Editorial Feature

An Introduction to AI in Pharma

Integrating artificial intelligence (AI) in pharma continues to impact the broader drug discovery and manufacturing sector. By harnessing the power of machine learning algorithms and predictive modeling, AI in pharma will enhance efficiency, accelerate research efforts, and foster innovation within the pharmaceutical industry.

An Introduction to AI in Pharma

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AI in Pharma: The Current Global Market

AI in pharma has experienced substantial growth in recent times, with a global market value increasing from $1.24 billion in 2022 to $1.64 billion in 2023 with a 32.8% compound annual growth rate (CAGR), whereby market research indicates that the global AI in pharma market is projected to increase year-on-year within this decade, thus showcasing a robust CAGR. The increasing adoption of AI in pharma can be attributed to the pressing need for enhanced decision-making processes, cost reduction, and improved efficiency across all facets of the industry.

AI in Pharma: Case Study 1 - Accelerating Drug Discovery

The use of AI in pharma to expedite the drug discovery process is a remarkable benefit to the pharmaceutical industry, whereby traditional drug development methods often suffer from time-consuming processes, high costs, and a high failure rate. However, AI algorithms have demonstrated the capacity to revolutionize this realm by leveraging predictive modeling and deep learning techniques to analyze extensive datasets, discern patterns, and predict the efficacy of potential drug candidates.

For example, researchers at the University of Toronto were able to design and synthesize a novel drug candidate to treat hepatocellular carcinoma (HCC), a prevailing form of primary liver cancer, within a short span of just 30 days by using the AI-powered protein structure database known as AlphaFold. Thus, through the use of AI in pharma research, the research team successfully identified a novel compound with potential anti-cancer properties, significantly reducing the time and resources that would be required in traditional screening methods.

AI in Pharma: Case Study 2 - Optimizing Manufacturing Processes

AI in pharma is also making significant strides in optimizing pharmaceutical manufacturing processes, leading to improved production efficiency and high-quality outcomes. AI in pharma’s applications in real-time monitoring and predictive analytics, AI-powered systems can detect anomalies, identify potential bottlenecks, and optimize manufacturing parameters to enhance productivity and minimize waste.

For example, the pharmaceutical company Sanofi has successfully implemented AI in pharma with multiple AI-driven programs that improve predictive modeling and automate time-wasting activities, resulting in a substantial reduction in production time, decreased defects, and notable cost savings. An intricate example of AI in pharma is Sanofi’s use of AI-generated digital models. With Sanofi holding a vast library of lipid nanoparticles, that is, mRNA vaccine drug delivery systems, the AI-generated digital models were able to predict the lipid nanoparticles that are most effective in delivering to its cellular target - a process that would usually take months, only takes days via the power of AI in pharma.

AI in Pharma: Case Study 3 - Enhanced Candidate Analysis

Evaluating and selecting suitable candidates for clinical trials are pivotal aspects of pharmaceutical research. With the use of AI in pharma, AI algorithms are increasingly being employed to analyze extensive patient data, genetic information, and clinical trial results to identify potential candidates who are most likely to exhibit positive responses to specific treatments. Optimized trial designs are facilitated alongside risk mitigation and increased chance of successful outcomes.

Notably, several studies implemented an AI-driven system that identified eligible candidates for cancer trial enrolment, whereby a meta-analysis of such studies conducted by Chow et al. (2023) concluded that the use of AI in pharma candidate analysis was proven to be as effective as manual screening, if not better, regarding patient enrolment in cancer clinical trials. Moreover, the meta-analysis study also concluded that implementing AI in pharma offers a high level of efficiency, requiring less time and fewer human resources for patient screening processes.

AI in Pharma: The Future

It is evident that the future of AI in pharma holds immense promise. As technological advancements continue to unfold, AI-driven systems will become increasingly sophisticated, catapulting innovations within the pharmaceutical industry to new heights, whether it be in personalized medicine, precise drug targeting, and more efficient clinical trial designs.

Furthermore, AI-powered robotics and automation will streamline manufacturing processes, ensuring heightened quality control and improved efficiency. All in all, the global market for AI in pharma is ever-expanding, with its global market value projected to reach a sizable $4.61 billion by 2027, growing at a CAGR of 29.4%.

In conclusion, the incorporation of AI in pharma is revolutionizing multiple fronts, such as drug discovery, manufacturing processes, and candidate analysis. With ongoing advancements and future developments, AI in pharma is poised to reshape the sector's landscape, resulting in improved patient outcomes, reduced costs, and accelerated innovation.

How is AI Progressing Drug Discovery?

References and Further Reading

ltd R and M. AI In Pharma Global Market Report 2023 - Research and Markets [Internet]. www.researchandmarkets.com. [cited 2023 Jul 12]. Available from: https://www.researchandmarkets.com/reports/5767266/ai-in-pharma-global-market-report?utm_source=BW&utm_medium=PressRelease&utm_code=492nfr&utm_campaign=1694183+-+%2

Researchers use AI-powered database to design potential cancer drug in 30 days [Internet]. University of Toronto News. Available from: https://www.utoronto.ca/news/researchers-use-ai-powered-database-design-potential-cancer-drug-30-days

PR: Sanofi “all in” on artificial intelligence and data science to speed breakthroughs for patients [Internet]. www.sanofi.com. [cited 2023 Jul 12]. Available from: https://www.sanofi.com/en/media-room/press-releases/2023/2023-06-13-12-00-00-2687072

Chow R, Midroni J, Kaur J, Boldt G, Liu G, Eng L, et al. Use of artificial intelligence for cancer clinical trial enrollment: a systematic review and meta-analysis. 2023 Jan 23, 115(4), pp.365–74.

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Chi Cheng

Written by

Chi Cheng

Having graduated in Pharmacology BSc (Hons), followed by the completion of a Master of Science in Biomedical and Molecular Sciences, Chi’s interests spans widely across many areas of scientific enquiry within the life sciences and beyond. This has been demonstrated with his successful completion of modules relating to pharmacology, neuroscience, organic chemistry, biomedical science, as well as animal and plant biology, during his academic pursuits.


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