Role of Molybdenum-Containing Enzymes in the Biotransformation of the Novel Ghrelin Receptor Inverse Agonist PF-5190457: A Reverse Translational Bed-to-Bench Approach

Challenges and Opportunities for In Vitro-In Vivo Extrapolation of Aldehyde Oxidase-Mediated Clearance: Toward a Roadmap for Quantitative Translation

Underestimation of aldehyde oxidase (AO)-mediated clearance by current in vitro assays leads to uncertainty in human dose projections, thereby reducing the likelihood of success in drug development. In the present study we first evaluated the current drug development practices for AO substrates. Next, the overall predictive performance of in vitro-in vivo extrapolation of unbound hepatic intrinsic clearance (CLint,u) and unbound hepatic intrinsic clearance by AO (CLint,u,AO) was assessed using a comprehensive literature database of in vitro (human cytosol/S9/hepatocytes) and in vivo (intravenous/oral) data collated for 22 AO substrates (total of 100 datapoints from multiple studies). Correction for unbound fraction in the incubation was done by experimental data or in silico predictions. The fraction metabolized by AO (fmAO) determined via in vitro/in vivo approaches was found to be highly variable. The geometric mean fold errors (gmfe) for scaled CLint,u (mL/min/kg) were 10.4 for human hepatocytes, 5.6 for human liver cytosols, and 5.0 for human liver S9, respectively. Application of these gmfe's as empirical scaling factors improved predictions (45%-57% within twofold of observed) compared with no correction (11%-27% within twofold), with the scaling factors qualified by leave-one-out cross-validation. A road map for quantitative translation was then proposed following a critical evaluation on the in vitro and clinical methodology to estimate in vivo fmAO In conclusion, the study provides the most robust system-specific empirical scaling factors to date as a pragmatic approach for the prediction of in vivo CLint,u,AO in the early stages of drug development. SIGNIFICANCE STATEMENT: Confidence remains low when predicting in vivo clearance of AO substrates using in vitro systems, leading to de-prioritization of AO substrates from the drug development pipeline to mitigate risk of unexpected and costly in vivo impact. The current study establishes a set of empirical scaling factors as a pragmatic tool to improve predictability of in vivo AO clearance. Developing clinical pharmacology strategies for AO substrates by utilizing mass balance/clinical drug-drug interaction data will help build confidence in fmAO.

In Silico Prediction of Metabolic Reaction Catalyzed by Human Aldehyde Oxidase

Aldehyde oxidase (AOX) plays an important role in drug metabolism. Human AOX (hAOX) is widely distributed in the body, and there are some differences between species. Currently, animal models cannot accurately predict the metabolism of hAOX. Therefore, more and more in silico models have been constructed for the prediction of the hAOX metabolism. These models are based on molecular docking and quantum chemistry theory, which are time-consuming and difficult to automate. Therefore, in this study, we compared traditional machine learning methods, graph convolutional neural network methods, and sequence-based methods with limited data, and proposed a ligand-based model for the metabolism prediction catalyzed by hAOX. Compared with the published models, our model achieved better performance (ACC = 0.91, F1 = 0.77). What's more, we built a web server to predict the sites of metabolism (SOMs) for hAOX. In summary, this study provides a convenient and automatable model and builds a web server named Meta-hAOX for accelerating the drug design and optimization stage.

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.

Roles of selected non-P450 human oxidoreductase enzymes in protective and toxic effects of chemicals: review and compilation of reactions

This is an overview of the metabolic reactions of drugs, natural products, physiological compounds, and other (general) chemicals catalyzed by flavin monooxygenase (FMO), monoamine oxidase (MAO), NAD(P)H quinone oxidoreductase (NQO), and molybdenum hydroxylase enzymes (aldehyde oxidase (AOX) and xanthine oxidoreductase (XOR)), including roles as substrates, inducers, and inhibitors of the enzymes. The metabolism and bioactivation of selected examples of each group (i.e., drugs, "general chemicals," natural products, and physiological compounds) are discussed. We identified a higher fraction of bioactivation reactions for FMO enzymes compared to other enzymes, predominately involving drugs and general chemicals. With MAO enzymes, physiological compounds predominate as substrates, and some products lead to unwanted side effects or illness. AOX and XOR enzymes are molybdenum hydroxylases that catalyze the oxidation of various heteroaromatic rings and aldehydes and the reduction of a number of different functional groups. While neither of these two enzymes contributes substantially to the metabolism of currently marketed drugs, AOX has become a frequently encountered route of metabolism among drug discovery programs in the past 10-15 years. XOR has even less of a role in the metabolism of clinical drugs and preclinical drug candidates than AOX, likely due to narrower substrate specificity.

Therapeutic potential of GHSR-1A antagonism in alcohol dependence, a review

Growth hormone secretagogue receptor type 1A (GHSR-1A) is a functional receptor of orexigenic peptide ghrelin and is highly expressed in mesolimbic dopaminergic systems that regulate incentive value of artificial reward in substance abuse. Interestingly, GHSR-1A has also shown ligand-independent constitutive activity. Alcohol use disorder (AUD) is one of the growing concerns worldwide as it involves complex neuro-psycho-endocrinological interactions. Positive correlation of acylated ghrelin and alcohol-induced human brain response in the right and left ventral striatum are evident. In the last decade, the beneficial effects of ghrelin receptor (GHSR-1A) antagonism to suppress artificial reward circuitries and induce self-control for alcohol consumption have drawn significant attention from researchers. In this updated review, we summarize the available recent preclinical, clinical, and experimental data to discuss functional, molecular actions of central ghrelin-GHSR-1A signaling in different craving levels for alcohol as well as to promote "GHSR-1A antagonism" as one of the potential therapies in early abstinence.

Synthesis of PF-6870961, a major hydroxy metabolite of the novel ghrelin receptor inverse agonist PF-5190457

Preclinical and human studies have indicated involvement of the ghrelin system in alcohol-related behaviors illuminating the possibility of using ghrelin receptor blockers as a pharmacological intervention for alcohol use disorder (AUD). Preliminary data from a recently conducted phase 1b human study with a ghrelin receptor inverse agonist, PF-5190457 (2-(2-methylimidazo[2,1-b][1,3thiazol-6-yl)-1-{2-(1R)-5-(6-methylpyrimidin-4-yl)-2,3-dihydro-1H-inden-1-yl]-2,7-diazaspiro[3.5]non-7-ylethanone), provided evidence on the safety and tolerability of this compound when co-administered with alcohol. Furthermore, the study revealed important information on the biotransformation pathways for this compound and prompted the discovery and then synthesis of a newly identified major metabolite, PF-6870961 ((R)-1-(2-(5-(2-hydroxy-6-methylpyrimidin-4-yl)-2,3-dihydro-1H-inden-1-yl)-2,7-diazaspiro[3.5]nonan-7-yl)-2-(2-methylimidazo[2,1-b]thiazol-6-yl)ethan-1-one). The metabolite was synthesized and fully characterized through a design that enabled it to be prepared in useful quantities. The synthesis provided direct access to the recently discovered PF-6870961 and is allowing researchers to conduct additional and deeper evaluation of its in vitro and in vivo properties.

A Population Pharmacokinetic Analysis of PF-5190457, a Novel Ghrelin Receptor Inverse Agonist in Healthy Volunteers and in Heavy Alcohol Drinkers

BACKGROUND AND OBJECTIVES

The ghrelin receptor (GHS-R1a) is a potential target for alcohol use disorders. PF-5190457 is the first inverse agonist of GHS-R1a to progress to clinical development with potential to treat alcohol use disorder. We present a population pharmacokinetic model for PF-5190457 in non-heavy (alcohol consumption status = 0) and heavy alcohol drinkers (alcohol consumption status = 1), and identify relevant factors that can influence its pharmacokinetics.

METHODS

Plasma concentration-time data from non-heavy (n = 35) and heavy drinkers (n = 12) were pooled for the population pharmacokinetic model development. The influence of various covariates including alcohol consumption status was evaluated. The accuracy, precision, and robustness of the model were also evaluated using bootstrapping and visual predictive checks.

RESULTS

A two-compartment model best described the pharmacokinetics of PF-5190457. The apparent volume of distribution of 44.5 L, apparent clearance of 72.0 L/h, apparent peripheral volume of distribution of 271 L, apparent distributional clearance of 28.7 L/h, and first-order absorption rate constant of 0.27/h were accurate and precise. The apparent volume of distribution was 3.8-fold higher (169 L) in heavy drinkers, and correlated with a lower maximum plasma concentration in heavy drinkers compared with non-heavy drinkers at the same dose; and a corresponding reduced incidence of somnolence in heavy drinkers at doses > 50 mg.

CONCLUSIONS

This work provides an accurate, precise, and robust two-compartment model that describes the pharmacokinetics of PF-5190457 and suggests a possible link of PF-5190457 pharmacokinetics with somnolence.

TRIAL REGISTRATION

ClinicalTrials.gov identifier numbers NCT01247896 and NCT02039349.

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.