Published Originally in Chemical Industry Digest July 2023
Abstract
The article highlights the increasing adoption of artificial intelligence (AI) and allied technologies in the pharma- ceutical and life science industry. It discusses the significant impact of AI in drug discovery, molecular research, mRNA therapeutics, smart chemical manufacturing processes, and smart labeling for regulatory compliance. The potential of AI in these areas is projected to generate substantial value for patients and improve overall efficien- cy in the industry. However, there are still areas where AI has yet to demonstrate positive outcomes. The article concludes by emphasizing the future potential of AI in the pharma sector and its ability to revolutionize various aspects of the industry.
Introduction
Emerging technologies like artificial intelligence and machine learning are making their mark on every aspect of our lives; and pharma and life science are no exception. With the recent advances in the areas of artificial intelligence including the sudden shift of trends towards techniques like Generative AI and LLMs (Large Language Models), pharma players are set to embrace AI techniques for rapid transformation and generating value for patients or end users. In recent studies, life science industries seem to have understood the value of rapid AI adoptions in the areas of drug discovery, molecule design, mRNA, etc. to address the ever-evolving drug landscape. This is pushing pharma companies to move away from conventional ways of IT to large-velocity AI analysis for rapid value generation.
The application areas for AI or allied technologies are quite diverse. They enable organizations to reimagine business cases, and strategies, streamline manufacturing, and most importantly enhance the drug discovery pipeline, molecule research, product intelligence and many more.
To shed more light on why it is important to adopt AI in the life science industry, we came across a study which shows the significant value of using AI and allied technologies in drug discovery. In 2022, it was predicted that the volume and market share of AI-based drug discovery methods would total $0.6 billion, and it is bound to reach $4 billion by 2027. With this staggering growth of approximately 45%, most of the pharma life science industries are now taking steps towards equipping R&D (Research & Development) units with AI capabilities for faster drug development to improve patient care.
Here is a comprehensive outlook on how AI and allied technologies in the pharma and life science industry are making a difference-
- AI in drug discovery – Drug discovery is considered one of the most critical components of the drug development lifecycle which significantly impacts patients around the world. The use of AI and natural language processing technology to quickly identify new chemical compositions that can treat diseases like cancer is the subject of extensive research and investigation at the moment. With the advancements in the areas of transformers and large language models like GPTs (ChatGPT, 2023), researchers are trying to find ways to mine large volumes of data for screening and processing databases of articles explaining existing chemical compositions, studies of molecular designs, etc. This information is used to create and generate prospective chemical compositions for novel medicinal molecules using an AI model. Some of the large pharma giants have invested in creating dedicated teams which are geared towards enabling organizations with AI-driven drug discovery at a faster pace.
- Molecular research – With rapid growth and developments in the AI space, new methodologies have been introduced in the R&D business of pharma life sciences to investigate the potential use of machine learning to analyze large volumes of data. Such analysis and insights from diverse data sets help businesses explore new paradigms of the indications resulting in the rapid discovery of chemical molecules.
- Machine learning based ext-gen mRNA – mRNA therapeutics is undergoing various transformations from theoretical aspects to more practical real-world usage. Machine learning plays a pivotal role in the design of mRNA such as chemical modifications to enhance the performance and lower the risks associated with the same. With these techniques, the models can scan through large literature to optimize aspects like target identification, selection processes as well as design of key components of mRNA, etc.
- Smart Chemical manufacturing processes – Some life science businesses are preparing for a pattern shift in the domain of manufacturing processes, moving away from conventional procedures to more proactive methods of creating chemicals. To achieve proactive quality across manufacturing, the life sciences industry is now adopting AI-based technologies not just to improve productivity and cost savings but to also work towards enabling sustainable production to reduce environmental impacts. One of the major areas where AI and allied technologies are helping the life science industry is work on reducing waste generation from manufacturing. Based on the historical data captured during the process, AI models can analyze and predict the potential scenarios where excess waste was produced and can suggest ways on reducing the waste. AI-based recommendations have shown noteworthy results where a slight modification to existing processes has helped in reducing waste effectively.
- Smart labelling for regulatory compliance – Labelling is a very crucial task as part of any pharmaceutical life science organization. The regulatory bodies are continually establishing new rules and recommendations for labelling procedures. Some of the life science organizations have key issues when it comes to labelling. Due to flaws or challenges in the labelling process, approximately 50% of recalls are caused, which adds a significant overhead.
The process of labelling can be greatly expedited by utilizing AI and related technologies, such as Natural Language Processing. The models can offer near real time analysis of the changes in the labelling, ways to adjust the labels and cascade the updates to the business teams for effective regulatory processes. This has helped major pharma organizations significantly reduce the overhead of product recalls.
The examples mentioned above are a few of the most important use cases where artificial intelligence (AI) and related technologies have demonstrated their utility and continue to revolutionize how businesses employ these cutting-edge tools.
Additionally, there are notable applications in chemical literature where researchers have used AI-based language models for analyzing chemical substance information. Such analysis is useful in cases where it is crucial to understand the temporal evolution of substance-level information from the articles.
“Some life science businesses are preparing for a pattern shift in the domain of manufacturing processes, moving away from conventional procedures to more proactive methods of creating chemicals. To achieve proactive quality across manufacturing, the life sciences industry is now adopting AI-based technologies not just to improve productivity and cost savings but to also work towards enabling sustainable production to reduce environmental impacts”.
Future of AI in the Pharma Sector
To conclude, there is tremendous potential in leveraging AI and allied technologies-based products in the pharma life sciences and chemical domain to better design and deliver value out of drugs and molecules for the end users. Significant research from academia and industry professionals has been published in various journals providing proof points to test the hypothesis and compare the performance of the models. There are numerous emerging applications of AI in the chemical domain which are gaining attention across industries.
The major advantage of using AI models can be its generalizability. The models can be trained on a large corpus of various publications, patents, documents, etc. which can provide knowledge for better decisions. The use of AI and advanced analytics in R&D business units is extremely critical to achieve significant value generation for patients. Investing in technologies of AI, Machine Learning, Natural Language Processing and so on will provide a significant boost to ROI (Return on investment) for R&D spending by increasing efficiency, reduction in clinical trial failures, cutting costs across the value chain, and enabling sustainable technology platforms.
Even though AI and similar technologies have seen tremendous advances in the chemical or life science business, there are still areas of organic synthetic chemistry where AI is yet to generate any positive outcomes. We will witness a wave of revolutions in the chemical domain with the rise of transformers like ChatGPT (ChatGPT, 2023) or large language models in open-source communities, along with improvements and lessons from existing use cases to help wake up previously dormant business sectors and increase AI’s overall business productivity.
