Abstract
Lung cancer causes a large number of deaths per year. Until now, a cure for this disease has not been found or developed. Finding an effective drug through traditional experimental methods invariably costs millions of dollars and takes several years. It is imperative that computational methods be developed to integrate several types of existing information to identify candidate drugs for further study, which could reduce the cost and time of development. In this study, we tried to advance this effort by proposing a computational method to identify candidate drugs for non-small cell lung cancer (NSCLC), a major type of lung cancer. The method used three steps: (1) preliminary screening, (2) screening compounds by an association test and a permutation test, (3) screening compounds using an EM clustering algorithm. In the first step, based on the chemical-chemical interaction information reported in STITCH, a well-known database that reports interactions between chemicals and proteins, and approved NSCLC drugs, compounds that can interact with at least one approved NSCLC drug were picked. In the second step, the association test selected compounds that can interact with at least one NSCLC-related chemical and at least one NSCLC-related gene, and subsequently, the permutation test was used to discard nonspecific compounds from the remaining compounds. In the final step, core compounds were selected using a powerful clustering algorithm, the EM algorithm. Six putative compounds, protoporphyrin IX, hematoporphyrin, canertinib, lapatinib, pelitinib, and dacomitinib, were identified by this method. Previously published data show that all of the selected compounds have been reported to possess anti-NSCLC activity, indicating high probabilities of these compounds being novel candidate drugs for NSCLC.
Highlights
Lung cancer is a major cause of cancer-related deaths worldwide [1], and the number of deaths has shown an increasing trend over the past fifteen years [2] despite improvements in research and development (R&D) and increased investments in R&D
Of the five features derived from the five scores of chemical-chemical interactions between c and approved non-small cell lung cancer (NSCLC) drugs, we only described how to extract a feature from the “Similarity” score; the others can be obtained in a similar way
This study used a computational method for identifying novel putative compounds of NSCLC, which were deemed to have anti-NSCLC activity
Summary
Lung cancer is a major cause of cancer-related deaths worldwide [1], and the number of deaths has shown an increasing trend over the past fifteen years [2] despite improvements in research and development (R&D) and increased investments in R&D. Many previous studies based on in silico predictions have been carried out to analyze the structure-activity relationships (SARs) of anti-NSCLC chemicals and identify promising chemicals that can act as substitutes for approved NSCLC drugs. The above methods primarily used the structures of chemicals to discover compounds that have anti-NSCLC activity. Lu et al developed a novel computational model by using chemical-/protein-chemical interaction information and identified promising chemicals with potential anti-NSCLC activity that were structurally dissimilar to drugs approved for NSCLC [7]. Of the nineteen compounds identified, only six were found to have anti-NSCLC activity This method needs to be improved, the concept of identifying drug candidates by integrating chemical-/protein-chemical interactions is a suitable approach. We tried to extend this method by using additional related information and more powerful computational tools
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