Protein-ligand-based pharmacophores: generation and utility assessment in computational ligand profiling.

Ligand profiling is an emerging computational method for predicting the most likely targets of a bioactive compound and therefore anticipating adverse reactions, side effects and drug repurposing. A few encouraging successes have already been reported using ligand 2-D similarity searches and protein-ligand docking. The current study describes the use of receptor-ligand-derived pharmacophore searches as a tool to link ligands to putative targets. A database of 68,056 pharmacophores was first derived from 8,166 high-resolution protein-ligand complexes. In order to limit the number of queries, a maximum of 10 pharmacophores was generated for each complex according to their predicted selectivity. Pharmacophore search was compared to ligand-centric (2-D and 3-D similarity searches) and docking methods in profiling a set of 157 diverse ligands against a panel of 2,556 unique targets of known X-ray structure. As expected, ligand-based methods outperformed, in most of the cases, structure-based approaches in ranking the true targets among the top 1% scoring entries. However, we could identify ligands for which only a single method was successful. Receptor-ligand-based pharmacophore search is notably a fast and reliable alternative to docking when few ligand information is available for some targets. Overall, the present study suggests that a workflow using the best profiling method according to the protein-ligand context is the best strategy to follow. We notably present concrete guidelines for selecting the optimal computational method according to simple ligand and binding site properties.

How large does a compound screening collection need to be?

Increasingly, chemical libraries are being produced which are focused on a biological target or group of related targets, rather than simply being constructed in a combinatorial fashion. A screening collection compiled from such libraries will contain multiple analogues of a number of discrete series of compounds. The question arises as to how many analogues are necessary to represent each series in order to ensure that an active series will be identified. Based on a simple probabilistic argument and supported by in-house screening data, guidelines are given for the number of compounds necessary to achieve a "hit", or series of hits, at various levels of certainty. Obtaining more than one hit from the same series is useful since this gives early acquisition of SAR (structure-activity relationship) and confirms a hit is not a singleton. We show that screening collections composed of only small numbers of analogues of each series are sub-optimal for SAR acquisition. Based on these studies, we recommend a minimum series size of about 200 compounds. This gives a high probability of confirmatory SAR (i.e. at least two hits from the same series). More substantial early SAR (at least 5 hits from the same series) can be gained by using series of about 650 compounds each. With this level of information being generated, more accurate assessment of the likely success of the series in hit-to-lead and later stage development becomes possible.

Chemogenomics: structuring the drug discovery process to gene families.

In the post-genomic era, if all proteins in a gene family can putatively be identified, how can drug discovery effectively tackle so many novel targets that might lack structural and small-molecule inhibitory data? In response, chemogenomics, a new approach that guides drug discovery based on gene families, has been developed. By integrating all information available within a protein family (sequence, SAR data, protein structure), chemogenomics can efficiently enable cross-SAR exploitation, directed compound selection and early identification of optimum selectivity panel members. This review examines recent developments in chemogenomics technologies and illustrates their predictive capabilities with successful examples from two of the major protein families: protein kinases and G-protein-coupled receptors.

Bringing kinases into focus: efficient drug design through the use of chemogenomic toolkits.

The study of protein target families, as opposed to single targets, has become a very powerful tool in chemogenomics-led drug discovery. By integrating comprehensive chemoinformatics and bioinformatics databases with customised analytical tools, a 'Toolkit' approach for the target family is possible, thus allowing predictions of the ligand class, affinity, selectivity and likely off-target issues to be made for the guidance of the medicinal chemist. In this review, we highlight the development and application of the Toolkit approach to the protein kinase superfamily, drawing on examples from lead optimisation studies and the design of focused libraries for lead discovery.

Computational design strategies for combinatorial libraries.

Medicinal chemistry principles are being increasingly applied to the design of smaller, high purity, information-rich libraries. Recent computational advances in statistical methodology, the design of libraries to reduce ADMET problems, targeting protein families and revisiting natural products as sources of inspiration for scaffolds and reagents are all areas of progressive research.

Changes in cancer incidence patterns among a northeastern American Indian population: 1955-1969 versus 1990-2004.

PURPOSE: This manuscript examines shifts in patterns of cancer incidence among the Seneca Nation of Indians (SNI) for the interval 1955-1969 compared to 1990-2004. METHODS: A retrospective cohort design was used to examine cancer incidence among the SNI during 2 time intervals: 1955-1969 and 1990-2004. Person-years at risk were multiplied by cancer incidence rates for New York State, exclusive of New York City, over 5-year intervals. A computer-aided match with the New York State Cancer Registry was used to identify incident cancers. Overall and site-specific standardized incidence ratios (SIRs = observed/expected x 100), and 95% confidence intervals (CIs), were calculated for both time periods. RESULTS: During the earlier interval, deficits in overall cancer incidence were noted among males (SIR = 56, CI 36-82) and females (SIR = 71, CI 50-98), and for female breast cancers (SIR = 21, CI 4-62). During the more recent intervals, deficits in overall cancer incidence persisted among both genders (males SIR = 63, CI 52-77; females SIR = 67, CI 55-80). Deficits were also noted among males for cancers of the lung (SIR = 60, CI 33-98), prostate (SIR = 51, CI = 33-76) and bladder (SIR = 17, CI = 2-61) and among females for breast (SIR = 33, CI = 20-53) and uterus (SIR = 36, CI = 10-92). No cancer sites demonstrated increased incidence. Persons ages 60-69 years, 70-79 years, and ages 80+ years tended to exhibit deficits in overall incidence. CONCLUSIONS: Despite marked changes over time, deficits in overall cancer incidence have persisted between the time intervals studied. Tribal-specific cancer data are important for the development and implementation of comprehensive cancer control plans which align with local needs.

Fifty years of cancer in an American Indian population.

BACKGROUND: A clear understanding of cancer patterns among American Indian tribal groups has been complicated by a variety of issues. A retrospective cohort study design was applied to a Seneca Nation of Indians (SNI) cohort for the period from 1955 through 2004. METHODS: Incident cancers were identified through a computer match with the New York State Cancer Registry. Standardized incidence ratios (SIRs) and 95% confidence intervals were calculated for the overall interval as well as for each of the 5 10-year intervals. The SNI cohort consisted of 3935 men and 4193 women with a total of 120,403 person-years. RESULTS: Significant deficits in cancer incidence were noted among men for all sites combined (SIR, 69), and for lung (SIR, 59), prostate (SIR, 54), urinary bladder (SIR, 8), and Hodgkin lymphoma (SIR, 0); no cancer sites were identified with significantly elevated incidence. Women demonstrated significantly reduced cancer incidence for all sites combined (SIR, 70) and for breast (SIR, 39), colorectal (SIR, 72), ovary (SIR, 37), uterus (SIR, 42), bladder (SIR, 20), pancreas (SIR, 10), and non-Hodgkin lymphoma (SIR, 39); elevated incidence was noted for cancers of the lung (SIR, 139) and liver (SIR, 405). CONCLUSIONS: To the authors' knowledge, the current study represents the most comprehensive investigation to date of cancer patterns among an American Indian tribal group and provides insights for the development of tribal cancer control programming.

Optimising the EVA descriptor for prediction of biological activity.

EVA is a multivariate molecular descriptor for use in QSAR studies. It is constructed from vibrational eigenvalues derived from either a quantum theoretical or molecular mechanical treatment of molecular structure. This paper applies the method to biological-activity data using measures of the inotropic potential of a range of Calcium channel agonists. The performance of the descriptor, as both an explanatory and a predictive tool, is analysed in relation to the way in which it is constructed using a rigorous statistical treatment. Its capabilities are examined in relation to those of previously published methodology which used a composite descriptor. It is shown to have improved performance and several procedural advantages, such as ease of calculation and operation. It is a 3-D structural descriptor which does not require prior co-alignment of structures for a QSAR study.