Applying AI to improve the pharmaceutical industry

By Emily Newton
Emily Newton discusses how pharmaceutical manufacturers can apply AI to improve production

AI innovations are starting to significantly impact how pharmaceutical manufacturers research, develop, and manufacture drugs and predict demand. However, despite the technology's potential, the industry as a whole is just now beginning to adopt AI at scale.

According to one recent report, almost 50% of global healthcare companies plan to implement AI strategies by 2025. Some experts believe artificial intelligence could even be key to the industry's future. These technology applications show how manufacturers can use AI to improve pharmaceutical production right now.

AI-Powered Drug Discovery, Development and Trials

Top pharmaceutical companies are already using AI to accelerate their research and development. Amgen and Roche recently began working with Owkin, the developer of “a collaborative research platform and unique AI drug development solutions.”

AI can also be used to open up research options and identify new drugs or alternative uses for existing, approved treatments. The technology has already been used to help pharmaceutical research teams develop several medications — including a new flu vaccine and a drug for pulmonary fibrosis developed by Insilico Medicine.

In both cases, the pattern-finding abilities of AI were leveraged to help researchers identify new, small-molecule drugs suitable for different applications. They inhibited novel biological targets or acted as an adjuvant in a flu vaccine.

The Insilico Medicine project utilised an end-to-end AI drug discovery platform to generate new drug-like molecules. The team behind it had previously published research on the use of AI in rapid “de novo small-molecule design.”

AI could soon become an essential tool for pharmaceutical research and development teams wanting to mitigate some of the extreme costs associated with drug discovery, development and trials. Drug discovery has become extremely expensive over the past few decades, and market research suggests that the process will likely cost more in the future. Without significant innovation, it will grow more impractical and inaccessible over time. 

AI can help drive down the cost of drug discovery by accelerating the process. An algorithm can help rule out dead ends or provide additional analysis to a research team. These tools can also help researchers take advantage of all available information — including new papers and unstructured data that an algorithm can't typically analyse.

Natural language processing, an AI technology that allows algorithms to extract information from plain language, could enable tools to use data from unprocessed studies and drug research literature.

Information from trials and drug development could be fed back into the AI algorithm, allowing it to quickly integrate new data and iterate. This could help the team develop additional drug candidates or analyse trial performance.

Smart Maintenance Strategies and Predictive Analytics

AI makes it possible to more effectively predict the future — enabling greater accuracy in market predictions and new predictive maintenance algorithms that can help manufacturers estimate when equipment will fail. 

As with research and development algorithms, predictive AI tools rely on vast amounts of existing data to make educated assumptions about what could happen. Historical sales data or stored equipment operational parameters could allow a well-trained predictive algorithm to estimate the future of the market or the potential for specific types of machine failure.

AI for Predictive Analytics in Pharmaceutical Manufacturing 

This analysis can also help pharmaceutical manufacturers make better decisions on site. It’s estimated that 50% of work can be automated. By analysing historical data, companies can improve their productivity and safety

The power of predictive forecasting can also extend beyond the market. New AI public health and epidemiology tools may be able to use available information to track the spread of disease or seasonal illnesses worldwide. The right AI tool could make predicting and managing disease outbreaks much easier. 

AI for Smart Preventive and Predictive Maintenance

Many pharmaceutical manufacturers have begun to use AI, combined with IoT devices and other smart maintenance tools, to analyse the performance of essential equipment.

Artificial intelligence can continuously examine data gathered by IoT devices, enabling a system that can automatically detect anomalies, manage errors and reduce the frequency of off-spec products.

Enough operational data makes it possible to train an AI algorithm that can correlate failure, inefficiency and machine error with certain operating conditions. For example, an ultrasonic whine or pattern of vibrations may suggest the breakage of a component that could lead to total equipment failure or cause product quality issues. 

This could cause errors that are intermittent but otherwise predictable. They would be difficult for an employee or technician to spot but easier for a computer with continuous information on the machine’s performance.

A predictive maintenance approach can offer significant benefits, including reduced operating costs, downtime and maintenance expenses. Downtime can be particularly expensive for the pharmaceutical industry, so these tools may provide substantial value for manufacturers.

New AI Health Care Products

Artificial intelligence may soon form the foundation for several new health care tools and digital therapeutics. It may be able to predict the progression of Alzheimer’s disease or more accurately diagnose patients with rare diseases. 

AI may be particularly useful in the areas of personalised treatment and precision medicine. It allows a manufacturer to customise a remedy for an individual patient, using their health care history, genetics and symptoms to identify the most likely treatments to help them.

New health care tools and digital therapeutics, powered by AI, could provide doctors with more accurate information on patient health or health record analysis — making it easier to identify the best possible treatment. Pharmaceutical manufacturers could use anonymised data analysis from these tools to drive drug development and research.

AI Is Helping Pharmaceutical Manufacturers Innovate

The pharmaceutical industry has been slow to adopt artificial intelligence — but recent partnerships suggest manufacturers are beginning to invest in the technology. They can benefit from AI in various ways. 

The technology can help streamline drug development, improve trials, reduce maintenance costs and even enable personalised medical treatments. Already, AI has been used to develop a vaccine and several novel therapies. The technology is also being used to identify alternative uses for existing drugs. 

These innovations have many positive implications for pharmaceutical development and production, paving the way for a better future.

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