Modeling subpopulations with the $MIXTURE subroutine in NONMEM: finding the individual probability of belonging to a subpopulation for the use in model analysis and improved decision making

Effect of Interindividual Variability in Metabolic Clearance and Relative Bioavailability on Rifampicin Exposure in Tuberculosis Patients with and without HIV Co-Infection: Does Formulation Quality Matter?

The present study aims to characterise the pharmacokinetics of rifampicin (RIF) in tuberculosis (TB) patients with and without HIV co-infection, considering the formation of 25-O-desacetyl-rifampicin (desRIF). It is hypothesised that the metabolite formation, HIV co-infection and drug formulation may further explain the interindividual variation in the exposure to RIF. Pharmacokinetic, clinical, and demographic data from TB patients with (TB-HIV+ group; n = 18) or without HIV (TB-HIV- group; n = 15) who were receiving RIF as part of a four-drug fixed-dose combination (FDC) regimen (RIF, isoniazid, pyrazinamide, and ethambutol) were analysed, along with the published literature data on the relative bioavailability of different formulations. A population pharmacokinetic model, including the formation of desRIF, was developed and compared to a model based solely on the parent drug. HIV co-infection does not alter the plasma exposure to RIF and the desRIF formation does not contribute to the observed variability in the RIF disposition. The relative bioavailability and RIF plasma exposure were significantly lower than previously reported for the standard regimen with FDC tablets. Furthermore, participants weighting less than 50 kg do not reach the same RIF plasma exposure as compared to those weighting >50 kg. In conclusion, as no covariate was identified other than body weight on CL/F and Vd/F, low systemic exposure to RIF is likely to be caused by the low bioavailability of the formulation.

Derivation and Clinical Utility of Safety Targets for Linezolid-Related Adverse Events in Drug-Resistant Tuberculosis Treatment

Long-term usage of linezolid can result in adverse events such as peripheral neuropathy, anemia and thrombocytopenia. Therapeutic drug monitoring data from 75 drug-resistant tuberculosis patients treated with linezolid were analyzed using a time-to-event (TTE) approach for peripheral neuropathy and anemia and indirect response modelling for thrombocytopenia. Different time-varying linezolid pharmacokinetic exposure indices (AUC0-24h,ss, Cav, Cmax and Cmin) and patient characteristics were investigated as risk factors. A treatment duration shorter than 3 months was considered dropout and was modelled using a TTE approach. An exposure-response relationship between linezolid Cmin and both peripheral neuropathy and anemia was found. The exposure index which best described the development of thrombocytopenia was AUC0-24h. The final TTE dropout model indicated an association between linezolid Cmin and dropout. New safety targets for each adverse event were proposed which can be used for individualized linezolid dosing. According to the model predictions at 6 months of treatment, a Cmin of 0.11 mg/L and 1.4 mg/L should not be exceeded to keep the cumulative probability to develop anemia and peripheral neuropathy below 20%. The AUC0-24h should be below 111 h·mg/L or 270 h·mg/L to prevent thrombocytopenia and severe thrombocytopenia, respectively. A clinical utility assessment showed that the currently recommended dose of 600 mg once daily is safer compared to a 300 mg BID dosing strategy considering all four safety endpoints.

Pharmacometric modeling of drug adverse effects: an application of mixture models in schizophrenia spectrum disorder patients treated with clozapine

Clozapine has superior efficacy to other antipsychotics yet is underutilized due to its adverse effects, such as neutropenia, weight gain, and tachycardia. The current investigation aimed to introduce a pharmacometric approach to simultaneously model drug adverse effects, with examples from schizophrenia spectrum patients receiving clozapine. The adverse drug effects were represented as a function of time by incorporating a mixture model to describe individual susceptibility to the adverse effects. Applications of the proposed method were presented by analyzing retrospective data from patients' medical records in Psychiatric Clinic, Penang General Hospital. Tachycardia, weight gain, and absolute neutrophils count (ANC) decrease were best described by an offset, a piecewise linear, and a transient surge function, respectively. 42.9% of the patients had all the adverse effects, including weight gain (0.01 kg/m² increase every week over a baseline of 24.7 kg/m² until stabilizing at 279 weeks), ANC decrease (20% decrease from 4540 cells/µL week 12-20.8), and tachycardia (14% constant increase over a baseline of 87.9 bpm for a clozapine maintenance dose of 450 mg daily). 32.5% of the patients had only tachycardia, while the remaining 24.6% had none of the adverse effects. A new pharmacometric approach was proposed to describe adverse drug effects with examples of clozapine-induced weight gain, ANC drop, and tachycardia. The current approach described the longitudinal time changes of continuous data while assessing patient susceptibility. Furthermore, the model revealed the possible co-existence of ANC drop and weight gain; thus, neutrophil monitoring might predict future changes in body weight.

Influence of NAT2 Genotype and Maturation on Isoniazid Exposure in Low-Birth-Weight and Preterm Infants With or Without Human Immunodeficiency Virus (HIV) Exposure

BACKGROUND

Isoniazid (INH) metabolism depends on the N-acetyl transferase 2 (NAT2) enzyme, whose maturation process remains unknown in low birth weight (LBW) and preterm infants. We aimed to assess INH exposure and safety in infants receiving oral tuberculosis prevention.

METHODS

This population pharmacokinetics (PK) analysis used INH and N-acetyl-isoniazid (ACL) concentrations in infants (BW ≤ 4 kg), including preterm, with follow-up for 6 months. PK parameters were described using nonlinear mixed effects modeling. Simulations were performed to assess INH exposure and optimal dosing regimens, using 2 targets: Cmax at 3-6 mg/L and area under the curve (AUC) ≥ 10.52 mg h/L.

RESULTS

We included 57 infants (79% preterm, 84% LBW) in the PK analysis, with a median (range) gestational age of 34 (28.7-39.4) weeks. At the time of sampling, postnatal age was 2.3 (0.2-7.3) months and weight (WT) was 3.7 (0.9-9.3) kg. NAT2 genotype was available in 43 (75.4%) patients (10 slow, 26 intermediate, and 7 fast metabolizers). Ninety percent of NAT2 maturation was attained by 4.4 post-natal months. WT, postmenstrual age, and NAT2 genotype significantly influenced INH exposure, with a 5-fold difference in AUC between slow and fast metabolizers for the same dose. INH appeared safe across the broad range of exposure for 61 infants included in the safety analysis.

CONCLUSIONS

In LBW/preterm infants, INH dosing needs frequent adjustment to account for growth and maturation. Pharmacogenetics-based dosing regimens is the most powerful approach to deliver safe and equalized exposures for all infants, because NAT2 genotype highly impacts INH pharmacokinetic variability.

External Evaluation of Risperidone Population Pharmacokinetic Models Using Opportunistic Pediatric Data

Risperidone is approved to treat schizophrenia in adolescents and autistic disorder and bipolar mania in children and adolescents. It is also used off-label in younger children for various psychiatric disorders. Several population pharmacokinetic models of risperidone and 9-OH-risperidone have been published. The objectives of this study were to assess whether opportunistically collected pediatric data can be used to evaluate risperidone population pharmacokinetic models externally and to identify a robust model for precision dosing in children. A total of 103 concentrations of risperidone and 112 concentrations of 9-OH-risperidone, collected from 62 pediatric patients (0.16–16.8 years of age), were used in the present study. The predictive performance of five published population pharmacokinetic models (four joint parent-metabolite models and one parent only) was assessed for accuracy and precision of the predictions using statistical criteria, goodness of fit plots, prediction-corrected visual predictive checks (pcVPCs), and normalized prediction distribution errors (NPDEs). The tested models produced similarly precise predictions (Root Mean Square Error [RMSE]) ranging from 0.021 to 0.027 nmol/ml for risperidone and 0.053–0.065 nmol/ml for 9-OH-risperidone). However, one of the models (a one-compartment mixture model with clearance estimated for three subpopulations) developed with a rich dataset presented fewer biases (Mean Percent Error [MPE, %] of 1.0% vs. 101.4, 146.9, 260.4, and 292.4%) for risperidone. In contrast, a model developed with fewer data and a more similar population to the one used for the external evaluation presented fewer biases for 9-OH-risperidone (MPE: 17% vs. 69.9, 47.8, and 82.9%). None of the models evaluated seemed to be generalizable to the population used in this analysis. All the models had a modest predictive performance, potentially suggesting that sources of inter-individual variability were not entirely captured and that opportunistic data from a highly heterogeneous population are likely not the most appropriate data to evaluate risperidone models externally.

Evaluation of the estimation and classification performance of NONMEM when applying mixture model for drug clearance

Maximum likelihood estimation of parameters involving mixture model is known to have significant and specific patterns of errors. Population pharmacokinetic (PopPK) modeling using NONMEM is no exception. A few relevant studies on estimation and classification performance were done, but a comprehensive study was not yet available. The current study aims to evaluate performance and likelihood ratio test (LRT)‐based true covariate detection rate when fitting a bimodal mixture of drug clearance (CL) in NONMEM. A large number of PopPK datasets with various settings were simulated and then estimated. The estimates were compared to the simulated values and summarized. The separation between the CL distributions of the two subpopulations is systematically overestimated. The major factor associated with the performance is the change in the minimum objective function value after removing the mixture component (dOFV). Other significant factors include estimated disparity index (DI), estimated mixing proportion, and number of subjects in the dataset. Small dOFV and large estimated DI are associated with the worst performance. Omitting a true mixture resulted in reduced true covariate detection rates. It is recommended that on top of routinely generated standard errors and model diagnostics, dOFV, and other factors when necessary, should be taken into account for the evaluation of performance when fitting mixture model using NONMEM. In addition, when fitting mixture model for CL is intended, the mixture component should be introduced prior to LRT‐based covariate model development for CL.

Quantifying the pharmacodynamics of morphine in the treatment of postoperative pain in preverbal children

While the pharmacokinetics of morphine in children have been studied extensively, little is known about the pharmacodynamics of morphine in this population. Here, we quantified the concentration‐effect relationship of morphine for postoperative pain in preverbal children between 0 and 3 years of age. For this, we applied item response theory modeling in the pharmacokinetic/pharmacodynamic analysis of COMFORT‐Behavior (COMFORT‐B) scale data from 2 previous clinical studies. In the model, we identified a sigmoid maximal efficacy model for the effect of morphine and found that in 26% of children, increasing morphine concentrations were not associated with lower pain scores (nonresponders to morphine up‐titration). In responders to morphine up‐titration, the COMFORT‐B score slowly decreases with increasing morphine concentrations at morphine concentrations >20 ng/mL. In nonresponding children, no decrease in COMFORT‐B score is expected. In general, lower baseline COMFORT‐B scores (2.1 points on average) in younger children (postnatal age <10.3 days) were found. Based on the model, we conclude that the percentage of children at a desirable COMFORT‐B score is maximized at a morphine concentration between 5 and 30 ng/mL for children aged <10 days, and between 5 and 40 ng/mL for children >10 days. These findings support a dosing regimen previously suggested by Krekels et al, which would put >95% of patients within this morphine target concentration range at steady state. Our modeling approach provides a promising platform for pharmacodynamic research of analgesics and sedatives in children.

Early Survival Prediction Framework in CD19-Specific CAR-T Cell Immunotherapy Using a Quantitative Systems Pharmacology Model

Chimeric antigen receptor (CAR)-T cell therapy has revolutionized treatment of relapsed/refractory non-Hodgkin lymphoma (NHL). However, since 36-60% of patients relapse, early response prediction is crucial. We present a novel population quantitative systems pharmacology model, integrating literature knowledge on physiology, immunology, and adoptive cell therapy together with 133 CAR-T cell phenotype, 1943 cytokine, and 48 metabolic tumor measurements. The model well described post-infusion concentrations of four CAR-T cell phenotypes and CD19+ metabolic tumor volume over 3 months after CAR-T cell infusion. Leveraging the model, we identified a low expansion subpopulation with significantly lower CAR-T cell expansion capacities amongst 19 NHL patients. Together with two patient-/therapy-related factors (autologous stem cell transplantation, CD4+/CD8+ T cells), the low expansion subpopulation explained 2/3 of the interindividual variability in the CAR-T cell expansion capacities. Moreover, the low expansion subpopulation had poor prognosis as only 1/4 of the low expansion subpopulation compared to 2/3 of the reference population were still alive after 24 months. We translated the expansion capacities into a clinical composite score (CCS) of 'Maximum naïve CAR-T cell concentrations/Baseline tumor burden' ratio and propose a CCSTN-value > 0.00136 (cells·µL-1·mL-1 as predictor for survival. Once validated in a larger cohort, the model will foster refining survival prediction and solutions to enhance NHL CAR-T cell therapy response.

Assessing Treatment Effects with Pharmacometric Models: A New Method that Addresses Problems with Standard Assessments

Longitudinal pharmacometric models offer many advantages in the analysis of clinical trial data, but potentially inflated type I error and biased drug effect estimates, as a consequence of model misspecifications and multiple testing, are main drawbacks. In this work, we used real data to compare these aspects for a standard approach (STD) and a new one using mixture models, called individual model averaging (IMA). Placebo arm data sets were obtained from three clinical studies assessing ADAS-Cog scores, Likert pain scores, and seizure frequency. By randomly (1:1) assigning patients in the above data sets to “treatment” or “placebo,” we created data sets where any significant drug effect was known to be a false positive. Repeating the process of random assignment and analysis for significant drug effect many times (N = 1000) for each of the 40 to 66 placebo-drug model combinations, statistics of the type I error and drug effect bias were obtained. Across all models and the three data types, the type I error was (5th, 25th, 50th, 75th, 95th percentiles) 4.1, 11.4, 40.6, 100.0, 100.0 for STD, and 1.6, 3.5, 4.3, 5.0, 6.0 for IMA. IMA showed no bias in the drug effect estimates, whereas in STD bias was frequently present. In conclusion, STD is associated with inflated type I error and risk of biased drug effect estimates. IMA demonstrated controlled type I error and no bias.

A Model-Informed Method for the Purpose of Precision Dosing of Isoniazid in Pulmonary Tuberculosis

Background and Objective

This study aimed to develop and evaluate a population pharmacokinetic model and limited sampling strategy for isoniazid to be used in model-based therapeutic drug monitoring.

Methods

A population pharmacokinetic model was developed based on isoniazid and acetyl-isoniazid pharmacokinetic data from seven studies with in total 466 patients from three continents. Three limited sampling strategies were tested based on the available sampling times in the dataset and practical considerations. The tested limited sampling strategies sampled at 2, 4, and 6 h, 2 and 4 h, and 2 h after dosing. The model-predicted area under the concentration–time curve from 0 to 24 h (AUC₂₄) and the peak concentration from the limited sampling strategies were compared to predictions using the full pharmacokinetic curve. Bias and precision were assessed using the mean error (ME) and the root mean square error (RMSE), both expressed as a percentage of the mean model-predicted AUC₂₄ or peak concentration on the full pharmacokinetic curve.

Results

Performance of the developed model was acceptable and the uncertainty in parameter estimations was generally low (the highest relative standard error was 39% coefficient of variation). The limited sampling strategy with sampling at 2 and 4 h was determined as most suitable with an ME of 1.1% and RMSE of 23.4% for AUC₂₄ prediction, and ME of 2.7% and RMSE of 23.8% for peak concentration prediction. For the performance of this strategy, it is important that data on both isoniazid and acetyl-isoniazid are used. If only data on isoniazid are available, a limited sampling strategy using 2, 4, and 6 h can be employed with an ME of 1.7% and RMSE of 20.9% for AUC₂₄ prediction, and ME of 1.2% and RMSE of 23.8% for peak concentration prediction.

Conclusions

A model-based therapeutic drug monitoring strategy for personalized dosing of isoniazid using sampling at 2 and 4 h after dosing was successfully developed. Prospective evaluation of this strategy will show how it performs in a clinical therapeutic drug monitoring setting.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40262-020-00971-2.

Understanding the drug exposure-response relationship of bedaquiline to predict efficacy for novel dosing regimens in the treatment of multidrug-resistant tuberculosis

AIMS

To externally validate an earlier characterized relationship between bedaquiline exposure and decline in bacterial load in a more difficult-to-treat patient population, and to explore the performances of alternative dosing regimens through simulations.

METHODS

The bedaquiline exposure-response relationship was validated using time-to-positivity data from 233 newly diagnosed or treatment-experienced patients with drug-resistant tuberculosis from the C209 open-label study. The significance of the exposure-response relationship on the bacterial clearance was compared to a constant drug effect model. Tuberculosis resistance type and the presence and duration of antituberculosis pre-treatment were evaluated as additional covariates. Alternative dosing regimens were simulated for tuberculosis patients with different types of drug resistance.

RESULTS

High bedaquiline concentrations were confirmed to be associated with faster bacterial load decline in patients, given that the exposure-effect relationship provided a significantly better fit than the constant drug effect (relative likelihood = 0.0003). The half-life of bacterial clearance was identified to be 22% longer in patients with pre-extensively drug-resistant (pre-XDR) tuberculosis (TB) and 86% longer in patients with extensively drug-resistant (XDR) TB, compared to patients with multidrug-resistant (MDR) TB. Achievement of the same treatment response for (pre-)XDR TB patients as for MDR TB patients would be possible by adjusting the dose and dosing frequency. Furthermore, daily bedaquiline administration as in the ZeNix regimen, was predicted to be as effective as the approved regimen.

CONCLUSION

The confirmed bedaquiline exposure-response relationship offers the possibility to predict efficacy under alternative dosing regimens, and provides a useful tool for potential treatment optimization.

Extension of Pharmacokinetic/Pharmacodynamic Time-Kill Studies To Include Lipopolysaccharide/Endotoxin Release from Escherichia coli Exposed to Cefuroxime

The release of inflammatory bacterial products, such as lipopolysaccharide (LPS)/endotoxin, may be increased upon the administration of antibiotics. An improved quantitative understanding of endotoxin release and its relation to antibiotic exposure and bacterial growth/killing may be gained by an integrated analysis of these processes. The aim of this work was to establish a mathematical model that relates Escherichia coli growth/killing dynamics at various cefuroxime concentrations to endotoxin release in vitro.

The release of inflammatory bacterial products, such as lipopolysaccharide (LPS)/endotoxin, may be increased upon the administration of antibiotics. An improved quantitative understanding of endotoxin release and its relation to antibiotic exposure and bacterial growth/killing may be gained by an integrated analysis of these processes. The aim of this work was to establish a mathematical model that relates Escherichia coli growth/killing dynamics at various cefuroxime concentrations to endotoxin release in vitro. Fifty-two time-kill experiments informed bacterial and endotoxin time courses and included both static (0×, 0.5×, 1×, 2×, 10×, and 50× MIC) and dynamic (0×, 15×, and 30× MIC) cefuroxime concentrations. A model for the antibiotic-bacterium interaction was established, and antibiotic-induced bacterial killing followed a sigmoidal Emax relation to the cefuroxime concentration (MIC-specific 50% effective concentration [EC₅₀], maximum antibiotic-induced killing rate [E_max] = 3.26 h⁻¹ and γ = 3.37). Endotoxin release was assessed in relation to the bacterial processes of growth, antibiotic-induced bacterial killing, and natural bacterial death and found to be quantitatively related to bacterial growth (0.000292 endotoxin units [EU]/CFU) and antibiotic-induced bacterial killing (0.00636 EU/CFU). Increased release following the administration of a second cefuroxime dose was described by the formation and subsequent antibiotic-induced killing of filaments (0.295 EU/CFU). Release due to growth was instantaneous, while release due to antibiotic-induced killing was delayed (mean transit time of 7.63 h). To conclude, the in vitro release of endotoxin is related to bacterial growth and antibiotic-induced killing, with higher rates of release upon the killing of formed filaments. Endotoxin release over 24 h is lowest when antibiotic exposure rapidly eradicates bacteria, while increased release is predicted to occur when growth and antibiotic-induced killing occur simultaneously.

Using Real-World Data to Guide Ustekinumab Dosing Strategies for Psoriasis: A Prospective Pharmacokinetic-Pharmacodynamic Study

Variation in response to biologic therapy for inflammatory diseases, such as psoriasis, is partly driven by variation in drug exposure. Real‐world psoriasis data were used to develop a pharmacokinetic/pharmacodynamic (PK/PD) model for the first‐line therapeutic antibody ustekinumab. The impact of differing dosing strategies on response was explored. Data were collected from a UK prospective multicenter observational cohort (491 patients on ustekinumab monotherapy, drug levels, and anti‐drug antibody measurements on 797 serum samples, 1,590 measurements of Psoriasis Area Severity Index (PASI)). Ustekinumab PKs were described with a linear one‐compartment model. A maximum effect (E_max) model inhibited progression of psoriatic skin lesions in the turnover PD mechanism describing PASI evolution while on treatment. A mixture model on half‐maximal effective concentration identified a potential nonresponder group, with simulations suggesting that, in future, the model could be incorporated into a Bayesian therapeutic drug monitoring “dashboard” to individualize dosing and improve treatment outcomes.

Development of visual predictive checks accounting for multimodal parameter distributions in mixture models

The assumption of interindividual variability being unimodally distributed in nonlinear mixed effects models does not hold when the population under study displays multimodal parameter distributions. Mixture models allow the identification of parameters characteristic to a subpopulation by describing these multimodalities. Visual predictive check (VPC) is a standard simulation based diagnostic tool, but not yet adapted to account for multimodal parameter distributions. Mixture model analysis provides the probability for an individual to belong to a subpopulation (IPmix) and the most likely subpopulation for an individual to belong to (MIXEST). Using simulated data examples, two implementation strategies were followed to split the data into subpopulations for the development of mixture model specific VPCs. The first strategy splits the observed and simulated data according to the MIXEST assignment. A shortcoming of the MIXEST-based allocation strategy was a biased allocation towards the dominating subpopulation. This shortcoming was avoided by splitting observed and simulated data according to the IPmix assignment. For illustration purpose, the approaches were also applied to an irinotecan mixture model demonstrating 36% lower clearance of irinotecan metabolite (SN-38) in individuals with UGT1A1 homo/heterozygote versus wild-type genotype. VPCs with segregated subpopulations were helpful in identifying model misspecifications which were not evident with standard VPCs. The new tool provides an enhanced power of evaluation of mixture models.

Population pharmacokinetic-pharmacodynamic modelling of the relationship between testosterone and prostate specific antigen in patients with prostate cancer during treatment with leuprorelin

AIMS

This investigation aimed to quantitatively characterize the relationship between the gonadotropin-releasing hormone agonist leuprorelin, testosterone (T) and prostate specific antigen (PSA) concentrations over time, to aid identification of a target T concentration that optimises the balance of the benefits of T suppression whilst reducing the risk of side effects related to futile over-suppression.

METHODS

Data from a single dose study to investigate the effect of leuprorelin in a 6-month depot formulation on T and PSA in prostate cancer patients were analysed using a population pharmacokinetic-pharmacodynamic modelling approach. The developed model was qualified using external data from 3 studies, in which the effect of different formulations of leuprorelin on T and PSA was evaluated in prostate cancer patients.

RESULTS

The effect of leuprorelin on the relationship between T and PSA was adequately characterized by the Romero model with minor modifications, combined with a turnover model to describe the delay in response between T and PSA. The data were significantly better described when assuming a minimum PSA level that is independent on the treatment-related reduction in T, as compared to a model with a proportional reduction in PSA and T.

CONCLUSIONS

The model-based analysis suggests that on a population level, reducing T concentrations below 35 ng/dL does not result in a further decrease in PSA levels (>95% of the minimal PSA level is reached). More data are required to support this relationship in the lower T and PSA range.

Adjustment of the area under the concentration curve by terminal rate constant for bioequivalence assessment in a parallel-group study of lamotrigine

AIM

A new strength of lamotrigine extended-release formulation unexpectedly failed to show bioequivalence with the existing strengths at the same dose in a parallel-group study. We report the post-hoc analyses conducted to identify the cause and propose an approach for future evaluations in similar situations.

METHODS

A seemingly bimodal distribution of the half-life among the study participants prompted the use of terminal-phase-rate-constant-adjusted area under the concentration curve as the endpoint for bioequivalence assessment. Population pharmacokinetic modelling was also performed to assess the bimodal distribution of apparent clearance and the potential treatment effects on bioavailability.

RESULTS

The cause for failing to achieve bioequivalence appeared to be a biased representation of a bimodal clearance distribution between the groups. The pharmacokinetic modelling with a mixture routine identified two subpopulations: 88% had a mean clearance of 1.99 l h-1 ; 12% had a mean clearance of 0.64 l h-1 . The low-clearance population was unequally represented by 13% and 4% of subjects in the reference and test groups, respectively, and treatment appeared to have no significant effect on oral bioavailability. The bioequivalence comparison using the adjusted area concluded with a 90% confidence interval of 0.91-1.06, suggesting that treatment had no significant effect on bioavailability and the formulations would meet regulatory criteria for bioequivalence.

CONCLUSIONS

The adjustment of the area under the concentration curve adjusted by terminal-phase rate constant should be considered for situational application in bioequivalence assessment when there are multiple clearance subpopulations in a parallel-group study.

A Mechanism-Based Population Pharmacokinetic Analysis Assessing the Feasibility of Efavirenz Dose Reduction to 400 mg in Pregnant Women

BACKGROUND

Reducing the dose of efavirenz can improve safety, reduce costs, and increase access for patients with HIV infection. According to the World Health Organization, a similar dosing strategy for all patient populations is desirable for universal roll-out; however, it remains unknown whether the 400 mg daily dose is adequate during pregnancy.

METHODS

We developed a mechanistic population pharmacokinetic model using pooled data from women included in seven studies (1968 samples, 774 collected during pregnancy). Total and free efavirenz exposure (AUC24 and C12) were predicted for 400 (reduced) and 600 mg (standard) doses in both pregnant and non-pregnant women.

RESULTS

Using a 400 mg dose, the median efavirenz total AUC24 and C12 during the third trimester of pregnancy were 91 and 87% of values among non-pregnant women, respectively. Furthermore, the median free efavirenz C12 and AUC24 were predicted to increase during pregnancy by 11 and 15%, respectively.

CONCLUSIONS

It was predicted that reduced-dose efavirenz provides adequate exposure during pregnancy. These findings warrant prospective confirmation.

Characterization of changes in HbA1c in patients with and without secondary failure after metformin treatments by a population pharmacodynamic analysis using mixture models

The objective of the present study was to develop a population pharmacodynamic (PPD) model to describe the glycated hemoglobin (HbA1c)-lowering effects of metformin in type 2 diabetes mellitus patients with and without secondary failure and to characterize changes in HbA1c levels in the two subpopulations using a mixture model. Information on patients was collected retrospectively from electronic medical records. In this study, the mixture model was used to characterize the bimodal effects of metformin. A PPD analysis was performed using NONMEM 7.3.0. A physiological indirect response model, based on 829 HbA1c levels of 69 patients, described the time course for the HbA1c-lowering effects of metformin. Evidence for the different effectiveness of metformin subpopulations was provided using the mixture model. In the final PPD model, the inhibition effect was constant over a study duration in a patient subpopulation without secondary failure. In contrast, the inhibition effect decreased as a function of time after start of metformin treatment in a subpopulation with secondary failure. These results indicated that HbA1c improvements appeared to deteriorate over time in patients with secondary failure. In a PPD analysis of metformin, it was possible to assign patients with secondary failure using the mixture model.

Inter-study variability of preclinical in vivo safety studies and translational exposure-QTc relationships--a PKPD meta-analysis

BACKGROUND AND PURPOSE

Preclinical cardiovascular safety studies (CVS) have been compared between facilities with respect to their sensitivity to detect drug-induced QTc prolongation (ΔQTc). Little is known about the consistency of quantitative ΔQTc predictions that are relevant for translation to humans.

EXPERIMENTAL APPROACH

We derived typical ΔQTc predictions at therapeutic exposure (ΔQTcTHER ) with 95% confidence intervals (95%CI) for 3 Kv 11.1 (hERG) channel blockers (moxifloxacin, dofetilide and sotalol) from a total of 14 CVS with variable designs in the conscious dog. Population pharmacokinetic-pharmacodynamic (PKPD) analysis of each study was followed by a meta-analysis (pooling 2-6 studies including 10-32 dogs per compound) to derive meta-predictions of typical ΔQTcTHER . Meta-predictions were used as a reference to evaluate the consistency of study predictions and to relate results to those found in the clinical literature.

KEY RESULTS

The 95%CIs of study-predicted ΔQTcTHER comprised in 13 out of 14 cases the meta-prediction. Overall inter-study variability (mean deviation from meta-prediction at upper level of therapeutic exposure) was 30% (range: 1-69%). Meta-ΔQTcTHER predictions for moxifloxacin, dofetilide and sotalol overlapped with reported clinical QTc prolongation when expressed as %-prolongation from baseline.

CONCLUSIONS AND IMPLICATIONS

Consistent exposure-ΔQTc predictions were obtained from single preclinical dog studies of highly variable designs by systematic PKPD analysis, which is suitable for translational purposes. The good preclinical-clinical pharmacodynamic correlations obtained suggest that such an analysis should be more routinely applied to increase the informative and predictive value of results obtained from animal experiments.

Modeling Alzheimer's Disease Progression Using Disease Onset Time and Disease Trajectory Concepts Applied to CDR-SOB Scores From ADNI

Disease-onset time (DOT) and disease trajectory concepts were applied to derive an Alzheimer's disease (AD) progression population model using the clinical dementia rating scale—sum of boxes (CDR-SOB) from the AD neuroimaging initiative (ADNI) database. The model enabled the estimation of a DOT and a disease trajectory for each patient. The model also allowed distinguishing fast and slow-progressing subpopulations according to the functional assessment questionnaire, normalized hippocampal volume, and CDR-SOB score at study entry. On the basis of these prognostic factors, 81% of the mild cognitive impairment (MCI) subjects could correctly be assigned to slow or fast progressers, and 77% of MCI to AD conversions could be predicted whereas the model described correctly 84% of the conversions. Finally, synchronization of the biomarker-time profiles on estimated individual DOT virtually expanded the population observation period from 3 to 8 years. DOT-disease trajectory model is a powerful approach that could be applied to many progressive diseases.

Amyotrophic lateral sclerosis disease progression model

Our objective was to develop: 1) a longitudinal model to describe amyotrophic lateral sclerosis (ALS) disease progression using the revised Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R); and 2) a probabilistic model to estimate the presence of clusters of trajectories in ALS progression over 12 months of treatment. Three hundred and thirty-eight patients treated with placebo from the PRO-ACT database were included in the analyses. A non-linear Weibull model best described the ALS disease progression, and a stepwise logistic regression approach was used to select the variables predicting a slow or fast disease progression. Results identified two clusters of trajectories: 1) slow disease progressors (46% of patients with a change from baseline of 13%); 2) fast disease progressors (54% of patients with a change from baseline of 49%). ROC curve analysis estimated the optimal cut-off for classifying patients as slow or fast disease progressors given ALSFRS-R measurements at 2-4 weeks. Results showed that the degree of ALS disease progression quantified by the ALSFRS-R symptomatic change on placebo is highly heterogeneous. In conclusion, this finding indicates the potential interest of disease progression models for implementing a population enrichment strategy to control the level of heterogeneity in the patients included in new trials.

Mathematical model approach to describe tumour response in mice after vaccine administration and its applicability to immune-stimulatory cytokine-based strategies

Immunotherapy is a growing therapeutic strategy in oncology based on the stimulation of innate and adaptive immune systems to induce the death of tumour cells. In this paper, we have developed a population semi-mechanistic model able to characterize the mechanisms implied in tumour growth dynamic after the administration of CyaA-E7, a vaccine able to target antigen to dendritic cells, thus triggering a potent immune response. The mathematical model developed presented the following main components: (1) tumour progression in the animals without treatment was described with a linear model, (2) vaccine effects were modelled assuming that vaccine triggers a non-instantaneous immune response inducing cell death. Delayed response was described with a series of two transit compartments, (3) a resistance effect decreasing vaccine efficiency was also incorporated through a regulator compartment dependent upon tumour size, and (4) a mixture model at the level of the elimination of the induced signal vaccine (k 2) to model tumour relapse after treatment, observed in a small percentage of animals (15.6%). The proposed model structure was successfully applied to describe antitumor effect of IL-12, suggesting its applicability to different immune-stimulatory therapies. In addition, a simulation exercise to evaluate in silico the impact on tumour size of possible combination therapies has been shown. This type of mathematical approaches may be helpful to maximize the information obtained from experiments in mice, reducing the number of animals and the cost of developing new antitumor immunotherapies.

A mechanism-based approach for absorption modeling: the Gastro-Intestinal Transit Time (GITT) model

Absorption models used in the estimation of pharmacokinetic drug characteristics from plasma concentration data are generally empirical and simple, utilizing no prior information on gastro-intestinal (GI) transit patterns. Our aim was to develop and evaluate an estimation strategy based on a mechanism-based model for drug absorption, which takes into account the tablet movement through the GI transit. This work is an extension of a previous model utilizing tablet movement characteristics derived from magnetic marker monitoring (MMM) and pharmacokinetic data. The new approach, which replaces MMM data with a GI transit model, was evaluated in data sets where MMM data were available (felodipine) or not available (diclofenac). Pharmacokinetic profiles in both datasets were well described by the model according to goodness-of-fit plots. Visual predictive checks showed the model to give superior simulation properties compared with a standard empirical approach (first-order absorption rate + lag-time). This model represents a step towards an integrated mechanism-based NLME model, where the use of physiological knowledge and in vitro–in vivo correlation helps fully characterize PK and generate hypotheses for new formulations or specific populations.

Population pharmacokinetics of telapristone (CDB-4124) and its active monodemethylated metabolite CDB-4453, with a mixture model for total clearance

Telapristone is a selective progesterone antagonist that is being developed for the long-term treatment of symptoms associated with endometriosis and uterine fibroids. The population pharmacokinetics of telapristone (CDB-4124) and CDB-4453 was investigated using nonlinear mixed-effects modeling. Data from two clinical studies (n = 32) were included in the analysis. A two-compartment (parent) one compartment (metabolite) mixture model (with two populations for apparent clearance) with first-order absorption and elimination adequately described the pharmacokinetics of telapristone and CDB-4453. Telapristone was rapidly absorbed with an absorption rate constant (Ka) of 1.26 h⁻¹. Moderate renal impairment resulted in a 74% decrease in Ka. The population estimates for oral clearance (CL/F) for the two populations were 11.6 and 3.34 L/h, respectively, with 25% of the subjects being allocated to the high-clearance group. Apparent volume of distribution for the central compartment (V2/F) was 37.4 L, apparent inter-compartmental clearance (Q/F) was 21.9 L/h, and apparent peripheral volume of distribution for the parent (V4/F) was 120 L. The ratio of the fraction of telapristone converted to CDB-4453 to the distribution volume of CDB-4453 (Fmet_est) was 0.20/L. Apparent volume of distribution of the metabolite compartment (V3/F) was fixed to 1 L and apparent clearance of the metabolite (CLM/F) was 2.43 L/h. A two-compartment parent-metabolite model adequately described the pharmacokinetics of telapristone and CDB-4453. The clearance of telapristone was separated into two populations and could be the result of metabolism via polymorphic CYP3A5.