Characterization of Contributing Factors to Variability in Morphine Clearance Through PBPK Modeling Implemented With OCT1 Transporter

PBPK-PD model for predicting morphine pharmacokinetics, CNS effects and naloxone antagonism in humans

Morphine and morphine-6-glucuronide (M6G) produce central nervous system (CNS) effects by activating mu-opioid receptors, while naloxone is used mainly for the reversal of opioid overdose, specifically for the fatal complication of respiratory depression, but also for alleviating opioid-induced side effects. In this study we developed a physiologically-based pharmacokinetic-pharmacodynamic (PBPK-PD) model to simultaneously predict pharmacokinetics and CNS effects (miosis, respiratory depression and analgesia) of morphine as well as antagonistic effects of naloxone against morphine. The pharmacokinetic and pharmacodynamic parameters were obtained from in vitro data, in silico, or animals. Pharmacokinetic and pharmacodynamic simulations were conducted using 39 and 36 clinical reports, respectively. The pharmacokinetics of morphine and M6G following oral or intravenous administration were simulated, and the PBPK-PD model was validated using clinical observations. The Emax model correlated CNS effects with free concentrations of morphine and M6G in brain parenchyma. The predicted CNS effects were compared with observations. Most clinical observations fell within the 5th-95th percentiles of simulations based on 1000 virtual individuals. Most of the simulated area under the concentration-time curve or peak concentrations also fell within 0.5-2-fold of observations. The contribution of morphine to CNS effects following intravenous or oral administration was larger than that of M6G. Pharmacokinetics and antagonistic effects of naloxone on CNS effects were also successfully predicted using the developed PBPK-PD model. In conclusion, the pharmacokinetics and pharmacodynamics of morphine and M6G, antagonistic effects of naloxone against morphine-induced CNS effects may be successfully predicted using the developed PBPK-PD model based on the parameters derived from in vitro, in silico, or animal studies.

Physiologically Based Pharmacokinetic (PBPK) Model Predictions of Disease Mediated Changes in Drug Disposition in Patients with Nonalcoholic Fatty Liver Disease (NAFLD)

PURPOSE

This study was designed to verify a virtual population representing patients with nonalcoholic fatty liver disease (NAFLD) to support the implementation of a physiologically based pharmacokinetic (PBPK) modeling approach for prediction of disease-related changes in drug pharmacokinetics.

METHODS

A virtual NAFLD patient population was developed in GastroPlus (v.9.8.2) by accounting for pathophysiological changes associated with the disease and proteomics-informed alterations in the abundance of metabolizing enzymes and transporters pertinent to drug disposition. The NAFLD population model was verified using exemplar drugs where elimination is influenced predominantly by cytochrome P450 (CYP) enzymes (chlorzoxazone, caffeine, midazolam, pioglitazone) or by transporters (rosuvastatin, 11C-metformin, morphine and the glucuronide metabolite of morphine).

RESULTS

PBPK model predictions of plasma concentrations of all the selected drugs and hepatic radioactivity levels of 11C-metformin were consistent with the clinically-observed data. Importantly, the PBPK simulations using the virtual NAFLD population model provided reliable estimates of the extent of changes in key pharmacokinetic parameters for the exemplar drugs, with mean predicted ratios (NAFLD patients divided by healthy individuals) within 0.80- to 1.25-fold of the clinically-reported values, except for midazolam (prediction-fold difference of 0.72).

CONCLUSION

A virtual NAFLD population model within the PBPK framework was successfully developed with good predictive capability of estimating disease-related changes in drug pharmacokinetics. This supports the use of a PBPK modeling approach for prediction of the pharmacokinetics of new investigational or repurposed drugs in patients with NAFLD and may help inform dose adjustments for drugs commonly used to treat comorbidities in this patient population.

Another Step Toward Qualification of Pediatric Physiologically Based Pharmacokinetic Models to Facilitate Inclusivity and Diversity in Pediatric Clinical Studies

Robust prediction of pharmacokinetics (PKs) in pediatric subjects of diverse ages, ethnicities, and morbidities is critical. Qualification of pediatric physiologically-based pharmacokinetic (P-PBPK) models is an essential step toward enabling precision dosing of these vulnerable groups. Twenty-two manuscripts involving P-PBPK predictions and corresponding observed PK data (e.g., area under the curve and clearance) for 22 small-molecule compounds metabolized by CYP (3A4, 1A2, and 2C9), UGT (1A9 and 2B7), FMO3, renal, non-renal, and complex routes were identified; ratios of mean predicted/observed (P/O) PK parameters were calculated. Seventy-eight of 115 mean predicted PK parameters were within 0.8 to 1.25-fold of observed data, 98 within 0.67 to 1.5-fold, 109 within 2-fold, and only 6 P/O ratios were outside of these bounds. A set of 12 CYP3A4-metabolized compounds and a set of 6 metabolized by other enzymes, CYP1A2 (1 compound), CYP2C9 (2 compounds), UGT1A9 (1 compound) and UGT2B7 (2 compounds) had 56 of 59 and 22 of 25 mean P/O ratios, respectively, that fell within the > 0.5 and < 2.0-fold boundaries. For compounds covering renal, non-renal, complex, and FM03 routes of elimination, 29 of 31 mean P/O ratios fell within the 0.67 to 1.5-fold bounds, including 4 of 5 P/O ratios from newborns. P-PBPK modeling and simulation is a strategic component of the complement of precision dosing methods and has a vital role to play in dose adjustment in vulnerable pediatric populations, such as those with disease or in different ethnic groups. Qualification of such models is an essential step toward acceptance of this methodology by regulators.

An Overview of Physiologically-Based Pharmacokinetic Models for Forensic Science

A physiologically-based pharmacokinetic (PBPK) model represents the structural components of the body with physiologically relevant compartments connected via blood flow rates described by mathematical equations to determine drug disposition. PBPK models are used in the pharmaceutical sector for drug development, precision medicine, and the chemical industry to predict safe levels of exposure during the registration of chemical substances. However, one area of application where PBPK models have been scarcely used is forensic science. In this review, we give an overview of PBPK models successfully developed for several illicit drugs and environmental chemicals that could be applied for forensic interpretation, highlighting the gaps, uncertainties, and limitations.

Predicting transporter mediated drug-drug interactions via static and dynamic physiologically based pharmacokinetic modeling: A comprehensive insight on where we are now and the way forward

The greater utilization and acceptance of physiologically-based pharmacokinetic (PBPK) modeling to evaluate the potential metabolic drug-drug interactions is evident by the plethora of literature, guidance's, and regulatory dossiers available in the literature. In contrast, it is not widely used to predict transporter-mediated DDI (tDDI). This is attributed to the unavailability of accurate transporter tissue expression levels, the absence of accurate in vitro to in vivo extrapolations (IVIVE), enzyme-transporter interplay, and a lack of specific probe substrates. Additionally, poor understanding of the inhibition/induction mechanisms coupled with the inability to determine unbound concentrations at the interaction site made tDDI assessment challenging. Despite these challenges, continuous improvements in IVIVE approaches enabled accurate tDDI predictions. Furthermore, the necessity of extrapolating tDDI's to special (pediatrics, pregnant, geriatrics) and diseased (renal, hepatic impaired) populations is gaining impetus and is encouraged by regulatory authorities. This review aims to visit the current state-of-the-art and summarizes contemporary knowledge on tDDI predictions. The current understanding and ability of static and dynamic PBPK models to predict tDDI are portrayed in detail. Peer-reviewed transporter abundance data in special and diseased populations from recent publications were compiled, enabling direct input into modeling tools for accurate tDDI predictions. A compilation of regulatory guidance's for tDDI's assessment and success stories from regulatory submissions are presented. Future perspectives and challenges of predicting tDDI in terms of in vitro system considerations, endogenous biomarkers, the use of empirical scaling factors, enzyme-transporter interplay, and acceptance criteria for model validation to meet the regulatory expectations were discussed.

Quantitative Proteomics in Translational Absorption, Distribution, Metabolism, and Excretion and Precision Medicine

A reliable translation of in vitro and preclinical data on drug absorption, distribution, metabolism, and excretion (ADME) to humans is important for safe and effective drug development. Precision medicine that is expected to provide the right clinical dose for the right patient at the right time requires a comprehensive understanding of population factors affecting drug disposition and response. Characterization of drug-metabolizing enzymes and transporters for the protein abundance and their interindividual as well as differential tissue and cross-species variabilities is important for translational ADME and precision medicine. This review first provides a brief overview of quantitative proteomics principles including liquid chromatography-tandem mass spectrometry tools, data acquisition approaches, proteomics sample preparation techniques, and quality controls for ensuring rigor and reproducibility in protein quantification data. Then, potential applications of quantitative proteomics in the translation of in vitro and preclinical data as well as prediction of interindividual variability are discussed in detail with tabulated examples. The applications of quantitative proteomics data in physiologically based pharmacokinetic modeling for ADME prediction are discussed with representative case examples. Finally, various considerations for reliable quantitative proteomics analysis for translational ADME and precision medicine and the future directions are discussed. SIGNIFICANCE STATEMENT: Quantitative proteomics analysis of drug-metabolizing enzymes and transporters in humans and preclinical species provides key physiological information that assists in the translation of in vitro and preclinical data to humans. This review provides the principles and applications of quantitative proteomics in characterizing in vitro, ex vivo, and preclinical models for translational research and interindividual variability prediction. Integration of these data into physiologically based pharmacokinetic modeling is proving to be critical for safe, effective, timely, and cost-effective drug development.

Clinical Implications of Altered Drug Transporter Abundance/Function and PBPK Modeling in Specific Populations: An ITC Perspective

The role of membrane transporters on pharmacokinetics (PKs), drug-drug interactions (DDIs), pharmacodynamics (PDs), and toxicity of drugs has been broadly recognized. However, our knowledge of modulation of transporter expression and/or function in the diseased patient population or specific populations, such as pediatrics or pregnancy, is still emerging. This white paper highlights recent advances in studying the changes in transporter expression and activity in various diseases (i.e., renal and hepatic impairment and cancer) and some specific populations (i.e., pediatrics and pregnancy) with the focus on clinical implications. Proposed alterations in transporter abundance and/or activity in diseased and specific populations are based on (i) quantitative transporter proteomic data and relative abundance in specific populations vs. healthy adults, (ii) clinical PKs, and emerging transporter biomarker and/or pharmacogenomic data, and (iii) physiologically-based pharmacokinetic modeling and simulation. The potential for altered PK, PD, and toxicity in these populations needs to be considered for drugs and their active metabolites in which transporter-mediated uptake/efflux is a major contributor to their absorption, distribution, and elimination pathways and/or associated DDI risk. In addition to best practices, this white paper discusses current challenges and knowledge gaps to study and quantitatively predict the effects of modulation in transporter activity in these populations, together with the perspectives from the International Transporter Consortium (ITC) on future directions.

Predicting Regional Respiratory Tissue and Systemic Concentrations of Orally Inhaled Drugs through a Novel PBPK Model

Oral inhalation (OI) of drugs is the route of choice to treat respiratory diseases or for recreational drug use (e.g., cannabis). After OI, the drug is deposited in and systemically absorbed from various regions of the respiratory tract. Measuring regional respiratory tissue drug concentrations at the site of action is important for evaluating the efficacy and safety of orally inhaled drugs (OIDs). Because such a measurement is routinely not possible in humans, the only alternative is to predict these concentrations, for example by physiologically based pharmacokinetic (PBPK) modeling. Therefore, we developed an OI-PBPK model to integrate the interplay between regional respiratory drug deposition and systemic absorption to predict regional respiratory tissue and systemic drug concentrations. We validated our OI-PBPK model by comparing the simulated and observed plasma concentration-time profiles of two OIDs, morphine and nicotine. Furthermore, we performed sensitivity analyses to quantitatively demonstrate the impact of key parameters on the extent and pattern of regional respiratory drug deposition, absorption, and the resulting regional respiratory tissue and systemic plasma concentrations. Our OI-PBPK model can be applied to predict regional respiratory tissue and systemic drug concentrations to optimize OID formulations, delivery systems, and dosing regimens. Furthermore, our model could be used to establish the bioequivalence of generic OIDs for which systemic plasma concentrations are not measurable or are not a good surrogate of the respiratory tissue drug concentrations. SIGNIFICANCE STATEMENT: Our OI-PBPK model is the first comprehensive model to predict regional respiratory deposition, as well as systemic and regional tissue concentrations of OIDs, especially at the drug's site of action, which is difficult to measure in humans. This model will help optimize OID formulations, delivery systems, dosing regimens, and bioequivalence assessment of generic OID. Furthermore, this model can be linked with organs-on-chips, pharmacodynamic and quantitative systems pharmacology models to predict and evaluate the safety and efficacy of OID.

An update on the use of allometric and other scaling methods to scale drug clearance in children: towards decision tables

INTRODUCTION

When pediatric data are not available for a drug, allometric and other methods are applied to scale drug clearance across the pediatric age-range from adult values. This is applied when designing first-in-child studies, but also for off-label drug prescription.

AREAS COVERED

This review provides an overview of the systematic accuracy of allometric and other pediatric clearance scaling methods compared to gold-standard PBPK predictions. The findings are summarized in decision tables to provide a priori guidance on the selection of appropriate pediatric clearance scaling methods for both novel drugs for which no pediatric data are available and existing drugs in clinical practice.

EXPERT OPINION

While allometric scaling principles are commonly used to scale pediatric clearance, there is no universal allometric exponent (i.e., 1, 0.75 or 0.67) that can accurately scale clearance for all drugs from adults to children of all ages. Therefore, pediatric scaling decision tables based on age, drug elimination route, binding plasma protein, fraction unbound, extraction ratio, and/or isoenzyme maturation are proposed to a priori select the appropriate (allometric) clearance scaling method, thereby reducing the need for full PBPK-based clearance predictions. Guidance on allometric scaling when estimating pediatric clearance values is provided as well.

Inhibitory effects of non-steroidal anti-inflammatory drugs on human liver microsomal morphine glucuronidation: Implications for drug-drug interaction liability

The inhibitory effects of fifteen NSAIDs from six structurally distinct classes on human liver microsomal morphine glucuronidation were investigated. K_i values of selected NSAIDs were generated and employed to assess DDI liability in vivo. Potent inhibition was observed for mefenamic acid and tolfenamic acid; respective IC₅₀ values for morphine 3- and 6-glucuronidation were 9.2 and 13.5 μM, and 5.3 and 8.3 μM. Diclofenac and celecoxib showed moderate inhibition with IC₅₀ values of 78 and 52 μM, and 83 and 214 μM, respectively. Estimated IC₅₀ values for the other NSAIDs screened were >100 μM. Mefenamic acid, diclofenac, and S-naproxen competitively inhibited morphine 3- and 6-glucuronidation, with the K_i values of 11 and 12 μM, 110 and 76 μM, and 319 and 650 μM, respectively. Using the static mechanistic IVIVE approach, an approximate 40% increase in the AUC of morphine was predicted when co-administered with mefenamic acid, whereas the increase was <10% with diclofenac and S-naproxen. PBPK modeling predicted <15% increases in the morphine AUC from diclofenac and S-naproxen inhibition in virtual healthy and cirrhotic subjects. The data suggest that potential clinically significant DDIs arising from NSAID inhibition of morphine glucuronidation is unlikely, with the possible exception of some fenamates.

Bridging the gap between in silico and in vivo by modeling opioid disposition in a kidney proximal tubule microphysiological system

Opioid overdose, dependence, and addiction are a major public health crisis. Patients with chronic kidney disease (CKD) are at high risk of opioid overdose, therefore novel methods that provide accurate prediction of renal clearance (CL_r) and systemic disposition of opioids in CKD patients can facilitate the optimization of therapeutic regimens. The present study aimed to predict renal clearance and systemic disposition of morphine and its active metabolite morphine-6-glucuronide (M6G) in CKD patients using a vascularized human proximal tubule microphysiological system (VPT-MPS) coupled with a parent-metabolite full body physiologically-based pharmacokinetic (PBPK) model. The VPT-MPS, populated with a human umbilical vein endothelial cell (HUVEC) channel and an adjacent human primary proximal tubular epithelial cells (PTEC) channel, successfully demonstrated secretory transport of morphine and M6G from the HUVEC channel into the PTEC channel. The in vitro data generated by VPT-MPS were incorporated into a mechanistic kidney model and parent-metabolite full body PBPK model to predict CL_r and systemic disposition of morphine and M6G, resulting in successful prediction of CL_r and the plasma concentration–time profiles in both healthy subjects and CKD patients. A microphysiological system together with mathematical modeling successfully predicted renal clearance and systemic disposition of opioids in CKD patients and healthy subjects.

Racial differences in opioid withdrawal syndrome among neonates with intrauterine opioid exposure

BACKGROUND

The aim of this study was to investigate the association between race and severe neonatal opioid withdrawal syndrome (NOWS) in infants exposed to intrauterine opioids.

METHODS

This is a prospective observational study on intrauterine opioid-exposed term infants. Exposure to opioids was based on maternal disclosure, urine, or umbilical cord drug screening. Severe NOWS was defined based on modified Finnegan scoring and the need for pharmacological intervention.

RESULTS

One hundred and fifty mother-infant pairs, 60 Black and 90 White with history of opioid exposure during pregnancy, were included. More White than Black infants developed NOWS that required pharmacological treatment, 70 vs. 40%: RR = 1.75 (1.25-2.45). In adjusted analysis, there was no significant association between race and the development of severe NOWS in mothers who attended opioid maintenance treatment program (OMTP). However, in mothers who did not attend OMTP, White race remained a significant factor associated with the development of severe NAS, RR = 1.69 (1.06, 2.69).

CONCLUSIONS

Severe NOWS that required pharmacological intervention was significantly higher in White than in Black infants born to mothers who did not attend OMTP. Larger studies are needed to evaluate the association between social as well as genetic factors and the development of NOWS.

IMPACT

There is a significant association between race and development of severe NOWS.

Pharmacogenomics for Drug Dosing in Children: Current Use, Knowledge, and Gaps

Pharmacogenomics research ranges from the discovery of genetic factors to explain interpatient variability in drug exposure and response to clinical implementation of this knowledge to improve pharmacotherapy. Medications with actionable pharmacogenomic associations are frequently used in children, and therefore pharmacogenomics-guided precision medicine is readily applicable to the pediatric population. Although heritable genetics are considered immutable, the impact of genetic variation in pharmacogenes is modified by other factors such as age-dependent changes in gene expression. Early evidence has emerged indicating that the interaction between ontogeny and pharmacogenomics determines whether or how genetics-based dosing algorithms should be adjusted in children versus adults. However, there is still a paucity of data describing pharmacogenomic associations in patient populations across the life span. Future research is much needed to evaluate the impact of pharmacogenomics on drug dosing specific to the pediatric population, along with consideration of other developmental and physiological factors uniquely related to drug disposition in this population.

Physiologically based pharmacokinetic/pharmacodynamic model for the prediction of morphine brain disposition and analgesia in adults and children

Morphine is a widely used opioid analgesic, which shows large differences in clinical response in children, even when aiming for equivalent plasma drug concentrations. Age-dependent brain disposition of morphine could contribute to this variability, as developmental increase in blood-brain barrier (BBB) P-glycoprotein (Pgp) expression has been reported. In addition, age-related pharmacodynamics might also explain the variability in effect. To assess the influence of these processes on morphine effectiveness, a multi-compartment brain physiologically based pharmacokinetic/pharmacodynamic (PB-PK/PD) model was developed in R (Version 3.6.2). Active Pgp-mediated morphine transport was measured in MDCKII-Pgp cells grown on transwell filters and translated by an in vitro-in vivo extrapolation approach, which included developmental Pgp expression. Passive BBB permeability of morphine and its active metabolite morphine-6-glucuronide (M6G) and their pharmacodynamic parameters were derived from experiments reported in literature. Model simulations after single dose morphine were compared with measured and published concentrations of morphine and M6G in plasma, brain extracellular fluid (ECF) and cerebrospinal fluid (CSF), as well as published drug responses in children (1 day– 16 years) and adults. Visual predictive checks indicated acceptable overlays between simulated and measured morphine and M6G concentration-time profiles and prediction errors were between 1 and -1. Incorporation of active Pgp-mediated BBB transport into the PB-PK/PD model resulted in a 1.3-fold reduced brain exposure in adults, indicating only a modest contribution on brain disposition. Analgesic effect-time profiles could be described reasonably well for older children and adults, but were largely underpredicted for neonates. In summary, an age-appropriate morphine PB-PK/PD model was developed for the prediction of brain pharmacokinetics and analgesic effects. In the neonatal population, pharmacodynamic characteristics, but not brain drug disposition, appear to be altered compared to adults and older children, which may explain the reported differences in analgesic effect.

Developmental processes in children can affect pharmacokinetics: “what the body does to the drug” as well as pharmacodynamics: “what the drug does to the body”. A typical example is morphine, of which the analgesic response is variable and particularly neonates suffer more often from respiratory depression, even when receiving doses corrected for differences in elimination. One way to mathematically incorporate developmental processes is by employing physiologically based pharmacokinetic/pharmacodynamic (PB-PK/PD) models, where physiological differences between individuals are incorporated. In this study, we developed a morphine PB-PK/PD model to predict brain drug disposition as well as analgesic response in adults and children, as both processes could potentially contribute to developmental variability in the effect of morphine. We found that age-related variation in BBB expression of the main morphine efflux transporter P-glycoprotein was not responsible for differences in brain exposure. In contrast, pharmacodynamic modelling suggested an increased sensitivity to morphine in neonates.

Physiologically-Based Pharmacokinetic Model of Morphine and Morphine-3-Glucuronide in Nonalcoholic Steatohepatitis

Nonalcoholic steatohepatitis (NASH), the progressive form of nonalcoholic fatty liver disease, is increasing in prevalence. NASH-related alterations in hepatic protein expression (e.g., transporters) and in overall physiology may affect drug exposure by altering drug disposition and elimination. The aim of this study was to build a physiologically-based pharmacokinetic (PBPK) model to predict drug exposure in NASH by incorporating NASH-related changes in hepatic transporters. Morphine and morphine-3-glucuronide (M3G) were used as model compounds. A PBPK model of morphine with permeability-limited hepatic disposition was extended to include M3G disposition and enterohepatic recycling (EHR). The model captured the area under the plasma concentration-time curve (AUC) of morphine and M3G after intravenous morphine administration within 0.82-fold and 1.94-fold of observed values from 3 independent clinical studies for healthy adult subjects (6, 10, and 14 individuals). When NASH-related changes in multidrug resistance-associated protein 2 (MRP2) and MRP3 were incorporated into the model, the predicted M3G mean AUC in NASH was 1.34-fold higher compared to healthy subjects, which is slightly lower than the observed value (1.63-fold). Exploratory simulations on other physiological changes occurring in NASH (e.g., moderate decreases in glomerular filtration rate and portal vein blood flow) revealed that the effect of transporter changes was most prominent. Additionally, NASH-related transporter changes resulted in decreased morphine EHR, which could be important for drugs with extensive EHR. This study is an important first step to predict drug disposition in complex diseases such as NASH using PBPK modeling.

Potential implications of DMET ontogeny on the disposition of commonly prescribed drugs in neonatal and pediatric intensive care units

Introduction: Pediatric patients, especially neonates and infants, are more susceptible to adverse drug events as compared to adults. In particular, immature small molecule drug metabolism and excretion can result in higher incidences of pediatric toxicity than adults if the pediatric dose is not adjusted.Area covered: We reviewed the top 29 small molecule drugs prescribed in neonatal and pediatric intensive care units and compiled the mechanisms of their metabolism and excretion. The ontogeny of Phase I and II drug metabolizing enzymes and transporters (DMETs), particularly relevant to these drugs, are summarized. The potential effects of DMET ontogeny on the metabolism and excretion of the top pediatric drugs were predicted. The current regulatory requirements and recommendations regarding safe and effective use of drugs in children are discussed. A few representative examples of the use of ontogeny-informed physiologically based pharmacokinetic (PBPK) models are highlighted.Expert opinion: Empirical prediction of pediatric drug dosing based on body weight or body-surface area from the adult parameters can be inaccurate because DMETs are not mature in children and the age-dependent maturation of these proteins is different. Ontogeny-informed-PBPK modeling provides a better alternative to predict the pharmacokinetics of drugs in children.

Model-Informed Pediatric Dose Selection for Dapagliflozin by Incorporating Developmental Changes

This analysis reports a quantitative modeling and simulation approach for oral dapagliflozin, a primarily uridine diphosphate-glucuronosyltransferase (UGT)-metabolized human sodium-glucose cotransporter 2 selective inhibitor. A mechanistic dapagliflozin physiologically based pharmacokinetic (PBPK) model was developed using in vitro metabolism and clinical pharmacokinetic (PK) data and verified for context of use (e.g., exposure predictions in pediatric subjects aged 1 month to 18 years). Dapagliflozin exposure is challenging to predict in pediatric populations owing to differences in UGT1A9 ontogeny maturation and paucity of clinical PK data in younger age groups. Based on the exposure-response relationship of dapagliflozin, twofold acceptance criteria were applied between model-predicted and observed drug exposures and PK parameters (area under the curve and maximum drug concentration) in various scenarios, including monotherapy in healthy adults (single/multiple dose), monotherapy in hepatically or renally impaired patients, and drug-drug interactions with UGT1A9 modulators, such as mefenamic acid and rifampin. The PBPK model captured the observed exposure within twofold of the observed monotherapy data in adults and adolescents and in special population. As a guide to determining dosing regimens in pediatric studies, the verified PBPK model, along with UGT enzyme ontogeny maturation understanding, was used for predictions of dapagliflozin monotherapy exposures in pediatric subjects aged 1 month to 18 years that best matched exposure in adult patients with a 10-mg single dose of dapagliflozin.

Application of Model Informed Precision Dosing to Address the Impact of Pregnancy Stage and CYP2D6 Phenotype on Foetal Morphine Exposure

Guidance regarding the effect of codeine and its metabolites on foetal development is limited by small studies and inconsistent findings. The primary objective was to use physiologically based pharmacokinetic modelling to investigate the impact of gestational stage and maternal CYP2D6 phenotype on foetal morphine exposure following codeine administration. Full body physiologically based pharmacokinetic models were developed and verified for codeine and morphine using Simcyp (version 19.1). The impact of gestational age and maternal CYP2D6 phenotype on foetal and maternal morphine and codeine exposure following oral codeine administration was modelled in a cohort of 250 pregnant females and foetuses at gestational weeks 0 (mothers only), 6, 12, 24 and 36. Consistent with the known effect on codeine metabolism, a clinically meaningful (> 1.65-fold) increase in foetal morphine AUC was observed in the CYP2D6 UM phenotype cohort compared to the CYP2D6 EM and PM phenotype cohorts. The mean (95% CI) foetal morphine AUC in the CYP2D6 UM cohort of 0.988 (0.902 to 1.073) ng/mL.h was 1.8-fold higher than the CYP2D6 EM cohort of 0.546 (0.492 to 0.600) ng/mL.h (p < 0.001). Despite a 2.8-fold increase in maternal CYP2D6 protein abundance between gestational weeks 6 and 36, the mean foetal morphine AUC in the CYP2D6 EM and UM phenotype cohorts reduced by 1.55- and 1.75-fold, respectively, over this period. Maternal CYP2D6 phenotype is a significant determinant of foetal morphine AUC. Simulations suggest that the greatest risk with respect to foetal morphine exposure is during the first trimester of pregnancy, particularly in CYP2D6 UM phenotype mothers.

Use of normalized prediction distribution errors for assessing population physiologically-based pharmacokinetic model adequacy

Currently employed methods for qualifying population physiologically-based pharmacokinetic (Pop-PBPK) model predictions of continuous outcomes (e.g., concentration-time data) fail to account for within-subject correlations and the presence of residual error. In this study, we propose a new method for evaluating Pop-PBPK model predictions that account for such features. The approach focuses on deriving Pop-PBPK-specific normalized prediction distribution errors (NPDE), a metric that is commonly used for population pharmacokinetic model validation. We describe specific methodological steps for computing NPDE for Pop-PBPK models and define three measures for evaluating model performance: mean of NPDE, goodness-of-fit plots, and the magnitude of residual error. Utility of the proposed evaluation approach was demonstrated using two simulation-based study designs (positive and negative control studies) as well as pharmacokinetic data from a real-world clinical trial. For the positive-control simulation study, where observations and model simulations were generated under the same Pop-PBPK model, the NPDE-based approach denoted a congruency between model predictions and observed data (mean of NPDE =  - 0.01). In contrast, for the negative-control simulation study, where model simulations and observed data were generated under different Pop-PBPK models, the NPDE-based method asserted that model simulations and observed data were incongruent (mean of NPDE =  - 0.29). When employed to evaluate a previously developed clindamycin PBPK model against prospectively collected plasma concentration data from 29 children, the NPDE-based method qualified the model predictions as successful (mean of NPDE = 0). However, when pediatric subpopulations (e.g., infants) were evaluated, the approach revealed potential biases that should be explored.

The Ontogeny of UDP-glucuronosyltransferase Enzymes, Recommendations for Future Profiling Studies and Application Through Physiologically Based Pharmacokinetic Modelling

Limited understanding of drug pharmacokinetics in children is one of the major challenges in paediatric drug development. This is most critical in neonates and infants owing to rapid changes in physiological functions, especially in the activity of drug-metabolising enzymes. Paediatric physiologically based pharmacokinetic models that integrate ontogeny functions for cytochrome P450 enzymes have aided our understanding of drug exposure in children, including those under the age of 2 years. Paediatric physiologically based pharmacokinetic models have consequently been recognised by the European Medicines Agency and the US Food and Drug Administration as innovative tools in paediatric drug development and regulatory decision making. However, little is currently known about age-related changes in UDP-glucuronosyltransferase-mediated metabolism, which represents the most important conjugation reaction for xenobiotics. Therefore, the objective of the review was to conduct a thorough literature survey to summarise our current understanding of age-related changes in UDP-glucuronosyltransferases as well as associated clinical and experimental sources of variance. Our findings indicate that there are distinct differences in UDP-glucuronosyltransferase expression and activity between isoforms for different age groups. In addition, there is substantial variability between individuals and laboratories reported for human liver microsomes, which results in part from a lack of standardised experimental conditions. Therefore, we provide a number of best practice recommendations for experimental conditions, which ultimately may help improve the quality of data used for quantitative clinical pharmacology approaches, and thus for safe and effective pharmacotherapy in children.

State-of-the-Art Review on Physiologically Based Pharmacokinetic Modeling in Pediatric Drug Development

Physiologically based pharmacokinetic modeling and simulation is an important tool for predicting the pharmacokinetics, pharmacodynamics, and safety of drugs in pediatrics. Physiologically based pharmacokinetic modeling is applied in pediatric drug development for first-time-in-pediatric dose selection, simulation-based trial design, correlation with target organ toxicities, risk assessment by investigating possible drug-drug interactions, real-time assessment of pharmacokinetic-safety relationships, and assessment of non-systemic biodistribution targets. This review summarizes the details of a physiologically based pharmacokinetic modeling approach in pediatric drug research, emphasizing reports on pediatric physiologically based pharmacokinetic models of individual drugs. We also compare and contrast the strategies employed by various researchers in pediatric physiologically based pharmacokinetic modeling and provide a comprehensive overview of physiologically based pharmacokinetic modeling strategies and approaches in pediatrics. We discuss the impact of physiologically based pharmacokinetic models on regulatory reviews and product labels in the field of pediatric pharmacotherapy. Additionally, we examine in detail the current limitations and future directions of physiologically based pharmacokinetic modeling in pediatrics with regard to the ability to predict plasma concentrations and pharmacokinetic parameters. Despite the skepticism and concern in the pediatric community about the reliability of physiologically based pharmacokinetic models, there is substantial evidence that pediatric physiologically based pharmacokinetic models have been used successfully to predict differences in pharmacokinetics between adults and children for several drugs. It is obvious that the use of physiologically based pharmacokinetic modeling to support various stages of pediatric drug development is highly attractive and will rapidly increase, provided the robustness and reliability of these techniques are well established.

Electronic Health Record-Embedded Decision Support Platform for Morphine Precision Dosing in Neonates

Morphine is the opioid most commonly used for neonatal pain management. In intravenous form, it is administered as continuous infusions and intermittent injections, mostly based on empirically established protocols. Inadequate pain control in neonates can cause long-term adverse consequences; however, providing appropriate individualized morphine dosing is particularly challenging due to the interplay of rapid natural physiological changes and multiple life-sustaining procedures in patients who cannot describe their symptoms. At most institutions, morphine dosing in neonates is largely carried out as an iterative process using a wide range of starting doses and then titrating to effect based on clinical response and side effects using pain scores and levels of sedation. Our background data show that neonates exhibit large variability in morphine clearance resulting in a wide range of exposures, which are poorly predicted by dose alone. Here, we describe the development and implementation of an electronic health record-integrated, model-informed decision support platform for the precision dosing of morphine in the management of neonatal pain. The platform supports pharmacokinetic model-informed dosing guidance and has functionality to incorporate real-time drug concentration information. The feedback is inserted directly into prescribers' workflows so that they can make data-informed decisions. The expected outcomes are better clinical efficacy and safety with fewer side effects in the neonatal population.

Physiologically-Based Pharmacokinetic Models for Evaluating Membrane Transporter Mediated Drug-Drug Interactions: Current Capabilities, Case Studies, Future Opportunities, and Recommendations

Physiologically-based pharmacokinetic (PBPK) modeling has been extensively used to quantitatively translate in vitro data and evaluate temporal effects from drug-drug interactions (DDIs), arising due to reversible enzyme and transporter inhibition, irreversible time-dependent inhibition, enzyme induction, and/or suppression. PBPK modeling has now gained reasonable acceptance with the regulatory authorities for the cytochrome-P450-mediated DDIs and is routinely used. However, the application of PBPK for transporter-mediated DDIs (tDDI) in drug development is relatively uncommon. Because the predictive performance of PBPK models for tDDI is not well established, here, we represent and discuss examples of PBPK analyses included in regulatory submission (the US Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the Pharmaceuticals and Medical Devices Agency (PMDA)) across various tDDIs. The goal of this collaborative effort (involving scientists representing 17 pharmaceutical companies in the Consortium and from academia) is to reflect on the use of current databases and models to address tDDIs. This challenges the common perceptions on applications of PBPK for tDDIs and further delves into the requirements to improve such PBPK predictions. This review provides a reflection on the current trends in PBPK modeling for tDDIs and provides a framework to promote continuous use, verification, and improvement in industrialization of the transporter PBPK modeling.

Utilizing Pediatric Physiologically Based Pharmacokinetic Models to Examine Factors That Contribute to Methadone Pharmacokinetic Variability in Neonatal Abstinence Syndrome Patients

Chronic intrauterine exposure to psychoactive drugs often results in neonatal abstinence syndrome (NAS). NAS is the symptomatic drug withdrawal in newborns that generally occurs after in utero chronic opioid exposure. Methadone is an opioid analgesic commonly prescribed for pharmacologic management of NAS. It exhibits high pharmacokinetic (PK) variability. The current study used physiologically based PK modeling to predict the PK profile of methadone in 20 newborns treated for NAS. The physiologically based PK simulations adequately predicted the PK profile of the clinical data for 45% of the patients. Sensitivity analyses were conducted to explore contributing factors to methadone PK variability. The data suggest that P450 enzymatic activity impacts the clearance of methadone in virtual adults and neonates, while the contribution of cardiac output may be negligible. Understanding maturational and/or pharmacogenetic changes in cytochrome P450 enzymatic activity may further explain the large PK variability of methadone in newborns with NAS and will help individualized treatment.

Computational framework for predictive PBPK-PD-Tox simulations of opioids and antidotes

The primary goal of this work was to develop a computational tool to enable personalized prediction of pharmacological disposition and associated responses for opioids and antidotes. Here we present a computational framework for physiologically-based pharmacokinetic (PBPK) modeling of an opioid (morphine) and an antidote (naloxone). At present, the model is solely personalized according to an individual's mass. These PK models are integrated with a minimal pharmacodynamic model of respiratory depression induction (associated with opioid administration) and reversal (associated with antidote administration). The model was developed and validated on human data for IV administration of morphine and naloxone. The model can be further extended to consider different routes of administration, as well as to study different combinations of opioid receptor agonists and antagonists. This work provides the framework for a tool that could be used in model-based management of pain, pharmacological treatment of opioid addiction, appropriate use of antidotes for opioid overdose and evaluation of abuse deterrent formulations.

[Message from the President of the Japanese Society for the Study of Xenobiotics]

The Japanese Society for the Study of Xenobiotics has focused on drug development and clinical pharmacotherapy by applying cutting-edge technology and reviewing drugs. One specific area of focus has been the reverse-translational approach. This approach uses data from the investigation of clinical problems and from the detailed revisiting of preclinical studies to discover new pharmacotherapies and new methods to prevent drug-induced side effects. In 2017, the U.S. Food and Drug Administration restricted the use of prescription codeine cough-and-cold medicines in children. This decision was based on concerns about the effects of the extensive metabolite morphine in cytochrome P450 2D6 ultra-rapid metabolizers. However, there are few reports on the side effects of dihydrocodeine in Japanese children. Our laboratory measured serum concentrations of dihydrocodeine in a 1-month-old infant with respiratory depression who was given dihydrocodeine phosphate in a suspected case of overdosing. Levels of dihydrocodeine and its primary metabolite, dihydromorphine, were high in this infant. However, the molar ratios of glucuronide conjugates of dihydrocodeine and dihydromorphine were lower than those found in a 3-year-old and a 6-year-old child used as references. To support and facilitate reverse-translational research, the elimination of regional differences in the methodologies used for liquid chromatography to measure drug concentrations and for the genotyping of drug-metabolizing enzymes should become the focus in hospitals, laboratories, and doctoral programs.

Age- and Genotype-Dependent Variability in the Protein Abundance and Activity of Six Major Uridine Diphosphate-Glucuronosyltransferases in Human Liver

The ontogeny of hepatic uridine diphosphate-glucuronosyltransferases (UGTs) was investigated by determining their protein abundance in human liver microsomes isolated from 136 pediatric (0-18 years) and 35 adult (age >18 years) donors using liquid chromatography / tandem mass spectrometry (LC-MS/MS) proteomics. Microsomal protein abundances of UGT1A1, UGT1A4, UGT1A6, UGT1A9, UGT2B7, and UGT2B15 increased by ∼8, 55, 35, 33, 8, and 3-fold from neonates to adults, respectively. The estimated age at which 50% of the adult protein abundance is observed for these UGT isoforms was between 2.6-10.3 years. Measured in vitro activity was generally consistent with the protein data. UGT1A1 protein abundance was associated with multiple single nucleotide polymorphisms exhibiting noticeable ontogeny-genotype interplay. UGT2B15 rs1902023 (*2) was associated with decreased protein activity without any change in protein abundance. Taken together, these data are invaluable to facilitate the prediction of drug disposition in children using physiologically based pharmacokinetic modeling as demonstrated here for zidovudine and morphine.

Prolonged central apnoea after intravenous morphine administration in a 12-year-old male with a UGT1A1 loss-of-function polymorphism

ADVERSE EVENT

Repeated and prolonged episodes of central apnoea and hypoxia after receiving intravenous morphine for analgesia and ketamine for sedation.

DRUG IMPLICATED

Intravenous morphine sulfate.

THE PATIENT

Previously healthy 12-year-old male with no history of sleep apnoea who presented with distal tibia and fibula fracture.

EVIDENCE THAT LINKS DRUG TO EVENT

Pharmacogenomic testing revealed that the patient was homozygous for the T allele at the rs887829 SNP in UGT1A1, an enzyme involved in the metabolism of morphine. This polymorphism is a loss-of-function variant, leading to impaired metabolism of morphine.

MECHANISM

Morphine is metabolized by UDP-glucuronosyltransferase (UGT)-2B7 and UGT1A1 to form its major metabolites morphine-3-glucuronide (M3G) and morphine-6-glucuronide (M6G). Our patient was a poor metabolizer through UGT1A1, likely leading to increased respiratory depression as morphine has greater respiratory depressant effects compared to its metabolites.

IMPLICATIONS

When appropriate, physicians should enquire about prior receipt of opioids, in both the patient and family, to be better prepared for potential adverse reactions. In the patient with excessive sedation or respiratory depression to standard doses of morphine, genetic testing may be warranted, especially if there is a family or past history that supports a metabolic defect in morphine metabolism and/or excretion.

Influence of OCT1 Ontogeny and Genetic Variation on Morphine Disposition in Critically Ill Neonates: Lessons From PBPK Modeling and Clinical Study

Morphine is commonly used for analgesia in the neonatal intensive care unit (NICU) despite having highly variable pharmacokinetics (PKs) between individual patients. The pharmacogenetic (PG) effect of variants at the loci of organic cation transporter 1 (OCT1) and UDP-glucuronosyltransferase 2B7 (UGT2B7) on age-dependent morphine clearance were evaluated in a cohort of critically ill neonatal patients using an opportunistic sampling design. Our primary results demonstrate the significant influence of OCT1 genotype (P < 0.05) and gestational age (P ≤ 0.005) on morphine PKs. A physiologically based pharmacokinetic (PBPK) model for morphine that accounted for OCT1 ontogeny and PG effect in post-term neonates adequately described the clinically observed variability in morphine PKs. This study serves as a proof of concept for genotype-dependent drug transporter ontogeny in neonates.

Abundance of Phase 1 and 2 Drug-Metabolizing Enzymes in Alcoholic and Hepatitis C Cirrhotic Livers: A Quantitative Targeted Proteomics Study

To predict the impact of liver cirrhosis on hepatic drug clearance using physiologically based pharmacokinetic (PBPK) modeling, we compared the protein abundance of various phase 1 and phase 2 drug-metabolizing enzymes (DMEs) in S9 fractions of alcoholic (n = 27) or hepatitis C (HCV, n = 30) cirrhotic versus noncirrhotic (control) livers (n = 25). The S9 total protein content was significantly lower in alcoholic or HCV cirrhotic versus control livers (i.e., 38.3 ± 8.3, 32.3 ± 12.8, vs. 51.1 ± 20.7 mg/g liver, respectively). In general, alcoholic cirrhosis was associated with a larger decrease in the DME abundance than HCV cirrhosis; however, only the abundance of UGT1A4, alcohol dehydrogenase (ADH)1A, and ADH1B was significantly lower in alcoholic versus HCV cirrhotic livers. When normalized to per gram of tissue, the abundance of nine DMEs (UGT1A6, UGT1A4, CYP3A4, UGT2B7, CYP1A2, ADH1A, ADH1B, aldehyde oxidase (AOX)1, and carboxylesterase (CES)1) in alcoholic cirrhosis and five DMEs (UGT1A6, UGT1A4, CYP3A4, UGT2B7, and CYP1A2) in HCV cirrhosis was <25% of that in control livers. The abundance of most DMEs in cirrhotic livers was 25% to 50% of control livers. CES2 abundance was not affected by cirrhosis. Integration of UGT2B7 abundance in cirrhotic livers into the liver cirrhosis (Child Pugh C) model of Simcyp improved the prediction of zidovudine and morphine PK in subjects with Child Pugh C liver cirrhosis. These data demonstrate that protein abundance data, combined with PBPK modeling and simulation, can be a powerful tool to predict drug disposition in special populations.

PBPK Model of Morphine Incorporating Developmental Changes in Hepatic OCT1 and UGT2B7 Proteins to Explain the Variability in Clearances in Neonates and Small Infants

Morphine has large pharmacokinetic variability, which is further complicated by developmental changes in neonates and small infants. The impacts of organic cation transporter 1 (OCT1) genotype and changes in blood-flow on morphine clearance (CL) were previously demonstrated in children, whereas changes in UDP-glucuronosyltransferase 2B7 (UGT2B7) activity showed a small effect. This study, targeting neonates and small infants, was designed to assess the influence of developmental changes in OCT1 and UGT2B7 protein expression and modified blood-flow on morphine CL using physiologically based pharmacokinetic (PBPK) modeling. The implementation of these three age-dependent factors into the pediatric system platform resulted in reasonable prediction for an age-dependent increase in morphine CL in these populations. Sensitivity of morphine CL to changes in cardiac output increased with age up to 3 years, whereas sensitivity to changes in UGT2B7 activity decreased. This study suggests that morphine exhibits age-dependent extraction, likely due to the developmental increase in OCT1 and UGT2B7 protein expression/activity and hepatic blood-flow.

Influence of Transporter Polymorphisms on Drug Disposition and Response: A Perspective From the International Transporter Consortium

Advances in genomic technologies have led to a wealth of information identifying genetic polymorphisms in membrane transporters, specifically how these polymorphisms affect drug disposition and response. This review describes the current perspective of the International Transporter Consortium (ITC) on clinically important polymorphisms in membrane transporters. ITC suggests that, in addition to previously recommended polymorphisms in ABCG2 (BCRP) and SLCO1B1 (OATP1B1), polymorphisms in the emerging transporter, SLC22A1 (OCT1), be considered during drug development. Collectively, polymorphisms in these transporters are important determinants of interindividual differences in the levels, toxicities, and response to many drugs.

Update of the Scientific Opinion on opium alkaloids in poppy seeds

Poppy seeds are obtained from the opium poppy (Papaver somniferum L.). They are used as food and to produce edible oil. The opium poppy plant contains narcotic alkaloids such as morphine and codeine. Poppy seeds do not contain the opium alkaloids, but can become contaminated with alkaloids as a result of pest damage and during harvesting. The European Commission asked EFSA to provide an update of the Scientific Opinion on opium alkaloids in poppy seeds. The assessment is based on data on morphine, codeine, thebaine, oripavine, noscapine and papaverine in poppy seed samples. The CONTAM Panel confirms the acute reference dose (ARfD) of 10 μg morphine/kg body weight (bw) and concluded that the concentration of codeine in the poppy seed samples should be taken into account by converting codeine to morphine equivalents, using a factor of 0.2. The ARfD is therefore a group ARfD for morphine and codeine, expressed in morphine equivalents. Mean and high levels of dietary exposure to morphine equivalents from poppy seeds considered to have high levels of opium alkaloids (i.e. poppy seeds from varieties primarily grown for pharmaceutical use) exceed the ARfD in most age groups. For poppy seeds considered to have relatively low concentrations of opium alkaloids (i.e. primarily varieties for food use), some exceedance of the ARfD is also seen at high levels of dietary exposure in most surveys. For noscapine and papaverine, the available data do not allow making a hazard characterisation. However, comparison of the dietary exposure to the recommended therapeutical doses does not suggest a health concern for these alkaloids. For thebaine and oripavine, no risk characterisation was done due to insufficient data. However, for thebaine, limited evidence indicates a higher acute lethality than for morphine and the estimated exposure could present a health risk.

Critical Issues and Optimized Practices in Quantification of Protein Abundance Level to Determine Interindividual Variability in DMET Proteins by LC-MS/MS Proteomics

Protein quantification data on drug metabolizing enzymes and transporters (collectively referred as DMET proteins) in human tissues are useful in predicting interindividual variability in drug disposition. While targeted proteomics is an emerging technique for quantification of DMET proteins, the methodology involves significant technical challenges especially when multiple samples are analyzed in a single study over a long period of time. Therefore, it is important to thoroughly address the critical variables that could affect DMET protein quantification.