Biocomputing and Media Research Lab

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NIH-R01:
An Automated High-Throughput Phenotypic Screen for Schistosomiasis Drug Discovery

    National Institute of Allergy and Infectious Diseases
  • Award Number: 1R01AI089896-01
  • Principal Investigator: R. Singh (SFSU), C. Caffrey (UCSF), M. Arkin (UCSF)
  • PD: C. Caffrey

Abstract

Schistosomiasis is a neglected tropical disease infecting over 200 million people and putting approximately 800 million people at risk. The disease, if left untreated, leads to a variety of c linical manifestations that undermine social and economic development in areas of high transmission. Treatment relies on a single drug, praziquantel (PZQ). The success of PZQ has stifled drug development for novel entities, yet relying on a single drug to treat such large populations adds to the risk of resistance to PZQ and eventual therapy failure. The World Health Organization has therefore declared schistosomiasis a disease for which new therapies are urgently needed. Unfortunately, the traditional phenotypic screens, using adult-stage S. mansoni, are low-throughput and incompatible with modern high-throughput screen (HTS) systems. In contrast, and as part of an ongoing pre-clinical drug development program, the present proposal sets out to turn a novel, moderate-throughput phenotypic screen (MTS), designed in-house, into a fully automated, quantitative HTS to accelerate the discovery of novel anti-schistosomal chemotherapies. The proposal is built on a foundation of collaborations between the PIs at San Francisco State University and UC San Francisco. Each partner contributes vital expertise to removing the bottlenecks to earlier screens and to developing critical computational tools.


Research Foci

Computational drug discovery against: Schistosomiasis, Filariasis


Students


Publications

  • L. Rojo-Arreola, T. Long, D. Asarnow, B.M. Suzuki, R. Singh and C.R. Caffrey, "Chemical and genetic validation of the statin drug target for the potential treatment of the helminth disease, schistosomiasis," PLoS ONE, 9(1): e87594, 2014. (Corresponding author C.R. Caffrey). [PDF]
  • D. Asarnow and R. Singh, "Segmenting the Etiological Agent of Schistosomiasis for High-Content Screening," IEEE Transactions on Medical Imaging, vol. 32, no. 6, pp. 1007-10018, 2013. (Corresponding author R. Singh). [PDF]
  • A. Shimoide, I. Kimball, A. Gutierrez, H. Lim, I. Yoon, J. T. Birmingham, R. Singh* and M. Fuse*, "Quantification and Analysis of Ecdysis in the Hornworm Manduca Sexta Using Machine Vision-based Tracking", Invertebrate Neuroscience, 2012, (*R. Singh, and M. Fuse Joint Corresponding Authors). [PDF]
  • H. Lee*, A. Moody-Davis, U. Saha, B. Suzuki, D. Asarnow, S. Chen, M. Arkin, C. Caffrey, and R. Singh*, "Quantification and Clustering of Phenotypic Screening Data Using Time Series Analysis for Chemotherapy of Schistosomiasis", BMC Genomics, 12 (Suppl 1):S4, 2012 (*H. Lee and R. Singh equal contributors. Corresponding author R. Singh). [PDF]
  • C. Marcellino, J. Gut, K. C. Lim, R. Singh, J. McKerrow, J. Sakanari, "WormAssay: A Novel Computer Application for Whole-Plate Screening of Macroscopic Parasites", PLoS Neglected Tropical Diseases, Vol. 6(1):e1494, 2012 (Corresponding author C. Marcellino). [PDF]
  • R. Singh, "Quantitative High-Content Screening-Based Drug Discovery against Helminthic Diseases", in Parasitic Helminths: Targets, Screens, Drugs, and Vaccines, Ed. C. Caffrey, Wiley-Blackwell, pp. 159-179 2012. [PDF]
  • H. Lee and R. Singh, "Symbolic Representation and Clustering of Bio-Medical Time-Series Data Using Non-Parametric Segmentation and Cluster Ensemble," IEEE International Symposium on Computer Based Medical Systems (CBMS), pp. 1-6, 2012. [PDF]
  • D. Asarnow and R. Singh, "Segmentation of Parasites for High-Content Screening using Phase Congruency and Grayscale Morphology", International Symposium on Visual Computing (ISVC), Lecture Notes in Computer Science, Vol. 7431, pp. 51-60, Springer, 2012. [PDF]
  • U. Saha and R. Singh, "Vision-Based Tracking of Complex Macroparasites for High-Content Phenotypic Drug Screening", International Symposium on Visual Computing (ISVC), Lecture Notes in Computer Science, Vol. 7432, pp. 104-114, Springer, 2012. [PDF]
  • A. Moody-Davis, L. Mennillo and R. Singh, "Region Based Segmentation of Parasites for High-Throughput Screening," G. Bebis et al. (Eds.): International Symposium on Visiual Computing, Part I, LNCS 6938, pp. 44-54, 2011. [PDF]
  • R. Singh, V. Popescu, L. Mennillo, B. Suzuki, and C. Caffrey, "Association Rule Discovery in Time-Series Phenomic Data", Bioimage Informatics, Carnegie-Mellon University, 2010 (peer-reviewed poster).
  • R. Singh, M. Pittas, I. Heskia, F. Xu, J. H. McKerrow, and C. Caffrey, "Automated Image-Based Phenotypic Screening for High-Throughput Drug Discovery", IEEE Symposium on Computer-Based Medical Systems (CBMS), pp. 1-8, 2009. [PDF]
  • R. Singh, M. Pittas, I. Heskia, F. Xu, J. H. McKerrow, and C. Caffrey, "Automated Image-Based Phenotypic Screening of Multi-Cellular Pathogens for High-Throughput Drug Discovery", Bioimage Informatics, Howard Hughes Medical Institute, Janelia Farm, 2009 (peer-reviewed poster).
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