Determination of plasma concentration reference ranges for oral aripiprazole, olanzapine, and quetiapine.

BACKGROUND: Schizophrenia is a common disease which is commonly managed using antipsychotic medications (APS). Inadequate response and lack of adherence often prevent optimal therapeutic effectiveness. Monitoring APS concentrations can be useful to help improve outcomes for the patient. AIMS: The aim of this study was to develop "reference ranges" for oral aripiprazole, olanzapine, and quetiapine to allow clinicians to understand expected variability in patients treated with APS. The reference ranges were constructed to account for different oral doses, sampling times, and variability both between, and within, subjects. METHODS: Population pharmacokinetic models were used to simulate plasma concentrations over time under different doses and population demographics. The references were validated against external data both numerically and graphically. RESULTS: Reference ranges for oral aripiprazole, olanzapine, and quetiapine were derived and successfully validated against the external data. The 80% reference range for aripiprazole following a 2-mg oral dose was 14.7-41.6 ng/mL 0-4 h post dose and 10.6-37.1 ng/mL 20-24 h post dose. These ranges increased to 221-624 ng/mL 0-4 h post dose following administration of a 30-mg dose, and 159-557 ng/mL 20-24 h post dose. The 80% reference range 0-4 h post dose was 22.5-67.1 ng/mL following a 15-mg dose once daily of oral olanzapine, and 179-768 ng/mL following a 150-mg dose once daily of oral quetiapine. CONCLUSIONS: Comparing individual patients' APS levels with reference ranges, along with a full clinical assessment, could provide important insights to help a clinician optimize APS therapy.

Simplification of a pharmacokinetic model for red blood cell methotrexate disposition.

PURPOSE: A pharmacokinetic (PK) model is available for describing the time course of the concentrations of methotrexate (MTX or MTXGlu1) and its active polyglutamated metabolites (MTXGlu2-5) in red blood cells (RBCs). In this study, we aimed to simplify the MTX PK model and to optimise the blood sampling schedules for use in future studies. METHODS: A proper lumping technique was used to simplify the original MTX RBC PK model. The sum of predicted RBC MTXGlu3-5 concentrations in both the simplified and original models was compared. The sampling schedules for MTXGlu3-5 or all MTX polyglutamates in RBCs were optimised using the Population OPTimal design (POPT) software. RESULTS: The MTX RBC PK model was simplified into a three-state model. The maximum of the absolute value of relative difference in the sum of predicted RBC MTXGlu3-5 concentrations over time was 6.3 %. A five blood sample design was identified for estimating parameters of the simplified model. CONCLUSIONS: This study illustrates the application of model simplification processes to an existing model for MTX RBC PK. The same techniques illustrated in our study may be adopted by other studies with similar interest.

Comparison of intracellular methotrexate kinetics in red blood cells with the kinetics in other cell types.

AIM: To assess the similarities in intracellular pharmacokinetics (PK) of methotrexate (MTX) in red blood cells (RBCs) and other cell lines. METHODS: Three previously published PK models for intracellular MTX and MTX polyglutamate (MTXGlu2-5 ) concentrations were used: (i) a model for the kinetics in RBCs, (ii) a model for the kinetics in human breast cancer cells (HBCCs) and (iii) a model for the kinetics in various white blood cell (WBC) lines. All three models were used to simulate the response in a typical individual receiving 10 mg oral MTX once weekly and the predicted steady-state concentrations (Css ) and time to Css (tss ) were compared. RESULTS: The HBCC model showed a lower Css for MTXGlu2 and 3 and higher Css for MTXGlu4 and 5 compared with the RBC PK model, while tss and overall intracellular MTX exposure appeared similar. The WBC PK model showed much lower Css for the parent MTXGlu1 and of tss for all MTXGlun , as well as a much lower cumulative Css for MTXGlu2-7 for the majority of the WBC cell lines. CONCLUSION: RBC kinetics of MTX differ from the kinetics in other cell types such as WBCs and HBCCs to a variable degree. It is possible that similarly diverse profiles may exist across other cell lines, including those on the causal path in rheumatoid arthritis. Hence, there may not necessarily be a clear link between RBC MTX concentrations and disease control in rheumatoid arthritis.

Parameterisation affects identifiability of population models.

Identifiability is an important aspect of model development. In this work, using a simple one compartment population pharmacokinetic model, we show that identifiability of the variances of the random effects parameters are affected by the parameterisation of the fixed effects parameters.

A semi-mechanistic red blood cell survival model provides some insight into red blood cell destruction mechanisms.

Most mathematical models developed for the survival of haematological cell populations, in particular red blood cells (RBCs), follow the principle of parsimony. They focus on the predominant destruction mechanism of age-related cell death (senescence) and do not account for within subject variability in the RBC lifespan. However, assessment of the underlying physiological destruction mechanisms can be of interest in pathological conditions that affect RBC survival, for example sickle cell anaemia or anaemia of chronic kidney disease. We have previously proposed a semi-mechanistic RBC survival model which accounts for four different types of RBC destruction mechanisms. In this work, it is shown that the proposed model in combination with informative RBC survival data is able to provide a deeper insight into RBC destruction mechanisms. The proposed model was applied in a non-linear mixed effect modelling framework to biotin derived RBC survival data available from literature. Three mechanisms were estimable based on the available data of twelve subjects, including random destruction, senescence and destruction due to delayed failure. It was possible to identify three subjects with a decreased RBC survival in the study population. These three subjects all showed differences in the contribution of the estimated destruction mechanisms: an increased random destruction, versus an accelerated senescence, versus a combination of both.

Bounded integer model-based analysis of psoriasis area and severity index in patients with moderate-to-severe plaque psoriasis receiving BI 730357.

BI 730357 is investigated as an oral treatment of plaque psoriasis. We analyzed the impact of three dosage regimens on the Psoriasis Area and Severity Index (PASI) response with modeling based on phase I and II data from 109 healthy subjects and 274 patients with moderate-to-severe plaque psoriasis. The pharmacokinetics (PK) was characterized by a two-compartment model with dual absorption paths and a first-order elimination. Higher baseline C-reactive protein was associated with lower clearance and patients generally had lower clearance compared with healthy subjects. A bounded integer PK/pharmacodynamic model characterized the effect on the observed PASI. The maximum drug effect was largest for patients with no prior biologic use, smaller for patients with prior use of non-interleukin-17 inhibitors, and smallest for patients with prior interleukin-17 inhibitor use. The models allowed robust simulation of large patient populations, predicting a plateau in PASI outcomes for BI 730357 exposure above 2000 nmol/L.

A pharmacokinetic model for the glycation of albumin.

Glycated haemoglobin (HbA1c) concentrations can be falsely lowered in circumstances when red blood cell (RBC) survival is reduced, e.g. in patients with chronic kidney disease (CKD). Glycated albumin (GA) has been suggested as an alternative marker of glycaemic control in these patients since it is independent of the RBC life span. The primary aim of this work was to develop a pharmacokinetic model that describes the time course of GA. The secondary aim was to assess the performance of GA as marker for glycaemic control in comparison to HbA1c based on simulations. For the second aim, three different scenarios were considered in the simulations: 1) assessment of the effect of large intra-day fluctuations in mean blood glucose on GA concentrations, 2) initiation of antidiabetic treatment on the GA profile, and 3) a hypothetical phase II study for a new antidiabetic compound. The GA model, as well as a previously developed HbA1c model described literature data well. GA concentrations appear to be stable even in the presence of high intra-day fluctuations in mean blood glucose concentrations. Simulation of a decrease in mean blood glucose concentrations resulted in a faster change in GA compared to HbA1c. GA also provided a time to 90 % power of the effect of a hypothetical antidiabetic drug that was 16 days shorter than when using HbA1c. These results indicate that GA could be used as alternative marker to assess blood glucose control in diabetic patients with CKD and also to follow an individual patient over time.

A statistical model for red blood cell survival.

A statistical model for the survival time of red blood cells (RBCs) with a continuous distribution of cell lifespans is presented. The underlying distribution of RBC lifespans is derived from a probability density function with a bathtub-shaped hazard curve, and accounts for death of RBCs due to senescence (age-dependent increasing hazard rate) and random destruction (constant hazard), as well as for death due to initial or delayed failures and neocytolysis (equivalent to early red cell mortality). The model yields survival times similar to those of previously published studies of RBC survival and is easily amenable to inclusion of drug effects and haemolytic disorders.

Evaluation of red blood cell labelling methods based on a statistical model for red blood cell survival.

The aim of this work is to compare different labelling methods that are commonly used to estimate the lifespan of red blood cells (RBCs), e.g. in anaemia of renal failure, where the effect of treatment with erythropoietin depends on the lifespan of RBCs. A previously developed model for the survival time of RBCs that accounts for plausible physiological processes of RBC destruction was used to simulate ideal random and cohort labelling methods for RBCs, as well as the flaws associated with these methods (e.g. reuse of label and loss of the label from the surviving RBCs). Random labelling with radioactive chromium and cohort labelling using heavy nitrogen were considered. Blood sampling times were determined for RBC survival studies using both labelling methods by applying the theory of optimal design. It was assessed whether the underlying parameter values of the model are estimable from these studies, and the precision of the parameter estimates were calculated. In theory, parameter estimation would be possible for both types of ideal labelling methods without flaws. However, flaws associated with random labelling are significant and not all parameters controlling RBC survival in the model can be estimated with good precision. In contrast, cohort labelling shows good precision in the parameter estimates even in the presence of reuse and prolonged incorporation of the label. A model based analysis of RBC survival studies is recommended in future to account for limitations in methodology as well as likely causes of RBC destruction.

Modeling red blood cell survival data.

Anaemia of chronic kidney disease (CKD) is a common complication in patients with renal impairment, especially in end-stage renal failure. As well as erythropoietin deficiency, decreased red blood cell survival is a contributing factor. However, it remains unclear which mechanism underlies the altered survival of red blood cells (RBCs). In this work a previously developed statistical model for RBC survival was applied to clinical data obtained from 14 patients with CKD undergoing hemodialysis as well as 14 healthy controls using radioactive chromium (5¹Cr) as random labelling method. A classical two-stage approach and a full population analysis were applied to estimate the key parameters controlling random destruction and senescence in the model. Estimating random destruction was preferred over estimating an accelerated senescence in both approaches and both groups as it provided the better fit to the data. Due to significant nonspecific random loss of the label from the cells that cannot be quantified directly only a relative RBC survival can be obtained from data using 5¹Cr as labelling method. Nevertheless, RBC survival was found to be significantly reduced in CKD patients compared to the controls with a relative reduction of 20-30% depending on the analysis method used.

Parametric Time-to-Event Model for Acute Exacerbations in Idiopathic Pulmonary Fibrosis.

We describe a parametric time-to-event model for idiopathic pulmonary fibrosis (IPF) exacerbations and identify predictors of exacerbation risk using data obtained for the tyrosine-kinase inhibitor nintedanib in two phase III studies (INPULSIS-1/2). Parametric survival analysis was performed on time to first exacerbation (censoring on day 372), with univariate analysis to select statistically significant covariates (P = 0.05). Multivariate covariate models were developed using stepwise covariate modeling with forward inclusion (P = 0.05) and backward elimination (P = 0.01). Sixty-three first exacerbation events were reported across 1,061 subjects in the INPULSIS studies. Baseline and decline of forced vital capacity (FVC)/percent-predicted FVC (%pFVC), supplemental oxygen use, baseline CO diffusing capacity and age were statistically significant in the univariate analysis. The final covariate model included decline in FVC to week 52, baseline %pFVC, supplemental oxygen use, and age. The developed model may be used to identify patients at high risk of IPF exacerbations and accelerate development of novel treatments.

A human challenge model for respiratory syncytial virus kinetics, the pharmacological effect of a novel fusion inhibitor, and the modelling of symptoms scores.

Respiratory syncytial virus (RSV) causes acute lower respiratory tract infections, and is a major cause of hospital admissions and death in young children. Limited treatments currently exist that can prevent or minimise exacerbation of the disease. The aims of this work were: 1) to develop a population pharmacodynamic model to describe RSV kinetics (RSVK) in nasal lavage, 2) evaluate the impact of an investigational fusion inhibitor, JNJ-53718678, on RSVK, and 3) determine the relationship between RSVK and symptoms scores. The best model to fit the RSVK data was a target-cell limited viral kinetics model previously developed for influenza A infections (Baccam et al., 2006), which included a series of compartments for infected, non-producing and infected, and producing cell populations. The model was adapted to account for longer incubation times seen in RSV, by including 4 additional transit compartments, with the virus elimination rate constant and initial number of target cells fixed to literature values to ensure model parameter identifiability. Between-subject variability was included on the infection rate constant and virus production rate constant. The effect of JNJ-53718678 on RSVK was best described by a non-dose dependent transformation of the infectious virions into a non-infectious state, with a proportional odds model successfully describing symptoms scores, using individual model predicted viral loads as predictor.

Assessment of the relationship between methotrexate polyglutamates in red blood cells and clinical response in patients commencing methotrexate for rheumatoid arthritis.

BACKGROUND AND OBJECTIVES: Therapeutic drug monitoring in patients with rheumatoid arthritis (RA) receiving methotrexate (MTX, MTXGlu1) has not been established. In this study, we aim to explore the relationship between red blood cell (RBC) concentrations of MTX and its polyglutamate metabolites (MTXGlu(n); n = 2, 3, 4, 5) and clinical response in RA patients commencing MTX. METHODS: The binding activity of MTXGlu(n) to three putative enzymes involved in the MTX mechanism of action­dihydrofolate reductase, thymidylate synthase, and 5-aminoimidazole-4-carboxamide ribonucleotide transformylase­was simulated. RBC MTXGlu(n) concentrations that gave the highest inhibition activity across all three enzymes were linked with the disease activity score DAS28-3v (C-reactive protein [CRP]). A population pharmacokinetic-pharmacodynamic model was developed to describe the relationship between RBC MTX polyglutamate concentrations and clinical response in 12 RA patients commencing MTX. RESULTS: The highest inhibition activity was with RBC MTXGlu(3-5). These polyglutamates were further evaluated for their relationship with DAS28-3v (CRP). Three of the 12 patients had a high DAS28-3v (CRP) at baseline (mean = 6.1) and showed a delayed response to MTX treatment. The remaining nine patients with a lower DAS28-3v (CRP) baseline (mean = 3.6) showed an immediate response. The developed MTX pharmacokinetic-pharmacodynamic model provided an acceptable description of the observed DAS28-3v (CRP) across all patients. CONCLUSIONS: The developed model describes a longitudinal relationship between RBC MTXGlu(3-5) concentrations and DAS28-3v (CRP) in patients with RA commencing MTX. Further work is required to determine whether measurement of RBC MTX polyglutamates might be useful for dose individualisation in patients with RA.

A population pharmacokinetic model for low-dose methotrexate and its polyglutamated metabolites in red blood cells.

BACKGROUND: Measurement of intracellular concentrations of methotrexate (MTX) and its polyglutamated metabolites (MTXGlu(2-5)) in red blood cells (RBCs) has been suggested as a potential means of monitoring low-dose MTX treatment of rheumatoid arthritis (RA). However, a possible correlation between RBC MTX and MTXGlu2-5 concentrations and clinical outcomes of MTX treatment in RA is debated. A better understanding of the dose-concentration-time relationship of MTX and MTXGlu(2-5) in RBCs by population pharmacokinetic modelling is desirable and will facilitate assessing a potential RBC concentration-effect relationship in the future. AIM: The purpose of this analysis was to describe the pharmacokinetics of MTX and MTXGlu(2-5) in RBCs. Secondary objectives included investigation of deglutamation reactions and the loss of MTX and MTXGlu(2-5) from the RBC. METHODS: A model was developed using NONMEM(®) version 7.2 based on RBC data obtained from 48 patients with RA receiving once-weekly low-dose MTX treatment. This model was linked to a fixed two-compartment model that was used to describe the pharmacokinetics of MTX in the plasma. A series of five compartments were used to describe the intracellular pharmacokinetics of MTX and MTXGlu(2-5) in RBCs. Biologically plausible covariates were tested for a significant effect on MTX plasma clearance and the intracellular volume of distribution of all MTX species in RBCs ([Formula: see text]). The developed model was used to test hypotheses related to the enzymatic deglutamation of MTXGlu(2-5) and potential loss of MTXGlu(2-5) from RBCs. RESULTS: The final RBC pharmacokinetic model required the intracellular volumes of distribution for the parent and metabolites to be set to the value estimated for the parent drug MTX alone, and the rate constants describing the polyglutamation steps were fixed at literature values. Significant covariates included effect of body surface area-adjusted estimated glomerular filtration rate on renal plasma clearance and effect of allometrically scaled total body weight with a fixed exponent of 0.75 on non-renal plasma clearance of MTX. The only significant covariate with an effect on [Formula: see text] was mean corpuscular volume (MCV). The model supported single deglutamation steps and a single mechanism of MTX and MTXGlu(2-5) loss from RBCs. CONCLUSIONS: The developed model enabled acceptable description of the intracellular kinetics of MTX and MTXGlu(2-5) in RBCs. In the future it can form the basis of a full pharmacokinetic-pharmacodynamic model to assess the time-RBC concentration-effect relationship of low-dose MTX treatment in RA.