Innovations In Clinical Neuroscience

ISCTM Supplement 2015

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

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[ 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 ] Innovations in CLINICAL NEUROSCIENCE 15S used for the treatment of type II diabetes for over a decade, with 22+ million patient years of exposure data, it was felt to meet this criterion. In c onclusion, in a fresh approach to AD treatment trials, this study examines a non-amyloid treatment mechanism and targets the role of synaptic energetics via effects on mitochondria in this disease process. This lengthy and innovative clinical trial is overviewed in the context of a design that is powered to address two primary objectives: 1) evaluating the utility of genetic biomarkers to assign risk of developing onset of cognitive decline due to AD over a five-year timeframe and 2) testing the ability of pioglitazone to delay the onset of MCI due to AD in cognitively normal subjects. This program aims to provide new insight into predicting the age related risk for AD as well as evaluating a new therapeutic approach to forestalling the cognitive decline associated with AD, an avenue which seems much more promising than changing the course of illness in the midst of robust symptoms and pathophysiology. Rationale for using pioglitazone. Peroxisome proliferator-activated receptor gamma (PPARy) agonists, including pioglitazone, modulate a number of pathways implicated in the pathogenesis of AD: energy metabolism, insulin sensitivity, lipid metabolism, amyloid beta homeostasis, and inflammation. 14 PPARy agonists also play critical roles in energy metabolism due to their direct effects on mitochondrial function, biogenesis, and ultimately ATP production in neuronal glucose utilization. Pioglitazone has salutary effects in mouse models of AD and in small sample-sized human studies. 15,16 Furthermore, any pharmaceutical agent used in cognitively normal people must be supported by extensive patient exposure data and be well tolerated if it is to be used in an extended trial design. Given the substantial patient experience and exposure with pioglitazone (22+ million patient years), with this agent known to be well tolerated and relatively safe in a vast majority of patients, part of the rationale with being able to understand its e ffectiveness at delaying MCI in AD will be carried-out using this drug. Study design and considerations. The dementia field is moving toward earlier treatment of AD and particularly treatments that delay the onset of the disease. A Phase III clinical trial is currently ongoing by Takeda and Zinfandel Pharmaceuticals to delay the onset of MCI using pioglitazone in a multicenter, double-blind, randomized, placebo-controlled fashion. The trial is poised to enroll approximately 5,800 cognitively normal subjects (male and female), 4,622 subjects deemed to be of high risk for AD (Caucasian), 600 subjects considered at low risk for AD (Caucasian), and 578 non-Caucasian subjects (which includes 60 subjects who are low risk). The current trial design rather more closely resembles that of an epidemiology study recruiting large numbers of healthy elderly subjects while monitoring them for a number of years (5 years), with treatment arms geared to delay the onset of MCI. As instruments that generate outcome measures in AD trials require greater symptomatology to be present than MCI in detecting cognitive changes, the traditional AD trial techniques needed to be modified for MCI, preferably in accordance with a draft United States Food and Drug Administration (FDA) guidance overview detailing clinical research endpoints for prior onset of frank dementia. Patient selection queries. Particularly with respect to patient selection, FDA does support subject enrichment strategies and guidance on endpoint measures which have appropriately been incorporated into the present detailed initiative. However, there are many questions related to study methodology that need to be addressed during the development of a trial design for delaying MCI rather than treating AD. For instance, in the diagnosis of AD there are cognitive endpoints and functional co-primary outcome measures that need to be established, but MCI by definition does not have a f unctional decrement that is overtly measurable, thus a different outcome measure without a functional co- primary must be determined. Furthermore, in such a study as this, how is it possible to accomplish a typical Phase II trial goal when treatment trial duration is five years and the sample size prohibits dose finding studies? Rather, could translational medicine and biomarkers be used to guide the dose selection in subjects? Although using biomarkers to enrich the subject population may be possible, can data generated from this approach be used to support outcome measures? As it appears that regulatory agencies at this time are not comfortable with using biomarkers as a primary outcome measure in cognitive impairment, might they be more willing to accept biomarkers as supportive findings? With respect to the latter, would biomarkers that offer support in outcome measures need to be done in all patients? Lastly, does the cognitive demand in MCI translate well in subjects from different countries and how does one carry out a multi- country trial using different languages and cultures when measuring MCI; can one generalize results from a given study population to patients speaking different languages in other countries that are not part of the study? These are just a number of issues, without definitive answers, that were considered prior to launching this trial's initiative. Rationale for study design. Given recent failures of AD treatment trials in symptomatic individuals with late MCI and AD, the present study is based on a population subtype that is at the beginning of the AD spectrum. The current study attempts to put forth and evaluate a risk algorithm in cognitively normal patients through use of genetic biomarkers in stratification of risk and treatment response. Specifically, the algorithm will yield a determination of risk in

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