Biomedical subjects

Sung Jin Cho

Publications and source records attributed to Sung Jin Cho.

5 recordsLinked to original sources

PubMed2003

Hox genes from the earthworm Perionyx excavatus.

The Hox genes of the oligochaete, Perionyx excavatus, were surveyed using PCR and phylogenetic analysis. We were able to identify 11 different Hox gene fragments. Comparative and phylogenetic analyses revealed that this oligochaete would have at least five Hox genes of the anterior group, including three copies of labial-type, five of the central group and one of the posterior group. This is the first report regarding sequence information and phylogenetic analysis of Hox genes in the earthworm.

Animals↗

PubMed2002

Prediction of aqueous solubility of organic compounds using a quantitative structure-property relationship.

A quantitative structure-property relationship (QSPR) was developed for predicting the aqueous solubility of drug-like compounds from their chemical structures. A set of 321 structurally diverse drugs or related compounds, with their intrinsic aqueous solubility collected from literature, was used in this analysis. The data were divided into a training set (n = 267) for building the model and a randomly chosen testing set (n = 54) for assessing the predictive ability of the model. A series of molecular descriptors was calculated directly from chemical structures and a set of eight descriptors, including dipole moment, surface area, volume, molecular weight, number of rotatable bonds/total bonds, number of hydrogen-bond acceptors, number of hydrogen-bond donors and density, was chosen for the final model. The eight-descriptor model generated by multiple linear regression was further optimized by a genetic algorithm guided selection method. The model has a correlation coefficient (r) of 0.95 and a root-mean-square (rms) error of 0.56 log unit. It predicts the solubility of testing set compounds with a reasonable degree of accuracy (r = 0.84 and rms = 0.86 log unit). The present model can serve as a tool for medicinal chemists to guide their early synthetic efforts in arriving at appropriate analogs.

Algorithms↗

PubMed2002

Testing non-additivity of biological activity in a combinatorial library.

Combinatorial chemistry offers new opportunities to generate and analyze QSAR data. Traditional QSAR attempts to correlate activity with structure. With combinatorial chemistry, it is possible to correlate activity directly with the reagents used in a combinatorial library. If one can determine which reagents lead to the compounds of highest activities, it may then be possible to predict active compounds in virtual libraries of 10(6) to 10(10) compounds. This would greatly facilitate library design and provide confidence that the best compounds are being considered for synthesis. An important question is whether the activity of a product molecule can be considered as a sum of its components. This is referred to as additivity between reagents. If there is non-additivity, it is necessary to identify and include the non-additive terms in the model in order to improve QSAR models. Presented here are methods for developing QSAR models relating compound activity to reagents and a method for detecting the second effects of side-chain non-additivity. If the reagents in a library are shown to be additive in their contribution to activity, simple QSAR based on additive models can be applied confidently to reagents. Testing non-additivity can also guide the synthesis of the library. If the contributions are shown to be additive then the strategy for library synthesis may be shifted to include many reagents of a given type but not to make all combinations. The result is more efficient use of resources. In the analysis of percent inhibition data of a combinatorial library an additive model using reagents as descriptors yields a R(2) of 0.43. Application of this method is probably appropriate for HTS single point data while methods employing topological or pharmacophore based descriptors would be necessary to adequately model IC50 data.

Combinatorial Chemistry Techniques↗

PubMedDate not supplied

Genetic Algorithm guided Selection: variable selection and subset selection.

A novel Genetic Algorithm guided Selection method, GAS, has been described. The method utilizes a simple encoding scheme which can represent both compounds and variables used to construct a QSAR/QSPR model. A genetic algorithm is then utilized to simultaneously optimize the encoded variables that include both descriptors and compound subsets. The GAS method generates multiple models each applying to a subset of the compounds. Typically the subsets represent clusters with different chemotypes. Also a procedure based on molecular similarity is presented to determine which model should be applied to a given test set compound. The variable selection method implemented in GAS has been tested and compared using the Selwood data set (n = 31 compounds; v = 53 descriptors). The results showed that the method is comparable to other published methods. The subset selection method implemented in GAS has been first tested using an artificial data set (n = 100 points; v = 1 descriptor) to examine its ability to subset data points and second applied to analyze the XLOGP data set (n = 1831 compounds; v = 126 descriptors). The method is able to correctly identify artificial data points belonging to various subsets. The analysis of the XLOGP data set shows that the subset selection method can be useful in improving a QSAR/QSPR model when the variable selection method fails.

Algorithms↗

PubMedDate not supplied

Manganese superoxide dismutase expression correlates with a poor prognosis in gastric cancer.

OBJECTIVE: The biologic significance of superoxide dismutase (SOD) in transformed gastric carcinoma cells is unclear. The aim of this study was to examine the role of SOD as a prognostic indicator of gastric carcinomas and its association with other clinicopathological factors. METHODS: Expression of MnSOD and Cu/ZnSOD was evaluated immunohistochemically in gastric carcinomas and adenomas using tissue-array methods. The correlation between SOD immunoreactivity and clinical outcome or clinicopathological factors was investigated. RESULTS: MnSOD expression was associated with a poor patient prognosis and was strong in advanced gastric cancer, in the well-differentiated type and in the presence of lymphoid stroma (p < 0.05). On the other hand, there was no association between Cu/ZnSOD expression and patient survival. Cu/ZnSOD immunoreactivity was strong in advanced gastric cancer and in the presence of lymphoid stroma (p < 0.05) and was weak in signet ring cell type. CONCLUSION: MnSOD immunoreactivity is significantly associated with poor outcome in gastric carcinoma patients although both MnSOD and Cu/ZnSOD have a connection with several clinicopathological parameters with some overlap.

Adenocarcinoma↗