Biomedical subjects

Richard Rotondo

Publications and source records attributed to Richard Rotondo.

4 recordsLinked to original sources

PubMed2006

Bond-based global and local (bond, group and bond-type) quadratic indices and their applications to computer-aided molecular design. 1. QSPR studies of diverse sets of organic chemicals.

The concept of atom-based quadratic indices is extended to a series of molecular descriptors (MDs) (both total and local) based on adjacency between edges. The kth edge-adjacency matrix (E ( k )) denotes the matrix of bond-based quadratic indices (non-stochastic) with respect to the canonical basis set. The kth "stochastic" edge-adjacency matrix, ES ( k ), is here proposed as a new molecular representation easily calculated from E ( k ). Then, the kth stochastic bond-based quadratic indices are calculated using ES ( k ) as operators of quadratic transformations. The study of six representative physicochemical properties of octane isomers was used to compare the ability of both series of MDs to produce significant quantitative structure-property relationship (QSPR) models. Moreover, the general performance of the new MDs in this QSPR study has been evaluated with respect to other 2D/3D well-known sets of indices and the obtained results shown a quite satisfactory behavior of the present method. The novel bond-level MDs were also used for the description and prediction of the boiling point of 28 alkyl-alcohols and to the modeling of the specific rate constant (log k) of 34 derivatives of 2-furylethylenes. These models were statistically significant and showed very good stability to data variation in leave-one-out (LOO) cross-validation experiment. The comparison with other approaches (edge- and vertices-based connectivity indices, total and local spectral moments, and quantum chemical descriptors as well as E-state/biomolecular encounter parameters) expose a good behavior of our method in this QSPR studies. The approach described in this report appears to be a very promising structural invariant, useful for QSPR/QSAR studies, similarity/diversity analysis, and computer-aided "rational" molecular (drug) design.

Chemical Phenomena↗

PubMed2006

TOMOCOMD-CARDD descriptors-based virtual screening of tyrosinase inhibitors: evaluation of different classification model combinations using bond-based linear indices.

A new set of bond-level molecular descriptors (bond-based linear indices) are used here in QSAR (quantitative structure-activity relationship) studies of tyrosinase inhibitors, for finding functions that discriminate between the tyrosinase inhibitor compounds and inactive ones. A database of 246 compounds was collected for this study; all organic chemicals were reported as tyrosinase inhibitors; they had great structural diversity. This dataset can be considered as a helpful tool, not only for theoretical chemists but also for other researchers in this area. The set used as inactive has 412 drugs with other clinical uses. Twelve LDA-based QSAR models were obtained, the first six using the non-stochastic total and local bond-based linear indices as well as the last six ones, the stochastic molecular descriptors. The best two discriminant models computed using the non-stochastic and stochastic molecular descriptors (Eqs. , respectively) had globally good classifications of 98.95% and 89.75% in the training set, with high Matthews correlation coefficients (C) of 0.98 and 0.78. The external prediction sets had accuracies of 98.89% and 89.44%, and (C) values of 0.98 and 0.78, for models 7 and 13, respectively. A virtual screening of compounds reported in the literature with such activity was carried out, to prove the ability of present models to search for tyrosinase inhibitors, not included in the training or test set. At the end, the fitted discriminant functions were used in the selection/identification of new ethylsteroids isolated from herbal plants, looking for tyrosinase inhibitory activity. A good behavior is shown between the theoretical and experimental results on mushroom tyrosinase enzyme. It might be highlighted that all the compounds showed values under 10microM and that ES2 (IC(50)=1.25microM) showed higher activity in the inhibition against the enzyme than reference compounds kojic acid (IC(50)=16.67microM) and l-mimosine (IC(50)=3.68microM). In addition, a comparison with other established methods was carried to prove the adequate discriminatory performance of the molecular descriptors used here. The present algorithm provided useful clues that can be used to speed up in the identification of new tyrosinase inhibitor compounds.

Agaricales↗

PubMed2006

Atom-based stochastic and non-stochastic 3D-chiral bilinear indices and their applications to central chirality codification.

Non-stochastic and stochastic 2D bilinear indices have been generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. In order to evaluate the effectiveness of this novel approach in drug design we have modeled the angiotensin-converting enzyme inhibitory activity of perindoprilate's sigma-stereoisomers combinatorial library. Two linear discriminant analysis models, using non-stochastic and stochastic linear indices, were obtained. The models had shown an accuracy of 95.65% for the training set and 100% for the external prediction set. Next the prediction of the sigma-receptor antagonists of chiral 3-(3-hydroxyphenyl)piperidines by multiple linear regression analysis was carried out. Two statistically significant QSAR models were obtained when non-stochastic (R(2)=0.953 and s=0.238) and stochastic (R(2)=0.961 and s=0.219) 3D-chiral bilinear indices were used. These models showed adequate predictive power (assessed by the leave-one-out cross-validation experiment) yielding values of q(2)=0.935 (s(cv)=0.259) and q(2)=0.946 (s(cv)=0.235), respectively. Finally, the prediction of the corticosteroid-binding globulin binding affinity of steroids set was performed. The obtained results are rather similar to most of the 3D-QSAR approaches reported so far. The validation of this method was achieved by comparison with previous reports applied to the same data set. The non-stochastic and stochastic 3D-chiral linear indices appear to provide a very interesting alternative to other more common 3D-QSAR descriptors.

Angiotensin-Converting Enzyme Inhibitors↗

PubMed2006

Predicting antitrichomonal activity: a computational screening using atom-based bilinear indices and experimental proofs.

Existing Trichomonas vaginalis therapies are out of reach for most trichomoniasis people in developing countries and, where available, they are limited by their toxicity (mainly in pregnant women) and their cost. New antitrichomonal agents are needed to combat emerging metronidazole-resistant trichomoniasis and reduce the side effects associated with currently available drugs. Toward this end, atom-based bilinear indices, a new TOMOCOMD-CARDD molecular descriptor, and linear discriminant analysis (LDA) were used to discover novel, potent, and non-toxic lead trichomonacidal chemicals. Two discriminant functions were obtained with the use of non-stochastic and stochastic atom-type bilinear indices for heteroatoms and H-bonding of heteroatoms. These atomic-level molecular descriptors were calculated using a weighting scheme that includes four atomic labels, namely atomic masses, van der Waals volumes, atomic polarizabilities, and atomic electronegativities in Pauling scale. The obtained LDA-based QSAR models, using non-stochastic and stochastic indices, were able to classify correctly 94.51% (90.63%) and 93.41% (93.75%) of the chemicals in training (test) sets, respectively. They showed large Matthews' correlation coefficients (C); 0.89 (0.79) and 0.87 (0.85), for the training (test) sets, correspondingly. The result of predictions on the 15% full-out cross-validation test also evidenced the robustness and predictive power of the obtained models. In addition, canonical regression analyses corroborated the statistical quality of these models (R(can) of 0.749 and of 0.845, correspondingly); they were also used to compute biological activity canonical scores for each compound. On the other hand, a close inspection of the molecular descriptors included in both equations showed that several of these molecular fingerprints are strongly interrelated with each other. Therefore, these models were orthogonalized using the Randić orthogonalization procedure. These classification functions were then applied to find new lead antitrichomonal agents and six compounds were selected as possible active compounds by computational screening. The designed compounds were synthesized and tested for in vitro activity against T. vaginalis. Out of the six compounds that were designed, and synthesized, three molecules (chemicals VA5-5a, VA5-5c, and VA5-12b) showed high to moderate cytocidal activity at the concentration of 10 microg/ml, other two compounds (VA5-8pre and VA5-8) showed high cytocidal and cytostatic activity at the concentration of 100 microg/ml and 10 microg/ml, correspondingly, and the remaining chemical (compound VA5-5e) was inactive at these assayed concentrations. Nonetheless, these compounds possess structural features not seen in known trichomonacidal compounds and thus can serve as excellent leads for further optimization of antitrichomonal activity. The LDA-based QSAR models presented here can be considered as a computer-assisted system that could potentially significantly reduce the number of synthesized and tested compounds and increase the chance of finding new chemical entities with antitrichomonal activity.

Algorithms↗