Functional genomics to new drug targets

Kramer, Richard S.; Cohen, Dalia

doi:10.1038/nrd1552

Cited by 158 publications

(75 citation statements)

References 74 publications

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“…This number is probably constrained by the limited knowledge from the 348 known successful targets and limited structural information for a large percentage of targets. Rapid progress in genomics (Kramer and Cohen, 2004), structural genomics (Hajduk et al, 2005), and proteomics (Ryan and Patterson, 2002) is revolutionizing target discovery. In addition to high-throughput technologies (Ilag et al, 2002) and cellular (Jackson and Harrington, 2005) and physiological studies (Lindsay, 2005;Sams-Dodd, 2005), various in silico methods are being developed.…”

Section: Discussionmentioning

confidence: 99%

“…Intensive efforts in target search (Chiesi et al, 2001;Matter, 2001;Walke et al, 2001;Ilag et al, 2002;Zheng et al, 2006b) have led to the discovery of Ͼ1000 research targets (targeted by investigational agents only) (Zheng et al, 2006b). These targets have been derived from analysis of disease relevance, functional roles, expression profiles, and loss-of-function genetics between normal and disease states (Ryan and Patterson, 2002;Nicolette and Miller, 2003;Kramer and Cohen, 2004;Austen and Dohrmann, 2005;Jackson and Harrington, 2005;Lindsay, 2005;Sams-Dodd, 2005). Many of them have been targeted by target-selective leads (Simmons, 2006;Zheng et al, 2006b).…”

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confidence: 99%

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What Are Next Generation Innovative Therapeutic Targets? Clues from Genetic, Structural, Physicochemical, and Systems Profiles of Successful Targets

Zhu

Han²,

Chen³

et al. 2009

J Pharmacol Exp Ther

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Low target discovery rate has been linked to inadequate consideration of multiple factors that collectively contribute to druggability. These factors include sequence, structural, physicochemical, and systems profiles. Methods individually exploring each of these profiles for target identification have been developed, but they have not been collectively used. We evaluated the collective capability of these methods in identifying promising targets from 1019 research targets based on the multiple profiles of up to 348 successful targets. The collective method combining at least three profiles identified 50, 25, 10, and 4% of the 30, 84, 41, and 864 phase III, II, I, and nonclinical trial targets as promising, including eight to nine targets of positive phase III results. This method dropped 89% of the 19 discontinued clinical trial targets and 97% of the 65 targets failed in high-throughput screening or knockout studies. Collective consideration of multiple profiles demonstrated promising potential in identifying innovative targets.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

What Are Next Generation Innovative Therapeutic Targets? Clues from Genetic, Structural, Physicochemical, and Systems Profiles of Successful Targets

Zhu

Han²,

Chen³

et al. 2009

J Pharmacol Exp Ther

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“…"Knocking down" a protein by using antisense provides the opportunity to induce transient changes in gene expression during physiologically significant time windows (Robinson et al, 1997;Kramer and Cohen, 2004). Antisense has been successfully used to downregulate BDNF expression: BDNF antisense injected into the mouse cerebellum caused a memory retrieval deficit in mice that had been radial arm maze trained (Mizuno et al, 2000), injection of BDNF antisense into the mouse dentate gyrus caused learning deficits in passive avoidance memory (Ma et al, 1998), and BDNF antisense injection caused impaired performance of a passive avoidance task by chicks (Johnston and Rose, 2001).…”

Section: Introductionmentioning

confidence: 99%

Brain-Derived Neurotrophic Factor Is Critically Involved in Thermal-Experience-Dependent Developmental Plasticity

Katz¹,

Meiri²

2006

J. Neurosci.

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All species exhibit critical period for sensory development, yet very little is known about the molecules involved in the changes in the network wiring that underlies this process. Here the role of brain-derived neurotrophic factor (BDNF) in the critical period of thermal control establishment in chicks was investigated.Neuroanatomically, the body temperature is balanced by the preoptic anterior hypothalamus (PO/AH) and controlled by thermosensitive neurons. Exposure to hot or cold conditions during the critical period of temperature control development causes a plastic change in the ratio between heat-and cold-sensitive cells and can modulate temperature tolerance.It was found that expression of BDNF mRNA but not of NGF or neurotrophin-3 was induced in the PO/AH of 3-d-old chicks during both heat and cold exposure. The peak of BDNF induction in both heat and cold exposure occurred after 6 h, with, respectively, threefold and sevenfold increases in its mRNA expression.To prove the concept that BDNF activation is a critical step in thermal-experience-dependent plasticity, BDNF was "knocked down" using antisense. It was found that, when BDNF in the PO/AH was inhibited by 80% during the third postnatal day, thermal establishment was impaired, and, after 1 week, the chicks' body temperature was reduced by 0.5°C. Furthermore, later in life, their reaction to thermal challenge was altered, and they exhibited a pronounced reduction in their ability to maintain their body temperature and body weight under harsh conditions. Together, these results prove that BDNF is critically involved in thermal-experience-dependent development.

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“…The study of proteins is crucial in understanding and combating disease through identification of proteins, discovering disease biomarkers, studying protein involvement in specific metabolic pathways, and identifying protein targets in drug discovery (1,2). An important technique that is used in these studies to quantify and identify peptides and/or proteins present in simple and complex mixtures is ESI-LCMS.…”

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confidence: 99%

Absolute Quantification of Proteins by LCMSE

Silva

Gorenstein

et al. 2006

Molecular & Cellular Proteomics

1,379

1,292

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Relative quantification methods have dominated the quantitative proteomics field. There is a need, however, to conduct absolute quantification studies to accurately model and understand the complex molecular biology that results in proteome variability among biological samples. A new method of absolute quantification of proteins is described. This method is based on the discovery of an unexpected relationship between MS signal response and protein concentration: the average MS signal response for the three most intense tryptic peptides per mole of protein is constant within a coefficient of variation of less than ؎10%. Given an internal standard, this relationship is used to calculate a universal signal response factor. The universal signal response factor (counts/mol) was shown to be the same for all proteins tested in this study. A controlled set of six exogenous proteins of varying concentrations was studied in the absence and presence of human serum. The absolute quantity of the standard proteins was determined with a relative error of less than ؎15%. The average MS signal responses of the three most intense peptides from each protein were plotted against their calculated protein concentrations, and this plot resulted in a linear relationship with an R 2 value of 0.9939. The analyses were applied to determine the absolute concentration of 11 common serum proteins, and these concentrations were then compared with known values available in the literature. Additionally within an unfractionated Escherichia coli lysate, a subset of identified proteins known to exist as functional complexes was studied. The calculated absolute quantities were used to accurately determine their stoichiometry. Molecular & Cellular Proteomics 5:144 -156, 2006.The study of proteins is crucial in understanding and combating disease through identification of proteins, discovering disease biomarkers, studying protein involvement in specific metabolic pathways, and identifying protein targets in drug discovery (1, 2). An important technique that is used in these studies to quantify and identify peptides and/or proteins present in simple and complex mixtures is ESI-LCMS.To date a majority of the quantitative proteomic analyses have been performed using stable isotope labeling strategies such as ICAT (3), iTRAQ TM (4), SILAC (stable isotope labeling by amino acids in cell culture) (5), and 18 O labeling (6, 7). These methodologies require complex, time-consuming sample preparation and can be relatively expensive.Recently there have been numerous reports applying labelfree methods to monitor the relative abundance of protein between different conditions (8 -11). Relative quantification provides information regarding specific protein abundance changes between two conditions caused by an induced perturbation (environment-induced, drug-induced, and diseaseinduced). These studies require comparison of identical proteolytic peptides in each of the two experiments to accurately determine relative ratios of the particular protein(s) of interest.Rel...

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Functional genomics to new drug targets

Cited by 158 publications

References 74 publications

What Are Next Generation Innovative Therapeutic Targets? Clues from Genetic, Structural, Physicochemical, and Systems Profiles of Successful Targets

What Are Next Generation Innovative Therapeutic Targets? Clues from Genetic, Structural, Physicochemical, and Systems Profiles of Successful Targets

Brain-Derived Neurotrophic Factor Is Critically Involved in Thermal-Experience-Dependent Developmental Plasticity

Absolute Quantification of Proteins by LCMSE

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