Huge investments in pharmaceutical research and development over the past two decades have not produced many new drugs to help treat serious neuropsychiatric diseases. The increasing failure rate for new drugs at all phases of clinical trials is at least partially to blame for this productivity crisis. Despite extensive precinical testing the drugs either fail to show an improvement over existing medications or they cause heretofore unpredictable side effects that mitigate their usefulness. The status quo for preclinica testing-in vitro assays of specific drug targets, in vivo assays of low-resolution global responses or high-resolution local responses to drug application, and behavioral assays-suffer from being too labor intensive, semi-quantitative, and limited in the information they provide about brain-wide drug effects. The preclinical data that are generated from these types of tests fail to adequately predict the effects of the drug in a clinical trial setting. Our overall goal is to deveop a tool for drug developers that can be used at the preclinical stage to help reduce the risk of clinical trial failures for new drugs. Certerra's drug-screening Pharmacomap technology is based on visualizing drug-evoked neural activation throughout the entire mouse brain at celular resolution, using an automated high throughout microscopy called serial two-photon (STP) tomography. Employing this technology to track the induction of the activity-regulated immediate early gene c-fos, we can generate brainwide maps of drug-evoked neural activation, which we call pharmacomaps. In this project we aim to develop a database based on the pharmacomaps data of the most commonly prescribed drugs for the treatment of neuropsychiatric illness. We will then correlate patterns of brain-wide neural activation in the mouse with the drugs' clinical effects in humans. This work will set the stage for a revolution in the way preclinical assessment of new pharmaceutical agents is done. Implementation of the pharmacomap technology on a new drug will generate whole brain data that can be interpreted within the statistically powerful context of preexisting parallel datasets. Ultimately, we envision the use of the Pharmacomap technology as a routine screen that will help mitigate the loss of investment that comes with failures during late stages of clinical trials and, thus, accelerate the development of new therapeutic agents for neuropsychiatric illnesses.
There is an acknowledged bottleneck within the drug development pipeline at the clinical trial level that impedes the process of making new and promising therapeutic drugs available to patients. Using the Pharmacomap approach detailed in this application drug developers will be able to increase the predictability of clinical trials fornew drugs based on a database of high-resolution cellular activation information for other similar compounds, thereby accelerating the process of getting new and better treatments for neuropsychiatric illness to the patient.
