Performance of longitudinal item response theory models in shortened or partial assessments

This work evaluates the performance of longitudinal item response (IR) theory models in shortened assessments using an existing model for part II and III of the MDS-UPDRS score. Based on the item information content, the assessment was reduced by removal of items in multiple increments and the models’ ability to recover the item characteristics of the remaining items at each level was evaluated. This evaluation was done for both simulated and real data. The metric of comparison in both cases was the item information function. For real data, the impact of shortening on the estimated disease progression and drug effect was also studied. In the simulated data setting, the item characteristics did not differ between the full and the shortened assessments down to the lowest level of information remaining; indicating a considerable independence between items. In contrast when reducing the assessment in a real data setting, a substantial change in item information was observed for some of the items. Disease progression and drug effect estimates also decreased in the reduced assessments. These changes indicate a shift in the measured construct of the shortened assessment and warrant caution when comparing results from a partial assessment with results from the full assessment.

Comparing Circulating Tumor Cell Counts with Dynamic Tumor Size Changes as Predictor of Overall Survival: A Quantitative Modeling Framework

PURPOSE

Quantitative relationships between treatment-induced changes in tumor size and circulating tumor cell (CTC) counts, and their links to overall survival (OS), are lacking. We present a population modeling framework identifying and quantifying such relationships, based on longitudinal data collected in patients with metastatic colorectal cancer (mCRC) to evaluate the value of tumor size and CTC counts as predictors of OS.

EXPERIMENTAL DESIGN

A pharmacometric approach (i.e., population pharmacodynamic modeling) was used to characterize the changes in tumor size and CTC count and evaluate them as predictors of OS in 451 patients with mCRC treated with chemotherapy and targeted therapy in a prospectively randomized phase III study (CAIRO2).

RESULTS

A tumor size model of tumor quiescence and drug resistance was used to characterize the tumor size time-course, and was, in addition to the total normalized dose (i.e., of all administered drugs) in a given cycle, related to the CTC counts through a negative binomial model (CTC model). Tumor size changes did not contribute additional predictive value when the mean CTC count was a predictor of OS. Treatment reduced the typical mean count from 1.43 to 0.477 (HR = 3.94). The modeling framework was applied to explore whether dose modifications (increased and reduced) would result in a CTC count below 1/7.5 mL after 1 to 2 weeks of treatment.

CONCLUSIONS

Time-varying CTC counts can be useful for early predicting OS in patients with mCRC, and may therefore have potential for model-based treatment individualization. Although tumor size was connected to CTC, its link to OS was weaker.

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.

Population pharmacokinetics of colistin and the relation to survival in critically ill patients infected with colistin susceptible and carbapenem-resistant bacteria

OBJECTIVES

The aim was to analyse the population pharmacokinetics of colistin and to explore the relationship between colistin exposure and time to death.

METHODS

Patients included in the AIDA randomized controlled trial were treated with colistin for severe infections caused by carbapenem-resistant Gram-negative bacteria. All subjects received a 9 million units (MU) loading dose, followed by a 4.5 MU twice daily maintenance dose, with dose reduction if creatinine clearance (CrCL) < 50 mL/min. Individual colistin exposures were estimated from the developed population pharmacokinetic model and an optimized two-sample per patient sampling design. Time to death was evaluated in a parametric survival analysis.

RESULTS

Out of 406 randomized patients, 349 contributed pharmacokinetic data. The median (90% range) colistin plasma concentration was 0.44 (0.14-1.59) mg/L at 15 minutes after the end of first infusion. In samples drawn 10 hr after a maintenance dose, concentrations were >2 mg/L in 94% (195/208) and 44% (38/87) of patients with CrCL ≤120 mL/min, and >120 mL/min, respectively. Colistin methanesulfonate sodium (CMS) and colistin clearances were strongly dependent on CrCL. High colistin exposure to MIC ratio was associated with increased hazard of death in the multivariate analysis (adjusted hazard ratio (95% CI): 1.07 (1.03-1.12)). Other significant predictors included SOFA score at baseline (HR 1.24 (1.19-1.30) per score increase), age and Acinetobacter or Pseudomonas as index pathogen.

DISCUSSION

The population pharmacokinetic model predicted that >90% of the patients had colistin concentrations >2 mg/L at steady state, but only 66% at 4 hr after start of treatment. High colistin exposure was associated with poor kidney function, and was not related to a prolonged survival.

Prediction of exposure-driven myelotoxicity of continuous infusion 5-fluorouracil by a semi-physiological pharmacokinetic-pharmacodynamic model in gastrointestinal cancer patients

Purpose

To describe 5-fluorouracil (5FU) pharmacokinetics, myelotoxicity and respective covariates using a simultaneous nonlinear mixed effect modelling approach.

Methods

Thirty patients with gastrointestinal cancer received 5FU 650 or 1000 mg/m²/day as 5-day continuous venous infusion (14 of whom also received cisplatin 20 mg/m²/day). 5FU and 5-fluoro-5,6-dihydrouracil (5FUH2) plasma concentrations were described by a pharmacokinetic model using NONMEM. Absolute leukocyte counts were described by a semi-mechanistic myelosuppression model. Covariate relationships were evaluated to explain the possible sources of variability in 5FU pharmacokinetics and pharmacodynamics.

Results

Total clearance of 5FU correlated with body surface area (BSA). Population estimate for total clearance was 249 L/h. Clearances of 5FU and 5FUH2 fractionally changed by 77%/m² difference from the median BSA. 5FU central and peripheral volumes of distribution were 5.56 L and 28.5 L, respectively. Estimated 5FUH2 clearance and volume of distribution were 121 L/h and 96.7 L, respectively. Baseline leukocyte count of 6.86 × 10⁹/L, as well as mean leukocyte transit time of 281 h accounting for time delay between proliferating and circulating cells, was estimated. The relationship between 5FU plasma concentrations and absolute leukocyte count was found to be linear. A higher degree of myelosuppression was attributed to combination therapy (slope = 2.82 L/mg) with cisplatin as compared to 5FU monotherapy (slope = 1.17 L/mg).

Conclusions

BSA should be taken into account for predicting 5FU exposure. Myelosuppression was influenced by 5FU exposure and concomitant administration of cisplatin.

Electronic supplementary material

The online version of this article (10.1007/s00280-019-04028-5) contains supplementary material, which is available to authorized users.

Handling underlying discrete variables with bivariate mixed hidden Markov models in NONMEM

Non-linear mixed effects models typically deal with stochasticity in observed processes but models accounting for only observed processes may not be the most appropriate for all data. Hidden Markov models (HMMs) characterize the relationship between observed and hidden variables where the hidden variables can represent an underlying and unmeasurable disease status for example. Adding stochasticity to HMMs results in mixed HMMs (MHMMs) which potentially allow for the characterization of variability in unobservable processes. Further, HMMs can be extended to include more than one observation source and are then multivariate HMMs. In this work MHMMs were developed and applied in a chronic obstructive pulmonary disease example. The two hidden states included in the model were remission and exacerbation and two observation sources were considered, patient reported outcomes (PROs) and forced expiratory volume (FEV1). Estimation properties in the software NONMEM of model parameters were investigated with and without random and covariate effect parameters. The influence of including random and covariate effects of varying magnitudes on the parameters in the model was quantified and a power analysis was performed to compare the power of a single bivariate MHMM with two separate univariate MHMMs. A bivariate MHMM was developed for simulating and analysing hypothetical COPD data consisting of PRO and FEV1 measurements collected every week for 60 weeks. Parameter precision was high for all parameters with the exception of the variance of the transition rate dictating the transition from remission to exacerbation (relative root mean squared error [RRMSE] > 150%). Parameter precision was better with higher magnitudes of the transition probability parameters. A drug effect was included on the transition rate probability and the precision of the drug effect parameter improved with increasing magnitude of the parameter. The power to detect the drug effect was improved by utilizing a bivariate MHMM model over the univariate MHMM models where the number of subject required for 80% power was 25 with the bivariate MHMM model versus 63 in the univariate MHMM FEV1 model and > 100 in the univariate MHMM PRO model. The results advocates for the use of bivariate MHMM models when implementation is possible.

Bayesian Forecasting Utilizing Bleeding Information to Support Dose Individualization of Factor VIII

Bayesian forecasting for dose individualization of prophylactic factor VIII replacement therapy using pharmacokinetic samples is challenged by large interindividual variability in the bleeding risk. A pharmacokinetic‐repeated time‐to‐event model‐based forecasting approach was developed to contrast the ability to predict the future occurrence of bleeds based on individual (i) pharmacokinetic, (ii) bleeding, and (iii) pharmacokinetic, bleeding and covariate information using observed data from the Long‐Term Efficacy Open‐Label Program in Severe Hemophilia A Disease (LEOPOLD) clinical trials (172 severe hemophilia A patients taking prophylactic treatment). The predictive performance assessed by the area under receiver operating characteristic (ROC) curves was 0.67 (95% confidence interval (CI), 0.65–0.69), 0.78 (95% CI, 0.76–0.80), and 0.79 (95% CI, 0.77–0.81) for patients ≥ 12 years when using pharmacokinetics, bleeds, and all data, respectively, suggesting that individual bleed information adds value to the optimization of prophylactic dosing regimens in severe hemophilia A. Further steps to optimize the proposed tool for factor VIII dose adaptation in the clinic are required.

Plasma and CSF pharmacokinetics of meropenem in neonates and young infants: results from the NeoMero studies

Background

Sepsis and bacterial meningitis are major causes of mortality and morbidity in neonates and infants. Meropenem, a broad-spectrum antibiotic, is not licensed for use in neonates and infants below 3 months of age and sufficient information on its plasma and CSF disposition and dosing in neonates and infants is lacking.

Objectives

To determine plasma and CSF pharmacokinetics of meropenem in neonates and young infants and the link between pharmacokinetics and clinical outcomes in babies with late-onset sepsis (LOS).

Methods

Data were collected in two recently conducted studies, i.e. NeoMero-1 (neonatal LOS) and NeoMero-2 (neonatal meningitis). Optimally timed plasma samples (n = 401) from 167 patients and opportunistic CSF samples (n = 78) from 56 patients were analysed.

Results

A one-compartment model with allometric scaling and fixed maturation gave adequate fit to both plasma and CSF data; the CL and volume (standardized to 70 kg) were 16.7 (95% CI 14.7, 18.9) L/h and 38.6 (95% CI 34.9, 43.4) L, respectively. CSF penetration was low (8%), but rose with increasing CSF protein, with 40% penetration predicted at a protein concentration of 6 g/L. Increased infusion time improved plasma target attainment, but lowered CSF concentrations. For 24 patients with culture-proven Gram-negative LOS, pharmacodynamic target attainment was similar regardless of the test-of-cure visit outcome.

Conclusions

Simulations showed that longer infusions increase plasma PTA but decrease CSF PTA. CSF penetration is worsened with long infusions so increasing dose frequency to achieve therapeutic targets should be considered.

Relating Nicotine Plasma Concentration to Momentary Craving Across Four Nicotine Replacement Therapy Formulations

Tobacco use is a major health concern. To assist smoking cessation, nicotine replacement therapy (NRT) is used to reduce nicotine craving. We quantitatively described the relationship between nicotine pharmacokinetics (PKs) from NRTs and momentary craving, linking two different pharmacodynamic (PD) scales for measuring craving. The dataset comprised retrospective data from 17 clinical studies and included 1,077 adult smokers with 39,802 craving observations from four formulations: lozenge, gum, mouth spray, and patch. A PK/PD model was developed that linked individual predicted nicotine concentrations with the categorical and visual analogue PD scales through a joint bounded integer model. A maximum effect model, accounting for acute tolerance development, successfully related nicotine concentrations to momentary craving. Results showed that all formulations were similarly effective in reducing craving, albeit with a fourfold lower potency for the patch. Women were found to have a higher maximal effect of nicotine to reduce craving, compared with men.

Relationship between factor VIII activity, bleeds and individual characteristics in severe hemophilia A patients

Pharmacokinetic-based prophylaxis of replacement factor VIII (FVIII) products has been encouraged in recent years, but the relationship between exposure (factor VIII activity) and response (bleeding frequency) remains unclear. The aim of this study was to characterize the relationship between FVIII dose, plasma FVIII activity, and bleeding patterns and individual characteristics in severe hemophilia A patients. Pooled pharmacokinetic and bleeding data during prophylactic treatment with BAY 81-8973 (octocog alfa) were obtained from the three LEOPOLD trials. The population pharmacokinetics of FVIII activity and longitudinal bleeding frequency, as well as bleeding severity, were described using non-linear mixed effects modeling in NONMEM. In total, 183 patients [median age 22 years (range, 1-61); weight 60 kg (11-124)] contributed with 1,535 plasma FVIII activity observations, 633 bleeds and 11 patient/study characteristics [median observation period 12 months (3.1-13.1)]. A parametric repeated time-to-categorical bleed model, guided by plasma FVIII activity from a 2-compartment population pharmacokinetic model, described the time to the occurrence of bleeds and their severity. Bleeding probability decreased with time of study, and a bleed was not found to affect the time of the next bleed. Several covariate effects were identified, including the bleeding history in the 12-month pre-study period increasing the bleeding hazard. However, unexplained inter-patient variability in the phenotypic bleeding pattern remained large (111%CV). Further studies to translate the model into a tool for dose individualization that considers the individual bleeding risk are required. Research was based on a post-hoc analysis of the LEOPOLD studies registered at clinicaltrials.gov identifiers: 01029340, 01233258 and 01311648.

Item Response Model Adaptation for Analyzing Data from Different Versions of Parkinson's Disease Rating Scales

Purpose

The aim of this work was to allow combination of information from recent and historical trials in Parkinson’s Disease (PD) by developing bridging methodology between two versions of the clinical endpoint.

Methods

A previously developed Item Response Model (IRM), that described longitudinal changes in Movement Disorder Society (MDS) sponsored revision of Unified Parkinson’s Disease Rating Scale (UPDRS) [MDS–UPDRS] data from the De Novo PD cohort in Parkinson’s Progression Markers Initiative, was first adapted to describe baseline UPDRS data from two clinical trials, one in subjects with early PD and another in subjects with advanced PD. Assuming similar IRM structure, items of the UPDRS version were mapped to those in the MDS-UPDRS version. Subsequently, the longitudinal changes in the placebo arm of the advanced PD study were characterized.

Results

The parameters reflecting differences in the shared items between endpoints were successfully estimated, and the model diagnostics indicated that mapping was better for early PD subjects (closer to De Novo cohort) than for advanced PD subjects. Disease progression for placebo in advanced PD patients was relatively shallow.

Conclusion

An IRM able to handle two variants of clinical PD endpoints was developed; it can improve the utilization of data from diverse sources and diverse disease populations.

Electronic supplementary material

The online version of this article (10.1007/s11095-019-2668-6) contains supplementary material, which is available to authorized users.

Sensitivity of Pegfilgrastim Pharmacokinetic and Pharmacodynamic Parameters to Product Differences in Similarity Studies

In this work, a previously developed pegfilgrastim (PG) population pharmacokinetic-pharmacodynamic (PKPD) model was used to evaluate potential factors of importance in the assessment of PG PK and PD similarity. Absolute neutrophil count (ANC) was the modelled PD variable. A two-way cross-over study was simulated where a reference PG and a potentially biosimilar test product were administered to healthy volunteers. Differences in delivered dose amounts or potency between the products were simulated. A different baseline absolute neutrophil count (ANC) was also considered. Additionally, the power to conclude PK or PD similarity based on areas under the PG concentration-time curve (AUC) and ANC-time curve (AUEC) were calculated. Delivered dose differences between the products led to a greater than dose proportional differences in AUC but not in AUEC, respectively. A 10% dose difference from a 6 mg dose resulted in 51% and 7% differences in AUC and AUEC, respectively. These differences were more pronounced with low baseline ANC. Potency differences up to 50% were not associated with large differences in either AUCs or AUECs. The power to conclude PK similarity was affected by the simulated dose difference; with a 4% dose difference from 6 mg the power was approximately 29% with 250 subjects. The power to conclude PD similarity was high for all delivered dose differences and sample sizes.

A Bounded Integer Model for Rating and Composite Scale Data

Rating and composite scales are commonly used to assess treatment efficacy. The two main strategies for modelling such endpoints are to treat them as a continuous or an ordered categorical variable (CV or OC). Both strategies have disadvantages, including making assumptions that violate the integer nature of the data (CV) and requiring many parameters for scales with many response categories (OC). We present a method, called the bounded integer (BI) model, which utilises the probit function with fixed cut-offs to estimate the probability of a certain score through a latent variable. This method was successfully implemented to describe six data sets from four different therapeutic areas: Parkinson's disease, Alzheimer's disease, schizophrenia, and neuropathic pain. Five scales were investigated, ranging from 11 to 181 categories. The fit (likelihood) was better for the BI model than for corresponding OC or CV models (ΔAIC range 11-1555) in all cases but one (∆AIC - 63), while the number of parameters was the same or lower. Markovian elements were successfully implemented within the method. The performance in external validation, assessed through cross-validation, was also in favour of the new model (ΔOFV range 22-1694) except in one case (∆OFV - 70). A residual for diagnostic purposes is discussed. This study shows that the BI model respects the integer nature of data and is parsimonious in terms of number of estimated parameters.

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.

Handling interoccasion variability in model-based dose individualization using therapeutic drug monitoring data

AIMS

This study aims to assess approaches to handle interoccasion variability (IOV) in a model-based therapeutic drug monitoring (TDM) context, using a population pharmacokinetic model of coagulation factor VIII as example.

METHODS

We assessed 5 model-based TDM approaches: empirical Bayes estimates (EBEs) from a model including IOV, with individualized doses calculated based on individual parameters either (i) including or (ii) excluding variability related to IOV; and EBEs from a model excluding IOV by (iii) setting IOV to zero, (iv) summing variances of interindividual variability (IIV) and IOV into a single IIV term, or (v) re-estimating the model without IOV. The impact of varying IOV magnitudes (0-50%) and number of occasions/observations was explored. The approaches were compared with conventional weight-based dosing. Predictive performance was assessed with the prediction error percentiles.

RESULTS

When IOV was lower than IIV, the accuracy was good for all approaches (50^th percentile of the prediction error [P50] <7.4%), but the precision varied substantially between IOV magnitudes (P97.5 61-528%). Approach (ii) was the most precise forecasting method across a wide range of scenarios, particularly in case of sparse sampling or high magnitudes of IOV. Weight-based dosing led to less precise predictions than the model-based TDM approaches in most scenarios.

CONCLUSIONS

Based on the studied scenarios and theoretical expectations, the best approach to handle IOV in model-based dose individualization is to include IOV in the generation of the EBEs but exclude the portion of unexplained variability related to IOV in the individual parameters used to calculate the future dose.

A Novel Method for Analysing Frequent Observations from Questionnaires in Order to Model Patient-Reported Outcomes: Application to EXACT® Daily Diary Data from COPD Patients

Chronic obstructive pulmonary disease (COPD) is a progressive lung disease with approximately 174 million cases worldwide. Electronic questionnaires are increasingly used for collecting patient-reported-outcome (PRO) data about disease symptoms. Our aim was to leverage PRO data, collected to record COPD disease symptoms, in a general modelling framework to enable interpretation of PRO observations in relation to disease progression and potential to predict exacerbations. The data were collected daily over a year, in a prospective, observational study. The e-questionnaire, the EXAcerbations of COPD Tool (EXACT®) included 14 items (i.e. questions) with 4 or 5 ordered categorical response options. An item response theory (IRT) model was used to relate the responses from each item to the underlying latent variable (which we refer to as disease severity), and on each item level, Markov models (MM) with 4 or 5 categories were applied to describe the dependence between consecutive observations. Minimal continuous time MMs were used and parameterised using ordinary differential equations. One hundred twenty-seven COPD patients were included (median age 67 years, 54% male, 39% current smokers), providing approximately 40,000 observations per EXACT® item. The final model suggested that, with time, patients more often reported the same scores as the previous day, i.e. the scores were more stable. The modelled COPD disease severity change over time varied markedly between subjects, but was small in the typical individual. This is the first IRT model with Markovian properties; our analysis proved them necessary for predicting symptom-defined exacerbations.

Translation Between Two Models; Application with Integrated Glucose Homeostasis Models

PURPOSE

For some biological systems, there exist several models with somewhat different features and perspectives. We propose an evaluation method for NLME models by analyzing real and simulated data from the model of main interest using a structurally different, but similar, NLME model. We showcase this method using the Integrated Glucose Insulin (IGI) model and the Integrated Minimal Model (IMM). Additionally, we try to map parameters carrying similar information between the two models.

METHODS

A bootstrap of real data and simulated datasets from both the IMM and IGI models were analyzed with the two models. Important parameters of the IMM were mapped to IGI parameters using a large IMM simulated dataset analyzed under the IGI model.

RESULTS

Comparison of the parameters estimated from real data and data simulated with the IMM and analyzed with the IGI model demonstrated differences between real and IMM-simulated data. Comparison of the parameters estimated from real data and data simulated with the IGI model and analyzed with the IMM also demonstrated differences but to a lower extent. The strongest parameter correlations were found for: insulin-dependent glucose clearance (IGI) ~ insulin sensitivity (IMM); insulin-independent glucose clearance (IGI) ~ glucose effectiveness (IMM); and insulin effect parameter (IGI) ~ insulin action (IMM).

CONCLUSIONS

We demonstrated a new approach to investigate models' ability to simulate real-life-like data, and the information captured in each model in comparison to real data, and the IMM clinically used parameters were successfully mapped to their corresponding IGI parameters.

Variability Attribution for Automated Model Building

We investigated the possible advantages of using linearization to evaluate models of residual unexplained variability (RUV) for automated model building in a similar fashion to the recently developed method “residual modeling.” Residual modeling, although fast and easy to automate, cannot identify the impact of implementing the needed RUV model on the imprecision of the rest of model parameters. We used six RUV models to be tested with 12 real data examples. Each example was first linearized; then, we assessed the agreement in improvement of fit between the base model and its extended models for linearization and conventional analysis, in comparison to residual modeling performance. Afterward, we compared the estimates of parameters’ variabilities and their uncertainties obtained by linearization to conventional analysis. Linearization accurately identified and quantified the nature and magnitude of RUV model misspecification similar to residual modeling. In addition, linearization identified the direction of change and quantified the magnitude of this change in variability parameters and their uncertainties. This method is implemented in the software package PsN for automated model building/evaluation with continuous data.

Model-Based Conditional Weighted Residuals Analysis for Structural Model Assessment

Nonlinear mixed effects models are widely used to describe longitudinal data to improve the efficiency of drug development process or increase the understanding of the studied disease. In such settings, the appropriateness of the modeling assumptions is critical in order to draw correct conclusions and must be carefully assessed for any substantial violations. Here, we propose a new method for structure model assessment, based on assessment of bias in conditional weighted residuals (CWRES). We illustrate this method by assessing prediction bias in two integrated models for glucose homeostasis, the integrated glucose-insulin (IGI) model, and the integrated minimal model (IMM). One dataset was simulated from each model then analyzed with the two models. CWRES outputted from each model fitting were modeled to capture systematic trends in CWRES as well as the magnitude of structural model misspecifications in terms of difference in objective function values (ΔOFV_Bias). The estimates of CWRES bias were used to calculate the corresponding bias in conditional predictions by the inversion of first-order conditional estimation method’s covariance equation. Time, glucose, and insulin concentration predictions were the investigated independent variables. The new method identified correctly the bias in glucose sub-model of the integrated minimal model (IMM), when this bias occurred, and calculated the absolute and proportional magnitude of the resulting bias. CWRES bias versus the independent variables agreed well with the true trends of misspecification. This method is fast easily automated diagnostic tool for model development/evaluation process, and it is already implemented as part of the Perl-speaks-NONMEM software.

Modeling Exposure-Driven Adverse Event Time Courses in Oncology Exemplified by Afatinib

Models were developed to characterize the relationship between afatinib exposure and diarrhea and rash/acne adverse event (AE) trajectories, and their predictive ability was assessed. Based on pooled data from seven phase II/III clinical studies including 998 patients, mixed‐effects models for ordered categorical data were applied to describe daily AE severity. Clinical trial simulation aided by trial execution models was used for internal and external model evaluation. The final exposure‐safety model consisted of longitudinal logistic regression models with first‐order Markov elements for both AEs. Drug exposure was included as daily area under the concentration‐time curve (AUC), and drug effects on the AEs were correlated. Clinical trial simulation allowed adequate prediction of maximum AE grades and AE severity time courses but overestimated the proportion of AE‐dependent dose reductions and discontinuations. Both diarrhea and rash/acne were correlated with afatinib exposure. The developed modeling framework allows a prospective comparison of dosing strategies and study designs with respect to safety.

A non-linear mixed effect model for innate immune response: In vivo kinetics of endotoxin and its induction of the cytokines tumor necrosis factor alpha and interleukin-6

Endotoxin, a component of the outer membrane of Gram-negative bacteria, has been extensively studied as a stimulator of the innate immune response. However, the temporal aspects and exposure-response relationship of endotoxin and resulting cytokine induction and tolerance development is less well defined. The aim of this work was to establish an in silico model that simultaneously captures and connects the in vivo time-courses of endotoxin, tumor necrosis factor alpha (TNF-α), interleukin-6 (IL-6), and associated tolerance development. Data from six studies of porcine endotoxemia in anesthetized piglets (n = 116) were combined and used in the analysis, with purified endotoxin (Escherichia coli O111:B4) being infused intravenously for 1–30 h in rates of 0.063–16.0 μg/kg/h across studies. All data were modelled simultaneously by means of importance sampling in the non-linear mixed effects modelling software NONMEM. The infused endotoxin followed one-compartment disposition and non-linear elimination, and stimulated the production of TNF-α to describe the rapid increase in plasma concentration. Tolerance development, observed as declining TNF-α concentration with continued infusion of endotoxin, was also driven by endotoxin as a concentration-dependent increase in the potency parameter related to TNF-α production (EC₅₀). Production of IL-6 was stimulated by both endotoxin and TNF-α, and four consecutive transit compartments described delayed increase in plasma IL-6. A model which simultaneously account for the time-courses of endotoxin and two immune response markers, the cytokines TNF-α and IL-6, as well as the development of endotoxin tolerance, was successfully established. This model-based approach is unique in its description of the time-courses and their interrelation and may be applied within research on immune response to bacterial endotoxin, or in pre-clinical pharmaceutical research when dealing with study design or translational aspects.

Population pharmacokinetics of oral ivermectin in venous plasma and dried blood spots in healthy volunteers

AIMS

The anthelminthic ivermectin is receiving new attention as it is being repurposed for new indications such as mass drug administrations for the treatment of scabies or in malaria vector control. As its pharmacokinetics are still poorly understood, we aimed to characterize the population pharmacokinetics of ivermectin in plasma and dried blood spots (DBS), a sampling method better suited to field trials, with special focus on the influence of body composition and enterohepatic circulation.

METHODS

We performed a clinical trial in 12 healthy volunteers who each received a single oral dose of 12 mg ivermectin, and collected peripheral venous and capillary DBS samples. We determined ivermectin concentrations in plasma and DBS by liquid chromatography tandem mass spectrometry using a fully automated and scalable extraction system for DBS sample processing. Pharmacokinetic data were analysed using non-linear mixed effects modelling.

RESULTS

A two-compartment model with a transit absorption model, first-order elimination, and weight as an influential covariate on central volume of distribution and clearance best described the data. The model estimates (inter-individual variability) for a 70 kg subject were: apparent population clearance 7.7 (25%) l h^-1 , and central and peripheral volumes of distribution 89 (10%) l and 234 (20%) l, respectively. Concentrations obtained from DBS samples were strongly linearly correlated (R²  = 0.97) with plasma concentrations, and on average 30% lower.

CONCLUSION

The model accurately depicts population pharmacokinetics of plasma and DBS concentrations over time for oral ivermectin. The proposed analytical workflow is scalable and applicable to the requirements of mass drug administrations.

A Population Pharmacokinetic-Pharmacodynamic Model of Pegfilgrastim

Neutropenia and febrile neutropenia (FN) are serious side effects of cytotoxic chemotherapy which may be alleviated with the administration of recombinant granulocyte colony-stimulating factor (GCSF) derivatives, such as pegfilgrastim (PG) which increases absolute neutrophil count (ANC). In this work, a population pharmacokinetic-pharmacodynamic (PKPD) model was developed based on data obtained from healthy volunteers receiving multiple administrations of PG. The developed model was a bidirectional PKPD model, where PG stimulated the proliferation, maturation, and margination of neutrophils and where circulating neutrophils in turn increased the elimination of PG. Simulations from the developed model show disproportionate changes in response with changes in dose. A dose increase of 10% from the 6 mg therapeutic dose taken as a reference leads to area under the curve (AUC) increases of ~50 and ~5% for PK and PD, respectively. A full random effects covariate model showed that little of the parameter variability could be explained by sex, age, body size, and race. As a consequence, little of the secondary parameter variability (C_max and AUC of PG and ANC) could be explained by these covariates.

Model-Based Prediction of Plasma Concentration and Enterohepatic Circulation of Total Bile Acids in Humans

Bile acids released postprandially can modify the rate and extent of lipophilic compounds' absorption. This study aimed to predict the enterohepatic circulation (EHC) of total bile acids (TBAs) in response to caloric intake from their spillover in plasma. A model for TBA EHC was combined with a previously developed gastric emptying (GE) model. Longitudinal gallbladder volumes and TBA plasma concentration data from 30 subjects studied after ingestion of four different test drinks were supplemented with literature data. Postprandial gallbladder refilling periods were implemented to improve model predictions. The TBA hepatic extraction was reduced with the high-fat drink. Basal and nutrient-induced gallbladder emptying rates were altered by type 2 diabetes (T2D). The model was predictive of the central trend and the variability of gallbladder volume and TBA plasma concentration for all test drinks. Integration of this model within physiological pharmacokinetic modeling frameworks could improve the predictions for lipophilic compounds' absorption considerably.