Thought Leadership with Dr. Stephen Liggett, Director of the Future Medicines Institute.
“‘May you live in interesting times’ is a familiar phrase and that is certainly the current status quo for medicine.
Whilst the advances in technology that drive the development of new healthcare products are not a curse, the pace of change is alarming.
The benefits of medical advances always lag significantly behind their scientific foundations. Ten years is a rapid timeline to develop a drug from a solid research base. Once approved, it may take another ten years for that drug to find a therapeutic application for patients.
The first GLP-1 agonist was approved by the US FDA in 2005 and yet it is still difficult to predict the impact they will have. The comparative rate of technological change is on a much higher trajectory.
For context the first iPhone was released in 2007 two years later. The capabilities offered by Machine Learning and Artificial Intelligence (AI) offer infinite opportunities to enhance healthcare delivery and the development of healthcare products.
Realising benefits for patients can be difficult to navigate, with the integration and consequences for clinical practice often forgotten about or a narrow scope applied during testing.
Clinical trials are a perfect environment to use AI in a wide variety of contexts with applications ranging from clinical site selection to primary endpoints. This range shows use can be from improving operational efficiency to generating clinical evidence that will support product approvals and future medicines.
The magnitude of risk for here is not equal and the rapid deployment of AI to streamline clinical trials is relatively simple compared to establishing new ways of assessing the efficacy of investigational medicines.
There have been very few primary endpoints utilizing AI despite the potential advantages offered by this technology and this is not surprising given the time to adoption within the governing frameworks.
This is compounded by the absence of detailed guidance from regulators despite the encouraging initiatives they have launched. The common principles published by the FDA and EMA provide a starting point for AI use within the medicine lifecycle but risk aversion in this highly regulated industry will continue to limit the number of use cases in at least the short term.
Drug discovery is being advanced by new technologies such as those available in the Future Medicines Institute (FMI).
There are so many tools available that can expedite the drug discovery process and produce molecules that have a higher probably of regulatory approval. This can be through the application of bioinformatics, computational chemistry, robotics, miniaturization, and high-throughput testing. Utilising patient samples and datasets is not new but the ability to scale this approach significantly and then extract the value in a shortened timeframe is expanding the number of assets built around the patient.
The development of drugs driven by the patient’s biology enables personalisation or precision of the developed treatments. This can simply be using a biomarker that stratifies a disease into additional subgroups and enables drugs to target the disease mechanism more accurately.
Advances in our understanding of biology and increased options in the modalities we can use provide further tools we can utilize. Next generation antibody development, live therapeutics, cell and gene therapies, and mRNA therapeutics provide more potent and targeted treatments for patients. Curative therapies for terminal diseases presenting exceptional results during clinical trials are not a rarity. There is so much optimism in what future medicines can do for patients.
It may seem a bit obvious but whether the clinical intervention is personalized, predictive or preventative it needs to be centered on the needs of the patient and in the context of clinical practice.
Developing software as a medical device that can optimize diagnosis can’t happen in isolation of the care that needs to be provided. Reducing missed diagnosis doesn’t deliver clinical benefits without adequate treatment and support being available ina system with no additional capacity.
Generating data with no follow-up or that cannot be standardized and actioned delivers little benefit and risks diverting resources away from patient care.
Most new drugs are developed for the US market and focus on therapeutic indications that present as the biggest markets. This approach will continue to generate tremendous drugs and significant revenues, however my hope is for the wide range of new technologies to simplify and expedite scientific discovery into patient benefits.
With a patient centric approach these new technologies supported by a pragmatic regulatory environment can reduce the cost and time to develop healthcare products. When these technologies unlock the generational delay and remove the financial burden of our scientific advances future medicines will have arrived.”
