Phytochemicals in Anticancer Drug Development.

BACKGROUND: In spite of major technological advances in conventional therapies, cancer continues to remain the leading cause of mortality worldwide. Phytochemicals are gradually emerging as a rich source of effective but safer agents against many life-threatening diseases. METHODS: Various phytochemicals with reported anticancer activity have been simply categorized into major phytoconstituents- alkaloids, polyphenols, saponins, tannins and terpenoids. RESULTS: The adverse effects associated with currently available anticancer medications may be overcome by using plant-derived compounds either alone or in combination. Exploration of plant kingdom may provide new leads for the accelerated development of new anticancer agents. CONCLUSION: Although numerous potent synthetic drugs have been introduced for cancer chemotherapy, yet their serious toxicity concerns to normal cells apart from drug resistance have emerged as the major obstacles for their clinical utility over a prolonged duration of time. Current status and potential of phytochemicals and their derivatives in cancer therapy have been briefly reviewed in the present manuscript.

Ayurvedic Medicine for the Treatment of Dementia: Mechanistic Aspects.

Ayurvedic medicine is a personalized system of traditional medicine native to India and the Indian subcontinent. It is based on a holistic view of treatment which promotes and supports equilibrium in different aspects of human life: the body, mind, and soul. Popular Ayurvedic medicinal plants and formulations that are used to slow down brain aging and enhance memory include Ashwagandha (Withania somnifera), Turmeric (Curcuma longa), Brahmi (Bacopa monnieri), Shankhpushpi (Convolvulus pluricaulis, Evolvulus alsinoides, and other species), gotu kola (Centella asiatica), and guggulu (Commiphora mukul and related species) and a formulation known as Brahmi Ghrita, containing Brahmi, Vaca (Acorus calamus), Kustha (Saussurea lappa), Shankhpushpi, and Purana Ghrita (old clarified butter/old ghee). The rationale for the utilization of Ayurvedic medicinal plants has depended mostly on traditional usage, with little scientific data on signal transduction processes, efficacy, and safety. However, in recent years, pharmacological and toxicological studies have begun to be published and receive attention from scientists for verification of their claimed pharmacological and therapeutic effects. The purpose of this review is to outline the molecular mechanisms, signal transduction processes, and sites of action of some Ayurvedic medicinal plants. It is hoped that this description can be further explored with modern scientific methods, to reveal new therapeutic leads and jump-start more studies on the use of Ayurvedic medicine for prevention and treatment of dementia.

Color additives: legislative perspective in the United States, Europe, Australia, and India.

In most countries, their regulatory bodies which control food, drugs, and cosmetics are responsible for regulating all the color additives to ensure that they are safe with those products, ensuring that they contain only approved ingredients, and that they are accurately labeled. Permitted colors are subject to rigorous safety standards prior to their approval and prior to the release of their specific use. Although a colorant may have approval in several countries, its use, level of use, specifications, acceptance criteria, and labeling requirements may differ from one country to another. In this review, we have explored the legislative and regulatory framework regarding the color additives for use in drugs and cosmetics.

Selection of color additives: a regulatory view.

Color additives have a unique place in the categories of the excipients. However, most of the color additives are complex heterogeneous organic compounds. In pharmaceuticals, colors are used in various oral (solid, liquid) and topical dosage form. Different regulatory authorities have their own specific set of regulation for registration, approval, and control of color additives. However, at this time of globalization, selection of appropriate color is not an easy task when a company wants to sale its product in many countries. In this article, the authors have explored various important factors which should be considered in the selection of color additives.

Diverse models for the prediction of HIV integrase inhibitory activity of substituted quinolone carboxylic acids.

In the present study both classification and correlation techniques of diverse nature were successfully employed for the development of models for the prediction of human immunodeficiency virus (HIV) integrase inhibitory activity using a dataset comprising 50 analogs of quinolone carboxylic acid. The values of various molecular descriptors (MDs) for each analog in the dataset were computed using the MDS V-life science QSAR plus module. The values of other MDs which are not part of MDS V-life science were computed using an in-house computer program. A decision tree (DT) was constructed for the HIV integrase inhibitory activity to determine the importance of MDs. The DT learned the information from the input data with an accuracy of 98% and correctly predicted the cross-validated (10 fold) data with an accuracy of 96%. Three MDs, E-state contribution descriptor (SssOHE), molecular connectivity topochemical index ($\chi {}^{{\rm A}} $), and eccentric connectivity topochemical index ($\xi _{{\rm C}}^{{\rm C}} $), were used to develop the models using moving average analysis (MAA). The accuracy of classification of single descriptor based models using MAA was found to vary from a minimum of 96% to a maximum of 98%. The statistical significance of the models was assessed through specificity, sensitivity, overall accuracy, Mathew's correlation coefficient, and intercorrelation analysis. The widely used methods like multiple linear regression, partial least squares, and principal component regression were employed for development of correlation models. The models were generated on a training set of 36 molecules. The models had a correlation coefficient (r(2) ) of 0.86 to 0.92, significant cross validated correlation coefficient (q(2) ) of 0.79 to 0.85, F-test from 63.2 to 93.06, r(2) for external test set (pred_r(2) ) from 0.69, coefficient of correlation of predicted dataset (pred_ r(2) Se) of 0.77, and degree of freedom from 27 to 30. Alignment independent descriptors, SsOHE-index, SaaCHE index, SssCH2, and x log P were found to be the most important descriptors for the development of correlation models for the prediction of HIV integrase inhibitory activity.

Superaugmented eccentric distance sum connectivity indices: novel highly discriminating topological descriptors for QSAR/QSPR.

Four highly discriminating fourth-generation topological indices (TIs), termed as superaugmented eccentric distance sum connectivity indices, as well as their topochemical versions (denoted by , , and ), have been conceptualized in this study. The values of these indices for all possible structures with three, four, and five vertices containing one heteroatom were computed using an in-house computer program. The proposed superaugmented eccentric distance sum connectivity topochemical indices exhibited exceptionally high discriminating power, low degeneracy, and high sensitivity toward both the presence and the relative position of heteroatom(s) for all possible structures with five vertices containing at least one heteroatom. Intercorrelation analysis revealed the absence of correlation of proposed indices with Zagreb indices and the molecular connectivity index. Subsequently, the proposed TIs were successfully utilized for the development of models for the prediction of checkpoint kinase inhibitory activity of 2-arylbenzimidazoles. A data set comprising 47 differently substituted analogs of 2-arylbenzimidazoles was selected for the study. The values of various TIs for each analog in the data set were computed using an in-house computer program. The resulting data were analyzed, and suitable models were developed through decision tree (DT), random forest (RF), and moving average analysis (MAA). The performance of the models was assessed by calculating the specificity, sensitivity, overall accuracy, and Mathew's correlation coefficient. A decision tree was constructed for the checkpoint kinase inhibitory activity to determine the importance of topological indices. The decision tree identified the proposed TIs -, - as the most important indices. The decision tree learned the information from the input data with an accuracy of 96% and correctly predicted the cross-validated (10-fold) data with an accuracy of 77%. Random forest correctly predicted the checkpoint kinase inhibitory activity with an accuracy of 83%. The single index-based models were also developed for the prediction of checkpoint kinase inhibitory activity using MAA. The accuracy of prediction of single index-based models derived through MAA was found to vary from a minimum of 90% to a maximum of 95%. Exceptionally high discriminating power, low degeneracy, and high sensitivity toward branching and presence of heteroatom of proposed indices can be of immense use in drug design, isomer discrimination, similarity/dissimilarity studies, quantitative structure activity/property relationships, lead optimization, and combinatorial library design.

Models for the prediction of receptor tyrosine kinase inhibitory activity of substituted 3-aminoindazole analogues.

The inhibition of tumor angiogenesis has become a compelling approach in the development of anticancer drugs. In the present study, topological models were developed through decision tree and moving average analysis using a data set comprising 42 analogues of 3-aminoindazoles. A total of 22 descriptors (distance based, adjacency based, pendenticity and distance-cum-adjacency based) were used. The values of all 22 topological indices for each analogue in the dataset were computed using an in-house computer program. A decision tree was constructed for the receptor tyrosine kinase KDR (kinase insert domain receptor) inhibitory activity to determine the importance of topological indices. The decision tree learned the information from the input data with an accuracy of 88%. Three independent topological models were also developed for prediction of receptor tyrosine kinase inhibitory (KDR) activity using moving average analysis. The models developed were also found to be sensitive towards the prediction of other receptor tyrosine kinases i.e. FLT3 (fms-like tyrosine kinase-3) and cKIT inhibitory activity. The accuracy of classification of single index based models using moving average analysis was found to be 88%. The performance of models was assessed by calculating precision, sensitivity, overall accuracy and Mathew's correlation coefficient (MCC). The significance of the models was also assessed by intercorrelation analysis.

Models for antitubercular activity of 5â-O-[(N-Acyl)sulfamoyl]adenosines.

The relationship between topological indices and antitubercular activity of 5â-O-[(N-Acyl)sulfamoyl]adenosines has been investigated. A data set consisting of 31 analogues of 5â-O-[(N-Acyl)sulfamoyl]adenosines was selected for the present study. The values of numerous topostructural and topochemical indices for each of 31 differently substituted analogues of the data set were computed using an in-house computer program. Resulting data was analyzed and suitable models were developed through decision tree, random forest and moving average analysis (MAA). The goodness of the models was assessed by calculating overall accuracy of prediction, sensitivity, specificity and Mathews correlation coefficient. Pendentic eccentricity index â a novel highly discriminating, non-correlating pendenticity based topochemical descriptor â was also conceptualized and successfully utilized for the development of a model for antitubercular activity of 5â-O-[(N-Acyl)sulfamoyl]adenosines. The proposed index exhibited not only high sensitivity towards both the presence as well as relative position(s) of pendent/heteroatom(s) but also led to significant reduction in degeneracy. Random forest correctly classified the analogues into active and inactive with an accuracy of 67.74%. A decision tree was also employed for determining the importance of molecular descriptors. The decision tree learned the information from the input data with an accuracy of 100% and correctly predicted the cross-validated (10 fold) data with accuracy up to 77.4%. Statistical significance of proposed models was also investigated using intercorrelation analysis. Accuracy of prediction of proposed MAA models ranged from 90.4 to 91.6%.

Development of modified dosage form for enhancement of dissolution rate through amalgamation of solid dispersion and cube sugar or sintering technology using famotidine as a model drug.

An amalgamation of solid dispersion and cube sugar or sintering technologies was utilized for preparing a high dissolution rate, fast-release dosage form for poorly water soluble drug(s). Famotidine was employed as a model drug. Solid dispersion particles of famotidine were prepared using the fusion method employing xylitol as an hydrophilic carrier, and the particles' solid state performance was characterized by means of differential scanning calorimetry, Fourier transformed infrared spectroscopy, and X-ray powder diffractometry. Solid dispersion particles of famotidine were encompassed directly into tablets in a manner similar to that adopted for cube sugar production and sintering technology. The effect of different particle sizes of solid dispersion was also studied in relation to tablet disintegration. The resulting tablets were only rapidly disintegrating owing to capillarity but also ensure the rapid dissolution of poorly water-soluble drug when compared to other marketed products.

Predicting anti-HIV activity of dimethylaminopyridin-2-ones: computational approach using topochemical descriptors.

In this study, relationship between the topochemical indices and anti-HIV activity of dimethylaminopyridin-2-ones has been investigated. Three topochemical indices of diverse nature, i.e. Wiener's topochemical index--a distance-based topochemical descriptor, molecular connectivity topochemical index--an adjacency based topochemical descriptor and augmented eccentric connectivity topochemical index--an adjacency-cum-distance-based topochemical descriptor, were used for the present investigations. The values of the Wiener's topochemical index, molecular connectivity topochemical index and augmented eccentric connectivity topochemical index for each of the 103 analogues comprising the data set were computed using an in-house computer program. Resulting data were analyzed and suitable models were developed after the identification of the active ranges. Subsequently, a biological activity was assigned to each compound using these models, which was then compared with the reported anti-HIV activity. Statistical significance of proposed models was further investigated using chi-squared test and intercorrelation analysis. Accuracy of prediction of anti-HIV activity was found to vary from 81 to 85% using these models.

Topochemical models for prediction of telomerase inhibitory activity of flavonoids.

In the present study, the relationship between the topochemical indices and telomerase inhibitory activity of flavonoids has been investigated. Three topochemical indices, Wiener's topochemical index (a distance-based topochemical descriptor), molecular connectivity topochemical index (an adjacency-based topochemical descriptor) and superadjacency topochemical index (an adjacency cum distance-based topochemical descriptor) were used for the present investigation. The values of the Wiener's topochemical index, molecular connectivity topochemical index and superadjacency topochemical index for each of the 30 analogues comprising the data set were computed using an in-house computer program. Resultant data was analysed and suitable models were developed after identification of the active ranges. Subsequently, a biological activity was assigned to each analogue involved in the data set using these models, which was then compared with the reported telomerase inhibitory activity. Statistical significance of proposed models was investigated using intercorrelation analysis. Accuracy of prediction using proposed models was found to vary from 80% to 83%.

Topochemical models for the prediction of voltage-gated sodium channel binding activity of hydantoins and related non-hydantoins.

The relationship of Wiener's topochemical index-a distance based topochemical index, molecular connectivity topochemical index-an adjacency based topochemical index and eccentric connectivity topochemical index-an adjacency-cum-distance based topochemical index with sodium channel binding activity has been investigated. A dataset comprising 50 hydantoins and related non-hydantoins was selected. The dataset was divided equally into training and test sets. The values of the three topochemical indices for all the compounds present in both the training and test sets were computed using an in-house computer program. The resulting data was analyzed and suitable models were developed after identification of the active ranges in the training set. Subsequently, a biological activity was assigned to each compound involved in the training set using these models, which was then compared with the reported sodium channel binding activity. An accuracy of prediction of the order of >99% was observed using the proposed models. Cross-validation of these models using the test set revealed an exceptionally high accuracy of approximately 95%.

Validation of topochemical models for the prediction of permeability through the blood-brain barrier.

Recently published topochemical models for permeability through the blood-brain barrier were validated and cross-validated in the present study. Five models based on three topochemical indices, Wiener's topochemical index - a distance-based topochemical descriptor, molecular connectivity topochemical index - an adjacency-based topochemical descriptor and eccentric connectivity topochemical index - an adjacency-cum-distance based topochemical descriptor, for permeability of structurally and chemically diverse molecules through blood-brain barrier were used in the present investigation. A data set comprising 62 structurally and chemically diverse compounds was selected. This data set was divided into two sets of 31 compounds each - one to serve as the validation set and other as the cross-validation set. The values of all the three-topochemical indices in the original as well as in the normalized form for each of the 31 compounds of the validation set were computed using an in-house computer program. Resultant data was analyzed and each compound was assigned a permeability characteristic using topochemical models, which was then compared with the reported permeability through the blood-brain barrier. Accuracy of prediction of these models was calculated. The same procedure was similarly followed for the cross-validation set. Studies revealed accuracy of prediction of the order of 70-80% during validation. Surprisingly, very high predictability of the order of 77-91% was observed during cross-validation. High predictability observed during validation as well as cross-validation authenticates topochemical models for prediction of permeability through the blood-brain barrier.

Prediction of h5-HT2A receptor antagonistic activity of arylindoles: computational approach using topochemical descriptors.

Relationship between the topochemical indices and h5-HT2A receptor antagonistic activity of arylindoles has been investigated. Three topochemical indices, Wiener's topochemical index--a distance-based topochemical descriptor, molecular connectivity topochemical index--an adjacency-based topochemical descriptor and eccentric connectivity topochemical index--an adjacency-cum-distance based topochemical descriptor, were used for the present investigation. A data set comprising 31 differently substituted arylindoles was selected for the present study. The values of the Wiener's topochemical index, molecular connectivity topochemical index and eccentric connectivity topochemical index were computed for all the analogues involved in the data set using an in-house computer program. Resultant data was analyzed and suitable models were developed after identification of the active ranges. Subsequently, a biological activity was assigned to each analogue using these models, which was then compared with the reported h5-HT2A receptor antagonistic activity. Accuracy of prediction was found to vary from a minimum of approximately 81% to a maximum of approximately 84%.

Topological model for the prediction of MRP1 inhibitory activity of pyrrolopyrimidines and templates derived from pyrrolopyrimidine.

Relationship between Wiener's index--a distance-based topological descriptor and multidrug-resistance-associated protein (MRP1) inhibitory activity of pyrrolopyrimidines and their derivatives has been investigated. A dataset comprising of 82 analogues of pyrrolopyrimidine was selected for the present study. The values of Wiener's index were computed for each of the 82 analogues using an in-house computer program. Resultant data were analyzed and a suitable model was developed after identification of the active range. Subsequently, a biological activity was assigned to each analogue involved in the dataset using this model, which was then compared with the reported MRP1 inhibitory activity. Accuracy of prediction was found to be 88% using model based on Wiener's index.

Topochemical models for prediction of anti-tumor activity of 3-aminopyrazoles.

Relationship between topochemical indices and inhibition of CDK2/cyclin A by 3-aminopyrazoles was investigated using a data set comprising of 42 3-aminopyrazoles. Three topochemical indices--the Wiener's topochemical index--a distance based topochemical index, atomic molecular connectivity index--an adjacency based topochemical index and superadjacency topochemical index--an adjacency-cum-distance based topochemical index were used for the present investigations. The values of Wiener's topochemical index, atomic molecular connectivity index and superadjacency topochemical index for each of the 42 compounds comprising the data set were computed using an in-house computer program. Resultant data was subsequently analyzed and suitable models were developed after identification of the active ranges. Subsequently, a biological activity was assigned to each of the compounds using these models, which was then compared with the reported CDK2/cyclin A inhibitory activity. High accuracy of prediction ranging from 86 to 89% was observed using these models.

Topochemical models for prediction of cyclin-dependent kinase 2 inhibitory activity of indole-2-ones.

The relationship between the topochemical indices and cyclin-dependent kinase 2 (CDK2) inhibitory activity of indole-2-ones has been investigated. The relationship of topochemical versions of well known topological indices of Wiener's index--a distance-based topological descriptor, molecular connectivity index, an adjacency-based topological descriptor and eccentric connectivity index--an adjacency-cum-distance based topological descriptor with CDK2 inhibitory activity of indole-2-ones has been investigated. A data set comprising 67 analogues of substituted indole-2-ones was selected for the present investigation. The values of the Wiener's topochemical index, molecular connectivity topochemical index and eccentric connectivity topochemical index for each of 67 analogues comprising the data set were computed. The resulting data was analyzed and suitable models developed after identification of the active ranges. Subsequently, a biological activity was assigned to each analogue in the data set using these models, which was then compared with the reported CDK2 inhibitory activity. Accuracy of prediction was found to vary from a minimum of 88% for a model based upon molecular connectivity topochemical index to a maximum of approximately 90% for model based upon eccentric connectivity topochemical index.

Topological models for the prediction of HIV-protease inhibitory activity of tetrahydropyrimidin-2-ones.

Relationship between the topological indices and HIV-protease inhibitory activity of tetrahydropyrimidine-2-ones has been investigated. Three topological indices, Wiener's index--a distance based topological descriptor, Zagreb group parameter--an adjacency based topological descriptor and eccentric connectivity index--an adjacency-cum-distance based topological descriptor were used for the present investigations. A dataset comprising of 80 substituted tetrahydropyrimidine-2-one analogues was selected for the present studies. The values of the Wiener's index, Zagreb group parameter and eccentric connectivity index for each of the 80 compounds comprising the dataset were computed using an in-house computer program. The dataset was divided randomly into training and test sets. Resultant data was analyzed and suitable models were developed after identifying the active ranges in the training set. Subsequently, a biological activity was assigned to each of the compound involved in the test set using these models, which was then compared with the reported HIV-protease inhibitory activity. Accuracy of prediction using these models was found to vary from a minimum of approximately 86% to a maximum of approximately 88%.

Predicting anti-HIV activity of 2,3-diaryl-1,3-thiazolidin-4-ones: computational approach using reformed eccentric connectivity index.

The eccentric connectivity index, which has recently been employed successfully for the development of numerous mathematical models for the prediction of biological activities of diverse nature, has been reformed to overcome its limitations caused by degeneracy and insensitivity towards heteroatoms. The reformed eccentric connectivity index, termed the eccentric connectivity topochemical index, overcomes the limitations of the eccentric connectivity index by exhibiting very low degeneracy and displaying sensitivity to both the presence and relative position of heteroatoms without compromizing the discriminating power of the eccentric connectivity index. The relationship of the eccentric connectivity topochemical index, eccentric connectivity index and Wiener's index with regard to the anti-HIV activity of 2, 3-diaryl-1, 3-thiazolidin-4-one derivatives was subsequently investigated. The values of the eccentric connectivity topochemical index, the eccentric connectivity index and Wiener's index of each of 31 analogues comprizing the data set were computed using in-house computer program. Resultant data was analyzed and suitable models developed after identification of active ranges. Subsequently, each derivative was assigned a biological activity using these models, which was then compared with the reported anti-HIV activity. The accuracy of prediction using these models was found to vary from 81 to 90%. The proposed index offers a vast potential for virtual screening of combinatorial libraries, structure property/activity studies and drug design. [figure]. Basic structure of 2,3-diaryl-1, 3-thiazoidin-4-ones.

Predicting anti-HIV activity of TIBO derivatives: a computational approach using a novel topological descriptor.

A novel highly discriminating adjacency-cum-distance-based topological descriptor, termed the adjacent eccentric distance sum index, has been conceptualized and its discriminating power investigated with regard to the anti-HIV activity of 4,5,6,7-tetrahydro-imidazo-[4,5,1- jk] [1,4] benzodiazepin-2 (1 H)-one (TIBO) derivatives. The discriminating power of the adjacent eccentric distance sum index was compared with that of the eccentric connectivity index - another adjacency-cum-distance-based topological descriptor. The values of the eccentric connectivity index and the adjacent eccentric distance sum index of each of 121 analogues comprising the data set were computed and active ranges were identified. Subsequently, a biological activity was assigned to each analogue involved in the data set and this was then compared with the reported anti-HIV activity. Excellent correlations were observed between anti-HIV activity and both the topological descriptors. Although the overall accuracy of prediction was found to be approximately 84% in case of the eccentric connectivity index and approximately 86% in case of adjacent eccentric distance sum index, the predictability using the adjacent eccentric distance sum index in the active range itself was >92%. The proposed index offers a vast potential for structure-activity/property studies.