In-Silico Pharmacodynamics

Diagnosis and therapy of oral squamous cell carcinoma

Oral squamous cell carcinoma ranks among the top ten most common cancers worldwide. Despite the success in diagnosis and therapy during the past 30 years, oral squamous cell carcinoma still belongs to the tumor types with a very unfavorable prognosis. In an effort to identify genomic alterations with prognostic relevance, we applied the comparative genomic hybridization technique on oral squamous cell carcinoma. The tumors exhibited from five up to 47 DNA copy number alterations, indicating a considerable degree of genomic imbalance. Out of 35 tumors, 19 showed a gain of chromosome band 7p12. Genomic imbalances were investigated by hierarchical cluster analysis and clustered image mapping to investigate whether genomic profiles correlate with clinical data. Results of the present investigation show that profiling of genomic imbalances in general, and especially of the epidermal growth factor receptor (EGFR) on 7p12, may be suitable as prognostic factors. In order to identify small-molecule inhibitors for EGFR, we established a database of 531 natural compounds derived from medicinal plants used in traditional Chinese medicine. Candidate compounds were identified by correlation analysis using the Kendall tau-test of IC50 values of tumor cell lines and microarray-based EGFR mRNA expression. Further validation was performed by molecular docking studies using the AutoDock program with the crystal structure of EGFR tyrosine kinase domain as docking template. We estimate these results will be a further step toward the ultimate goal of individualized, patient-adapted tumor treatment based on tumor molecular profiling.

targetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis

Background

Tuberculosis still remains one of the largest killer infectious diseases, warranting the identification of newer targets and drugs. Identification and validation of appropriate targets for designing drugs are critical steps in drug discovery, which are at present major bottle-necks. A majority of drugs in current clinical use for many diseases have been designed without the knowledge of the targets, perhaps because standard methodologies to identify such targets in a high-throughput fashion do not really exist. With different kinds of 'omics' data that are now available, computational approaches can be powerful means of obtaining short-lists of possible targets for further experimental validation.

Results

We report a comprehensive in silico target identification pipeline, targetTB, for Mycobacterium tuberculosis. The pipeline incorporates a network analysis of the protein-protein interactome, a flux balance analysis of the reactome, experimentally derived phenotype essentiality data, sequence analyses and a structural assessment of targetability, using novel algorithms recently developed by us. Using flux balance analysis and network analysis, proteins critical for survival of M. tuberculosis are first identified, followed by comparative genomics with the host, finally incorporating a novel structural analysis of the binding sites to assess the feasibility of a protein as a target. Further analyses include correlation with expression data and non-similarity to gut flora proteins as well as 'anti-targets' in the host, leading to the identification of 451 high-confidence targets. Through phylogenetic profiling against 228 pathogen genomes, shortlisted targets have been further explored to identify broad-spectrum antibiotic targets, while also identifying those specific to tuberculosis. Targets that address mycobacterial persistence and drug resistance mechanisms are also analysed.

Conclusion

The pipeline developed provides rational schema for drug target identification that are likely to have high rates of success, which is expected to save enormous amounts of money, resources and time in the drug discovery process. A thorough comparison with previously suggested targets in the literature demonstrates the usefulness of the integrated approach used in our study, highlighting the importance of systems-level analyses in particular. The method has the potential to be used as a general strategy for target identification and validation and hence significantly impact most drug discovery programmes.

High-throughput electronic biology: mining information for drug discovery

The vast range of in silico resources that are available in life sciences research hold much promise towards aiding the drug discovery process. To fully realize this opportunity, computational scientists must consider the practical issues of data integration and identify how best to apply these resources scientifically. In this article we describe in silico approaches that are driven towards the identification of testable laboratory hypotheses; we also address common challenges in the field. We focus on flexible, high-throughput techniques, which may be initiated independently of 'wet-lab' experimentation, and which may be applied to multiple disease areas. The utility of these approaches in drug discovery highlights the contribution that in silico techniques can make and emphasizes the need for collaboration between the areas of disease research and computational science.