Successful identification of key chemical structure modifications that lead to improved ADME profiles.
The results of a new method developed to identify well defined structural transformations that are key to improve a certain ADME profile are presented in this work. In particular Naïve Bayesian statistics and SciTegic FCFP_6 molecular fingerprints have been used to extract, from a dataset of 1,169 compounds with known in vitro UGT glucuronidation clearance, those changes in chemical structure that lead to a significant increase in this property. The effectiveness in achieving that goal of the thus found 55,987 transformations has been quantified and compared to classical medicinal chemistry transformations. The conclusion is that on average the new transformations found via in silico methods induce increases of UGT clearance by twofold, whilst the classical transformations are on average unable to alter that endpoint significantly in any direction. When both types of transformations are combined via substructural searches (SSS) the average twofold increase in glucuronidation is maintained. The implications of these findings for the drug design process are also discussed, in particular when compared to other methods previously described in the literature to address the question 'Which compound do I make next?'
Cucurull-Sanchez
Lourdes
L
Department of Pharmacokinetics, Pfizer PGRD, Sandwich, Kent CT13 9NJ, UK. Lourdes.Cucurull-Sanchez@Pfizer.com
D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D015195:Drug Design; D014453:Glucuronosyltransferase / Q000378:metabolism; D008657:Metabolic Clearance Rate; D008956:Models, Chemical; D015394:Molecular Structure; D010599:Pharmacokinetics; D021281:Quantitative Structure-Activity Relationship
D014453:Glucuronosyltransferase
J Comput Aided Mol Des. 2009 Feb;23(2):97-103
18802667
J Chem Inf Model. 2007 Jul-Aug;47(4):1294-302
17539596
J Chem Inf Model. 2006 Jan-Feb;46(1):180-92
16426054
Chem Rev. 1996 Dec 19;96(8):3147-3176
11848856
J Chem Inf Model. 2006 May-Jun;46(3):1124-33
16711732
J Med Chem. 2005 Mar 10;48(5):1638-48
15743205
J Biomol Screen. 2005 Oct;10(7):682-6
16170046
J Med Chem. 2006 Mar 23;49(6):2103-9
16539399
J Med Chem. 2006 Nov 16;49(23):6672-82
17154498
Curr Opin Drug Discov Devel. 2001 Jul;4(4):471-8
11727312
J Chem Inf Comput Sci. 2002 Jan-Feb;42(1):103-8
11855973
J Chem Inf Model. 2006 May-Jun;46(3):1188-93
16711738
Curr Med Chem. 2005;12(1):23-49
15638729
Br J Clin Pharmacol. 2006 Apr;61(4):427-39
16542204
J Chem Inf Model. 2006 Mar-Apr;46(2):462-70
16562973
J Med Chem. 2003 Jul 17;46(15):3257-74
12852756
Adv Drug Deliv Rev. 2001 Mar 1;46(1-3):3-26
11259830
J Chem Inf Model. 2006 Mar-Apr;46(2):677-85
16562998
J Med Chem. 2005 Jun 16;48(12):4031-9
15943476
Bioorg Med Chem. 2006 Oct 15;14(20):7011-22
16870456
J Med Chem. 2004 Aug 26;47(18):4463-70
15317458
J Chem Inf Comput Sci. 1998 May-Jun;38(3):511-22
9611787
J Med Chem. 2005 May 5;48(9):3164-70
15857122
J Med Chem. 2008 Feb 14;51(3):553-64
18173228