The ultimate path that precision medicines will take is unclear, although it is already apparent that diseases that hitherto were classed as ‘untreatable’ can potentially be treated (and, in a few cases, are being treated). These conditions are genetic diseases and certain cancers.
While scientific thinking has advanced and technologies have followed suit, the ethical issues that stem from these innovations are lagging. These ethical issues relate to privacy, data security, and law. To tailor treatments for patients requires the capture and analysis of vast amount of patient-centric data. However, doubts are being expressed as to how well-equipped global health systems are to handle such data and over the level of maturity that exists in terms of ethical and legal regulation.
Many of these issues are being flagged by the scientific community, as with a recent paper published in the journal Precision Oncology; yet the level of discussion around the legal and ethical issues at the governmental level is not advanced and the ramifications are not registering in the public discourse to any appreciable degree.
What is precision medicine?
The history of medicine has largely been one based on the production of generic medicines, a ‘one-size-fits-all’ approach to the treatment of disease. Here, an active ingredient in the medicine is designed to target a specific region of the body or to simulate the immune system against an infectious agent.
In the past decade, there has been a driver towards ‘precision medicine’ (the preferred term in the U.S.) or ‘personalized medicine’ (the preferred term in the European Union). This means creating medications that are tailored towards a specific characteristics of an individual (or smaller group of patients sharing a common phenotype). This could be, for instance, treating a rare disease based on a person’s genetic makeup creating a drug around the genetic profile of an individual’s tumor. Gene therapy is an example of a scientific breakthrough fostering current and potential precision medicines.
Drivers for precision medicines include a desire to treat specific conditions relating to individuals where common medicines have not proven effective and improvements in scientific understanding and technology. In time, there may also be economic factors linked to ‘bespoke’ treatments.
Advances in Next Generation Sequencing to sequence genomes have helped to pave the way for precision medicines, rapidly identifying or ‘sequencing’ large sections of a person’s genome, together with, from the infectious agent perspective, the continuing insights from the Human Microbiome Project and Human Genome Project (vast data collection initiatives that produce their own matters of consent, privacy, and inclusivity).
Importance of data
Precision medicine is reliant upon data. Data analysis is necessary to determine decisions, treatments, and practices, and to make predictions about patients’ health trajectories and likely treatment outcomes. The accuracy of data analysis is a factor of algorithms and advances in machine learning, harnessed to produce a deep understanding of the health and disease attributes unique to each individual.
Building a robust regulatory framework
Data gathering systems to support precision medicine draw upon health record data and laboratory test systems, to enable decisions. Government regulation of healthcare systems will need to be advanced in order to introduce improvements in areas like data architecture and consensus on data use.
The level of data being collected about individual patients is far more comprehensive and detailed than the types of data collected through conventional medicine. In addition to the usual personally identifiable information and health status, the precision medicine process requires details of a person’s genetics, lifestyle, and environment.
There are also issues of data ownership, particularly as the patient is curated between health institutions, academia, and private corporations, where siloed data sources are integrated to enable scientists to make better decisions through predictive risk modeling. For this, data scientists will need to have secure systems in place. Healthcare data is a regular target for criminals and hence a cybersecurity issue.
This also introduces the concept of patient consent. If the data from one patient is pooled together with other patients, should that patient be told and be able to track how their data is used? And what safeguards are in place for rendering the data anonymous?
Should data from a patient lead to a remarkable new treatment that goes beyond the specific need of the individual patient is that patient entitled to consent or to be financially reimbursed for the use of these data? Ultimately, who owns medical data?
Equally, a too restrictive approach to data sharing may simply tether precision medicine to the starting block. For example, Jeffrey Kahn, the Andreas C. Dracopoulos director of the Johns Hopkins Berman Institute of Bioethics said in October 2022: “A healthcare system built on de-identification emphasizes privacy but sacrifices our duties of clinical care to patients… It limits the effectiveness of science and prevents any attempt to share the broader benefits of research discoveries with those who contribute to its success.”
The use of artificial intelligence to interpret patient data and to make longitudinal predictions also brings with it ethical considerations, not least due to inherent, and so far inescapable, bias built into algorithms.
Several of these issues have preoccupied the legal process in medicine, or they have preoccupied other fields (as with the ethics of machine learning), yet they are combining in new ways in the era of advanced medicine and they are producing new issues for ethical and legal preoccupation. Moreover, the specialist nature of precision treatments makes them difficult to regulate within traditional frameworks, meaning that agencies must update current policies and regulations. Should these be at the level of the nation-state or joined up to create a level playing field?
Broad principles for a regulatory framework will need processes for monitoring data quality; a system for assessing the validity of algorithms; a process for seeking patient consent, ensuring anonymity and protecting patient data; and a governance structure to articulate the appropriate conditions of data use.
However these answers are reached and the final form that fit-for-purpose regulations take, it is time for governments to catch up.
Dr. Tim Sandle is a practicing microbiologist and science & technology journalist. The author of 30 books, Tim’s journalism has been published on Digital Journal and Pharmaceutical Microbiology Resources. Tim is a visiting tutor at the University of Manchester, U.K. and University College London.