Alessandro Pedretti MetaPies, an annotated database for metabolism analysis and prediction: results and future perspectives L’Aquila November 21, 2011

The MetaPies project 1.Database structure definition and input forms. Objective: classification and analysis of metabolic reactions of different substrates with a view to developing reliable prediction models. Research phases: 2.Collecting data: exploited criteria and rules. 4.Property profiling: enzyme’s substrate/metabolite property space. 3.First general analysis: relative relevance of each metabolic reaction.

Phase 1: MetaPies database Microsoft Access database application. Reports for: generic analysis; metabolite analysis; generation analysis. Forms to simplify the input.

Phase 2: meta-analysis of the literature A systematic search of metabolic studies was carried out in the primary literature, namely Chem Res Tox ( ), Drug Metab Dispos ( ), and Xenobiotica ( ). The focus was on drugs and other xenobiotics at the exclusion of endogenous compounds, except when the latter are used as drugs (e.g. steroids). The meta-analysis involved studies in humans or mammalian animals, carried out either: 1) in vivo; 2) in cellular systems; 3) at subcellular or enzymatic level. Each substrate was analyzed separately, avoiding duplicates. Regio- and stereo-isomers were considered as distinct substrates (substrate selectivity) or metabolites (product selectivity).

Metabolite classification In each paper, the reported metabolites were classified according to: the type of reaction that produced them (28 different types); the enzyme (super)family or category that produced them (16 families); the metabolic generation to which they belonged (1st, 2nd, and 3rd or more); whether they were pharmacologically active; whether they were reactive and/or toxic. General counts Number of analyzed papers: 903 Number of distinct substrates: 1107 Number of distinct metabolites: 6767 Metabolites per substrate: 5.8 Number of active metabolites: 201 Number of toxic metabolites: 473

Phase 3: distribution of metabolites into the three major reaction classes Global distribution Distribution for each generation

Distribution of metabolites within each reaction class Red-ox reactionsConjugations Hydrolisis

Reactions and metabolites Active metabolites Reactive / toxic metabolites

Phase 4: substrate profiling MetaPies Substrates Metabolites Reactions ODBC PubChem 3D structures HTTP 3D structures of substrates and metabolites Molecular descriptors Reactions New database ODBC Microsoft Excel Molecular descriptors DDE

Substrate profiling 2 For each molecule in the database, a set of 2D/3D properties are automatically calculated: Descriptive: SMILES, InChI, InChI Key, functional group codes, molecular formula. Constitutional: number of atoms, heavy atoms, chiral atoms, bonds, unsatured bonds, angles, torsions, flexible torsions, rings, H-bond acceptors and donors. Structural: mass, volume, radius of gyration, SAS, PSA, ovality. Physicochemical: charge, dipole, lipole, virtual logP.

Substrate profiling: structural results 11 % 17 % 16 % 8 % 28 % 14 % Relative abundance of the main functional grou- ps for all collected substrates Red-ox Conjugation

Enzyme specific property space Marketed All substrates Red-ox Conjugation Hydrolase Rotors Virtual logP Oxidoreductases prefer more rigid and less polar substrates. Conjugation enzymes prefer more rigid and more polar substrates. Hydrolases prefer more flexible and less polar substrates. Metabolic substrates are more apolar and more rigid molecules.

Redox and property space Csp3 Csp2/1 C=O Nox Rotors Virtual logP Substrates of C=O/CH-OH oxidations are in general more polar. Substrates of Csp3 oxidation are in general more flexible.

Conclusions Recently, we used the MetaPies data to predict the probability to generate specific metabolites starting from substrates not included in the database. Considering the substrate property space required for each type of reaction, it was possible to identify the most probable metabolites and these results were in agreement with the experimental data. MetaPies is a valuable source of metabolic information and we explored only the iceberg tip.

Acknowledgements Bernard Testa Giulio Vistoli