Identification and human exposure prediction of two aldehyde oxidase-mediated metabolites of a methylquinoline-containing drug candidate

Preparation of 18F-Labeled Tracers Targeting Fibroblast Activation Protein via Sulfur [18F]Fluoride Exchange Reaction

Early detection and treatment of cancers can significantly increase patient prognosis and enhance the quality of life of affected patients. The emerging significance of the tumor microenvironment (TME) as a new frontier for cancer diagnosis and therapy may be exploited by radiolabeled tracers for diagnostic imaging techniques such as positron emission tomography (PET). Cancer-associated fibroblasts (CAFs) within the TME are identified by biomarkers such as fibroblast activation protein alpha (FAPα), which are expressed on their surfaces. Targeting FAPα using small-molecule 18F-labeled inhibitors (FAPIs) has recently garnered significant attention for non-invasive tumor visualization using PET. Herein, two potent aryl-fluorosulfate-based FAPIs, 12 and 13, were synthetically prepared, and their inhibition potency was determined using a fluorimetric FAP assay to be IC50 9.63 and 4.17 nM, respectively. Radiofluorination was performed via the sulfur [18F]fluoride exchange ([18F]SuFEx) reaction to furnish [18F]12 and [18F]13 in high activity yields (AY) of 39-56% and molar activities (Am) between 20-55 GBq/µmol. In vitro experiments focused on the stability of the radiolabeled FAPIs after incubation with human serum, liver microsomes and liver cytosol. Preliminary PET studies of the radioligands were performed in healthy mice to investigate the in vivo biodistribution and 18F defluorination rate. Fast pharmacokinetics for the FAP-targeting tracers were retained and considerable bone uptake, caused by either 18F defluorination or radioligand accumulation, was observed. In summary, our findings demonstrate the efficiency of [18F]SuFEx as a radiolabeling method as well as its advantages and limitations with respect to PET tracer development.

Drug clearance by aldehyde oxidase: can we avoid clinical failure?

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 (CLb_AO). This value was compared with observed blood clearance (CLb_-obs), establishing cut-off CLb_AO 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.

Recent developments in predicting CYP-independent metabolism

As lead optimization efforts have successfully reduced metabolic liabilities due to cytochrome P450 (CYP)-mediated metabolism, there has been an increase in the frequency of involvement of non-CYP enzymes in the metabolism of investigational compounds. Although there have been numerous notable advancements in the characterization of non-CYP enzymes with respect to their localization, reaction mechanisms, species differences and identification of typical substrates, accurate prediction of non-CYP-mediated clearance, with a particular emphasis with the difficulties in accounting for any extrahepatic contributions, remains a challenge. The current manuscript comprehensively summarizes the recent advancements in the prediction of drug metabolism and the in vitro to in vitro extrapolation of clearance for substrates of non-CYP drug metabolizing enzymes.

Biotransformation: Impact and Application of Metabolism in Drug Discovery

Biotransformation has a huge impact on the efficacy and safety of drugs. Ultimately the effects of metabolism can be the lynchpin in the discovery and development cycle of a new drug. This article discusses the impact and application of biotransformation of drugs by mammalian systems, microorganisms, and recombinant enzymes, covering active and reactive metabolites, the impact of the gut microbiome on metabolism, and how insights gained from biotransformation studies can influence drug design from the combined perspectives of a CRO specializing in a range of biotransformation techniques and pharma biotransformation scientists. We include a commentary on how biology-driven approaches can complement medicinal chemistry strategies in drug optimization and the in vitro and surrogate systems available to explore and exploit biotransformation.

Metabolism by Aldehyde Oxidase: Drug Design and Complementary Approaches to Challenges in Drug Discovery

Aldehyde oxidase (AO) catalyzes oxidations of azaheterocycles and aldehydes, amide hydrolysis, and diverse reductions. AO substrates are rare among marketed drugs, and many candidates failed due to poor pharmacokinetics, interspecies differences, and adverse effects. As most issues arise from complex and poorly understood AO biology, an effective solution is to stop or decrease AO metabolism. This perspective focuses on rational drug design approaches to modulate AO-mediated metabolism in drug discovery. AO biological aspects are also covered, as they are complementary to chemical design and important when selecting the experimental system for risk assessment. The authors' recommendation is an early consideration of AO-mediated metabolism supported by computational and in vitro experimental methods but not an automatic avoidance of AO structural flags, many of which are versatile and valuable building blocks. Preferably, consideration of AO-mediated metabolism should be part of the multiparametric drug optimization process, with the goal to improve overall drug-like properties.