A collaborative approach to targeted treatment development for schizophrenia: a qualitative evaluation of the NIMH-MATRICS project.

INTRODUCTION

In 2002, the National Institute of Mental Health (NIMH) initiated a multistakeholder research process designed to stimulate the development and evaluation of medications targeting the cognitive deficits associated with schizophrenia. The first phase, Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS), sought consensus on laboratory measures for cognition, clinical trial outcome measures, and drug registration requirements. MATRICS constitutes a unique drug development model because it targeted a specific endophenotype of schizophrenia and because it engaged academic, industry, and government stakeholders in a consensus-oriented process. This study offers a preliminary qualitative evaluation of the NIMH-MATRICS project.

METHOD

Interview data are used to describe how MATRICS participants regard 3 aspects of the development of cognitive medications: the definition of the treatment target, stakeholders' role in the early development process, and anticipated dissemination complexities.

RESULTS

MATRICS participants describe the treatment target in highly varied ways and envision a wide range of public health benefits. MATRICS is perceived as inclusive, despite minimal representation from some end users. According to informants, clinical detection, documentation, and monitoring of cognition and functioning may prove problematic. More thoroughly than non-industry-employed informants, industry-employed MATRICS participants articulate strategies by which treatments can be integrated into clinical practice.

DISCUSSION

The MATRICS process did not produce a clinical concept of cognitive impairment in schizophrenia, and significant challenges remain to be addressed regarding the rational clinical use of novel pharmaceuticals for cognition. Broader inclusion of end users in translational science projects may streamline implementation and facilitate improvements in real-world outcomes.