1.
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.
2.
3.
Ernest II, Charles, et al.
(författare)
Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model
2014
Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 41:6, s. 639-654
Tidskriftsartikel (refereegranskat) abstract
D-optimal designs for discrete-type responses have been derived using generalized linear mixed models, simulation based methods and analytical approximations for computing the fisher information matrix (FIM) of non-linear mixed effect models with homogeneous probabilities over time. In this work, D-optimal designs using an analytical approximation of the FIM for a dichotomous, non-homogeneous, Markov-chain phase advanced sleep non-linear mixed effect model was investigated. The non-linear mixed effect model consisted of transition probabilities of dichotomous sleep data estimated as logistic functions using piecewise linear functions. Theoretical linear and nonlinear dose effects were added to the transition probabilities to modify the probability of being in either sleep stage. D-optimal designs were computed by determining an analytical approximation the FIM for each Markov component (one where the previous state was awake and another where the previous state was asleep). Each Markov component FIM was weighted either equally or by the average probability of response being awake or asleep over the night and summed to derive the total FIM (FIMtotal). The reference designs were placebo, 0.1, 1-, 6-, 10- and 20-mg dosing for a 2- to 6-way crossover study in six dosing groups. Optimized design variables were dose and number of subjects in each dose group. The designs were validated using stochastic simulation/re-estimation (SSE). Contrary to expectations, the predicted parameter uncertainty obtained via FIMtotal was larger than the uncertainty in parameter estimates computed by SSE. Nevertheless, the D-optimal designs decreased the uncertainty of parameter estimates relative to the reference designs. Additionally, the improvement for the D-optimal designs were more pronounced using SSE than predicted via FIMtotal. Through the use of an approximate analytic solution and weighting schemes, the FIMtotal for a non-homogeneous, dichotomous Markov-chain phase advanced sleep model was computed and provided more efficient trial designs and increased nonlinear mixed-effects modeling parameter precision.
4.
Faraj, Alan
(författare)
Pharmacometric models to inform dose selection and study design : Applied in hemophilia and tuberculosis
2024
Doktorsavhandling (övrigt vetenskapligt/konstnärligt) abstract
While tuberculosis is a global pandemic, hemophilia is a rare disease which many have not heard of. Due to tuberculosis mainly being a problem in developing countries and hemophilia being a rare disease, they are not as heard of as other diseases such as cancer or metabolic diseases which are on the rise in Western societies. The quality of life for patients suffering from these diseases is notably impaired and novel drugs are warranted to further improve the treatment and management of both diseases. As market incentives are a limiting factor, it is important that the efforts that are taken to develop novel drugs are carried out in an informative manner. One strategy to incorporate as much information as possible to inform decision making in drug development is to use pharmacometric methods. Such strategies enable simultaneous analysis of different types of data that are generated during drug development programs. In this thesis, the aim was to develop and apply pharmacometric models to facilitate dose selection and study designs in clinical programs that aim at developing new drugs for tuberculosis and hemophilia. A standardized analysis approach of early clinical trials studying drugs against tuberculosis was presented including power calculations that showed the number of patients needed to detect drug effects. Such efforts are important as showing drug effect in early trials will aid decision making into significantly longer and costlier late trials. The approach was used to analyze a clinical trial studying if the current dose of meropenem can be lowered without negatively impacting drug effects and improving the already poor tolerability of the drug. The study found that lowering the dose may lower activity without any improvement of the tolerability properties. Furthermore, population pharmacokinetic models were developed for two novel hemostatic drugs in development for prophylactic and on-demand treatment of hemophilia. Based on the models, clinical trials in adult and pediatric subjects were supported. One of the trials were performed and it was showed with a model-based analysis that the new drug which is given subcutanously has similar efficacy as current intravenously given standard of care alternatives. Using the developed models, different strategies for designing pharmacokinetic trials in children was also presented. In conclusion, the work performed within this thesis has contributed to the development of new drugs against tuberculosis and hemophilia.
5.
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.
6.
Hennig, Stefanie, et al.
(författare)
Application of the Optimal Design Approach to Improve a Pretransplant Drug Dose Finding Design for Ciclosporin
2012
Ingår i: Journal of clinical pharmacology. - : Wiley. - 0091-2700 .- 1552-4604. ; 52:3, s. 347-360
Tidskriftsartikel (refereegranskat) abstract
A time and sampling intensive pretransplant test dose design was to be reduced, but at the same time optimized so that there was no loss in the precision of predicting the individual pharmacokinetic (PK) estimates of posttransplant dosing. The following variables were optimized simultaneously: sampling times, ciclosporin dose, time of second dose, infusion duration, and administration order, using a published ciclosporin population PK model as prior information. The original design was reduced from 22 samples to 6 samples/patient and both doses (intravenous oral) were administered within 8 hours. Compared with the prior information given by the published ciclosporin population PK model, the expected standard deviations (SDs) of the individual parameters for clearance and bioavailability could be reduced by, on average, 40% under the optimized sparse designs. The gain of performing the original rich design compared with the optimal reduced design, considering the standard errors of the parameter estimates, was found to be minimal. This application demonstrates, in a practical clinical scenario, how optimal design techniques may be used to improve diagnostic procedures given available software and methods.
7.
Juul, Rasmus Vestergaard, et al.
(författare)
A Pharmacokinetic-Pharmacodynamic Model of Morphine Exposure and Subsequent Morphine Consumption in Postoperative Pain
2016
Ingår i: Pharmaceutical research. - : Springer Science and Business Media LLC. - 0724-8741 .- 1573-904X. ; 33:5, s. 1093-1103
Tidskriftsartikel (refereegranskat) abstract
To characterize the pharmacokinetic-pharmacodynamic (PK-PD) relationship between exposure of morphine and subsequent morphine consumption and to develop simulation tools for model validation. Dose, formulation and time of morphine administration was available from a published study in 63 patients receiving intravenous, oral immediate release or oral controlled release morphine on request after hip surgery. The PK-PD relationship between predicted exposure of morphine and morphine consumption was modeled using repeated time to event (RTTE) modeling in NONMEM. To validate the RTTE model, a visual predictive check method was developed with simulated morphine consumption given the exposure of preceding morphine administration. The probability of requesting morphine was found to be significantly related to the exposure of morphine as well as night/day. Oral controlled release morphine was more effective than intravenous and oral immediate release formulations at equivalent average concentrations. Maximum effect was obtained for 8 h by oral controlled release doses a parts per thousand yenaEuro parts per thousand 15 mg, where probability of requesting a new dose was reduced to 20% for a typical patient. This study demonstrates the first quantitative link between exposure of morphine and subsequent morphine consumption and introduces an efficient visual predictive check approach with simulation of adaptive dosing.
8.
Juul, Rasmus Vestergaard, et al.
(författare)
Analysis of opioid consumption in clinical trials : a simulation based analysis of power of four approaches
2017
Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : SPRINGER/PLENUM PUBLISHERS. - 1567-567X .- 1573-8744. ; 44:4, s. 325-333
Tidskriftsartikel (refereegranskat) abstract
Inconsistent trial design and analysis is a key reason that few advances in postoperative pain management have been made from clinical trials analyzing opioid consumption data. This study aimed to compare four different approaches to analyze opioid consumption data. A repeated time-to-event (RTTE) model in NONMEM was used to simulate clinical trials of morphine consumption with and without a hypothetical adjuvant analgesic in doses equivalent to 15-62% reduction in morphine consumption. Trials were simulated with duration of 24-96 h. Monte Carlo simulation and re-estimation were performed to determine sample size required to demonstrate efficacy with 80% power using t test, Mann-Whitney rank sum test, time-to-event (TTE) modeling and RTTE modeling. Precision of efficacy estimates for RTTE models were evaluated in 500 simulations. A sample size of 50 patients was required to detect 37% morphine sparing effect with at least 80% power in a 24 h trial with RTTE modeling whereas the required sample size was 200 for Mann-Whitney, 180 for t-test and 76 for TTE models. Extending the trial duration from 24 to 96 h reduced the required sample size by 3.1 fold with RTTE modeling. Precise estimate of potency was obtained with a RTTE model accounting for both morphine effects and time-varying covariates on opioid consumption. An RTTE analysis approach proved better suited for demonstrating efficacy of opioid sparing analgesics than traditional statistical tests as a lower sample size was required due the ability to account for time-varying factors including PK.
9.
Keutzer, Lina, et al.
(författare)
Machine Learning and Pharmacometrics for Prediction of Pharmacokinetic Data : Differences, Similarities and Challenges Illustrated with Rifampicin
2022
Ingår i: Pharmaceutics. - : MDPI. - 1999-4923. ; 14:8
Tidskriftsartikel (refereegranskat) abstract
Pharmacometrics (PM) and machine learning (ML) are both valuable for drug development to characterize pharmacokinetics (PK) and pharmacodynamics (PD). Pharmacokinetic/pharmacodynamic (PKPD) analysis using PM provides mechanistic insight into biological processes but is time- and labor-intensive. In contrast, ML models are much quicker trained, but offer less mechanistic insights. The opportunity of using ML predictions of drug PK as input for a PKPD model could strongly accelerate analysis efforts. Here exemplified by rifampicin, a widely used antibiotic, we explore the ability of different ML algorithms to predict drug PK. Based on simulated data, we trained linear regressions (LASSO), Gradient Boosting Machines, XGBoost and Random Forest to predict the plasma concentration-time series and rifampicin area under the concentration-versus-time curve from 0-24 h (AUC(0-24h)) after repeated dosing. XGBoost performed best for prediction of the entire PK series (R-2: 0.84, root mean square error (RMSE): 6.9 mg/L, mean absolute error (MAE): 4.0 mg/L) for the scenario with the largest data size. For AUC(0-24h) prediction, LASSO showed the highest performance (R-2: 0.97, RMSE: 29.1 h center dot mg/L, MAE: 18.8 h center dot mg/L). Increasing the number of plasma concentrations per patient (0, 2 or 6 concentrations per occasion) improved model performance. For example, for AUC(0-24h) prediction using LASSO, the R-2 was 0.41, 0.69 and 0.97 when using predictors only (no plasma concentrations), 2 or 6 plasma concentrations per occasion as input, respectively. Run times for the ML models ranged from 1.0 s to 8 min, while the run time for the PM model was more than 3 h. Furthermore, building a PM model is more time- and labor-intensive compared with ML. ML predictions of drug PK could thus be used as input into a PKPD model, enabling time-efficient analysis.
10.
Lledó-García, Rocío, et al.
(författare)
Ethically Attractive Dose-Finding Designs for Drugs With a Narrow Therapeutic Index
2012
Ingår i: Journal of clinical pharmacology. - : Wiley. - 0091-2700 .- 1552-4604. ; 52:1, s. 29-38
Tidskriftsartikel (refereegranskat) abstract
A simulation-based comparison study on the relative merits of dose-control trials (DCTs) with exposure-response analysis versus concentration-control trials (CCTs) for drugs with narrow therapeutic index showed that DCT designs are more informative about the exposure-response relationship. The authors revisit the question employing optimal design methodology and propose strategies for designing ethically attractive trials for these drugs, balancing between individual-collective risk and informativeness. An optimal study was performed considering a hypothetical immunosuppressant agent with 2 clinical end points. Different scenarios were optimized applying cost-based designs (unwanted events vs number of sub-jects/trial or maximal individual risk). Dose/exposure targets and number of subjects per trial/arm were optimized. Prior information inclusion on baseline risks was evaluated. DCTs were more informative, needing smaller studies to provide the same information as CCTs. Using the number of unwanted events-rather than subjects-as cost resulted in ethically more attractive designs. Including prior baseline risk information reduced the number of subject/events and allowed the use of targets closer to the optimal. Designing dose-finding trials for some narrow therapeutic index drugs may be improved by using DCTs with exposure-response analysis, cost-based designs, prior information, and optimal design analysis providing information on the ethical trade-off between individual risk and information gain.
