Development and validation of a methotrexate adherence assay.

BACKGROUND: The 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. OBJECTIVE: To 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. METHODS: 20 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). RESULTS: A 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%. CONCLUSION: Non-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.

Modelling of atorvastatin pharmacokinetics and the identification of the effect of a BCRP polymorphism in the Japanese population.

AIM: Ethnicity plays a modulating role in atorvastatin pharmacokinetics (PK), with Asian patients reported to have higher exposure compared with Caucasians. Therefore, it is difficult to safely extrapolate atorvastatin PK data and models across ethnic groups. This work aims to develop a population PK model for atorvastatin and its pharmacologically active metabolites specifically for the Japanese population. Subsequently, it aimed to identify genetic polymorphisms affecting atorvastatin PK in this population. METHODS: Atorvastatin acid (ATA) and ortho-hydroxy-atorvastatin acid (o-OH-ATA) plasma concentrations, clinical/demographic characteristics and genotypes for 18 (3, 3, 1, 1, 7, 2 and 1 in the ABCB1, ABCG2, CYP3A4, CYP3A5, SLCO1B1, SLCO2B1 and PPARA genes, respectively) genetic polymorphisms were collected from 27 Japanese individuals (taking 10 mg atorvastatin once daily) and analysed using a population PK modelling approach. RESULTS: The population PK model developed (one-compartment for ATA linked through metabolite formation to an additional compartment describing the disposition of o-OH-ATA) accurately described the observed data and the associated population variability. Our analysis suggested that patients carrying one variant allele for the rs2622604 polymorphism (ABCG2) show a 55% (95% confidence interval: 16-131%) increase in atorvastatin oral bioavailability relative to the value in individuals without the variant allele. CONCLUSION: The current work reports the identification in the Japanese population of a BCRP polymorphism, not previously associated with the PK of any statin, that markedly increases ATA and o-OH-ATA exposure. The model developed may be of clinical importance to guide dosing recommendations tailored specifically for the Japanese.

Model-based evaluation of the impact of formulation and food intake on the complex oral absorption of mavoglurant in healthy subjects.

PURPOSE: To compare the pharmacokinetics of intravenous (IV), oral immediate-release (IR) and oral modified-release (MR) formulations of mavoglurant in healthy subjects, and to assess the food effect on the MR formulation's input characteristics. METHODS: Plasma concentration-time data from two clinical studies in healthy volunteers were pooled and analysed using NONMEM®. Drug entry into the systemic circulation was modelled using a sum of inverse Gaussian (IG) functions as an input rate function, which was estimated specifically for each formulation and food state. RESULTS: Mavoglurant pharmacokinetics was best described by a two-compartment model with a sum of either two or three IG functions as input function. The mean absolute bioavailability from the MR formulation (0.387) was less than from the IR formulation (0.436). The MR formulation pharmacokinetics were significantly impacted by food: bioavailability was higher (0.508) and the input process was shorter (complete in approximately 36 versus 12 h for the fasted and fed states, respectively). CONCLUSIONS: Modelling and simulation of mavoglurant pharmacokinetics indicate that the MR formulation might provide a slightly lower steady-state concentration range with lower peaks (possibly better drug tolerance) than the IR formulation, and that the MR formulation's input properties strongly depend on the food conditions at drug administration.

Application of a Bayesian approach to physiological modelling of mavoglurant population pharmacokinetics.

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.

Incorporation of stochastic variability in mechanistic population pharmacokinetic models: handling the physiological constraints using normal transformations.

The utilisation of physiologically-based pharmacokinetic models for the analysis of population data is an approach with progressively increasing impact. However, as we move from empirical to complex mechanistic model structures, incorporation of stochastic variability in model parameters can be challenging due to the physiological constraints that may arise. Here, we investigated the most common types of constraints faced in mechanistic pharmacokinetic modelling and explored techniques for handling them during a population data analysis. An efficient way to impose stochastic variability on the parameters of interest without neglecting the underlying physiological constraints is through the assumption that they follow a distribution with support and properties matching the underlying physiology. It was found that two distributions that arise through transformations of the normal, the logit-normal generalisation and the logistic-normal, are excellent for such an application as not only they can satisfy the physiological constraints but also offer high flexibility during characterisation of the parameters' distribution. The statistical properties and practical advantages/disadvantages of these distributions for such an application were clearly displayed in the context of different modelling examples. Finally, a simulation study clearly illustrated the practical gains of the utilisation of the described techniques, as omission of population variability in physiological systems parameters leads to a biased/misplaced stochastic model with mechanistically incorrect variance structure. The current methodological work aims to facilitate the use of mechanistic/physiologically-based models for the analysis of population pharmacokinetic clinical data.

Predicting survival of pancreatic cancer patients treated with gemcitabine using longitudinal tumour size data.

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.

Reduction of a Whole-Body Physiologically Based Pharmacokinetic Model to Stabilise the Bayesian Analysis of Clinical Data.

Whole-body physiologically based pharmacokinetic (PBPK) models are increasingly used in drug development for their ability to predict drug concentrations in clinically relevant tissues and to extrapolate across species, experimental conditions and sub-populations. A whole-body PBPK model can be fitted to clinical data using a Bayesian population approach. However, the analysis might be time consuming and numerically unstable if prior information on the model parameters is too vague given the complexity of the system. We suggest an approach where (i) a whole-body PBPK model is formally reduced using a Bayesian proper lumping method to retain the mechanistic interpretation of the system and account for parameter uncertainty, (ii) the simplified model is fitted to clinical data using Markov Chain Monte Carlo techniques and (iii) the optimised reduced PBPK model is used for extrapolation. A previously developed 16-compartment whole-body PBPK model for mavoglurant was reduced to 7 compartments while preserving plasma concentration-time profiles (median and variance) and giving emphasis to the brain (target site) and the liver (elimination site). The reduced model was numerically more stable than the whole-body model for the Bayesian analysis of mavoglurant pharmacokinetic data in healthy adult volunteers. Finally, the reduced yet mechanistic model could easily be scaled from adults to children and predict mavoglurant pharmacokinetics in children aged from 3 to 11 years with similar performance compared with the whole-body model. This study is a first example of the practicality of formal reduction of complex mechanistic models for Bayesian inference in drug development.

Development and validation of a simple reversed-phase HPLC method for the determination of camptothecin in animal organs following administration in solid lipid nanoparticles.

A simple, sensitive and specific high-performance liquid chromatography (HPLC) assay for the quantification of camptothecin (CPT), a potent anticancer candidate, incorporated into solid lipid nanoparticles (SLN) in several rat organs (brain, heart, kidneys liver, lung, spleen) and serum was developed and validated. The sample pre-treatment involved organs homogenisation followed by CPT extraction. The samples were injected onto an analytical reversed-phase (RP) Mediterranea™ Sea18 column maintained at 30°C. The chromatographic separation was achieved by gradient elution consisting of triethyamine buffer pH 5.5 and acetonitrile at a flow rate of 1.2 mL/min in 16 min of run time and retention time of 9.8 min (lactone). Fluorescence detection was used at the excitation and emission of 360 and 440 nm, respectively. The calibration curves in the different organs, serum and in PB3 were linear (r(2)>0.9999) over CPT concentrations ranging from 1 to 200 ng/mL or 0.5 to 200 ng/mL (n=6), respectively. The method was shown to be specific, accurate (between 94.4±4.5% and 108.9±0.6%) and precise at the intra-day and inter-day levels as reflected by the coefficient of variation (CV<6.3%) at three different concentrations (10, 50 and 100 ng/mL) in all matrices. Stability studies showed that CPT was stable in all matrices after 24h of incubation at room temperature (RT), after 24 h in the autosampler or after three freeze/thaw cycles. The mean recoveries of CPT in suspension, loaded into SLN and in a physical mixture with SLN at three concentrations of 10, 50 and 200 ng/mL were higher than 86.4%. The detection limit (DL) was ≤0.2 ng/mL and the quantification limit (QL) was ≤0.5 ng/mL. The method developed is reliable, precise and accurate and can be used in the determination of CPT amount in rat organ samples after i.v. administration of CPT in suspension, in physical mixture with SLN and incorporated in SLN.