Connectivity Mapping for Candidate Therapeutics Identification Using Next Generation Sequencing RNA-Seq Data

The advent of next generation sequencing technologies (NGS) has expanded the area of genomic research, offering high coverage and increased sensitivity over older microarray platforms. Although the current cost of next generation sequencing is still exceeding that of microarray approaches, the rapid advances in NGS will likely make it the platform of choice for future research in differential gene expression. Connectivity mapping is a procedure for examining the connections among diseases, genes and drugs by differential gene expression initially based on microarray technology, with which a large collection of compound-induced reference gene expression profiles have been accumulated. In this work, we aim to test the feasibility of incorporating NGS RNA-Seq data into the current connectivity mapping framework by utilizing the microarray based reference profiles and the construction of a differentially expressed gene signature from a NGS dataset. This would allow for the establishment of connections between the NGS gene signature and those microarray reference profiles, alleviating the associated incurring cost of re-creating drug profiles with NGS technology. We examined the connectivity mapping approach on a publicly available NGS dataset with androgen stimulation of LNCaP cells in order to extract candidate compounds that could inhibit the proliferative phenotype of LNCaP cells and to elucidate their potential in a laboratory setting. In addition, we also analyzed an independent microarray dataset of similar experimental settings. We found a high level of concordance between the top compounds identified using the gene signatures from the two datasets. The nicotine derivative cotinine was returned as the top candidate among the overlapping compounds with potential to suppress this proliferative phenotype. Subsequent lab experiments validated this connectivity mapping hit, showing that cotinine inhibits cell proliferation in an androgen dependent manner. Thus the results in this study suggest a promising prospect of integrating NGS data with connectivity mapping. © 2013 McArt et al.

Pharmacological evaluation of selective α2c-adrenergic agonists in experimental animal models of nasal congestion

Nasal congestion is one of the most troublesome symptoms of many upper airways diseases. We characterized the effect of selective α2c-adrenergic agonists in animal models of nasal congestion. In porcine mucosa tissue, compound A and compound B contracted nasal veins with only modest effects on arteries. In in vivo experiments, we examined the nasal decongestant dose-response characteristics, pharmacokinetic/pharmacodynamic relationship, duration of action, potential development of tolerance, and topical efficacy of α2c-adrenergic agonists. Acoustic rhinometry was used to determine nasal cavity dimensions following intranasal compound 48/80 (1%, 75 µl). In feline experiments, compound 48/80 decreased nasal cavity volume and minimum cross-sectional areas by 77% and 40%, respectively. Oral administration of compound A (0.1-3.0 mg/kg), compound B (0.3-5.0 mg/kg), and d-pseudoephedrine (0.3 and 1.0 mg/kg) produced dose-dependent decongestion. Unlike d-pseudoephedrine, compounds A and B did not alter systolic blood pressure. The plasma exposure of compound A to produce a robust decongestion (EC(80)) was 500 nM, which related well to the duration of action of approximately 4.0 hours. No tolerance to the decongestant effect of compound A (1.0 mg/kg p.o.) was observed. To study the topical efficacies of compounds A and B, the drugs were given topically 30 minutes after compound 48/80 (a therapeutic paradigm) where both agents reversed nasal congestion. Finally, nasal-decongestive activity was confirmed in the dog. We demonstrate that α2c-adrenergic agonists behave as nasal decongestants without cardiovascular actions in animal models of upper airway congestion.