Artificial intelligence (AI) has the potential to significantly transform the pharmaceutical industry. From drug discovery to inventory management, the integration of AI offers numerous benefits including faster identification of drug candidates, improved patient outcomes, optimized manufacturing processes, and lowered product loss. The implementation of AI tools will not only streamline operations but also allow pharma companies to respond swiftly to new challenges. This article discusses the exceptional benefits of AI in pharmaceuticals. However, before discussing these, it’s necessary to understand the applications of AI.
Applications of AI in the Pharmaceutical Industry
The applications of AI in the pharmaceutical industry are exhaustive.
- AI in drug discovery: AI tools can identify target and binding molecules and predict proteins, genes, and mechanisms involved in specific diseases. Furthermore, AI can also predict the efficacy of developed candidates and analyze potential preclinical failures.
- AI in clinical trials: By sifting through large databases of patient records, AI can identify patients fitting the criteria for clinical trials. Furthermore, machine learning (ML) algorithms can predict possible failures in clinical trial design, allowing researchers to address them before commencing them.
- AI in pharma compliance: Natural language processing (NLP) and ML algorithms can simplify data management, create regulatory submission documents, track regulatory updates, identify potential non-compliance issues in other processes, allowing pharma companies to improve their overall regulatory compliance.
- AI in pharma supply chain: AI tools can analyze historical data and market trends to identify changes in product demand, which will optimize inventory management and reduce the chances of overstocking and understocking.
- AI in manufacturing: Predictive analytics can use historical data to identify potential manufacturing issues and equipment maintenance due to wear-and-tear, which will reduce equipment downtime. AI can also be used to optimize processes, automate interdependent steps, detect defects, and monitor quality in real-time.
Here, we’ve provided briefs of each application. If you’re interested in getting more in-depth information, Pharma Now has published comprehensive articles on the possible implementation of AI in pharmaceuticals.
What’s stopping pharma companies from using AI?
There’s a complicated relationship between AI and pharmaceutical companies. As everyone knows, the pharmaceutical industry is heavily regulated, and every change must comply with regulatory guidelines. These make the implementation of AI challenging. Interestingly, the implementation of AI is not as straightforward as many may believe. There are many sizeable barriers that pharma companies have to tackle in their quest to implement AI:
- Data challenges: AI models require vast amounts of high-quality data, which is hard to obtain. Data often comes from various sources, which limits AI’s effectiveness. Furthermore, even if the right data is obtained, companies need to ensure compliance with global data privacy and security laws, which is difficult.
- Regulatory challenges: Regulatory guidelines and standards for AI in pharmaceuticals are still evolving, making implementation while ensuring compliance tricky. Companies need to demonstrate the reliability, accuracy, and reproducibility of AI models, which is time-consuming and complex.
- Integration challenges: AI needs to be implemented in existing processes and workflows without disrupting operations, which may be infeasible technically. Additionally, AI implementation may require significant changes to existing processes, which may face resistance from employees.
- Employee challenges: There is a limited talent pool who are expert in AI and pharmaceuticals. Hence, companies need to upskill employees or find hire new employees from a small talent pool. These challenges can slow down implementation plans.
- Financial challenges: The cost of developing, training, and deploying AI models is high. Additionally, companies also need to invest in resources to regularly clean, label, and structure the obtained data before it can be used by AI models, which also expensive.
What’s the current scenario?
Currently, many pharma companies are testing AI models. Johnson & Johnson is using AI to improve surgical procedures, discover new drugs, and recruit clinical trial volunteers. Pfizer has been using AI in pharmacovigilance and safety monitoring since 2014. Novatis has established an AI framework for risk and compliance management. Moderna aims to implement AI in all areas of the company. These are only a few examples. Many more pharma companies are looking at new ways to implement AI tools to simplify and optimize existing processes, all to ensure they provide better products to patients.
What does the future really look like?
As many experts (rightly) claim, AI is the future.
The implementation of AI may be challenging, expensive, time-consuming, and many other things. But AI’s benefits significantly outweigh these cons. So, we believe that slowly but surely, AI will be adopted by more and more pharma companies in the future. Process automation and optimization with the help of AI will result in improved drug quality, resulting in improved patient outcomes and much better healthcare! This implementation will be subject to regulatory approval — as is the case with everything in the pharma sector — but as regulatory guidelines evolve to include these updates, we can expect rapid adoption of AI!
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