Personalised medicine is a medical model that tailors treatment to individuals based on genetic, epigenomic, and clinical information (Mathur & Sutton, 2017). Also called precision medicine (Boguski et al., 2009), P4 medicine (Flores et al., 2013) or stratified medicine (Trusheim et al., 2007), it is anticipated to have a major effect on both the development of new medications and diagnostics in clinical practice.
While the principles of personalised medicine have been central to clinical practice since the very first efforts to classify disorders and to prescribe a specific treatment on the basis of a precise diagnosis (Egnew, 2009), many technological breakthroughs have been drivers in the advance of personalised medicine, including the recent convergence of genetics, epigenetics, imaging, and informatics (Jameson & Longo, 2015).
These technologies could potentially allow clinicians to shift the emphasis from reaction to prevention, using molecular markers that signal the risk of disease before clinical symptoms appear; select an optimal therapy, avoiding the practice of trial-and-error prescriptions; make medications safer, taking into account variations in genes that impact metabolism; and reduce the cost of both clinical trials and health care in general (Abrahams & Silver, 2009).
For instance, recent research has shown promise in mapping the genetics of clinical depression (Nurnberger, 2017). The China Oxford and VCU Experimental Research on Genetic Epidemiology study analysed the cases of more than 5,000 women suffering from major depressive disorder (MDD) and used low coverage sequencing to genotype both these women and a control group of equivalent size; they identified two genome-wide significant loci contributing to the risk of MDD, one near the SIRT1 gene, and the other in an intron of the LHPP gene (Cai et al., 2015).
While MDD is most likely highly polygenic (Ripke et al., 2013) and many additional genetic markers remain to be discovered, the results of this study are encouraging and indicate that low sequence coverage of a large number of individuals can be an effective way to infer genetic signals (Cai et al., 2015). These results are in line with previous research indicating that depression is highly heritable, with relatives of individuals with milder forms of depression having a greater risk of MDD compared to relatives of individuals without any mood disorders (Lewinsohn et al., 2003).
While research has indicated it is possible to use genetic markers to separate patients from healthy controls at the single-subject level, what about distinguishing between different disorders? One study used magnetic resonance imaging (MRI) to analyse neuroanatomical biomarkers for both patients with schizophrenia and patients with MDD, and compared the neurodiagnostic classification with a traditional diagnosis determined by two experienced psychiatrists (Koutsouleris et al., 2015).
The study found that the neuroanatomical diagnosis was correct in 80% of patients with MDD and 72% of patients with schizophrenia, suggesting that neuroanatomical data may provide a generalisable diagnostic tool to distinguish schizophrenia from MDD early in the course of psychosis.
However, despite these promising early results, some researchers are questioning the relevance of personalised medicine, arguing that few genetic markers are specific enough to provide clinically actionable information that could not otherwise be obtained through regular clinical practice (Coote & Joyner, 2015). In the case of psychiatric conditions such as MDD and schizophrenia in particular, the cause is probably heterogeneous, making the search for specific biomarkers challenging (Alda, 2013).
But it could equally be argued that uncovering the heterogeneous biological basis of individual symptoms may prove to be more helpful in understanding the pathophysiology of these mental illnesses, compared to forcing a large number of co-occurring symptoms to fit together under one specific marker (Ozomaro et al., 2013).
Personalised medicine may also cause important ethical concerns, from data-sharing and genetic privacy, communication of the complexities associated with pharmacogenomics testing, and consent (Atutornu & Hayre, 2018; Feiler et al., 2017). The last two concerns are particularly relevant in patients with mental disorders such as MDD and schizophrenia where informed consent is more difficult to obtain (Amer, 2013; Gupta & Kharawala, 2012; Van Staden & Krüger, 2003), although these ethical considerations could be mitigated by developing appropriate social infrastructures with guidelines to ensure the responsible implementation of personalised medicine solutions in mental health (Evers, 2009).
In conclusion, personalised medicine is still in its infancy, and will require more research, as well as specific ethical guidelines to ensure it delivers on its promise of a more efficient, more effective, and safer medicine, especially in the field of mental health.
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