Estimation of Circulating Drug Metabolite Exposure in Human Using In Vitro Data and Physiologically Based Pharmacokinetic Modeling: Example of a High Metabolite/Parent Drug Ratio

The Importance of Tracking "Missing" Metabolites: How and Why?

Technologies currently employed to find and identify drug metabolites in complex biological matrices generally yield results that offer a comprehensive picture of the drug metabolite profile. However, drug metabolites can be missed or are captured only late in the drug development process. This could be due to a variety of factors, such as metabolism that results in partial loss of the molecule, covalent bonding to macromolecules, the drug being metabolized in specific human tissues, or poor ionization in a mass spectrometer. These scenarios often draw a great deal of attention from chemistry, safety assessment, and pharmacology. This review will summarize scenarios of missing metabolites, why they are missing, and associated uncovering strategies from deeper investigations. Uncovering previously missed metabolites can have ramifications in drug development with toxicological and pharmacological consequences, and knowledge of these can help in the design of new drugs.

Empirical scaling factor for predicting human pharmacokinetic profiles of disproportionate metabolites using the Css-MRTpo method and chimeric mice with humanised livers

Predicting plasma concentration-time profiles of disproportionate metabolites in humans is crucial for evaluating metabolites according to the Safety Testing guidelines. We evaluated Css-MRTpo, an empirical method, using chimeric mice with humanised livers capable of generating human-disproportionate metabolites. Azilsartan and AZ-M2 were administered to humanised chimeric mice, and pharmacokinetic parameters were obtained. Pharmacokinetic data for DS-1971a and DS-M1 in humanised chimeric mice were obtained from the literature. The human plasma concentration-time profiles of these compounds were simulated using the Css-MRTpo method. Azilsartan, DS-1971a, and PF-04937319 produced human disproportionate metabolites, AZ-M2, DS-M1, and PF-M1, respectively. The predicted human pharmacokinetic profiles of PF-04937319 and PF-M1 were obtained from a previous study, and their outcomes were re-evaluated. Our findings revealed that the plasma concentrations of the three metabolites were unexpectedly underpredicted, whereas the three unchanged drugs were reasonably predicted. Further, the introduction of the empirical scaling factor of 3, obtained from six model compounds, improved the predictability of metabolites, suggesting the potential usefulness of the Css-MRTpo method in combination with humanised chimeric mice for predicting the pharmacokinetic profiles of disproportionate metabolites at the early stage of new drug development.

Physiologically Based Pharmacokinetic Modeling for Quantitative Prediction of Exposure to a Human Disproportionate Metabolite of the Selective NaV1.7 Inhibitor DS-1971a, a Mixed Substrate of Cytochrome P450 and Aldehyde Oxidase, Using Chimeric Mice With Humanized Liver

In a previous study on the human mass balance of DS-1971a, a selective NaV1.7 inhibitor, its CYP2C8-dependent metabolite M1 was identified as a human disproportionate metabolite. The present study assessed the usefulness of pharmacokinetic evaluation in chimeric mice grafted with human hepatocytes (PXB-mice) and physiologically based pharmacokinetic (PBPK) simulation of M1. After oral administration of radiolabeled DS-1971a, the most abundant metabolite in the plasma, urine, and feces of PXB-mice was M1, while those of control SCID mice were aldehyde oxidase-related metabolites including M4, suggesting a drastic difference in the metabolism between these mouse strains. From a qualitative perspective, the metabolite profile observed in PXB-mice was remarkably similar to that in humans, but the quantitative evaluation indicated that the area under the plasma concentration-time curve (AUC) ratio of M1 to DS-1971a (M1/P ratio) was approximately only half of that in humans. A PXB-mouse-derived PBPK model was then constructed to achieve a more accurate prediction, giving an M1/P ratio (1.3) closer to that in humans (1.6) than the observed value in PXB-mice (0.69). In addition, simulated maximum plasma concentration and AUC values of M1 (3429 ng/ml and 17,116 ng·h/ml, respectively) were similar to those in humans (3180 ng/ml and 18,400 ng·h/ml, respectively). These results suggest that PBPK modeling incorporating pharmacokinetic parameters obtained with PXB-mice is useful for quantitatively predicting exposure to human disproportionate metabolites. SIGNIFICANCE STATEMENT: The quantitative prediction of human disproportionate metabolites remains challenging. This paper reports on a successful case study on the practical estimation of exposure (C max and AUC) to DS-1971a and its CYP2C8-dependent, human disproportionate metabolite M1, by PBPK simulation utilizing pharmacokinetic parameters obtained from PXB-mice and in vitro kinetics in human liver fractions. This work adds to the growing knowledge regarding metabolite exposure estimation by static and dynamic models.

CYP2C8-Mediated Formation of a Human Disproportionate Metabolite of the Selective NaV1.7 Inhibitor DS-1971a, a Mixed Cytochrome P450 and Aldehyde Oxidase Substrate

Predicting human disproportionate metabolites is difficult, especially when drugs undergo species-specific metabolism mediated by cytochrome P450s (P450s) and/or non-P450 enzymes. This study assessed human metabolites of DS-1971a, a potent Nav1.7-selective blocker, by performing human mass balance studies and characterizing DS-1971a metabolites, in accordance with the Metabolites in Safety Testing guidance. In addition, we investigated the mechanism by which the major human disproportionate metabolite (M1) was formed. After oral administration of radiolabeled DS-1971a, the major metabolites in human plasma were P450-mediated monoxidized metabolites M1 and M2 with area under the curve ratios of 27% and 10% of total drug-related exposure, respectively; the minor metabolites were dioxidized metabolites produced by aldehyde oxidase and P450s. By comparing exposure levels of M1 and M2 between humans and safety assessment animals, M1 but not M2 was found to be a human disproportionate metabolite, requiring further characterization under the Metabolites in Safety Testing guidance. Incubation studies with human liver microsomes indicated that CYP2C8 was responsible for the formation of M1. Docking simulation indicated that, in the formation of M1 and M2, there would be hydrogen bonding and/or electrostatic interactions between the pyrimidine and sulfonamide moieties of DS-1971a and amino acid residues Ser100, Ile102, Ile106, Thr107, and Asn217 in CYP2C8, and that the cyclohexane ring of DS-1971a would be located near the heme iron of CYP2C8. These results clearly indicate that M1 is the predominant metabolite in humans and a human disproportionate metabolite due to species-specific differences in metabolism. SIGNIFICANCE STATEMENT: This report is the first to show a human disproportionate metabolite generated by CYP2C8-mediated primary metabolism. We clearly demonstrate that DS-1971a, a mixed aldehyde oxidase and cytochrome P450 substrate, was predominantly metabolized by CYP2C8 to form M1, a human disproportionate metabolite. Species differences in the formation of M1 highlight the regio- and stereoselective metabolism by CYP2C8, and the proposed interaction between DS-1971a and CYP2C8 provides new knowledge of CYP2C8-mediated metabolism of cyclohexane-containing substrates.

Population Pharmacokinetic Model for Tramadol and O-desmethyltramadol in Older Patients

BACKGROUND AND OBJECTIVES

Tramadol is commonly prescribed to manage chronic pain in older patients. However, there is a gap in the literature describing the pharmacokinetic parameters for tramadol and its active metabolite (O-desmethyltramadol [ODT]) in this population. The objective of this study was to develop and evaluate a population pharmacokinetic model for tramadol and ODT in older patients.

METHODS

Twenty-one patients who received an extended-release oral tramadol dose (25-100 mg) were recruited. Tramadol and ODT concentrations were determined using a validated liquid chromatography/tandem mass spectrometry method. A population pharmacokinetic model was developed using non-linear mixed-effects modelling. The performance of the model was assessed by visual predictive check.

RESULTS

A two-compartment, first-order absorption model with linear elimination best described the tramadol concentration data. The absorption rate constant was 2.96/h (between-subject variability [BSV] 37.8%), apparent volume of distribution for the central compartment (V₁/F) was 0.373 l (73.8%), apparent volume of distribution for the peripheral compartment (V₂/F) was 0.379 l (97.4%), inter-compartmental clearance (Q) was 0.0426 l/h (2.19%) and apparent clearance (CL/F) was 0.00604 l/h (6.61%). The apparent rate of metabolism of tramadol to ODT (k_t) was 0.0492 l/h (78.5%) and apparent clearance for ODT (CL_m) was 0.143 l/h (21.6%). Identification of Seniors at Risk score (ISAR) and creatinine clearance (CrCL) were the only covariates included in the final model, where a higher value for the ISAR increased the maximum concentration (C_max) of tramadol and reduced the BSV in Q from 4.71 to 2.19%. A higher value of CrCL reduced tramadol C_max and half-life (T_1/2) and reduced the BSV in V₂/F (from 148 to 97.4%) and in CL/F (from 78.9 to 6.61%).

CONCLUSION

Exposure to tramadol increased with increased frailty and reduced CrCL. Prescribers should consider patients frailty status and CrCL to minimise the risk of tramadol toxicity in such cohort of patients.

Simultaneous determination of ZL-01, a novel nucleotide prodrug, and its metabolites in rat plasma by LC-MS/MS: Application to pharmacokinetic study

ZL-01 is a novel dual-prodrug which shows promise to be an antiviral candidate for hepatitis C virus. Here we have established a liquid chromatography tandem mass spectrometry (LC-MS/MS) method for simultaneous determination of ZL-01 and its four metabolites (M1, M7, M8, and M9) in rat plasma with special consideration of ex vivo ZL-01, M1, and M7 stability. Several factors affecting the stability were investigated. EDTA and citric acid solution (1 M) were added to plasma to maintain the stability of analytes. The protein-precipitation method was selected with acetonitrile containing sofosbuvir as internal standard (IS). Adequate separation of ZL-01 and its metabolites was achieved on XSelect HSS T3 (3.5 µm, 4.6 × 150 mm) column by a gradient-elution with a mobile phase consisting of 0.1% formic acid and acetonitrile at a flow rate of 0.5 mL/min. The detection was performed on a triple quadrupole tandem mass spectrometer by multiple reaction monitoring (MRM) mode to monitor the precursor-to-product ion transitions of m/z 599.2→418.5 for ZL-01, m/z 529.7→398.2 for M1, m/z 330.5→182.0 for M7, m/z 260.3→112.1 for M8, m/z 261.3→113.2 for M9 and m/z 530.4→243.4 for IS. The calibration curves exhibited good linearity (r＞0.997) for all components. The lower limit of quantitation (LLOQ) was in the range of 1-2 ng/mL. The intra-day and inter-day precisions (RSD) at three different levels were both less than 10.2% and the accuracies (RE) ranged from -3.7-7.6%. The matrix effect and extraction recovery of them ranged from 84% to 110.3% and 88.3-106.3%. This LC-MS/MS method for the simultaneous quantitation of ZL-01 and its metabolites was developed successfully and applied in the pharmacokinetic studies of these in rats. Pharmacokinetic results indicated ZL-01 would be metabolized rapidly and M8 might be the main metabolites after oral absorption.

Prediction of Metabolite-to-Parent Drug Exposure: Derivation and Application of a Mechanistic Static Model

In the development of new drugs, the prediction of metabolite‐to‐parent plasma exposure ratio in humans prior to administration in a clinical study has emerged as an important need. In this work, we derived a mechanistic static model based on first principles to estimate metabolite‐to‐parent plasma exposure ratio, considering the contribution of liver and gut metabolism and drug transport. Knowledge (or assumptions) of mechanisms of clearance and organs involved is required. Input parameters needed included intrinsic clearance, fraction of clearance to the metabolite of interest, various binding values, and, in some cases, active transport clearance. The principles are illustrated with four drugs that yield six metabolites, with one in which clearance is dependent on a pathway subject to genetic polymorphism. Overall, the approach yielded metabolite‐to‐parent ratios within about twofold of the actual values and, thus, can be valuable in decision making in the drug development process.

A novel Css-MRTpo approach to simulate oral plasma concentration-time profiles of the partial glucokinase activator PF-04937319 and its disproportionate N-demethylated metabolite in humans using chimeric mice with humanized livers

A Css-MRTpo superposition method was devised to predict (retrospectively) oral plasma concentration-time profiles of PF-04937319 and its MIST-related metabolite, M1, in humans using chimeric mice with humanized liver.Original PK data were taken from a published report in which PF-04937319 and M1 were given to chimeric mice orally and/or intravenously. Human CL and Vss were predicted by single-species allometry and MRTiv,pred were calculated as Vss,pred/CL,pred. MRTpo,human were assumed to be MRTiv,pred plus MAT or mean metabolite formation time (MFT). Human Css was calculated by dividing the corrected oral dose by Vss,pred.Chronological sampling time and measured plasma concentrations were corrected by MRTpo,human and Css,human, respectively, and transformed to the corresponding values in humans.The obtained concentration-time profile of PF-04937319 was superimposed well with the observed data after single and repeated oral administration to humans. The transformed plasma concentration of M1 was somewhat lower than the observed value, but a slow increase of the simulated metabolite reflected gradual increase of observed M1 on Day 1. Transformed M1 gave an almost-flat concentration-time profile on Day 14, which was consistent with the curve observed in humans. Application of this novel method to other MIST-related compounds is discussed.