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  • Title: In silico prediction of acyl glucuronide reactivity.
    Author: Potter T, Lewis R, Luker T, Bonnert R, Bernstein MA, Birkinshaw TN, Thom S, Wenlock M, Paine S.
    Journal: J Comput Aided Mol Des; 2011 Nov; 25(11):997-1005. PubMed ID: 22042375.
    Abstract:
    Drugs and drug candidates containing a carboxylic acid moiety, including many widely used non-steroidal anti-inflammatory drugs (NSAIDs) are often metabolized to form acyl glucuronides (AGs). NSAIDs such as Ibuprofen are amongst the most widely used drugs on the market, whereas similar carboxylic acid drugs such as Suprofen have been withdrawn due to adverse events. Although the link between these AG metabolites and toxicity is not proven, there is circumstantial literature evidence to suggest that more reactive acyl glucuronides may, in some cases, present a greater risk of exhibiting toxic effects. We wished therefore to rank the reactivity of potential new carboxylate-containing drug candidates, and performed kinetic studies on synthetic acyl glucuronides to benchmark our key compounds. Driven by the desire to quickly rank the reactivity of compounds without the need for lengthy synthesis of the acyl glucuronide, a correlation was established between the degradation half-life of the acyl glucuronide and the half life for the hydrolysis of the more readily available methyl ester derivative. This finding enabled a considerable broadening of chemical property space to be investigated. The need for kinetic measurements was subsequently eliminated altogether by correlating the methyl ester hydrolysis half-life with the predicted (13)C NMR chemical shift of the carbonyl carbon together with readily available steric descriptors in a PLS model. This completely in silico prediction of acyl glucuronide reactivity is applicable within the earliest stages of drug design with low cost and acceptable accuracy to guide intelligent molecular design. This reactivity data will be useful alongside the more complex additional pharmacokinetic exposure and distribution data that is generated later in the drug discovery process for assessing the overall toxicological risk of acidic drugs.
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