From data to information and from information to knowledge. Ismael Zamora
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1 2015
2 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
3 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 Sep 6;50(18): Ahlström MM, et al J Med Chem Nov 1;50(22): % of compound metabolized In HLM (30 min)
4 The Context: Drug Metabolism in Discovery Clearance data Crivori P, et al J Comput Aided Mol Des Mar;18(3):
5 Our proposal to increase Metabolism knowledge based on HRMS Server Pending Experiments Approved Experiments Analysis Tools I. Zamora et al Drug Discovery Today: Technologies, , (1), e199 e205
6 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
7 Peak detection and structure elucidation HLM incubation DRM Analysis >90% success (unsupervised) LC/MSMS MS Mass-MetaSite Zelesky V, et al Bioanalysis May;5(10): Li AC, et al Xenobiotica Apr;43(4): Bonn B et al Rapid Commun Mass Spectrom Nov 15;24(21): MSMS Metabolite Prediction Mass & Fragmentation recognition >80% success at rank 1 >95% ranks 1-3 (unsupervised) From data to information
8 Clearance data Can structure of a metabolite be elucidated? From data to information
9 HRMS Clearance data in Drug Discovery: Déjà Vu function From information to knowledge
10 HRMS Clearance data in Drug Discovery: SoM Prediction Zamora I, Afzelius L, Cruciani G. J Med Chem Jun 5;46(12): From information to knowledge
11 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
12 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
13 Fragment Analysis Number of reactions that suffer a particular substructure From information to knowledge
14 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
15 Cross platform comparison: Molecules Tested Medium to high clearance compounds. Compounds with known metabolites and series for the same family 2N-569S 10P R Z-0822 Dextromethorphan Nefazadone Saquinavir Verapamil Busprione
16 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, ,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, ,5 0,03 4 Waters 2 15,5 0,2 9 80,3 0,03 1 Waters 3 14,1 0, ,2 0,16 1 Waters 4 15,6 0, ,4 0,03 1 Waters 5 15,5 0, ,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, ,0 0,03 3
17 Cross platform Comparison: Soft Spot Analysis
18 Cross platform comparison: Soft Spot Analysis
19 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.
20 HRMS Clearance data in Drug Discovery: Comparison of metabolic pathways in a congeneric series of compounds From information to knowledge
21 HRMS Clearance data in Drug Discovery: Interaction analysis Bonn B, et ai. Drug Metab Dispos Nov;36(11): doi: From information to knowledge
22 HRMS Clearance data in Drug Discovery: Comparing metabolites across experiments From information to knowledge
23 HRMS Clearance data in Drug Discovery: Comparing metabolites across experiments From information to knowledge
24 HRMS Clearance data in Drug Discovery: Metabolic Pathway From information to knowledge
25 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 Mar;49(3): Fontaine F, et al ChemMedChem Mar;4(3): From information to knowledge
26 HRMS Clearance data in Drug Discovery: Designing compounds with better properties From information to knowledge
27 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
28 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
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