Are There Differences between Launched Drugs, Clinical Candidates, and Commercially Available Compounds?

Ohno, Kazuki; Nagahara, Yuichi; Tsunoyama, Kazuhisa; Orita, Masaya

doi:10.1021/ci100023s

Cited by 16 publications

(20 citation statements)

References 25 publications

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“…To this end, and while we and others (e.g. Dobson et al 2009; Feher and Schmidt 2003; Khanna and Ranganathan 2011; Medina-Franco and Maggiora 2014; Ohno et al 2010) have recognised that marketed drugs do differ structurally from most molecules in drug discovery libraries, despite their ‘biogenic bias’ (Hert et al 2009), we sought to see how similar such non-marketed drug molecules or compounds are to marketed drugs when we compare them in the same way. The comparison is not entirely favourable to metabolites since we already know (Fig.…”

Section: Resultsmentioning

confidence: 79%

A ‘rule of 0.5’ for the metabolite-likeness of approved pharmaceutical drugs

et al. 2014

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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.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-014-0733-z) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 79%

A ‘rule of 0.5’ for the metabolite-likeness of approved pharmaceutical drugs

et al. 2014

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“…We used this scoring function to rank order the following small molecule databases for their kinase-likeness: The National Cancer Institute database (NCI), [54] the Natural Products database (NPD), [10] and the National Institute of Health's Molecular Libraries Small Molecule Repository (MLSMR), [55] all of which are commonly used in screening for novel kinase actives. We further validated our results by rank ordering the same databases using another correlation matrix-based scoring function originally developed by Ohno et al [56]. …”

Section: Introductionmentioning

confidence: 68%

“…A similar approach has already been applied by Ohno et al to rank order various test databases for drug-likeness [56]. In this methodology we first computed the correlation coefficient between every pair of descriptors in our descriptor set using Pipeline Pilot software.…”

Section: Methodsmentioning

confidence: 99%

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A physicochemical descriptor-based scoring scheme for effective and rapid filtering of kinase-like chemical space

et al. 2012

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BackgroundThe current chemical space of known small molecules is estimated to exceed 1060 structures. Though the largest physical compound repositories contain only a few tens of millions of unique compounds, virtual screening of databases of this size is still difficult. In recent years, the application of physicochemical descriptor-based profiling, such as Lipinski's rule-of-five for drug-likeness and Oprea's criteria of lead-likeness, as early stage filters in drug discovery has gained widespread acceptance. In the current study, we outline a kinase-likeness scoring function based on known kinase inhibitors.ResultsThe method employs a collection of 22,615 known kinase inhibitors from the ChEMBL database. A kinase-likeness score is computed using statistical analysis of nine key physicochemical descriptors for these inhibitors. Based on this score, the kinase-likeness of four publicly and commercially available databases, i.e., National Cancer Institute database (NCI), the Natural Products database (NPD), the National Institute of Health's Molecular Libraries Small Molecule Repository (MLSMR), and the World Drug Index (WDI) database, is analyzed. Three of these databases, i.e., NCI, NPD, and MLSMR are frequently used in the virtual screening of kinase inhibitors, while the fourth WDI database is for comparison since it covers a wide range of known chemical space. Based on the kinase-likeness score, a kinase-focused library is also developed and tested against three different kinase targets selected from three different branches of the human kinome tree.ConclusionsOur proposed methodology is one of the first that explores how the narrow chemical space of kinase inhibitors and its relevant physicochemical information can be utilized to build kinase-focused libraries and prioritize pre-existing compound databases for screening. We have shown that focused libraries generated by filtering compounds using the kinase-likeness score have, on average, better docking scores than an equivalent number of randomly selected compounds. Beyond library design, our findings also impact the broader efforts to identify kinase inhibitors by screening pre-existing compound libraries. Currently, the NCI library is the most commonly used database for screening kinase inhibitors. Our research suggests that other libraries, such as MLSMR, are more kinase-like and should be given priority in kinase screenings.

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“…These studies lead us to use the Pearson distribution system practically. The author researched the independent component analysis of the blind separation problem (Kato et al 2009) and drug discovery (Ohno et al 2010). And also, "the Pearson diffusions" are focused on by some researchers (Nagahara 1996;Forman and Sørensen 2008;Sørensen 2009;Shaw 2009).…”

Section: Introductionmentioning

confidence: 99%