Abstract: PURPOSE: Transport across the human nail under hydration can be modeled as hindered transport across aqueous pore pathways. As such, nail permselectivity to charged species can be manipulated by changing the ionic strength of the system in transungual delivery to treat nail diseases. The present study investigated the effects of ionic strength upon transungual passive and iontophoretic transport. METHODS: Transungual passive and anodal iontophoretic transport experiments of tetraethylammonium ion (TEA) were conducted under symmetric conditions in which the donor and receiver had the same ionic strength in vitro. Experiments under asymmetric conditions were performed to mimic the in vivo conditions. Prior to the transport studies, TEA uptake studies were performed to assess the partitioning of TEA into the nail. RESULTS: Permselectivity towards TEA was inversely related to ionic strength in both passive and iontophoretic transport. The permeability and transference number of TEA were higher at lower ionic strengths under the symmetric conditions due to increased partitioning of TEA into the nail. Transference numbers were smaller under the asymmetric conditions compared with their symmetric counterparts. CONCLUSIONS: The results demonstrate significant ionic strength effects upon the partitioning and transport of a cationic permeant in transungual transport, which may be instrumental in the development of transungual delivery systems.

Abstract: We present here the estimation of the upper limit of the number of molecular targets in the human genome that represent an opportunity for further therapeutic treatment. We select around approximately 6300 human proteins that are similar to sequences of known protein targets collected from DrugBank database. Our bioinformatics study estimates the size of 'druggable' human genome to be around 20% of human proteome, i.e. the number of the possible protein targets for small-molecule drug design in medicinal chemistry. We do not take into account any toxicity prediction, the three-dimensional characteristics of the active site in the predicted 'druggable' protein families, or detailed chemical analysis of known inhibitors/drugs. Instead we rely on remote homology detection method Meta-BASIC, which is based on sequence and structural similarity. The prepared dataset of all predicted protein targets from human genome presents the unique opportunity for developing and benchmarking various in silico chemo/bio-informatics methods in the context of the virtual high throughput screening.

Abstract: BACKGROUND: Chemogenomics is an emerging inter-disciplinary approach to drug discovery that combines traditional ligand-based approaches with biological information on drug targets and lies at the interface of chemistry, biology and informatics. The ultimate goal in chemogenomics is to understand molecular recognition between all possible ligands and all possible drug targets. Protein and ligand space have previously been studied as separate entities, but chemogenomics studies deal with large datasets that cover parts of the joint protein-ligand space. Since drug discovery has traditionally focused on ligand optimization, the chemical space has been studied extensively. The protein space has been studied to some extent, typically for the purpose of classification of proteins into functional and structural classes. Since chemogenomics deals not only with ligands but also with the macromolecules the ligands interact with, it is of interest to find means to explore, compare and visualize protein-ligand subspaces. RESULTS: Two chemogenomics protein-ligand interaction datasets were prepared for this study. The first dataset covers the known structural protein-ligand space, and includes all non-redundant protein-ligand interactions found in the worldwide Protein Data Bank (PDB). The second dataset contains all approved drugs and drug targets stored in the DrugBank database, and represents the approved drug-drug target space. To capture biological and physicochemical features of the chemogenomics datasets, sequence-based descriptors were computed for the proteins, and 0, 1 and 2 dimensional descriptors for the ligands. Principal component analysis (PCA) was used to analyze the multidimensional data and to create global models of protein-ligand space. The nearest neighbour method, computed using the principal components, was used to obtain a measure of overlap between the datasets. CONCLUSION: In this study, we present an approach to visualize protein-ligand spaces from a chemogenomics perspective, where both ligand and protein features are taken into account. The method can be applied to any protein-ligand interaction dataset. Here, the approach is applied to analyze the structural protein-ligand space and the protein-ligand space of all approved drugs and their targets. We show that this approach can be used to visualize and compare chemogenomics datasets, and possibly to identify cross-interaction complexes in protein-ligand space.

Abstract: Aggregation of a biotherapeutic is of significant concern and judicious process and formulation development is required to minimize aggregate levels in the final product. Aggregation of a protein in solution is driven by intrinsic and extrinsic factors. In this work we have focused on aggregation as an intrinsic property of the molecule. We have studied the sequences and Fab structures of commercial and non-commercial antibody sequences for their vulnerability towards aggregation by using sequence based computational tools to identify potential aggregation-prone motifs or regions. The mAbs in our dataset contain 2 to 8 aggregation-prone motifs per heavy and light chain pair. Some of these motifs are located in variable domains, primarily in CDRs. Most aggregation-prone motifs are rich in beta branched aliphatic and aromatic residues. Hydroxyl-containing Ser/Thr residues are also found in several aggregation-prone motifs while charged residues are rare. The motifs found in light chain CDR3 are glutamine (Q)/asparagine (N) rich. These motifs are similar to the reported aggregation promoting regions found in prion and amyloidogenic proteins that are also rich in Q/N, aliphatic and aromatic residues. The implication is that one possible mechanism for aggregation of mAbs may be through formation of cross-beta structures and fibrils. Mapping on the available Fab-receptor/antigen complex structures reveals that these motifs in CDRs might also contribute significantly towards receptor/antigen binding. Our analysis identifies the opportunity and tools for simultaneous optimization of the therapeutic protein sequence for potency and specificity while reducing vulnerability towards aggregation.

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: BACKGROUND: Discovering that drug entities already approved for one disease are effective treatments for other distinct diseases can be highly beneficial and cost effective. To do this predictively, our conjecture is that a semantic infrastructure linking mechanistic relationships between pharmacologic entities and multidimensional knowledge of biological systems and disease processes will be highly enabling. RESULTS: To develop a knowledge framework capable of modeling and interconnecting drug actions and disease mechanisms across diverse biological systems contexts, we designed a Disease-Drug Correlation Ontology (DDCO), formalized in OWL, that integrates multiple ontologies, controlled vocabularies, and data schemas and interlinks these with diverse datasets extracted from pharmacological and biological domains. Using the complex disease Systemic Lupus Erythematosus (SLE) as an example, a high-dimensional pharmacome-diseasome graph network was generated as RDF XML, and subjected to graph-theoretic proximity and connectivity analytic approaches to rank drugs versus the compendium of SLE-associated genes, pathways, and clinical features. Tamoxifen, a current candidate therapeutic for SLE, was the highest ranked drug. CONCLUSION: This early stage demonstration highlights critical directions to follow that will enable translational pharmacotherapeutic research. The uniform application of Semantic Web methodology to problems in data integration, knowledge representation, and analysis provides an efficient and potentially powerful means to allow mining of drug action and disease mechanism relationships. Further improvements in semantic representation of mechanistic relationships will provide a fertile basis for accelerated drug repositioning, reasoning, and discovery across the spectrum of human disease.

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.