On 28th and 29th May 2024, I attended the Digi-Tech Pharma and AI conference in London. This was a much smaller conference compared to the Oncology Professionals Conference I attended previous year, however a wide range of topics were discussed over the course of the two days. In particular, I was interested to learn about the role of artificial intelligence (AI) in drug development and trials, as I am aware of the growing influence of AI across many aspects of day-to-day life, but wanted to know more about how this may affect the pharmaceutical industry.

One focus of the talks was about the use of AI in diagnostics and drug development. For example, the ‘Every Cure’ platform, which uses a bank of data from 22,000 drugs and 3000 diseases to train AI to match approved drugs to diseases of which they have not yet been used to treat. By identifying the repurposing potential of existing drugs, this can cut down on the substantial time and money required to develop an entirely new drug, therefore increasing accessibility to valuable treatments. Similar biobanking methods are also being used in the ‘100,000 Genomes Project’, which uses whole genomic sequencing to diagnose cancer and rare diseases.

Another focus of the conference was on the emergence of precision medicine – applying technological advances and AI to health data in order to better manage patient conditions. For example, I learnt that a ‘multi-omics’ approach is being used in oncology to train AI using large amounts of biological data. This can then be used across all stages of the clinical pathway to interpret new data (e.g. imaging) to improve early disease detection, aid clinical decision making, create personalised treatment plans and predict clinical outcomes.

Other talks focussed on real-world data in drug development, such as using smart wearable devices and apps to collect patient data (e.g. vital signs), whilst saving time and money. Such devices are being used in clinical trials to streamline patient selection processes and enhance trial precision by utilising real-time data. In some cases, the collection of real-world data is being used to create software that understands patients and can feed information directly back to them. For example, the ‘Woebot’ mental health app which uses a cognitive behavioural therapy approach in combination with AI to analyse user input, challenge negative thinking and promote user wellbeing.

Going forward, there was also discussion of how trial data may be integrated using digital technology to improve efficiency, such as through the direct transfer of data from patient electronic medical records to trial electronic data capture platforms. AI models can also be implemented to analyse real-world data to predict trial outcomes and aid study planning and monitoring. However, there are some limitations to applying these methods to clinical trials, as there often needs to be human action at some point in the process in order to avoid errors and navigate the complexities of the healthcare system.

Overall, the conference was a brilliant opportunity to understand how digital technology and AI is increasingly being used in the pharma industry and to foresee how this might change the landscape of drug development and patient data going forward. It was interesting to discuss with fellow attendees how although such advances are predicted to save significant time and money, they require financial investment and a shift in perspectives to address longstanding healthcare practices and rightful concerns over data protection. Regardless, it is clear that digital health and AI have the potential to optimise the industry and provide digital solutions on both a patient and business level.

Published: 25 June 2024

Author: Lydia Ainsworth