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A novel HPLC MS/MS assay for the biochemical detection of adherence to tofacitinib with evidence of sensitive measurement using real-world patient samples is reported.

Coproporphyrin I (CPI) is an endogenous biomarker of organic anion transporting polypeptides 1B transporter (OATP1B). CPI plasma baseline was reported to increase with severity of chronic kidney disease (CKD). Further, ratio of CPI area under the plasma concentration-time curve in the presence/absence of OATP1B inhibitor rifampicin (AUCR) was higher in CKD patients compared to healthy subjects, in contrast to pitavastatin (a clinical OATP1B probe). This study investigated mechanism(s) contributing to altered CPI baseline in CKD patients by extending previously developed physiologically-based pharmacokinetic model to this patient population. CKD-related covariates were evaluated in a step-wise manner on CPI fraction unbound in plasma (fu,p), OATP1B-mediated hepatic uptake clearance (CLactive), renal clearance (CLR), and endogenous synthesis (ksyn). CPI model successfully recovered increased baseline and rifampicin-mediated AUCR in CKD patients by accounting for the following disease-related changes: 13% increase in fu,p, 29% and 39% decrease in CLactive in mild and moderate-to-severe CKD, respectively, decrease in CLR proportional to decline in GFR, and 27% decrease in ksyn in severe CKD. Almost complete decline in CPI renal elimination in severe CKD increased its fraction transported by OATP1B, rationalizing differences in the CPI-rifampicin interaction observed between healthy and CKD patients. In conclusion, mechanistic modelling performed here supports CKD-related decrease in OATP1B function to inform prospective PBPK modelling of OATP1B-mediated DDIs in these patients. Monitoring of CPI allows detection of CKD-drug interaction risk for OATP1B drugs with combined hepatic and renal elimination which may be under-estimated by extrapolating the interaction risk based on pitavastatin data in healthy subjects.

Aim To quantify the utility of a terminal-phase adjusted area under the concentration curve method in increasing the probability of a correct and conclusive outcome of a bioequivalence (BE) trial for highly variable drugs when clearance (CL) varies more than the volume of distribution (V). Methods Data from a large population of subjects were generated with variability in CL and V, and used to simulate a two-period, two-sequence crossover BE trial. The 90% confidence interval for formulation comparison was determined following BE assessment using the area under the concentration curve (AUC) ratio test, and the proposed terminal-phase adjusted AUC ratio test. An outcome of bioequivalent, non-bioequivalent or inconclusive was then assigned according to predefined BE limits. Results When CL is more variable than V, the proposed approach would enhance the probability of correctly assigning bioequivalent or non-bioequivalent and reduce the risk of an inconclusive trial. For a hypothetical drug with between-subject variability of 35% for CL and 10% for V, when the true test-reference ratio of bioavailability is 1.15, a cross-over study of n=14 subjects analyzed by the proposed method would have 80% or 20% probability of claiming bioequivalent or non-bioequivalent, compared to 22%, 46% or 32% probability of claiming bioequivalent, non-bioequivalent or inconclusive using the standard AUC ratio test. Conclusions The terminal-phase adjusted AUC ratio test represents a simple and readily applicable approach to enhance the BE assessment of drug products when CL varies more than V.

Physiologically based pharmacokinetic (PBPK) models are increasingly used in drug development to simulate changes in both systemic and tissue exposures that arise as a result of changes in enzyme and/or transporter activity. Verification of these model-based simulations of tissue exposure is challenging in the case of transporter-mediated drug–drug interactions (tDDI), in particular as these may lead to differential effects on substrate exposure in plasma and tissues/organs of interest. Gadoxetate, a promising magnetic resonance imaging (MRI) contrast agent, is a substrate of organic-anion-transporting polypeptide 1B1 (OATP1B1) and multidrug resistance-associated protein 2 (MRP2). In this study, we developed a gadoxetate PBPK model and explored the use of liver-imaging data to achieve and refine in vitro–in vivo extrapolation (IVIVE) of gadoxetate hepatic transporter kinetic data. In addition, PBPK modeling was used to investigate gadoxetate hepatic tDDI with rifampicin i.v. 10 mg/kg. In vivo dynamic contrast-enhanced (DCE) MRI data of gadoxetate in rat blood, spleen, and liver were used in this analysis. Gadoxetate in vitro uptake kinetic data were generated in plated rat hepatocytes. Mean (%CV) in vitro hepatocyte uptake unbound Michaelis–Menten constant (Km,u) of gadoxetate was 106 μM (17%) (n = 4 rats), and active saturable uptake accounted for 94% of total uptake into hepatocytes. PBPK–IVIVE of these data (bottom-up approach) captured reasonably systemic exposure, but underestimated the in vivo gadoxetate DCE–MRI profiles and elimination from the liver. Therefore, in vivo rat DCE–MRI liver data were subsequently used to refine gadoxetate transporter kinetic parameters in the PBPK model (top-down approach). Active uptake into the hepatocytes refined by the liver-imaging data was one order of magnitude higher than the one predicted by the IVIVE approach. Finally, the PBPK model was fitted to the gadoxetate DCE–MRI data (blood, spleen, and liver) obtained with and without coadministered rifampicin. Rifampicin was estimated to inhibit active uptake transport of gadoxetate into the liver by 96%. The current analysis highlighted the importance of gadoxetate liver data for PBPK model refinement, which was not feasible when using the blood data alone, as is common in PBPK modeling applications. The results of our study demonstrate the utility of organ-imaging data in evaluating and refining PBPK transporter IVIVE to support the subsequent model use for quantitative evaluation of hepatic tDDI.

Coproporphyrin I (CPI) is an endogenous biomarker of organic anion transporting polypeptides 1B (OATP1B) activity and associated drug-drug interactions. In this study, a minimal physiologicallybased pharmacokinetic model was developed to investigate the impact of OATP1B1 genotype (c.521T>C), ethnicity, and sex on CPI pharmacokinetics and inter-individual variability in its baseline. The model implemented mechanistic descriptions of CPI hepatic transport between liver blood and liver tissue and renal excretion. Key model parameters (e.g., endogenous CPI synthesis rate, CPI hepatic uptake clearance) were estimated by fitting the model simultaneously to three independent CPI clinical datasets (plasma and urine data) obtained from Caucasian (n=16, male and female) and Asian-Indian (n=26, all male) subjects, with c.521 variants (TT, TC, and CC). The optimized CPI model successfully described the observed data using three covariates (c.521T>C genotype, ethnicity, and sex). Hepatic active uptake of CPI decreased by 75% in 521CC relative to the wild type and by 37% in Asian-Indians relative to Caucasians. CPI synthesis was 24% lower in female relative to male. Parameter sensitivity analysis showed marginal impact of the assumption of CPI synthesis site (blood or liver), resulting in comparable recovery of plasma and urine CPI data. Lower magnitude of CPI-drug interaction was simulated in 521CC subjects, suggesting the risk of under-estimation of CPI-drug interaction without prior OATP1B1 genotyping. The CPI model incorporates key covariates contributing to inter-individual variability in its baseline and highlights the utility of the CPI modelling to facilitate the design of prospective clinical studies to maximise the sensitivity of this biomarker.

Clinical assessment of drug-drug interactions (DDIs) in children is not a common practice in drug development. Therefore, physiologically-based pharmacokinetic (PBPK) modeling can be beneficial for informing drug labeling. Using ivabradine and its metabolite (both cytochrome P450 3A4 enzyme (CYP3A4) substrates), the objectives were (i) to scale ivabradine-metabolite adult PBPK/PD to pediatrics, (ii) to predict the DDIs with a strong CYP3A4 inhibitor, and (iii) to compare the sensitivity of children to DDIs using two CYP3A4 hepatic ontogeny functions: Salem and Upreti. A scaled parent-metabolite PBPK/PD model from adults to children satisfactorily predicted pharmacokinetics (PK) and pharmacodynamics (PD) in 74 children (0.5-18 years) regardless of CYP3A4 hepatic ontogeny function applied. However, using the Salem ontogeny, mean predicted parent and metabolite area under the concentration-time curve over 12 hours (AUC12h ) and heart rate change from baseline were 2-fold, 1.5-fold, and 1.4-fold higher in young children (0.5-3 years old) compared with Upreti ontogeny, respectively. Despite these differences, choice of appropriate hepatic CYP3A4 ontogeny was challenging due to sparse PK and PD data. Different sensitivity to ivabradine-ketoconazole DDIs was simulated in young children relative to adults depending on the choice of hepatic CYP3A4 ontogeny. Predicted ivabradine and metabolite AUCDDI /AUCcontrol were 2-fold lower in the youngest children (0.5-1 year old) compared with adults (Salem function). In contrast, the Upreti function predicted comparable ivabradine DDIs across all age groups, although predicted metabolite AUCDDI/ AUCcontrol was 1.3-fold higher between the youngest children and adults. In the case of PD, differences in predicted DDIs were minor across age groups and between both functions. Current work highlights the importance of careful consideration of hepatic CYP3A4 ontogeny function and implications on labeling recommendations in the pediatric population.

Aims Chemotherapy-induced neutropenia has been associated with an increase in overall survival in non-small cell lung cancer patients. Therefore, neutrophil counts could be an interesting biomarker for drug efficacy as well as linked directly to toxicity. For drugs where neutropenia is dose limiting, neutrophil counts might be used for monitoring drug effect and for dosing optimisation. Methods The relationship between drug effect on the first cycle neutrophil counts and patient survival was explored in a Phase III clinical trial where patients with non-small cell lung cancer were treated with docetaxel. Once the association has been established, dosing optimisation was performed for patients with severe toxicities (neutropenia) without compromising drug efficacy (overall survival). Results After taking into account baseline prognostic factors, such as Eastern Cooperative Oncology Group performance status, smoking status, liver metastasis, tumour burden, neutrophil counts and albumin levels, a model-predicted drug effect on the first cycle neutrophil counts was strongly associated with patient survival (P = .005). Utilising this relationship in a dose optimisation algorithm, 194 out of 366 patients would have benefited from a dose reduction after the first cycle of docetaxel. The simulated 1-year survival probabilities associated with the original dose and the individualised dose were not different. Conclusion The strong relationship between drug effect on the first cycle neutrophil counts and patient survival suggests that this variable could be used to individualise dosing, possibly without needing pharmacokinetic samples. The algorithm highlights that doses could be reduced in case of severe haematological toxicities without compromising drug efficacy.

Purpose Poor adherence to dietary/behaviour modifications as interventions for hypercholesterolemia in paediatric patients often necessitates the initiation of statin therapy. The aim of this study was to develop a joint population pharmacokinetic model for simvastatin and four metabolites in children and adolescents to investigate sources of variability in simvastatin acid exposure in this patient population, in addition to SLCO1B1 genotype status. Methods Plasma concentrations of simvastatin and its four metabolites, demographic and polymorphism data for OATP1B1 and CYP3A5 were analysed utilising a population pharmacokinetic modelling approach from an existing single oral dose (10 mg C (rs4149056) on the pharmacokinetics of the active metabolite simvastatin acid in children/adolescents, consistent with adult data. In addition, age was identified as a covariate affecting elimination clearances of 6-hydroxymethyl simvastatin acid and 3, 5 dihydrodiol simvastatin metabolites. Conclusion The model developed describes the pharmacokinetics of simvastatin and its metabolites in children/adolescents capturing the effects of both c.521T>C and age on variability in exposure in this patient population. This joint simvastatin metabolite model is envisaged to facilitate optimisation of simvastatin dosing in children/adolescents. Electronic supplementary material The online version of this article (10.1007/s00228-019-02697-y) contains supplementary material, which is available to authorized users.

AimTo investigate total serum levobupivacaine concentrations after a caudal‐epidural loading dose followed by a maintenance infusion in infants aged 3‐6 months over 48 hours.BackgroundThe local anaesthetic, levobupivacaine, is the safer enantiomer of racemic bupivacaine. Present protocols for levobupivacaine are based on studies and pharmacokinetic modelling with racemic bupivacaine. This study will inform clinical practice in this age group and validate the pharmacokinetic model for levobupivacaine infusions in infants, aged 3‐6months.MethodsThe clinical trial was conducted in 8 infants aged 3‐6 months, undergoing bladder exstrophy repair. Pharmacokinetic modelling allowed optimisation of clinical sampling to measure total levobupivacaine and α1‐acid glycoprotein and prediction of the effect of α1‐acid glycoprotein on levobupivacaine plasma protein binding.ResultsThe observed median total levobupivacaine serum concentration was 0.30 mg.L−1 (range: 0.20‐0.70 mg.L−1) at 1 hour after the loading dose of 2 mg.kg−1. The median total levobupivacaine concentration after 47 hours of infusion, at 0.2 mg.h−1.kg−1, was 1.21 mg.L−1 (0.07‐1.85 mg.L−1). Concentrations of α1‐acid glycoprotein were found to rise throughout the study period. Pharmacokinetic modelling suggested that unbound levobupivacaine quickly reached steady state at a concentration of approximately 0.03 mg.L−1.ConclusionThis study examines the pharmacokinetics of levobupivacaine after a loading dose (given over 5 minutes) followed by a maintenance infusion in infants 3‐6 months. The study allows the development of a pharmacokinetic model, combining levobupivacaine and α1‐acid glycoprotein data. Modelling indicates that unbound levobupivacaine quickly reaches steady state once the infusion is started. Simulations suggest that it may be possible to continue the infusion beyond 48 hours.

Risdiplam (Evrysdi) improves motor neuron function in patients with spinal muscular atrophy (SMA) and has been approved for the treatment of patients ≥2 months old. Risdiplam exhibits time-dependent inhibition of cytochrome P450 (CYP) 3A in vitro. While many pediatric patients receive risdiplam, a drug-drug interaction (DDI) study in pediatric patients with SMA was not feasible. Therefore, a novel physiologically-based pharmacokinetic (PBPK) model-based strategy was proposed to extrapolate DDI risk from healthy adults to children with SMA in an iterative manner. A clinical DDI study was performed in healthy adults at relevant risdiplam exposures observed in children. Risdiplam caused an 1.11-fold increase in the ratio of midazolam area under the curve with and without risdiplam (AUCR)), suggesting an 18-fold lower in vivo CYP3A inactivation constant compared with the in vitro value. A pediatric PBPK model for risdiplam was validated with independent data and combined with a validated midazolam pediatric PBPK model to extrapolate DDI from adults to pediatric patients with SMA. The impact of selected intestinal and hepatic CYP3A ontogenies on the DDI susceptibility in children relative to adults was investigated. The PBPK analysis suggests that primary CYP3A inhibition by risdiplam occurs in the intestine rather than the liver. The PBPK-predicted risdiplam CYP3A inhibition risk in pediatric patients with SMA aged 2 months-18 years was negligible (midazolam AUCR of 1.09-1.18) and included in the US prescribing information of risdiplam. Comprehensive evaluation of the sensitivity of predicted CYP3A DDI on selected intestinal and hepatic CYP3A ontogeny functions, together with PBPK model-based strategy proposed here, aim to guide and facilitate DDI extrapolations in pediatric populations.

Model-informed precision dosing (MIPD) has become synonymous with modern approaches for individualizing drug therapy, in which the characteristics of each patient are considered as opposed to applying a one-size-fits-all alternative. This review provides a brief account of the current knowledge, practices, and opinions on MIPD while defining an achievable vision for MIPD in clinical care based on available evidence. We begin with a historical perspective on variability in dose requirements and then discuss technical aspects of MIPD, including the need for clinical decision support tools, practical validation, and implementation of MIPD in health care. We also discuss novel ways to characterize patient variability beyond the common perceptions of genetic control. Finally, we address current debates on MIPD from the perspectives of the new drug development, health-care economics, and drug regulations. Expected final online publication date for the Annual Review of Pharmacology and Toxicology, Volume 61 is January 7, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Aim: To quantify the utility of the rate-adjusted area under the concentration curve method in increasing the probability of a correct and conclusive outcome of a bioequivalence (BE) trial for highly variable drugs when clearance (CL) varies more than volume of distribution (V). Methods. Data from a large population of subjects were generated with variability in CL and V parameters and used to simulate a two-period, crossover BE trial. The 90% confidence interval for formulation comparison was determined following BE assessment using the area under the concentration curve (AUC) ratio test, and the proposed rate-adjusted AUC ratio method. An outcome of bioequivalent, non-bioequivalent or inconclusive was then assigned in relation to predefined BE limits. Results: We illustrate the utility of the rate-adjusted AUC method for BE testing when CL varies more than V. The approach is expected to enhance the probability of correctly assigning BE or non-BE and to increase study power to further reduce the risk of an inconclusive trial. Conclusions: The rate-adjusted AUC method represents a simple and readily applicable approach to enhance the BE assessment of drug products when CL varies more than V.

Ivabradine and its metabolite both demonstrate heart rate–reducing effect (If current inhibitors) and undergo CYP3A4 metabolism. The purpose of this study was to develop a joint parent-metabolite physiologically based pharmacokinetic (PBPK)/pharmacodynamic (PD) model to predict the PK and PD of ivabradine and its metabolite following intravenous (i.v.) or oral administration (alone or co-administered with CYP3A4 inhibitors). Firstly, a parent-metabolite disposition model was developed and optimised using individual plasma concentration-time data following i.v. administration of ivabradine or metabolite within a Bayesian framework. Secondly, the model was extended and combined with a mechanistic intestinal model to account for oral absorption and drug-drug interactions (DDIs) with CYP3A4 inhibitors (ketoconazole, grapefruit juice). Lastly, a PD model was linked to the PBPK model to relate parent and metabolite PK to heart rate (HR) reduction. The disposition model described successfully parent-metabolite PK following i.v. administration. Following integration of a gut model, the PBPK model adequately predicted plasma concentration profiles and the DDI risk (92% and 85% of predicted AUC+inhibitor/AUCcontrol and Cmax+inhibitor/Cmaxcontrol for ivabradine and metabolite within the prediction limits). Ivabradine-metabolite PBPK model was linked to PD by using the simulated unbound parent-metabolite concentrations in the heart. This approach successfully predicted the effects of both entities on HR (observed vs predicted − 7.7/− 5.9 bpm and − 15.8/− 14.0 bpm, control and ketoconazole group, respectively). This study provides a framework for PBPK/PD modelling of a parent-metabolite and can be scaled to other populations or used for investigation of untested scenarios (e.g. evaluation of DDI risk in special populations).

Our objective was to develop a population pharmacokinetic (PK) model in order to evaluate the currently recommended dosing regimen in term and preterm neonates. By using an optimal design approach, a prospective PK study was designed and implemented in 60 neonates with postmenstrual ages (PMA) of 26 to 43 weeks. A loading dose of 16 mg/kg was administered at day 1, followed by a maintenance dose of 8 mg/kg daily. Plasma concentrations were quantified by high-pressure liquid chromatography–mass spectrometry. Population PK (popPK) analysis was performed using NONMEM software. Monte-Carlo (MC) simulations were performed to evaluate currently recommended dosing based on a pharmacodynamic index of area under the concentration-time curve (AUC)/MIC ratio of ≥400. A two-compartment model with linear elimination best described the data by the following equations: clearance (CL) = 0.0227 × (weight [wt]/1,765)(0.75) × (estimated creatinine clearance [eCRCL]/22)(0.672), central compartment volume of distribution (V1) = 0.283 (wt/1,765), intercompartmental clearance (Q) = 0.151 (wt/1,765)(0.75), and peripheral compartment volume (V2) = 0.541 (wt/1,765). The interindividual variability estimates for CL, V1, and V2 were 36.5%, 45.7%, and 51.4%, respectively. Current weight (wt) and estimated creatinine clearance (eCRCL) significantly explained the observed variability. MC simulation demonstrated that, with the current dosing regimen, an AUC/MIC ratio of ≥400 was reached by only 68.5% of neonates with wt of

Glomerular filtration rate (GFR) is an important measure of renal function. Various models for its maturation have recently been compared; however, these have used markers, which are subject to different renal elimination processes. Inulin clearance data (a purer probe of GFR) collected from the literature were used to determine age‐related changes in GFR aspects of renal drug excretion in pediatrics. An ontogeny model was derived using a best‐fit model with various combinations of covariates such as postnatal age, gestational age at birth, and body weight. The model was applied to the prediction of systemic clearance of amikacin, gentamicin, vancomycin, and gadobutrol. During neonatal life, GFR increased as a function of both gestational age at birth and postnatal age, hence implying an impact of birth and a discrepancy in GFR for neonates with the same postmenstrual age depending on gestational age at birth (ie, neonates who were outside the womb longer had higher GFR, on average). The difference in GFR between pre‐term and full‐term neonates with the same postmenstrual age was negligible from beyond 1.25 years. Considering both postnatal age and gestational age at birth in GFR ontogeny models is important because postmenstrual age alone ignores the impact of birth. Most GFR models use covariates of body size in addition to age. Therefore, prediction from these models will also depend on the change in anthropometric characteristics with age. The latter may not be similar in various ethnic groups, and this makes the head‐to‐head comparison of models very challenging.

Purpose During oncology clinical trials, tumour size (TS) measurements are commonly used to monitor disease progression and to assess drug efficacy. We explored inter-operator variability within a subset of a phase III clinical trial conducted from August 1995 to February 1997 and its impact on drug effect evaluation using a tumour growth inhibition model. Methods One hundred twenty lesions were measured twice at each time point; once at the hospital and once at the centralised centre. A visual analysis was performed to identify trends within the profiles over time. Linear regression and relative error ratios were used to explore the inter-operator variability of raw TS measurements and model-based estimates. Results While correlation between patient-level estimates of drug effect was poor (r2 = 0.28), variability between the study-level estimates was much less affected (9%). Conclusions The global evaluation of drug effect using modelling approaches might not be affected by inter-operator variability. However, the exploration of covariates for drug effect and the characterisation of an exposure–tumour shrinkage relationship seems limited by the high measurement variability that translates to a poor correlation of individual drug effect estimates. This might be addressed by the use of more precise computer-aided measurement methods.

BackgroundSevere infections of the respiratory tracts of critically ill patients are common and associated with excess morbidity and mortality. Piperacillin is commonly used to treat pulmonary infections in critically ill patients. Adequate antibiotic concentration in the epithelial lining fluid (ELF) of the lung is essential for successful treatment of pulmonary infection.ObjectivesTo compare piperacillin pharmacokinetics/pharmacodynamics in the serum and ELF of healthy volunteers and critically ill patients.MethodsPiperacillin concentrations in the serum and ELF of healthy volunteers and critically ill patients were compared using population methodologies.ResultsMedian piperacillin exposure was significantly higher in the serum and the ELF of critically ill patients compared with healthy volunteers. The IQR for serum piperacillin exposure in critically ill patients was six times greater than for healthy volunteers. The IQR for piperacillin exposure in the ELF of critically ill patients was four times greater than for healthy volunteers. The median pulmonary piperacillin penetration ratio was 0.31 in healthy volunteers and 0.54 in critically ill patients.ConclusionsGreater variability in serum and ELF piperacillin concentrations is observed in critically ill patients compared with healthy adult subjects and must be considered in the development of dosage regimens. Pulmonary penetration of antimicrobial agents should be studied in critically ill patients, as well as healthy volunteers, during drug development to ensure appropriate dosing of patients with pneumonia.

The prospect of precision dosing in oncology is attractive for several reasons. Many anticancer drugs display narrow therapeutic indices, where suboptimal therapy may lead to severe patient outcomes. Clinical study participant recruitment is seldom extended beyond the intended patient population, leading to difficulties in patient recruitment in dedicated clinical trials. The high rate of non-responders and high cost of cancer therapy warrant novel solutions to increase clinical effectiveness and cost-benefit, pharmacokinetic (PK) modeling and model-informed precision dosing (MIPD) can help to maximize these. PK modeling provides a quantitative framework to account for inter-individual variability in drug exposure, the influence of covariates and extrapolation to special populations or drug-drug interactions, using physiologically-based PK (PBPK) modeling. Here we present the current state of PK modeling in precision dosing of anticancer drugs and illustrate its utility, based on an extensive literature review and numerous case examples from both pharmaceutical industry and healthcare focused research. While some great progress has been made in implementing model-informed dosage guidance in the drug label and much research has been carried out to address clinically relevant dosing questions, the uptake of MIPD has been modest in healthcare. The success of PK modeling in industry has been made possible through collaborative efforts between regulators, industry and academia. Collaboration between academia, healthcare and industry, and financial support for research into patient benefit, cost-benefit and clinical effectiveness of these approaches is imperative for wider adaption of PK modeling in precision dosing of anticancer drugs

Background Dabigatran etexilate (DABE) has been suggested as a clinical probe for intestinal P-glycoprotein (P-gp)-mediated drug-drug interaction (DDI) studies and, as an alternative to digoxin. Clinical DDI data with various P-gp inhibitors demonstrated a dose-dependent inhibition of P-gp with DABE. The aims of this study were to develop a joint DABE (prodrug)-dabigatran reduced physiologically-based-pharmacokinetic (PBPK) model and to evaluate its ability to predict differences in P-gp DDI magnitude between a microdose and a therapeutic dose of DABE. Methods A joint DABE-dabigatran PBPK model was developed with a mechanistic intestinal model accounting for the regional P-gp distribution in the gastrointestinal tract. Model input parameters were estimated using DABE and dabigatran pharmacokinetic (PK) clinical data obtained after administration of DABE alone or with a strong P-gp inhibitor, itraconazole, and over a wide range of DABE doses (from 375 µg to 400 mg). Subsequently, the model was used to predict extent of DDI with additional P-gp inhibitors and with different DABE doses. Results The reduced DABE-dabigatran PBPK model successfully described plasma concentrations of both prodrug and metabolite following administration of DABE at different dose levels and when co-administered with itraconazole. The model was able to capture the dose dependency in P-gp mediated DDI. Predicted magnitude of itraconazole P-gp DDI was higher at the microdose (predicted vs. observed median fold-increase in AUC+inh/AUCcontrol (min-max) = 5.88 (4.29–7.93) vs. 6.92 (4.96–9.66) ) compared to the therapeutic dose (predicted median fold-increase in AUC+inh/AUCcontrol = 3.48 (2.37–4.84) ). In addition, the reduced DABE-dabigatran PBPK model predicted successfully the extent of DDI with verapamil and clarithromycin as P-gp inhibitors. Model-based simulations of dose staggering predicted the maximum inhibition of P-gp when DABE microdose was concomitantly administered with itraconazole solution; simulations also highlighted dosing intervals required to minimise the DDI risk depending on the DABE dose administered (microdose vs. therapeutic). Conclusions This study provides a modelling framework for the evaluation of P-gp inhibitory potential of new molecular entities using DABE as a clinical probe. Simulations of dose staggering and regional differences in the extent of intestinal P-gp inhibition for DABE microdose and therapeutic dose provide model-based guidance for design of prospective clinical P-gp DDI studies.

Inadequate dose selection for confirmatory trials is currently still one of the most challenging issues in drug development, as illustrated by high rates of late-stage attritions in clinical development and postmarketing commitments required by regulatory institutions. In an effort to shift the current paradigm in dose and regimen selection and highlight the availability and usefulness of well-established and regulatory-acceptable methods, the European Medicines Agency (EMA) in collaboration with the European Federation of Pharmaceutical Industries Association (EFPIA) hosted a multistakeholder workshop on dose finding (London 4-5 December 2014). Some methodologies that could constitute a toolkit for drug developers and regulators were presented. These methods are described in the present report: they include five advanced methods for data analysis (empirical regression models, pharmacometrics models, quantitative systems pharmacology models, MCP-Mod, and model averaging) and three methods for study design optimization (Fisher information matrix (FIM)-based methods, clinical trial simulations, and adaptive studies). Pairwise comparisons were also discussed during the workshop; however, mostly for historical reasons. This paper discusses the added value and limitations of these methods as well as challenges for their implementation. Some applications in different therapeutic areas are also summarized, in line with the discussions at the workshop. There was agreement at the workshop on the fact that selection of dose for phase III is an estimation problem and should not be addressed via hypothesis testing. Dose selection for phase III trials should be informed by well-designed dose-finding studies; however, the specific choice of method(s) will depend on several aspects and it is not possible to recommend a generalized decision tree. There are many valuable methods available, the methods are not mutually exclusive, and they should be used in conjunction to ensure a scientifically rigorous understanding of the dosing rationale.

Imatinib, a tyrosine kinase inhibitor, is the drug of choice for the treatment of chronic myeloid leukemia in Nigeria. Several studies have established interindividual and interpopulation variations in imatinib disposition although no pharmacokinetic study have been conducted in an African population since the introduction of the drug. This study explored a population pharmacokinetic approach to investigate the disposition of imatinib in Nigerians and examined the involvement of some covariates including genetic factors in the variability of the drug disposition with a view to optimize the use of the drug in this population. A total of 250 plasma concentrations from 126 chronic myeloid leukemia patients were quantified using a validated method. A population pharmacokinetic model was fitted to the data using NONMEM VII software, and the influences of 12 covariates were investigated. The mean population-derived apparent steady-state clearance, elimination half-life, area under the concentration-time curve over 24 hours, and volume of distribution were 17.2 ± 1.8 L/h., 12.05 ± 2.1 hours, 23.26 ± 0.6 μg·h/mL, and 299 ± 20.4 L, respectively. Whole blood count, ethnicity, CYP3A5*3, and ABCB1 C3435T were found to have significant influence on the apparent clearance, while the interindividual variability in clearance and interoccasion variability in bioavailability were 17.4% and 20.4%, respectively. There was a wide variability in apparent clearance and area under the curve compared to those reported in other populations. Thus, treatment with a standard dose of imatinib in this population may not produce the desired effect in most of the patients, whereas continuous exposure to a low drug concentration could lead to pharmacokinetic-derived resistance. The authors suggest the need for therapeutic drug monitoring-guided dose individualization in this population.

In physiologically based pharmacokinetic (PBPK) modelling, the large number of input parameters, limited amount of available data and the structural model complexity generally hinder simultaneous estimation of uncertain and/or unknown parameters. These parameters are generally subject to estimation. However, the approaches taken for parameter estimation vary widely. Global sensitivity analyses are proposed as a method to systematically determine the most influential parameters that can be subject to estimation. Herein, a global sensitivity analysis was conducted to identify the key drug and physiological parameters influencing drug disposition in PBPK models and to potentially reduce the PBPK model dimensionality. The impact of these parameters was evaluated on the tissue-to-unbound plasma partition coefficients (Kpus) predicted by the Rodgers and Rowland model using Latin hypercube sampling combined to partial rank correlation coefficients (PRCC). For most drug classes, PRCC showed that LogP and fraction unbound in plasma (fup) were generally the most influential parameters for Kpu predictions. For strong bases, blood:plasma partitioning was one of the most influential parameter. Uncertainty in tissue composition parameters had a large impact on Kpu and Vss predictions for all classes. Among tissue composition parameters, changes in Kpu outputs were especially attributed to changes in tissue acidic phospholipid concentrations and extracellular protein tissue:plasma ratio values. In conclusion, this work demonstrates that for parameter estimation involving PBPK models and dimensionality reduction purposes, less influential parameters might be assigned fixed values depending on the parameter space, while influential parameters could be subject to parameters estimation. QC 20200205

BackgroundThe first-line therapy for rheumatoid arthritis (RA) is weekly oral methotrexate (MTX) at low dosages (7.5–25 mg/week). However, ~40% of patients are non-adherent which may explain why some do not respond and need to start more expensive biological therapies. To monitor adherence more accurately and develop strategies to improve it, a validated objective MTX adherence test is required.ObjectiveTo develop and validate the diagnostic accuracy of a novel MTX adherence assay using high-performance liquid chromatography–selected reaction monitoring– mass spectrometry (HPLC-SRM-MS) based biochemical analysis of drug levels.Methods20 patients with RA underwent MTX pharmacokinetic assessment using HPLC-SRM-MS MTX plasma concentration analysis over a 6-day period. Directly observed therapy was the reference standard. Pharmacokinetic model validation was performed using independent plasma samples from real-world patients (n=50) with self-reported times of drug administration. Following assay optimisation, the sensitivity of the assay to detect adherence was established using samples from an observational cohort study (n=138).ResultsA two-compartment pharmacokinetic model was developed and validated. Simulations described the sensitivity required for MTX detection over 7 days; subsequent assay optimisation and retesting of samples confirmed that all patients were correctly identified as MTX adherers. Using real-world samples, the assays sensitivity was 95%.ConclusionNon-adherence to MTX is common and can have a significant effect on disease activity. HPLC-SRM-MS plasma analysis accurately detects MTX adherence. The validated objective test could easily be used in clinic to identify patients requiring adherence support.

Endogenous biomarkers can be clinically relevant tools for the assessment of transporter function in vivo and corresponding drug-drug interactions (DDIs). The aim of this study was to perform systematic evaluation of plasma data obtained for 20 endogenous molecules in the same healthy subjects (n = 8-12) in the absence and presence of organic anion transporting polypeptide (OATP) inhibitor rifampicin (600 mg, single dose). The extent of rifampicin DDI magnitude [the ratio of the plasma concentration-time area under the curve (AUCR)], estimated fraction transported (fT), and baseline variability was compared across the biomarkers and relative to rosuvastatin and coproporphyrin I (CPI). Out of the 20 biomarkers investigated tetradecanedioate (TDA), hexadecanedioate (HDA), glycocholic acid, glycodeoxycholic acid (GDCA), taurodeoxycholic acid (TDCA), and coproporphyrin III (CPIII) showed the high AUCR (2.1-8.5) and fT (0.5-0.76) values, indicative of substantial OATP1B-mediated transport. A significant positive correlation was observed between the individual GDCA and TDCA AUCRs and the magnitude of rosuvastatin-rifampicin interaction. The CPI and CPIII AUCRs were significantly correlated, but no clear trend was established with the rosuvastatin AUCR. Moderate interindividual variability (15%-62%) in baseline exposure and AUCR was observed for TDA, HDA, and CPIII. In contrast, bile acids demonstrated high interindividual variability (69%-113%) and significant decreases in baseline plasma concentrations during the first 4 hours. This comprehensive analysis in the same individuals confirms that none of the biomarkers supersede CPI in the evaluation of OATP1B-mediated DDI risk. Monitoring of CPI and GDCA/TDCA may be beneficial for dual OATP1B/sodium-taurocholate cotransporting polypeptide inhibitors with consideration of challenges associated with large inter- and intraindividual variability observed for bile acids. Benefit of monitoring combined biomarkers (CPI, one bile acid and one fatty acid) needs to be confirmed with larger data sets and against multiple OATP1B clinical probes and perpetrators.

Purpose Measures derived from longitudinal tumour size data have been increasingly utilised to predict survival of patients with solid tumours. The aim of this study was to examine the prognostic value of such measures for patients with metastatic pancreatic cancer undergoing gemcitabine therapy. Methods The control data from two Phase III studies were retrospectively used to develop (271 patients) and validate (398 patients) survival models. Firstly, 31 baseline variables were screened from the training set using penalised Cox regression. Secondly, tumour shrinkage metrics were interpolated for each patient by hierarchical modelling of the tumour size time-series. Subsequently, survival models were built by applying two approaches: the first aimed at incorporating model-derived tumour size metrics in a parametric model, and the second simply aimed at identifying empirical factors using Cox regression. Finally, the performance of the models in predicting patient survival was evaluated on the validation set. Results Depending on the modelling approach applied, albumin, body surface area, neutrophil, baseline tumour size and tumour shrinkage measures were identified as potential prognostic factors. The distributional assumption on survival times appeared to affect the identification of risk factors but not the ability to describe the training data. The two survival modelling approaches performed similarly in predicting the validation data. Conclusions A parametric model that incorporates model-derived tumour shrinkage metrics in addition to other baseline variables could predict reasonably well survival of patients with metastatic pancreatic cancer. However, the predictive performance was not significantly better than a simple Cox model that incorporates only baseline characteristics. Electronic supplementary material The online version of this article (doi:10.1007/s00280-016-2994-x) contains supplementary material, which is available to authorized users.

ObjectivesBeing able to predict which patients with COVID-19 are going to deteriorate is important to help identify patients for clinical and research practice. Clinical prediction models play a critical role in this process, but current models are of limited value because they are typically restricted to baseline predictors and do not always use contemporary statistical methods. We sought to explore the benefits of incorporating dynamic changes in routinely measured biomarkers, non-linear effects and applying ‘state-of-the-art’ statistical methods in the development of a prognostic model to predict death in hospitalised patients with COVID-19.DesignThe data were analysed from admissions with COVID-19 to three hospital sites. Exploratory data analysis included a graphical approach to partial correlations. Dynamic biomarkers were considered up to 5 days following admission rather than depending solely on baseline or single time-point data. Marked departures from linear effects of covariates were identified by employing smoothing splines within a generalised additive modelling framework.Setting3 secondary and tertiary level centres in Greater Manchester, the UK.Participants392 hospitalised patients with a diagnosis of COVID-19.Results392 patients with a COVID-19 diagnosis were identified. Area under the receiver operating characteristic curve increased from 0.73 using admission data alone to 0.75 when also considering results of baseline blood samples and to 0.83 when considering dynamic values of routinely collected markers. There was clear non-linearity in the association of age with patient outcome.ConclusionsThis study shows that clinical prediction models to predict death in hospitalised patients with COVID-19 can be improved by taking into account both non-linear effects in covariates such as age and dynamic changes in values of biomarkers.

Objectives Cetuximab dosing is based on body surface area (BSA), an approach that is associated with significant wastage due to available vial sizes. NHS England recently introduced an alternative strategy for cetuximab dosing based on dose banding. The aim of this work was to investigate approaches to cetuximab dosing to improve its cost-effectiveness. Methods A simulation study using a population pharmacokinetic model was used to assess the performance of dosing strategies using exposure, probability of target attainment and cost. Two dosage regimens (500 and 400/250 mg/m2) were investigated; 5% and 10% dose banding, fixed and optimised dosing strategies were evaluated and compared to BSA strategy. Key findings The percentage of the total cost associated with wastage for the 400/250 mg/m2 regimen were 8.75%, 5.13%, 3.61%, 9.2% and 0% for BSA; 5 and 10% bands; fixed and optimal strategies, respectively. Similar results were obtained for 500 mg/m2 regimen. In comparison with BSA strategy, other strategies have comparable or improved performance. Optimised strategy showed consistent performance and ensures equal exposure and probability of target attainment. Conclusions Cost-effectiveness of cetuximab treatment can be improved with alternative strategies by reducing wastage without compromising exposure.

Interleukin-1 receptor antagonist, a naturally-occurring antagonist to the pro-inflammatory cytokine Interleukin-1, is already in clinical use. In experimental models of stroke, Interleukin-1 receptor antagonist in cerebrospinal fluid has been associated with cerebral neuroprotection and in a phase I clinical trial in patients with subarachnoid haemorrhage it crosses the blood-cerebrospinal fluid barrier. The aims of the current work were to design a dose-ranging clinical study in patients and to analyse the plasma and cerebrospinal fluid data obtained using a population pharmacokinetic modelling approach. The study was designed using prior information: a published population pharmacokinetic model and associated parameter estimates. Simulations were carried out to identify combinations of intravenous bolus and 4 h infusion doses that could achieve a concentration of 100 ng/ml in cerebrospinal fluid within approximately 30 min. The most informative time points for plasma and cerebrospinal fluid were obtained prospectively; optimisation identified five sampling time points that were included in the 15 time points in the present study design. All plasma and cerebrospinal fluid concentration data from previous and current studies were combined for updated analysis. The result of the simulations showed that a dosage regimen of 500 mg intravenous bolus and 10 mg/kg/h could achieve the target concentration, however four other regimens that represent a stepwise increase in maximum concentration were also selected. Analysis of the updated data showed improvement in parameter accuracy and predictive performance of the model; the percentage relative standard errors for fixed and random-effects parameters were

Mavoglurant (MVG) is an antagonist at the metabotropic glutamate receptor-5 currently under clinical development at Novartis Pharma AG for the treatment of central nervous system diseases. The aim of this study was to develop and optimise a population whole-body physiologically-based pharmacokinetic (WBPBPK) model for MVG, to predict the impact of drug-drug interaction (DDI) and age on its pharmacokinetics. In a first step, the model was fitted to intravenous (IV) data from a clinical study in adults using a Bayesian approach. In a second step, the optimised model was used together with a mechanistic absorption model for exploratory Monte Carlo simulations. The ability of the model to predict MVG pharmacokinetics when orally co-administered with ketoconazole in adults or administered alone in 3-11 year-old children was evaluated using data from three other clinical studies. The population model provided a good description of both the median trend and variability in MVG plasma pharmacokinetics following IV administration in adults. The Bayesian approach offered a continuous flow of information from pre-clinical to clinical studies. Prediction of the DDI with ketoconazole was consistent with the results of a non-compartmental analysis of the clinical data (threefold increase in systemic exposure). Scaling of the WBPBPK model allowed reasonable extrapolation of MVG pharmacokinetics from adults to children. The model can be used to predict plasma and brain (target site) concentration-time profiles following oral administration of various immediate-release formulations of MVG alone or when co-administered with other drugs, in adults as well as in children.

AIMS: To extend the physiologically based pharmacokinetic (PBPK) model developed for 6-mercaptopurine to account for intracellular metabolism and to explore the role of genetic polymorphism in the TPMT enzyme on the pharmacokinetics of 6-mercaptopurine. METHODS: The developed PBPK model was extended for 6-mercaptopurine to account for intracellular metabolism and genetic polymorphism in TPMT activity. System and drug specific parameters were obtained from the literature or estimated using plasma or intracellular red blood cell concentrations of 6-mercaptopurine and its metabolites. Age-dependent changes in parameters were implemented for scaling, and variability was also introduced for simulation. The model was validated using published data. RESULTS: The model was extended successfully. Parameter estimation and model predictions were satisfactory. Prediction of intracellular red blood cell concentrations of 6-thioguanine nucleotide for different TPMT phenotypes (in a clinical study that compared conventional and individualized dosing) showed results that were consistent with observed values and reported incidence of haematopoietic toxicity. Following conventional dosing, the predicted mean concentrations for homozygous and heterozygous variants, respectively, were about 10 times and two times the levels for wild-type. However, following individualized dosing, the mean concentration was around the same level for the three phenotypes despite different doses. CONCLUSIONS: The developed PBPK model has been extended for 6-mercaptopurine and can be used to predict plasma 6-mercaptopurine and tissue concentration of 6-mercaptopurine, 6-thioguanine nucleotide and 6-methylmercaptopurine ribonucleotide in adults and children. Predictions of reported data from clinical studies showed satisfactory results. The model may help to improve 6-mercaptopurine dosing, achieve better clinical outcome and reduce toxicity.

Pembrolizumab and nivolumab are highly selective anti-programmed cell death 1 (PD-1) antibodies approved for the treatment of advanced malignancies. Variable exposure and significant wastage have been associated with body size dosing of monoclonal antibodies (mAbs). The following dosing strategies were evaluated using simulations: body weight, dose banding, fixed dose, and pharmacokinetic (PK)-based methods. The relative cost to body weight dosing for band, fixed 150 mg and 200 mg, and PK-derived strategies were -15%, -25%, + 7%, and -16% for pembrolizumab and -8%, -6%, and -10% for band, fixed, and PK-derived strategies for nivolumab, respectively. Relative to mg/kg doses, the median exposures were -1.0%, -4.6%, + 27.1%, and +3.0% for band, fixed 150 mg, fixed 200 mg, and PK-derived strategies, respectively, for pembrolizumab and -3.1%, + 1.9%, and +1.4% for band, fixed 240 mg, and PK-derived strategies, respectively, for nivolumab. Significant wastage can be reduced by alternative dosing strategies without compromising exposure and efficacy.

OBJECTIVEAneurysmal subarachnoid hemorrhage (aSAH) is a devastating cerebrovascular event with long-term morbidity and mortality. Patients who survive the initial bleeding are likely to suffer further early brain injury arising from a plethora of pathological processes. These may result in a worsening of outcome or death in approximately 25% of patients and may contribute to longer-term cognitive dysfunction in survivors. Inflammation, mediated by the cytokine interleukin-1 (IL-1), is an important contributor to cerebral ischemia after diverse forms of brain injury, including aSAH. Its effects are attenuated by its naturally occurring antagonist, IL-1 receptor antagonist (IL-1Ra [anakinra]). The authors hypothesized that administration of additional subcutaneous IL-1Ra would reduce inflammation and associated plasma markers associated with poor outcome following aSAH.METHODSThis was a randomized, open-label, single-blinded study of 100 mg subcutaneous IL-1Ra, administered twice daily in patients with aSAH, starting within 3 days of ictus and continuing until 21 days postictus or discharge from the neurosurgical center, whichever was earlier. Blood samples were taken at admission (baseline) and at Days 3–8, 14, and 21 postictus for measurement of inflammatory markers. The primary outcome was difference in plasma IL-6 measured as area under the curve between Days 3 and 8, corrected for baseline value. Secondary outcome measures included similar area under the curve analyses for other inflammatory markers, plasma pharmacokinetics for IL-1Ra, and clinical outcome at 6 months.RESULTSInterleukin-1Ra significantly reduced levels of IL-6 and C-reactive protein (p < 0.001). Fibrinogen levels were also reduced in the active arm of the study (p < 0.002). Subcutaneous IL-1Ra was safe, well tolerated, and had a predictable plasma pharmacokinetic profile. Although the study was not powered to investigate clinical effect, scores of the Glasgow Outcome Scale–extended at 6 months were better in the active group; however, this outcome did not reach statistical significance.CONCLUSIONSSubcutaneous IL-1Ra is safe and well tolerated in aSAH. It is effective in reducing peripheral inflammation. These data support a Phase III study investigating the effect of IL-1Ra on outcome following aSAH.Clinical trial registration no.: EudraCT: 2011-001855-35 (www.clinicaltrialsregister.eu)

Objective The minimal model is used to estimate insulin sensitivity in patients with diabetes, following an intravenous glucose tolerance test (IVGTT). Issues have been reported regarding parameter estimation, including correlation between insulin sensitivity and action parameters. The objective was to reduce these issues, by modifying the input of glucose in the test. Methods Data were available for 24 volunteers following an IVGTT and glucose clamp test. Correlation between parameters was explored using likelihood heatmaps. An integrated glucose–insulin model was used to simulate glucose and insulin concentrations following new glucose inputs. The improved input for the test was selected by finding the minimum inverse of the determinant of the Fisher information matrix. Key findings When the minimal model was fitted to the IVGTT data, there was clear correlation between the insulin parameters. With the glucose clamp, all parameters were correlated and badly estimated. The modified input, a bolus dose followed by constant infusion, resulted in improvement in parameter estimation and reduction in parameter correlation. Conclusions It is possible to reduce the issues with parameter estimation in the minimal model by modifying the glucose input, leading to a simplified test deign and a reduction in the total amount of glucose infused.

Item does not contain fulltext Patient groups prone to polypharmacy and special subpopulations are susceptible to suboptimal treatment. Refined dosing in special populations is imperative to improve therapeutic response and/or lowering the risk of toxicity. Model-informed precision dosing (MIPD) may improve treatment outcomes by achieving the optimal dose for an individual patient. There is, however, relatively little published evidence of large-scale utility and impact of MIPD, where it is often implemented as local collaborative efforts between academia and healthcare. This article highlights some successful applications of bringing MIPD to clinical care and proposes strategies for wider integration in healthcare. Considerations are brought up herein that will need addressing to see MIPD become "widespread clinical practice," among those, wider interdisciplinary collaborations and the necessity for further evidence-based efficacy and cost-benefit analysis of MIPD in healthcare. The implications of MIPD on regulatory policies and pharmaceutical development are also discussed as part of the roadmap.

Valproic acid is an anti-convulscant drug that is widely used in the treatment of different types of epilepsy and since its introduction the clinical use has increased rapidly both as a sole agent and in combination therapies. The mechanism of action has been linked to blockade of voltage-dependent sodium channels and potentiation of GABAergic transmission. The most widely used route of administration of Valproic acid is oral, although it can also be given intravenously and rectally and its pharmacokinetics has been studied extensively. The aim of this work was to develop a physiologically based pharmacokinetic model for plasma and tissue/organ prediction in children and adults following intravenous and oral dosing of Valproic acid. The plasma/tissue concentration profile will be used for clinical trial simulation in Dravet syndrome, a rare form of epilepsy in children where the combination of Valproic acid, stiripentol and clobazam has shown remarkable results. A physiologically based pharmacokinetic model was developed with compartments for gut lumen, enterocyte, gut tissue, systemic blood, kidney, liver, brain, spleen, muscle and rest of body. System and drug specific parameters for the model were obtained from the literature from in vitro and in vivo experiments. The model was initially developed for adults and scaled to children using age-dependent changes in anatomical and physiological parameters and ontogeny functions for enzyme maturation assuming the same elimination pathways in adults and children. The results from the model validation showed satisfactory prediction of plasma concentration both in terms of mean prediction and variability in children and adults following intravenous and oral dosing especially after single doses. The model also adequately predicts clearance in children. Due to limited distribution of Valproic acid into tissues, the concentration in plasma is about 8-9 times higher than tissues/organs. The model could help to improve clinical outcome in the treatment of Dravet syndrome through dose optimisation.

To develop a physiologically based pharmacokinetic model in adults and children for clobazam, its active metabolite norclobazam and stiripentol and to account for significant clinical interaction that has been reported when clobazam and stiripentol are co-administered. A PBPK model with ten compartments was developed. An in vitro-in vivo extrapolation technique was used to scale clearance in children for clobazam and norclobazam and clearance parameters for stiripentol were obtained from fitting. Other drug and system parameters were obtained from the literature. The tissue/blood partition coefficients adequately predict observed volume of distribution for clobazam and stiripentol. In a clinical study in children where clobazam was administered alone and co-administered with stiripentol, the predicted and observed minimum concentration at steady state (mean and 95% confidence interval) during clobazam monotherapy were 0.19 (0.05–0.49 mg/L) and 0.20 (0.17–0.23 mg/L), respectively, and predicted and observed norclobazam concentrations were 0.49 (0.16–1.38 mg/L) and 0.95 (0.91–0.99 mg/L), respectively. From an interaction study with stiripentol the predicted stiripentol concentration was 10.12 (2.51–39.36 mg/L) and the observed concentration was 10.0 (8.3–11.7 mg/L); the predicted clobazam concentration was 0.29 (0.07–1.05 mg/L) and the observed concentration was 0.31 (0.24–0.38 mg/L); and the predicted norclobazam concentration was 2.30 (0.45–5.53 mg/L) and the observed concentration was 4.32 (3.77–4.87 mg/L). The PBPK model adequately described observed data and the extent of interaction between clobazam/norclobazam and stiripentol.

6-mercaptopurine (6-MP) is a purine antimetabolite and prodrug that undergoes extensive intracellular metabolism to produce thionucleotides, active metabolites which have cytotoxic and immunosuppressive properties. Combination therapies involving 6-MP and methotrexate have shown remarkable results in the cure of childhood acute lymphoblastic leukaemia (ALL) in the last 30 years. 6-MP undergoes very extensive intestinal and hepatic metabolism following oral dosing due to the activity of xanthine oxidase leading to very low and highly variable bioavailability and methotrexate has been demonstrated as an inhibitor of xanthine oxidase. Despite the success recorded in the use of 6-MP in ALL, there is still lack of effect and life threatening toxicity in some patients due to variability in the pharmacokinetics of 6-MP. Also, dose adjustment during treatment is still based on toxicity. The aim of the current work was to develop a mechanistic model that can be used to simulate trial outcomes and help to improve dose individualisation and dosage regimen optimisation. A physiological based pharmacokinetic model was proposed for 6-MP, this model has compartments for stomach, gut lumen, enterocyte, gut tissue, spleen, liver vascular, liver tissue, kidney vascular, kidney tissue, skin, bone marrow, thymus, muscle, rest of body and red blood cells. The model was based on the assumption of the same elimination pathways in adults and children. Parameters of the model include physiological parameters and drug-specific parameter which were obtained from the literature or estimated using plasma and red blood cell concentration data. Age-dependent changes in parameters were implemented for scaling and variability was also introduced on the parameters for prediction. Inhibition of 6-MP first-pass effect by methotrexate was implemented to predict observed clinical interaction between the two drugs. The model was developed successfully and plasma and red blood cell concentrations were adequately predicted both in terms of mean prediction and variability. The predicted interaction between 6-MP and methotrexate was slightly lower than the reported clinical interaction between the two drugs. The model can be used to predict plasma and tissue concentration in adults and children following oral and intravenous dosing and may ultimately help to improve treatment outcome in childhood ALL patients.

Methotrexate is an antimetabolite and antifolate drug that is widely used in the treatment of malignancies and auto-immune disorders. In childhood acute lymphoblastic leukaemia, methotrexate is often combined with 6-mercaptopurine and both of them have been shown to be very effective for maintenance of remission. Large variability in the pharmacokinetics of methotrexate has led to increasing use of therapeutic drug monitoring in its clinical use to identify patients with high risk of toxicity and optimise clinical outcome. A physiologically based pharmacokinetic model was developed for methotrexate for oral and intravenous dosing and adults and paediatric use. The model has compartments for stomach, gut lumen, enterocyte, gut tissue, spleen, liver vascular, liver tissue, gall bladder, systemic plasma, red blood cells, kidney vascular, kidney tissue, skin, bone marrow, thymus, muscle and rest of body. A mechanistic model was also developed for the kidney to account for renal clearance of methotrexate via filtration and secretion. Variability on system and drug specific parameters was incorporated in the model to reflect observed clinical data and assuming the same pathways in adults and children, age-dependent changes in body size, organ volumes and plasma flows, the model was scaled to children. The model was developed successfully for adults and parameters such as net secretion clearance, biliary transit time and red blood cell distribution and binding parameters were estimated from published adult profiles. A relationship between fraction absorbed and dose using reported mean bioavailability data in the literature was also established. The model also incorporates non-linear binding in some tissues that has been described in the literature. Predictions using this model provide adequate description of observed plasma concentration data in adults and children. The model can be used to predict plasma and tissue concentrations of methotrexate following intravenous and oral dosing in adults and children and therefore help to improve clinical outcome.