A 'rule of 0.5' for the metabolite-likeness of approved pharmaceutical drugs.
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ABSTRACT:
SUBMITTER: O Hagan S
PROVIDER: S-EPMC4342520 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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.
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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.
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Project description:Drug-likeness is a vital consideration when selecting compounds in the early stage of drug discovery. A series of drug-like properties are needed to predict the drug-likeness of a given compound and provide useful guidelines to increase the likelihood of converting lead compounds into drugs. Experimental physicochemical properties, pharmacokinetic/toxicokinetic properties and maximum dosages of approved small-molecule drugs from multiple text-based unstructured data resources have been manually assembled, curated, further digitized and processed into structured data, which are deposited in the Database of Digital Properties of approved Drugs (DDPD). DDPD 1.0 contains 30 212 drug property entries, including 2250 approved drugs and 32 properties, in a standardized value/unit format. Moreover, two analysis tools are provided to examine the drug-likeness features of given molecules based on the collected property data of approved drugs. Additionally, three case studies are presented to demonstrate how users can utilize the database. We believe that this database will be a valuable resource for the drug discovery and development field. Database URL: http://www.inbirg.com/ddpd.
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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.
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Project description:AimsThis study compares physicians' recall of the claims of benefits on cardiovascular disease and diabetes made by pharmaceutical sales representatives for drugs approved on the basis of a surrogate outcome, i.e., an off-label claim, compared with those approved on the basis of a serious morbidity or mortality (clinical) outcome.MethodsPhysicians in primary care practices in Montreal, Vancouver, Sacramento and Toulouse, who saw sales representatives as part of their usual practice and served a non-referral population, were contacted in blocks of 25 from a randomized list of all physicians practising in the relevant metropolitan area. We compared how frequently physicians reported that sales reps made claims of serious morbidity or mortality (clinically meaningful) benefits for drugs approved on the basis of surrogate outcomes vs. drugs approved on the basis of clinical outcomes.ResultsThere were 448 promotions for 58 unique brand name cardiovascular and diabetes drugs. Claims of clinically meaningful benefit were reported in 156 (45%) of the 347 promotions for surrogate outcome drugs, constituting unwarranted efficacy claims, i.e., off-label promotion. Claims of clinical benefit were reported in 72 of the 101 promotions (71%) for drugs approved on the basis of clinical outcomes, adjusted OR = 0.3 (95% CI 0.2, 0.6), P < 0.001.ConclusionsClaims of efficacy made in sales visit promotions for drugs approved only on the basis of surrogate outcomes extended beyond the regulator-approved efficacy information for the product in almost half of promotions. Unapproved claims of drug efficacy constitute a form of off-label promotion and merit greater attention from regulators.
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Project description:Chemical space maps help visualize similarities within molecular sets. However, there are many different molecular similarity measures resulting in a confusing number of possible comparisons. To overcome this limitation, we exploit the fact that tools designed for reaction informatics also work for alchemical processes that do not obey Lavoisier's principle, such as the transmutation of lead into gold. We start by using the differential reaction fingerprint (DRFP) to create tree-maps (TMAPs) representing the chemical space of pairs of drugs selected as being similar according to various molecular fingerprints. We then use the Transformer-based RXNMapper model to understand structural relationships between drugs, and its confidence score to distinguish between pairs related by chemically feasible transformations and pairs related by alchemical transmutations. This analysis reveals a diversity of structural similarity relationships that are otherwise difficult to analyze simultaneously. We exemplify this approach by visualizing FDA-approved drugs, EGFR inhibitors, and polymyxin B analogs.
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Project description:This SuperSeries is composed of the SubSeries listed below.
2024-01-19 | GSE240520 | GEO