| 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.
|
|
| 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.
|
|
| 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.
|
|
| 4. |
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
| 19. |
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
