Analysis of System Structure–Function Relationships

Fliri, Anton; Loging, William; Volkmann, Robert A.

doi:10.1002/cmdc.200700153

Cited by 28 publications

(31 citation statements)

References 13 publications

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“…The integrative data representation and the predictive method discussed here fall within the scope of the emerging field of systems pharmacology (26). In recent years, systems pharmacologic approaches have been applied successfully to various problems, such as identifying new targets for existing drugs (23,24,(27)(28)(29)(30) or understanding ADEs (21,25,(31)(32)(33)(34)(35). Here, we extend our earlier work on developing network-based methods for predicting drug adverse events and drugdrug interactions.…”

Section: Discussionmentioning

confidence: 88%

Predicting Adverse Drug Events Using Pharmacological Network Models

Cami

Arnold²,

Manzi³

et al. 2011

Sci. Transl. Med.

119

140

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Early and accurate identification of adverse drug events (ADEs) is critically important for public health. We have developed a novel approach for predicting ADEs, called predictive pharmacosafety networks (PPNs). PPNs integrate the network structure formed by known drug-ADE relationships with information on specific drugs and adverse events to predict likely unknown ADEs. Rather than waiting for sufficient post-market evidence to accumulate for a given ADE, this predictive approach relies on leveraging existing, contextual drug safety information, thereby having the potential to identify certain ADEs earlier. We constructed a network representation of drug-ADE associations for 809 drugs and 852 ADEs on the basis of a snapshot of a widely used drug safety database from 2005 and supplemented these data with additional pharmacological information. We trained a logistic regression model to predict unknown drug-ADE associations that were not listed in the 2005 snapshot. We evaluated the model's performance by comparing these predictions with the new drug-ADE associations that appeared in a 2010 snapshot of the same drug safety database. The proposed model achieved an AUROC (area under the receiver operating characteristic curve) statistic of 0.87, with a sensitivity of 0.42 given a specificity of 0.95. These findings suggest that predictive network methods can be useful for predicting unknown ADEs.

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Section: Discussionmentioning

confidence: 88%

Predicting Adverse Drug Events Using Pharmacological Network Models

Cami

Arnold²,

Manzi³

et al. 2011

Sci. Transl. Med.

119

140

View full text Add to dashboard Cite

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“…In the DRSN, the connections between drugs and subpathways revealed the drug dual effects on the human body. Some researchers have indicated that drugs binding to similar proteins tend to cause similar effects and these affected proteins may interact with each other to form a biological subpathway [21], [61]–[63]. Thus, if two drugs were connected to the same subpathways in our network, they were likely to cause the same therapeutic or side effects.…”

Section: Resultsmentioning

confidence: 98%

Characterizing the Network of Drugs and Their Affected Metabolic Subpathways

Shang

Wang

et al. 2012

PLoS ONE

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A fundamental issue in biology and medicine is illustration of the overall drug impact which is always the consequence of changes in local regions of metabolic pathways (subpathways). To gain insights into the global relationship between drugs and their affected metabolic subpathways, we constructed a drug–metabolic subpathway network (DRSN). This network included 3925 significant drug–metabolic subpathway associations representing drug dual effects. Through analyses based on network biology, we found that if drugs were linked to the same subpathways in the DRSN, they tended to share the same indications and side effects. Furthermore, if drugs shared more subpathways, they tended to share more side effects. We then calculated the association score by integrating drug-affected subpathways and disease-related subpathways to quantify the extent of the associations between each drug class and disease class. The results showed some close drug–disease associations such as sex hormone drugs and cancer suggesting drug dual effects. Surprisingly, most drugs displayed close associations with their side effects rather than their indications. To further investigate the mechanism of drug dual effects, we classified all the subpathways in the DRSN into therapeutic and non-therapeutic subpathways representing drug therapeutic effects and side effects. Compared to drug side effects, the therapeutic effects tended to work through tissue-specific genes and these genes tend to be expressed in the adrenal gland, liver and kidney; while drug side effects always occurred in the liver, bone marrow and trachea. Taken together, the DRSN could provide great insights into understanding the global relationship between drugs and metabolic subpathways.

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“…Finally, Bork and colleagues, building from the knowledge from Fliri et al . that drugs with similar in vitro protein binding profiles trend towards displaying similar side effects, 74, 75 examined the side effects of 746 marketed drugs. 1018 drug-drug relations appeared, 261 of which exist from chemically dissimilar drugs in different therapeutic categories.…”

Section: Current Development In Molecular Network For Drug Discoverymentioning

confidence: 99%

Molecular Networks in Drug Discovery

Morrow

Tian

Zhang³

2010

Crit Rev Biomed Eng

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Despite the dramatic increase of global spending on drug discovery and development, the approval rate for new drugs is declining, due chiefly to toxicity and undesirable side effects. Simultaneously, the growth of available biomedical data in the post-genomic era has provided fresh insight into the nature of redundant and compensatory drug-target pathways. This stagnation in drug approval can be overcome by the novel concept of polypharmacology, which is built on the fundamental concept that drugs modulate multiple targets. Polypharmacology can be studied with molecular networks which integrate multidisciplinary concepts including cheminformatics, bioinformatics, and systems biology. In silico techniques such as structure and ligand-based approaches can be employed to study molecular networks and reduce costs by predicting adverse drug reactions and toxicity in the early stage of drug development. By amalgamating strides in this informatics-driven era, designing polypharmacological drugs with molecular network technology exemplifies the next generation of therapeutics with less off-target properties and toxicity. In this review, we will first describe the challenges in drug discovery, and showcase successes using multi-target drugs toward diseases such as cancer and mood disorders. We will then focus on recent development of in silico polypharmacology predictions. Finally, our technologies in molecular network analysis will be presented.

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Analysis of System Structure–Function Relationships

Cited by 28 publications

References 13 publications

Predicting Adverse Drug Events Using Pharmacological Network Models

Predicting Adverse Drug Events Using Pharmacological Network Models

Characterizing the Network of Drugs and Their Affected Metabolic Subpathways

Molecular Networks in Drug Discovery

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