An Automated High Throughput Phenotypic Screen for Schistosomiasis Drug Discovery

Investigator: Conor R. Caffrey, PhD
Sponsor: NIH National Institute of Allergy and Infectious Disease

Location(s): United States


Schistosomiasis is a tropical parasitic disease infecting over 200 million people. Treatment relies on a single drug, praziquantel (PZQ). In the absence of back-up drugs with PZQ's therapeutic spectrum, the risk of resistance to PZQ and eventual drug failure is a major concern. Traditional phenotypic screens, using adult- stage S. mansoni, are low-throughput and incompatible with modern high-throughput screen (HTS) systems. In keeping with the NIAID's mission, the present proposal aims to turn a newly developed, moderate- throughput phenotypic screen (MTS), into a fully automated, quantitative HTS to accelerate drug discovery for this infectious disease. The proposal involves three PIs with ongoing collaborations and respective biological, screening-technology and bio-computational skills who are focused on just this goal. As a first research track for this proposal, we will utilize in-house automation and a high-content screening (HCS) system to significantly increase throughput over our published MTS approach. The proposal will involve; expanding robotic plating of the parasite, developing protocols for bright-field and fluorescence-based microscopy and adapting commercial image-analysis software to identify (segment), quantitatively describe and track the motion of parasites with a view to prioritizing compounds for further pre-clinical development. Because commercial HCS analysis tools are not likely optimized for recording the complex and dynamic phenotypes displayed by this multicellular parasite, we will also pursue a second and parallel track of research. Specifically, we will develop de novo an automated image-analysis screening technology to define, identify, and quantify the range of phenotypic responses (morphological and behavioral) possible in this parasite. Ultimately, both the experimental and computation tracks will together produce a standardized HTS protocol and a comprehensive, quantitative suite of image-analysis programs to categorize parasite phenotypes. Such rigor will facilitate the screening of large numbers of potential compounds and their prioritization into the secondary and tertiary screening assays available in-house. We also intend to make the algorithmic framework including its methods and implementations, publicly available.

 The major goal of the project is to turn a moderate-throughput phenotypic screen (MTS) system for schistosomiasis, into a fully automated, quantitative high-throughput screen (HTS). By so doing, the rate of discovery of drugs to treat this global tropical disease will be increased.