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AI in Pharma

In the last decade, the pharmaceutical industry has taken more of an interest in artificial intelligence (AI). Drug discovery, development, product formulation and virtually every other aspect within the industry is seeing advancement with AI in pharma. 

In a recent survey of pharma executives, about 50% believe that technologies like AI will relieve pressure on their companies and allow them to bring new drugs to market more quickly.

Increased usage of AI in pharma has been perpetuated by the continued advances we are seeing in these technologies, making the hurdle of adoption much lower than in previous decades. 

 

Investing in AI in Pharma

A primary reason for increased investment in artificial intelligence in pharma is the acknowledgement that data can and should be used more wisely to inform better decision-making. Machine learning allows companies to learn from historical data in a way that is nearly impossible for a human and then to apply those learnings to optimize an array of processes. 

AI technologies are currently being applied to robotic process automation, virtual agents, natural language processing, predictive analytics and autonomous vehicles. 

Investment in this type of advanced technology will also be relevant for other aspects of the healthcare industry, such as in tools for insurers, physicians, regulators and patients. AI in pharma brings a more cost-effective way to develop new drugs, crack down on cybersecurity threats, improve pharmacovigilance (PV) and much more. Enormous cost savings are associated with these kinds of investments and are predicted to increase exponentially across many sectors.  

As technology continues to evolve, deep learning through the contextualization of industrial data will soon be a necessity. What are the best use cases of AI for the current and future of the industry? 

 

Five Major Uses for AI in Pharma

1. Drug Discovery

Getting a new therapy to market is a long and costly process, and most drugs don’t ever make it to clinical trials. Failing a candidate early means the company spends time on the most viable drug candidates, thus one of the best use cases for artificial intelligence in the pharma industry is in drug discovery. 

Overall, the use of AI can accelerate drug research and development, yielding a commercialized medicine that makes its way to consumers much more quickly. Several pharma companies are even patenting their own drug discovery platforms which help with target identification as well as drug design. 

Machine learning software algorithms, intelligent pharma AI platforms and predictive analytics are also helping to repurpose failed drugs. Compounds that failed prior clinical trials can be evaluated for alternative diseases, encapsulated in a new way to extend bioavailability or be delivered in combination with other medications not considered before.  

2. Drug Development

The use of AI in pharma can accelerate drug development timelines as well. By harnessing the power of AI to design medicines with manufacturing in mind, companies can also proactively address challenges of tech transfer and scale-up, which are often the biggest bottlenecks when moving a drug from development to production. With the use of artificial intelligence in the pharma industry, companies can identify learnings and continuously improve their processes to enable more realistic storage and shipping conditions, optimize distribution channels and ultimately provide broader access to medicines.

3. Better Quality Control

Once a drug reaches production, the goal is to produce it quickly and consistently, while maintaining the highest quality. Failed batches mean wasted time and materials and ultimately, a delay in getting the product to the patient. Improving yield and throughput without failure means more saleable product, and this can be achieved using digital tools like multivariate analytics and process analytical technologies. 

Disruptions in the production line need to be minimized, and by harnessing the power of AI in pharma and predictive maintenance analytics, the company can better anticipate potential failures and ultimately reduce downtime. AI and machine learning are proving instrumental in improving efficiencies across the product lifecycle by keeping production lines operational. According to McKinsey, pharmaceutical companies show an estimated 70% improvement in overall equipment effectiveness with the use of artificial intelligence. Naturally, this can have a trickle-down effect on pharma’s ability to make medicine more available and less expensive, without sacrificing margins.   

4. Accelerating Product Release by Reducing Data Silos

Releasing a single lot of pharmaceutical product has traditionally been a very manual and labor-intensive process – from ensuring quality materials are sourced to processes being executed properly according to SOPs and subsequently released with all the required documentation, audit trails and signatures. A single lot release could easily generate a ream of paper. With automation and digital in pharma, the possibility of bridging Operations with Quality Control can become a reality. Documentation is accessed through a central repository, making sign-off significantly easier and faster. This means that product is not sitting in inventory and can be shipped sooner. Going digital with analytics in pharma means greater efficiencies across the board so the company can see shorter review cycles, improved rates of production and profitability. 

By bringing together data from predictive analytics and planning and scheduling, the on-time delivery of medicines can be further optimized. 

5. Real World Data and Real-World Evidence

There is a wealth of post-market surveillance data for any drug that reaches the marketplace, and these data are mined to understand negative outcomes as well as expand intended use scenarios. Because of the volume of data being generated from a variety of sources, AI in pharma has been a huge enabler for analyzing the data to inform better decision making. In addition, these real-world data can help companies identify more targeted cohorts for future clinical trials, which can equate to millions of dollars saved, and again, faster time to market for future development projects.

 

FAQs

How has AI changed the pharmaceutical industry?

Artificial intelligence has changed the pharmaceutical industry by making it easier to identify, develop and produce new drugs. It has also enabled faster and easier tech transfer and scale-up.

How do pharmaceutical companies use artificial intelligence?

AI in pharma is used to predict the effects of drugs on the human body. Using predictive maintenance tools and data analytics for pharma, the industry can also improve the execution of clinical trials, production, post-market surveillance and more.