NICEdrug as a prominent method for the prediction of metabolic fate of drugs in the liver: study of antiplatelet drugs

A Zisaki, KC Soh, M Seijo, N Hadadi, V Hatzimanikatis. Ecole Polytechnique Federale de Lausanne, Laboratory of Computational Systems Biotechnology, CH-1015, Switzerland

Computational methods for the prediction and analysis of drug metabolism can accelerate discovery and understanding of this critical process to drive safer drug development and prescription. The most common computational methods employ databases, expert knowledge and artificial intelligence, quantitative structure activity relationships (QSMRs and QSARs), and rule-based approaches (Darvas et al, 1987, Klopman et al, 1994, Greene et al, 1999, Jaworska et al, 2002). Applications of these methods have clearly demonstrated the importance and utility of computational methods in the assessment of the metabolic fate of drugs.

The Biochemical Network Integrated Computational Explorer (BNICE), a computational framework that is able to generate every possible biochemical reaction based on a set of enzyme reaction rules and starting compounds, is distinguished by other methods because of the database of generalized enzyme reaction rules, which are based on enzyme commission (EC) classification system (Hatzimanikatis et al, 2004, 2005, Li et al, 2004). These rules are manual curated and derived from all the known biochemical reactions at the 3rd level EC class for each rule. This allows a classification of the enzyme rules, which is consistent with all the known biochemical reactions, protein structures, genomic sequences, and enzyme properties that follow the EC classification.

NICEdrug is a new framework that builds upon the BNICE method for the study and analysis of the metabolic fate of drug compounds. Our current database of reaction rules can reproduce 86% of the reactions described in a human liver metabolic reconstruction (Jerby et al, 2010), and 71% of the reference cytochrome P450 reactions (Testa and Kramer, 2007).

We then applied the NICEdrug framework for the study of the clopidogrel and prasugrel metabolism in the liver. Both of them are thienopyridines are widely used as antiplatelet drugs and their active forms prevent ADP from binding to the P2Y12 receptor. Although prasugrel has shown a faster onset of activity and a low rate of non-responsiveness, clopidogrel has shown a variance in response, which can be attributed to genetic effects (Shuldiner et al, 2009).

Clopidogrel and prasugrel are pro-drugs and they have to be converted to thiol-containing active metabolites in order to act therapeutically. Although clopidogrel is metabolized to its active thiolactones in the liver by P450, prasugrel must undergo first a hydrolysis by carboxylesterases during intestinal absorption and then followed by the P450 action in the liver (Farid et al, 2010, Dobesh et al, 2009).

Our results identify one overall pathway in the case of clopidogrel and three in the case of prasugrel and these pathways were also assessed to be thermodynamically feasible. Hence, using NICEdrug,

we effectively reproduce the first step of both pro-drugs phase I metabolism and the tautomerase reactions in their metabolism. In the case of prasugrel, two alternative pathways for the early steps of metabolism were identified. The second step in which the P450 isozymes are involved, suggests the participation of an esterase. Recent in vitro and clinical studies have verified these results (Bouman et al, 2011, Talameh and McLeod, 2011, Simon et al, 2011), which demonstrates the capability of NICEdrug in the pharmaceutical research.

References

Bouman HJ, Schömig E, van Werkum JW, Velder J, Hackeng CM, Hirschhäuser C, Waldmann C, Schmalz HG, ten Berg JM, Taubert D. Paraoxonase-1 is a major determinant of clopidogrel efficacy. Nat Med. 2011;17(1):110-6.

Darvas F. MetabolExpert, an expert system for the predicting metabolism of sybstances. In: Kaiser K, editor. QSAR in environmental toxicology Riedel: Dordrecht. pp.71-81.

Dobesh PP. Pharmacokinetics and pharmacodynamics of prasugrel, a thienopyridine P2Y12 inhibitor. Pharmacokinetics and pharmacodynamics of prasugrel, a thienopyridine P2Y12 inhibitor. Pharmacotherapy. 2009;29(9):1089-102.

Farid NA, Kurihara A, Wrighton SA. Metabolism and disposition of the thienopyridine antiplatelet drugs ticlopidine, clopidogrel, and prasugrel in humans. J Clin Pharmacol. 2010 ;50(2):126-42.

Greene N, Judson PN, Langowski JJ, Marchant CA. SAR QSAR Environ Res. Knowledge-based expert systems for toxicity and metabolism prediction: DEREK, StAR and METEOR.1999;10(2-3):299-314.

Hatzimanikatis V, Li C, Ionita JA, Broadbelt LJ. Metabolic networks: enzyme function and metabolite structure. Curr Opin Struct Biol.; 2004 14(3):300-6.

Hatzimanikatis V, Li C, Ionita JA, Henry CS, Jankowski MD, Broadbelt LJ. Exploring the diversity of complex metabolic networks. Bioinformatics. 2005 15;21(8):1603-9.

Jaworska J, Dimitrov S, Nikolova N, Mekenyan O. Probabilistic assessment of biodegradability based on metabolic pathways: catabol system. SAR QSAR Environ Res. 2002 Mar;13(2):307-23

Jerby L, Shlomi T, Ruppin E. Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol Syst Biol. 2010 7;6:401.

Klopman G, Tu M, Talafous J. J Chem Inf Comput Sci. 1997 Mar-Apr;37(2):329-34. META. 3. A genetic algorithm for metabolic transform priorities optimization. J Chem Inf Comput Sci. 1997 Mar-Apr;37(2):329-34.

Li C, Henry C, Jankowski M, Ionita J, Hatzimanikatis V, Broadbelt L. Computational discovery of biochemical routes to specialty chemicals Chemical Engineering Science 59 (2004) 5051 – 5060

Shuldiner AR, O\'Connell JR, Bliden KP, Gandhi A, Ryan K, Horenstein RB, Damcott CM, Pakyz R, Tantry US, Gibson Q, Pollin TI, Post W, Parsa A, Mitchell BD, Faraday N, Herzog W, Gurbel PA. Association of cytochrome P450 2C19 genotype with the antiplatelet effect and clinical efficacy of clopidogrel therapy. JAMA. 2009 26;302(8):849-57.

Simon T, Steg PG, Becquemont L, Verstuyft C, Kotti S, Schiele F, Ferrari E, Drouet E, Grollier G, Danchin N. Effect of paraoxonase-1 polymorphism on clinical outcomes in patients treated with clopidogrel after an acute myocardial infarction. Clin Pharmacol Ther. 2011;90(4):561-7.

Talameh JA, McLeod HL. PON1 Q192R and clopidogrel: a case of the winner\'s curse or inadequate replication? Clin Pharmacol Ther. 2011;90(6):771-4

Testa B. and Kramer S. The biochemistry of drug metabolism: principles, redox reactions, hydrolyses, VHCA and Wiley-VCH, 2007, vol 1.