Innovations In Clinical Neuroscience

ISCTM Supplement 2015

A peer-reviewed, evidence-based journal for clinicians in the field of neuroscience

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Innovations in CLINICAL NEUROSCIENCE [ V O L U M E 1 2 , N U M B E R 3 – 4 , S U P P L E M E N T A , M A R C H – A P R I L 2 0 1 5 ] 36S symptoms, as well as lifestyle choices and nosological entities among patients with schizophrenia spectrum disorders. When considering the p otential value of actigraphy in clinical trials of treatments for schizophrenia, a major benefit is that large, objective datasets allow for centralized analysis. The application of actigraphy is simple and well tolerated. The procedure also tolerates missing values reasonable well. The added value is in continuous recording of activity and circadian rhythms. This in combination with experience based sampling enables a focus on disease trajectories. Among the problems are that more details are needed, including prospective assessment of medication and occupational status. Furthermore, there is a need for additional patient self-report (which is not always reliable) and activity/sleep protocol. The putative value of actigraphy in clinical trials lies in the ability to 1) identify subgroups (e.g., low/high physical activity, nosological subgroups); 2) monitor dimensions, such as negative syndrome and abnormal motor behavior; and 3) use as an outcome marker of physical activity, avolition, and quality of life. In addition, actigraphy could serve as a simple marker of target engagement, depending in the property of the compound of interest. THE USE OF BIOMARKERS IN CLINICAL TRIALS OF SCHIZOPHRENIA (D. Goff) Biomarkers traditionally reflect specific drug targets based on illness models or drug mechanisms, or they provide information about drug metabolism. While a drug's primary mechanism of action or its metabolism may be captured by relatively simple assays, illness models increasingly posit complex network dysregulation or impairment of plasticity. To represent these complex models may require much more sophisticated approaches than have been employed in the past. Potential roles for biomarkers in clinical trials includes establishing target engagement in order to guide dosing, measuring CNS activity as a means of monitoring therapeutic response, and subtyping patients according to likelihood of response in order to enhance s ensitivity for detecting drug effects and ultimately to guide a personalized medicine approach. Despite over a decade of research, very few commercial biomarkers are available for clinicians treating schizophrenia. Most characterize hepatic CYP 450 isoenzymes to predict drug metabolism. Others have been developed to predict therapeutic response to clozapine or risk for agranulocytosis or metabolic side effects, but these biomarkers remain of unclear clinical utility. A major problem faced by investigators working with "big data" approaches to biomarker development, such genome-wide association studies (GWAS) and the "omics" (e.g., metabolomics, proteomics, methylomics) is the risk of false positive findings and a corresponding failure to replicate positive results. One example of an approach to overcome these concerns is the finding by Malholtra et al in which single nucleotide polymorphisms (SNPs) associated with antipsychotic weight gain in a GWAS discovery sample were then subjected to three replication cohorts before identifying a single SNP as a potential biomarker. 68 In another example of genetic biomarkers, Roffman et al followed up on a previous report of a relationship between low serum folate concentrations and negative symptoms 69 and demonstrated an association between negative symptoms and a gene that regulates activity of methylene tetrahydrofolate reductase (MTHFR), an enzyme required for folate to participate in methylation reactions. 70 In a subsequent placebo-controlled clinical trial, folate supplementation was without benefit in the full sample, but associated with significant improvement of negative symptoms when subjects were categorized according to the MTHFR genetic marker. 71 Subsequent investigation of genes involved in the folate pathway, including absorption, metabolism (activation), and methylation function identified five genes that, when combined, better predict negative s ymptoms. 7 2 T he five SNPS identified by Roffman and colleagues were subsequently demonstrated to predict response to folate supplementation in a larger replication trial. 73 While the focus increasingly is on GWAS and other "big data" approaches, this finding serves as a model of a hypothesis-driven approach to biomarker discovery. Using a different strategy, Zhang et al 74 examined 74 candidate genes in 279 subjects from the Clinical Antipsychotic Trials of Intervention Effectiveness study (CATIE) cohort using clozapine therapy as a proxy for treatment resistance. They then found that three BDNF SNPS were associated with this marker for treatment resistance, providing compelling evidence for BDNF genotype as a potential predictive biomarker. Neuroimaging provides an increasingly important measure of brain structure or activity that may provide a more sensitive marker for target engagement and treatment impact on brain function than behavioral response. Most current work is examining functional MRI or PET as biomarkers; 75,76 however, imaging markers of brain structure may also provide information about treatment effects. In one example, Eack et al 77 monitored regional gray matter volume during a two-year study of cognitive enhancement therapy in participants with early stage schizophrenia and found significant effects suggestive of neuroprotection or enhancement of neuroplasticity. This effect was significant in the amygdala, fusiform gyrus, hippocampus and parahippocampal gyrus. With the exception of CSF and imaging markers, most biomarkers under development are assays of components of peripheral blood. The value of peripheral measures remains debated, since it is unclear the degree to which many candidate markers reflect CNS status. Depending on

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