While precision medicine holds great promise for both patients and the growth of global markets, there are significant obstacles to be overcome to enable its widespread adoption in a context which ensures equality of access. In this blog, we’ll explore how mind-bending volumes of data, a lack of trained specialists, and the huge costs of drug development all pose challenges which must be addressed to ensure successful implementation of precision medicine strategies internationally.
In 2018, 90% of the world’s data had been produced in the two years prior – and the pace of data production has only continued to increase since then. In the context of health data, this presents unprecedented challenges when considering how best to unify, store, and format information in order to make it securely accessible to researchers and healthcare institutions across the globe.
For example, a report published by UK-based organization The Health Foundation in 2022 cited, “a lack of linked data with detailed clinical information and issues with data quality” as some of the biggest barriers preventing the full potential of the health data being fully realized.
The introduction of guidelines such as the 2016 FAIR Guiding Principles for scientific data management and stewardship aims to improve the Findability, Accessibility, Interoperability, and Reuse of scientific data, emphasizing the need for computational systems to be able to find, access, interoperate, and reuse data with minimal human input. Such principles will not only enable researchers to fully harness the insights contained within huge volumes of data, but also to play a key role in the successful internationalization of precision medicine initiatives.
Data security, as well as which data can be stored and for how long, also represents a sticking point for the future of precision medicine. The complexities of diverse regulatory environments across different countries and territories mean that ensuring data use and storage practices abide by the laws of different geographies (which demand different levels of rigor) requires a nuanced strategic approach.
A 2020 study from the London School of Economics and Political Science put the average cost of bringing a drug to market at $1.3 billion. While this is a significantly lower number than previous studies which placed the average cost at $2.8 billion, the costs of drug development, with an overall time frame of more than 10-15 years per treatment, are still huge. High failure rates in clinical trials also present a significant challenge, with a 2022 study citing that 90% of drug candidates fail once already approved for Phase 1 trials (or beyond). Notably, that figure doesn’t take into account targets in the preclinical phase. These challenges are further compounded in a precision medicine context where identifying and sequencing large numbers of patients from rare and ultra rare disease populations can increase costs significantly.
However, the 2023 study from Institute of Cancer Policy and Queen’s University Belfast suggests that precision medicine approaches within oncology could make a new drug up to $1 billion cheaper to develop than in a non-precision context, despite oncology historically being one of the most costly domains in which to develop treatments. The research provides evidence to support the hypothesis that drug development that utilizes a companion diagnostic approach (such as genomic sequencing of tumors), has the potential to reduce the enormous costs associated with delivering new treatments.
It’s important to note, however, that the study also highlighted the enormous range within the cost of drug development, as well as return on investment. Professor Richard Sullivan, Director at the Institute of Cancer Policy and Co-Director of the Centre for Conflict & Health Research, said of the research: “This study illuminates extraordinary differences in R&D costs per cancer drug, as well as some staggering returns on investment. One thing that is abundantly clear is that there is no connection between the prices set for individual drugs and their prior R&D costs, or subsequent returns on investment."
As precision medicine initiatives continue to expand across both R&D and healthcare, and costs of genetic testing continue to fall thanks to the development of Next Generation Sequencing technologies, a paucity of trained specialists able to meet the demands of this rapidly developing field is proving a significant setback.
For example, the NHS cites a 30% rise in the number of computed tomography (CT) scans ordered in the UK between 2013 and 2016, during which the number of qualified radiologists only increased by 3% each year. Alongside this logistical challenge, a number of studies also demonstrate that as radiologists are forced to work faster their interpretation error rate rises.
AI has the potential to address the shortage of specialists, particularly in precision medicine, by enhancing the work of healthcare professionals and researchers in this field. For example, AI can help geneticists and data analysts by quickly processing and interpreting large-scale genomic data, identifying patterns, and generating insights that would otherwise take much longer to uncover. This allows genetic counselors and clinicians to make faster, more informed decisions about patient care. AI can also assist with precision medicine research, enabling more efficient analysis of clinical trial data or genomic variations, ultimately accelerating the development of tailored treatments. By automating time-consuming tasks and providing decision support, AI can help bridge the gap between the rising demand for precision medicine and the limited number of trained specialists.
Ensuring that there are enough practitioners to meet demand, that they receive the relevant training to give them the skills and understanding to successfully deliver precision medicine approaches, and that they understand when it is appropriate to do so is vital to integrating these practices into care and research. Establishing new roles and a greater number of positions for precision medicine specialists such as genetic counselors, data analysts and software developers is another key piece of the puzzle. As AI continues to evolve, it’s important to focus on its responsible use—ensuring that AI tools are designed and deployed ethically, with transparency and oversight. AI should not replace human expertise but serve as a tool to empower specialists, enabling them to make more accurate and timely decisions.
To learn more about the future of precision medicine in the international market, download our whitepaper, “Global market expansion and the future of precision medicine: The case for internationalization”