“…Five diverse protein targets were tested: human cyclooxygenase-2 [8], dihydrofolate reductase [9,10], thrombin [11,12], antiestrogen [13] and HIV reverse transcriptase [14]. The datasets used for training and testing comprised of both active and inactive compounds from the subset of the MDL drug data report [1].…”
In many cases at the beginning of an HTS-campaign, some information about active molecules is already available. Often known active compounds (such as substrate analogues, natural products, inhibitors of a related protein or ligands published by a pharmaceutical company) are identified in low-throughput validation studies of the biochemical target. In this study we evaluate the effectiveness of a support vector machine applied for those compounds and used to classify a collection with unknown activity. This approach was aimed at reducing the number of compounds to be tested against the given target. Our method predicts the biological activity of chemical compounds based on only the atom pairs (AP) two dimensional topological descriptors. The supervised support vector machine (SVM) method herein is trained on compounds from the MDL drug data report (MDDR) known to be active for specific protein target. For detailed analysis, five different biological targets were selected including cyclooxygenase-2, dihydrofolate reductase, thrombin, HIV-reverse transcriptase and antagonists of the estrogen receptor. The accuracy of compound identification was estimated using the recall and precision values. The sensitivities for all protein targets exceeded 80% and the classification performance reached 100% for selected targets. In another application of the method, we addressed the absence of an initial set of active compounds for a selected protein target at the beginning of an HTS-campaign. In such a case, virtual high-throughput screening (vHTS) is usually applied by using a flexible docking procedure. However, the vHTS experiment typically contains a large percentage of false positives that should be verified by costly and time-consuming experimental follow-up assays. The subsequent use of our machine learning method was found to improve the speed (since the docking procedure was not required for all compounds from the database) and also the accuracy of the HTS hit lists (the enrichment factor).
“…Five diverse protein targets were tested: human cyclooxygenase-2 [8], dihydrofolate reductase [9,10], thrombin [11,12], antiestrogen [13] and HIV reverse transcriptase [14]. The datasets used for training and testing comprised of both active and inactive compounds from the subset of the MDL drug data report [1].…”
In many cases at the beginning of an HTS-campaign, some information about active molecules is already available. Often known active compounds (such as substrate analogues, natural products, inhibitors of a related protein or ligands published by a pharmaceutical company) are identified in low-throughput validation studies of the biochemical target. In this study we evaluate the effectiveness of a support vector machine applied for those compounds and used to classify a collection with unknown activity. This approach was aimed at reducing the number of compounds to be tested against the given target. Our method predicts the biological activity of chemical compounds based on only the atom pairs (AP) two dimensional topological descriptors. The supervised support vector machine (SVM) method herein is trained on compounds from the MDL drug data report (MDDR) known to be active for specific protein target. For detailed analysis, five different biological targets were selected including cyclooxygenase-2, dihydrofolate reductase, thrombin, HIV-reverse transcriptase and antagonists of the estrogen receptor. The accuracy of compound identification was estimated using the recall and precision values. The sensitivities for all protein targets exceeded 80% and the classification performance reached 100% for selected targets. In another application of the method, we addressed the absence of an initial set of active compounds for a selected protein target at the beginning of an HTS-campaign. In such a case, virtual high-throughput screening (vHTS) is usually applied by using a flexible docking procedure. However, the vHTS experiment typically contains a large percentage of false positives that should be verified by costly and time-consuming experimental follow-up assays. The subsequent use of our machine learning method was found to improve the speed (since the docking procedure was not required for all compounds from the database) and also the accuracy of the HTS hit lists (the enrichment factor).
“…Additional argatroban-type inhibitors have been developed by Novartis, the non-charged analogs 91-92 have inhibition constants between 20-25 nM [131,132]. The most favorable overall profile was found for 93 (K i = 57 nM, CGH1484), which contains a solubilising piperazine ring combined with a morpholine substituent.…”
Section: Secondary Amides Of Arginine and Arginine Surrogatesmentioning
The trypsin-like serine protease thrombin is a multifunctional key enzyme at the final step of the coagulation cascade and is involved in the regulation of hemostasis and thrombosis. An increased activation of coagulation can result in severe thromboembolic disorders, one of the major reasons responsible for mortality and morbidity in western world. Therefore, an effective, safe, and orally available thrombin inhibitor could be a useful anticoagulant drug for the daily prophylaxis of venous and arterial thrombosis and prevention of myocardial infarction for high-risk patients. Synthetic thrombin inhibitors have a long history; initial compounds were derived from electrophilic ketone- and aldehyde-analogs of arginine. First potent leads of non-covalent inhibitors were developed in the early eighties, which were further optimised in the nineties, after the X-ray structure of thrombin became available. In the meantime a huge number of highly active and selective inhibitors has been published, however, only a few of them have an appropriate pharmacokinetic and pharmacodynamic overall profile, which could justify their further development. Very recently, with Ximelagatran a first orally available thrombin inhibitor has been approved in France for the prevention of venous thromboembolic events in major orthopaedic surgery after successful clinical phase III. However, it still has to be awaited, whether the extensive clinical use of Ximelagatran can demonstrate for the first time that direct thrombin inhibitors offer a real benefit in terms of efficacy and safety over established antithrombotic therapies. This review summarizes the current status of synthetic thrombin inhibitors with a focus on more recently published and promising new compounds.
“…These problems seem to be circumvented to some degree by the recent report of a large number of weakly basic inhibitors, which do not contain the above-mentioned S1 anchoring group but incorporate isosters of these moieties, as well as other structural modifications that reduce the highly basic p K a of the parent inhibitors (in the range of 11−13) (). Among the most promising new S1 anchoring groups recently reported are the following moieties: oxoguanidine (A) (p K a 7) (); hydroxyguanidine (B) (p K a 9) (); acylguanidine (C) (p K a 7.6) (); aminohydrazone (D) (p K a 8.7) (); benzamidrazone (E) (p K a 8.9) (); sulfonylguanidine (F) (p K a 8.3) ( , ); sulfonylaminoguanidine (G) (p K a 8.4) ( , ); imidazole (H) (p K a around 7) (); 1-aminoisoquinoline (I) (p K a 7.5) (); 2-aminopyridine (J) (p K a around 7) (); benzylamine (K) (p K a 9.4) (); aniline (L) (p K a not provided but stated as “neutral”) ( , ); benzonitrile (M) (); and arylsulfonyldicyandiamide (N) (p K a of 7.9−8.2) () among others (Figure ). Compounds incorporating some of these groups, such as LB-30057 ( 1 ), CGH 1668 ( 2 ), L-375378 ( 3 ), or the Organon derivative 4 (Figure ), are currently under clinical investigation as antithrombotic drugs ( , ).…”
mentioning
confidence: 99%
“…Compounds incorporating some of these groups, such as LB-30057 ( 1 ), CGH 1668 ( 2 ), L-375378 ( 3 ), or the Organon derivative 4 (Figure ), are currently under clinical investigation as antithrombotic drugs ( , ). 1 Examples of S1 anchoring groups with reduced basicity incorporated in thrombin inhibitors: A, oxoguanidine (); B, hydroxyguanidine (); C, acylguanidine (); D, aminohydrazone (); E, benzamidrazone (); F, sulfonylguanidine ( , ); G, sulfonylaminoguanidine ( , ); H, imidazole (); I, 1-aminoisoquinoline (); J, 2-aminopyridine (); K, benzylamine (); L, aniline ( , ), M, benzonitrile (); N, arylsulfonyldicyandiamide (). 2 Examples of thrombin inhibitors: 1 , LB-30057; 2 , CGH 1668; 3 , L-375378; 4 , 2-(7-methoxynaphthalene-2-sulfonylamido)-3-(4-amino-5-azabenzothiophen-2-yl)propanoic acid 4-methylpiperidide; 5 , 4-[α-(4-toluenesulfonylureido)phenylalanylamido]benzenesulfonylguanidine; 6 , 4-[(2-ethyl-4,6-diphenylpyridinium)- N -propylcarboxamido]benzenesulfonyl- N -1-aminoguanidine; 7 , 4-aminobenzenesulfonylguanidine; 8 , 4-aminobenzenesulfonyl- N -1-aminoguanidine.…”
mentioning
confidence: 99%
“…Examples of S1 anchoring groups with reduced basicity incorporated in thrombin inhibitors: A, oxoguanidine (); B, hydroxyguanidine (); C, acylguanidine (); D, aminohydrazone (); E, benzamidrazone (); F, sulfonylguanidine ( , ); G, sulfonylaminoguanidine ( , ); H, imidazole (); I, 1-aminoisoquinoline (); J, 2-aminopyridine (); K, benzylamine (); L, aniline ( , ), M, benzonitrile (); N, arylsulfonyldicyandiamide ().…”
To prepare weakly basic thrombin inhibitors with modified S1 anchoring groups, two series of compounds were synthesized by reaction of guanidine or aminoguanidine with acyl halides and N,N-disubstituted carbamoyl chlorides. pK(a) measurements of these acylated guanidines/aminoguanidines showed a reduced basicity, with pK(a) values in the range of 8.4-8.7. These molecules typically showed inhibition constants in the range of 150-425 nM against thrombin and 360-965 nM against trypsin, even though some bulky derivatives, such as N,N-diphenylcarbamoylguanidine/aminoguanidine and their congeners, showed much stronger thrombin inhibitory activity, with inhibition constants in the range of 24-42 nM. Unexpectedly, very long incubation times with both proteases revealed that aminoguanidine derivatives behaved as irreversible inhibitors. To assess the molecular basis responsible for the high affinity observed for these molecules toward thrombin, the crystal structure of the thrombin-hirugen-N,N-diphenylcarbamoylaminoguanidine complex has been solved at 1.90 A resolution. The structural analysis of the complex revealed an unexpected interaction mode with the protease, resulting in an N,N-diphenylcarbamoyl intermediate covalently bound to the catalytic serine as a consequence of its hydrolysis together with the release of the aminoguanidine moiety. Surprisingly, in this covalent adduct a phenyl group was found in the S1 specificity pocket, which usually recognizes positively charged residues. These findings provide new insights in the design of low basicity serine protease inhibitors.
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