Current Drug Discovery Technologies - Volume 14, Issue 4, 2017

Background: Hit identification is the starting point of small-molecule drug discovery and is therefore very important to the pharmaceutical industry. One of the most important approaches to identify a new hit is to screen a compound library using an in vitro assay. High-throughput screening has made great contributions to drug discovery since the 1990s but requires expensive equipment and facilities, and its success depends on the size of the compound library. Recent progress in the development of compound libraries has provided more efficient ways to identify new hits for novel drug targets, thereby helping to promote the development of the pharmaceutical industry, especially for firstin- class drugs. Methods: A multistage and systematic research of articles published between 1986 and 2017 has been performed, which was organized into 5 sections and discussed in detail. Results: In this review, the sources and classification of compound libraries are summarized. The progress made in combinatorial libraries and DNA-encoded libraries is reviewed. Library design methods, especially for focused libraries, are introduced in detail. In the final part, the status of the compound libraries at WuXi is reported. Conclusion: The progress related to compound libraries, especially drug template libraries, DELs, and focused libraries, will help to identify better hits for novel drug targets and promote the development of the pharmaceutical industry. Moreover, these libraries can facilitate hit identification, which benefits most research organizations, including academics and small companies.

Background: Traditional quantitative structure - property / activity relationships (QSPRs/QSARs) are based on representation of molecular structure by molecular graph or simplified molecular input-line entry system (SMILES). It is an attractive idea to develop predictive models for large molecules in general and for peptides in particular. However, the representation of these molecules by molecular graph or SMILES is problematic owing to large size of these molecules. A possible alternative of SMILES is the representation of peptides via sequence of abbreviations of amino acids. Method: Models for hemolysis and cytotoxicity of peptides are suggested. These models are based on representation of the peptides by sequences of amino acids. Correlation weights, which are calculated for each amino acid using the Monte Carlo method are basis for quantitative sequence - activity relationships (QSAR) for antimicrobial peptides. The correlation weights are the basis for optimal descriptors, which are correlated with experimental data for hemolysis and cytotoxicity. The basic hypothesis is that if optimal descriptors are correlated with endpoints of peptides for the training set, they should also correlate with the endpoints for validation set. Results: Checking up of correlations between the above-mentioned descriptors and antimicrobial activity of peptides (cytotoxicity or hemolysis) has shown that these models have good predictive potential. Conclusion: Suggested approach can be used as a tool to develop predictive models of biological activity of peptides as a mathematical function of sequences of amino acids.

Background: Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of intestinal absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches. Methods: Six Artificial intelligence methods namely, Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis were used for prediction of absorption of compounds. Results: Prediction accuracy of Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54%, 88.33%, 84.30%, 86.51%, 79.07% and 80.08% respectively. Conclusion: Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development.

Background: Quinolone scaffolds are widely used for the synthesis of a number of medicinal compounds with variety of biological activity. In view of the reported antimicrobial activity of various fluoroquinolones, the structure activity studies of various substituted quinolones, which proved the importance of the C-7 substituents to exhibit potent antimicrobial activities. Objective: Based on the structural activity relationship at C-7 position it was rationalized to design and synthesize new quinolone derivatives with increasing bulk at C-7 position of the main 6-fluoroquinolone scaffold. Methods: A novel series of 1-cyclopropyl-6-fluoro-4-oxo-7-{4-[2-(4-substituted-phenyl)- 2-(substituted)-ethyl]-1-piperazinyl}-1,4-dihydroquinoline-3-carboxylic acid derivatives were synthesized by reacting 1-cyclopropyl-6-fluoro-4-oxo-7-(piperazin-1-yl)-1,4- dihydroquinoline-3-carboxylic acid with 2-bromo-4-(substituted) acetophenone in the presence of sodium bicarbonate to obtain 1-cyclopropyl-6-fluoro-7-{4-[2-(4- substitutedphenyl)-2-oxoethyl]-1-piperazinyl}-4-oxo-1,4-dihydroquinoline-3-carboxylic acids 2a-2d. Compound 2a-2d underwent further reaction with different substituted hydrazide, hydroxylamine hydrochloride or methoxylamine in glacial acetic acid to give 3a-7d. In vitro antibacterial activity of the synthesized compounds 3a-7d was studied and the MIC value was determined by the broth dilution method. Result: Among all the synthesized compounds 3a-7d some compounds showed antimicrobial activity in comparison to the reference standard ciprofloxacin. Conclusion: The compound 6d showed the reasonable good antibacterial activity among all the tested compounds. To understand antibacterial data on structural basis and the interaction of binding sites with bacterial protein receptor, the docking studies were carried out using topoisomerase II DNA gyrase enzymes (PDB ID. 2XCT) by shrodinger's maestro program.

Background: Recent studies have sought to draw attention of biological activity of Campylandra aurantiaca. The aim of the present study was to evaluate the effect of flavonoid enriched fraction of Campylandra aurantiaca (FEFCA) on in vitro and in vivo antioxidant and DNA protective effect in mouse peritoneal macrophages cells. Methods: FEFCAwas characterized by HPLC analysis. The in vitro antioxidant activities of FEFCA was measured by different in vitro assays like 1, 1-diphenyl-2-picrylhydrazil radical (DPPH), superoxide anions, nitric oxide and hydroxyl radicals scavenging methods. Isolated mouse peritoneal macrophages were oxidized by carbon tetra chloride (CCl4) in animal model; subsequently the protective effect of FEFCA was determined in terms of estimation of antioxidant enzyme and the damage to DNA of the cells. Results: FEFCA exhibited both in vitro antioxidant activities in a concentration dependent manner. FEFCA significantly (*p < 0.05) attenuated the oxidative DNA damage of mouse peritoneal macrophage cells induced by CCl4 in an in vivo assay. Conclusion: Therefore FEFCA showed good free radical scavenging activity as well as reduced oxidative DNA damage in mouse peritoneal macrophages in animal model.

Background: This work reports the safety profiling, in vivo hypoglycemic and pharmacokinetic studies of three phytoceuticals viz. conventional and sustained release tablets and microspheres each containing a polyherbal product phytocomposite (PHC) as the active ingredient. PHC is prepared from the leaf extracts of Ficus benghalensis: Syzigium cumini: Ocimum sanctum mixed in the weight ratio of 1:1:2. Further no observed adverse effect level (NOAEL), maximum recommended starting dose (MRSD) in human and prediction of human pharmacokinetic parameters have been accomplished by allometric equations. Methods: Acute and sub chronic studies of the phytoceuticals were done as per OECD and in vivo hypoglycemic studies in STZ induced diabetic rats. Plasma concentrations of the active constituent rutin (pharmacologically active compound of PHC) were determined by HPLC and other pharmacokinetic parameters using PK Solver. Repeated dose toxicity was carried out to determine the NOAEL value, MRSD estimated using allometric formulas of body surface area and clearance (CL) and volume of distribution (Vd) predicted by allometric equations of single species scaling. Results: Phytoceuticals showed a wide range of safety profile with a significant lowering of blood gluco-lipid level. The values of the pharmacokinetic parameters for different doses of phytoceuticals showed that the active concentration was maintained in plasma level and each formulation complied with their relevant quality criteria. NOAEL value was 5000 mg/kg/body weight and MRSD was 4864.86 mg. Conclusion: Phytoceuticals prepared are safe and effectively controlled blood gluco lipid level. Animal to human dose extrapolation and prediction of human pharmacokinetic parameters by allometry was convenient.