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A suite of phenotypic assays to ensure pipeline diversity when prioritizing drug-like Cryptosporidium growth inhibitors.

Rajiv S JumaniMuhammad M HasanErin E StebbinsLiam DonnellyPeter MillerConnor KlopferKovi BessoffJose E TeixeiraMelissa S LoveCase W McNamaraChristopher D Huston
Published in: Nature communications (2019)
Cryptosporidiosis is a leading cause of life-threatening diarrhea in children, and the only currently approved drug is ineffective in malnourished children and immunocompromised people. Large-scale phenotypic screens are ongoing to identify anticryptosporidial compounds, but optimal approaches to prioritize inhibitors and establish a mechanistically diverse drug development pipeline are unknown. Here, we present a panel of medium-throughput mode of action assays that enable testing of compounds in several stages of the Cryptosporidium life cycle. Phenotypic profiles are given for thirty-nine anticryptosporidials. Using a clustering algorithm, the compounds sort by phenotypic profile into distinct groups of inhibitors that are either chemical analogs (i.e. same molecular mechanism of action (MMOA)) or known to have similar MMOA. Furthermore, compounds belonging to multiple phenotypic clusters are efficacious in a chronic mouse model of cryptosporidiosis. This suite of phenotypic assays should ensure a drug development pipeline with diverse MMOA without the need to identify underlying mechanisms.
Keyphrases
  • high throughput
  • mouse model
  • young adults
  • life cycle
  • machine learning
  • emergency department
  • deep learning
  • intensive care unit
  • gene expression
  • drug induced
  • single molecule
  • irritable bowel syndrome
  • data analysis