A convergent approach to side-chain homologated derivatives of 1α,25-dihydroxyergocalciferol.

As part of our program on search for vitamin D analogs with selective biological properties, such as low or negligible calcemic action, we describe here an efficient and versatile synthetic approach to derivatives of 1α,25-dihydroxyvitamin D2 with homologated side-chains and substitution at C24 for biological evaluation.

Comparative study to predict toxic modes of action of phenols from molecular structures.

Quantitative structure-activity relationship models for the prediction of mode of toxic action (MOA) of 221 phenols to the ciliated protozoan Tetrahymena pyriformis using atom-based quadratic indices are reported. The phenols represent a variety of MOAs including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles and soft electrophiles. Linear discriminant analysis (LDA), and four machine learning techniques (ML), namely k-nearest neighbours (k-NN), support vector machine (SVM), classification trees (CTs) and artificial neural networks (ANNs), have been used to develop several models with higher accuracies and predictive capabilities for distinguishing between four MOAs. Most of them showed global accuracy of over 90%, and false alarm rate values were below 2.9% for the training set. Cross-validation, complementary subsets and external test set were performed, with good behaviour in all cases. Our models compare favourably with other previously published models, and in general the models obtained with ML techniques show better results than those developed with linear techniques. We developed unsupervised and supervised consensus, and these results were better than our ML models, the results of rule-based approach and other ensemble models previously published. This investigation highlights the merits of ML-based techniques as an alternative to other more traditional methods for modelling MOA.

Design, Synthesis, and Evaluation of Antineoplastic Activity of Novel Carbocyclic Nucleosides.

Cancer is the leading cause of death among men and women under age 85. Every year, millions of individuals are diagnosed with cancer. But finding new drugs is a complex, expensive, and very time-consuming task. Over the past decade, the cancer research community has begun to address the in silico modeling approaches, such as Quantitative Structure-Activity Relationships (QSAR), as an important alternative tool for targeting potential anticancer drugs. With the compilation of a large dataset of nucleosides synthesized in our laboratories, or elsewhere, and tested in a single cytotoxic assay under the same experimental conditions, we recognized a unique opportunity to attempt to build predictive QSAR models. Early efforts with 2D classification models built from part of this dataset were very encouraging. Here we report a further detailed evaluation of classification models to flag potential anticancer activities derived from a variety of 3D molecular representations. A quantitative 3D-model model that discriminates anticancer compounds from the inactive ones was attained, which allowed the correct classification of 82 % of compounds in such a large and diverse dataset, with only 5 % of false inactives and 11 % of false actives. The model developed here was then used to select and design a new series of nucleosides, by classifying beforehand them as active/inactive anticancer compounds. From the compounds so designed, 22 were synthesized and evaluated for their inhibitory effects on the proliferation of murine leukemia cells (L1210/0), of which 86 % were well-classified as active or inactive, and only two were false actives, corroborating the good predictive ability of the present discriminant model. The results of this study thus provide a valuable tool for the design of novel potent anticancer nucleoside analogues.

A TOPological Sub-structural Molecular Design (TOPS-MODE)-QSAR approach for modeling the antiproliferative activity against murine leukemia tumor cell line (L1210).

Lately, Quantitative Structure-Activity Relationship (QSAR) studies have been afar used to predict anticancer activity taking into account different molecular descriptors, statistical techniques, cell lines and data set of congeneric and non-congeneric compounds. Herein we report a QSAR study based on a TOPological Sub-structural Molecular Design (TOPS-MODE) approach, aiming at predicting the anticancer leukemia activity of a diverse data set of indolocarbazoles derivatives. Finally, several aspects of the structural activity relationships are discussed in terms of the contribution of different bonds to the anticancer activity, thereby making the relationship between structure and biological activity more transparent.

Probing the anticancer activity of nucleoside analogues: a QSAR model approach using an internally consistent training set.

The cancer research community has begun to address the in silico modeling approaches, such as quantitative structure-activity relationships (QSAR), as an important alternative tool for screening potential anticancer drugs. With the compilation of a large dataset of nucleosides synthesized in our laboratories, or elsewhere, and tested in a single cytotoxic assay under the same experimental conditions, we recognized a unique opportunity to attempt to build predictive QSAR models. Here, we report a systematic evaluation of classification models to probe anticancer activity, based on linear discriminant analysis along with 2D-molecular descriptors. This strategy afforded a final QSAR model with very good overall accuracy and predictability on external data. Finally, we search for similarities between the natural nucleosides, present in RNA/DNA, and the active nucleosides well-predicted by the model. The structural information then gathered and the QSAR model per se shall aid in the future design of novel potent anticancer nucleosides.

Synthesis and QSAR study of the anticancer activity of some novel indane carbocyclic nucleosides.

A set of 14 indane carbocyclic nucleosides were synthesized and experimentally assayed for their inhibitory effects in the proliferation of murine leukemia (L1210/0) and human T-lymphocyte (Molt4/C8, CEM/0) cells. The compounds have promising inhibitory activity judging from the IC(50) values obtained for all these cellular lines. Multiple linear regression analysis was then applied to build up consistent QSAR models based on quantum mechanics-derived molecular descriptors. The derived models reproduce well the experimental data of both three cells (r(2) >/=0.90), display a good predictive power and are, above all, easily interpretable. They show that frontier-orbital energies and hydrophobicity are mainly responsible for the activity of the synthesized compounds and also, suggest similar mechanisms of action. The final QSAR-models involve only two descriptors: the lowest unoccupied molecular orbital energy and the solvent accessible-hydrophobic surface area, but describe a sound correlation between predicted and experimental activity data (r(2)=0.931, r(2)=0.936 and r(2)=0.931 for the cells L1210/0, Molt4/C8 and CEM/0, respectively).

New carbocyclic nucleosides derived from indan.

Seven new carbocyclic nucleosides derived from indan (1a-g) were efficiently prepared from 1,2-indanedimethanol vía Mitsunobu reaction with 6-chloroadenine and subsequent introduction of the appropriate substituent.

Synthesis and antiviral and antineoplastic activities of some novel carbocyclic guanosine analogues with a cyclobutane ring.

Cyclobutyl nucleoside analogues containing guanine and 8-azaguanine (compounds 5-10) were prepared from (1R,cis)-3-aminomethyl-2,2-dimethylcyclobutylmethanol (1). All were evaluated as antiviral and antitumoral agents in a variety of assay systems. Compounds 6 and 7 showed a noteworthy activity against a respiratory syncytial virus and compound 10 was moderately active against vaccinia virus. Only compound 5 showed some cytostatic activity.