Traditionally, medicine has treated patients with a one-size-fits-all approach, but Dr Rhianna Goozee, Senior Medical Writer, discusses how personalised treatment is already changing the way we treat patients.
For those young people experiencing the hallucinations and delusions of psychosis for the first time, effective treatment early on is key to limiting the impact of the condition on their life. It can also help avoid the repeated episodes and hospitalisations that characterise psychosis.
In the past, managing and treating psychosis has been approached as a ‘trial and error’ process, with doctors using their clinical experience to determine the most suitable treatments for their patients. This can be a quick process for some, but for others, they must first try several drugs before finding one that works, while the patient’s health and quality of life continues to deteriorate.
During my research career, I used machine learning (an application of artificial intelligence) to identify patterns in brain imaging data that could predict how well a patient responds to an antipsychotic drug. The aim was to be able to predict whether a particular patient will respond to a particular drug. Being able to predict treatment response can enable personalised treatment from the start – delivering the right treatment to the right patient at the right time based on a simple predictive test at diagnosis.
The use of brain imaging to direct treatment decisions in psychiatry remains a long way from being used regularly in clinical practice. But other medical fields are already personalising treatment using other kinds of biological markers – genes, blood molecules or other biological characteristics that indicate disease, disease risk or response to treatment.
Research in the field of cancer has led the way by using genomics (the mapping of an organism’s full set of genes) to determine eligibility for treatment, as well as for screening and predicting risk of recurrence. Now, other therapy areas are beginning to catch up as genomics starts to inform management approaches elsewhere.
For example, cardiovascular medicine has traditionally relied on non-invasive diagnostics and symptom-based management but genomics is beginning to uncover mechanisms underlying cardiovascular diseases (CVDs). Novel genomic information can be combined with known environmental and lifestyle risk factors to inform personalised management – from reducing risk to managing overt disease.
The benefits of targeted treatments may be clear for patients. And this new era of personalised medicine is likely to be welcomed by the tech-savvy, well-informed consumers who represent current and future patients. But what’s it in for pharmaceutical companies?
Personalised medicine offers the opportunity to identify new therapeutic targets, but also opens the way for optimising and repurposing existing drugs. Targeting smaller subgroups of patients may also reduce time to market, for example by enabling a drug to gain orphan drug status – in which companies are offered incentives to develop a drug where it’s the only available treatment for a rare disease. In these cases, a pharmacodiagnostic test can be used to identify those patients who are eligible to receive a therapy.
Better characterisation of the patient population who will benefit from a drug may also mean that smaller, quicker trials can be conducted that produce robust data. And of course, targeted therapies can help achieve the overarching aims of improving care and achieving better outcomes for patients.
Personalised treatment recognises that while two patients may have the same clinical label, the specific biology underlying each individual’s disease will likely differ. This realisation has initiated a shift in the way we approach healthcare. A shift that comes a thousand years after the Greek physician Hippocrates observed the importance of personalised disease management when he said, “It’s far more important to know what person the disease has than what disease the person has.”