2. Academic Validation
  3. From Experiments to a Fast Easy-to-Use Computational Methodology to Predict Human Aldehyde Oxidase Selectivity and Metabolic Reactions

From Experiments to a Fast Easy-to-Use Computational Methodology to Predict Human Aldehyde Oxidase Selectivity and Metabolic Reactions

  • J Med Chem. 2018 Jan 11;61(1):360-371. doi: 10.1021/acs.jmedchem.7b01552.
Gabriele Cruciani 1 2,  Nicolò Milani 1,  Paolo Benedetti 1 2,  Susan Lepri 1,  Lucia Cesarini 1,  Massimo Baroni 3,  Francesca Spyrakis 4,  Sara Tortorella 2 5,  Edoardo Mosconi 2 6,  Laura Goracci 1 2
Affiliations

Affiliations

  • 1 Department of Chemistry, Biology and Biotechnology, University of Perugia , via Elce di Sotto 8, 06123 Perugia, Italy.
  • 2 Consortium for Computational Molecular and Materials Sciences (CMS) , via Elce di Sotto 8, 06123 Perugia, Italy.
  • 3 Molecular Discovery Ltd , Centennial Park, Borehamwood, Hertfordshire, United Kingdom.
  • 4 Department of Drug Science and Technology, University of Turin , via P. Giuria 9, 10125 Turin, Italy.
  • 5 Molecular Horizon srl , via Montelino 32, 06084 Bettona, Italy.
  • 6 Computational Laboratory for Hybrid/Organic Photovoltaics, National Research Council-Institute of Molecular Science and Technologies , Via Elce di Sotto 8, I-06123 Perugia, Italy.
PMID: 29240409 DOI: 10.1021/acs.jmedchem.7b01552
Abstract

Aldehyde oxidase (AOX) is a molibdo-flavoenzyme that has raised great interest in recent years, since its contribution in xenobiotic metabolism has not always been identified before clinical trials, with consequent negative effects on the fate of new potential drugs. The fundamental role of AOX in metabolizing xenobiotics is also due to the attempt of medicinal chemists to stabilize candidates toward Cytochrome P450 activity, which increases the risk for new compounds to be susceptible to AOX nucleophile attack. Therefore, novel strategies to predict the potential liability of new entities toward the AOX Enzyme are urgently needed to increase effectiveness, reduce costs, and prioritize experimental studies. In the present work, we present the most up-to-date computational method to predict liability toward human AOX (hAOX), for applications in drug design and pharmacokinetic optimization. The method was developed using a large data set of homogeneous experimental data, which is also disclosed as Supporting Information .

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