  Infections from parasitic nematodes (or roundworms) contribute to a
significant disease burden and productivity losses for humans and livestock.
The limited number of anthelmintics (or antinematode drugs) available today to
treat these infections are rapidly losing their efficacy as multidrug
resistance in parasites becomes a global health challenge. We propose an
engineering approach to discover an anthelmintic drug combination that is more
potent at killing wild-type Caenorhabditis elegans worms than four individual
drugs. In the experiment, freely swimming single worms are enclosed in
microfluidic drug environments to assess the centroid velocity and track
curvature of worm movements. After analyzing the behavioral data in every
iteration, the feedback system control (FSC) scheme is used to predict new drug
combinations to test. Through a differential evolutionary search, the winning
drug combination is reached that produces minimal centroid velocity and high
track curvature, while requiring each drug in less than their EC50
concentrations. The FSC approach is model-less and does not need any
information on the drug pharmacology, signaling pathways, or animal biology.
Toward combating multidrug resistance, the method presented here is applicable
to the discovery of new potent combinations of available anthelmintics on C.
elegans, parasitic nematodes, and other small model organisms.

Quantitative Methods;Systems and Control;Image and Video Processing;Systems and Control