design of effective and efficient manufacturing processes.”

To improve pharma manufacturing processes and resolve production issues, organizations need to be able to draw on as much of their in-house knowledge as possible. AI has a valuable role to play here, and in this article, we’ll explore how AI and pharma manufacturing can be combined to help pharmaceutical businesses improve product quality and manufacturing efficiency to ultimately drive towards better patient outcomes.

Pharma manufacturing: an overview

First of all, let’s start by briefly looking at some key concerns in pharma manufacturing. The synthesis of pharmaceutical drugs on an industrial scale, and producing them in the form of pills, liquids, or aerosols for use in medicine, has a range of factors that define quality:

  • The physical strength of the products to withstand packing, shipping, dispensing and use by patients
  • Uniformity of the products’ weight and drug content, to ensure safe use by patients and cost-effectiveness for the business
  • Bioavailability of products: the speed and extent to which a drug enters circulation to have the desired effect
  • Chemical and physical stability of products over extended periods of time, before they degrade and lose effectiveness
  • Uniformity of the product’s appearance to ensure consistent branding and foster a positive brand with consumers

Quality control has always been paramount in pharma manufacturing, but during challenging times, it is even more vital that businesses do all they can to ensure products meet the standards set out to them by regulators, internal controls and ultimately, expectations from healthcare professionals and end-patients.

Challenges in quality control

Businesses devote considerable resources toward quality control in pharma manufacturing. Deviation investigations are a crucial part of the process of findind where and how products have deviated from defined quality standards and an enabler for the development of modifications to reduce the occurence of irregularities.

There are several common pain points that make deviation investigations more difficult and hamper quality control:

  • High product volumes — difficult to gain visibility on the breadth of items produced across multiple production sites, at all times
  • Greater supply chain complexity — issues with shipping controls, compliance, and certification can easily arise
  • More advanced products — more factors requiring visibility and more areas where errors can occur in manufacturing

Data visibility is a problem across supply chains and elsewhere in the manufacturing process. Businesses need to harness a wide range of data including deviation reports, SCADA system data, batch records, and more. Implementing more effective knowledge management, and providing quick, easy, and complete access to different types of data can positively impact pharma manufacturing quality control.

Corralling pharma companies’ large volumes of complex data from disparate sources has been challenging. Today, effective use of AI can help.

AI and pharma manufacturing

Discovering your data

Machine learning and other AI technologies are ideally suited to dealing with large volumes of complex data. These technologies allow pharmaceutical companies to discover new insights and optimizations and make predictions so they can seize future opportunities or avoid problems.

This can assist with optimizing pharma manufacturing supply chains and even factory machinery itself, by sifting through masses of product data to spot anomalous batches, identifying where existing or future failures may occur, and locating inefficiencies and bottlenecks.

Integrating your knowledge base

AI can also facilitate better access to in-house knowledge by mining it for relevant information. Sophisticated tools like Element AI Knowledge Scout can integrate data from a range of disparate systems, and make organizational information easily available to users via simple queries in natural language, as you would with a search engine.

Pharma companies can break down their data silos, organize their information resources, and make them available to everyone that needs them. This includes both structured data, which has already been labelled and prepared for processing, and even raw, unstructured data which would otherwise go untapped. When all this valuable data is readily available to QA specialists, they’re able to make better informed decisions to resolve deviations and ultimately improve pharma manufacturing processes.

Realizing the power within your data

Although the pharmaceutical sector boasts access to a wealth of data, the data is often siloed or exists as tacit information, rendering it a challenge to unlock its full potential. Herein lies the core value of combining AI and pharma: when AI becomes the key to the data goldmine. It’s a value that more and more businesses are realizing.


Want to learn more about how AI and pharma manufacturing can combine to give your business the advantage? Get in touch with the team at Element AI today.