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Identifying problematic drugs based on the characteristics of their targets.

Lopes TJ, Shoemaker JE, Matsuoka Y, Kawaoka Y, Kitano H - Front Pharmacol (2015)

Bottom Line: We used this strategy to identify properties that make certain proteins more likely to cause harmful effects when targeted; such proteins usually have domains commonly found throughout the human proteome.This approach enabled us to distinguish between approved and problematic drugs with an accuracy of 60-70%.Moreover, our approach can be applied as soon as candidate drugs are available, as demonstrated with predictions for more than 5000 experimental drugs.

View Article: PubMed Central - PubMed

Affiliation: Japan Science and Technology Agency ERATO Kawaoka Infection-Induced Host Responses Project Minato-ku, Japan ; Department of Pathobiological Sciences, School of Veterinary Medicine, Influenza Research Institute, University of Wisconsin-Madison Madison, WI, USA ; Division of Virology, Department of Microbiology and Immunology, Institute of Medical Science, University of Tokyo Tokyo, Japan.

ABSTRACT
Identifying promising compounds during the early stages of drug development is a major challenge for both academia and the pharmaceutical industry. The difficulties are even more pronounced when we consider multi-target pharmacology, where the compounds often target more than one protein, or multiple compounds are used together. Here, we address this problem by using machine learning and network analysis to process sequence and interaction data from human proteins to identify promising compounds. We used this strategy to identify properties that make certain proteins more likely to cause harmful effects when targeted; such proteins usually have domains commonly found throughout the human proteome. Additionally, since currently marketed drugs hit multiple targets simultaneously, we combined the information from individual proteins to devise a score that quantifies the likelihood of a compound being harmful to humans. This approach enabled us to distinguish between approved and problematic drugs with an accuracy of 60-70%. Moreover, our approach can be applied as soon as candidate drugs are available, as demonstrated with predictions for more than 5000 experimental drugs. These resources are available at http://sourceforge.net/projects/psin/.

No MeSH data available.


Related in: MedlinePlus

(A–D) In general, targets of problematic drugs have high degrees and closeness centralities in the PSIN and PPI networks. However, their betweenness values are not significantly different from the targets of approved drugs in either protein network (One-Way ANOVA, ***p < < 0.0001 and *p>0.05, sample sizes for each group are the same as depicted in Figure 2A). The closeness from the targets of both networks was close to two main values, differing by only decimal digits; therefore, we rounded the values to their closest integer, namely 17 or 19 in the PSIN and 14 or 18 in the PPI. While three PSIN centrality measures were found to be strong indicators of the differences between targets of problematic and approved drugs, the centrality measures of the PPI network could also detect these differences, albeit in a moderate fashion (Tukey's Honest Significance Difference—Supplementary Figure 2). Overall, this likely stems from the fact that the current PPIs still have only ~10,000 proteins and numerous false-positive interactions; with new proteins and high-quality interactions being constantly added, we expect this to change in the future.
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Figure 3: (A–D) In general, targets of problematic drugs have high degrees and closeness centralities in the PSIN and PPI networks. However, their betweenness values are not significantly different from the targets of approved drugs in either protein network (One-Way ANOVA, ***p < < 0.0001 and *p>0.05, sample sizes for each group are the same as depicted in Figure 2A). The closeness from the targets of both networks was close to two main values, differing by only decimal digits; therefore, we rounded the values to their closest integer, namely 17 or 19 in the PSIN and 14 or 18 in the PPI. While three PSIN centrality measures were found to be strong indicators of the differences between targets of problematic and approved drugs, the centrality measures of the PPI network could also detect these differences, albeit in a moderate fashion (Tukey's Honest Significance Difference—Supplementary Figure 2). Overall, this likely stems from the fact that the current PPIs still have only ~10,000 proteins and numerous false-positive interactions; with new proteins and high-quality interactions being constantly added, we expect this to change in the future.

Mentions: Compared with proteins targeted by the approved drugs, those targeted by problematic compounds had a significantly higher degree in both networks, and much lower closeness centrality and Burt's constraint values (Figure 3; for each centrality measure, One-Way ANOVA, p < 0.0001, followed by Tukey's HSD test; Supplementary Figure 2). In contrast, we observed no significant differences in the betweenness values of proteins targeted by problematic and those targeted by approved drugs in the PSIN or in the PPI network. These findings indicate that while targets of approved drugs have protein domains that are not shared among many other proteins and are involved in fewer interactions, targets of problematic drugs have domains that are more common throughout the proteome and have more protein interactions reported.


Identifying problematic drugs based on the characteristics of their targets.

Lopes TJ, Shoemaker JE, Matsuoka Y, Kawaoka Y, Kitano H - Front Pharmacol (2015)

(A–D) In general, targets of problematic drugs have high degrees and closeness centralities in the PSIN and PPI networks. However, their betweenness values are not significantly different from the targets of approved drugs in either protein network (One-Way ANOVA, ***p < < 0.0001 and *p>0.05, sample sizes for each group are the same as depicted in Figure 2A). The closeness from the targets of both networks was close to two main values, differing by only decimal digits; therefore, we rounded the values to their closest integer, namely 17 or 19 in the PSIN and 14 or 18 in the PPI. While three PSIN centrality measures were found to be strong indicators of the differences between targets of problematic and approved drugs, the centrality measures of the PPI network could also detect these differences, albeit in a moderate fashion (Tukey's Honest Significance Difference—Supplementary Figure 2). Overall, this likely stems from the fact that the current PPIs still have only ~10,000 proteins and numerous false-positive interactions; with new proteins and high-quality interactions being constantly added, we expect this to change in the future.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4555035&req=5

Figure 3: (A–D) In general, targets of problematic drugs have high degrees and closeness centralities in the PSIN and PPI networks. However, their betweenness values are not significantly different from the targets of approved drugs in either protein network (One-Way ANOVA, ***p < < 0.0001 and *p>0.05, sample sizes for each group are the same as depicted in Figure 2A). The closeness from the targets of both networks was close to two main values, differing by only decimal digits; therefore, we rounded the values to their closest integer, namely 17 or 19 in the PSIN and 14 or 18 in the PPI. While three PSIN centrality measures were found to be strong indicators of the differences between targets of problematic and approved drugs, the centrality measures of the PPI network could also detect these differences, albeit in a moderate fashion (Tukey's Honest Significance Difference—Supplementary Figure 2). Overall, this likely stems from the fact that the current PPIs still have only ~10,000 proteins and numerous false-positive interactions; with new proteins and high-quality interactions being constantly added, we expect this to change in the future.
Mentions: Compared with proteins targeted by the approved drugs, those targeted by problematic compounds had a significantly higher degree in both networks, and much lower closeness centrality and Burt's constraint values (Figure 3; for each centrality measure, One-Way ANOVA, p < 0.0001, followed by Tukey's HSD test; Supplementary Figure 2). In contrast, we observed no significant differences in the betweenness values of proteins targeted by problematic and those targeted by approved drugs in the PSIN or in the PPI network. These findings indicate that while targets of approved drugs have protein domains that are not shared among many other proteins and are involved in fewer interactions, targets of problematic drugs have domains that are more common throughout the proteome and have more protein interactions reported.

Bottom Line: We used this strategy to identify properties that make certain proteins more likely to cause harmful effects when targeted; such proteins usually have domains commonly found throughout the human proteome.This approach enabled us to distinguish between approved and problematic drugs with an accuracy of 60-70%.Moreover, our approach can be applied as soon as candidate drugs are available, as demonstrated with predictions for more than 5000 experimental drugs.

View Article: PubMed Central - PubMed

Affiliation: Japan Science and Technology Agency ERATO Kawaoka Infection-Induced Host Responses Project Minato-ku, Japan ; Department of Pathobiological Sciences, School of Veterinary Medicine, Influenza Research Institute, University of Wisconsin-Madison Madison, WI, USA ; Division of Virology, Department of Microbiology and Immunology, Institute of Medical Science, University of Tokyo Tokyo, Japan.

ABSTRACT
Identifying promising compounds during the early stages of drug development is a major challenge for both academia and the pharmaceutical industry. The difficulties are even more pronounced when we consider multi-target pharmacology, where the compounds often target more than one protein, or multiple compounds are used together. Here, we address this problem by using machine learning and network analysis to process sequence and interaction data from human proteins to identify promising compounds. We used this strategy to identify properties that make certain proteins more likely to cause harmful effects when targeted; such proteins usually have domains commonly found throughout the human proteome. Additionally, since currently marketed drugs hit multiple targets simultaneously, we combined the information from individual proteins to devise a score that quantifies the likelihood of a compound being harmful to humans. This approach enabled us to distinguish between approved and problematic drugs with an accuracy of 60-70%. Moreover, our approach can be applied as soon as candidate drugs are available, as demonstrated with predictions for more than 5000 experimental drugs. These resources are available at http://sourceforge.net/projects/psin/.

No MeSH data available.


Related in: MedlinePlus