pf-04455242 and Disease-Models--Animal

When Zika virus emerged as a public health emergency there were no drugs or vaccines approved for its prevention or treatment. We used a high-throughput screen for Zika virus protease inhibitors to identify several inhibitors of Zika virus infection. We expressed the NS2B-NS3 Zika virus protease and conducted a biochemical screen for small-molecule inhibitors. A quantitative structure-activity relationship model was employed to virtually screen ∼138,000 compounds, which increased the identification of active compounds, while decreasing screening time and resources. Candidate inhibitors were validated in several viral infection assays. Small molecules with favorable clinical profiles, especially the five-lipoxygenase-activating protein inhibitor, MK-591, inhibited the Zika virus protease and infection in neural stem cells. Members of the tetracycline family of antibiotics were more potent inhibitors of Zika virus infection than the protease, suggesting they may have multiple mechanisms of action. The most potent tetracycline, methacycline, reduced the amount of Zika virus present in the brain and the severity of Zika virus-induced motor deficits in an immunocompetent mouse model. As Food and Drug Administration-approved drugs, the tetracyclines could be quickly translated to the clinic. The compounds identified through our screening paradigm have the potential to be used as prophylactics for patients traveling to endemic regions or for the treatment of the neurological complications of Zika virus infection.

Topics: Animals; Antiviral Agents; Artificial Intelligence; Chlorocebus aethiops; Disease Models, Animal; Drug Evaluation, Preclinical; High-Throughput Screening Assays; Immunocompetence; Inhibitory Concentration 50; Methacycline; Mice, Inbred C57BL; Protease Inhibitors; Quantitative Structure-Activity Relationship; Small Molecule Libraries; Vero Cells; Zika Virus; Zika Virus Infection

By use of parallel chemistry coupled with physicochemical property design, a series of selective κ opioid antagonists have been discovered. The parallel chemistry strategy utilized key monomer building blocks to rapidly expand the desired SAR space. The potency and selectivity of the in vitro κ antagonism were confirmed in the tail-flick analgesia model. This model was used to build an exposure-response relationship between the κ K(i) and the free brain drug levels. This strategy identified 2-methyl-N-((2'-(pyrrolidin-1-ylsulfonyl)biphenyl-4-yl)methyl)propan-1-amine, PF-4455242, which entered phase 1 clinical testing and has demonstrated target engagement in healthy volunteers.

Topics: 3,4-Dichloro-N-methyl-N-(2-(1-pyrrolidinyl)-cyclohexyl)-benzeneacetamide, (trans)-Isomer; Analgesics; Animals; Area Under Curve; Biphenyl Compounds; Brain; Disease Models, Animal; Dogs; Drug Design; Drug Discovery; Haplorhini; Humans; Metabolic Clearance Rate; Mice; Microsomes, Liver; Models, Chemical; Molecular Structure; Morphine; Narcotic Antagonists; Pain; Rats; Rats, Sprague-Dawley; Receptors, Opioid, kappa; Receptors, Opioid, mu; Structure-Activity Relationship; Sulfonamides