Piers Gillett

Background: Demonstrating safety and efficacy of new medical treatments requires clinical trials but clinical trials are costly and may not provide value proportionate to their costs. As most health systems have limited resources, it is therefore important to identify the trials with the highest value. Tools exist to assess elements of a clinical trial such as statistical validity but are not wholistic in their valuation of a clinical trial. This study aims to develop a measure of clinical trials value and provide an online tool for clinical trial prioritisation. Methods: A search of the academic and grey literature and stakeholder consultation was undertaken to identify a set of criteria to aid clinical trial valuation using multi-criteria decision analysis. Swing weighting and ranking exercises were used to calculate appropriate weights of each of the included criteria and to estimate the partial-value function for each underlying metric. The set of criteria and their respective weights were applied to the results of six different clinical trials to calculate their value. Results: Seven criteria were identified: ‘unmet need’, ‘size of target population’, ‘eligible participants can access the trial’, ‘patient outcomes’, ‘total trial cost’, ‘academic impact’ and ‘use of trial results’. The survey had 80 complete sets of responses (51% response rate). A trial designed to address an ‘Unmet Need’ was most commonly ranked as the most important with a weight of 24.4%, followed by trials demonstrating improved ‘Patient Outcomes’ with a weight of 21.2%. The value calculated for each trial allowed for their clear delineation and thus a final value ranking for each of the six trials. Conclusion: We confirmed that the use of the decision tool for valuing clinical trials is feasible and that the results are face valid based on the evaluation of six trials. A proof-of-concept applying this tool to a larger set of trials with an external validation is currently underway.

Background Demonstrating safety and efficacy of new medical treatments requires clinical trials. But clinical trials are costly and may not provide value proportionate to their costs. In health systems with limited resources, it is important to identify the trials with the highest value. Tools exist to assess elements of a clinical trial such as statistical validity but are not wholistic in their valuation of a clinical trial. This study aims to develop a measure of clinical trials value and provide an online tool for clinical trial prioritisation. Methods A search of the academic and grey literature and expert consultation was undertaken to identify a set of metrics to aid clinical trial valuation using multi-criteria decision analysis. Swing weighting and ranking exercises were used to calculate appropriate weights of each of the included metrics and to estimate the partial-value function for each underlying metric. The set of metrics and their respective weights were applied to the results of six different clinical trials to calculate their value. Results Seven metrics were identified: ‘unmet need’, ‘size of target population’, ‘eligible participants can access the trial’, ‘patient outcomes’, ‘total trial cost’, ‘academic impact’ and ‘use of trial results’. The survey had 80 complete sets of responses (51% response rate). A trial designed to address an ‘Unmet Need’ was most commonly ranked as the most important with a weight of 24.4%, followed by trials demonstrating improved ‘Patient Outcomes’ with a weight of 21.2%. The value calculated for each trial allowed for their clear delineation and thus a final value ranking for each of the six trials. Conclusion We confirmed that the use of the decision tool for valuing clinical trials is feasible and that the results are face valid based on the evaluation of six trials. A proof-of-concept applying this tool to a larger set of trials with an external validation is currently underway.

60This chapter describes the role of microRNA dysregulation in the development of schizophrenia (SCZ) with particular reference to functional changes arising from this dysregulation. The chapter begins with evidence from both postmortem and peripheral blood studies that microRNAs are indeed dysregulated in SCZ. This dysregulation is believed to arise in part from genetic mutations in the microRNA transcript; therefore, a thorough review of the studies that identify microRNA single-nucleotide polymorphisms (SNPs) in SCZ population is subsequently provided. Particular emphasis is placed on the mechanism by which these microRNA SNPs may give rise to functional microRNA and downstream gene expression changes. In addition to these local SNPs, a second subset of studies indicates that microRNA dysregulation in SCZ may arise in part from global mutations in the microRNA biogenesis pathway. These studies are discussed with particular reference to the links that have been discovered between dysregulation of genes involved in the microRNA biogenesis pathway and copy number variations that are established candidate risk factors for SCZ, such as the 22q11.2 microdeletion. This evidence is then followed by an interrogation of the pharmacological studies, some of which have shown that antipsychotic medication used in the treatment of SCZ has a measurable effect on microRNA expression. Finally, as the information related above is highly heterogeneous, an attempt is made to collate the evidence around the mechanisms through which these diverse microRNA candidate SNPs affect mRNA expression and how this in turn has downstream effects on the expression of genes and proteins that have already been associated with SCZ. To this end, there is a detailed examination of studies that have examined the functional implications of microRNA dysregulation on SCZ-related targets. Evidence is presented from groups that have employed pathway analysis to determine the downstream effects of microRNA dysregulation, which have consistently implicated pathways involved in nervous system development and functioning. In conclusion, this chapter provides a thorough review of the body of evidence surrounding microRNA dysregulation in SCZ with the aim of determining the functional significance of this dysregulation. This investigation may indicate potential convergence pathways for a genetically heterogeneous disorder while simultaneously shedding light on the method by which differential microRNA expression produces dysregulation in these pathways.

Post-mortem brain investigations of schizophrenia have generated swathes of data in the last few decades implicating candidate genes and proteins, the relation of these findings to peripheral biomarker indicators and symptomatology remain to be elucidated. While biomarkers for disease do not have to be involved with underlying pathophysiology and may be largely indicative of diagnosis or prognosis, the ideal may be a biomarker that is involved in underlying disease processes and which is therefore more likely to change with progression of the illness as well as potentially being more responsive to treatment. One of the main difficulties in conducting biomarker investigations for major psychiatric disorders is the relative inconsistency in clinical diagnoses between disorders such as bipolar and schizophrenia. This has led some researchers to investigate biomarkers associated with core symptoms of these disorders, such as psychosis. The aim of this review is to evaluate the contribution of post-mortem brain investigations to elucidating the pathophysiology pathways involved in schizophrenia and psychosis, with an emphasis on major neurotransmitter systems that have been implicated. This data will then be compared to functional neuroimaging findings as well as findings from blood based gene expression investigations in schizophrenia in order to highlight the relative overlap in pathological processes between these different modalities used to elucidate pathogenesis of schizophrenia. In addition we will cover some recent and exciting findings demonstrating microRNA dysregulation in both the blood and the brain in patients with schizophrenia. These changes are pertinent to the topic due to their known role in post-transcriptional modification of gene expression with the potential to contribute or underlie gene expression changes observed in schizophrenia. Finally, we will discuss how post-mortem studies may aid future biomarker investigations.