J Chem Inf Model - Using molecular features of xenobiotics to predict hepatic gene expression response.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ model(2656) set(1616) predict(1553) }
{ chang(1828) time(1643) increas(1301) }
{ drug(1928) target(777) effect(648) }
{ can(774) often(719) complex(702) }
{ spatial(1525) area(1432) region(1030) }
{ bind(1733) structur(1185) ligand(1036) }
{ featur(1941) imag(1645) propos(1176) }
{ howev(809) still(633) remain(590) }
{ compound(1573) activ(1297) structur(1058) }
{ time(1939) patient(1703) rate(768) }
{ import(1318) role(1303) understand(862) }
{ data(1737) use(1416) pattern(1282) }
{ take(945) account(800) differ(722) }
{ assess(1506) score(1403) qualiti(1306) }
{ error(1145) method(1030) estim(1020) }
{ model(2341) predict(2261) use(1141) }
{ group(2977) signific(1463) compar(1072) }
{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
{ analysi(2126) use(1163) compon(1037) }
{ use(976) code(926) identifi(902) }
{ method(2212) result(1239) propos(1039) }
{ imag(2830) propos(1344) filter(1198) }
{ data(1714) softwar(1251) tool(1186) }
{ model(2220) cell(1177) simul(1124) }
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{ visual(1396) interact(850) tool(830) }
{ studi(1119) effect(1106) posit(819) }
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{ ehr(2073) health(1662) electron(1139) }
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{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
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{ data(3008) multipl(1320) sourc(1022) }
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{ high(1669) rate(1365) level(1280) }
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{ use(1733) differ(960) four(931) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
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{ method(1969) cluster(1462) data(1082) }

Resumo

Despite recent advances in molecular medicine and rational drug design, many drugs still fail because toxic effects arise at the cellular and tissue level. In order to better understand these effects, cellular assays can generate high-throughput measurements of gene expression changes induced by small molecules. However, our understanding of how the chemical features of small molecules influence gene expression is very limited. Therefore, we investigated the extent to which chemical features of small molecules can reliably be associated with significant changes in gene expression. Specifically, we analyzed the gene expression response of rat liver cells to 170 different drugs and searched for genes whose expression could be related to chemical features alone. Surprisingly, we can predict the up-regulation of 87 genes (increased expression of at least 1.5 times compared to controls). We show an average cross-validation predictive area under the receiver operating characteristic curve (AUROC) of 0.7 or greater for each of these 87 genes. We applied our method to an external data set of rat liver gene expression response to a novel drug and achieved an AUROC of 0.7. We also validated our approach by predicting up-regulation of Cytochrome P450 1A2 (CYP1A2) in three drugs known to induce CYP1A2 that were not in our data set. Finally, a detailed analysis of the CYP1A2 predictor allowed us to identify which fragments made significant contributions to the predictive scores.

Resumo Limpo

despit recent advanc molecular medicin ration drug design mani drug still fail toxic effect aris cellular tissu level order better understand effect cellular assay can generat highthroughput measur gene express chang induc small molecul howev understand chemic featur small molecul influenc gene express limit therefor investig extent chemic featur small molecul can reliabl associ signific chang gene express specif analyz gene express respons rat liver cell differ drug search gene whose express relat chemic featur alon surpris can predict upregul gene increas express least time compar control show averag crossvalid predict area receiv oper characterist curv auroc greater gene appli method extern data set rat liver gene express respons novel drug achiev auroc also valid approach predict upregul cytochrom p cypa three drug known induc cypa data set final detail analysi cypa predictor allow us identifi fragment made signific contribut predict score

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