Characterization of Atomoxetine Biotransformation and Implications for Development of PBPK Models for Dose Individualization in Children

Population Pharmacokinetic Analysis of Atomoxetine and its Metabolites in Children and Adolescents with Attention-Deficit/Hyperactivity Disorder

Atomoxetine (ATX) is a non-stimulant used to treat attention-deficit/hyperactivity disorder (ADHD) and systemic exposure is highly variable due to polymorphic cytochrome P450 2D6 (CYP2D6) activity. The objective of this study was to characterize the time course of ATX and metabolites (4-hydroxyatomoxetine (4-OH); N-desmethylatomoxetine (NDA); and 2-carboxymethylatomoxetine (2-COOH)) exposure following oral ATX dosing in children with ADHD to support individualized dosing. A nonlinear mixed-effect modeling approach was used to analyze ATX, 4-OH, and NDA plasma and urine, and 2-COOH urine profiles obtained over 24-72 hours from children with ADHD (n = 23) following a single oral ATX dose. Demographics and CYP2D6 activity score (AS) were evaluated as covariates. Simulations were performed to explore the ATX dosing in subjects with various CYP2D6 AS. A simultaneous pharmacokinetic modeling approach was used in which a model for ATX, 4-OH, and NDA in plasma and urine, and 2-COOH in urine was developed. Plasma ATX, 4-OH, and NDA were modeled using two-compartment models with first-order elimination. CYP2D6 AS was a significant determinant of ATX apparent oral clearance (CL/F), fraction metabolized to 4-OH, and systemic exposure of NDA. CL/F of ATX varied almost 7-fold across the CYP2D6 AS groups: AS 2: 20.02 L/hour; AS 1: 19.00 L/hour; AS 0.5: 7.47 L/hour; and AS 0: 3.10 L/hour. The developed model closely captures observed ATX, 4-OH, and NDA plasma and urine, and 2-COOH urine profiles. Application of the model shows the potential for AS-based dosing recommendations for improved individualized dosing.

Construction of a fused grid-based CYP2C18-Template system and its application to drug metabolism

Detailed estimation of cytochrome P450 (CYP)-mediated metabolisms of medicine and other chemicals is necessary for the efficacy and safety assessments. Data on the metabolisms mediated by minor CYP enzymes like CYP2C18 are often not available in metabolisms and safety assessments of chemicals except for medical drugs developed recently. A ligand-accessible space in the active site of human CYP2C18 was thus reconstituted as a fused grid-based Template with the use of structural data of its ligands. An evaluation system of CYP2C18-mediated metabolism was then developed on Template with the introduction of the idea of movement and fastening of ligands after Trigger-residue contact. Reciprocal comparison of the data of simulations on Template with experimental results suggested a unified way of the interaction of CYP2C18, in similar to the CYP2C8 interaction (Drug Metab Pharmacokinet 2023, in press). These experiments also displayed the roles of initial Trigger-residue-localizations on their distinct catalyses among human CYP2C enzymes. Simulation experiments for over 130 reactions of CYP2C18 ligands supported the system established.

Impact of CYP2C:TG Haplotype on CYP2C19 Substrates Clearance In Vivo, Protein Content, and In Vitro Activity

A novel haplotype composed of two non-coding variants, CYP2C18 NM_000772.3:c.*31T (rs2860840) and NM_000772.2:c.819+2182G (rs11188059), referred to as "CYP2C:TG," was recently associated with ultrarapid metabolism of various CYP2C19 substrates. As the underlying mechanism and clinical relevance of this effect remain uncertain, we analyzed existing in vivo and in vitro data to determine the magnitude of the CYP2C:TG haplotype effect. We assessed variability in pharmacokinetics of CYP2C19 substrates, including citalopram, sertraline, voriconazole, omeprazole, pantoprazole, and rabeprazole in 222 healthy volunteers receiving one of these six drugs. We also determined its impact on CYP2C8, CYP2C9, CYP2C18, and CYP2C19 protein abundance in 135 human liver tissue samples, and on CYP2C18/CYP2C19 activity in vitro using N-desmethyl atomoxetine formation. No effects were observed according to CYP2C:TG haplotype or to CYP2C19*1+TG alleles (i.e., CYP2C19 alleles containing the CYP2C:TG haplotype). In contrast, CYP2C19 intermediate (e.g., CYP2C19*1/*2) and poor metabolizers (e.g., CYP2C19*2/*2) showed significantly higher exposure in vivo, lower CYP2C19 protein abundance in human liver microsomes, and lower activity in vitro compared with normal, rapid (i.e., CYP2C19*1/*17), and ultrarapid metabolizers (i.e., CYP2C19*17/*17). Moreover, a tendency toward lower exposure was observed in ultrarapid metabolizers compared with rapid metabolizers and normal metabolizers. Furthermore, when the CYP2C19*17 allele was present, CYP2C18 protein abundance was increased suggesting that genetic variation in CYP2C19 may be relevant to the overall metabolism of certain drugs by regulating not only its expression levels, but also those of CYP2C18. Considering all available data, we conclude that there is insufficient evidence supporting clinical CYP2C:TG testing to inform drug therapy.

The Utility of Mixed Effects Models in the Evaluation of Complex Genomic Traits In Vitro

In pharmacogenomic studies, the use of human liver microsomes as a model system to evaluate the impact of complex genomic traits (i.e., linkage-disequilibrium patterns, coding, and non-coding variation, etc.) on efficiency of drug metabolism is challenging. To accurately predict the true effect size of genomic traits requires large richly sampled datasets representative of the study population. Moreover, the acquisition of this data can be labor-intensive if the study design or bioanalytical methods are not high throughput, and it is potentially unfeasible if the abundance of sample needed for experiments is limited. To overcome these challenges, we developed a novel strategic approach using non-linear mixed effects models (NLME) to determine enzyme kinetic parameters for individual liver specimens using sparse data. This method can facilitate evaluation of the impact that complex genomic traits have on the metabolism of xenobiotics in vitro when tissue and other resources are limited. In addition to facilitating the accrual of data, it allows for rigorous testing of covariates as sources of kinetic parameter variability. In this in silico study, we present a practical application of such an approach using previously published in vitro cytochrome P450 (CYP) 2D6 data and explore the impact of sparse sampling, and experimental error on known kinetic parameter estimates of CYP2D6 mediated formation of 4-hydroxy-atomoxetine in human liver microsomes. SIGNIFICANCE STATEMENT: This study presents a novel non-linear mixed effects model (NLME)-based framework for evaluating the impact of complex genomic traits on saturable processes described by a Michaelis-Menten kinetics in vitro using sparse data. The utility of this approach extends beyond gene variant associations, including determination of covariate effects on in vitro kinetic parameters and reduced demand for precious experimental material.

Personalizing atomoxetine dosing in children with ADHD: what can we learn from current supporting evidence

PURPOSE

There is marked heterogeneity in treatment response of atomoxetine in patients with attention deficit/hyperactivity disorder (ADHD), especially for the pediatric population. This review aims to evaluate current evidence to characterize the dose-exposure relationship, establish clinically relevant metrics for systemic exposure to atomoxetine, define a therapeutic exposure range, and to provide a dose-adaptation strategy before implementing personalized dosing for atomoxetine in children with ADHD.

METHODS

A comprehensive search was performed across electronic databases (PubMed and Embase) covering the period of January 1, 1985 to July 10, 2022, to summarize recent advances in the pharmacokinetics, pharmacogenomics/pharmacogenetics (PGx), therapeutic drug monitoring (TDM), physiologically based pharmacokinetics (PBPK), and population pharmacokinetics (PPK) of atomoxetine in children with ADHD.

RESULTS

Some factors affecting the pharmacokinetics of atomoxetine were summarized, including food, CYP2D6 and CYP2C19 phenotypes, and drug‒drug interactions (DDIs). The association between treatment response and genetic polymorphisms of genes encoding pharmacological targets, such as norepinephrine transporter (NET/SLC6A2) and dopamine β hydroxylase (DBH), was also discussed. Based on well-developed and validated assays for monitoring plasma concentrations of atomoxetine, the therapeutic reference range in pediatric patients with ADHD proposed by several studies was summarized. However, supporting evidence on the relationship between systemic atomoxetine exposure levels and clinical response was far from sufficient.

CONCLUSION

Personalizing atomoxetine dosage may be even more complex than anticipated thus far, but elucidating the best way to tailor the non-stimulant to a patient's individual need will be achieved by combining two strategies: detailed research in linking the pharmacokinetics and pharmacodynamics in pediatric patients, and better understanding in nature and causes of ADHD, as well as environmental stressors.

Characterisation of seven medications approved for attention-deficit/hyperactivity disorder using in vitro models of hepatic metabolism

The metabolism of most medications approved for the treatment of attention deficit/hyperactivity disorder (ADHD) is not fully understood.In vitro studies using cryopreserved, plated human hepatocytes (cPHHs) and pooled human liver microsomes (HLMs) were performed to more thoroughly characterise the metabolism of several ADHD medications.The use of enzyme-specific chemical inhibitors indicated a role for CYP2D6 in atomoxetine (ATX) metabolism, and roles for CYP3A4/5 in guanfacine (GUA) metabolism.The 4-hydroxy-atomoxetine and N-desmethyl-atomoxetine pathways represented 98.4% and 1.5% of ATX metabolism in cPHHs, respectively. The 3-OH-guanfacine pathway represented at least 2.6% of GUA metabolism in cPHHs, and 71% in HLMs.The major metabolising enzyme for methylphenidate (MPH) and dexmethylphenidate (dMPH) could not be identified using these methods because these compounds were too unstable. Hydrolysis of these medications was spontaneous and did not require the presence of protein to occur.Clonidine (CLD), amphetamine (AMPH), and dextroamphetamine (dAMPH) did not deplete substantially in cPHHs nor HLMs, suggesting that these compounds may not undergo considerable hepatic metabolism. The major circulating metabolites of AMPH and dAMPH (benzoic acid and hippuric acid) were not observed in either system, and therefore could not be characterised. Additionally, inhibition experiments suggested a very minimal role for CYP2D6 in CLD and AMPH metabolism.

MetaClass, a Comprehensive Classification System for Predicting the Occurrence of Metabolic Reactions Based on the MetaQSAR Database

(1) Background: Machine learning algorithms are finding fruitful applications in predicting the ADME profile of new molecules, with a particular focus on metabolism predictions. However, the development of comprehensive metabolism predictors is hampered by the lack of highly accurate metabolic resources. Hence, we recently proposed a manually curated metabolic database (MetaQSAR), the level of accuracy of which is well suited to the development of predictive models. (2) Methods: MetaQSAR was used to extract datasets to predict the metabolic reactions subdivided into major classes, classes and subclasses. The collected datasets comprised a total of 3788 first-generation metabolic reactions. Predictive models were developed by using standard random forest algorithms and sets of physicochemical, stereo-electronic and constitutional descriptors. (3) Results: The developed models showed satisfactory performance, especially for hydrolyses and conjugations, while redox reactions were predicted with greater difficulty, which was reasonable as they depend on many complex features that are not properly encoded by the included descriptors. (4) Conclusions: The generated models allowed a precise comparison of the propensity of each metabolic reaction to be predicted and the factors affecting their predictability were discussed in detail. Overall, the study led to the development of a freely downloadable global predictor, MetaClass, which correctly predicts 80% of the reported reactions, as assessed by an explorative validation analysis on an external dataset, with an overall MCC = 0.44.

Metabolic N-Dealkylation and N-Oxidation as Elucidators of the Role of Alkylamino Moieties in Drugs Acting at Various Receptors

Metabolic reactions that occur at alkylamino moieties may provide insight into the roles of these moieties when they are parts of drug molecules that act at different receptors. N-dealkylation of N,N-dialkylamino moieties has been associated with retaining, attenuation or loss of pharmacologic activities of metabolites compared to their parent drugs. Further, N-dealkylation has resulted in clinically used drugs, activation of prodrugs, change of receptor selectivity, and providing potential for developing fully-fledged drugs. While both secondary and tertiary alkylamino moieties (open chain aliphatic or heterocyclic) are metabolized by CYP450 isozymes oxidative N-dealkylation, only tertiary alkylamino moieties are subject to metabolic N-oxidation by Flavin-containing monooxygenase (FMO) to give N-oxide products. In this review, two aspects will be examined after surveying the metabolism of representative alkylamino-moieties-containing drugs that act at various receptors (i) the pharmacologic activities and relevant physicochemical properties (basicity and polarity) of the metabolites with respect to their parent drugs and (ii) the role of alkylamino moieties on the molecular docking of drugs in receptors. Such information is illuminative in structure-based drug design considering that fully-fledged metabolite drugs and metabolite prodrugs have been, respectively, developed from N-desalkyl and N-oxide metabolites.

Metabolic profiling of norepinephrine reuptake inhibitor atomoxetine

Atomoxetine (ATX), a selective and potent inhibitor of the presynaptic norepinephrine transporter, is used mainly to treat attention-deficit hyperactivity disorder. Although multiple adverse effects associated with ATX have been reported including severe liver injuries, the mechanisms of ATX-related toxicity remain largely unknown. Metabolism frequently contributes to adverse effects of a drug through reactive metabolites, and the bioactivation status of ATX is still not investigated yet. Here, we systematically investigated ATX metabolism, bioactivation, species difference in human, mouse, and rat liver microsomes (HLM, MLM, and RLM) and in mice using metabolomic approaches as mice and rats are commonly used animal models for the studies of drug toxicity. We identified thirty one ATX metabolites and adducts in LMs and mice, 16 of which are novel. In LMs, we uncovered two methoxyamine-trapped aldehydes, two cyclization metabolites, detoluene-ATX, and ATX-N-hydroxylation for the first time. Detoluene-ATX and one cyclization metabolite were also observed in mice. Using chemical inhibitors and recombinant CYP enzymes, we demonstrated that CYP2C8 and CYP2B6 mainly contribute to the formation of aldehyde; CYP2D6 is the dominant enzyme for the formation of ATX cyclization and detoluene-ATX; CYP3A4 is major enzyme responsible for the hydroxylamine formation. The findings concerning aldehydes should be very useful to further elucidate the mechanistic aspects of adverse effects associated with ATX from metabolic angles. Additionally, the species differences for each metabolite should be helpful to investigate the contribution of specific metabolites to ATX toxicity and possible drug-drug interactions in suitable models.

Gene-Based Dose Optimization in Children

Pharmacogenetics is a key component of precision medicine. Genetic variation in drug metabolism enzymes can lead to variable exposure to drugs and metabolites, potentially leading to inefficacy and drug toxicity. Although the evidence for pharmacogenetic associations in children is not as extensive as for adults, there are several drugs across diverse therapeutic areas with robust pediatric data indicating important, and relatively common, drug-gene interactions. Guidelines to assist gene-based dose optimization are available for codeine, thiopurine drugs, selective serotonin reuptake inhibitors, atomoxetine, tacrolimus, and voriconazole. For each of these drugs, there is an opportunity to clinically implement precision medicine approaches with children for whom genetic test results are known or are obtained at the time of prescribing. For many more drugs that are commonly used in pediatric patients, additional investigation is needed to determine the genetic factors influencing appropriate dose.

Simple pharmacokinetic models accounting for drug monitoring results of atomoxetine and its 4-hydroxylated metabolites in Japanese pediatric patients genotyped for cytochrome P450 2D6

Atomoxetine is an approved medicine for attention-deficit/hyperactivity disorder and a cytochrome P450 2D6 (CYP2D6) probe substrate. Simple physiologically based pharmacokinetic (PBPK) models and compartment models were set up to account for drug monitoring results of 33 Japanese patients (6-15 years of age) to help establish the correct dosage for the evaluation of clinical outcomes. The steady-state one-point drug monitoring data for the most participants indicated the extensive biotransformation of atomoxetine to 4-hydroxyatomoxetine under individually prescribed doses of atomoxetine. However, 5 participants (with impaired CYP2D6 activity scores based on the CYP2D6 genotypes) showed high plasma concentrations of atomoxetine (0.53-1.5 μM) compared with those of total 4-hydroxyatomoxetine (0.49-1.4 μM). Results from full PBPK models using the in-built Japanese pediatric system of software Simcyp, one-compartment models, and new simple PBPK models (using parameters that reflected the subjects' small body size and normal/reduced CYP2D6-dependent clearance) could overlay one-point measured drug/metabolite plasma concentrations from almost common 28 participants within threefold ranges. Validated one-compartment or simple PBPK models can be used to predict steady-state plasma concentrations of atomoxetine and/or its primary metabolites in Japanese pediatric patients (>6 years) who took a variety of individualized doses in a clinical setting.

Clinical Pharmacogenetics Implementation Consortium Guideline for Cytochrome P450 (CYP)2D6 Genotype and Atomoxetine Therapy

Atomoxetine is a nonstimulant medication used to treat attention-deficit/hyperactivity disorder (ADHD). Cytochrome P450 (CYP)2D6 polymorphisms influence the metabolism of atomoxetine thereby affecting drug efficacy and safety. We summarize evidence from the published literature supporting these associations and provide therapeutic recommendations for atomoxetine based on CYP2D6 genotype (updates at www.cpicpgx.org).

Physiologically based pharmacokinetic modelling of atomoxetine with regard to CYP2D6 genotypes

Atomoxetine is a norepinephrine reuptake inhibitor indicated in the treatment of attention-deficit/hyperactivity disorder. It is primarily metabolized by CYP2D6 to its equipotent metabolite, 4-hydroxyatomoxetine, which promptly undergoes further glucuronidation to an inactive 4-HAT-O-glucuronide. Clinical trials have shown that decreased CYP2D6 activity leads to substantially elevated atomoxetine exposure and increase in adverse reactions. The aim of this study was to to develop a pharmacologically based pharmacokinetic (PBPK) model of atomoxetine in different CYP2D6 genotypes. A single 20 mg dose of atomoxetine was given to 19 healthy Korean individuals with CYP2D6*wt/*wt (*wt = *1 or *2) or CYP2D6*10/*10 genotype. Based on the results of this pharmacokinetic study, a PBPK model for CYP2D6*wt/*wt individuals was developed. This model was scaled to those with CYP2D6*10/*10 genotype, as well as CYP2D6 poor metabolisers. We validated this model by comparing the predicted pharmacokinetic parameters with diverse results from the literature. The presented PBPK model describes the pharmacokinetics after single and repeated oral atomoxetine doses with regard to CYP2D6 genotype and phenotype. This model could be utilized for identification of appropriate dosages of atomoxetine in patients with reduced CYP2D6 activity to minimize the adverse events, and to enable personalised medicine.

Challenges and Opportunities for Increasing the Knowledge Base Related to Drug Biotransformation and Pharmacokinetics during Growth and Development

It is generally acknowledged that there is a need and role for informative pharmacokinetic models to improve predictions and simulation as well as individualization of drug therapy in pediatric populations of different ages and developmental stages. This special issue contains more than 20 papers responding to the challenge of providing new information on scaling factors, ontogeny functions for drug metabolizing enzymes and transporters, the mechanisms underlying the observed developmental trajectories for these gene products, age-dependent changes in physiologic processes affecting drug disposition in children, as well as in vitro and in vivo studies describing the relative contribution of ontogeny and genetic factors as sources of variability in drug disposition in children. Considered together, these contributions serve to illustrate some of the current limitations regarding sample availability, number, and quality, but also provide a framework that allows for the potential value of the results of a given study to be interpreted within the context of these limitations. Among the challenges for the future are improving our understanding of the mechanisms regulating age-dependent changes in factors influencing drug disposition and response, thereby facilitating generalization to systems lacking detailed data, better integrating age-dependent changes in pharmacokinetics with age-dependent changes in pharmacodynamics, and allowing better predictability and individualization of drug disposition and response across the pediatric age spectrum.