Mounting pressures from research and development (R&D) costs and complex regulations are pushing life sciences companies to urgently explore advanced technologies like artificial intelligence (AI).
Traditional drug development is slow, inefficient and has a 90% failure rate [1]. Poor digital connectivity hinders data analysis for improving R&D and operations. Effectively capturing and utilizing data across the entire pharmaceutical process — from discovery to commercialization — is crucial for faster, more efficient medicine delivery.
However, many companies struggle with siloed legacy systems and limited human analysis. Next-generation agentic AI may offer a solution to these data obstacles in drug development and manufacturing.
In this blog series, we will be exploring the key challenges facing pharmaceutical companies at each stage of the product lifecycle to understand how agentic AI can support in optimizing efficiency and maximizing the chances of successful development, manufacturing and commercialization in the life science industry.
In this first instalment, we will discuss the barriers hindering effective R&D.
Four key obstacles to R&D success
Life sciences enterprises are navigating multiple challenges that risk undermining effective and efficient R&D:
Time constraints: The pressure to quickly launch new medications due to heightened global competition necessitates faster drug development timelines.
Secure, reliable supply of raw materials for research: The increasing demand for sophisticated, patient-specific treatments, especially autologous cell and gene therapies (CGTs), introduces significant hurdles in supply chain logistics and formulation for R&D as well as other stages.
Siloed data impeding continuous improvement: Difficulties retrieving data from different stages of drug development and manufacturing, such as production logs and supply chain details, hinder research insights and efforts to accelerate the transition from discovery to clinical trials.
Regulatory complexity must be navigated: For European life sciences companies, the expanding web of regulations within the European Union and surrounding markets adds further complexities that can impede the speed of drug development.
Breaking barriers with agentic AI
Outdated workflows and static automation are struggling to meet these complex R&D challenges. Agentic AI offers a powerful solution by fostering virtual communication across the R&D phase and beyond, enhancing decision-making and enabling scalable execution, thereby overcoming these limitations.
Unlike traditional AI, which analyzes data for human-led action, agentic AI employs autonomous agents that can act on recommendations without intervention. When integrated into a life sciences company's digital infrastructure, this technology can rapidly combine and analyze diverse datasets, significantly accelerating data analysis and generating efficiency-optimizing actions much faster than humanly possible.
Redefining R&D
Agentic AI can accelerate and optimize various stages of pharmaceutical R&D. In drug discovery, it autonomously analyzes biological data to identify and validate targets. For analytical and formulation development, it designs in silico experiments and explores chemical spaces to optimize drug candidates.
In clinical research, agentic AI aids in preclinical analysis, trial management and safety monitoring. Furthermore, it streamlines the transfer to clinical manufacturing by identifying efficiencies and optimizing timelines. Strategically, it enhances real-world evidence sourcing for approvals and improves the quality and impact assessment of regulatory submissions.
Key benefits
For life sciences companies in particular, agentic AI can offer a number of benefits at the R&D stage, transforming efficiency and increasing the likelihood of drug research translating into clinical and commercial success:
More efficient drug discovery processes
Better-informed strategic decisions regarding assets
Faster progression of drug candidates into pre-clinical and clinical trials
Higher success rates for initial regulatory submissions
More effective evaluation of the impact of regulatory changes.
Accessing agentic AI in drug research through expert support
Agentic AI can revolutionize the entire life sciences development lifecycle, spanning from early R&D through manufacturing and commercialization. This technology can integrate disparate digital systems across all departments involved in the product lifecycle. Consequently, this interconnectivity allows for enhanced knowledge sharing and learning between functions, ultimately boosting efficiency across the product's entire lifespan.
However, implementing agentic AI into existing digital infrastructure poses a challenge for pharmaceutical companies. This must be overcome to ensure agentic AI fulfills its transformative potential for drug R&D and the rest of the product lifecycle.
To find out more about how agentic AI can be successfully harnessed in life sciences research and beyond, and to find out how Cognizant and Microsoft can help, download our latest eBook now https://www.cognizant.com/emea/en/cmp/agentic-ai-in-life-sciences.
References
[1] Why 90% of clinical drug development fails and how to improve it?, Duxin Sun et. al., 2022