Accelerating the future: pioneering a new era in pharma R&D and scientific and technological innovations
This week we have published the fifth and sixth predictions from our Accelerating the future: Life Sciences and Healthcare predictions 2030 report. These latest two predictions explore the Convergence of AI technologies and human expertise in pharma R&D and Interdependent innovations in science and technology are resharing treatment paradigms. Both consider what the world of medicine will look like in 2030. Our fifth prediction explores how end-to-end digitalisation, automation, and integration of AI technologies has transformed R&D, enabling quicker and more accurate decision-making in drug discover and significantly improved the productivity, efficiency and patient-centricity of clinical trials. The sixth prediction examines how innovative science and technologies that are available today, like quantum computing and AI, genomics and population health data profiles, have transformed the diagnostic and treatment paradigms. Our blog this week highlights the key insights in these two predictions.
Accelerating R&D return on investment and access to innovations for patients
By 2030, end to end digitalisation, automation and integration of AI technologies have enabled pharma companies to speed up decision making across the R&D value chain, improving R&D productivity. GenAI models, insilico research, digital twins and computational science have improved the speed and accuracy of drug discovery. Virtual and hybrid clinical trials, powered by cloud-enabled data platforms, quantum computing, data visualisation and AI have improved clinical trial design, has expedited trial recruitment and enhanced the diversity and retention of trail participants while optimising trial logistics.
Real-world data and multi-omics technology combined with electronic clinical outcomes assessments (eCOA) provide deeper insights into drug performance. Moreover, AI has streamlined trial design and documentation and reporting, while a focus on ‘sustainability by design’ principles and adoption of science-based carbon reduction targets has helped reduce the environmental impact of R&D (see Figure 1). These innovations have accelerated R&D timelines, enabled more efficient, patient-centric drug development and ensure innovations reach patients and realise value sooner.
Figure 1. Transforming pharma R&D through the strategic application of GenAI
Source: Adapted from Deloitte’s ‘Unleash AI’s potential’ report.
Since 2023, the return on investment in R&D innovation has improved year-on-year. Key drivers of this success include strategic alliances, outsourcing, and earlier and ongoing dialogues with regulators. This approach has not only increased the success rates of treatments for diseases that were once considered untreatable but has also paved the way for new blockbuster drugs targeting conditions including non-communicable and previously untreatable diseases. The success of GLP-1 drugs in treating obesity in the mid-2020s has been particularly influential in the establishment of new funding models that focus on preventive therapies including mRNA vaccines for cancers and other non- communicable diseases.
This transformation of the R&D ecosystem is dependent on overcoming a number of cross-cutting constraints such as not having the right skills and talent, the need for new funding and business models, compliance with emerging regulations, and digitalisation and data governance. However, we believe that by 2030, the R&D talent pool will have been strengthened by creating an agile adaptable workforce, bringing together engineering, computational science, and biotechnology and by investing in AI, digital, genomics, and medical skills. Collaboration between R&D, commercial and manufacturing teams and partnerships with research institutions has enhanced the R&D talent pool. Moreover, new collaborations between government and private investors, pharma companies and research institutions have addressed the funding challenges.
Pharma companies have proactively addressed regulatory changes by incorporating ‘quality-by-design’ principles and leveraging real-world evidence. A robust data ecosystem, powered by AI, cloud storge and advanced analytics, and a deep culture of cyber vigilance with strengthened technology enabled resilience and advanced third party risk management practices have strengthened data security and resilience.
How can GenAI accelerate this future?
Deloitte research shows that a top-10 biopharma company with an average revenue of US$65‑75bn could obtain between $5‑7bn of value by scaling the use of AI over five years. By leveraging GenAI's ability to analyse vast datasets and model complex biological processes, researchers can accelerate the identification and validation of drug candidates. A collaboration between AI-driven analysis and human expertise enhances the identification of potential drug targets and enables personalised treatment options tailored to an individual's genetic profile, saving both time and resources in the process. In clinical development, GenAI can improve the diversity of trial participants, conduct real-time data analysis to detect patterns and potential issues, and support the management of decentralised trials. It can also assist in the collection and processing of real-world evidence for regulatory submission. Gen AI as the potential to enhances efficiency, reduces timelines and improve the clinical trial experience for both clinicians and participants, ultimately leading to faster delivery of innovative therapies.
Emergence of interdependent innovations in science and technology
The rapid transformation of R&D, alongside the development and adoption of scientific and technological innovations, are reshaping diagnostics and treatment paradigms (see Figure 2). Integration of genomic data with clinical data increases the speed and accuracy of diagnoses and treatment plans, improving survival rates. Other technologies, such as metabolomics, proteomic and microbiomics, also provide a better understanding of the patient’s biology and offer innovative treatment options for various diseases. Integration of data from multiples sources, such as MedTech devices and wearables, alongside the use of quantum computing and AI-driven technologies, enables the identification of real-time population health profiles, facilitating the identification of disease drivers and the creation of advanced, personalised treatments. Innovation will also extend to prevention therapies, enhanced by the development of vaccines (mRNA platforms and other novel approaches).
Innovative drug delivery systems, such as nanomaterials and microrobots, enhance treatment precision and minimise side effects while decreasing healthcare costs. Meanwhile, breakthroughs in neurotechnology and personalised mental health have enabled the development of new therapies for brain-related diseases, with quantum computing and advances in neurotechnology, such as brain-computer interfaces, and customised mental health treatment plans based on genetics and biomarkers revolutionising neurological and psychological care.
Achieving this prediction, also requires overcoming the same cross-cutting constraints identified above. By 2030, a skilled workforce proficient in areas like computational biology and chemistry, AI engineering and bioinformatics, and regulatory compliance has helped realise our view of the future. Outcomes‑based funding is the dominant model in government and private financing initiatives. Regulatory bodies are adapting to balance innovation with patient safety, incorporating real-world evidence and AI-driven pharmacovigilance. Omics data sharing has proved crucial to diagnosis and treatment decisions using collaborative reporting and analysis software, databases and knowledge sharing. Digitalisation is end-to-end with data protected using encrypted servers, secure cloud storage and robust transmission protocols. Firewalls and intrusion prevention systems protect against unauthorised access. Data backup and disaster recovery measures help reduce and restore system failures and breaches. Companies have established real time monitoring, cyber threat modelling and analysis, threat mitigation and remediation.
How can GenAI accelerate this future?
Used responsibly, GenAI can analyse large datasets, and combine genomics, clinical history, lifestyle and social determinants of health to provide deeper insights. Developments in GenAI can support clinicians’ decision-making, including improving the speed and accuracy of diagnosis, the breadth and depth of treatment options, adaptation of treatment plans, optimising medication doses and predicting adverse drug reactions. Its ability to synthesise vast scientific literature provides clinicians and researchers with rapid access to the latest evidence. Furthermore, GenAI facilitates patient engagement through personalised interventions, virtual coaching, and improved communication by creating tailored materials in multiple languages and literacy levels. Beyond direct patient care, GenAI strengthens supply chain management and enhances medical imaging analysis, ultimately improving public health outcomes and accelerating the development of innovative treatments.
Conclusion
The convergence of AI, data and human ingenuity has heralded the potential for more precise diagnostics and the advent of 5P medicine and is reshaping pharma R&D to deliver a future where treatment paradigms are redefined. By 2030, we will be on the path towards diseases being predicted and prevented with precision, treatments tailored to an individual’s unique genetic makeup and healthcare delivered seamlessly through virtual and physical channels. This future hinges on fostering a dynamic ecosystem where a highly skilled and technological savvy workforce, empowered by cutting-edge technologies and collaborative partnerships, drives innovation at an unprecedented pace.
We would like to acknowledge the contributions of Ditto Antony towards the drafting of this blog.
