Advancing drug discovery through systems biology

Davidov, Eugene; Holland, Joanne M.; Marple, Edward W.; Naylor, Stephen

doi:10.1016/s1359-6446(03)02600-x

Cited by 93 publications

(49 citation statements)

References 40 publications

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“…To develop a novel predictive model for PSA recurrence in prostate cancer patients treated with prostatectomy, we chose to apply a systems pathology methodology (9)(10)(11). Systems pathology makes use of novel technologies in object-oriented image analysis, pattern recognition, and quantitative biomarker multiplexing.…”

Section: Introductionmentioning

confidence: 99%

Improved prediction of prostate cancer recurrence through systems pathology

Cordon‐Cardo

Kotsianti²,

Verbel³

et al. 2007

J. Clin. Invest.

102

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We have developed an integrated, multidisciplinary methodology, termed systems pathology, to generate highly accurate predictive tools for complex diseases, using prostate cancer for the prototype. To predict the recurrence of prostate cancer following radical prostatectomy, defined by rising serum prostate-specific antigen (PSA), we used machine learning to develop a model based on clinicopathologic variables, histologic tumor characteristics, and cell type-specific quantification of biomarkers. The initial study was based on a cohort of 323 patients and identified that high levels of the androgen receptor, as detected by immunohistochemistry, were associated with a reduced time to PSA recurrence. The model predicted recurrence with high accuracy, as indicated by a concordance index in the validation set of 0.82, sensitivity of 96%, and specificity of 72%. We extended this approach, employing quantitative multiplex immunofluorescence, on an expanded cohort of 682 patients. The model again predicted PSA recurrence with high accuracy, concordance index being 0.77, sensitivity of 77% and specificity of 72%. The androgen receptor was selected, along with 5 clinicopathologic features (seminal vesicle invasion, biopsy Gleason score, extracapsular extension, preoperative PSA, and dominant prostatectomy Gleason grade) as well as 2 histologic features (texture of epithelial nuclei and cytoplasm in tumor only regions). This robust platform has broad applications in patient diagnosis, treatment management, and prognostication.

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Section: Introductionmentioning

confidence: 99%

Improved prediction of prostate cancer recurrence through systems pathology

Cordon‐Cardo

Kotsianti²,

Verbel³

et al. 2007

J. Clin. Invest.

102

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“…Defining a system by the dynamic state of its metabolome relies on the effective integration of omics data because the metabolic state of the system is largely derived from the global expression of its genome and proteome [Davidov et al, 2003]. Recent advances enable the production of metabolic network models reconstructed from genome sequences, as well as experimental measurements of much of the metabolome [Douglas B.…”

Section: Stochastic Simulationmentioning

confidence: 99%

Stochastic Simulation for Biochemical Reaction Networks in Infectious Disease

Singh¹,

Shinde²

2012

Medicinal Chemistry and Drug Design

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“…Metabolomics illustrates this amply. There is also the view that the perceived decrease in the effectiveness of the target-based drug discovery process [3][4][5], including the still-high levels of attrition [6], means that we must move towards understanding organisms at something more akin to a whole-system level [7][8][9][10][11].…”

Section: Systems Biology and Metabolic Modelling In The 21st Centurymentioning

confidence: 99%

Systems biology, metabolic modelling and metabolomics in drug discovery and development

Kell

2006

Drug Discovery Today

266

163

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Unlike signalling pathways, metabolic networks are subject to strict stoichiometric constraints. Metabolomics amplifies changes in the proteome, and represents more closely the phenotype of an organism. Recent advances enable the production (and computer-readable encoding as SBML) of metabolic network models reconstructed from genome sequences, as well as experimental measurements of much of the metabolome. There is increasing convergence between the number of human metabolites estimated via genomics ( 3000) and the number measured experimentally. It is thus both timely, and now possible, to bring these two approaches together as an integrated (if distributed) whole to help understand the genesis of metabolic biomarkers, the progress of disease, and the modes of action, efficacy, off-target effects and toxicity of pharmaceutical drugs. Systems biology and metabolic modelling in the 21st CenturyAlthough there are many individual definitions, most commentators (including this one [1,2]) take it that systems biology involves an iterative interplay between more or less high-throughput and high-content 'wet' experiments, technology development, theory and computational modelling, and that it is the involvement of computational modelling, in particular, in the process that sets systems biology apart from the more traditional and more reductionist molecular biology. Metabolomics illustrates this amply. There is also the view that the perceived decrease in the effectiveness of the target-based drug discovery process [3][4][5], including the still-high levels of attrition [6], means that we must move towards understanding organisms at something more akin to a whole-system level [7][8][9][10][11].The question then arises as to what part of a system one might first beneficially model? Although there has, unsurprisingly, been considerable interest in modelling the major signalling pathways (e.g. Refs [12,13]), there are several reasons why it is timely to turn our attention to the level of small molecule metabolism, which is the focus of this review. Metabolism is more discriminatingIt has long been known, and proven through the formalism of metabolic control analysis [7,10,[14][15][16], that whereas small changes in the concentrations of enzymes (and the transcripts that encode them) have only small effects on the fluxes through metabolic pathways, they have substantial effects on the concentrations of metabolic intermediates. Because the metabolome (nominally the concentrations of 'all' the metabolites measured in a system of interest [17]) is downstream of the proteome, it is thereby 'amplified' both in theory [18] and in practice [19,20] and represents a more sensitive level of organisation than do the macromolecular 'omes for understanding a complex biological system, and the changes in it that might be occasioned by disease or pharmaceutical intervention [21,22]. Metabolic reconstruction is now mature and timelyAn attractive feature for the purposes of modelling is that metabolism, in contrast to signalling pathways, ...

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Advancing drug discovery through systems biology

Cited by 93 publications

References 40 publications

Improved prediction of prostate cancer recurrence through systems pathology

Improved prediction of prostate cancer recurrence through systems pathology

Stochastic Simulation for Biochemical Reaction Networks in Infectious Disease

Systems biology, metabolic modelling and metabolomics in drug discovery and development

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