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1.
  • Amilon, Carl, et al. (författare)
  • Population Pharmacodynamic Modeling of Eflornithine-Based Treatments Against Late-Stage Gambiense Human African Trypanosomiasis and Efficacy Predictions of L-eflornithine-Based Therapy
  • 2022
  • Ingår i: The AAPS journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 24:3
  • Tidskriftsartikel (refereegranskat)abstract
    • (Carl Amilon and Mikael Boberg contributed equally to this work) Eflornithine is a recommended treatment against late-stage gambiense human African trypanosomiasis, a neglected tropical disease. Standard dosing of eflornithine consists of repeated intravenous infusions of a racemic mixture of L- and D-eflornithine. Data from three clinical studies, (i) eflornithine intravenous monotherapy, (ii) nifurtimox-eflornithine combination therapy, and (iii) eflornithine oral monotherapy, were pooled and analyzed using a time-to-event pharmacodynamic modeling approach, supported by in vitro activity data of the individual enantiomers. Our aim was to assess (i) the efficacy of the eflornithine regimens in a time-to-event analysis and (ii) the feasibility of an L-eflornithine-based therapy integrating clinical and preclinical data. A pharmacodynamic time-to-event model was used to estimate the total dose of eflornithine, associated with 50% reduction in baseline hazard, when administered as monotherapy or in the nifurtimox-eflornithine combination therapy. The estimated total doses were 159, 60 and 291 g for intravenous eflornithine monotherapy, nifurtimox-eflornithine combination therapy and oral eflornithine monotherapy, respectively. Simulations suggested that L-eflornithine achieves a higher predicted median survival, compared to when racemate is administered, as treatment against late-stage gambiense human African trypanosomiasis. Our findings showed that oral L-eflornithine-based monotherapy would not result in adequate efficacy, even at high dose, and warrants further investigations to assess the potential of oral L-eflornithine-based treatment in combination with other treatments such as nifurtimox. An all-oral eflornithine-based regimen would provide easier access to treatment and reduce burden on patients and healthcare systems in gambiense human African trypanosomiasis endemic areas. Graphical abstract.
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2.
  • Aoki, Yasunori, et al. (författare)
  • Preconditioning of Nonlinear Mixed Effects Models for Stabilisation of Variance-Covariance Matrix Computations
  • 2016
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 18:2, s. 505-518
  • Tidskriftsartikel (refereegranskat)abstract
    • As the importance of pharmacometric analysis increases, more and more complex mathematical models are introduced and computational error resulting from computational instability starts to become a bottleneck in the analysis. We propose a preconditioning method for non-linear mixed effects models used in pharmacometric analyses to stabilise the computation of the variance-covariance matrix. Roughly speaking, the method reparameterises the model with a linear combination of the original model parameters so that the Hessian matrix of the likelihood of the reparameterised model becomes close to an identity matrix. This approach will reduce the influence of computational error, for example rounding error, to the final computational result. We present numerical experiments demonstrating that the stabilisation of the computation using the proposed method can recover failed variance-covariance matrix computations, and reveal non-identifiability of the model parameters.
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3.
  • Arrington, Leticia, et al. (författare)
  • Comparison of Two Methods for Determining Item Characteristic Functions and Latent Variable Time-Course for Pharmacometric Item Response Models
  • 2024
  • Ingår i: AAPS Journal. - : Springer. - 1550-7416. ; 26
  • Tidskriftsartikel (refereegranskat)abstract
    • There are examples in the literature demonstrating different approaches to defining the item characteristic functions (ICF) and characterizing the latent variable time-course within a pharmacometrics item response theory (IRT) framework. One such method estimates both the ICF and latent variable time-course simultaneously, and another method establishes the ICF first then models the latent variable directly. To date, a direct comparison of the "simultaneous" and "sequential" methodologies described in this work has not yet been systematically investigated. Item parameters from a graded response IRT model developed from Parkinson's Progression Marker Initiative (PPMI) study data were used as simulation parameters. Each method was evaluated under the following conditions: (i) with and without drug effect and (ii) slow progression rate with smaller sample size and rapid progression rate with larger sample size. Overall, the methods performed similarly, with low bias and good precision for key parameters and hypothesis testing for drug effect. The ICF parameters were well determined when the model was correctly specified, with an increase in precision in the scenario with rapid progression. In terms of drug effect, both methods had large estimation bias for the slow progression rate; however, this bias can be considered small relative to overall progression rate. Both methods demonstrated type 1 error control and similar discrimination between model with and without drug effect. The simultaneous method was slightly more precise than the sequential method while the sequential method was more robust towards longitudinal model misspecification and offers practical advantages in model building.
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5.
  • Backhaus, Thomas, 1967 (författare)
  • Environmental Risk Assessment of Pharmaceutical Mixtures: Demands, Gaps, and Possible Bridges
  • 2016
  • Ingår i: Aaps Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 18:4, s. 804-813
  • Tidskriftsartikel (refereegranskat)abstract
    • The ecotoxicological risk of pharmaceutical mixtures typically exceeds the risk of each individual compound, which calls specific attention to the fact that monitoring surveys routinely find complex pharmaceutical mixtures in various environmental compartments. However, although the body of evidence on the ecotoxicology of pharmaceutical mixtures is quite consistent, the current guidelines for the environmental risk assessment of pharmaceuticals often do not explicitly address mixture effects. Data availability and acceptable methods often limit such assessments. A tiered approach that begins with summing up individual risk quotients, i.e., the ratio between the predicted or measured environmental concentration and the predicted no effect concentration (PNEC) is therefore suggested in this paper, in order to improve the realism of the environmental risk assessment of pharmaceuticals. Additionally, the use of a mixture-specific assessment factor, as well as the classical mixture toxicity concepts of concentration addition and independent action is explored. Finally, specific attention is given to the exposure-based waiving of environmental risk assessments, as currently implemented in screening or pre-screening phases (tier 0 in Europe, categorical exclusion in the USA), since even low, individually non-toxic concentrations might combine to produce substantial mixture effects.
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6.
  • Bender, Brendan, et al. (författare)
  • A Mechanistic Pharmacokinetic Model Elucidating the Disposition of Trastuzumab Emtansine (T-DM1), an Antibody-Drug Conjugate (ADC) for Treatment of Metastatic Breast Cancer
  • 2014
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 16:5, s. 994-1008
  • Tidskriftsartikel (refereegranskat)abstract
    • Trastuzumab emtansine (T-DM1) is an antibody-drug conjugate (ADC) therapeutic for treatment of human epidermal growth factor receptor 2 (HER2)-positive cancers. The T-DM1 dose product contains a mixture of drug-to-antibody ratio (DAR) moieties whereby the small molecule DM1 is chemically conjugated to trastuzumab antibody. The pharmacokinetics (PK) underlying this system and other ADCs are complex and have not been elucidated. Accordingly, we have developed two PK modeling approaches from preclinical data to conceptualize and understand T-DM1 PK, to quantify rates of DM1 deconjugation, and to elucidate the link between trastuzumab, T-DM1, and DAR measurements. Preclinical data included PK studies in rats (n = 34) and cynomolgus monkeys (n = 18) at doses ranging from 0.3 to 30 mg/kg and in vitro plasma stability. T-DM1 and total trastuzumab (TT) plasma concentrations were measured by enzyme-linked immunosorbent assay. Individual DAR moieties were measured by affinity capture liquid chromatography-mass spectrophotometry. Two PK modeling approaches were developed for T-DM1 using NONMEM 7.2 software: a mechanistic model fit simultaneously to TT and DAR concentrations and a reduced model fit simultaneously to TT and T-DM1 concentrations. DAR moieties were well described with a three-compartmental model and DM1 deconjugation in the central compartment. DM1 deconjugated fastest from the more highly loaded trastuzumab molecules (i.e., DAR moieties that are a parts per thousand yen3 DM1 per trastuzumab). T-DM1 clearance (CL) was 2-fold faster than TT CL due to deconjugation. The two modeling approaches provide flexibility based on available analytical measurements for T-DM1 and a framework for designing ADC studies and PK-pharmacodynamic modeling of ADC efficacy- and toxicity-related endpoints.
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7.
  • Bergstrand, Martin, et al. (författare)
  • Handling data below the limit of quantification in mixed effect models.
  • 2009
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 11:2, s. 371-380
  • Tidskriftsartikel (refereegranskat)abstract
    • The purpose of this study is to investigate the impact of observations below the limit of quantification (BQL) occurring in three distinctly different ways and assess the best method for prevention of bias in parameter estimates and for illustrating model fit using visual predictive checks (VPCs). Three typical ways in which BQL can occur in a model was investigated with simulations from three different models and different levels of the limit of quantification (LOQ). Model A was used to represent a case with BQL observations in an absorption phase of a PK model whereas model B represented a case with BQL observations in the elimination phase. The third model, C, an indirect response model illustrated a case where the variable of interest in some cases decreases below the LOQ before returning towards baseline. Different approaches for handling of BQL data were compared with estimation of the full dataset for 100 simulated datasets following models A, B, and C. An improved standard for VPCs was suggested to better evaluate simulation properties both for data above and below LOQ. Omission of BQL data was associated with substantial bias in parameter estimates for all tested models even for seemingly small amounts of censored data. Best performance was seen when the likelihood of being below LOQ was incorporated into the model. In the tested examples this method generated overall unbiased parameter estimates. Results following substitution of BQL observations with LOQ/2 were in some cases shown to introduce bias and were always suboptimal to the best method. The new standard VPCs was found to identify model misfit more clearly than VPCs of data above LOQ only.
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8.
  • Bergstrand, Martin, 1977-, et al. (författare)
  • Prediction-Corrected Visual Predictive Checks for Diagnosing Nonlinear Mixed-Effects Models
  • 2011
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 13:2, s. 143-151
  • Tidskriftsartikel (refereegranskat)abstract
    • Informative diagnostic tools are vital to the development of useful mixed-effects models. The Visual Predictive Check (VPC) is a popular tool for evaluating the performance of population PK and PKPD models. Ideally, a VPC will diagnose both the fixed and random effects in a mixed-effects model. In many cases, this can be done by comparing different percentiles of the observed data to percentiles of simulated data, generally grouped together within bins of an independent variable. However, the diagnostic value of a VPC can be hampered by binning across a large variability in dose and/or influential covariates. VPCs can also be misleading if applied to data following adaptive designs such as dose adjustments. The prediction-corrected VPC (pcVPC) offers a solution to these problems while retaining the visual interpretation of the traditional VPC. In a pcVPC, the variability coming from binning across independent variables is removed by normalizing the observed and simulated dependent variable based on the typical population prediction for the median independent variable in the bin. The principal benefit with the pcVPC has been explored by application to both simulated and real examples of PK and PKPD models. The investigated examples demonstrate that pcVPCs have an enhanced ability to diagnose model misspecification especially with respect to random effects models in a range of situations. The pcVPC was in contrast to traditional VPCs shown to be readily applicable to data from studies with a priori and/or a posteriori dose adaptations.
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9.
  • Bergström, Christel A. S., et al. (författare)
  • Lipophilicity in Drug Development : Too Much or Not Enough?
  • 2016
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 18:5, s. 1095-1100
  • Tidskriftsartikel (refereegranskat)abstract
    • A round table discussion was held during the AAPS Annual Meeting on October 27, 2015, with the somewhat provocative topic of whether we need more or less lipophilic compounds in drug development. The session was attended by more than 250 participants, and the feedback was very positive as this round table became a forum for the exchange of ideas from scientists within the academia and industry. Most importantly, the discussion highlighted the difference in approaches to compound selection and development strategies in various companies and organizations. As moderators of this session, we are writing this report to highlight the points and counterpoints made at the session and to bring the importance of the dialogue and debate to the forefront of discussions on how to select the best drug development candidates to enable efficient delivery and, hence, treatment of diseases.
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10.
  • Bizzotto, Roberto, et al. (författare)
  • Multinomial Logistic Functions in Markov Chain Models of Sleep Architecture : Internal and External Validation and Covariate Analysis
  • 2011
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 13:3, s. 445-463
  • Tidskriftsartikel (refereegranskat)abstract
    • Mixed-effect Markov chain models have been recently proposed to characterize the time course of transition probabilities between sleep stages in insomniac patients. The most recent one, based on multinomial logistic functions, was used as a base to develop a final model combining the strengths of the existing ones. This final model was validated on placebo data applying also new diagnostic methods and then used for the inclusion of potential age, gender, and BMI effects. Internal validation was performed through simplified posterior predictive check (sPPC), visual predictive check (VPC) for categorical data, and new visual methods based on stochastic simulation and estimation and called visual estimation check (VEC). External validation mainly relied on the evaluation of the objective function value and sPPC. Covariate effects were identified through stepwise covariate modeling within NONMEM VI. New model features were introduced in the model, providing significant sPPC improvements. Outcomes from VPC, VEC, and external validation were generally very good. Age, gender, and BMI were found to be statistically significant covariates, but their inclusion did not improve substantially the model's predictive performance. In summary, an improved model for sleep internal architecture has been developed and suitably validated in insomniac patients treated with placebo. Thereafter, covariate effects have been included into the final model.
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11.
  • Bjugård Nyberg, Henrik, et al. (författare)
  • Saddle-Reset for Robust Parameter Estimation and Identifiability Analysis of Nonlinear Mixed Effects Models
  • 2020
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 22:4
  • Tidskriftsartikel (refereegranskat)abstract
    • Parameter estimation of a nonlinear model based on maximizing the likelihood using gradient-based numerical optimization methods can often fail due to premature termination of the optimization algorithm. One reason for such failure is that these numerical optimization methods cannot distinguish between the minimum, maximum, and a saddle point; hence, the parameters found by these optimization algorithms can possibly be in any of these three stationary points on the likelihood surface. We have found that for maximization of the likelihood for nonlinear mixed effects models used in pharmaceutical development, the optimization algorithm Broyden-Fletcher-Goldfarb-Shanno (BFGS) often terminates in saddle points, and we propose an algorithm, saddle-reset, to avoid the termination at saddle points, based on the second partial derivative test. In this algorithm, we use the approximated Hessian matrix at the point where BFGS terminates, perturb the point in the direction of the eigenvector associated with the lowest eigenvalue, and restart the BFGS algorithm. We have implemented this algorithm in industry standard software for nonlinear mixed effects modeling (NONMEM, version 7.4 and up) and showed that it can be used to avoid termination of parameter estimation at saddle points, as well as unveil practical parameter non-identifiability. We demonstrate this using four published pharmacometric models and two models specifically designed to be practically non-identifiable.
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12.
  • Björnsson, Marcus, et al. (författare)
  • Performance of Nonlinear Mixed Effects Models in the Presence of Informative Dropout
  • 2015
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 17:1, s. 245-255
  • Tidskriftsartikel (refereegranskat)abstract
    • Informative dropout can lead to bias in statistical analyses if not handled appropriately. The objective of this simulation study was to investigate the performance of nonlinear mixed effects models with regard to bias and precision, with and without handling informative dropout. An efficacy variable and dropout depending on that efficacy variable were simulated and model parameters were reestimated, with or without including a dropout model. The Laplace and FOCE-I estimation methods in NONMEM 7, and the stochastic simulations and estimations (SSE) functionality in PsN, were used in the analysis. For the base scenario, bias was low, less than 5% for all fixed effects parameters, when a dropout model was used in the estimations. When a dropout model was not included, bias increased up to 8% for the Laplace method and up to 21% if the FOCE-I estimation method was applied. The bias increased with decreasing number of observations per subject, increasing placebo effect and increasing dropout rate, but was relatively unaffected by the number of subjects in the study. This study illustrates that ignoring informative dropout can lead to biased parameters in nonlinear mixed effects modeling, but even in cases with few observations or high dropout rate, the bias is relatively low and only translates into small effects on predictions of the underlying effect variable. A dropout model is, however, crucial in the presence of informative dropout in order to make realistic simulations of trial outcomes.
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13.
  • Bocci, G., et al. (författare)
  • State of the Art and Uses for the Biopharmaceutics Drug Disposition Classification System (BDDCS): New Additions, Revisions, and Citation References
  • 2022
  • Ingår i: Aaps Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 24:2
  • Tidskriftsartikel (refereegranskat)abstract
    • The Biopharmaceutics Drug Disposition Classification system (BDDCS) is a four-class approach based on water solubility and extent of metabolism/permeability rate. Based on the BDDCS class to which a drug is assigned, it is possible to predict the role of metabolic enzymes and transporters on the drug disposition of a new molecular entity (NME) prior to its administration to animals or humans. Here, we report a total of 1475 drugs and active metabolites to which the BDDCS is applied. Of these, 379 are new entries, and 1096 are revisions of former classification studies with the addition of references for the approved maximum dose strength, extent of the systemically available drug excreted unchanged in the urine, and lowest solubility over the pH range 1.0-6.8 when such information is available in the literature. We detail revised class assignments of previously misclassified drugs and the literature analyses to classify new drugs. We review the process of solubility assessment for NMEs prior to drug dosing in humans and approved dose classification, as well as the comparison of Biopharmaceutics Classification System (BCS) versus BDDCS assignment. We detail the uses of BDDCS in predicting, prior to dosing animals or humans, disposition characteristics, potential brain penetration, food effect, and drug-induced liver injury (DILI) potential. This work provides an update on the current status of the BDDCS and its uses in the drug development process.
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14.
  • Brekkan, Ari, et al. (författare)
  • A Population Pharmacokinetic-Pharmacodynamic Model of Pegfilgrastim
  • 2018
  • Ingår i: AAPS Journal. - : SPRINGER. - 1550-7416. ; 20:5
  • Tidskriftsartikel (refereegranskat)abstract
    • 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 similar to 50 and similar to 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.
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15.
  • Brekkan, Ari, et al. (författare)
  • Sensitivity of Pegfilgrastim Pharmacokinetic and Pharmacodynamic Parameters to Product Differences in Similarity Studies
  • 2019
  • Ingår i: AAPS Journal. - : Springer. - 1550-7416. ; 21:85
  • Tidskriftsartikel (refereegranskat)abstract
    • 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.
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16.
  • Buatois, Simon, et al. (författare)
  • Comparison of Model Averaging and Model Selection in Dose Finding Trials Analyzed by Nonlinear Mixed Effect Models
  • 2018
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 20:3
  • Tidskriftsartikel (refereegranskat)abstract
    • In drug development, pharmacometric approaches consist in identifying via a model selection (MS) process the model structure that best describes the data. However, making predictions using a selected model ignores model structure uncertainty, which could impair predictive performance. To overcome this drawback, model averaging (MA) takes into account the uncertainty across a set of candidate models by weighting them as a function of an information criterion. Our primary objective was to use clinical trial simulations (CTSs) to compare model selection (MS) with model averaging (MA) in dose finding clinical trials, based on the AIC information criterion. A secondary aim of this analysis was to challenge the use of AIC by comparing MA and MS using five different information criteria. CTSs were based on a nonlinear mixed effect model characterizing the time course of visual acuity in wet age-related macular degeneration patients. Predictive performances of the modeling approaches were evaluated using three performance criteria focused on the main objectives of a phase II clinical trial. In this framework, MA adequately described the data and showed better predictive performance than MS, increasing the likelihood of accurately characterizing the dose-response relationship and defining the minimum effective dose. Moreover, regardless of the modeling approach, AIC was associated with the best predictive performances.
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17.
  • Cardilin, Tim, 1989, et al. (författare)
  • Tumor Static Concentration Curves in Combination Therapy
  • 2017
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 19:2, s. 456-467
  • Tidskriftsartikel (refereegranskat)abstract
    • © 2016 The Author(s) Combination therapies are widely accepted as a cornerstone for treatment of different cancer types. A tumor growth inhibition (TGI) model is developed for combinations of cetuximab and cisplatin obtained from xenograft mice. Unlike traditional TGI models, both natural cell growth and cell death are considered explicitly. The growth rate was estimated to 0.006 h−1 and the natural cell death to 0.0039 h−1 resulting in a tumor doubling time of 14 days. The tumor static concentrations (TSC) are predicted for each individual compound. When the compounds are given as single-agents, the required concentrations were computed to be 506 μg · mL−1 and 56 ng · mL−1 for cetuximab and cisplatin, respectively. A TSC curve is constructed for different combinations of the two drugs, which separates concentration combinations into regions of tumor shrinkage and tumor growth. The more concave the TSC curve is, the lower is the total exposure to test compounds necessary to achieve tumor regression. The TSC curve for cetuximab and cisplatin showed weak concavity. TSC values and TSC curves were estimated that predict tumor regression for 95% of the population by taking between-subject variability into account. The TSC concept is further discussed for different concentration-effect relationships and for combinations of three or more compounds.
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18.
  • Chasseloup, Estelle, et al. (författare)
  • Assessing Treatment Effects with Pharmacometric Models : A New Method that Addresses Problems with Standard Assessments
  • 2021
  • Ingår i: AAPS Journal. - : Springer. - 1550-7416. ; 23:3
  • Tidskriftsartikel (refereegranskat)abstract
    • 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.
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19.
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20.
  • Chen, Chunli, et al. (författare)
  • Comparisons of analysis methods for assessment of pharmacodynamic interactions including design recommendations
  • 2018
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 20
  • Tidskriftsartikel (refereegranskat)abstract
    • Quantitative evaluation of potential pharmacodynamic (PD) interactions is important in tuberculosis drug development in order to optimize Phase 2b drug selection and ultimately to define clinical combination regimens. In this work, we used simulations to (1) evaluate different analysis methods for detecting PD interactions between two hypothetical anti-tubercular drugs in in vitro time-kill experiments, and (2) provide design recommendations for evaluation of PD interactions. The model used for all simulations was the Multistate Tuberculosis Pharmacometric (MTP) model linked to the General Pharmacodynamic Interaction (GPDI) model. Simulated data were re-estimated using the MTP–GPDI model implemented in Bliss Independence or Loewe Additivity, or using a conventional model such as an Empirical Bliss Independence-based model or the Greco model based on Loewe Additivity. The GPDI model correctly characterized different PD interactions (antagonism, synergism, or asymmetric interaction), regardless of the underlying additivity criterion. The commonly used conventional models were not able to characterize asymmetric PD interactions, i.e., concentration-dependent synergism and antagonism. An optimized experimental design was developed that correctly identified interactions in ≥ 94% of the evaluated scenarios using the MTP–GPDI model approach. The MTP–GPDI model approach was proved to provide advantages to other conventional models for assessing PD interactions of anti-tubercular drugs and provides key information for selection of drug combinations for Phase 2b evaluation.
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21.
  • Choy, Steve, et al. (författare)
  • Modeling the Disease Progression from Healthy to Overt Diabetes in ZDSD Rats
  • 2016
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 18:5, s. 1203-1212
  • Tidskriftsartikel (refereegranskat)abstract
    • Studying the critical transitional phase between healthy to overtly diabetic in type 2 diabetes mellitus (T2DM) is of interest, but acquiring such clinical data is impractical due to ethical concerns and would require a long study duration. A population model using Zucker diabetic Sprague-Dawley (ZDSD) rats was developed to describe this transition through altering insulin sensitivity (IS, %) as a result of accumulating excess body weight and beta-cell function (BCF, %) to affect glucose-insulin homeostasis. Body weight, fasting plasma glucose (FPG), and fasting serum insulin (FSI) were collected biweekly over 24 weeks from ZDSD rats (n = 23) starting at age 7 weeks. A semi-mechanistic model previously developed with clinical data was adapted to rat data with BCF and IS estimated relative to humans. Non-linear mixed-effect model estimation was performed using NONMEM. Baseline IS and BCF were 41% compared to healthy humans. BCF was described with a non-linear rise which peaked at 14 weeks before gradually declining to a negligible level. A component for excess growth reflecting obesity was used to affect IS, and a glucose-dependent renal effect exerted a two- to sixfold increase on the elimination of glucose. A glucose-dependent weight loss effect towards the end of experiment was implemented. A semi-mechanistic model to describe the dynamics of glucose and insulin was successfully developed for a rat population, transitioning from healthy to advanced diabetes. It is also shown that weight loss can be modeled to mimic the glucotoxicity phenomenon seen in advanced hyperglycemia.
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22.
  • Choy, Steve, et al. (författare)
  • Modelling the disease progression from healthy to overt diabetes in ZDSD rats
  • Ingår i: AAPS Journal. - 1550-7416.
  • Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
    • Introduction: Studying the critical transitional phase between healthy to overtly diabetic in type 2 diabetes mellitus (T2DM) is of interest, but acquiring such clinical data is impractical due to ethical concerns and the long study duration required. ZDSD rats are a strain of rats bred specifically to spontaneously develop T2DM, and a population model using ZDSD rats was developed to describe this transition through altering insulin sensitivity (IS) as a result of accumulating excess body weight and β-cell function (BCF) to affect glucose-insulin homeostasis.Methods and Materials: Body weight, fasting plasma glucose (FPG), and fasting serum insulin (FSI) were collected over 24 weeks from ZDSD rats (n=23) at age 7 weeks. A semi-mechanistic model previously developed with clinical data was adapted to rat data with BCF and IS estimated relative to humans. Non-linear mixed-effect model estimation was performed using NONMEM 7.3 with first-order interaction.Results and Discussion: Baseline IS and BCF were 41% compared to healthy humans. BCF was described with a non-linear rise which peaked at 14 weeks before gradually declining to a negligible level. A component for excess growth reflecting obesity was used to affect IS, and a FPG-dependent urine effect exerted a 2 to 6-fold increase on the elimination of FPG.Conclusion:  A semi-mechanistic model to describe the dynamics of glucose and insulin was successfully developed for a rat population, transitioning from healthy to advanced diabetes. It is also shown that weight loss can be modeled to mimic the “starvation in the midst of plenty” phenomenon seen in advanced hyperglycemia.
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23.
  • Diao, Xingxing, et al. (författare)
  • In Vitro and In Vivo Human Metabolism of Synthetic Cannabinoids FDU-PB-22 and FUB-PB-22
  • 2016
  • Ingår i: AAPS Journal. - : SPRINGER. - 1550-7416. ; 18:2, s. 455-464
  • Tidskriftsartikel (refereegranskat)abstract
    • In 2014, FDU-PB-22 and FUB-PB-22, two novel synthetic cannabinoids, were detected in herbal blends in Japan, Russia, and Germany and were quickly added to their scheduled drugs list. Unfortunately, no human metabolism data are currently available, making it challenging to confirm their intake. The present study aims to identify appropriate analytical markers by investigating FDU-PB-22 and FUB-PB-22 metabolism in human hepatocytes and confirm the results in authentic urine specimens. For metabolic stability, 1 mu M FDU-PB-22 and FUB-PB-22 was incubated with human liver microsomes for up to 1 h; for metabolite profiling, 10 mu M was incubated with human hepatocytes for 3 h. Two authentic urine specimens from FDU-PB-22 and FUB-PB-22 positive cases were analyzed after beta-glucuronidase hydrolysis. Metabolite identification in hepatocyte samples and urine specimens was accomplished by high-resolution mass spectrometry using information-dependent acquisition. Both FDU-PB-22 and FUB-PB-22 were rapidly metabolized in HLM with half-lives of 12.4 and 11.5 min, respectively. In human hepatocyte samples, we identified seven metabolites for both compounds, generated by ester hydrolysis and further hydroxylation and/or glucuronidation. After ester hydrolysis, FDU-PB-22 and FUB-PB-22 yielded the samemetabolite M7, fluorobenzylindole-3-carboxylic acid (FBI-COOH). M7 and M6 (hydroxylated FBI-COOH) were the major metabolites. In authentic urine specimens after beta-glucuronidase hydrolysis, M6 and M7 also were the predominant metabolites. Based on our study, we recommend M6 (hydroxylated FBI-COOH) and M7 (FBI-COOH) as suitable urinary markers for documenting FDU-PB-22 and/or FUB-PB-22 intake.
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24.
  • Dickinson, Paul A, et al. (författare)
  • Clinical relevance of dissolution testing in quality by design
  • 2008
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 10:2, s. 380-390
  • Forskningsöversikt (refereegranskat)abstract
    • Quality by design (QbD) has recently been introduced in pharmaceutical product development in a regulatory context and the process of implementing such concepts in the drug approval process is presently on-going. This has the potential to allow for a more flexible regulatory approach based on understanding and optimisation of how design of a product and its manufacturing process may affect product quality. Thus, adding restrictions to manufacturing beyond what can be motivated by clinical quality brings no benefits but only additional costs. This leads to a challenge for biopharmaceutical scientists to link clinical product performance to critical manufacturing attributes. In vitro dissolution testing is clearly a key tool for this purpose and the present bioequivalence guidelines and biopharmaceutical classification system (BCS) provides a platform for regulatory applications of in vitro dissolution as a marker for consistency in clinical outcomes. However, the application of these concepts might need to be further developed in the context of QbD to take advantage of the higher level of understanding that is implied and displayed in regulatory documentation utilising QbD concepts. Aspects that should be considered include identification of rate limiting steps in the absorption process that can be linked to pharmacokinetic variables and used for prediction of bioavailability variables, in vivo relevance of in vitro dissolution test conditions and performance/interpretation of specific bioavailability studies on critical formulation/process variables. This article will give some examples and suggestions how clinical relevance of dissolution testing can be achieved in the context of QbD derived from a specific case study for a BCS II compound.
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25.
  • Dubbelboer, Ilse R, et al. (författare)
  • Porcine and Human In Vivo Simulations for Doxorubicin-Containing Formulations Used in Locoregional Hepatocellular Carcinoma Treatment
  • 2018
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 20:6
  • Tidskriftsartikel (refereegranskat)abstract
    • It is important to be able to simulate and predict formulation effects on the pharmacokinetics of a drug in order to optimize effectivity in clinical practice and drug development. Two formulations containing doxorubicin are used in the treatment of hepatocellular carcinoma (HCC): a Lipiodol-based emulsion (LIPDOX) and a loadable microbead system (DEBDOX). Although equally effective, the formulations are vastly different, and little is known about the parameters affecting doxorubicin release in vivo. However, mathematical modeling can be used to predict doxorubicin release properties from these formulations and its in vivo pharmacokinetic (PK) profiles. A porcine semi-physiologically based pharmacokinetic (PBPK) model was scaled to a human physiologically based biopharmaceutical (PBBP) model that was altered to include HCC. DOX in vitro and in vivo release data from LIPDOX or DEBDOX were collected from the literature and combined with these in silico models. The simulated pharmacokinetic profiles were then compared with observed porcine and human HCC patient data. DOX pharmacokinetic profiles of LIPDOX-treated HCC patients were best predicted from release data sets acquired by in vitro methods that did not use a diffusion barrier. For the DEBDOX group, the best predictions were from the in vitro release method with a low ion concentration and a reduced loading dose. The in silico modeling combined with historical release data was effective in predicting in vivo plasma exposure. This can give useful insights into the release method properties necessary for correct in vivo predictions of pharmacokinetic profiles of HCC patients dosed with LIPDOX or DEBDOX.
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26.
  • Eriksson, Johanna, et al. (författare)
  • Drug Absorption Parameters Obtained Using the Isolated Perfused Rat Lung Model Are Predictive of Rat In Vivo Lung Absorption
  • 2020
  • Ingår i: AAPS Journal. - : SPRINGER. - 1550-7416. ; 22:3
  • Tidskriftsartikel (refereegranskat)abstract
    • The ex vivo isolated perfused rat lung (IPL) model has been demonstrated to be a useful tool during drug development for studying pulmonary drug absorption. This study aims to investigate the potential use of IPL data to predict rat in vivo lung absorption. Absorption parameters determined from IPL data (ex vivo input parameters) in combination with intravenously determined pharmacokinetic data were used in a biopharmaceutics model to predict experimental rat in vivo plasma concentration-time profiles and lung amount after inhalation of five different inhalation compounds. The performance of simulations using ex vivo input parameters was compared with simulations using in vitro input parameters, to determine whether and to what extent predictability could be improved by using input parameters determined from the more complex ex vivo model. Simulations using ex vivo input parameters were within twofold average difference (AAFE < 2) from experimental in vivo data for all compounds except one. Furthermore, simulations using ex vivo input parameters performed significantly better than simulations using in vitro input parameters in predicting in vivo lung absorption. It could therefore be advantageous to base predictions of drug performance on IPL data rather than on in vitro data during drug development to increase mechanistic understanding of pulmonary drug absorption and to better understand how different substance properties and formulations might affect in vivo behavior of inhalation compounds.
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27.
  • Fang, Lanyan, et al. (författare)
  • The Role of Model Master Files for Sharing, Acceptance, and Communication with FDA
  • 2024
  • Ingår i: AAPS Journal. - : Springer. - 1550-7416. ; 26:2
  • Tidskriftsartikel (refereegranskat)abstract
    • With the evolving role of Model Integrated Evidence (MIE) in generic drug development and regulatory applications, the need for improving Model Sharing, Acceptance, and Communication with the FDA is warranted. Model Master File (MMF) refers to a quantitative model or a modeling platform that has undergone sufficient model Verification & Validation to be recognized as sharable intellectual property that is acceptable for regulatory purposes. MMF provides a framework for regulatorily acceptable modeling practice, which can be used with confidence to support MIE by both the industry and the U.S. Food and Drug Administration (FDA). In 2022, the FDA and the Center for Research on Complex Generics (CRCG) hosted a virtual public workshop to discuss the best practices for utilizing modeling approaches to support generic product development. This report summarizes the presentations and panel discussions of the workshop symposium entitled "Model Sharing, Acceptance, and Communication with the FDA". The symposium and this report serve as a kick-off discussion for further utilities of MMF and best practices of utilizing MMF in drug development and regulatory submissions. The potential advantages of MMFs have garnered acknowledgment from model developers, industries, and the FDA throughout the workshop. To foster a unified comprehension of MMFs and establish best practices for their application, further dialogue and cooperation among stakeholders are imperative. To this end, a subsequent workshop is scheduled for May 2-3, 2024, in Rockville, Maryland, aiming to delve into the practical facets and best practices of MMFs pertinent to regulatory submissions involving modeling and simulation methodologies.
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28.
  • Feasel, Michael G., et al. (författare)
  • Metabolism of Carfentanil, an Ultra-Potent Opioid, in Human Liver Microsomes and Human Hepatocytes by High-Resolution Mass Spectrometry
  • 2016
  • Ingår i: AAPS Journal. - : SPRINGER. - 1550-7416. ; 18:6, s. 1489-1499
  • Tidskriftsartikel (refereegranskat)abstract
    • Carfentanil is an ultra-potent synthetic opioid. No human carfentanil metabolism data are available. Reportedly, Russian police forces used carfentanil and remifentanil to resolve a hostage situation in Moscow in 2002. This alleged use prompted interest in the pharmacology and toxicology of carfentanil in humans. Our study was conducted to identify human carfentanil metabolites and to assess carfentanils metabolic clearance, which could contribute to its acute toxicity in humans. We used Simulations Pluss ADMET Predictor (TM) and Molecular Discoverys MetaSite (TM) to predict possible metabolite formation. Both programs gave similar results that were generally good but did not capture all metabolites seen in vitro. We incubated carfentanil with human hepatocytes for up to 1 h and analyzed samples on a Sciex 3200 QTRAP mass spectrometer to measure parent compound depletion and extrapolated that to represent intrinsic clearance. Pooled primary human hepatocytes were then incubated with carfentanil up to 6 h and analyzed for metabolite identification on a Sciex 5600+ TripleTOF (QTOF) high-resolution mass spectrometer. MS and MS/MS analyses elucidated the structures of the most abundant metabolites. Twelve metabolites were identified in total. N-Dealkylation and monohydroxylation of the piperidine ring were the dominant metabolic pathways. Two N-oxide metabolites and one glucuronide metabolite were observed. Surprisingly, ester hydrolysis was not a major metabolic pathway for carfentanil. While the human liver microsomal system demonstrated rapid clearance by CYP enzymes, the hepatocyte incubations showed much slower clearance, possibly providing some insight into the long duration of carfentanils effects.
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29.
  • Gabrielsson, Johan (författare)
  • Michaelis-Menten from an In Vivo Perspective: Open Versus Closed Systems
  • 2018
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 20
  • Tidskriftsartikel (refereegranskat)abstract
    • After a century of applications of the seminal Michaelis-Menten equation since its advent it is timely to scrutinise its principal parts from an in vivo point of view. Thus, the Michaelis-Menten system was revisited in which enzymatic turnover, i.e. synthesis and elimination was incorporated. To the best of our knowledge, previous studies of the Michaelis-Menten system have been mainly based on the assumption that the total pool of enzyme, free and bound, is constant. However, in fact this may not always be the case, particularly for chronic indications. Chronic (periodic) administration of drugs is often related to induction or inhibition of enzymatic processes and even changes in the free enzymatic load per se. This may account for the fact that translation of in vitro metabolism data have shown to give systematic deviations from experimental in vivo data. Interspecies extrapolations of metabolic data are often challenged by poor predictability due to insufficient power of applied functions and methods. By incorporating enzyme turnover, a more mechanistic expression of substrate, free enzyme and substrate-enzyme complex concentrations is derived. In particular, it is shown that whereas in closed systems there is a threshold for chronic dosing beyond which the substrate concentration keeps rising, in open systems involving enzyme turnover this is no longer the case. However, in the presence of slow enzyme turnover, after an initial period of adjustment which may be quite long, the relation between substrate concentration and dose rate reduces to a linear expression. This new open framework is also applicable to transporter systems.
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30.
  • Gabrielsson, Johan (författare)
  • New Equilibrium Models of Drug-Receptor Interactions Derived from Target-Mediated Drug Disposition
  • 2018
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 20
  • Tidskriftsartikel (refereegranskat)abstract
    • In vivo analyses of pharmacological data are traditionally based on a closed system approach not incorporating turnover of target and ligand-target kinetics, but mainly focussing on ligand-target binding properties. This study incorporates information about target and ligand-target kinetics parallel to binding. In a previous paper, steady-state relationships between target- and ligand-target complex versus ligand exposure were derived and a new expression of in vivo potency was derived for a circulating target. This communication is extending the equilibrium relationships and in vivo potency expression for (i) two separate targets competing for one ligand, (ii) two different ligands competing for a single target and (iii) a single ligand-target interaction located in tissue. The derived expressions of the in vivo potencies will be useful both in drug-related discovery projects and mechanistic studies. The equilibrium states of two targets and one ligand may have implications in safety assessment, whilst the equilibrium states of two competing ligands for one target may cast light on when pharmacodynamic drug-drug interactions are important. The proposed equilibrium expressions for a peripherally located target may also be useful for small molecule interactions with extravascularly located targets. Including target turnover, ligand-target complex kinetics and binding properties in expressions of potency and efficacy will improve our understanding of within and between-individual (and across species) variability. The new expressions of potencies highlight the fact that the level of drug-induced target suppression is very much governed by target turnover properties rather than by the target expression level as such.
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31.
  • Gabrielsson, Johan, et al. (författare)
  • Pattern Recognition in Pharmacodynamic Data Analysis
  • 2016
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 18:1, s. 64-91
  • Tidskriftsartikel (refereegranskat)abstract
    • Pattern recognition is a key element in pharmacodynamic analyses as a first step to identify drug action and selection of a pharmacodynamic model. The essence of this process is going from data to insight through exploratory data analysis. There are few formal strategies that scientists typically use when the experiment has been done and data collected. This report attempts to ameliorate this deficit by identifying the properties of a pharmacodynamic model via dissection of the pattern revealed in response-time data. Pattern recognition in pharmacodynamic analyses contrasts with pharmacokinetic analyses with respect to time course. Thus, the time course of drug in plasma usually differs markedly from the time course of the biomarker response, as a consequence of a myriad of interactions (transport to biophase, binding to target, activation of target and downstream mediators, physiological response, cascade and amplification of biosignals, homeostatic feedback) between the events of exposure to test compound and the occurrence of the biomarker response. Homing in on this important—but less often addressed—element, 20 datasets of varying complexity were analyzed, and from this, we summarize a set of points to consider, specifically addressing baseline behavior, number of phases in the response-time course, time delays between concentration- and response-time courses, peak shifts in response with increasing doses, saturation, and other potential nonlinearities. These strategies will hopefully give a better understanding of the complete pharmacodynamic response-time profile. © 2015, American Association of Pharmaceutical Scientists.
  •  
32.
  • Gabrielsson, Johan (författare)
  • Pattern Recognition in Pharmacokinetic Data Analysis
  • 2016
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 18, s. 47-63
  • Tidskriftsartikel (refereegranskat)abstract
    • Pattern recognition is a key element in pharmacokinetic data analyses when first selecting a model to be regressed to data. We call this process going from data to insight and it is an important aspect of exploratory data analysis (EDA). But there are very few formal ways or strategies that scientists typically use when the experiment has been done and data collected. This report deals with identifying the properties of a kinetic model by dissecting the pattern that concentration-time data reveal. Pattern recognition is a pivotal activity when modeling kinetic data, because a rigorous strategy is essential for dissecting the determinants behind concentration-time courses. First, we extend a commonly used relationship for calculation of the number of potential model parameters by simultaneously utilizing all concentration-time courses. Then, a set of points to consider are proposed that specifically addresses exploratory data analyses, number of phases in the concentration-time course, baseline behavior, time delays, peak shifts with increasing doses, flip-flop phenomena, saturation, and other potential nonlinearities that an experienced eye catches in the data. Finally, we set up a series of equations related to the patterns. In other words, we look at what causes the shapes that make up the concentration-time course and propose a strategy to construct a model. By practicing pattern recognition, one can significantly improve the quality and timeliness of data analysis and model building. A consequence of this is a better understanding of the complete concentration-time profile.
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33.
  • Gabrielsson, Johan (författare)
  • Pharmacokinetic Steady-States Highlight Interesting Target-Mediated Disposition Properties
  • 2017
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 19, s. 772-786
  • Tidskriftsartikel (refereegranskat)abstract
    • In this paper, we derive explicit expressions for the concentrations of ligand L, target R and ligand-target complex RL at steady state for the classical model describing target-mediated drug disposition, in the presence of a constant-rate infusion of ligand. We demonstrate that graphing the steady-state values of ligand, target and ligand-target complex, we obtain striking and often singular patterns, which yield a great deal of insight and understanding about the underlying processes. Deriving explicit expressions for the dependence of L, R and RL on the infusion rate, and displaying graphs of the relations between L, R and RL, we give qualitative and quantitive information for the experimentalist about the processes involved. Understanding target turnover is pivotal for optimising these processes when target-mediated drug disposition (TMDD) prevails. By a combination of mathematical analysis and simulations, we also show that the evolution of the three concentration profiles towards their respective steady-states can be quite complex, especially for lower infusion rates. We also show how parameter estimates obtained from iv bolus studies can be used to derive steady-state concentrations of ligand, target and complex. The latter may serve as a template for future experimental designs.
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34.
  • Gennemark, Peter, 1974, et al. (författare)
  • Optimal Design in Population Kinetic Experiments by Set-Valued Methods
  • 2011
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 13:4, s. 495-507
  • Tidskriftsartikel (refereegranskat)abstract
    • We propose a new method for optimal experimental design of population pharmacometric experiments based on global search methods using interval analysis; all variables and parameters are represented as intervals rather than real numbers. The evaluation of a specific design is based on multiple simulations and parameter estimations. The method requires no prior point estimates for the parameters, since the parameters can incorporate any level of uncertainty. In this respect, it is similar to robust optimal design. Representing sampling times and covariates like doses by intervals gives a direct way of optimizing with rigorous sampling and dose intervals that can be useful in clinical practice. Furthermore, the method works on underdetermined problems for which traditional methods typically fail.
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35.
  • Germovsek, Eva, et al. (författare)
  • A Novel Method for Analysing Frequent Observations from Questionnaires in Order to Model Patient-Reported Outcomes : Application to EXACT (R) Daily Diary Data from COPD Patients
  • 2019
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 21:4
  • Tidskriftsartikel (refereegranskat)abstract
    • 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 (R)) 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 67years, 54% male, 39% current smokers), providing approximately 40,000 observations per EXACT (R) 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.
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36.
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37.
  • Gottipati, Gopichand, et al. (författare)
  • Modeling a Composite Score in Parkinson's Disease Using Item Response Theory
  • 2017
  • Ingår i: AAPS Journal. - : SPRINGER. - 1550-7416. ; 19:3, s. 837-845
  • Tidskriftsartikel (refereegranskat)abstract
    • In the current work, we present the methodology for development of an Item Response Theory model within a non-linear mixed effects framework to characterize the longitudinal changes of the Movement Disorder Society (sponsored revision) of Unified Parkinson's Disease Rating Scale (MDS-UPDRS) endpoint in Parkinson's disease (PD). The data were obtained from Parkinson's Progression Markers Initiative database and included 163,070 observations up to 48 months from 430 subjects belonging to De Novo PD cohort. The probability of obtaining a score, reported for each of the items in the questionnaire, was modeled as a function of the subject's disability. Initially, a single latent variable model was explored to characterize the disease progression over time. However, based on the understanding of the questionnaire set-up and the results of a residuals-based diagnostic tool, a three latent variable model with a mixture implementation was able to adequately describe longitudinal changes not only at the total score level but also at each individual item level. The linear progression rates obtained for the patient-reported items and the non-sided items were similar, each of which roughly take about 50 months for a typical subject to progress linearly from the baseline by one standard deviation. However for the sided items, it was found that the better side deteriorates quicker than the disabled side. This study presents a framework for analyzing MDS-UPDRS data, which can be adapted to more traditional UPDRS data collected in PD clinical trials and result in more efficient designs and analyses of such studies.
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38.
  • Hammarlund-Udenaes, Margareta (författare)
  • Microdialysis as an Important Technique in Systems Pharmacology : a Historical and Methodological Review
  • 2017
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 19:5, s. 1294-1303
  • Forskningsöversikt (refereegranskat)abstract
    • Microdialysis has contributed with very important knowledge to the understanding of target-specific concentrations and their relationship to pharmacodynamic effects from a systems pharmacology perspective, aiding in the global understanding of drug effects. This review focuses on the historical development of microdialysis as a method to quantify the pharmacologically very important unbound tissue concentrations and of recent findings relating to modeling microdialysis data to extrapolate from rodents to humans, understanding distribution of drugs in different tissues and disease conditions. Quantitative microdialysis developed very rapidly during the early 1990s. Method development was in focus in the early years including development of quantitative microdialysis, to be able to estimate true extracellular concentrations. Microdialysis has significantly contributed to the understanding of active transport at the blood-brain barrier and in other organs. Examples are presented where microdialysis together with modeling has increased the knowledge on tissue distribution between species, in overweight patients and in tumors, and in metabolite contribution to drug effects. More integrated metabolomic studies are still sparse within the microdialysis field, although a great potential for tissue and disease-specific measurements is evident.
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39.
  • Heimbach, Tycho, et al. (författare)
  • Dissolution and Translational Modeling Strategies Toward Establishing an In Vitro-In Vivo Link : a Workshop Summary Report
  • 2019
  • Ingår i: AAPS Journal. - : SPRINGER. - 1550-7416. ; 21:2
  • Tidskriftsartikel (refereegranskat)abstract
    • This publication summarizes the proceedings of day 2 of a 3-day workshop on Dissolution and Translational Modeling Strategies Enabling Patient-Centric Product Development. Patient-centric drug product development from a drug product quality perspective necessitates the establishment of clinically relevant drug product specifications via an in vitro-in vivo link. Modeling and simulation offer a path to establish this link; in this regard, physiologically based modeling has been implemented successfully to support regulatory decision-making and drug product labeling. In this manuscript, case studies of physiologically based biopharmaceutics modeling (PBBM) applied to drug product quality are presented and summarized. These case studies exemplify a possible path to achieve an in vitro-in vivo link and encompass (a) development of biopredictive dissolution methods to support biowaivers, (b) model-informed formulation selection, (c) predicting clinical formulation performance, and (d) defining a safe space for regulatory flexibility via virtual bioequivalence (BE). Workflows for the development and verification of absorption models/PBBM and for the establishment of a safe space using dissolution as an input are described with examples. Breakout session discussions on topics, such as current challenges and some best practices in model development and verification, are included as part of the Supplementary material.
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40.
  • Hénin, Emilie, et al. (författare)
  • A mechanism-Based Approach for Absorption Modeling : The Gastro-Intestinal Transit Time (GITT) Model
  • 2012
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 14:2, s. 155-163
  • Tidskriftsartikel (refereegranskat)abstract
    • 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.
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41.
  • Hooker, Andrew C., et al. (författare)
  • Simultaneous population optimal design for pharmacokinetic-pharmacodynamic experiments.
  • 2005
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 7:4, s. E759-85
  • Tidskriftsartikel (refereegranskat)abstract
    • Multiple outputs or measurement types are commonly gathered in biological experiments. Often, these experiments are expensive (such as clinical drug trials) or require careful design to achieve the desired information content. Optimal experimental design protocols could help alleviate the cost and increase the accuracy of these experiments. In general, optimal design techniques ignore between-individual variability, but even work that incorporates it (population optimal design) has treated simultaneous multiple output experiments separately by computing the optimal design sequentially, first finding the optimal design for one output (eg, a pharmacokinetic [PK] measurement) and then determining the design for the second output (eg, a pharmacodynamic [PD] measurement). Theoretically, this procedure can lead to biased and imprecise results when the second model parameters are also included in the first model (as in PK-PD models). We present methods and tools for simultaneous population D-optimal experimental designs, which simultaneously compute the design of multiple output experiments, allowing for correlation between model parameters. We then apply these methods to simulated PK-PD experiments. We compare the new simultaneous designs to sequential designs that first compute the PK design, fix the PK parameters, and then compute the PD design in an experiment. We find that both population designs yield similar results in designs for low sample number experiments, with simultaneous designs being possibly superior in situations in which the number of samples is unevenly distributed between outputs. Simultaneous population D-optimality is a potentially useful tool in the emerging field of experimental design.
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42.
  • Ibrahim, Moustafa M. A., et al. (författare)
  • Model-Based Conditional Weighted Residuals Analysis for Structural Model Assessment
  • 2019
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 21:3
  • Tidskriftsartikel (refereegranskat)abstract
    • 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 (ΔOFVBias). 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.
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43.
  • Ibrahim, Moustafa M. A., et al. (författare)
  • Model-Based Residual Post-Processing for Residual Model Identification
  • 2018
  • Ingår i: AAPS Journal. - : SPRINGER. - 1550-7416. ; 20:5
  • Tidskriftsartikel (refereegranskat)abstract
    • The purpose of this study was to investigate if model-based post-processing of common diagnostics can be used as a diagnostic tool to quantitatively identify model misspecifications and rectifying actions. The main investigated diagnostic is conditional weighted residuals (CWRES). We have selected to showcase this principle with residual unexplained variability (RUV) models, where the new diagnostic tool is used to scan extended RUV models and assess in a fast and robust way whether, and what, extensions are expected to provide a superior description of data. The extended RUV models evaluated were autocorrelated errors, dynamic transform both sides, inter-individual variability on RUV, power error model, t-distributed errors, and time-varying error magnitude. The agreement in improvement in goodness-of-fit between implementing these extended RUV models on the original model and implementing these extended RUV models on CWRES was evaluated in real and simulated data examples. Real data exercise was applied to three other diagnostics: conditional weighted residuals with interaction (CWRESI), individual weighted residuals (IWRES), and normalized prediction distribution errors (NPDE). CWRES modeling typically predicted (i) the nature of model misspecifications, (ii) the magnitude of the expected improvement in fit in terms of difference in objective function value (Delta OFV), and (iii) the parameter estimates associated with the model extension. Alternative metrics (CWRESI, IWRES, and NPDE) also provided valuable information, but with a lower predictive performance of Delta OFV compared to CWRES. This method is a fast and easily automated diagnostic tool for RUV model development/evaluation process; it is already implemented in the software package PsN.
  •  
44.
  • Ibrahim, Moustafa M. A., et al. (författare)
  • Variability Attribution for Automated Model Building
  • 2019
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 21:3
  • Tidskriftsartikel (refereegranskat)abstract
    • 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.
  •  
45.
  • Janssen, Julie M, et al. (författare)
  • Population Pharmacokinetics of Intracellular 5-Fluorouridine 5'-Triphosphate and its Relationship with Hand-and-Foot Syndrome in Patients Treated with Capecitabine.
  • 2021
  • Ingår i: AAPS Journal. - : Springer Nature. - 1550-7416. ; 23:1, s. 23-
  • Tidskriftsartikel (refereegranskat)abstract
    • Capecitabine is an oral pro-drug of 5-fluorouracil. Patients with solid tumours who are treated with capecitabine may develop hand-and-foot syndrome (HFS) as side effect. This might be a result of accumulation of intracellular metabolites. We characterised the pharmacokinetics (PK) of 5-fluorouridine 5'-triphosphate (FUTP) in peripheral blood mononuclear cells (PBMCs) and assessed the relationship between exposure to capecitabine or its metabolites and the development of HFS. Plasma and intracellular capecitabine PK data and ordered categorical HFS data was available. A previously developed model describing the PK of capecitabine and metabolites was extended to describe the intracellular FUTP concentrations. Subsequently, a continuous-time Markov model was developed to describe the development of HFS during treatment with capecitabine. The influences of capecitabine and metabolite concentrations on the development of HFS were evaluated. The PK of intracellular FUTP was described by an one-compartment model with first-order elimination (ke,FUTP was 0.028 h-1 (95% confidence interval 0.022-0.039)) where the FUTP influx rate was proportional to the 5-FU plasma concentrations. The predicted individual intracellular FUTP concentration was identified as a significant predictor for the development and severity of HFS. Simulations demonstrated a clear exposure-response relationship. The intracellular FUTP concentrations were successfully described and a significant relationship between these intracellular concentrations and the development and severity of HFS was identified. This model can be used to simulate future dosing regimens and thereby optimise treatment with capecitabine.
  •  
46.
  • Johansson, Åsa M., et al. (författare)
  • Comparison of Methods for Handling Missing Covariate Data
  • 2013
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 15:4, s. 1232-1241
  • Tidskriftsartikel (refereegranskat)abstract
    • Missing covariate data is a common problem in nonlinear mixed effects modelling of clinical data. The aim of this study was to implement and compare methods for handling missing covariate data in nonlinear mixed effects modelling under different missing data mechanisms. Simulations generated data for 200 individuals with a 50% difference in clearance between males and females. Three different types of missing data mechanisms were simulated and information about sex was missing for 50% of the individuals. Six methods for handling the missing covariate were compared in a stochastic simulations and estimations study where 200 data sets were simulated. The methods were compared according to bias and precision of parameter estimates. Multiple imputation based on weight and response, full maximum likelihood modelling using information on weight and full maximum likelihood modelling where the proportion of males among the individuals lacking information about sex was estimated (EST) gave precise and unbiased estimates in the presence of missing data when data were missing completely at random or missing at random. When data were missing not at random, the only method resulting in low bias and high parameter precision was EST.
  •  
47.
  • Johansson, Åsa M., et al. (författare)
  • Multiple Imputation of Missing Covariates in NONMEM and Evaluation of the Method's Sensitivity to eta-Shrinkage
  • 2013
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 15:4, s. 1035-1042
  • Tidskriftsartikel (refereegranskat)abstract
    • Multiple imputation (MI) is an approach widely used in statistical analysis of incomplete data. However, its application to missing data problems in nonlinear mixed-effects modelling is limited. The objective was to implement a four-step MI method for handling missing covariate data in NONMEM and to evaluate the method's sensitivity to eta-shrinkage. Four steps were needed; (1) estimation of empirical Bayes estimates (EBEs) using a base model without the partly missing covariate, (2) a regression model for the covariate values given the EBEs from subjects with covariate information, (3) imputation of covariates using the regression model and (4) estimation of the population model. Steps (3) and (4) were repeated several times. The procedure was automated in PsN and is now available as the mimp functionality (http://psn.sourceforge.net/).. The method's sensitivity to shrinkage in EBEs was evaluated in a simulation study where the covariate was missing according to a missing at random type of missing data mechanism. The eta-shrinkage was increased in steps from 4.5 to 54%. Two hundred datasets were simulated and analysed for each scenario. When shrinkage was low the MI method gave unbiased and precise estimates of all population parameters. With increased shrinkage the estimates became less precise but remained unbiased.
  •  
48.
  • Karlsson, Kristin C., et al. (författare)
  • 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
  • 2009
  • Ingår i: AAPS Journal. - : Springer. - 1550-7416. ; 11:1, s. 148-154
  • Tidskriftsartikel (refereegranskat)abstract
    • In nonlinear mixed effects modeling using NONMEM, mixture models can be used for multimodal distributions of parameters. The fraction of individuals belonging to each of the subpopulations can be estimated, and the most probable subpopulation for each patient is output (MIXEST(k)). The objective function value (OFV) that is minimized is the sum of the OFVs for each patient (OFV(i)), which in turn is the sum across the k subpopulations (OFV(i,k)). The OFV(i,k) values can be used together with the total probability in the population of belonging to subpopulation k to calculate the individual probability of belonging to the subpopulation (IP(k)). Our objective was to explore the information gained by using IP(k) instead of or in addition to MIXEST(k) in the analysis of mixture models. Two real data sets described previously by mixture models as well as simulations were used to explore the use of IP(k) and the precision of individual parameter values based on IP(k) and MIXEST(k). For both real data-based mixture models, a substantial fraction (11% and 26%) of the patients had IP(k) values not close to 0 or 1 (IP(k) between 0.25 and 0.75). Simulations of eight different scenarios showed that individual parameter estimates based on MIXEST were less precise than those based on IP(k), as the root mean squared error was reduced for IP(k) in all scenarios. A probability estimate such as IP(k) provides more detailed information about each individual than the discrete MIXEST(k). Individual parameter estimates based on IP(k) should be preferable whenever individual parameter estimates are to be used as study output or for simulations.
  •  
49.
  • Karlsson, Kristin E., et al. (författare)
  • Modeling Disease Progression in Acute Stroke Using Clinical Assessment Scales
  • 2010
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 12:4, s. 683-691
  • Tidskriftsartikel (refereegranskat)abstract
    • This article demonstrates techniques for describing and predicting disease progression in acute stroke by modeling scores measured using clinical assessment scales, accommodating dropout as an additional source of information. Scores assessed using the National Institutes of Health Stroke Scale and the Barthel Index in acute stroke patients were used to model the time course of disease progression. Simultaneous continuous and probabilistic models for describing the nature and magnitude of score changes were developed, and used to model the trajectory of disease progression using scale scores. The models described the observed data well, and exhibited good simulation properties. Applications include longitudinal analysis of stroke scale data, clinical trial simulation, and prognostic forecasting. Based upon experience in other areas, it is likely that application of this modeling methodology will enable reductions in the number of patients needed to carry out clinical studies of treatments for acute stroke.
  •  
50.
  • Karlsson, Kristin E., et al. (författare)
  • Performance of three estimation methods in repeated time-to-event modeling
  • 2011
  • Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 13:1, s. 83-91
  • Tidskriftsartikel (refereegranskat)abstract
    • It is not uncommon that the outcome measurements, symptoms or side effects, of a clinical trial belong to the family of event type data, e.g., bleeding episodes or emesis events. Event data is often low in information content and the mixed-effects modeling software NONMEM has previously been shown to perform poorly with low information ordered categorical data. The aim of this investigation was to assess the performance of the Laplace method, the stochastic approximation expectation-maximization (SAEM) method, and the importance sampling method when modeling repeated time-to-event data. The Laplace method already existed, whereas the two latter methods have recently become available in NONMEM 7. A stochastic simulation and estimation study was performed to assess the performance of the three estimation methods when applied to a repeated time-to-event model with a constant hazard associated with an exponential interindividual variability. Various conditions were investigated, ranging from rare to frequent events and from low to high interindividual variability. The method performance was assessed by parameter bias and precision. Due to the lack of information content under conditions where very few events were observed, all three methods exhibit parameter bias and imprecision, however most pronounced by the Laplace method. The performance of the SAEM and importance sampling were generally higher than Laplace when the frequency of individuals with events was less than 43%, while at frequencies above that all methods were equal in performance.
  •  
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