title: Variation of substituents on pirarubicin for enhancing response to hepatocellular carcinoma and pattern recognition analysis to determine analogy to parent drug.
abstract: Hepatocellular carcinoma arises from hepatocytes, it is the most common primary malignant tumor of the liver and accounts for the majority of liver cancers. Pirarubicin is a compound analog to the antineoplastic anthracycline antibiotic doxorubicin. Thereby as an intercalator of DNA, pirarubicin has shown efficacy in reducing tumor activity of hepatocellular carcinoma. Utilizing in silico optimized similarity of structure search of data base and replacement by isosteres, a total of nine analogs to pirarubicin were generated. The molecular size of ring substituents (approximately a base pair) were preserved and isosteres chosen to preserve the weak electrostatic properties permitting DNA intercalation. Only a small range in molecular weight and volume was permitted in order to preserve intercalation activity. Standard deviations for polar surface area, number of heavy atoms, molecular weight, molecular volume, and number of oxygens and nitrogens, were only 3.98%, 1.76%, 16.5%, 2.51%, and 0%, respectively, of the overall average for these properties. Variation of the target substituent resulted in insignificant changes in many properties; there was a striking variation in the lipophilic property Log P. Values of Log P ranged from 0.142 to 2.078 inclusive of pirarubicin and plays an important role in bioavailability. Hierarchical cluster analysis indicated that all analogs, except analog 6, were substantially similar to pirarubicin. A similar outcome was obtained from non-metric multidimensional scaling of descriptors. However detrended correspondence analysis distinguished both analogs 6 and 5 from the remaining analogs and pirarubicin. Nonhierarchical K-means clustering analysis determined greater differentiation among the analogs and pirarubicin. Analysis of similarity (ANOSIM) affirmed all compounds to be highly similar which shows structural optimization was achieved.
authors.affiliation: University of Nebraska, College of Arts & Sciences, Chemistry Department, Omaha, Nebraska 68182, USA. rbartzatt@mail.unomaha.edu
authors.forename: Ronald L
authors.initials: RL
authors.lastname: Bartzatt
mesh_terms: D000970:Antineoplastic Agents / Q000138:chemical synthesis / Q000737:chemistry* / Q000494:pharmacology; D006528:Carcinoma, Hepatocellular / Q000188:drug therapy; D016000:Cluster Analysis; D004247:DNA / Q000737:chemistry; D019992:Databases as Topic; D004317:Doxorubicin / Q000031:analogs & derivatives* / Q000737:chemistry / Q000494:pharmacology; D055808:Drug Discovery; D006801:Humans; D008113:Liver Neoplasms / Q000188:drug therapy; D008956:Models, Chemical; D008970:Molecular Weight; D015999:Multivariate Analysis; D017672:Nitrogen Compounds / Q000138:chemical synthesis / Q000737:chemistry / Q000494:pharmacology; D017601:Oxygen Compounds / Q000138:chemical synthesis / Q000737:chemistry / Q000494:pharmacology; D010363:Pattern Recognition, Automated / Q000379:methods; D055672:Static Electricity
chemical_list: D000970:Antineoplastic Agents; D017672:Nitrogen Compounds; D017601:Oxygen Compounds; D004317:Doxorubicin; D004247:DNA; C027260:pirarubicin