Ayurveda, the traditional Indian medicine is one of the most ancient, yet living medicinal traditions. In the present work, we developed an in silico library of natural products from Ayurveda medicine, coupled with structural information, plant origin and traditional therapeutic use. Following this, we compared their structures with those of drugs from DrugBank and we constructed a structural similarity network. Information on the traditional therapeutic use of the plants was integrated in the network in order to provide further evidence for the predicted biologically active natural compounds. We hereby present a number of examples where the traditional medicinal use of the plant matches with the medicinal use of the drug that is structurally similar to a plant component. With this approach, we have brought to light a number of obscure compounds of natural origin (e.g. kanugin, norruffscine, isoazadirolide) that could provide the basis and inspiration for further lead development. Apart from the identification of novel natural leads in drug discovery, we envisage that this integrated in silico ethnopharmacology approach could find applications in the elucidation of the molecular basis of Ayurveda medicine and in drug repurposing.

The MET receptor tyrosine kinase (MET) gene is a proto-oncogene whose abnormal activation can trigger tumor growth, angiogenesis, and metastasis. Numerous mutations have been identified that lead to over-activation of the gene, including well characterized amplification and somatic mutations. Mutations that lead to the skipping of MET exon 14 are likely to be highly predictive of response to MET inhibition. Exon 14 contains a motif that is required for the efficient recruitment of the ubiquitin ligase, CBL, which targets MET for degradation. Loss of exon 14 leads to prolonged stability of the MET protein which causes increased signaling upon stimulation by hepatocyte growth factor. Over 200 distinct mutations have been identified that cause MET exon 14 skipping ranging from single nucleotide mutations to large insertion/deletions. Due to this complexity, detecting MET exon 14 skipping mutations using DNA-based NGS hybridization capture methods is challenging. RNA-based testing can circumvent the complexity of DNA-based changes. We developed a RT-qPCR assay that detects MET exon 14 skipping independent of the causal mutation. Analytical validation using RNA admixtures demonstrated 1% sensitivity of detecting MET exon 14 mutant transcripts. 100 FFPE lung adenocarcinomas were screened with the RT-qPCR assay. Of 100, two samples (2%) were identified as positive for MET exon 14 skipping. The two positive samples along with three negative samples were orthogonally confirmed using the Illumina TruSight Tumor 170 hybridization capture-based targeted panel assay that simultaneously analyzes DNA and RNA. Overall, the number of samples identified positive for MET exon 14 by RT-qPCR test is in line with the frequency observed from other studies in lung adenocarcinomas of about ~3%. The assay can detect as little as 1% transcripts with MET exon 14 skipping suggesting that we have developed an accurate and sensitive RNA-based assay for use in clinical trials. Citation Format: Mukund Patel, Honey Polur, Patrick Hurban. Sensitive detection of MET exon 14 skipping by RT-qPCR and next generation sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1626.

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