Presumed late recurrence of Acanthamoeba keratitis exacerbated by exposure to topical corticosteroids

Development of a Machine Learning-Based Cysticidal Assay and Identification of an Amebicidal and Cysticidal Marine Microbial Metabolite against Acanthamoeba

Traditional cysticidal assays for Acanthamoeba species revolve around treating cysts with compounds and manually observing the culture for evidence of excystation. This method is time-consuming, labor-intensive, and low throughput. We adapted and trained a YOLOv3 machine learning, object detection neural network to recognize Acanthamoeba castellanii trophozoites and cysts in microscopy images to develop an automated cysticidal assay. This trained neural network was used to count trophozoites in wells treated with compounds of interest to determine if a compound treatment was cysticidal. We validated this new assay with known cysticidal and noncysticidal compounds. In addition, we undertook a large-scale bioluminescence-based screen of 9,286 structurally unique marine microbial metabolite fractions against the trophozoites of A. castellanii and identified 29 trophocidal hits. These hits were then subjected to this machine learning-based automated cysticidal assay. One marine microbial metabolite fraction was identified as both trophocidal and cysticidal. IMPORTANCE The free-living Acanthamoeba can exist as a trophozoite or cyst and both stages can cause painful blinding keratitis. Infection recurrence occurs in approximately 10% of cases due to the lack of efficient drugs that can kill both trophozoites and cysts. Therefore, the discovery of therapeutics that are effective against both stages is a critical unmet need to avert blindness. Current efforts to identify new anti-Acanthamoeba compounds rely primarily upon assays that target the trophozoite stage of the parasite. We adapted and trained a machine learning, object detection neural network to recognize Acanthamoeba trophozoites and cysts in microscopy images. Our machine learning-based cysticidal assay improved throughput, demonstrated high specificity, and had an exquisite ability to identify noncysticidal compounds. We combined this cysticidal assay with our bioluminescence-based trophocidal assay to screen about 9,000 structurally unique marine microbial metabolites against A. castellanii. Our screen identified a marine metabolite that was both trophocidal and cysticidal.

Discovery of repurposing drug candidates for the treatment of diseases caused by pathogenic free-living amoebae

Diseases caused by pathogenic free-living amoebae include primary amoebic meningoencephalitis (Naegleria fowleri), granulomatous amoebic encephalitis (Acanthamoeba spp.), Acanthamoeba keratitis, and Balamuthia amoebic encephalitis (Balamuthia mandrillaris). Each of these are difficult to treat and have high morbidity and mortality rates due to lack of effective therapeutics. Since repurposing drugs is an ideal strategy for orphan diseases, we conducted a high throughput phenotypic screen of 12,000 compounds from the Calibr ReFRAME library. We discovered a total of 58 potent inhibitors (IC50 <1 μM) against N. fowleri (n = 19), A. castellanii (n = 12), and B. mandrillaris (n = 27) plus an additional 90 micromolar inhibitors. Of these, 113 inhibitors have never been reported to have activity against Naegleria, Acanthamoeba or Balamuthia. Rapid onset of action is important for new anti-amoeba drugs and we identified 19 compounds that inhibit N. fowleri in vitro within 24 hours (halofuginone, NVP-HSP990, fumagillin, bardoxolone, belaronib, and BPH-942, solithromycin, nitracrine, quisinostat, pabinostat, pracinostat, dacinostat, fimepinostat, sanguinarium, radicicol, acriflavine, REP3132, BC-3205 and PF-4287881). These compounds inhibit N. fowleri in vitro faster than any of the drugs currently used for chemotherapy. The results of these studies demonstrate the utility of phenotypic screens for discovery of new drugs for pathogenic free-living amoebae, including Acanthamoeba for the first time. Given that many of the repurposed drugs have known mechanisms of action, these compounds can be used to validate new targets for structure-based drug design.