# Reasoning with chemically induced dynamic phenotypes in whole-organism assays

> **NIH NIH R21** · SAN FRANCISCO STATE UNIVERSITY · 2020 · $233,375

## Abstract

ABSTRACT
Schistosomiasis, caused by a parasitic flatworm, is a disease of the poor. It infects over 200 million people worldwide and
places another 800 million at risk. Current treatment and control of this disease relies on just one drug, praziquantel (PZQ)
- a precarious situation should drug resistance emerge. The therapeutic profile of PZQ is also not ideal. The World Health
Organization has therefore declared schistosomiasis a disease for which new therapies are urgently needed.
 Drug discovery for schistosomiasis in particular (and helmintic diseases in general) is traditionally based on
phenotypic screening, whereby the parasite(s) are exposed to compounds and their systemic responses, such as changes in
shape, appearance, and motion, are analyzed to identify hits. Analyzing the temporally varying, high-dimensional, and
information-rich output from phenotypic screening of a complex macroparasite is, however, non-trivial. This fact is
underlined by the complete absence of any database(s) or analysis tools for disease-causing helminths that would allow
analysis and reasoning with dynamic phenotypic data. To address this need, we formulate the following two aims:
 Under Aim 1, we propose to develop the first quantitative and publicly available database of the schistosome’s time-
varying response to chemical probes. The database will support content-based querying of dynamic phenotypes using
time-series matching. The information in this database will underpin structure-activity relationship (SAR) studies with the
drug targets and associated small molecule chemistries that we have validated. This phenotypic record will also aid
understanding of the molecular mechanism of action (MMoA) of various chemistries and serve as a reference for
phenotypes elicited using other compounds by researchers worldwide.
 Under Aim 2, we will develop algorithmic methods for analyzing the time-varying phenotypic responses of the
schistosome parasite. These methods will allow scientists to match, compare, cluster, and quantitatively reason-with
dynamic (i.e. temporally varying) phenotypes. In particular, scientists will be able to: (1) objectively compare phenotypic
responses of parasites to identify similar effects, even when they occur due to structurally distinct compounds, (2) relate
phenotypic effects observed in different studies conducted under varying conditions, (3) stratify the phenotypic variability
within and across parasite populations, and (4) prioritize compounds based on quantitative reasoning with dynamic and
complex phenotypic responses.
 Results from both aims will be made freely available to biologists worldwide through a public database and software
developed by us. Our proposal constitutes an innovative point of progress in (a) developing algorithmic methods and
datasets for reasoning-with and understanding the phenome of the etiological agent of schistosomiasis and leveraging it
for drug discovery and (b) establishing a rigorous analysis fram...

## Key facts

- **NIH application ID:** 9932912
- **Project number:** 5R21AI146719-02
- **Recipient organization:** SAN FRANCISCO STATE UNIVERSITY
- **Principal Investigator:** Conor Caffrey
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $233,375
- **Award type:** 5
- **Project period:** 2019-06-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9932912

## Citation

> US National Institutes of Health, RePORTER application 9932912, Reasoning with chemically induced dynamic phenotypes in whole-organism assays (5R21AI146719-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/9932912. Licensed CC0.

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