2015
The Context: Drug Metabolism in Discovery CYP Inhibition IC50 Met Stab HLM Clint/%Remaining Coordination to heme: Type II/I Met Stab Hep Clint/%Remaining Met ID Type of Inhibition Competitive/Non Competitive Met Stab CRP Clint/%Remaining Time Dependent Yes/Maybe/No KCN Yes/Maybe/No GSH Trapping Yes/Maybe/No
The Context: Drug Metabolism in Discovery Clearance data 0 % of compound metabolized In HLM (30 min) 94 % of compound metabolized In HLM (30 min) Ahlström MM, et al J Med Chem. 2007 Sep 6;50(18):4444-52. Ahlström MM, et al J Med Chem. 2007 Nov 1;50(22):5382-91. 10 % of compound metabolized In HLM (30 min)
The Context: Drug Metabolism in Discovery Clearance data Crivori P, et al J Comput Aided Mol Des. 2004 Mar;18(3):155-66.
Our proposal to increase Metabolism knowledge based on HRMS Server Pending Experiments Approved Experiments Analysis Tools I. Zamora et al Drug Discovery Today: Technologies, 2013 10, (1), e199 e205
Low concentration: 2 mm or lower It has to be fast Structure elucidation Clearance data Can the metabolites be detected? Can be done fast? Dextromephorphan 3A4 2 mm T1/2 = 11 min Clint= 1.2 ml/min*mg From data to information
Peak detection and structure elucidation HLM incubation DRM Analysis >90% success (unsupervised) LC/MSMS MS Mass-MetaSite Zelesky V, et al Bioanalysis. 2013 May;5(10):1165-79. Li AC, et al Xenobiotica. 2013 Apr;43(4):390-8. Bonn B et al Rapid Commun Mass Spectrom. 2010 Nov 15;24(21):3127-38. MSMS Metabolite Prediction Mass & Fragmentation recognition >80% success at rank 1 >95% ranks 1-3 (unsupervised) From data to information
Clearance data Can structure of a metabolite be elucidated? From data to information
HRMS Clearance data in Drug Discovery: Déjà Vu function From information to knowledge
HRMS Clearance data in Drug Discovery: SoM Prediction Zamora I, Afzelius L, Cruciani G. J Med Chem. 2003 Jun 5;46(12):2313-24. From information to knowledge
Mass-MetaSite WebMetabase GSH Trapping work-flow: Setting up the system Properties Units Flags Groups Filters Protocol Definition Macros Auto- Process Definition Settings From information to knowledge
Fragment Analysis This analysis tool is looking in how many times a molecular substructure is present in the parent compounds and how many times it is metabolized following different mechanisms Substructure s that have been found in the experiment analyzed Number of times the substructure appear and number of time it is metabolized Reactions that have been obtained in any of the substructures analyzed From information to knowledge
Fragment Analysis Number of reactions that suffer a particular substructure From information to knowledge
HRMS Clearance data in Drug Discovery: Kinetic analysis A. Zimmerlin et al, Drug Discovery Today: Technologies 2013, 10( 1), e191 e198 From information to knowledge
Cross platform comparison: Molecules Tested Medium to high clearance compounds. Compounds with known metabolites and series for the same family 2N-569S 10P-909 10R-0650 7Z-0822 Dextromethorphan Nefazadone Saquinavir Verapamil Busprione
Cross Platform comparison: Short LC time seems to give good peak resolution Buspirone Dextromethorphan Clint Clint System Half Life # Half Life # (ml/min*mg (ml/min*mg (min) metabolites (min) metabolites ) ) Agilent 1 18,9 0,1 8 20,0 0,14 1 Agilent 2 17,7 0,2 8 98,0 0,03 1 Agilent 3 26,8 0,1 9 72,2 0,04 3 Agilent 4 25,0 0,1 8 94,3 0,03 3 Thermo 1 8,7 0,3 4 64,8 0,04 1 Thermo 2 14,0 0,2 8 67,3 0,04 2 Thermo 3 15,3 0,2 11 75,1 0,04 1 Thermo 4 15,8 0,2 9 42,8 0,06 1 Thermo 5 14,1 0,2 9 69,8 0,04 1 Thermo 6 14,9 0,2 7 26,4 0,10 1 Waters 1 16,0 0,2 10 79,5 0,03 4 Waters 2 15,5 0,2 9 80,3 0,03 1 Waters 3 14,1 0,2 11 17,2 0,16 1 Waters 4 15,6 0,2 6 102,4 0,03 1 Waters 5 15,5 0,2 10 68,8 0,04 1 Waters 6 13,9 0,2 7 66,1 0,04 2 Waters 7 16,1 0,2 7 95,0 0,03 3 Sciex 1 16,4 0,2 7 96,0 0,03 1 Sciex 2 14,5 0,2 7 53,4 0,05 1 Sciex 3 14,2 0,2 6 32,3 0,09 1 Sciex 4 12,9 0,2 4 38,2 0,07 3 Bruker 1 14,4 0,2 8 108,0 0,03 3
Cross platform Comparison: Soft Spot Analysis
Cross platform comparison: Soft Spot Analysis
Cross Platform Comparison: Conclusions 1. Big difference in file size depending on acquisition parameters. 2. Processing time is not directly correlate to file size. 3. Processing time depending on untarged (longer)/targeted(shorter) approaches 4. Short LC times do not seem to affect peak quality (4 minutes run gave nice peaks) 5. There is a difference in the number of metabolite detected, although this might be due to the processing and filtering conditions applied. 6. Fragmentation of the molecules is quite conservative over the systems 7. The soft spot results are quite similar across the different instruments.
HRMS Clearance data in Drug Discovery: Comparison of metabolic pathways in a congeneric series of compounds From information to knowledge
HRMS Clearance data in Drug Discovery: Interaction analysis Bonn B, et ai. Drug Metab Dispos. 2008 Nov;36(11):2199-210. doi: From information to knowledge
HRMS Clearance data in Drug Discovery: Comparing metabolites across experiments From information to knowledge
HRMS Clearance data in Drug Discovery: Comparing metabolites across experiments From information to knowledge
HRMS Clearance data in Drug Discovery: Metabolic Pathway From information to knowledge
HRMS Clearance data in Drug Discovery: Designing compounds with better properties Can the system propose a chemical replacement to try to stabilize this metabolite? Bergmann R et al J Chem Inf Model. 2009 Mar;49(3):658-69 Fontaine F, et al ChemMedChem. 2009 Mar;4(3):427-39 From information to knowledge
HRMS Clearance data in Drug Discovery: Designing compounds with better properties From information to knowledge
How to design new/better compounds in a faster way? Site of Metabolism prediction Kinetic analysis: rate limiting step Dejá Vu: old data to the service of the new structure elucidation MS Metabolic profile. One compound many experimental protocols Series Analysis. Structure Metabolism Tables MSMS Design new compounds Metabolic pathway analysis HRMS Search function Interaction Map
Co-Authors and contributors Pompeu Fabra University: Esra Cece Tatiana Radchenko Molecular Discovery/Lead Molecular Design: Luca Morettoni Fabien Fontaine Blanca Serra Guillem Plasencia Xavier Pascual Nadia Zara Daniele Gabriele Cruciani Laura Goracci