Dataset Information

A 'rule of 0.5' for the metabolite-likeness of approved pharmaceutical drugs.

ABSTRACT: We exploit the recent availability of a community reconstruction of the human metabolic network ('Recon2') to study how close in structural terms are marketed drugs to the nearest known metabolite(s) that Recon2 contains. While other encodings using different kinds of chemical fingerprints give greater differences, we find using the 166 Public MDL Molecular Access (MACCS) keys that 90 % of marketed drugs have a Tanimoto similarity of more than 0.5 to the (structurally) 'nearest' human metabolite. This suggests a 'rule of 0.5' mnemonic for assessing the metabolite-like properties that characterise successful, marketed drugs. Multiobjective clustering leads to a similar conclusion, while artificial (synthetic) structures are seen to be less human-metabolite-like. This 'rule of 0.5' may have considerable predictive value in chemical biology and drug discovery, and may represent a powerful filter for decision making processes.

SUBMITTER: O Hagan S

PROVIDER: S-EPMC4342520 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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A 'rule of 0.5' for the metabolite-likeness of approved pharmaceutical drugs.

O Hagan Steve S Swainston Neil N Handl Julia J Kell Douglas B DB

Metabolomics : Official journal of the Metabolomic Society 20140919 2

We exploit the recent availability of a community reconstruction of the human metabolic network ('Recon2') to study how close in structural terms are marketed drugs to the nearest known metabolite(s) that Recon2 contains. While other encodings using different kinds of chemical fingerprints give greater differences, we find using the 166 Public MDL Molecular Access (MACCS) keys that 90 % of marketed drugs have a Tanimoto similarity of more than 0.5 to the (structurally) 'nearest' human metabolite ...[more]

PMID: 25750602

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Project description:While the entirety of 'Chemical Space' is huge (and assumed to contain between 10(63) and 10(200) 'small molecules'), distinct subsets of this space can nonetheless be defined according to certain structural parameters. An example of such a subspace is the chemical space spanned by endogenous metabolites, defined as 'naturally occurring' products of an organisms' metabolism. In order to understand this part of chemical space in more detail, we analyzed the chemical space populated by human metabolites in two ways. Firstly, in order to understand metabolite space better, we performed Principal Component Analysis (PCA), hierarchical clustering and scaffold analysis of metabolites and non-metabolites in order to analyze which chemical features are characteristic for both classes of compounds. Here we found that heteroatom (both oxygen and nitrogen) content, as well as the presence of particular ring systems was able to distinguish both groups of compounds. Secondly, we established which molecular descriptors and classifiers are capable of distinguishing metabolites from non-metabolites, by assigning a 'metabolite-likeness' score. It was found that the combination of MDL Public Keys and Random Forest exhibited best overall classification performance with an AUC value of 99.13%, a specificity of 99.84% and a selectivity of 88.79%. This performance is slightly better than previous classifiers; and interestingly we found that drugs occupy two distinct areas of metabolite-likeness, the one being more 'synthetic' and the other being more 'metabolite-like'. Also, on a truly prospective dataset of 457 compounds, 95.84% correct classification was achieved. Overall, we are confident that we contributed to the tasks of classifying metabolites, as well as to understanding metabolite chemical space better. This knowledge can now be used in the development of new drugs that need to resemble metabolites, and in our work particularly for assessing the metabolite-likeness of candidate molecules during metabolite identification in the metabolomics field.

Project description:OBJECTIVES:To define a series of clinical trial transparency measures and apply them to large pharmaceutical and biotechnology companies and their 2014 FDA-approved drugs. DESIGN:Cross-sectional descriptive analysis of all clinical trials supporting 2014 Food and Drugs Administration (FDA)-approved new drug applications (NDAs) for novel drugs sponsored by large companies. DATA SOURCES:Data from over 45 sources, including Drugs@FDA.gov, ClinicalTrials.gov, corporate and international registries; PubMed, Google Scholar, EMBASE, corporate press releases, Securities and Exchange Commission (SEC) filings and personal communications with drug manufacturers. OUTCOME MEASURES:Trial registration, results reporting, clinical study report (CSR) synopsis sharing, biomedical journal publication, and FDA Amendments Acts (FDAAA) compliance, analysed on the drug level. RESULTS:The FDA approved 19 novel new drugs, sponsored by 11 large companies, involving 553 trials, in 2014. We analysed 505 relevant trials. Per drug, a median of 100% (IQR 86%-100%) of trials in patients were registered, 71% (IQR 57%-100%) reported results or shared a CSR synopsis, 80% (70%-100%) were published and 96% (80%-100%) were publicly available in some form by 13 months after FDA approval. Disclosure rates were lower at FDA approval (65%) and improved significantly by 6 months post FDA approval. Per drug, a median of 100% (IQR 75%-100%) of FDAAA-applicable trials were compliant. Half of reviewed drugs had publicly disclosed results for all trials in patients in our sample. One trial was uniquely registered in a corporate registry, and not ClinicalTrials.gov; 0 trials were uniquely registered in international registries. CONCLUSIONS:Among large pharmaceutical companies and new drugs, clinical trial transparency is high based on several standards, although opportunities for improvement remain. Transparency is markedly higher for trials in patients than among all trials supporting drug approval, including trials in healthy volunteers. Ongoing efforts to publicly track companies' transparency records and recognise exemplary companies may encourage further progress.

Project description:BackgroundRecently, the metabolite-likeness of the drug space has emerged and has opened a new possibility for exploring human metabolite-like candidates in drug discovery. However, the applicability of metabolite-likeness in drug discovery has been largely unexplored. Moreover, there are no reports on its applications for the repositioning of drugs to possible enzyme modulators, although enzyme-drug relations could be directly inferred from the similarity relationships between enzyme's metabolites and drugs.MethodsWe constructed a drug-metabolite structural similarity matrix, which contains 1,861 FDA-approved drugs and 1,110 human intermediary metabolites scored with the Tanimoto similarity. To verify the metabolite-likeness measure for drug repositioning, we analyzed 17 known antimetabolite drugs that resemble the innate metabolites of their eleven target enzymes as the gold standard positives. Highly scored drugs were selected as possible modulators of enzymes for their corresponding metabolites. Then, we assessed the performance of metabolite-likeness with a receiver operating characteristic analysis and compared it with other drug-target prediction methods. We set the similarity threshold for drug repositioning candidates of new enzyme modulators based on maximization of the Youden's index. We also carried out literature surveys for supporting the drug repositioning results based on the metabolite-likeness.ResultsIn this paper, we applied metabolite-likeness to repurpose FDA-approved drugs to disease-associated enzyme modulators that resemble human innate metabolites. All antimetabolite drugs were mapped with their known 11 target enzymes with statistically significant similarity values to the corresponding metabolites. The comparison with other drug-target prediction methods showed the higher performance of metabolite-likeness for predicting enzyme modulators. After that, the drugs scored higher than similarity score of 0.654 were selected as possible modulators of enzymes for their corresponding metabolites. In addition, we showed that drug repositioning results of 10 enzymes were concordant with the literature evidence.ConclusionsThis study introduced a method to predict the repositioning of known drugs to possible modulators of disease associated enzymes using human metabolite-likeness. We demonstrated that this approach works correctly with known antimetabolite drugs and showed that the proposed method has better performance compared to other drug target prediction methods in terms of enzyme modulators prediction. This study as a proof-of-concept showed how to apply metabolite-likeness to drug repositioning as well as potential in further expansion as we acquire more disease associated metabolite-target protein relations.

Dataset Information

A 'rule of 0.5' for the metabolite-likeness of approved pharmaceutical drugs.

Publications

A 'rule of 0.5' for the metabolite-likeness of approved pharmaceutical drugs.

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