Although antipsychotic drugs (APDs) are effective in improving symptoms for many patients with schizophrenia, they can also induce serious side effects and a large proportion of patients remain symptomatic despite treatment. Predictors of drug response are lacking and there is little empirical data available to guide clinicians in prognosis and clinical decision-making. Pharmacogenetics holds the promise of providing a useful tool to provide personalized treatment with optimal efficacy while minimizing side effects. However, pharmacogenetic studies to date have not yet provided clinically usable data, leaving this promise unfulfilled. In this K23 application, I proposed a structured training plan with the goals of gaining advanced training in clinical trial study design, pharmacogenomic analysis, and mechanisms of APD-induced weight gain, under the guidance of my experienced mentorship team. The objective is to become an independent investigator who can test practical pharmacogenetic hypotheses in randomized clinical trials. To accompany the training plan, I will conduct two separate but related pharmacogenetic research projects. Project 1 is a randomized, double-blind clinical trial, in which the clinical utility of pharmacogenetic testing in guiding AP treatment will be examined. Specifically, patients with schizophrenia will be recruited and genotyped for CYP2D6 prospectively. Patients who are poor or intermediate CYP2D6 metabolizers will be randomized to treatment with risperidone in either a treatment-as-usual condition (TAU) or a low-dose-titration condition (LDT). The primary hypothesis is that these patients, who may be at heightened vulnerability to side effects due to poor drug clearance, will have fewer side effects when assigned to the LDT condition compared to those in TAU group. This study will provide one of the first prospective data regarding the clinical value of pharmacogenetic testing in schizophrenia treatment. In Project 2, I propose to analyze a large pharmacogenetic database from several first-episode or drug-na?ve cohorts to build a genetic model predicting APD response, particularly APD-induced weight gain, with high sensitivity and specificity. This large dataset consists of more than 1200 patients with minimal prior drug exposure and ensured medication adherence, overcoming many limitations in prior pharmacogenetic studies. Taken together, this application aims to utilize these data to better inform treatment decisions for schizophrenia patients, and also potentially provide molecular targets for new drug discovery. At the end of the combined training and research plan outlined in this application, I will be well positioned to design a randomized trial to test pharmacogenetic strategies aimed at minimizing APD-induced weight gain, for submission as an independent R01 application.

Public Health Relevance

Many schizophrenia patients discontinue antipsychotic drug treatment due to lack of efficacy or intolerable side effects, and predictors of drug response are lacking. In this study, we seek to examine the clinical utility of pharmacogenetic testing in guiding treatment and attempt to identify genetic markers to predict antipsychotic drug response. These results may help to provide personalized treatment with these patients.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Mentored Patient-Oriented Research Career Development Award (K23)
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Pathophysiological Basis of Mental Disorders and Addictions Study Section (PMDA)
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Chavez, Mark
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Feinstein Institute for Medical Research
United States
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Zhang, Jian-Ping; Aitchison, Katherine J; Malhotra, Anil K (2014) The 12th Annual Pharmacogenetics in Psychiatry meeting report. Psychiatr Genet 24:218-20
Zhang, Jian-Ping; Lencz, Todd; Geisler, Stephen et al. (2013) Genetic variation in BDNF is associated with antipsychotic treatment resistance in patients with schizophrenia. Schizophr Res 146:285-8
Zhang, Jian-Ping; Malhotra, Anil K (2013) Pharmacogenetics of antipsychotics: recent progress and methodological issues. Expert Opin Drug Metab Toxicol 9:183-91