Drug clearance by aldehyde oxidase: can we avoid clinical failure?. (3rd August 2022)

• Record Type: Journal Article
• Authors: Toselli, Francesca; Golding, Melanie; Nicolaï, Johan; Gillent, Eric; Chanteux, Hugues
• Is Part Of: Xenobiotica. Volume 52:Number 8(2022)
• Page Start: 890
• Page End: 903
• Publication Date: 2022-08-03
• Subjects: Aldehyde oxidase -- drug metabolism -- oxidation -- pharmacokinetics -- inhibition -- clearance -- IVIVC -- fraction-metabolised; Metabolism -- Periodicals; Drugs -- Physiological effect -- Periodicals; Food additives -- Periodicals; Chemicals -- Physiological effect -- Periodicals; Biochemistry -- Periodicals; Pharmaceutical Preparations -- metabolism -- Periodicals; Metabolism -- Periodicals; 574.133
• DOI: 10.1080/00498254.2022.2129519
• Languages: English
• ISSNs: 0049-8254

Abstract

Abstract: Despite increased awareness of aldehyde oxidase (AO) as a major drug-metabolising enzyme, predicting the pharmacokinetics of its substrates remains challenging. Several drug candidates have been terminated due to high clearance, which were subsequently discovered to be AO substrates. Even retrospective extrapolation of human clearance, from models more sensitive to AO activity, often resulted in underprediction. The questions of the current work thus were: Is there an acceptable degree of in vitro AO metabolism that does not result in high in vivo human clearance? And, if so, how can this be predicted? We built an in vitro/in vivo correlation using known AO substrates, combining multiple in vitro parameters to calculate the blood metabolic clearance mediated by AO (CLbAO ). This value was compared with observed blood clearance (CLb-obs ), establishing cut-off CLbAO values, to discriminate between low and high CLb-obs . The model was validated using additional literature compounds, and CLb-obs was predicted in the correct category. This simple, categorical, semi-quantitative yet multi-factorial model is readily applicable in drug discovery. Further, it is valuable for high-clearance compounds, as it predicts the CLb group, rather than an exact CLb value, for the substrates of this poorly-characterised enzyme.