2018 AACR Drug Target Explorer

<div>In the modern drug discovery process, high-throughput screens of drugs are a common and important step in the identification of novel treatments. Frequently, these screens are phenotypic; i.e. they test compounds with known or unknown mechanisms of action in a biological model and evaluate a phenotype. While these types of screens facilitate the identification of active molecules, they also present challenges, including:</div><div><br></div><div>(1) Identifying the mechanism(s) of action of a compound</div><div>(2) literature frequently disagrees on drug targets</div><div>(3) Identifying common targets within screen hits</div><div>(4) Interpretation of polypharmacologic compounds</div><div>(5) Identifying structurally/functionally related molecules </div><div><br></div><div>Multiple tools and databases exist that address these challenges. The majority of these tools allow users to explore drug-target relationships. However, none of the tools fulfill all of the criteria listed in Table 1 of the poster. To address this, we developed the Drug-Target Explorer. This tool enables the user to:</div><div><br></div><div><br></div><div>(1) look up targets for molecules,</div><div>(2) explore networks of targets and drugs,</div><div>(3) perform gene list enrichment of targets</div><div>(4) compare query molecules to cancer screening datasets</div><div>(5) discover bioactive molecules using a query target</div><div><br></div><div>We anticipate that the users will include biologists and chemists involved in drug discovery who are interested in performing hypothesis generation of human targets for novel molecules, identifying off-targets for bioactive small molecules of interest, and exploring of the polypharmacologic nature of small molecules.</div

Novel Hits in the Correction of ΔF508-Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) Protein: Synthesis, Pharmacological, and ADME Evaluation of Tetrahydropyrido[4,3‑<i>d</i>]pyrimidines for the Potential Treatment of Cystic Fibrosis

Cystic fibrosis (CF) is a lethal genetic disease caused by mutations of the gene encoding the cystic fibrosis transmembrane conductance regulator (CFTR) with a prevalence of the ΔF508 mutation. Whereas the detailed mechanisms underlying disease have yet to be fully elucidated, recent breakthroughs in clinical trials have demonstrated that CFTR dysfunction can be corrected by drug-like molecules. On the basis of this success, a screening campaign was carried out, seeking new drug-like compounds able to rescue ΔF508-CFTR that led to the discovery of a novel series of correctors based on a tetrahydropyrido­[4,3-<i>d</i>]­pyrimidine core. These molecules proved to be soluble, cell-permeable, and active in a disease relevant functional-assay. The series was then further optimized with emphasis on biological data from multiple cell systems while keeping physicochemical properties under strict control. The pharmacological and ADME profile of this corrector series hold promise for the development of more efficacious compounds to be explored for therapeutic use in CF

Fused 3‑Hydroxy-3-trifluoromethylpyrazoles Inhibit Mutant Huntingtin Toxicity

Here, we describe the selection and optimization of a chemical series active in both a full-length and a fragment-based Huntington’s disease (HD) assay. Twenty-four thousand small molecules were screened in a phenotypic HD assay, identifying a series of compounds bearing a 3-hydroxy-3-trifluoromethylpyrazole moiety as able to revert the toxicity induced by full-length mutant Htt by up to 50%. A chemical exploration around the series led to the identification of compound <b>4f</b>, which demonstrated to be active in a Htt171–82Q rat primary striatal neuron assay and a PC12-Exon-1 based assay. This compound was selected for testing in R6/2 mice, in which it was well-tolerated and showed a positive effect on body weight and a positive trend in preventing ventricular volume enlargment. These studies provide strong rationale for further testing the potential benefits of 3-hydroxy-3-trifluoromethylpyrazoles in treating HD

Kinome changes across human isogenic merlin-wildtype and -deficient AC and SC pairs.

<p><b>(A-B)</b> Top baseline kinome changes across human AC and SC isogenic pairs. Data presented are the mean log2 fold change from 3 experiments, error bars are standard error. <b>(C)</b> Pan-kinome (left) drug induced perturbations relative to DMSO control contain clusters of induced/repressed kinases (right). Each condition is the median log 2 fold change of three replicate experiments relative to vehicle (DMSO) control, with any kinase having >33% non-detection rate removed (grey: kinase not detected in any run). Cell lines treated with HDAC inhibitors (Panobinostat and CUDC-907) cluster most closely.</p

Kinomic response to drug treatment.

<p><b>(A)</b> Ranking the top 10 most-induced and most-suppressed kinases for each of the treatments at 24 hours stresses the similarities in drug response across cell lines, and also the differences between human and mouse cell lines. <b>(B)</b> Drug induced perturbations in HS-01 and Syn5 shown in a kinome tree plot. <b>(C)</b> Kinases that were represented in every cell line were used to generate a MIB-binding response correlation matrix. <b>(D-E)</b> Two of the most-prominent clusters include kinases highly-correlated with PTK2 (FAK1) or RPS6KA1 (p90 RSK) and STK3/4. <b>(F)</b> The most-highly correlated kinases are INSR and IGF1R (corr = 0.91), primarily being induced by GSK458-treatment at 24 hours. <b>(G)</b> The next-most correlated kinase pair across the entire dataset is PTK2 and AAK1 (corr = 0.87). Interestingly, these kinases are preferentially induced in merlin-deficient cell lines.</p

Meningioma, schwannoma, arachnoidal, and Schwann cell lines used for Synodos drug screens.

<p>Meningioma, schwannoma, arachnoidal, and Schwann cell lines used for Synodos drug screens.</p

FDA approved or late–stage drugs selected for cell culture screening.

<p>FDA approved or late–stage drugs selected for cell culture screening.</p

Schematic diagram for drug testing with mechanistically based candidate drugs in a traditional <i>in vitro</i> to <i>in vivo</i> pipeline and simultaneous integrated transcriptome and kinome analysis of cells and murine tumors.

<p>Schematic diagram for drug testing with mechanistically based candidate drugs in a traditional <i>in vitro</i> to <i>in vivo</i> pipeline and simultaneous integrated transcriptome and kinome analysis of cells and murine tumors.</p

PCA and transcriptomic differences due to merlin deficiency in human AC and SC.

<p><b>(A)</b> Principal components analysis (PCA) of averaged, rank-normalized read counts (overlapping genes only) from RNA-seq of wild type and merlin-deficient human SC (red), mouse SC (orange), human AC (blue, Syn5, Syn1) and human MN cells (blue, Syn6). PC1 explains 61.5% of variance, while PC2 explains 25.5% of variance. <b>(B)</b> Volcano plots showing the significance and log2 fold-change (logFC) due to merlin deficiency for all gene transcripts reliably detected in the RNA-seq analysis. Yellow dots represent genes altered at [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0197350#pone.0197350.ref047" target="_blank">47</a>] adjusted significance of P<0.05. The location of the downregulated <i>NF2</i> gene, corresponding with ~7% of normal <i>NF2</i> expression, is labelled for HS01 vs HS11. In the Syn5 vs Syn1 AC comparison, fold-comparisons across the entire gene are not meaningful as there is no significant difference in the level of <i>NF2</i> transcripts in Syn5, but these produce no active merlin due to absence of exon 8 (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0197350#pone.0197350.s014" target="_blank">S5 Fig</a>), which was removed by CRISPR/Cas9. <b>(C)</b> Venn diagrams showing the relatively small degree of overlap between the downregulated (left) and upregulated genes (right) due to merlin deficiency in human AC and SC, respectively.</p

Drug screening outcomes are largely independent of merlin status.

<p><b>(A)</b> Estimated beta coefficients of different assay variables to differences in area under the curve (AUC) as determined by multiple linear modeling. Multiple linear modeling indicates that unlike merlin status, tumor type and treatment with a subset of drugs (CUDC-907, Bortezomib, Panobinostat, GSK2126458, Ganetespib, and Axitinib) are significantly associated with a reduction in cell viability as measured by Simpson AUC. <b>(B)</b> Hierarchical clustering demonstrates similarity of response (Simpson AUC) of all cell lines to tested drugs. Only drugs common to both cell types were included in the analysis. Based on the response of each cell line to the entire panel of drugs, the cell lines appear to cluster by cell type and merlin status.</p