Medscape.com USA: US President Obama, in early January of 2015, announced The National Institutes of Health Precision Medicine Initiative. This would strengthen efforts to use ‘big data’ (very large datasets) for extrapolating to individualized care in the clinic and to develop methods to ensure reproducibility of such data. Reaching the ultimate goals of precision medicine will also require discussions of the ethical issues involved in integrating and sharing big data from populations of study participants. There is even a new journal for peer review publications on this topic named the Journal of Precision Medicine. Publications on the use of the principles of precision medicine in neuropsychiatry are already available, also focusing on schizophrenia. They describe the need to focus on determining the level of risk, on the early pathophysiological mechanisms responsible for illness, and their combination with patient-specific molecular underpinnings of the disease, the goal being to provide personalized and individually entered treatments.
How Precise are Genetic Markers?
Recent studies using genome-wide association have resulted in the realisation that there are several genetic variations of modest risk that contribute to the increased familial loading observed in families of people with schizophrenia. These number as high as 108 independent loci.  Although each one alone only contributes a very small proportion of risk (i.e. 2% at most), combined, they may yield a higher risk (as much as approximately 7–9%, although this figure changes depending on how it is calculated) (unpublished presentations at the 2015 World Congress of Psychiatric Genetics). Recently, this calculation has become known as the Polygenic Risk Score and is used to examine intermediate features of the illness that are candidates for biological markers (such as with brain structure and function). However, despite predicting risk in a larger and larger percentage of people, it has not been shown to be specific for schizophrenia and is not sensitive enough, so that there is too high a percentage of false positives to be able to use this as a valid test for prediction. Most recently, structurally diverse alleles of the complement component 4 have been shown to be strongly associated with schizophrenia, underscoring the potential relevance of immunity in the etiopathogenesis of schizophrenia.
Genetic markers may also be used to try to predict how well patients respond to medications, and in predicting how likely it is for the patient to experience side-effects. This is highly relevant, since one would not want to be using a medication with potential risks if it could be predicted that no response will be achieved. Cytochrome p450 isoenzymes contributing to the metabolism of antipsychotics have been studied in this context. Particularly, CYP2D6 genetic variants have been found to be associated with either slow or rapid metabolism of various first and second generation antipsychotics. The data from studies of this enzyme are strong enough that the Food and Drug Administration in the United States has approved its use to identify slow metabolizers who are at risk to have higher blood levels, and thus side-effects of specific drugs, including clozapine, aripiprazole, risperidone, perphenazine, and others, although CYP2D6 variants are not known to correlate with treatment response. CYP3A4 and CYP2C19 have also been studied in antipsychotic treatment, but the results have not yet been replicated enough to be used in the clinic.  Although several studies indicate that consistent genetic markers may be available soon, none are yet employed in routine clinical care of schizophrenia patients.
How Precise are Brain Imaging Markers?
Numerous brain structure changes have been reported in schizophrenia. They appear to be already present prior to the onset of illness and some seem to predict whether someone at high risk converts to psychosis, but they may not be specific to schizophrenia. These include changes in both frontal and temporal cortices, overall cortical thickness, and the integrity of white matter. Similar to findings in genetics, multiple brain changes may represent combined patterns of brain abnormalities that are more predictive than any one change by itself.  MRI-based multivariate pattern classification has also been demonstrated to differentiate between schizophrenia and major depression with a considerable degree of accuracy, correctly identifying 80% patients with depression and 72% with schizophrenia.
Several brain functioning changes have also been reported in people at high risk. These include differences in language processing, working memory activity, and activity in the resting state.
On the molecular level, dopamine transmission alterations can be traced in the prodromal period, which may help to identify persons with a high risk of converting to full blown psychosis. Additionally, increased cingulate glutamate levels, as found in a proton magnetic resonance spectroscopy study, appear to predict nonresponse to antipsychotics in schizophrenia patients. However, albeit being highly interesting leads, the specificity of these imaging anomalies to schizophrenia has not been determined and many studies await independent replication.
One size fits all not successful so far..
Drug development in other fields of medicine, most notably oncology, is strongly moving toward molecular targets beyond clinical phenomenology, similar attempts in psychiatry have been the exception and unsuccessful so far. Recruiting for phase II and III clinical trials still relies exclusively on clinical descriptors. If patients were subdivided by, among others, genetic or metabolic determinants, the outcome of such trials may be different. It is not unlikely that the failure in bringing truly innovative antischizophrenia drugs to the field may, at least in part, be due to following a ‘one-size-fits-all’ principle to drug development, thereby ignoring the likely heterogeneity of the disorder. In a review of over 1600 antipsychotic treatment trials, Pich et al. found that only 18 used any biological marker as an outcome discriminator. It does appear that we need to change our strategies here
Conclusion
Precision medicine, as it relates to psychiatry, is still in its infancy as of this writing in early 2016, although these are exciting times that may bring individualized treatments to the forefront soon as early detection and prediction research studies applying biological markers progress.
With thanks to:
Lynn E. DeLisia and W. Wolfgang Fleischhackerb
Department of Psychiatry, VA Boston Healthcare System and Harvard Medical School, Brockton, Massachusetts, USA and Department of Psychiatry, Psychotherapy and Psychosomatics, Division of Psychiatry I, Medical University Innsbruck, Innsbruck, Austria
