Antineoplastic agents 1998

ANN-QSAR model for selection of anticancer leads from structurally heterogeneous series of compounds

Developing a model for predicting anticancer activity of any classes of organic compounds based on molecular structure is very important goal for medicinal chemist. Different molecular descriptors can be used to solve this problem. Stochastic molecular descriptors so-called the MARCH-INSIDE approach, shown to be very successful in drug design. Nevertheless, the structural diversity of compounds is so vast that we may need non-linear models such as artificial neural networks (ANN) instead of linear ones. SmartMLP-ANN analysis used to model the anticancer activity of organic compounds has shown high average accuracy of 93.79% (train performance) and predictability of 90.88% (validation performance) for the 8:3-MLP topology with different training and predicting series. This ANN model favourably compares with respect to a previous linear discriminant analysis (LDA) model [H. González-Díaz et al., J. Mol. Model 9 (2003) 395] that showed only 80.49% of accuracy and 79.34% of predictability. The present SmartMLP approach employed shorter training times of only 10h while previous models give accuracies of 70-89% only after 25-46 h of training. In order to illustrate the practical use of the model in bioorganic medicinal chemistry, we report the in silico prediction, and in vitro evaluation of six new synthetic tegafur analogues having IC(50) values in a broad range between 37.1 and 138 microgmL(-1) for leukemia (L1210/0) and human T-lymphocyte (Molt4/C8, CEM/0) cells. Theoretical predictions coincide very well with experimental results.

Synthesis of compounds with antiproliferative activity as analogues of prenylated natural products existing in Brazilian propolis

This work describes the syntheses and antitumoral properties of five prenyl compounds from which antiproliferative activities were predicted by using the TOPS-MODE approach, a computational method for drug design. The syntheses of 2-(3-methylbut-2-enyloxy)acetophenone (2), 2-hydroxy-5-(3-methylbut-2-enyl)acetophenone (3), 2-hydroxy-3-(1,1-dimethylallyl)acetophenone (4), and 5-(3-methylbut-2-enyl)-2-(3-methylbut-2-enyloxy)acetophenone (5) were realized by O-prenylation of phenolic compounds with prenyl bromide and by Claisen rearrangement, respectively. Reaction of 2-hydroxy-5-(3-methylbut-2-enyl)acetophenone 3 under Vilsmeier-Haack conditions with phosphoryl chloride and N,N-dimethylformamide yielded 6-(3-methylbut-2-enyl)chromone-3-carbaldehyde (6). The compounds were tested for their cytotoxicity toward a diverse panel of cultured human tumor cell lines. Compound 3 showed significant selective cytotoxic activity (IC(50) < 9 microg/ml).

Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer-aided molecular design I: discovery of anticancer compounds

A simple stochastic approach, designed to model the movement of electrons throughout chemical bonds, is introduced. This model makes use of a Markov matrix to codify useful structural information in QSAR. The self-return probabilities of this matrix throughout time ((SR)pi(k)) are then used as molecular descriptors. Firstly, a calculation of (SR)pi(k) is made for a large series of anticancer and non-anticancer chemicals. Then, k-Means Cluster Analysis allows us to split the data series into clusters and ensure a representative design of training and predicting series. Next, we develop a classification function through Linear Discriminant Analysis (LDA). This QSAR discriminates between anticancer compounds and non-active compounds with a correct global classification of 90.5% in the training series. The model also correctly classified 86.07% of the compounds in the predicting series. This classification function is then used to perform a virtual screening of a combinatorial library of coumarins. In this connection, the biological assay of some furocoumarins, selected by virtual screening using the present model, gives good results. In particular, a tetracyclic derivative of 5-methoxypsoralen (5-MOP) has an IC50 against HL-60 tumoral line around 6 to 10 times lower than those for 8-MOP and 5-MOP (reference drugs), respectively. Finally, application of Iso-contribution Zone Analysis (IZA) provides structural interpretation of the biological activity predicted with this QSAR.

Inhibition of tumor angiogenesis by synthetic receptor tyrosine kinase inhibitors

Protein tyrosine kinases have emerged as crucial targets for therapeutic intervention in cancer. More recently, growth factor ligands and their respective receptor tyrosine kinases (RTKs) have been shown to be required for tumor cell growth. This latter aspect includes tumor angiogenesis where the growth of tumors leads to compensatory effects on host cells in the tumor microenvironment leading to the growth of microvessels. The purpose of this review is to focus on synthetic chemical approaches to block RTKs associated with tumor angiogenesis as a means to limit the growth and spread of human tumors.

A novel approach for the virtual screening and rational design of anticancer compounds

A topological substructural approach to molecular design (TOSS-MODE) has been introduced for the selection and design of anticancer compounds. A quantitative model that discriminates anticancer compounds from the inactive ones in a training series was obtained. This model permits the correct classification of 91.43% of compounds in an external prediction set with only 1.43% of false actives and 7. 14% of false inactives. The model developed is then used in a simulation of a virtual search for Ras FTase inhibitors; 87% of the Ras FTase inhibitors used in this simulated search were correctly classified, thus indicating the ability of the TOSS-MODE model of finding lead compounds with novel structures and mechanism of action. Finally, a series of carbonucleosides was designed, and the compounds were classified as active/inactive anticancer compounds by using the model developed here. From the compounds so-designed, 20 were synthesized and evaluated experimentally for their antitumor effects on the proliferation of murine leukemia cells (L1210/0) and human T-lymphocyte cells (Molt4/C8 and CEM/0); 80% of these compounds were well-classified, as active or inactive, and only two pairs of isomeric compounds were false actives. The chloropurine derivatives were the most active compounds, especially compounds 6c, d.

Recent advances in the development of telomerase inhibitors for the treatment of cancer

Telomerase is an holoenzyme responsible for the maintenance of telomeres, the protein-nucleic acid structures which exist at the ends of eukaryotic chromosomes that serve to protect chromosomal stability and integrity. Telomerase activity is essential for the sustained proliferation of most immortal cells, including cancer cells. Since the discovery that telomerase activity is expressed in 85 - 90% of all human tumours and tumour-derived cell lines but not in most normal somatic cells, telomerase has become the focus of much attention as a novel and potentially highly-specific target for the development of new anticancer chemotherapeutics. Herein we review recent advances in the development of telomerase inhibitors for the treatment of cancer. To date, these have included antisense strategies, reverse transcriptase inhibitors and compounds capable of interacting with high-order telomeric DNA tetraplex ('G-quadruplex') structures to prevent enzyme access to the necessary linear telomere substrate. In addition, a number of telomerase-inhibitory therapies have been shown to synergistically enhance the effects of clinically-established anticancer drugs. Critical appraisal of each individual approach is provided, together with highlighted areas of likely future development. We also review recent developments in telomere and telomerase biology, of which a more detailed understanding would be essential in order to further develop the present classes of telomerase inhibitors into viable, clinically applicable therapies.