In Silico Receptorome Screening of Antipsychotic Drugs

The recent availability of a complete interaction matrix between 13 antipsychotic drugs and 34 protein targets (Roth et al. Nat. Rev. Drug Discov. 2004, 3, 353-359) allows to assess the performance of computational methods on their ability to anticipate the entire affinity profile of drugs across multiple targets. The analyses reveal that our current implementations, based on the similarity of drugs against a reference set of small molecules for which pharmacological data is available in the public domain, are able to predict 65 % of the 442 affinities within 1-log unit error, with a level of precision above 92 %. In spite of the relatively small scale of this validation study, the results are indicative that in silico receptorome screening of drugs offers an efficient and cost-effective complement to in vitro screening.

A canonical cation-π interaction stabilizes the agonist conformation of estrogen-like nuclear receptors

Representative crystal structures of the ligand-binding domain for the majority of nuclear receptors are currently available. A systematic comparative analysis of these structures identified an energetically favorable cation-π interaction that involves an amino acid located at the extreme C-terminal end and appears to form only in the agonist conformation of the estrogen receptor α, glucocorticoid, mineralocorticoid, progesterone, and androgen receptors. It is postulated that this cation-π interaction is used by members of the estrogen-like subfamily to provide additional stabilization to the transcriptional active conformation upon ligand binding.

iPHACE: integrative navigation in pharmacological space

SUMMARY

The increasing availability of experimentally determined binding affinities for drugs on multiple protein targets requires the design of specific mining and visualization tools that graphically integrate chemical and biological data in an efficient environment. With this aim, we developed iPHACE, an integrative web-based tool to navigate in the pharmacological space defined by small molecule drugs contained in the IUPHAR-DB, with additional interactions present in PDSP. Extending beyond traditional querying and filtering tools, iPHACE offers a means to extract knowledge from the target profile of drugs as well as from the drug profile of protein targets.

AVAILABILITY

iPHACE is available at http://cgl.imim.es/iphace/ (EU site) and http://agave.health.unm.edu/iphace/ (US mirror).

The topology of drug-target interaction networks: implicit dependence on drug properties and target families

The availability of interaction data between small molecule drugs and protein targets has increased substantially in recent years. Using seven different databases, we were able to assemble a total of 4767 unique interactions between 802 drugs and 480 targets, which means that on average every drug is currently acknowledged to interact with 6 targets. The application of network theory to the analysis of these data reveals an unexpectedly complex picture of drug-target interactions. The results confirm that the topology of drug-target networks depends implicitly on data completeness, drug properties, and target families. The implications for drug discovery are discussed.

Connecting Small Molecules to Nuclear Receptor Pathways

Many efforts are currently being made to connect small molecules to target proteins by extracting pharmacological data from bibliographic sources and storing them in annotated chemical libraries. Here, small molecules are further connected to biological pathways, with particular focus to pathways involving members of the nuclear receptor family. The results bring to light the relative importance for molecules on gaining selectivity at the target level, when the target has an intrinsic promiscuity at the pathway level, and highlight the implications for drug discovery to address current challenges related to poor drug efficacy and toxicity. Details on the main limitations encountered during the molecule-to-target-to-pathway annotation process are also discussed.

In silico pharmacology for drug discovery: applications to targets and beyond

Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediating the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets.

In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling

Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.

Applied information retrieval and multidisciplinary research: new mechanistic hypotheses in complex regional pain syndrome

Background

Collaborative efforts of physicians and basic scientists are often necessary in the investigation of complex disorders. Difficulties can arise, however, when large amounts of information need to reviewed. Advanced information retrieval can be beneficial in combining and reviewing data obtained from the various scientific fields. In this paper, a team of investigators with varying backgrounds has applied advanced information retrieval methods, in the form of text mining and entity relationship tools, to review the current literature, with the intention to generate new insights into the molecular mechanisms underlying a complex disorder. As an example of such a disorder the Complex Regional Pain Syndrome (CRPS) was chosen. CRPS is a painful and debilitating syndrome with a complex etiology that is still unraveled for a considerable part, resulting in suboptimal diagnosis and treatment.

Results

A text mining based approach combined with a simple network analysis identified Nuclear Factor kappa B (NFκB) as a possible central mediator in both the initiation and progression of CRPS.

Conclusion

The result shows the added value of a multidisciplinary approach combined with information retrieval in hypothesis discovery in biomedical research. The new hypothesis, which was derived in silico, provides a framework for further mechanistic studies into the underlying molecular mechanisms of CRPS and requires evaluation in clinical and epidemiological studies.

Ligand-based approach to in silico pharmacology: nuclear receptor profiling

Bioactive ligands are a valuable and increasingly accessible source of information about protein targets. On the basis of this statement, a list of 25 nuclear receptors was described by a series of bioactive ligands extracted directly from bibliographical sources, stored properly in an annotated chemical library, and mathematically represented using the recently reported SHED molecular descriptors. Analysis of this ligand information allowed for derivation of a threshold of nuclear receptor concern. If the similarity of one molecule to any of the molecules annotated to one particular nuclear receptor is below that threshold, the molecule receives an alert on the probability of having affinity below 10 microM for that nuclear receptor. On this basis, a linkage map was constructed that reveals the interaction network of nuclear receptors from the perspective of their active ligands. This ligand-based approach to nuclear receptor profiling was subsequently applied to four external chemical libraries of 10,000 molecules targeted to proteases, kinases, ion channels, and G protein-coupled receptors. The percentage of each library that returned an alert on at least one nuclear receptor was reasonably low and varied between 4.4 and 9.7%. In addition, ligand-based nuclear receptor profiling of a set of 2944 drugs provided an alert for 153 drugs. For some of them, namely, acitretin, telmisartan, phenyltoloxamine, tazarotene, and flumazenil, bibliographical evidence could be found indicating that those drugs may indeed have some potential off-target residual affinity for the nuclear receptors annotated. Overall, the present findings suggest that ligand-based approaches to protein family profiling appear as a promising means toward the establishment of novel tools for in silico pharmacology.

SHED: Shannon entropy descriptors from topological feature distributions

A novel set of molecular descriptors called SHED (SHannon Entropy Descriptors) is presented. They are derived from distributions of atom-centered feature pairs extracted directly from the topology of molecules. The value of a SHED is then obtained by applying the information-theoretical concept of Shannon entropy to quantify the variability in a feature-pair distribution. The collection of SHED values reflecting the overall distribution of pharmacophoric features in a molecule constitutes its SHED profile. Similarity between pairs of molecules is then assessed by calculating the Euclidean distance of their SHED profiles. Under the assumption that molecules having similar pharmacological profiles should contain similar features distributed in a similar manner, examples are given to show the ability of SHED for scaffold hopping in virtual chemical screening and pharmacological profiling compared to that of substructural BCI fingerprints and three-dimensional GRIND descriptors.

Representativity of target families in the Protein Data Bank: impact for family-directed structure-based drug discovery

Analysis of the population of enzyme structures in the Protein Data Bank across all levels of the functional classification based on enzyme commission (EC) numbers reveals that, in spite of the almost exponential growth in the number of structures deposited, progress in achieving complete occupancy at all EC levels is relatively slow. Moreover, inspection of the distribution of the population among the members of the different enzyme families uncovers a strong bias towards enzymes widely recognized as therapeutically relevant targets. The low representativity levels identified in some target families warn on the current scope and applicability of structure-based approaches to family-directed strategies in drug discovery.

FCP: functional coverage of the proteome by structures

MOTIVATION

Tools and resources for translating the remarkable growth witnessed in recent years in the number of protein structures determined experimentally into actual gain in the functional coverage of the proteome are becoming increasingly necessary. We introduce FCP, a publicly accessible web tool dedicated to analyzing the current state and trends of the population of structures within protein families. FCP offers both graphical and quantitative data on the degree of functional coverage of enzymes and nuclear receptors by existing structures, as well as on the bias observed in the distribution of structures along their respective functional classification schemes.

AVAILABILITY

http://cgl.imim.es/fcp

CONTACT

jmestres@imim.es.

Chemical and biological profiling of an annotated compound library directed to the nuclear receptor family

Nuclear receptors form a family of ligand-activated transcription factors that regulate a wide variety of biological processes and are thus generally considered relevant targets in drug discovery. We have constructed an annotated compound library directed to nuclear receptors (NRacl) as a means for integrating the chemical and biological data being generated within this family. Special care has been put in the appropriate storage of annotations by using hierarchical classification schemes for both molecules and nuclear receptors, which takes the ability to extract knowledge from annotated compound libraries to another level. Analysis of NRacl has ultimately led to the identification of scaffolds with highly promiscuous nuclear receptor profiles and to the classification of nuclear receptor groups with similar scaffold promiscuity patterns. This information can be exploited in the design of probing libraries for deorphanization activities as well as for devising screening batteries to address selectivity issues.

Unsupervised guided docking of covalently bound ligands

An approach for docking covalently bound ligands in protein enzymes or receptors was implemented in MacDOCK, a similarity-driven docking program based on DOCK 4.0. This approach was tested with a small number of covalent ligand-protein structures, using both native and non-native protein structures. In all cases, MacDOCK was able to generate orientations consistent with the known covalent binding mode of these complexes, with a performance similar to that of other docking programs. This method was also applied to search for known covalent thrombin inhibitors in a medium-sized molecular database (ca. 11,000 compounds). Detection of functional groups suitable for covalent docking was carried out automatically. A significant enrichment in known active molecules in the first 5% of the database was obtained, showing that MacDOCK can be used efficiently for the virtual screening of covalently bound ligands.

Mutual induced fit in cyclodextrin-rocuronium complexes

The binding of rocuronium bromide to 6-perdeoxy-6-per(4-carboxyphenyl)thio-gamma-cyclodextrin sodium salt, displays biphasic behaviour characteristic of the formation of a binary and 2 : 1 ternary guest-host complex in aqueous solution. Thermodynamic and structural data on this sequential complexation process can be rationalised within a single model involving switching of the conformational equilibria of both the rocuronium bromide and cyclodextrin molecules. Isothermal titration calorimetry (ITC), NMR and fluorescence experiments in solution, together with X-ray crystallography and molecular modelling, suggest that in order to induce encapsulation both rocuronium bromide and the modified cyclodextrin undergo conformational changes. Ring A of rocuronium bromide 'switches' from the more sterically encumbered chair to the sterically less demanding twist-boat, whilst the modified cyclodextrin "opens" its cavity to allow the steroid to enter. The recognition and mutual induced fit between cyclodextrin and steroid represents a classic example of dynamic host-guest chemistry.

Certification of occupational diseases as common diseases in a primary health care setting

BACKGROUND

It is often difficult to discern whether a disease is an occupational or common disease, especially in a primary care setting.

METHODS

From a randomly selected sample of 322 workers attending a Primary Health Care Center, 207 workers (response rate of 64.3%) agreed to participate. An occupational questionnaire was administered. General practitioners provided medical records for each worker. Medical records and occupational questionnaires were independently reviewed by three professionals. They assessed whether a relationship between disease and working conditions was probable or improbable.

RESULTS

Thirty-three of the 207 cases (15.9%) were considered probably related to working conditions according to the expert's opinion. The most frequent were musculoskeletal diseases (20 cases). Of the 207 workers, 74 (35.7%) judged that their diseases could be related to their working conditions.

CONCLUSIONS

A significant proportion of diseases attended in primary care setting was not recognized as occupational, and they were hence not reflected in official statistics.