A 12-gene pharmacogenetic panel to prevent adverse drug reactions: an open-label, multicentre, controlled, cluster-randomised crossover implementation study.

BACKGROUND: The benefit of pharmacogenetic testing before starting drug therapy has been well documented for several single gene-drug combinations. However, the clinical utility of a pre-emptive genotyping strategy using a pharmacogenetic panel has not been rigorously assessed. METHODS: We conducted an open-label, multicentre, controlled, cluster-randomised, crossover implementation study of a 12-gene pharmacogenetic panel in 18 hospitals, nine community health centres, and 28 community pharmacies in seven European countries (Austria, Greece, Italy, the Netherlands, Slovenia, Spain, and the UK). Patients aged 18 years or older receiving a first prescription for a drug clinically recommended in the guidelines of the Dutch Pharmacogenetics Working Group (ie, the index drug) as part of routine care were eligible for inclusion. Exclusion criteria included previous genetic testing for a gene relevant to the index drug, a planned duration of treatment of less than 7 consecutive days, and severe renal or liver insufficiency. All patients gave written informed consent before taking part in the study. Participants were genotyped for 50 germline variants in 12 genes, and those with an actionable variant (ie, a drug-gene interaction test result for which the Dutch Pharmacogenetics Working Group [DPWG] recommended a change to standard-of-care drug treatment) were treated according to DPWG recommendations. Patients in the control group received standard treatment. To prepare clinicians for pre-emptive pharmacogenetic testing, local teams were educated during a site-initiation visit and online educational material was made available. The primary outcome was the occurrence of clinically relevant adverse drug reactions within the 12-week follow-up period. Analyses were irrespective of patient adherence to the DPWG guidelines. The primary analysis was done using a gatekeeping analysis, in which outcomes in people with an actionable drug-gene interaction in the study group versus the control group were compared, and only if the difference was statistically significant was an analysis done that included all of the patients in the study. Outcomes were compared between the study and control groups, both for patients with an actionable drug-gene interaction test result (ie, a result for which the DPWG recommended a change to standard-of-care drug treatment) and for all patients who received at least one dose of index drug. The safety analysis included all participants who received at least one dose of a study drug. This study is registered with ClinicalTrials.gov, NCT03093818 and is closed to new participants. FINDINGS: Between March 7, 2017, and June 30, 2020, 41 696 patients were assessed for eligibility and 6944 (51·4 % female, 48·6% male; 97·7% self-reported European, Mediterranean, or Middle Eastern ethnicity) were enrolled and assigned to receive genotype-guided drug treatment (n=3342) or standard care (n=3602). 99 patients (52 [1·6%] of the study group and 47 [1·3%] of the control group) withdrew consent after group assignment. 652 participants (367 [11·0%] in the study group and 285 [7·9%] in the control group) were lost to follow-up. In patients with an actionable test result for the index drug (n=1558), a clinically relevant adverse drug reaction occurred in 152 (21·0%) of 725 patients in the study group and 231 (27·7%) of 833 patients in the control group (odds ratio [OR] 0·70 [95% CI 0·54-0·91]; p=0·0075), whereas for all patients, the incidence was 628 (21·5%) of 2923 patients in the study group and 934 (28·6%) of 3270 patients in the control group (OR 0·70 [95% CI 0·61-0·79]; p <0·0001). INTERPRETATION: Genotype-guided treatment using a 12-gene pharmacogenetic panel significantly reduced the incidence of clinically relevant adverse drug reactions and was feasible across diverse European health-care system organisations and settings. Large-scale implementation could help to make drug therapy increasingly safe. FUNDING: European Union Horizon 2020.

A pharmacometric multistate model for predicting long-term treatment outcomes of patients with pulmonary TB.

BACKGROUND: Studying long-term treatment outcomes of TB is time-consuming and impractical. Early and reliable biomarkers reflecting treatment response and capable of predicting long-term outcomes are urgently needed. OBJECTIVES: To develop a pharmacometric multistate model to evaluate the link between potential predictors and long-term outcomes. METHODS: Data were obtained from two Phase II clinical trials (TMC207-C208 and TMC207-C209) with bedaquiline on top of a multidrug background regimen. Patients were typically followed throughout a 24 week investigational treatment period plus a 96 week follow-up period. A five-state multistate model (active TB, converted, recurrent TB, dropout, and death) was developed to describe observed transitions. Evaluated predictors included patient characteristics, baseline TB disease severity and on-treatment biomarkers. RESULTS: A fast bacterial clearance in the first 2 weeks and low TB bacterial burden at baseline increased probability to achieve conversion, whereas patients with XDR-TB were less likely to reach conversion. Higher estimated mycobacterial load at the end of 24 week treatment increased the probability of recurrence. At 120 weeks, the model predicted 55% (95% prediction interval, 50%-60%), 6.5% (4.2%-9.0%) and 7.5% (5.2%-10%) of patients in converted, recurrent TB and death states, respectively. Simulations predicted a substantial increase of recurrence after 24 weeks in patients with slow bacterial clearance regardless of baseline bacterial burden. CONCLUSIONS: The developed multistate model successfully described TB treatment outcomes. The multistate modelling framework enables prediction of several outcomes simultaneously, and allows mechanistically sound investigation of novel promising predictors. This may help support future biomarker evaluation, clinical trial design and analysis.

Properties of the full random-effect modeling approach with missing covariate data.

During drug development, a key step is the identification of relevant covariates predicting between-subject variations in drug response. The full random effects model (FREM) is one of the full-covariate approaches used to identify relevant covariates in nonlinear mixed effects models. Here we explore the ability of FREM to handle missing (both missing completely at random (MCAR) and missing at random (MAR)) covariate data and compare it to the full fixed-effects model (FFEM) approach, applied either with complete case analysis or mean imputation. A global health dataset (20 421 children) was used to develop a FREM describing the changes of height for age Z-score (HAZ) over time. Simulated datasets (n = 1000) were generated with variable rates of missing (MCAR) covariate data (0%-90%) and different proportions of missing (MAR) data condition on either observed covariates or predicted HAZ. The three methods were used to re-estimate model and compared in terms of bias and precision which showed that FREM had only minor increases in bias and minor loss of precision at increasing percentages of missing (MCAR) covariate data and performed similarly in the MAR scenarios. Conversely, the FFEM approaches either collapsed at ≥ $$ \ge $$ 70% of missing (MCAR) covariate data (FFEM complete case analysis) or had large bias increases and loss of precision (FFEM with mean imputation). Our results suggest that FREM is an appropriate approach to covariate modeling for datasets with missing (MCAR and MAR) covariate data, such as in global health studies.

Characterization of anti-drug antibody dynamics using a bivariate mixed hidden-markov model by nonlinear-mixed effects approach.

Biological therapies may act as immunogenic triggers leading to the formation of anti-drug antibodies (ADAs). Population pharmacokinetic (PK) models can be used to characterize the relationship between ADA and drug disposition but often rely on the ADA bioassay results, which may not be sufficiently sensitive to inform on this characterization.In this work, a methodology that could help to further elucidate the underlying ADA production and impact on the drug disposition was explored. A mixed hidden-Markov model (MHMM) was developed to characterize the underlying (hidden) formation of ADA against the biologic, using certolizumab pegol (CZP), as a test drug. CZP is a PEGylated Fc free TNF-inhibitor used in the treatment of rheumatoid arthritis and other chronic inflammatory diseases.The bivariate MHMM used information from plasma drug concentrations and ADA measurements, from six clinical studies (n = 845), that were correlated through a bivariate Gaussian function to infer about two hidden states; production and no-production of ADA influencing PK. Estimation of inter-individual variability was not supported in this case. Parameters associated with the observed part of the model were reasonably well estimated while parameters associated with the hidden part were less precise. Individual state sequences obtained using a Viterbi algorithm suggested that the model was able to determine the start of ADA production for each individual, being a more assay-independent methodology than traditional population PK. The model serves as a basis for identification of covariates influencing the ADA formation, and thus has the potential to identify aspects that minimize its impact on PK and/or efficacy.

Training the next generation of pharmacometric modelers: a multisector perspective.

The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970's and early 1980's and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations.

Generation and application of avatars in pharmacometric modelling.

Simulations from population models have critical applications in drug discovery and development. Avatars or digital twins, defined as individual simulations matching clinical criteria of interest compared to observations from a real subject within a predefined margin of accuracy, may be a better option for simulations performed to inform future drug development stages in cases where an adequate model is not achievable. The aim of this work was to (1) investigate methods for generating avatars with pharmacometric models, and (2) explore the properties of the generated avatars to assess the impact of the different selection settings on the number of avatars per subject, their closeness to the individual observations, and the properties of the selected samples subset from the theoretical model parameters probability density function. Avatars were generated using different combinations of nature and number of clinical criteria, accuracy of agreement, and/or number of simulations for two examples models previously published (hemato-toxicity and integrated glucose-insulin model). The avatar distribution could be used to assess the appropriateness of the models assumed parameter distribution. Similarly it could be used to assess the models ability to properly describe the trajectories of the observations. Avatars can give nuanced information regarding the ability of a model to simulate data similar to the observations both at the population and at the individual level. Further potential applications for avatars may be as a diagnostic tool, an alternative to simulations with insurance to replicate key clinical features, and as an individual measure of model fit.

Joint longitudinal model-based meta-analysis of FEV1 and exacerbation rate in randomized COPD trials.

Model-based meta-analysis (MBMA) is an approach that integrates relevant summary level data from heterogeneously designed randomized controlled trials (RCTs). This study not only evaluated the predictability of a published MBMA for forced expiratory volume in one second (FEV1) and its link to annual exacerbation rate in patients with chronic obstructive pulmonary disease (COPD) but also included data from new RCTs. A comparative effectiveness analysis across all drugs was also performed. Aggregated level data were collected from RCTs published between July 2013 and November 2020 (n = 132 references comprising 156 studies) and combined with data used in the legacy MBMA (published RCTs up to July 2013 - n = 142). The augmented data (n = 298) were used to evaluate the predictive performance of the published MBMA using goodness-of-fit plots for assessment. Furthermore, the model was extended including drugs that were not available before July 2013, estimating a new set of parameters. The legacy MBMA model predicted the post-2013 FEV1 data well, and new estimated parameters were similar to those of drugs in the same class. However, the exacerbation model overpredicted the post-2013 mean annual exacerbation rate data. Inclusion of year when the study started on the pre-treatment placebo rate improved the model predictive performance perhaps explaining potential improvements in the disease management over time. The addition of new data to the legacy COPD MBMA enabled a more robust model with increased predictability performance for both endpoints FEV1 and mean annual exacerbation rate.

Population Pharmacokinetic and Concentration-QTc Analysis of Delamanid in Pediatric Participants with Multidrug-Resistant Tuberculosis.

A population pharmacokinetic analysis of delamanid and its major metabolite DM-6705 was conducted to characterize the pharmacokinetics of delamanid and DM-6705 in pediatric participants with multidrug-resistant tuberculosis (MDR-TB). Data from participants between the ages of 0.67 and 17 years, enrolled in 2 clinical trials, were utilized for the analysis. The final data set contained 634 delamanid and 706 DM-6705 valid plasma concentrations from 37 children. A transit model with three compartments best described the absorption of delamanid. Two-compartment models for each component with linear elimination were selected to characterize the dispositions of delamanid and DM-6705, respectively. The covariates included in the model were body weight on the apparent volume of distribution and apparent clearance (for both delamanid and DM-6705); formulation (dispersible versus film-coated tablet) on the mean absorption time; age, formulation, and dose on the bioavailability of delamanid; and age on the fraction of delamanid metabolized to DM-6705. Based on the simulations, doses for participants within different age/weight groups that result in delamanid exposure comparable to that in adults following the approved adult dose were calculated. By concentration-QTc (QTcB [QT corrected by Bazett's formula]) analysis, a significant positive correlation was detected with concentrations of DM-6705. However, the model-predicted upper bounds of the 90% confidence intervals of ΔQTc values were <10 ms at the simulated maximum concentration (Cmax) of DM-6705 following the administration of the maximum doses simulated. This suggests that the effect on the QT interval following the proposed dosing is unlikely to be clinically meaningful in children with MDR-TB who receive delamanid.

Ceftriaxone dosing based on the predicted probability of augmented renal clearance in critically ill patients with pneumonia.

OBJECTIVES: PTA of protein-unbound ceftriaxone may be compromised in critically ill patients with community-acquired pneumonia (CAP) with augmented renal clearance (ARC). We aimed to determine an optimized ceftriaxone dosage regimen based on the probability of developing ARC on the next day (PARC,d+1; www.arcpredictor.com). PATIENTS AND METHODS: Thirty-three patients enrolled in a prospective cohort study were admitted to the ICU with severe CAP and treated with ceftriaxone 2 g once daily. Patients contributed 259 total ceftriaxone concentrations, collected during 1 or 2 days (±7 samples/day). Unbound fractions of ceftriaxone were determined in all peak and trough samples (n = 76). Population pharmacokinetic modelling and simulation were performed using NONMEM7.4. Target attainment was defined as an unbound ceftriaxone concentration >4 mg/L throughout the dosing interval. RESULTS: A two-compartment population pharmacokinetic model described the data well. The maximal protein-bound ceftriaxone concentration decreased with lower serum albumin. Ceftriaxone clearance increased with body weight and PARC,d+1 determined on the previous day. A high PARC,d+1 was identified as a clinically relevant predictor for underexposure on the next day (area under the receiver operating characteristics curve 0.77). Body weight had a weak predictive value and was therefore considered clinically irrelevant. Serum albumin had no predictive value. An optimal PARC,d+1 threshold of 5.7% was identified (sensitivity 73%, specificity 69%). Stratified once- or twice-daily 2 g dosing when below or above the 5.7% PARC,d+1 cut-off, respectively, was predicted to result in 81% PTA compared with 47% PTA under population-level once-daily 2 g dosing. CONCLUSIONS: Critically ill patients with CAP with a high PARC,d+1 may benefit from twice-daily 2 g ceftriaxone dosing for achieving adequate exposure on the next day.

Improved Confidence in a Confirmatory Stage by Application of Item-Based Pharmacometrics Model: Illustration with a Phase III Active Comparator-Controlled Trial in COPD Patients.

PURPOSE: The current study aimed to illustrate how a non-linear mixed effect (NLME) model-based analysis may improve confidence in a Phase III trial through more precise estimates of the drug effect. METHODS: The FULFIL clinical trial was a Phase III study that compared 24 weeks of once daily inhaled triple therapy with twice daily inhaled dual therapy in patients with chronic obstructive pulmonary disease (COPD). Patient reported outcome data, obtained by using The Evaluating Respiratory Symptoms in COPD (E-RS:COPD) questionnaire, from the FULFIL study were analyzed using an NLME item-based response theory model (IRT). The change from baseline (CFB) in E-RS:COPD total score over 4-week intervals for each treatment arm was obtained using the IRT and compared with published results obtained with a mixed model repeated measures (MMRM) analysis. RESULTS: The IRT included a graded response model characterizing item parameters and a Weibull function combined with an offset function to describe the COPD symptoms-time course in patients receiving either triple therapy (n = 907) or dual therapy (n = 894). The IRT improved precision of the estimated drug effect compared to MMRM, resulting in a sample size of at least 3.64 times larger for the MMRM analysis to achieve the IRT precision in the CFB estimate. CONCLUSION: This study shows the advantage of IRT over MMRM with a direct comparison of the same primary endpoint for the two analyses using the same observed clinical trial data, resulting in an increased confidence in Phase III.

Pharmacokinetics and safety of high-dose rifampicin in children with TB: the Opti-Rif trial.

BACKGROUND: Rifampicin doses of 40 mg/kg in adults are safe and well tolerated, may shorten anti-TB treatment and improve outcomes, but have not been evaluated in children. OBJECTIVES: To characterize the pharmacokinetics and safety of high rifampicin doses in children with drug-susceptible TB. PATIENTS AND METHODS: The Opti-Rif trial enrolled dosing cohorts of 20 children aged 0-12 years, with incremental dose escalation with each subsequent cohort, until achievement of target exposures or safety concerns. Cohort 1 opened with a rifampicin dose of 15 mg/kg for 14 days, with a single higher dose (35 mg/kg) on day 15. Pharmacokinetic data from days 14 and 15 were analysed using population modelling and safety data reviewed. Incrementally increased rifampicin doses for the next cohort (days 1-14 and day 15) were simulated from the updated model, up to the dose expected to achieve the target exposure [235 mg/L·h, the geometric mean area under the concentration-time curve from 0 to 24 h (AUC0-24) among adults receiving a 35 mg/kg dose]. RESULTS: Sixty-two children were enrolled in three cohorts. The median age overall was 2.1 years (range = 0.4-11.7). Evaluated doses were ∼35 mg/kg (days 1-14) and ∼50 mg/kg (day 15) for cohort 2 and ∼60 mg/kg (days 1-14) and ∼75 mg/kg (day 15) for cohort 3. Approximately half of participants had an adverse event related to study rifampicin; none was grade 3 or higher. A 65-70 mg/kg rifampicin dose was needed in children to reach the target exposure. CONCLUSIONS: High rifampicin doses in children achieved target exposures and the doses evaluated were safe over 2 weeks.

Pharmacometrics-Based Considerations for the Design of a Pharmacogenomic Clinical Trial Assessing Irinotecan Safety.

PURPOSE: Pharmacometric models provide useful tools to aid the rational design of clinical trials. This study evaluates study design-, drug-, and patient-related features as well as analysis methods for their influence on the power to demonstrate a benefit of pharmacogenomics (PGx)-based dosing regarding myelotoxicity. METHODS: Two pharmacokinetic and one myelosuppression model were assembled to predict concentrations of irinotecan and its metabolite SN-38 given different UGT1A1 genotypes (poor metabolizers: CLSN-38: -36%) and neutropenia following conventional versus PGx-based dosing (350 versus 245 mg/m2 (-30%)). Study power was assessed given diverse scenarios (n = 50-400 patients/arm, parallel/crossover, varying magnitude of CLSN-38, exposure-response relationship, inter-individual variability) and using model-based data analysis versus conventional statistical testing. RESULTS: The magnitude of CLSN-38 reduction in poor metabolizers and the myelosuppressive potency of SN-38 markedly influenced the power to show a difference in grade 4 neutropenia (<0.5·109 cells/L) after PGx-based versus standard dosing. To achieve >80% power with traditional statistical analysis (χ2/McNemar's test, α = 0.05), 220/100 patients per treatment arm/sequence (parallel/crossover study) were required. The model-based analysis resulted in considerably smaller total sample sizes (n = 100/15 given parallel/crossover design) to obtain the same statistical power. CONCLUSIONS: The presented findings may help to avoid unfeasible trials and to rationalize the design of pharmacogenetic studies.

Improved numerical stability for the bounded integer model.

This article highlights some numerical challenges when implementing the bounded integer model for composite score modeling and suggests an improved implementation. The improvement is based on an approximation of the logarithm of the error function. After presenting the derivation of the improved implementation, the article compares the performance of the algorithm to a naive implementation of the log-likelihood using both simulations and a real data example. In the simulation setting, the improved algorithm yielded more precise and less biased parameter estimates when the within-subject variability was small and estimation was performed using the Laplace algorithm. The estimation results did not differ between implementations when the SAEM algorithm was used. For the real data example, bootstrap results differed between implementations with the improved implementation producing identical or better objective function values. Based on the findings in this article, the improved implementation is suggested as the new default log-likelihood implementation for the bounded integer model.

A general model for cell death and biomarker release from injured tissues.

Cellular response to insults may result in the initiation of different cell death processes. For many cases the cell death process will result in an acute release of cellular material that in some circumstances provides valuable information about the process (i.e. may represent a biomarker). The characteristics of the biomarker release is often informative and plays critical roles in clinical practice and toxicology research. The aim of this study is to develop a general, semi-mechanistic model to describe cell turnover and biomarker release by injured tissue that can be used for estimation in pharmacokinetic and (toxicokinetic)-pharmacodynamic studies. The model included three components: (1) natural tissue turnover, (2) biomarker release from cell death and its movement from the cell through the tissue into the blood, (3) different target insult mechanisms of cell death. We applied the general model to biomarker release profiles for four different cell insult causes. Our model simulations showed good agreements with reported data under both delayed release and rapid release cases. Additionally, we illustrate the use of the model to provide different biomarker profiles. We also provided details on interpreting parameters and their values for other researchers to customize its use. In conclusion, our general model provides a basic structure to study the kinetic behaviour of biomarker release and disposition after cellular insult.

Generating evidence for precision medicine: considerations made by the Ubiquitous Pharmacogenomics Consortium when designing and operationalizing the PREPARE study.

OBJECTIVES: Pharmacogenetic panel-based testing represents a new model for precision medicine. A sufficiently powered prospective study assessing the (cost-)effectiveness of a panel-based pharmacogenomics approach to guide pharmacotherapy is lacking. Therefore, the Ubiquitous Pharmacogenomics Consortium initiated the PREemptive Pharmacogenomic testing for prevention of Adverse drug Reactions (PREPARE) study. Here, we provide an overview of considerations made to mitigate multiple methodological challenges that emerged during the design. METHODS: An evaluation of considerations made when designing the PREPARE study across six domains: study aims and design, primary endpoint definition and collection of adverse drug events, inclusion and exclusion criteria, target population, pharmacogenomics intervention strategy, and statistical analyses. RESULTS: Challenges and respective solutions included: (1) defining and operationalizing a composite primary endpoint enabling measurement of the anticipated effect, by including only severe, causal, and drug genotype-associated adverse drug reactions; (2) avoiding overrepresentation of frequently prescribed drugs within the patient sample while maintaining external validity, by capping drugs of enrolment; (3) designing the pharmacogenomics intervention strategy to be applicable across ethnicities and healthcare settings; and (4) designing a statistical analysis plan to avoid dilution of effect by initially excluding patients without a gene-drug interaction in a gatekeeping analysis. CONCLUSION: Our design considerations will enable quantification of the collective clinical utility of a panel of pharmacogenomics-markers within one trial as a proof-of-concept for pharmacogenomics-guided pharmacotherapy across multiple actionable gene-drug interactions. These considerations may prove useful to other investigators aiming to generate evidence for precision medicine.

The effect of food on the pharmacokinetics of oral ivermectin.

BACKGROUND: Ivermectin is an older anthelminthic agent that is being studied more intensely given its potential for mass drug administration against scabies, malaria and other neglected tropical diseases. Its pharmacokinetics (PK) remain poorly characterized. Furthermore, the majority of PK trials are performed under fasted-state dosing conditions, and the effect of food is therefore not well known. To better plan and design field trials with ivermectin, a model that can account for both conditions would be valuable. OBJECTIVES: To develop a PK model and characterize the food effect with single oral doses of ivermectin. PATIENTS AND METHODS: We performed a population-based PK analysis of data pooled from two previous trials of a single dose of 12 mg ivermectin, one with dosing after a high-fat breakfast (n=12) and one with fasted-state dosing (n=3). RESULTS: The final model described concentration-time profiles after fed and fasted dosing accurately, and estimated the food effect associated with relative bioavailability to 1.18 (95% CI 1.10-1.67). CONCLUSIONS: In this analysis, the effect of a high-fat breakfast compared with a fasted-state administration of a single oral dose of 12 mg ivermectin was minimal.

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.

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.

Comparison of covariate selection methods with correlated covariates: prior information versus data information, or a mixture of both?

The inclusion of covariates in population models during drug development is a key step to understanding drug variability and support dosage regimen proposal, but high correlation among covariates often complicates the identification of the true covariate. We compared three covariate selection methods balancing data information and prior knowledge: (1) full fixed effect modelling (FFEM), with covariate selection prior to data analysis, (2) simplified stepwise covariate modelling (sSCM), data driven selection only, and (3) Prior-Adjusted Covariate Selection (PACS) mixing both. PACS penalizes the a priori less likely covariate model by adding to its objective function value (OFV) a prior probability-derived constant: [Formula: see text], Pr(X) being the probability of the more likely covariate. Simulations were performed to compare their external performance (average OFV in a validation dataset of 10,000 subjects) in selecting the true covariate between two highly correlated covariates: 0.5, 0.7, or 0.9, after a training step on datasets of 12, 25 or 100 subjects (increasing power). With low power data no method was superior, except FFEM when associated with highly correlated covariates ([Formula: see text]), sSCM and PACS suffering both from selection bias. For high power data, PACS and sSCM performed similarly, both superior to FFEM. PACS is an alternative for covariate selection considering both the expected power to identify an anticipated covariate relation and the probability of prior information being correct. A proposed strategy is to use FFEM whenever the expected power to distinguish between contending models is < 80%, PACS when > 80% but < 100%, and SCM when the expected power is 100%.

A longitudinal item response model for Aberrant Behavior Checklist (ABC) data from children with autism.

This manuscript aims to present the first item response theory (IRT) model within a pharmacometric framework to characterize the longitudinal changes of Aberrant Behavior Checklist (ABC) data in children with autism. Data were obtained from 120 patients, which included 20,880 observations of the 58 items for up to three months. Observed scores for each ABC item were modeled as a function of the subject's disability. Longitudinal IRT models with five latent disability variables based on ABC subscales were used to describe the irritability, lethargy, stereotypic behavior, hyperactivity, and inappropriate speech over time. The IRT pharmacometric models could accurately describe the longitudinal changes of the patient's disability while estimating different time-course of disability for the subscales. For all subscales, model-estimated disability was reduced following initiation of therapy, most markedly for hyperactivity. The developed framework provides a description of ABC longitudinal data that can be a suitable alternative to traditional ABC data collected in autism clinical trials. IRT is a powerful tool with the ability to capture the heterogeneous nature of ABC, which results in more accurate analysis in comparison to traditional approaches.