Artificial Intelligence in Pharma
The pharmaceutical industry has taken an increased interest in advanced analytics in the last decade with about 50% believing that technologies like artificial intelligence (AI) will relieve pressure on their companies and allow them to bring new drugs to market more quickly. From drug discovery and development to product formulation, manufacturing and post-market surveillance, we are seeing incredible advancements with the application of artificial intelligence in the pharma industry.
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 in pharma allows companies to learn from historical data in a way that is nearly impossible for a human and then 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.
These technologies allow organizations to create beneficial tools for physicians, insurers, consumers and regulators alike. As AI and machine learning improve, that value will continue to rise exponentially.
From developing new drugs, to cybersecurity, pharmacovigilance (PV) and everything in between, deep learning using artificial intelligence in pharma is becoming a growing necessity.
AI in pharma brings an easier, more cost-effective way to bring new products to the market.
One of the best use cases for artificial intelligence in pharma is in drug discovery. Overall, the use of AI can accelerate drug research and development, yielding a commercialized medicine that makes it way to consumers much more quickly. Machine learning algorithms and intelligent AI platforms are also helping to repurpose failed drugs. 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 the biggest bottlenecks when moving a drug from development to production. With the use of artificial intelligence in pharma, 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.
Quality Control Opportunities
Drug development and production are two other aspects of the pharma industry that could benefit greatly from AI.
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 predictive maintenance and process analytical technologies.
The use of predictive analytics in the pharmaceutical industry is also proving instrumental in improving efficiencies across the product lifecycle. 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. Predictive maintenance tools enable companies to move beyond traditional maintenance programs and towards more proactive approaches to minimize unplanned downtime. This allows Operations to take more control instead of being a victim to equipment failures.
Automation through Digital Transformation
Artificial intelligence in pharma can be used to help with more patient-centered clinical trials. Intelligent algorithms are built to better assess clinical outcomes, which can reduce overall clinical trial costs and timelines. With this stage of development being one of the most expensive, the use of AI for improving efficiencies is very attractive.
AI in the Pharma Supply Chain
In the world of big pharma, a supply chain tool that’s considered “self-healing” fits in perfectly. By incorporating artificial intelligence in pharma, organizations can better predict supply and demand.
Efficiencies can be gained across the supply chain in the pharmaceutical industry by using AI to optimize planning and scheduling across a multitude of variables that would be impossible for humans, allowing for much greater agility and resiliency across the pharma value chain network. This agility enables real-time accommodations to shifts in supply and demand, improved decision-making and the shift of workers’ responsibilities to more value-added tasks instead of being mired in the constant cycle of manually adjusting and readjusting planning and scheduling models.
By generating these novel insights, pharma manufacturers can see clear benefits of these data analytics on a grand scale.
Machine Learning in Post-Market Surveillance
Digital transformation in pharma extends beyond manufacturing. 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 expanded intended use scenarios. Because of the volume of data being generated from a variety of sources, AI 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 faster time to market for future development projects.
The top pharmaceutical companies are using AI technology to stay ahead of the curve either by using AI-driven drug discovery platforms or manufacturing software. Johnson & Johnson, Bristol-Myers Squibb, AbbVie, AstraZeneca, Pfizer, Roche, Merck, GSK and Sanofi are all examples of companies that are using AI in some way.
As artificial intelligence becomes more firmly embedded in the pharma industry, more and more companies are embracing AI so they don’t fall behind. Through the right machine learning platform, the pharma industry can collaborate easier on global drug development, drug discovery, tech transfer, scale-up, clinical trials and production.
How is AI used in pharma?
Artificial intelligence in pharma can be used to help design medicines during drug discovery, speed tech transfer and scale-up in drug development, maintain consistency and quality in manufacturing and build efficiencies across the whole continuum.
How is AI used in biotechnology?
Artificial intelligence in pharma is used similarly in both pharmaceutical and biopharmaceutical, with the goal of improving speed to market, enhancing overall efficiencies and accelerating product release.
What is 3D printing in pharmaceutical industry?
3D printing is a process of making three-dimensional solid objects from a digital file. This can be applied to 3D printing of actual medicines, with the first commercialized 3D-printed pharmaceutical approved in 2015.
What programming language is best for AI?
There are several languages that can be used for AI, but which to use depends on the project. Many argue that Python is the most user-friendly and therefore the best to use.
Optimizing Outcomes Across the Pharma Value Chain Network