Computational classification models for predicting the interaction of compounds with hepatic organic ion importers.

Hepatic transporters, a major determinant of pharmacokinetics, have been used to profile drug properties like efficacy. Among hepatic transporters, importers alter the concentration of the drug by facilitating the transport of a drug into a cell. Despite vast pharmacokinetic studies, the interacting mechanisms of the importers with its substrates or inhibitors are not well understood. Hence, we developed compound binary classification models of whether a compound is binder or nonbinder to a hepatic transporter with experimental data of 284 compounds for four representative hepatic importers, OATP1B1, OATP1B3, OAT2, and OCT1. Support Vector Machine (SVM) along with Genetic Algorithm (GA) was used to construct the classification models of binder versus nonbinder for each target importer. To construct the models, we prepared two data sets, a training data set from Fujitsu database (284 compounds) and an external validation data set from ChEMBL database (1738 compounds). Since an experimental classification criterion between binder and nonbinder has some ambiguity, there is an intrinsic limitation to expect high predictability of the binary classification models developed with the experimental data. The predictability of the classification models calculated with external validation sets were obtained as 77.72%, 84.31%, 84.21%, and 76.38 for OATP1B1, OATP1B3, OAT2, and OCT1, respectively.

Assessment of quantitative structure-activity relationship of toxicity prediction models for Korean chemical substance control legislation.

OBJECTIVES: For successful adoption of legislation controlling registration and assessment of chemical substances, it is important to obtain sufficient toxicological experimental evidence and other related information. It is also essential to obtain a sufficient number of predicted risk and toxicity results. Particularly, methods used in predicting toxicities of chemical substances during acquisition of required data, ultimately become an economic method for future dealings with new substances. Although the need for such methods is gradually increasing, the-required information about reliability and applicability range has not been systematically provided. METHODS: There are various representative environmental and human toxicity models based on quantitative structure-activity relationships (QSAR). Here, we secured the 10 representative QSAR-based prediction models and its information that can make predictions about substances that are expected to be regulated. We used models that predict and confirm usability of the information expected to be collected and submitted according to the legislation. After collecting and evaluating each predictive model and relevant data, we prepared methods quantifying the scientific validity and reliability, which are essential conditions for using predictive models. RESULTS: We calculated predicted values for the models. Furthermore, we deduced and compared adequacies of the models using the Alternative non-testing method assessed for Registration, Evaluation, Authorization, and Restriction of Chemicals Substances scoring system, and deduced the applicability domains for each model. Additionally, we calculated and compared inclusion rates of substances expected to be regulated, to confirm the applicability. CONCLUSIONS: We evaluated and compared the data, adequacy, and applicability of our selected QSAR-based toxicity prediction models, and included them in a database. Based on this data, we aimed to construct a system that can be used with predicted toxicity results. Furthermore, by presenting the suitability of individual predicted results, we aimed to provide a foundation that could be used in actual assessments and regulations.

De novo design of non-hydrogen-bonded helical pseudopeptides composed of oxanipecotic acid oligomers.

Ab initio calculation and circular dichroism experiments reveal that Oxa-oligomers adopted pronounced non-hydrogen-bonded helical structures.

Novel consecutive beta- and gamma-turn mimetics composed of alpha-aminooxy tripeptides.

[structure: see text] To develop novel consecutive beta- and gamma-turn mimetics, we designed and characterized alpha-aminooxy tripeptides (trimers) consisting of oxanipecotic acid dimer and alpha-aminooxy acid. According to FT-IR and NMR data, as well as ab initio quantum calculations, the trimers adopted unusual folded structures with consecutive beta- and gamma-turnlike conformations.