The biopharma industry has undergone a digital transformation, leveraging AI, automation, and patient-centric technologies to significantly accelerate R&D timelines and improve productivity. This digital-first approach, combined with strategic collaborations and innovative funding models, has led to faster delivery of groundbreaking therapies for previously untreatable diseases; new life-extending treatments for some of the most highly prevalent diseases such as cancers, diabetes, cardiovascular and neurodegenerative diseases; and a greater emphasis on preventative treatments. As a result, the return on investment (ROI) in biopharma innovation in 2030 has increased year-on-year since 2023.

The world in 2030 
 

• AI and advanced technologies accelerate drug discovery: GenAI, in-silico research, and advanced gene editing techniques are expediting and refining the drug discovery process, leading to more personalised and cost-effective treatments.
• Hybrid trials and data-driven optimisation: AI, along with quantum computing, streamlines drug development by optimising trial design, generating drug performance insights, and automating reporting. AI-enabled clinical trials enhance patient recruitment, monitoring, and data analysis, ultimately speeding up timelines. 
• Diversity, real-world evidence, and equitable drug development: The integration of real-world evidence, AI-driven clinical trial recruitment and retention, and a focus on diversity in clinical trials ensures the development of more equitable and representative healthcare solutions.
• Strategic partnerships and collaborations: Biopharma are pursing M&A to acquire innovative pipelines, replenishing assets impacted by the patent cliff, and bolster internal R&D capabilities, particularly in areas like AI-driven drug discovery and gene editing. This is complemented by strategic partnerships and collaborations to access cutting edge-technologies and accelerate the development of novel therapies.   
  

Overcoming cross-cutting constraints
 

There are four cross-cutting constraints that could affect the prediction (not having the right skills and talent, funding models, approach to regulation, and data governance in place). The prediction can be realised by turning the constraints into enablers, for example by:

• Fostering a workforce skilled in engineering, computational science, and biotechnology, while collaborating internally with clinicians, scientists and the supply chain and commercial functions, and externally with academia, contract research organisations, AI for drug discovery, big tech and metaverse companies to drive innovation.
• Adopting public-private partnerships, subscription-based drug access, and value-based care models to incentivise the development of novel therapies and shift the focus towards patient outcomes.
• Streamlining trial processes, risk-based monitoring, and robust cyber and data security measures to ensure compliance and accelerate approvals, while simultaneously navigating the evolving AI, pricing and access global regulation landscape.
  

Evidence in 2024  
 

• Success rates are higher for AI-discovered drugs: As of December 2023, 24 AI-discovered molecules have completed phase I trials, of which 21 were successful. This suggests a success rate for phase I trial AI-discovered molecules of 80-90%, substantially better than historical industry averages (40-65%).
• The potential of decentralised clinical trials (DCTs): Tufts Center for the Study of Drug Development found that DCTs are associated with reduced clinical trial timelines and substantial extra value to sponsors developing new drugs. If DCT methods are applied to both phase II and phase III trials, the value increased by US$20mn per drug that enters phase II, with a seven-fold increase of ROI. They also have lower screening failure rates and fewer protocol amendments. 
  

How AI/GenAI might impact R&D  
 

• Biopharma companies can unlock substantial financial value in R&D, by scaling up their AI capabilities for drug discovery and clinical trials.
• AI and GenAI can accelerate drug discovery by modelling protein structures, analysing molecule libraries to identify promising drug candidates, and enabling drug repurposing and personalised treatment options based on genetic profiles.
• GenAI can streamline clinical trials by automating documentation, optimising trial design and patient recruitment, and enhancing data analysis, leading to faster and more cost-effective drug development.
• GenAI can enable real-time monitoring of clinical trial data, facilitate the use of digital twins, and automate report generation, improving the efficiency and quality of evidence generated during trials.