title: Discovery of Novel Liver-Stage Antimalarials through Quantum Similarity.
abstract: Without quantum theory any understanding of molecular interactions is incomplete. In principal, chemistry, and even biology, can be fully derived from non-relativistic quantum mechanics. In practice, conventional quantum chemical calculations are computationally too intensive and time consuming to be useful for drug discovery on more than a limited basis. A previously described, original, quantum-based computational process for drug discovery and design bridges this gap between theory and practice, and allows the application of quantum methods to large-scale in silico identification of active compounds. Here, we show the results of this quantum-similarity approach applied to the discovery of novel liver-stage antimalarials. Testing of only five of the model-predicted compounds in vitro and in vivo hepatic stage drug inhibition assays with P. berghei identified four novel chemical structures representing three separate quantum classes of liver-stage antimalarials. All four compounds inhibited liver-stage Plasmodium as a single oral dose in the quantitative PCR mouse liver-stage sporozoites-challenge model. One of the newly identified compounds, cethromycin [ABT-773], a macrolide-quinoline hybrid, is a drug with an extensive (over 5,000 people) safety profile warranting its exploitation as a new weapon for the current effort of malaria eradication. The results of our molecular modeling exceed current state-of-the-art computational methods. Drug discovery through quantum similarity is data-driven, agnostic to any particular target or disease process that can evaluate multiple phenotypic, target-specific, or co-crystal structural data. This allows the incorporation of additional pharmacological requirements, as well as rapid exploration of novel chemical spaces for therapeutic applications.
authors.affiliation: W. Harry Feinstone Department of Molecular Micorbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
authors.forename: David J
authors.initials: DJ
authors.lastname: Sullivan
authors.affiliation: W. Harry Feinstone Department of Molecular Micorbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
authors.forename: Yi
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, United States of America.
authors.forename: Bryan T
authors.initials: BT
authors.lastname: Mott
authors.affiliation: Gradient Biomodeling LLC, Park City, Utah, United States of America.
authors.forename: Nikola
authors.initials: N
authors.lastname: Kaludov
authors.affiliation: Gradient Biomodeling LLC, Park City, Utah, United States of America.
authors.forename: Martin N
authors.initials: MN
authors.lastname: Martinov
mesh_terms: D000284:Administration, Oral; D000818:Animals; D000962:Antimalarials / Q000008:administration & dosage* / Q000138:chemical synthesis* / Q000737:chemistry; D019295:Computational Biology / Q000379:methods*; D058492:Drug Repositioning / Q000379:methods*; D048628:Ketolides / Q000008:administration & dosage / Q000138:chemical synthesis / Q000737:chemistry; D008288:Malaria / Q000188:drug therapy* / Q000469:parasitology; D051379:Mice; D010962:Plasmodium berghei / Q000187:drug effects; D011789:Quantum Theory; D013329:Structure-Activity Relationship
chemical_list: D000962:Antimalarials; D048628:Ketolides; C405499:cethromycin