David Evans

Diphenpyramide, a COX 1, 2 inhibitor, is also a RIO2 kinase small molecule ligand.

Several analogs of diphenpyramide are also RIO2 kinase small molecule ligand.

Diphenpyramide and one of its analogue show good selectivity against other kinases.

Integrating gene expression profiles with certain proteins can improve our understanding of the fundamental mechanisms in protein–ligand binding. This paper spotlights the integration of gene expression data and target prediction scores, providing insight into mechanism of action (MoA). Compounds are clustered based upon the similarity of their predicted protein targets and each cluster is linked to gene sets using Linear Models for Microarray Data. MLP analysis is used to generate gene sets… Show more

Integrating gene expression profiles with certain proteins can improve our understanding of the fundamental mechanisms in protein–ligand binding. This paper spotlights the integration of gene expression data and target prediction scores, providing insight into mechanism of action (MoA). Compounds are clustered based upon the similarity of their predicted protein targets and each cluster is linked to gene sets using Linear Models for Microarray Data. MLP analysis is used to generate gene sets based upon their biological processes and a qualitative search is performed on the homogeneous target-based compound clusters to identify pathways. Genes and proteins were linked through pathways for 6 of the 8 MCF7 and 6 of the 11 PC3 clusters. Three compound clusters are studied; (i) the target-driven cluster involving HSP90 inhibitors, geldanamycin and tanespimycin induces differential expression for HSP90-related genes and overlap with pathway response to unfolded protein. Gene expression results are in agreement with target prediction and pathway annotations add information to enable understanding of MoA. (ii) The antipsychotic cluster shows differential expression for genes LDLR and INSIG-1 and is predicted to target CYP2D6. Pathway steroid metabolic process links the protein and respective genes, hypothesizing the MoA for antipsychotics. A sub-cluster (verepamil and dexverepamil), although sharing similar protein targets with the antipsychotic drug cluster, has a lower intensity of expression profile on related genes, indicating that this method distinguishes close sub-clusters and suggests differences in their MoA. Lastly, (iii) the thiazolidinediones drug cluster predicted peroxisome proliferator activated receptor (PPAR) PPAR-alpha, PPAR-gamma, acyl CoA desaturase and significant differential expression of genes ANGPTL4, FABP4 and PRKCD [...] Show less

Given the increasing utilization of phenotypic screens in drug discovery also the subsequent mechanism-of-action analysis gains increased attention. Such analyses are frequently using in silico methods, which have become significantly more popular in recent years. However, identifying phenotype-specific mechanisms of action depends heavily on suitable phenotype identification in the first place, many of which rely on human input and are therefore inconsistent. In this work, we aimed at… Show more

Given the increasing utilization of phenotypic screens in drug discovery also the subsequent mechanism-of-action analysis gains increased attention. Such analyses are frequently using in silico methods, which have become significantly more popular in recent years. However, identifying phenotype-specific mechanisms of action depends heavily on suitable phenotype identification in the first place, many of which rely on human input and are therefore inconsistent. In this work, we aimed at analysing the impact that human phenotype classification has on subsequent in silico mechanism-of-action analysis. To this end, an image analysis application was implemented for the rapid identification of seven high-level phenotypes in Xenopus laevis tadpoles treated with compounds from the National Cancer Institute Diversity Set II. It was found that manual and automated phenotype classification were in agreement with some of the phenotypes (e.g. 73.9% agreement observed for General Morphology abnormality), while this was not the case in others (e.g. Melanophore Migration with 37.6% agreement between both annotations). Based on both annotations, protein targets of active compounds were predicted in silico, and decision trees were generated to understand mechanisms-of-action behind every phenotype. It was found that the automated phenotype categorisation greatly increased accuracy of the results mechanism-of-action model, where it improved the classification accuracy by 9.4%, as well as reducing the tree size by eight nodes and the number of leaves and the depth by three levels. Overall we conclude that consistent phenotype annotations seem to be generally crucial for successful subsequent mechanism-of-action analysis, and this is what we have shown here in the combination of in Xenopus laevis screens in combination with in silico mechanism-of-action analysis. Show less

Background: An in silico mechanism-of-action analysis protocol was developed, comprising molecule bioactivity profiling, annotation of predicted targets with pathways and calculation of enrichment factors to highlight targets and pathways more likely to be implicated in the studied phenotype. Results: The method was applied to a cytotoxicity phenotypic endpoint, with enriched targets/pathways found to be statistically significant when compared with 100 random datasets. Application on a smaller… Show more

Background: An in silico mechanism-of-action analysis protocol was developed, comprising molecule bioactivity profiling, annotation of predicted targets with pathways and calculation of enrichment factors to highlight targets and pathways more likely to be implicated in the studied phenotype. Results: The method was applied to a cytotoxicity phenotypic endpoint, with enriched targets/pathways found to be statistically significant when compared with 100 random datasets. Application on a smaller apoptotic set (10 molecules) did not allowed to obtain statistically relevant results, suggesting that the protocol requires modification such as analysis of the most frequently predicted targets/annotated pathways. Conclusion: Pathway annotations improved the mechanism-of-action information gained by target prediction alone, allowing a better interpretation of the predictions and providing better mapping of targets onto pathways. Show less