Abstract: PURPOSE: The aim of this study was to develop quantitative structure pharmacokinetic relationships (QSPKR) to correlate drug physicochemical properties (molecular weight, lipophilicity, and drug solubility), dose, salt form factor, and eye pigmentation factor to intravitreal half-life in the rabbit model. METHODS: Dataset derived from prior literature reports, which included molecules with complete structural diversity, was used to develop the QSPKR models. Entire dataset as well as subsets limited to albino rabbit data, pigmented rabbit data, acids, bases, zwitterions, neutral compounds, suspensions, and macromolecules were analyzed. Multiple linear regression analysis was carried out with noncollinear independent variables and the best-fit models were selected based on correlation coefficients and goodness of fit statistics. RESULTS: The analysis indicated that logarithm of MW (Log MW), lipophilicity (Log P or Log D) and dose number (dose/solubility at pH 7.4), are the most critical determinants of intravitreal half-life of the compounds analyzed. The best-fit models obtained from the entire dataset (Log t (1/2) = -0.178 + 0.267 Log MW - 0.093 Log D + 0.003 dose/solubility at pH 7.4 + 0.153 Pigmentation Factor and Log t (1/2) = -0.32 + 0.432 Log MW - 0.157 Log P + 0.003 dose/solubility at pH 7.4) predicted the various subsets well. Pigmented dataset and zwitterions were better predicted by Log P rather than Log D. CONCLUSIONS: The present study confirmed that intravitreal half-life could be better predicted by a group of variables (Log MW, Log P or Log D, dose number) rather than a single variable. In general, increasing Log MW and dose number, while reducing Log D or Log P would be beneficial for prolonging intravitreal half-life of drugs.

Abstract: Each Food and Drug Administration (FDA) consumer drug information file contains an inordinate amount of useful chemical, pharmaceutical, and pharmacological data. These files profile approved drugs by chemical structure, solubility, absorption, distribution, metabolism, elimination, toxicity (ADME/Tox), and possible adverse reactions. The ability to utilize this data in the classroom is a new approach to connect theory, technology, and reality. The KnowItAll((R)) Informatics System available through Bio-Rad Laboratories, Philadelphia, PA, offers fully integrated software and/or database desktop solutions. It holds a large collection of in silico ADME/Tox predictors and is a chemical informatics platform used to record experimental data. This project had three goals: (1) extract relevant information for 75 drugs from their freely available FDA drug files (limited to orally administrated drugs, pro-drugs, having a chemical structure), (2) build a database so this extracted FDA information is indexed for search and analysis, and when completed, (3) undergraduates involved in such a project should be capable of harvesting useful chemical, pharmaceutical, and pharmacological information; be adept in computational chemistry software tools; and should gain an enhanced vocabulary and new insights into organic chemistry, molecular biology, and physiology.

Abstract: A multiple criteria approach is presented, that is used to perform a comparative analysis of four recently developed combinatorial libraries to drugs, Molecular Libraries Small Molecule Repository (MLSMR) and natural products. The compound databases were assessed in terms of physicochemical properties, scaffolds, and fingerprints. The approach enables the analysis of property space coverage, degree of overlap between collections, scaffold and structural diversity, and overall structural novelty. The degree of overlap between combinatorial libraries and drugs was assessed using the R-NN curve methodology, which measures the density of chemical space around a query molecule embedded in the chemical space of a target collection. The combinatorial libraries studied in this work exhibit scaffolds that were not observed in the drug, MLSMR, and natural products databases. The fingerprint-based comparisons indicate that these combinatorial libraries are structurally different than current drugs. The R-NN curve methodology revealed that a proportion of molecules in the combinatorial libraries is located within the property space of the drugs. However, the R-NN analysis also showed that there are a significant number of molecules in several combinatorial libraries that are located in sparse regions of the drug space.

Abstract: Aminergic G protein-coupled receptors (GPCRs) have been a major focus of pharmaceutical research for many years. Due partly to the lack of reliable receptor structures, drug discovery efforts have been largely ligand-based. The recently determined X-ray structure of the beta(2)-adrenergic receptor offers an opportunity to investigate the advantages and limitations inherent in a structure-based approach to ligand discovery against this and related GPCR targets. Approximately 1 million commercially available, "lead-like" molecules were docked against the beta(2)-adrenergic receptor structure. On testing of 25 high-ranking molecules, 6 were active with binding affinities <4 microM, with the best molecule binding with a K(i) of 9 nM (95% confidence interval 7-10 nM). Five of these molecules were inverse agonists. The high hit rate, the high affinity of the most potent molecule, the discovery of unprecedented chemotypes among the new inhibitors, and the apparent bias toward inverse agonists among the docking hits, have implications for structure-based approaches against GPCRs that recognize small organic molecules.

Abstract: The increasing number of publicly available biological databases reflects the evolving need for managing and evaluating abundant and complex data in biological, clinical and computational research. Currently there are over 1000 biologically relevant databases in the public domain with varied content and diverse approaches to capturing and presenting data. This review summarizes the comparatively small niche of sophisticated databases and other resources that aim to enhance understanding of chemicals and their biological actions. The databases reviewed include 1 that emphasizes environmental chemicals and 9 that emphasize drugs and small molecules. These databases and their associated resources are incrementally strengthening the expanding field of toxicogenomics-based research by providing centralized sources of manually and computationally curated datasets and highly sophisticated tools for the meta-analysis of continually increasing environmental chemical, drug and small-molecule datasets.

Abstract: BACKGROUND: It is becoming increasingly accepted that a shift is needed from the traditional target-based approach of drug development towards an integrated perspective of drug action in biochemical systems. To make this change possible, the interaction networks connecting drug targets to all components of biological systems must be identified and characterized. RESULTS: We here present an integrative analysis of the interactions between drugs and metabolism by introducing the concept of metabolic drug scope. The metabolic drug scope represents the full set of metabolic compounds and reactions that are potentially affected by a drug. We constructed and analyzed the scopes of all US approved drugs having metabolic targets. Our analysis shows that the distribution of metabolic drug scopes is highly uneven, and that drugs can be classified into several categories based on their scopes. Some of them have small scopes corresponding to localized action, while others have large scopes corresponding to potential large-scale systemic action. These groups are well conserved throughout different topologies of the underlying metabolic network. They can furthermore be associated to specific drug therapeutic properties. CONCLUSION: These findings demonstrate the relevance of metabolic drug scopes to the characterization of drug-metabolism interactions and to understanding the mechanisms of drug action in a system-wide context.

Abstract: Understanding the molecular mechanisms underlying synergistic, potentiative and antagonistic effects of drug combinations could facilitate the discovery of novel efficacious combinations and multi-targeted agents. In this article, we describe an extensive investigation of the published literature on drug combinations for which the combination effect has been evaluated by rigorous analysis methods and for which relevant molecular interaction profiles of the drugs involved are available. Analysis of the 117 drug combinations identified reveals general and specific modes of action, and highlights the potential value of molecular interaction profiles in the discovery of novel multicomponent therapies.

Abstract: AIM: The search for molecules whose bioactivities are similar to those of given compounds or to optimize the initial lead compounds from high throughput screening has attracted increasing interest in recent years. Our goal is to provide a publically searchable database of scaffolds out from a large collection of existing chemical molecules. RESULTS: Although a number of in silico methods have emerged to facilitate this process, which has become known as "scaffold hopping" or "molecular hopping", there is an urgent need for a database system to provide such valuable data in the drug design field. Here we have systematically analyzed a collection of commercially available small molecule databases and a bioactive compound database to identify unique scaffolds and we have built a publically searchable database. The analysis of approximately 4,800,000 of these compounds identified 241,824 unique scaffolds, which are stored in a relational database (http://202.127.30.184:8080/db.html). Each entry in the database is associated with a molecular occurrence and includes its distribution of molecular properties, such as molecular weight, logP, hydrogen bond acceptor number, hydrogen bond donor number, rotatable bond number and ring number. More importantly, for scaffolds derived from the bioactive compounds database, it also contains the original compounds and their target information. CONCLUSION: This Web-based database system could help researchers in the fields of medicinal and organic chemistry to design novel molecules with properties similar to the original compounds, but built on novel scaffolds.

Abstract: The purpose of this research was to evaluate the difference between the pharmaceutical phosphate buffers and the gastrointestinal bicarbonates in dissolution of ketoprofen and indomethacin, to illustrate the dependence of buffer differential on biopharmaceutical properties of BCS II weak acids, and to recommend phosphate buffers equivalent to bicarbonates. The intrinsic dissolution rates of ketoprofen and indomethacin were experimentally measured using a rotating disk method at 37 degrees C in USP SIF/FaSSIF and various concentrations of bicarbonates. Theoretical models including an improved reaction plane model and a film model were applied to estimate the surrogate phosphate buffers equivalent to the bicarbonates. Experimental results show that the intrinsic dissolution rates of ketoprofen and indomethacin in USP and FaSSIF phosphate buffers are 1.5-3.0 times that in the 15 mM bicarbonates. Theoretical analysis demonstrates that the buffer differential is largely dependent on the drug pK(a) and second on solubility, and weakly dependent on the drug diffusivity. Further, in accordance with the drug pK(a), solubility and diffusivity, a simple phosphate surrogate was proposed to match an average bicarbonate value (15 mM) of the upper gastrointestinal region. Specifically, phosphate buffers of 13-15 mM and 3-4 mM were recommended for ketoprofen and indomethacin, respectively. For both ketoprofen and indomethacin, the intrinsic dissolution using the phosphate surrogate buffers closely approximated the 15 mM bicarbonate buffer. This work demonstrates the substantial difference between pharmaceutical phosphates and physiological bicarbonates in determining the drug intrinsic dissolution rates of BCS II weak acids, such as ketoprofen and indomethacin. Surrogate phosphates were recommended in order to closely reflect the in vivo dissolution of ketoprofen and indomethacin in gastrointestinal bicarbonates, which has significant implications for defining buffer systems for BCS II weak acids in developing in vitro bioequivalence dissolution methodology.

Abstract: The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users.