| 1. |
- Yngman, Gunnar, et al.
(author)
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An introduction of the full random effects model
- 2022
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In: CPT. - : John Wiley & Sons. - 2163-8306. ; 11:2, s. 149-160
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Journal article (peer-reviewed)abstract
- The full random-effects model (FREM) is a method for determining covariate effects in mixed-effects models. Covariates are modeled as random variables, described by mean and variance. The method captures the covariate effects in estimated covariances between individual parameters and covariates. This approach is robust against issues that may cause reduced performance in methods based on estimating fixed effects (e.g., correlated covariates where the effects cannot be simultaneously identified in fixed-effects methods). FREM covariate parameterization and transformation of covariate data records can be used to alter the covariate-parameter relation. Four relations (linear, log-linear, exponential, and power) were implemented and shown to provide estimates equivalent to their fixed-effects counterparts. Comparisons between FREM and mathematically equivalent full fixed-effects models (FFEMs) were performed in original and simulated data, in the presence and absence of non-normally distributed and highly correlated covariates. These comparisons show that both FREM and FFEM perform well in the examined cases, with a slightly better estimation accuracy of parameter interindividual variability (IIV) in FREM. In addition, FREM offers the unique advantage of letting a single estimation simultaneously provide covariate effect coefficient estimates and IIV estimates for any subset of the examined covariates, including the effect of each covariate in isolation. Such subsets can be used to apply the model across data sources with different sets of available covariates, or to communicate covariate effects in a way that is not conditional on other covariates.
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| 2. |
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| 3. |
- Faraj, Alan, et al.
(author)
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Model-based approaches to prospectively power pediatric pharmacokinetic trials with limited sample size
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Other publication (other academic/artistic)abstract
- Rare disease studies in pediatric subjects are challenging due to small sample sizes. Pharmacokinetic (PK) information in pediatric subjects is important and often used for matching strategy towards adults informing pediatric development program. Prior to studying PK in children, it is important to optimize the sparse sampling schedule and show that the study is designed to estimate key PK parameters with sufficient certainty. In this work, the sampling schedule in children was optimized for marzeptacog alfa activated (MarzAA) and dalcinonacog alfa (DalcA), two drugs in development for treatment of hemophilia. Subsequently, evaluation of different model-based approaches to calculate the power to estimate clearance (CL) and volume of distribution (V) using a fixed sample size (n=24) was performed. Usage of Bayesian priors (up to 2x inflation of the adult priors) performed well (power 80 %), but with lower power with decreasing informativeness (5x and 10x inflation of the adult priors), in particular for DalcA. Reusing the full adult model or a simplified model for standalone analysis of the pediatric data did not perform well (<80% power). Fixing the adult PK parameters except for CL and V performed well when pooling adult and pediatric data (power 100 %). In general, the power to estimate V alone or CL together with V was lower than for CL, indicating that the sampling schedules were more informative for CL. Although Bayesian prior approaches were shown to perform well without need of pooling data, other approaches that require less technical expertise and no need for simplification of the adult model were found to be good alternatives when pooling of data is possible.
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| 4. |
- Faraj, Alan
(author)
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Pharmacometric models to inform dose selection and study design : Applied in hemophilia and tuberculosis
- 2024
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Doctoral thesis (other academic/artistic)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.
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| 5. |
- Faraj, Alan, et al.
(author)
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Subcutaneous Marzeptacog Alfa (Activated) for On‐Demand Treatment of Bleeding Events in Subjects With Hemophilia A or B With Inhibitors
- 2024
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In: Clinical Pharmacology and Therapeutics. - : John Wiley & Sons. - 0009-9236 .- 1532-6535. ; 115:3, s. 498-505
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Journal article (peer-reviewed)abstract
- Marzeptacog alfa (MarzAA) is under development for subcutaneous treatment of episodic bleeds in patients with hemophilia A/B and was studied in a phase III trial evaluating MarzAA compared with standard-of-care (SoC) for on-demand use. The work presented here aimed to evaluate MarzAA and SoC treatment of bleeding events on a standardized four-point efficacy scale (poor, fair, good, and excellent). Two continuous-time Markov modeling approaches were explored; a four-state model analyzing all four categories of bleeding improvement and a two-state model analyzing a binarized outcome (treatment failure (poor/fair), and treatment success (good/excellent)). Different covariates impacting improvement of bleeding episodes as well as a putative relationship between MarzAA exposure and improvement of bleeding episodes were evaluated. In the final four-state model, higher baseline diastolic blood pressure and higher age (> 33 years of age) were found to negatively and positively impact improvement of bleeding condition, respectively. Bleeding events occurring in knees and ankles were found to improve faster than bleeding events at other locations. The covariate effects had most impact on early treatment success (≤ 3 hours) whereas at later timepoints (> 12 hours), treatment success was similar for all patients indicating that these covariates might be clinically relevant for early treatment response. A statistically significant relationship between MarzAA zero-order absorption and improvement of bleedings (P < 0.05) were identified albeit with low precision. No statistically significant difference in treatment response between MarzAA and intravenous SoC was identified, indicating the potential of MarzAA for treatment of episodic bleeding events with a favorable subcutaneous administration route.
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| 6. |
- Geroldinger, Martin, et al.
(author)
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Statistical recommendations for count, binary, and ordinal data in rare disease cross-over trials
- 2023
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In: Orphanet Journal of Rare Diseases. - : BioMed Central (BMC). - 1750-1172. ; 18:1
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Journal article (peer-reviewed)abstract
- BackgroundRecommendations for statistical methods in rare disease trials are scarce, especially for cross-over designs. As a result various state-of-the-art methodologies were compared as neutrally as possible using an illustrative data set from epidermolysis bullosa research to build recommendations for count, binary, and ordinal outcome variables. For this purpose, parametric (model averaging), semiparametric (generalized estimating equations type [GEE-like]) and nonparametric (generalized pairwise comparisons [GPC] and a marginal model implemented in the R package nparLD) methods were chosen by an international consortium of statisticians.ResultsIt was found that there is no uniformly best method for the aforementioned types of outcome variables, but in particular situations, there are methods that perform better than others. Especially if maximizing power is the primary goal, the prioritized unmatched GPC method was able to achieve particularly good results, besides being appropriate for prioritizing clinically relevant time points. Model averaging led to favorable results in some scenarios especially within the binary outcome setting and, like the GEE-like semiparametric method, also allows for considering period and carry-over effects properly. Inference based on the nonparametric marginal model was able to achieve high power, especially in the ordinal outcome scenario, despite small sample sizes due to separate testing of treatment periods, and is suitable when longitudinal and interaction effects have to be considered.ConclusionOverall, a balance has to be found between achieving high power, accounting for cross-over, period, or carry-over effects, and prioritizing clinically relevant time points.
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| 7. |
- Juul, Rasmus Vestergaard, et al.
(author)
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Analysis of opioid consumption in clinical trials : a simulation based analysis of power of four approaches
- 2017
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In: Journal of Pharmacokinetics and Pharmacodynamics. - : SPRINGER/PLENUM PUBLISHERS. - 1567-567X .- 1573-8744. ; 44:4, s. 325-333
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Journal article (peer-reviewed)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.
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| 8. |
- Keutzer, Lina, et al.
(author)
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Machine Learning and Pharmacometrics for Prediction of Pharmacokinetic Data : Differences, Similarities and Challenges Illustrated with Rifampicin
- 2022
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In: Pharmaceutics. - : MDPI. - 1999-4923. ; 14:8
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Journal article (peer-reviewed)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.
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| 9. |
- Nyberg, Joakim, 1978-, et al.
(author)
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Edoxaban Exposure-Response Analysis and Clinical Utility Index Assessment in Patients With Symptomatic Deep-Vein Thrombosis or Pulmonary Embolism
- 2016
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In: CPT. - : Wiley. - 2163-8306. ; 5:4, s. 222-232
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Journal article (peer-reviewed)abstract
- Edoxaban exposure-response relationships from the phase III study evaluating edoxaban for prevention and treatment of venous thromboembolism (VTE) in patients with acute deep vein thrombosis (DVT) and/or pulmonary embolism (PE) were assessed by parametric time-to-event analysis. Statistical significant exposure-response relationships were recurrent VTE with hazard ratio (HR) based on average edoxaban concentration at steady state (C-av) (HRCav) 50.98 (i.e., change in the HR with every 1 ng/mL increase of C-av); the composite of recurrent DVT and nonfatal PE with HRC(av)50.99; and the composite of recurrent DVT, nonfatal PE, and all-cause mortality HRC(av)50.98, and all death using maximal edoxaban concentration (C-max) with HR (C-max) 50.99. No statistical significant exposure-response relationships were found for clinically relevant bleeding or major adverse cardiovascular event. Results support the recommendation of once-daily edoxaban 60 mg, and a reduced 30 mg dose in patients with moderate renal impairment, body weight <= 60 kg, or use of P-glycoprotein inhibitors verapamil or quinidine.
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| 10. |
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| 11. |
- Nyberg, Joakim, 1978-
(author)
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Practical Optimal Experimental Design in Drug Development and Drug Treatment using Nonlinear Mixed Effects Models
- 2011
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Doctoral thesis (other academic/artistic)abstract
- The cost of releasing a new drug on the market has increased rapidly in the last decade. The reasons for this increase vary with the drug, but the need to make correct decisions earlier in the drug development process and to maximize the information gained throughout the process is evident. Optimal experimental design (OD) describes the procedure of maximizing relevant information in drug development and drug treatment processes. While various optimization criteria can be considered in OD, the most common is to optimize the unknown model parameters for an upcoming study. To date, OD has mainly been used to optimize the independent variables, e.g. sample times, but it can be used for any design variable in a study. This thesis addresses the OD of multiple continuous or discrete design variables for nonlinear mixed effects models. The methodology for optimizing and the optimization of different types of models with either continuous or discrete data are presented and the benefits of OD for such models are shown. A software tool for optimizing these models in parallel is developed and three OD examples are demonstrated: 1) optimization of an intravenous glucose tolerance test resulting in a reduction in the number of samples by a third, 2) optimization of drug compound screening experiments resulting in the estimation of nonlinear kinetics and 3) an individual dose-finding study for the treatment of children with ciclosporin before kidney transplantation resulting in a reduction in the number of blood samples to ~27% of the original number and an 83% reduction in the study duration. This thesis uses examples and methodology to show that studies in drug development and drug treatment can be optimized using nonlinear mixed effects OD. This provides a tool than can lower the cost and increase the overall efficiency of drug development and drug treatment.
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| 12. |
- Nyberg, Joakim, 1978-, et al.
(author)
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RETRACTED: Population Kinetics of 0.9% Saline Distribution in Hemorrhaged Awake and Isoflurane-anesthetized Volunteers
- 2019
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In: Anesthesiology. - : Ovid Technologies (Wolters Kluwer Health). - 0003-3022 .- 1528-1175. ; 131:3, s. 501-511
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Journal article (peer-reviewed)abstract
- Background: Population-based, pharmacokinetic modeling can be used to describe variability in fluid distribution and dilution between individuals and across populations. The authors hypothesized that dilution produced by crystalloid infusion after hemorrhage would be larger in anesthetized than in awake subjects and that population kinetic modeling would identify differences in covariates. Methods: Twelve healthy volunteers, seven females and five males, mean age 28 +/- 4.3 yr, underwent a randomized crossover study. Each subject participated in two separate sessions, separated by four weeks, in which they were assigned to an awake or an anesthetized arm. After a baseline period, hemorrhage (7 ml/kg during 20 min) was induced, immediately followed by a 25 ml/kg infusion during 20 min of 0.9% saline. Hemoglobin concentrations, sampled every 5 min for 60 min then every 10 min for an additional 120 min, were used for population kinetic modeling. Covariates, including body weight, sex, and study arm (awake or anesthetized), were tested in the model building. The change in dilution was studied by analyzing area under the curve and maximum plasma dilution. Results: Anesthetized subjects had larger plasma dilution than awake subjects. The analysis showed that females increased area under the curve and maximum plasma dilution by 17% (with 95% CI, 1.08 to 1.38 and 1.07 to 1.39) compared with men, and study arm (anesthetized increased area under the curve by 99% [0.88 to 2.45] and maximum plasma dilution by 35% [0.71 to 1.63]) impacted the plasma dilution whereas a 10-kg increase of body weight resulted in a small change (less than1% [0.93 to 1.20]) in area under the curve and maximum plasma dilution. Mean arterial pressure was lower in subjects while anesthetized (P < 0.001). Conclusions: In awake and anesthetized subjects subjected to controlled hemorrhage, plasma dilution increased with anesthesia, female sex, and lower body weight. Neither study arm nor body weight impact on area under the curve or maximum plasma dilution were statistically significant and therefore no effect can be established.
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