AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Circulating miRNA biomarkers for Alzheimer's disease.

A minimally invasive diagnostic assay for early detection of Alzheimer's disease (AD) is required to select optimal patient groups in clinical trials, monitor disease progression and response to treatment, and to better plan patient clinical care. Blood is an attractive source for biomarkers due to minimal discomfort to the patient, encouraging greater compliance in clinical trials and frequent testing. MiRNAs belong to the class of non-coding regulatory RNA molecules of ∼22 nt length and are now recognized to regulate ∼60% of all known genes through post-transcriptional gene silencing (RNAi). They have potential as useful biomarkers for clinical use because of their stability and ease of detection in many tissues, especially blood. Circulating profiles of miRNAs have been shown to discriminate different tumor types, indicate staging and progression of the disease and to be useful as prognostic markers. Recently their role in neurodegenerative diseases, both as diagnostic biomarkers as well as explaining basic disease etiology has come into focus. Here we report the discovery and validation of a unique circulating 7-miRNA signature (hsa-let-7d-5p, hsa-let-7g-5p, hsa-miR-15b-5p, hsa-miR-142-3p, hsa-miR-191-5p, hsa-miR-301a-3p and hsa-miR-545-3p) in plasma, which could distinguish AD patients from normal controls (NC) with >95% accuracy (AUC of 0.953). There was a >2 fold difference for all signature miRNAs between the AD and NC samples, with p-values<0.05. Pathway analysis, taking into account enriched target mRNAs for these signature miRNAs was also carried out, suggesting that the disturbance of multiple enzymatic pathways including lipid metabolism could play a role in AD etiology

Gene Expression Analyses Support Fallopian Tube Epithelium as the Cell of Origin of Epithelial Ovarian Cancer

Abstract: Folate receptor alpha (FOLR1/FRA) is reported to be overexpressed in epithelial ovarian cancers (EOC), especially the serous histotype. Further, while dysregulation of the folate-dependent 1-carbon cycle has been implicated in tumorogenesis, little is known relative to the potential mechanism of action of FOLR1 expression in these processes. We therefore investigated the expression of FOLR1, other folate receptors, and genes within the 1-carbon cycle in samples of EOC, normal ovary and fallopian tube on a custom TaqMan Low Density Array. Also included on this array were known markers of EOC such as MSLN, MUC16 and HE4. While few differences were observed in the expression profiles of genes in the 1-carbon cycle, genes previously considered to be overexpressed in EOC (e.g., FOLR1, MSLN, MUC16 and HE4) showed significantly increased expression when comparing EOC to normal ovary. However, when the comparator was changed to normal fallopian tube, these differences were abolished, supporting the hypothesis that EOC derives from fallopian fimbriae and, further, that markers previously considered to be upregulated or overexpressed in EOC are most likely not of ovarian origin, but fallopian i

Gene Expression Profiling Reveals Epithelial Mesenchymal Transition (EMT) Genes Can Selectively Differentiate Eribulin Sensitive Breast Cancer Cells

<div><p>Objectives</p><p>Eribulin mesylate is a synthetic macrocyclic ketone analog of the marine sponge natural product halichondrin B. Eribulin is a mechanistically unique inhibitor of microtubule dynamics. In this study, we investigated whether selective signal pathways were associated with eribulin activity compared to paclitaxel, which stabilizes microtubules, based on gene expression profiling of cell line panels of breast, endometrial, and ovarian cancer in vitro.</p><p>Results</p><p>We determined the sets of genes that were differentially altered between eribulin and paclitaxel treatment in breast, endometrial, and ovarian cancer cell line panels. Our unsupervised clustering analyses revealed that expression profiles of gene sets altered with treatments were correlated with the in vitro antiproliferative activities of the drugs. Several tubulin isotypes had significantly lower expression in cell lines treated with eribulin compared to paclitaxel. Pathway enrichment analyses of gene sets revealed that the common pathways altered between treatments in the 3 cancer panels were related to cytoskeleton remodeling and cell cycle regulation. The epithelial-mesenchymal transition (EMT) pathway was enriched in genes with significantly altered expression between the two drugs for breast and endometrial cancers, but not for ovarian cancer. Expression of genes from the EMT pathway correlated with eribulin sensitivity in breast cancer and with paclitaxel sensitivity in endometrial cancer. Alteration of expression profiles of EMT genes between sensitive and resistant cell lines allowed us to predict drug sensitivity for breast and endometrial cancers.</p><p>Conclusion</p><p>Gene expression analysis showed that gene sets that were altered between eribulin and paclitaxel correlated with drug in vitro antiproliferative activities in breast and endometrial cancer cell line panels. Among the panels, breast cancer provided the strongest differentiation between eribulin and paclitaxel sensitivities based on gene expression. In addition, EMT genes were predictive of eribulin sensitivity in the breast and endometrial cancer panels.</p></div

Overlap among gene signatures for the 3 cancer panels.

<p>We identified sets of genes with significantly altered gene expression profiles between eribulin and paclitaxel treatments for breast, ovarian, and endometrial cancer. The signature consisted of 327, 91, and 159 genes for breast, ovarian, and endometrial cancer, respectively. The percentage of genes having higher expression in cell lines treated with eribulin compared to paclitaxel is 76%, 56%, and 26% for breast, endometrial, and ovarian cancer, respectively.</p

The EMT expression profile correlates with eribulin sensitivity.

<p>Unsupervised hierarchical clustering defined groups of breast cancer cell lines with altered expression under eribulin treatment (left panel). The cell lines consisting of many upregulated EMT genes are more resistant to eribulin treatment (right panel, p = 0.06). The ER and HER2 status of cell lines are indicated in the parenthesis.</p

Pathway enrichment analysis of the three gene signatures altered between eribulin and paclitaxel treatments.

<p>Pathway enrichment analysis of the three gene signatures altered between eribulin and paclitaxel treatments.</p

Gene expression profiles of EMT pathway.

<p>A) The EMT pathway. The boxes show genes with significantly different expression between eribulin sensitive and resistant cell lines (red) and with significantly different expression between eribulin and paclitaxel treatments (blue). B) EMT gene expression between eribulin and paclitaxel. The plot shows the fold-changes of significantly altered genes between paclitaxel and eribulin (red: eribulin vs. control; green: paclitaxel vs. control; blue: eribulin vs. paclitaxel). C) Genes differentiating eribulin sensitivity. The plot shows the fold-changes of significantly altered genes between eribulin sensitive and resistant cell lines (red: resistant; green: sensitive; blue: resistant vs. sensitive).</p

Correlation of gene signatures with in vitro antiproliferative data.

<p>We performed unsupervised hierarchical clustering based on gene signatures for the 3 cancer panels. Significant (p<0.05) or marginally significant (p<0.1) p values are listed for the cell line panels where we identified clusters of cell lines with different sensitivities. For the EMT pathway we tested the predictive power of the expression profiles based on the elastic net regression model. The predicted and measured values (IC₅₀) were correlated based on the Pearson correlation coefficient. In cases where significant correlations existed, p values are listed. Significance of EMT pathway clustering was confirmed for breast cancer by qPCR (p = 0.05). *confirmed by TLDA (NS indicates not significant p values>0.1).</p><p>Correlation of gene signatures with in vitro antiproliferative data.</p

Correlation of tubulin expression with drug sensitivity and fold changes between drug treatments.

<p>Correlations were calculated based on the Pearson correlation, and p values between treatment and controls are based on paired t-test (NS indicates a not significant p value>0.1).</p><p>Correlation of tubulin expression with drug sensitivity and fold changes between drug treatments.</p