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The generics of biologic drugs play a significant role in pharmaceutics and therapeutics owing to their affordability to a group of patients with fatal diseases. Even though the challenges in manufacturing of these drugs are overcome by biosimilar applicants, the disputes on patents hamper the market entry of biosimilar. The countries such as United States, European Union, Japan, Canada etc. follow divergent legal pathways to avoid these conflicts. In this article, the patent dancing in US and patent linkage systems in various countries are elaborated. As for India, few generic manufacturers attempted to resolve patent disputes through lawsuits, yet no patent linkage system is established by legislation. [ABSTRACT FROM AUTHOR]

Quantitative structure-activity relationship (QSAR) modeling of 40 DEET-related mosquito repellents was carried out using calculated molecular descriptors. When the four different classes of descriptors (topochemical, topostructural, geometrical and quantum chemical) were used in a hierarchical fashion, topochemical descriptors were found to explain much of the variance in the data and this indicated the importance of the chemical nature of the atoms and groups towards repellency of these compounds. Ridge regression (RR) outperformed partial least square regression (PLS) and principal component regression (PCR). We also used descriptor thinning via a modified Gram-Schmidt algorithm prior to ridge regression. This resulted in a four-parameter model with a 20-fold cross-validated R2 of 0.734. The q2 (R2cv) reported here is the "true-q2" because the descriptor thinning was embedded within the cross-validation step. Inclusion of any calculated physicochemical property (secondary descriptor) did not result in improvement of the models built with the calculated molecular descriptors (primary descriptors). This result has great implications in the QSAR assisted design of new mosquito repellents because calculation of the primary descriptors does not require any input other than the molecular structure. [ABSTRACT FROM AUTHOR]

Vector Borne Diseases (VBD) present a serious threat to millions of people. In this paper various computational approaches towards new drugs design against some of them are reviewed. Malaria attracts particular attention of computational medicinal chemists. A promising strategy of the fight with VBD is usage of insect repellents. N,N-Diethyl-m-toluamide (DEET) has been the mostly used mosquito repellent for over five decades. Its mode of action is still a matter of intensive studies and debate. A possible mechanism of DEET activity is inactivation of odorant receptor proteins expressed in female mosquitoes, and being critical for finding a prey. In order to check possible interactions of DEET with such a transmembrane protein and to indicate a plausible biophore, we have constructed a hybrid "ab initio" model of Anopheles gambiae Odorant Receptor Protein 1 (AgOR1). The transmembrane regions of AgOR1 were predicted using 10 different bioinformatics algorithms and a consensus approach. A full torsional potential energy surface of DEET was determined using the AM1 method and low energy conformers were further optimized using the HF/6-31G method. DEET and a series of diastereomers of alternative repellent cyclohex-3-enyl 2-methylpiperidin-1-yl ketone (220) was docked to the AgOR1 model using the AutoDock 3.0.5 code, and possible interactions sites inside this GPCR AgOR1 were identified.

Several alignment free sequence comparison methods are available and they use similarity, based on a particular numerical descriptor of biological sequences. Any loss of information incurred in the transformation of a sequence into a numerical descriptor affects the results. A pool of descriptors that use different algorithms in their computation is expected to suffer minimum loss of information and an attempt is made in this direction to study the similarity of DNA sequences that are homogenous or heterogeneous. Several numerical descriptors for the characterization of DNA sequences are described, based on information theoretic approach, connectivity of vertex weighted line-graphs and those derived from the matrices obtained from the graphs constructed by depicting DNA sequences as a random walk on a Euclidean plane. The information theoretic descriptors were obtained based on the L-tuple approach for the combination of different numbers of bases. The connectivity type descriptors were calculated by converting the DNA sequence into vertex weighted graphs in which vertices (nucleotide) were assigned weights based on the pKa of the bases. The graphical representations were converted into numerical descriptors by constructing matrices. Computer programs were developed to calculate seventy DNA descriptors; 560 sequences of different types of organisms were used. After initial data analysis to eliminate almost perfectly correlated descriptors, orthogonal descriptors were obtained by performing principal component analysis. Principal components (PCs) were used to construct an N-dimensional similarity space wherein the 560 sequences were clustered by k-means cluster algorithm. Five principal components (orthogonal descriptors) were extracted and found to explain 92% of data variance. The PCs were used to cluster the sequences in a five-dimensional similarity space. The similarity-based dissimilarity clustering procedure using numerical descriptors was found to be effective for studying similarity/ dissimilarity of large number of sequences.

The use of chiral compounds as pharmaceuticals and agrochemicals continues to increase, warranting numerical characterization of chirality in order to develop structure-activity relationship models involving these compounds. Enantiomers are identical in all scalar properties and, hence, are not differentiated by topological indices and 3-D descriptors. Three distinct measures of chirality were developed to discriminate diastereomers and enantiomers. The novel topological indices treat chirality as a continuous measure, and hence we prefer to call it the Relative Chirality Index (RCI). Application of RCI in developing SAR is illustrated with the repellency data for the diastereomers of picaridin and AI3-37220.

Quantitative structure-activity relationship (QSAR) modelers often encounter the problem of multicollinearity owing to the availability of large numbers of computable molecular descriptors. Sparsity of the variables while using descriptors such as atom pairs increases the complexity. Three different predictor-thinning methods, namely, a modified Gram-Schmidt algorithm, a marginal soft thresholding algorithm, and LASSO (least absolute shrinkage and selection operator), were utilized to reduce the number of descriptors prior to developing linear models. Juvenile hormone (JH) activity of 304 compounds on Culex pipiens larvae was taken as the model data set, and predictor trimming of a large number of diverse descriptors comprising 268 global molecular descriptors (topostructural, topochemical, and geometrical), 13 quantum chemical descriptors, and 915 atom pairs (substructural counts) was applied prior to linear regression by the ridge regression method. The data set (N = 304) was split into five calibration data sets of random samples of sizes 60/110/160/210/260, and the remaining 244/194/144/94/44 compounds were used for validations. LASSO was not found to be a very effective method in handling a large set of descriptors because the number of predictors retained could not exceed the number of observations. The results indicated that the modified Gram-Schmidt algorithm could be used to trim the number of predictors in the global molecular descriptor set where collinearity of the descriptors was the major concern. On the contrary, the soft thresholding approach was found to be an effective tool in subset selection from a diverse set of descriptors having both sparsity and multicollinearity, as in the case of the combined set of atom pairs and global molecular descriptors. The final model developed after variable selection was dominated more by atom pairs, which indicated the important structural moieties that affect JH activity of the compounds. The success of the method reiterates the fact that QSAR or quantitative structure-property relationship (QSPR) models can be developed for a diverse set of compounds using properly parametrized and diverse sets of descriptors, of course, with the selection of the appropriate statistical tools.