One of the most fascinating rapidly growing fields of life science recruitment is the evolution of technologies that make precision medicine a possibility that can reduce harm and save lives.
To understand precision medicine and its benefits, however, we need to explore the concept in relation to what has in some circles been retroactively named “imprecise” medicine.
Interestingly enough, precision medicine in concept is not new; in the early days of prescription medicine, there was a distinction between extemporaneous prescriptions written for a specific persona and ailment and a more general remedy.
However, the difference between precision medicine and prescriptions is in the fundamental treatment of patients; whilst prescriptions rely on a one-drug-fits-all model, with alternatives for people who do not respond well to a particular medication, PM tailors healthcare interventions for the needs of different people.
This should not be confused with personalised medicine, where medical devices or medications are made specifically to treat a single individual patient, but instead focuses on different subgroups of patients subdivided best on genetic and molecular characteristics, as well as environmental and lifestyle factors.
The potential benefits are substantial for both patients and medical institutions. Customised medical products that factor in individual or subdivided circumstances reduce the risk of harmful interactions of medicines, improve the efficiency of treatments and by extension reduce the cost of healthcare.
It also fundamentally shifts the emphasis of medicine from reactive interventions to particular symptoms to preventative measures to stop a particular negative outcome from occurring. This, by extension, reduces people’s susceptibility to different diseases.
It can also improve the early detection of diseases where intervening only when symptoms start to emerge is too late, and the fundamental approach allows for custom strategies to prevent diseases based on patient need rather than their ability to handle the first-line treatment.
Conversely, it allows for more effective drugs to be prescribed sooner, avoiding the issues seen with certain conditions where a range of different drugs need to be tried to find one effective for a particular patient.
It relies heavily on diagnosis on a molecular level, such as checking for particular biomarkers, as well as genomic tests. This has required not only advanced diagnostics equipment to be developed but also the use of machine learning to interpret the huge amounts of data generated.
Artificial intelligence has already seen use in cardiovascular precision medicine as well as for clinical trials, with a study suggesting that machine learning had a 76 percent accuracy rate in predicting the outcomes of clinical trials.
This could potentially reduce or entirely remove entirely many of the trial-and-error inefficiencies of drug discovery and clinical trials, which are, in part, the reason why many medicines took a very long time to be approved.
There is a wealth of fascinating research in the field of precision medicine, primarily on the diagnostics front, such as a combination of spectrometry and machine learning to enable real-time imaging of the effects of medication in the body, a discovery believed to be impossible but one with considerable potential.