VTT-006, an anti-mitotic compound, binds to the Ndc80 complex and suppresses cancer cell growth in vitro.

Hec1 (Highly expressed in cancer 1) resides in the outer kinetochore where it works to facilitate proper kinetochore-microtubule interactions during mitosis. Hec1 is overexpressed in various cancers and its expression shows correlation with high tumour grade and poor patient prognosis. Chemical perturbation of Hec1 is anticipated to impair kinetochore-microtubule binding, activate the spindle assembly checkpoint (spindle checkpoint) and thereby suppress cell proliferation. In this study, we performed high-throughput screen to identify novel small molecules that target the Hec1 calponin homology domain (CHD), which is needed for normal microtubule attachments. 4 million compounds were first virtually fitted against the CHD, and the best hit molecules were evaluated in vitro. These approaches led to the identification of VTT-006, a 1,2-disubstituted-tetrahydro-beta-carboline derivative, which showed binding to recombinant Ndc80 complex and modulated Hec1 association with microtubules in vitro. VTT-006 treatment resulted in chromosome congression defects, reduced chromosome oscillations and induced loss of inter-kinetochore tension. Cells remained arrested in mitosis with an active spindle checkpoint for several hours before undergoing cell death. VTT-006 suppressed the growth of several cancer cell lines and enhanced the sensitivity of HeLa cells to Taxol. Our findings propose that VTT-006 is a potential anti-mitotic compound that disrupts M phase, impairs kinetochore-microtubule interactions, and activates the spindle checkpoint.

Centmitor-1, a novel acridinyl-acetohydrazide, possesses similar molecular interaction field and antimitotic cellular phenotype as rigosertib, on 01910.Na.

Mitosis is an attractive target for the development of new anticancer drugs. In a search for novel mitotic inhibitors, we virtually screened for low molecular weight compounds that would possess similar steric and electrostatic features, but different chemical structure than rigosertib (ON 01910.Na), a putative inhibitor of phosphoinositide 3-kinase (PI3K) and polo-like kinase 1 (Plk1) pathways. Highest scoring hit compounds were tested in cell-based assays for their ability to induce mitotic arrest. We identified a novel acridinyl-acetohydrazide, here named as Centmitor-1 (Cent-1), that possesses highly similar molecular interaction field as rigosertib. In cells, Cent-1 phenocopied the cellular effects of rigosertib and caused mitotic arrest characterized by chromosome alignment defects, multipolar spindles, centrosome fragmentation, and activated spindle assembly checkpoint. We compared the effects of Cent-1 and rigosertib on microtubules and found that both compounds modulated microtubule plus-ends and reduced microtubule dynamics. Also, mitotic spindle forces were affected by the compounds as tension across sister kinetochores was reduced in mitotic cells. Our results showed that both Cent-1 and rigosertib target processes that occur during mitosis as they had immediate antimitotic effects when added to cells during mitosis. Analysis of Plk1 activity in cells using a Förster resonance energy transfer (FRET)-based assay indicated that neither compound affected the activity of the kinase. Taken together, these findings suggest that Cent-1 and rigosertib elicit their antimitotic effects by targeting mitotic processes without impairment of Plk1 kinase activity.

Estimating error rates in bioactivity databases.

Bioactivity databases are routinely used in drug discovery to look-up and, using prediction tools, to predict potential targets for small molecules. These databases are typically manually curated from patents and scientific articles. Apart from errors in the source document, the human factor can cause errors during the extraction process. These errors can lead to wrong decisions in the early drug discovery process. In the current work, we have compared bioactivity data from three large databases (ChEMBL, Liceptor, and WOMBAT) who have curated data from the same source documents. As a result, we are able to report error rate estimates for individual activity parameters and individual bioactivity databases. Small molecule structures have the greatest estimated error rate followed by target, activity value, and activity type. This order is also reflected in supplier-specific error rate estimates. The results are also useful in identifying data points for recuration. We hope the results will lead to a more widespread awareness among scientists on the frequencies and types of errors in bioactivity data.

Analysis of commercial and public bioactivity databases.

Activity data for small molecules are invaluable in chemoinformatics. Various bioactivity databases exist containing detailed information of target proteins and quantitative binding data for small molecules extracted from journals and patents. In the current work, we have merged several public and commercial bioactivity databases into one bioactivity metabase. The molecular presentation, target information, and activity data of the vendor databases were standardized. The main motivation of the work was to create a single relational database which allows fast and simple data retrieval by in-house scientists. Second, we wanted to know the amount of overlap between databases by commercial and public vendors to see whether the former contain data complementing the latter. Third, we quantified the degree of inconsistency between data sources by comparing data points derived from the same scientific article cited by more than one vendor. We found that each data source contains unique data which is due to different scientific articles cited by the vendors. When comparing data derived from the same article we found that inconsistencies between the vendors are common. In conclusion, using databases of different vendors is still useful since the data overlap is not complete. It should be noted that this can be partially explained by the inconsistencies and errors in the source data.

Computational tools for polypharmacology and repurposing.

Most drugs act on a multitude of targets rather than on one single target. Polypharmacology, an upcoming branch of pharmaceutical science, deals with the recognition of these off-target activities of small chemical compounds. Due to the high amount of data to be processed, application of computational methods is indispensable in this area. This review summarizes the most important in silico approaches for polypharmacology. The described methods comprise network pharmacology, machine learning techniques and chemogenomic approaches. The use of these methods for drug repurposing as a branch of drug discovery and development is discussed. Furthermore, a broad range of prospective applications is summarized to give the reader an overview of possibilities and limitations of the described techniques.

Critical comparison of virtual screening methods against the MUV data set.

In the current work, we measure the performance of seven ligand-based virtual screening tools--five similarity search methods and two pharmacophore elucidators--against the MUV data set. For the similarity search tools, single active molecules as well as active compound sets clustered in terms of their chemical diversity were used as templates. Their score was calculated against all inactive and active compounds in their target class. Subsequently, the scores were used to calculate different performance metrics including enrichment factors and AUC values. We also studied the effect of data fusion on the results. To measure the performance of the pharmacophore tools, a set of active molecules was picked either random- or chemical diversity-based from each target class to build a pharmacophore model which was then used to screen the remaining compounds in the set. Our results indicate that template sets selected by their chemical diversity are the best choice for similarity search tools, whereas the optimal training sets for pharmacophore elucidators are based on random selection underscoring that pharmacophore modeling cannot be easily automated. We also suggest a number of improvements for future benchmark sets and discuss activity cliffs as a potential problem in ligand-based virtual screening.

Comparison of structure fingerprint and molecular interaction field based methods in explaining biological similarity of small molecules in cell-based screens.

In this work, we calculated the pair wise chemical similarity for a subset of small molecules screened against the NCI60 cancer cell line panel. Four different compound similarity calculation methods were used: Brutus, GRIND, Daylight and UNITY. The chemical similarity scores of each method were related to the biological similarity data set. The same was done also for combinations of methods. In the end, we had an estimate of biological similarity for a given chemical similarity score or combinations thereof. The data from above was used to identify chemical similarity ranges where combining two or more methods (data fusion) led to synergy. The results were also applied in ligand-based virtual screening using the DUD data set. In respect to their ability to enrich biologically similar compound pairs, the ranking of the four methods in descending performance is UNITY, Daylight, Brutus and GRIND. Combining methods resulted always in positive synergy within a restricted range of chemical similarity scores. We observed no negative synergy. We also noted that combining three or four methods had only limited added advantage compared to combining just two. In the virtual screening, using the estimated biological similarity for ranking compounds produced more consistent results than using the methods in isolation.

Distinct gene expression profiles in adult rat brains after acute MK-801 and cocaine treatments.

Uncompetitive N-methyl-D-aspartate (NMDA) receptor antagonists have been suggested to attenuate the self-administration and rewarding effects of psychostimulants. Microarrays containing 14,500 rat cDNAs were hybridized to identify alterations in gene expression levels in rat brain regions elicited by the uncompetitive NMDA receptor antagonist MK-801 (dizocilpine, 1 mg/kg), the dopamine agonist cocaine (20 mg/kg), or combined treatment (MK-801 15 min prior to cocaine) 4 h after injections. Total genes up regulated (Z-ratio >2) in parietal cortex and nucleus accumbens were 111 and 158, respectively. Total genes down regulated (Z-ratio <2) in the same tissues were 360 and 166, respectively. These genes fell into multiple molecular function gene ontology (GO) categories, but were highly represented in catalytic activities (44% of all genes), signal transduction (14%), protein (20%), nucleotide (18%), and nucleic acid (15%) binding. In nucleus accumbens, genes up regulated by MK-801 (87 genes) did not overlap those up regulated by cocaine (46 genes). Genes down regulated by MK-801 (33 genes) consisted of 2 overlapping genes with those down regulated by cocaine (89 genes). In parietal cortex, low numbers of overlapping regulated genes were also observed. Combined treatments also indicated low numbers (0-10) of genes commonly regulated when compared with single treatments alone. In situ hybridisation studies indicated significant increases in b-ZIP transcription factors (CREM, ICER, CBP, and c-fos) elicited by MK-801 and decreases in c-fos elicited by cocaine. The results indicate independent gene expression signatures following acute MK-801 and cocaine administration that appears to be largely non-overlapping and context dependent.