Formation and evaluation of mechanism-based chemical categories for regulatory read-across assessment of repeated-dose toxicity: A case of hemolytic anemia.

The aim of this study is to define chemical categories that can be applied to regulatory read-across assessments for repeated-dose toxicity, by classifying toxic substances based on their structures and mechanism of actions (MoAs). Hemolytic anemia, which often appears primarily, was examined as an example. An integrated database was constructed by collecting publicly available datasets on repeated-dose toxicity, in which 423 out of a total of 1518 chemicals were identified as capable of inducing hemolytic anemia. Subsequently, by grouping these chemicals based on their chemical structures and plausible MoAs on hemolytic substances, we identified the following categories: (i) anilines, (ii) nitrobenzenes, (iii) nitroanilines, (iv) dinitroanilines, (v) ethylene glycol alkyl ethers, (vi) hydroquinones, (vii) oximes, and (viii) hydrazines. In these categories, the toxicant and the measurable key events leading to hematotoxicity were identified, thereby allowing us to justify the categories and to discriminate the category substances. Moreover, toxicokinetics seems to critically affect the hemolytic levels of the category substances. Overall, the categories were validated through a comprehensive analysis of the collected information, while the utility was demonstrated by conducting a case study on the selected category. Further endeavors with this approach would attain categories for other organ toxicity endpoints.

Design and synthesis of novel allosteric MEK inhibitor CH4987655 as an orally available anticancer agent.

The MAP kinase pathway is one of the most important pathways involved in cell proliferation and differentiation, and its components are promising targets for antitumor drugs. Design and synthesis of a novel MEK inhibitor, based on the 3D-structural information of the target enzyme, and then multidimensional optimization including metabolic stability, physicochemical properties and safety profiles were effectively performed and led to the identification of a clinical candidate for an orally available potent MEK inhibitor, CH4987655, possessing a unique 3-oxo-oxazinane ring structure at the 5-position of the benzamide core structure. CH4987655 exhibits slow dissociation from the MEK enzyme, remarkable in vivo antitumor efficacy both in mono- and combination therapy, desirable metabolic stability, and insignificant MEK inhibition in mouse brain, implying few CNS-related side effects in human. An excellent PK profile and clear target inhibition in PBMC were demonstrated in a healthy volunteer clinical study.

Synthesis and biological activities of benzofuran antifungal agents targeting fungal N-myristoyltransferase.

The C-4 side chain modification of lead compound 1 has resulted in the identification of a potent and selective Candida albicans N-myristoyltransferase (CaNmt) inhibitor RO-09-4609, which exhibits antifungal activity against C. albicans in vitro. Further modification of its C-2 substituent has led to the discovery of RO-09-4879, which exhibits antifungal activity in vivo. The drug design is based on X-ray crystal analysis of a CaNmt complex with benzofuran derivative 4a. The optimization incorporates various biological investigations including a quasi in vivo assay and pharmacokinetic study. The computer aided drug design, synthesis, structure-activity relationships, and biological properties of RO-09-4879 are described in detail.

Development of a virtual screening method for identification of "frequent hitters" in compound libraries.

A computer-based method was developed for rapid and automatic identification of potential "frequent hitters". These compounds show up as hits in many different biological assays covering a wide range of targets. A scoring scheme was elaborated from substructure analysis, multivariate linear and nonlinear statistical methods applied to several sets of one and two-dimensional molecular descriptors. The final model is based on a three-layered neural network, yielding a predictive Matthews correlation coefficient of 0.81. This system was able to correctly classify 90% of the test set molecules in a 10-times cross-validation study. The method was applied to database filtering, yielding between 8% (compilation of trade drugs) and 35% (Available Chemicals Directory) potential frequent hitters. This filter will be a valuable tool for the prioritization of compounds from large databases, for compound purchase and biological testing, and for building new virtual libraries.