Chemoprotective antimalarials identified through quantitative high-throughput screening of Plasmodium blood and liver stage parasites.

The spread of Plasmodium falciparum parasites resistant to most first-line antimalarials creates an imperative to enrich the drug discovery pipeline, preferably with curative compounds that can also act prophylactically. We report a phenotypic quantitative high-throughput screen (qHTS), based on concentration-response curves, which was designed to identify compounds active against Plasmodium liver and asexual blood stage parasites. Our qHTS screened over 450,000 compounds, tested across a range of 5 to 11 concentrations, for activity against Plasmodium falciparum asexual blood stages. Active compounds were then filtered for unique structures and drug-like properties and subsequently screened in a P. berghei liver stage assay to identify novel dual-active antiplasmodial chemotypes. Hits from thiadiazine and pyrimidine azepine chemotypes were subsequently prioritized for resistance selection studies, yielding distinct mutations in P. falciparum cytochrome b, a validated antimalarial drug target. The thiadiazine chemotype was subjected to an initial medicinal chemistry campaign, yielding a metabolically stable analog with sub-micromolar potency. Our qHTS methodology and resulting dataset provides a large-scale resource to investigate Plasmodium liver and asexual blood stage parasite biology and inform further research to develop novel chemotypes as causal prophylactic antimalarials.

Exploring polypharmacology using a ROCS-based target fishing approach.

Polypharmacology has emerged as a new theme in drug discovery. In this paper, we studied polypharmacology using a ligand-based target fishing (LBTF) protocol. To implement the protocol, we first generated a chemogenomic database that links individual protein targets with a specified set of drugs or target representatives. Target profiles were then generated for a given query molecule by computing maximal shape/chemistry overlap between the query molecule and the drug sets assigned to each protein target. The overlap was computed using the program ROCS (Rapid Overlay of Chemical Structures). We validated this approach using the Directory of Useful Decoys (DUD). DUD contains 2950 active compounds, each with 36 property-matched decoys, against 40 protein targets. We chose a set of known drugs to represent each DUD target, and we carried out ligand-based virtual screens using data sets of DUD actives seeded into DUD decoys for each target. We computed Receiver Operator Characteristic (ROC) curves and associated area under the curve (AUC) values. For the majority of targets studied, the AUC values were significantly better than for the case of a random selection of compounds. In a second test, the method successfully identified off-targets for drugs such as rimantadine, propranolol, and domperidone that were consistent with those identified by recent experiments. The results from our ROCS-based target fishing approach are promising and have potential application in drug repurposing for single and multiple targets, identifying targets for orphan compounds, and adverse effect prediction.

Predictive models for cytochrome p450 isozymes based on quantitative high throughput screening data.

The human cytochrome P450 (CYP450) isozymes are the most important enzymes in the body to metabolize many endogenous and exogenous substances including environmental toxins and therapeutic drugs. Any unnecessary interactions between a small molecule and CYP450 isozymes may raise a potential to disarm the integrity of the protection. Accurately predicting the potential interactions between a small molecule and CYP450 isozymes is highly desirable for assessing the metabolic stability and toxicity of the molecule. The National Institutes of Health Chemical Genomics Center (NCGC) has screened a collection of over 17,000 compounds against the five major isozymes of CYP450 (1A2, 2C9, 2C19, 2D6, and 3A4) in a quantitative high throughput screening (qHTS) format. In this study, we developed support vector classification (SVC) models for these five isozymes using a set of customized generic atom types. The CYP450 data sets were randomly split into equal-sized training and test sets. The optimized SVC models exhibited high predictive power against the test sets for all five CYP450 isozymes with accuracies of 0.93, 0.89, 0.89, 0.85, and 0.87 for 1A2, 2C9, 2C19, 2D6, and 3A4, respectively, as measured by the area under the receiver operating characteristic (ROC) curves. The important atom types and features extracted from the five models are consistent with the structural preferences for different CYP450 substrates reported in the literature. We also identified novel features with significant discerning power to separate CYP450 actives from inactives. These models can be useful in prioritizing compounds in a drug discovery pipeline or recognizing the toxic potential of environmental chemicals.

Utilizing peptide structures as keys for unlocking challenging targets.

Three-dimensional structures of protein targets have proven to be extremely valuable for modern drug design and discovery. For cases where the structure of the protein is unattainable, such as G-protein coupled receptors (GPCRs), structural information on active ligands is still useful and helpful for deciphering the geometrical and chemical features of the active site. Peptides, constructed from easy-to-form amide backbones and featuring variable side-chains, have an inherent advantage in generating rapid quantitative structure-activity relationships (QSAR). Given the fact that peptides are natural ligands for many protein targets, structural investigation of a series of related peptides, typically carried out via nuclear magnetic resonance (NMR), can result in an accurate pharmacophore model. Such a model can be used for virtual screening, and to assist design of second-generation peptidomimetics with improved properties and design of non-peptidic leads. In this article, we will review examples in which a structural approach utilizing peptide ligands was employed to obtain a better understanding of the target active site. We will focus on cases where such information supplied guidance toward the discovery of small molecule ligands.