| 1. |
- Zhao, J. H., et al.
(författare)
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Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets
- 2023
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Ingår i: Nature Immunology. - : Springer Nature. - 1529-2908 .- 1529-2916. ; 24:9, s. 1540-1551
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Tidskriftsartikel (refereegranskat)abstract
- Circulating proteins have important functions in inflammation and a broad range of diseases. To identify genetic influences on inflammation-related proteins, we conducted a genome-wide protein quantitative trait locus (pQTL) study of 91 plasma proteins measured using the Olink Target platform in 14,824 participants. We identified 180 pQTLs (59 cis, 121 trans). Integration of pQTL data with eQTL and disease genome-wide association studies provided insight into pathogenesis, implicating lymphotoxin-alpha in multiple sclerosis. Using Mendelian randomization (MR) to assess causality in disease etiology, we identified both shared and distinct effects of specific proteins across immune-mediated diseases, including directionally discordant effects of CD40 on risk of rheumatoid arthritis versus multiple sclerosis and inflammatory bowel disease. MR implicated CXCL5 in the etiology of ulcerative colitis (UC) and we show elevated gut CXCL5 transcript expression in patients with UC. These results identify targets of existing drugs and provide a powerful resource to facilitate future drug target prioritization. Here the authors identify genetic effectors of the level of inflammation-related plasma proteins and use Mendelian randomization to identify proteins that contribute to immune-mediated disease risk.
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| 2. |
- Karlsson, Kristin E., et al.
(författare)
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Modeling Disease Progression in Acute Stroke Using Clinical Assessment Scales
- 2010
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Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 12:4, s. 683-691
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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.
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| 3. |
- Hamberg, Anna-Karin, et al.
(författare)
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Characterising variability in warfarin dose requirements in children using modelling and simulation
- 2013
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Ingår i: British Journal of Clinical Pharmacology. - : Wiley. - 0306-5251 .- 1365-2125. ; 78:1, s. 158-169
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Tidskriftsartikel (refereegranskat)abstract
- AIMS: Although genetic, clinical and demographic factors have been shown to explain approximately half of the inter-individual variability in warfarin dose requirement in adults, less is known about causes of dose variability in children. This study aimed to identify and quantify major genetic, clinical and demographic sources of warfarin dose variability in children using modelling and simulation.METHODS: Clinical, demographic and genetic data from 163 children with a median age of 6.3 years (range 0.06-18.9 years), covering over 183 years of warfarin therapy and 6445 INR observations were used to update and optimise a published adult pharmacometric warfarin model for use in children.RESULTS: Genotype effects in children were found to be comparable to what has been reported for adults, with CYP2C9 explaining up to a 4-fold difference in dose (CYP2C9 *1/*1 vs. *3/*3) and VKORC1 explaining up to a 2-fold difference in dose (VKORC1 G/G vs. A/A), respectively. The relationship between bodyweight and warfarin dose was non-linear, with a 3-fold difference in dose for a 4-fold difference in bodyweight. In addition, age, baseline and target INR, and time since initiation of therapy, but not CYP4F2 genotype, had a significant impact on typical warfarin dose requirements in children.CONCLUSIONS: The updated model provides quantitative estimates of major clinical, demographic and genetic factors impacting warfarin dose variability in children. With this new knowledge more individualised dosing regimens can be developed and prospectively evaluated in the pursuit of improving both efficacy and safety of warfarin therapy in children.
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| 4. |
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| 5. |
- Hamberg, Anna-Karin, 1964-
(författare)
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Pharmacometric Models for Individualisation of Warfarin in Adults and Children
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Warfarin is one of the most widely used anticoagulants. Therapy is complicated by warfarin’s narrow therapeutic range and pronounced variability in individual dose requirements. Although warfarin therapy is uncommon in children, it is crucial for children with certain congenital or acquired heart diseases. Treatment in children is especially difficult due to the lack of i) a decision support tool for efficient and consistent dose adjustments, and ii) a flexible warfarin formulation for accurate and reproducible dosing.The overall aim of this thesis was to develop a PKPD-based pharmacometric model for warfarin that describes the dose-response relationship over time, and to identify important predictors that influence individual dose requirements both in adults and children. Special emphasis was placed on investigating the contribution of genetic factors to the observed variability.A clinically useful pharmacometric model for warfarin has been developed using NONMEM. The model has been successfully reformulated into a KPD-model that describes the relationship between warfarin dose and INR response, and that is applicable to both adults and children. From a clinical perspective, this is a very important change since it allows the use of information on dose and INR that is available routinely. The model incorporates both patient and clinical characteristics, such as age, weight, CYP2C9 and VKORC1 genotype, and baseline and target INR, for the prediction of an individualised starting dose. It also enables the use of information from previous doses and INR observations to further individualise the dose a posteriori using a Bayesian forecasting method.The NONMEM model has been transferred to a user-friendly, platform independent tool to aid use in clinical practice. The tool can be used for a priori and a posteriori individualisation of warfarin therapy in both adults and children. The tool should ensure consistent dose adjustment practices, and provide more efficient individualisation of warfarin dosing in all patients, irrespective of age, body weight, CYP2C9 or VKORC1 genotype, baseline or target INR. The expected outcome is improved warfarin therapy compared with empirical dosing, with patients achieving a therapeutic and stable INR faster and avoiding high INRs that increase the risk of bleeding.
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| 6. |
- Hamberg, Anna-Karin, et al.
(författare)
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Predicting the relative importance of genetic, clinical and demographic factors on warfarin dose in children using pharmacometric modelling
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- It is difficult to predict anticoagulation response to warfarin in children mainly because of a wide inter-individual variability in warfarin dose requirement. The present study objective was to identify important predictors of dose in children and to optimize a previous NONMEM warfarin model for a priori and a posteriori dose and INR predictions in children. Data from 163 warfarin treated children with underlying heart disease (median age 6.3 years) were used. CYP2C9 and VKORC1 genotype caused up to 4-fold and 2-fold differences in warfarin dose requirement, respectively. Other important predictors of warfarin dose were bodyweight, age, baseline and target INR, and time since initiation of therapy with lower doses during the initiation. CYP4F2 genotype had only a marginal effect on dose. The present study findings will aid the development of a personalised approach to warfarin therapy in children, in the pursuit of improving both efficacy and safety of anticoagulation therapy.
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| 7. |
- Hamberg, Anna-Karin, 1964-, et al.
(författare)
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Warfarin dose prediction in children using pharmacometric bridging : comparison with published pharmacogenetic dosing algorithms
- 2013
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Ingår i: European Journal of Clinical Pharmacology. - : Springer Science and Business Media LLC. - 0031-6970 .- 1432-1041. ; 69:6, s. 1275-1283
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Tidskriftsartikel (refereegranskat)abstract
- PurposeNumerous studies have investigated causes of warfarin dose variability in adults whereas studies in children are limited both in numbers and size. Mechanism-based population modelling provides an opportunity to condense and propagate prior knowledge from one population to another. The main objectives with this study were to evaluate the predictive performance of a theoretically bridged adult warfarin model in children, and to compare accuracy in dose prediction relative to published warfarin algorithms for children.MethodAn adult population PK/PD-model for warfarin, with CYP2C9 and VKORC1 genotype, age and target INR as dose predictors, was bridged to children using allometric scaling methods. Its predictive properties were evaluated in an external dataset of children 0-18 years old, including comparison of dose prediction accuracy with three pharmacogenetics-based algorithms for children.ResultsOverall, the bridged model predicted INR response well in 64 warfarin treated Swedish children (median age 4.3 years), but with a tendency to over predict INR in children ≤ 2 years old. The bridged model predicted 20 of 49 children (41%) within ± 20% of actual maintenance dose (median age 7.2 years). In comparison the published dosing algorithms predicted 33-41% of the children within ± 20% of actual dose. Dose optimization with the bridged model based on up to three individual INR observations increased the proportion within ± 20% of actual dose to 70%.ConclusionA mechanism-based population model developed on adult data provides a promising first step towards more individualized warfarin therapy in children.
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| 8. |
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| 9. |
- Jonsson, Fredrik, et al.
(författare)
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The application of a Bayesian approach to the analysis of a complex, mechanistically based model
- 2007
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Ingår i: Journal of Biopharmaceutical Statistics. - : Informa UK Limited. - 1054-3406 .- 1520-5711. ; 17:1, s. 65-92
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Tidskriftsartikel (refereegranskat)abstract
- The Bayesian approach has been suggested as a suitable method in the context of mechanistic pharmacokinetic-pharmacodynamic (PK-PD) modeling, as it allows for efficient use of both data and prior knowledge regarding the drug or disease state. However, to this day, published examples of its application to real PK-PD problems have been scarce. We present an example of a fully Bayesian re-analysis of a previously published mechanistic model describing the time course of circulating neutrophils in stroke patients and healthy individuals. While priors could be established for all population parameters in the model, not all variability terms were known with any degree of precision. A sensitivity analysis around the assigned priors used was performed by testing three different sets of prior values for the population variance terms for which no data were available in the literature: “informative”, “semi-informative”, and “noninformative”, respectively. For all variability terms, inverse gamma distributions were used. It was possible to fit the model to the data using the “informative” priors. However, when the “semi-informative” and “noninformative” priors were used, it was impossible to accomplish convergence due to severe correlations between parameters. In addition, due to the complexity of the model, the process of defining priors and running the Markov chains was very time-consuming. We conclude that the present analysis represents a first example of the fully transparent application of Bayesian methods to a complex, mechanistic PK-PD problem with real data. The approach is time-consuming, but enables us to make use of all available information from data and scientific evidence. Thereby, it shows potential both for detection of data gaps and for more reliable predictions of various outcomes and “what if” scenarios.
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| 10. |
- Karlsson, Kristin E, 1975-
(författare)
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Benefits of Pharmacometric Model-Based Design and Analysis of Clinical Trials
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Quantitative pharmacokinetic-pharmacodynamic and disease progression models are the core of the science of pharmacometrics which has been identified as one of the strategies that can make drug development more effective. To adequately develop and utilize these models one needs to carefully consider the nature of the data, choice of appropriate estimation methods, model evaluation strategies, and, most importantly, the intended use of the model. The general aim of this thesis was to investigate how the use of pharmacometric models can improve the design and analysis of clinical trials within drug development. The development of pharmacometric models for clinical assessment scales in stroke and graded severity events, in this thesis, show the benefit of describing data as close to its true nature as possible, as it increases the predictive abilities and allows for mechanistic interpretations of the models. Performance of three estimation methods implemented in the mixed-effects modeling software NONMEM; 1) Laplace, 2) SAEM, and 3) Importance sampling, applied when modeling repeated time-to-event data, was investigated. The two latter methods are to be preferred if less than approximately half of the individuals experience events. In addition, predictive performance of two validation procedures, internal and external validation, was explored, with internal validation being preferred in most cases. Model-based analysis was compared to conventional methods by the use of clinical trial simulations and the power to detect a drug effect was improved with a pharmacometric design and analysis. Throughout this thesis several examples have shown the possibility of significantly reducing sample sizes in clinical trials with a pharmacometric model-based analysis. This approach will reduce time and costs spent in the development of new drug therapies, but foremost reduce the number of healthy volunteers and patients exposed to experimental drugs.
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| 11. |
- Karlsson, Kristin E, et al.
(författare)
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Comparisons of Analysis Methods for Proof-of-Concept Trials
- 2013
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Ingår i: CPT. - : Wiley. - 2163-8306. ; 2, s. e23-
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Tidskriftsartikel (refereegranskat)abstract
- Drug development struggles with high costs and time consuming processes. Hence, a need for new strategies has been accentuated by many stakeholders in drug development. This study proposes the use of pharmacometric models to rationalize drug development. Two simulated examples, within the therapeutic areas of acute stroke and type 2 diabetes, are utilized to compare a pharmacometric model–based analysis to a t-test with respect to study power of proof-of-concept (POC) trials. In all investigated examples and scenarios, the conventional statistical analysis resulted in several fold larger study sizes to achieve 80% power. For a scenario with a parallel design of one placebo group and one active dose arm, the difference between the conventional and pharmacometric approach was 4.3- and 8.4-fold, for the stroke and diabetes example, respectively. Although the model-based power depend on the model assumptions, in these scenarios, the pharmacometric model–based approach was demonstrated to permit drastic streamlining of POC trials.
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| 12. |
- Karlsson, Kristin E., et al.
(författare)
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Randomized exposure-controlled trials : Impact of randomization and analysis strategies
- 2007
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Ingår i: British Journal of Clinical Pharmacology. - : Wiley. - 0306-5251 .- 1365-2125. ; 64:3, s. 266-277
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Tidskriftsartikel (refereegranskat)abstract
- Aims: In the literature, five potential benefits of randomizing clinical trials on concentration levels, rather than dose, have been proposed: (i) statistical study power will increase; (ii) study power will be less sensitive to high variability in the pharmacokinetics (PK); (iii) the power of establishing an exposure-response relationship will be robust to correlations between PK and pharmacodynamics (PD); (iv) estimates of the exposure-response relationship are likely to be less biased; and (v) studies will provide a better control of exposure in situations with toxicity issues. The main aim of this study was to investigate if these five statements are valid when the trial results are evaluated using a model-based analysis. Methods: Quantitative relationships between drug dose, concentration, biomarker and clinical end-point were defined using pharmacometric models. Three randomization schemes for exposure-controlled trials, dose-controlled (RDCT), concentration-controlled (RCCT) and biomarker-controlled (RBCT), were simulated and analysed according to the models. Results: (i) The RCCT and RBCT had lower statistical power than RDCT in a model-based analysis; (ii) with a model-based analysis the power for an RDCT increased with increasing PK variability; (iii) the statistical power in a model-based analysis was robust to correlations between CL and EC 50 or Emax; (iv) under all conditions the bias was negligible (<3%); and (v) for studies with equal power RCCT could produce either more or fewer adverse events compared with an RDCT. Conclusion: Alternative randomization schemes may not have the proposed advantages if a model-based analysis is employed.
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| 13. |
- Gupta, Anubha, et al.
(författare)
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Brain distribution of cetirizine enantiomers : comparison of three different tissue-to-plasma partition coefficients : K(p), K(p,u), and K(p,uu)
- 2006
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Ingår i: Drug Metabolism And Disposition. - : American Society for Pharmacology & Experimental Therapeutics (ASPET). - 0090-9556 .- 1521-009X. ; 34:2, s. 318-323
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Tidskriftsartikel (refereegranskat)abstract
- The objective of this study was to compare the blood-brain barrier (BBB) transport and brain distribution of levo- (R-CZE) and dextrocetirizine (S-CZE). Microdialysis probes, calibrated using retrodialysis by drug, were placed into the frontal cortex and right jugular vein of eight guinea pigs. Racemic CZE (2.7 mg/kg) was administered as a 60-min i.v. infusion. Unbound and total concentrations of the enantiomers were measured in blood and brain with liquid chromatography-tandem mass spectrometry. The brain distribution of the CZE enantiomers were compared using the parameters K(p,) K(p,u,) K(p,uu), and V(u,br). K(p) compares total brain concentration to total plasma concentration, K(p,u) compensates for binding in plasma, whereas K(p,uu) also compensates for binding within the brain tissue and directly quantifies the transport across the BBB. V(u,br) describes binding within the brain. The stereoselective brain distribution indicated by the K(p) of 0.22 and 0.04 for S- and R-CZE, respectively, was caused by different binding to plasma proteins. The transport of the CZE enantiomers across the BBB was not stereoselective, since the K(p,uu) was 0.17 and 0.14 (N.S.) for S- and R-CZE, respectively. The K(p,uu) values show that the enantiomers are effluxed to a large extent across the BBB. The V(u,br) of approximately 2.5 ml/g brain was also similar for both the enantiomers, and the value indicates high binding to brain tissue. Thus, when determining stereoselectivity in brain distribution, it is important to study all factors governing this distribution, binding in blood and brain, and the BBB equilibrium.
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| 14. |
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| 15. |
- Gupta, Anubha, 1974-
(författare)
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Role of the Blood-Brain Barrier in Stereoselective Distribution and Delay in H1 Receptor Occupancy of Cetirizine in the Guinea Pig Brain
- 2006
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cetirizine, an H1-antihistamine, is prescribed for allergic disorders. It exists as a racemic mixture, with levocetirizine being the active enantiomer. The central nervous system side-effects of H1-antihistamines are caused by their penetration into the brain. In this thesis the plasma pharmacokinetics, transport across the blood-brain barrier (BBB) and H1 receptor occupancy of cetirizine enantiomers was investigated in vivo in guinea pigs. The transport across the BBB was quantified using the microdialysis technique. Stereoselective brain distribution was investigated by measuring both unbound and total concentrations in plasma and brain. The time aspects of the H1 receptor occupancy of levocetirizine was studied in the brain and the periphery.The plasma pharmacokinetics of cetirizine was stereoselective with clearance and volume of distribution of levocetirizine being approximately half that of dextrocetirizine. This was mainly due to the differences in plasma protein binding of the enantiomers. The stereoselectivity in brain distribution indicated by the partition coefficient Kp (total AUC ratio brain to plasma) was caused by stereoselective plasma protein binding. The transport across the BBB measured in this thesis by the unbound partition coefficient Kp,uu (unbound AUC ratio brain to plasma) was the same for the two enantiomers. Binding within the brain was also not significantly different. The H1 receptor occupancy of levocetirizine in brain lagged behind the plasma concentrations whereas it was not delayed with respect to the brain concentrations. This indicates that the delayed brain H1 receptor occupancy of levocetirizine is caused by a slow transport across the BBB.In summary, the results of this thesis emphasize the importance of measuring both the unbound and total concentrations in blood and brain to characterize stereoselective brain distribution. The thesis also emphasize the importance of taking local brain pharmacokinetics into consideration in understanding pharmacokinetic-pharmacodynamic relationships of drugs with central activity.
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| 16. |
- Gupta, Anubha, et al.
(författare)
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Stereoselective pharmacokinetics of cetirizine in the guinea pig : Role of protein binding
- 2006
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Ingår i: Biopharmaceutics & drug disposition. - : Wiley. - 0142-2782 .- 1099-081X. ; 27:6, s. 291-297
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Tidskriftsartikel (refereegranskat)abstract
- Purpose. To characterize the pharmacokinetics of cetirizine enantiomers in the guinea pig including protein binding in both the guinea pig and human plasma. Methods. Plasma concentrations of cetirizine enantiomers in the guinea pig were determined using a LC-MS/MS method after a short i.v. infusion (1, 2 and 4 mg/kg) of racemic cetirizine. Protein binding was determined using an in vitro equilibrium dialysis technique. A pharmacokinetic model was developed using NONMEM and the differences in pharmacokinetic parameters of levocetirizine and dextrocetirizine were estimated. Results. The plasma concentration time data of both the enantiomers were best described by a three-compartment pharmacokinetics model. The clearance (CL) of levocetirizine and dextrocetirizine was 1.2 and 2.7 ml/min, respectively, and the volume of distribution at steady state (V-ss) was 457 ml and 996 ml, respectively. The fraction unbound (f(u)) in guinea pig plasma for levocetirizine and dextrocetirizine was 7-10% and 16-21% while in human plasma, it was 8% and 12%, respectively. The factor describing the difference in the pharmacokinetic parameters of the cetirizine enantiomers was estimated to be 2.26. Conclusions. Cetirizine pharmacokinetics is stereoselective in the guinea pig. For levocetirizine, CL and V-ss were half those of dextrocetirizine, indicating that protein binding is an important factor affecting the pharmacokinetics of cetirizine. The effect of protein binding on the pharmacokinetics of the cetirizine enantiomers could be extrapolated to humans.
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| 17. |
- Hamberg, Anna-Karin, et al.
(författare)
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A Bayesian decision support tool for efficient dose individualization of warfarin in adults and children
- 2015
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Ingår i: BMC Medical Informatics and Decision Making. - : Springer Science and Business Media LLC. - 1472-6947. ; 15:7
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Tidskriftsartikel (refereegranskat)abstract
- Warfarin is the most widely prescribed anticoagulant for prevention and treatment of thromboembolic events. Although highly effective, the use of warfarin is limited by a narrow therapeutic range combined with a more than ten-fold difference in the dose required for adequate anticoagulation in adults. For each patient, an optimal dose that leads to a favourable balance between the wanted antithrombotic effect and the risk of bleeding, measured as the prothrombin time International Normalised Ratio (INR), must be found. A model capable of describing the time-course of the INR response to warfarin therapy can be used to aid dose selection, both before starting therapy (a priori dose prediction) and after therapy has been initiated (a posteriori dose revision). In this paper we describe the transfer of a population PKPD-model for warfarin developed in NONMEM to a platform independent decision support tool written in Java. The tool proved capable of solving a system of differential equations representing the pharmacokinetics and pharmacodynamics of warfarin, with a performance comparable to NONMEM. To estimate an a priori dose the user provides information on body weight, age, CYP2C9 and VKORC1 genotype, baseline and target INR. With addition of information about previous doses and INR observations, the tool will use a Bayesian forecasting method to suggest an a posteriori dose, i.e. the dose with the highest probability to result in the desired INR. Results are displayed as the predicted dose per day and per week, and graphically as the predicted INR curve. The tool can also be used to predict INR following any given dose regimen, e.g. a loading-dose regimen. We believe it will provide a clinically useful tool for initiating and maintaining warfarin therapy in the clinic. It will ensure consistent dose adjustment practices between prescribers, and provide more efficient individualization of warfarin dosing in both children and adults.
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| 18. |
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| 19. |
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| 20. |
- Jonsson, E. Niclas
(författare)
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Methodological studies on non-linear mixed effects model building
- 1998
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Population analysis of pharmacokinetic/pharmacodynamic (PK/PD) data by the use of non-linear mixed effects modeling offers a number of benefits over traditional methods of analyzing this type of data. These benefits include the possibility to appropriately characterize the typical PK/PD behaviour in the population as well as the associated variability when only a few observations are available for each individual. The price one has to pay, however, is a more complicated model structure and a corresponding increase in the complexity of the model building process.The work in this thesis demonstrates that there are a number of ways in which the probability of a successful population PK/PD model development can be increased. Using the best available dosage history, which was found to be identifiable in an objective way when two parallel dosing histories were collected, improves both the population parameter estimates and the individual parameter estimates. The individual estimates especially can be a crucial issue if methods that rely on their quality are to be used during the model building process. The sampling design also influences the quality of the individual estimates as well as the ability to detect the components of the inter-individual variabilitymodel. In the example studied it was possible to improve the original design with only small alterations to the protocol. The interpretability of the results and especially the extrapolation of the results of population studies are dependent on whether the assumptions made during the analysis can be justified or not. Using only the data set under study, it was, nonetheless, possible to check and sometimes justify the assumptions that was made. Identifying the relevant covariates is an important aspect of population model building, and ways to enhance a commonly used method (stepwize generalized additive modeling -GAM) were presented and evaluated. Especially the sensitivity to outliers could be reduced. Despite these improvements, the GAM has drawbacks and a method was therefore devised that does not suffer from these. A new method and strategy for inter-individual variability model building was also presented. Finally, more or less complicated methods like the ones presented herein will be of little practical use unless they are made accessible to the general data analyst community. Therefore a program that can serve as the vehicle for both existing and new methods was developed.
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| 21. |
- Jonsson, E Niclas, et al.
(författare)
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Population pharmacokinetics of levosimendan in patients with congestive heart failure
- 2003
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Ingår i: British Journal of Clinical Pharmacology. - : Wiley. - 0306-5251 .- 1365-2125. ; 55:6, s. 544-551
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Tidskriftsartikel (refereegranskat)abstract
- AIMS: The aim of this study was to characterize the population pharmacokinetics of levosimendan in patients with heart failure (NYHA grades III and IV) and its relationship to demographic factors, disease severity and concomitant use of digoxin and beta-blocking agents. METHODS: Data from two efficacy studies with levosimendan administered by intravenous infusion were combined (190 patients in total). The data were analysed using a nonlinear mixed-effects modelling approach as implemented in the NONMEM program. The model development was done in three sequential steps. First the best structural model was determined (e.g. a one-, two- or three-compartment pharmacokinetic model). This was followed by the identification and incorporation of important covariates into the model. Lastly the stochastic part of the model was refined. RESULTS: A two-compartment model best described levosimendan pharmacokinetics. Clearance and the central volume of distribution were found to increase linearly with bodyweight. No other covariates, including concomitant use of digoxin and beta-blocking agents, influenced the pharmacokinetics. In the final model, a 76-kg patient was estimated to have a clearance +/- s.e. of 13.3 +/- 0.4 l h-1 and a central volume of distribution of 16.8 +/- 0.79 l. The interindividual variability was estimated to be 39% and 60% for clearance and central volume of distribution, respectively. Weight changed clearance by 1.5% [95% confidence interval (CI) 0.9%, 2.1%] and the central volume of distribution by 0.9% (95% CI 0.5%, 1.3%) per kg. CONCLUSIONS: The population pharmacokinetics parameters of levosimendan in this patient group were comparable to those obtained by traditional methods in healthy volunteers and patients with mild heart failure. Bodyweight influenced the clearance and the central volume of distribution, which in practice is accounted for by weight adjusting doses. None of the other covariates, including digoxin and beta-blocking agents, significantly influenced the pharmacokinetics of levosimendan.
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| 22. |
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| 23. |
- Khandelwal, Akash, et al.
(författare)
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A Fast Method for Testing Covariates in Population PK/PD Models
- 2011
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Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 13:3, s. 464-472
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Tidskriftsartikel (refereegranskat)abstract
- The development of covariate models within the population modeling program like NONMEM is generally a time-consuming and non-trivial task. In this study, a fast procedure to approximate the change in objective function values of covariate-parameter models is presented and evaluated. The proposed method is a first-order conditional estimation (FOCE)-based linear approximation of the influence of covariates on the model predictions. Simulated and real datasets were used to compare this method with the conventional nonlinear mixed effect model using both first-order (FO) and FOCE approximations. The methods were mainly assessed in terms of difference in objective function values (Delta OFV) between base and covariate models. The FOCE linearization was superior to the FO linearization and showed a high degree of concordance with corresponding nonlinear models in Delta OFV. The linear and nonlinear FOCE models provided similar coefficient estimates and identified the same covariate-parameter relations as statistically significant or non-significant for the real and simulated datasets. The time required to fit tesaglitazar and docetaxel datasets with 4 and 15 parameter-covariate relations using the linearization method was 5.1 and 0.5 min compared with 152 and 34 h, respectively, with the nonlinear models. The FOCE linearization method allows for a fast estimation of covariate-parameter relations models with good concordance with the nonlinear models. This allows a more efficient model building and may allow the utilization of model building techniques that would otherwise be too time-consuming.
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| 24. |
- Kjellsson, Maria C., et al.
(författare)
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Comparison of proportional odds and differential odds models for mixed-effects analysis of categorical data
- 2008
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 35:5, s. 483-501
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Tidskriftsartikel (refereegranskat)abstract
- In this work a model for analyzing categorical data is presented; the differential odds model. Unlike the commonly used proportional odds model, this model does not assume that a covariate affects all categories equally on the log odds scale. The differential odds model was compared to the proportional odds model, by assessing statistical significance and improvement of predictive performance when applying the differential odds model to data previously analyzed using the proportional odds model. Three clinical studies; 3-category T-cell receptor density data, 5-category diarrhea data and 6-category sedation data, were re-analyzed with the differential odds model. As expected, no improvements were seen with T-cell receptor density and diarrhea data. However, for the more complex measurement sedation, the differential odds model provided both statistical improvements and improvements in simulation properties. The estimated actual critical value was for all data lower than the nominal value, using the number of added parameters as the degree of freedom, i.e. the differential odds model is statistically indicated to a less extent than expected. The differential odds model had the desired property of not being indicated when not necessary, but it may provide improvements when the data does not represent a categorization of continuous data.
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| 25. |
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| 26. |
- Lindbom, Lars, et al.
(författare)
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Perl-speaks-NONMEM (PsN) – a Perl module for NONMEM related programming
- 2004
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Ingår i: Computer Methods and Programs in Biomedicine. - : Elsevier BV. - 0169-2607 .- 1872-7565. ; 75:2, s. 85-94
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Tidskriftsartikel (refereegranskat)abstract
- The NONMEM program is the most widely used nonlinear regression software in population pharmacokinetic/pharmacodynamic (PK/PD) analyses. In this article we describe a programming library, Perl-speaks-NONMEM (PsN), intended for programmers that aim at using the computational capability of NONMEM in external applications. The library is object oriented and written in the programming language Perl. The classes of the library are built around NONMEM's data, model and output files. The specification of the NONMEM model is easily set or changed through the model and data file classes while the output from a model fit is accessed through the output file class. The classes have methods that help the programmer perform common repetitive tasks, e.g. summarising the output from a NONMEM run, setting the initial estimates of a model based on a previous run or truncating values over a certain threshold in the data file. PsN creates a basis for the development of high-level software using NONMEM as the regression tool.
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| 27. |
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| 28. |
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| 29. |
- Netterberg, Ida, et al.
(författare)
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A PK/PD Analysis of Circulating Biomarkers and Their Relationship to Tumor Response in Atezolizumab-Treated non-small Cell Lung Cancer Patients
- 2019
-
Ingår i: Clinical Pharmacology and Therapeutics. - : WILEY. - 0009-9236 .- 1532-6535. ; 105:2, s. 486-495
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Tidskriftsartikel (refereegranskat)abstract
- To assess circulating biomarkers as predictors of antitumor response to atezolizumab (anti-programmed death-ligand 1 (PD-L1), Tecentriq) serum pharmacokinetic (PK) and 95 plasma biomarkers were analyzed in 88 patients with relapsed/refractory non-small cell lung cancer (NSCLC) receiving atezolizumab i.v. q3w (10-20 mg/kg) in the PCD4989g phase I clinical trial. Following exploratory analyses, two plasma biomarkers were chosen for further study and correlation with change in tumor size (the sum of the longest diameter) was assessed in a pharmacokinetic/pharmacodynamic (PK/PD) tumor modeling framework. When longitudinal kinetics of biomarkers and tumor size were modeled, tumor shrinkage was found to significantly correlate with area under the curve (AUC), baseline factors (metastatic sites, liver metastases, and smoking status), and relative change in interleukin (IL)-18 level from baseline at day 21 (RCFBIL-18,d21). Although AUC was a major predictor of tumor shrinkage, the effect was estimated to dissipate with an average half-life of 80 days, whereas RCFBIL-18,d21 seemed relevant to the duration of the response.
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| 30. |
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| 31. |
- Nyberg, Joakim, et al.
(författare)
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Properties of the full random-effect modeling approach with missing covariate data
- 2024
-
Ingår i: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 43:5, s. 935-952
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Tidskriftsartikel (refereegranskat)abstract
- During drug development, a key step is the identification of relevant covariates predicting between-subject variations in drug response. The full random effects model (FREM) is one of the full-covariate approaches used to identify relevant covariates in nonlinear mixed effects models. Here we explore the ability of FREM to handle missing (both missing completely at random (MCAR) and missing at random (MAR)) covariate data and compare it to the full fixed-effects model (FFEM) approach, applied either with complete case analysis or mean imputation. A global health dataset (20 421 children) was used to develop a FREM describing the changes of height for age Z-score (HAZ) over time. Simulated datasets (n = 1000) were generated with variable rates of missing (MCAR) covariate data (0%-90%) and different proportions of missing (MAR) data condition on either observed covariates or predicted HAZ. The three methods were used to re-estimate model and compared in terms of bias and precision which showed that FREM had only minor increases in bias and minor loss of precision at increasing percentages of missing (MCAR) covariate data and performed similarly in the MAR scenarios. Conversely, the FFEM approaches either collapsed at ≥70% of missing (MCAR) covariate data (FFEM complete case analysis) or had large bias increases and loss of precision (FFEM with mean imputation). Our results suggest that FREM is an appropriate approach to covariate modeling for datasets with missing (MCAR and MAR) covariate data, such as in global health studies.
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| 32. |
- Rayner, Craig R., et al.
(författare)
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Population pharmacokinetics of oseltamivir when coadministered with probenecid.
- 2008
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Ingår i: Journal of clinical pharmacology. - : Wiley. - 0091-2700 .- 1552-4604. ; 48:8, s. 935-947
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Tidskriftsartikel (refereegranskat)abstract
- Oseltamivir is a potent, selective, oral neuraminidase inhibitor for the treatment and prophylaxis of influenza. Plasma concentrations of the active metabolite, oseltamivir carboxylate, are increased in the presence of probenecid, suggesting that the combination could allow for the use of reduced doses of oseltamivir. To investigate this proposal, we developed a population pharmacokinetic model and simulated the pharmacokinetics of candidate combination regimens of oral oseltamivir (45 mg and 30 mg twice a day) plus oral probenecid (500 mg/6 hourly). Probenecid plus oseltamivir 45 mg achieved all the pharmacokinetic parameters expected of oseltamivir alone, but combination with oseltamivir 30 mg and dose interval extension approaches did not. An oseltamivir-probenecid combination may compromise tolerability and enhance the potential for drug interactions. In addition, increased dosing requirements may affect compliance and attainment of optimal oseltamivir exposure, potentially facilitating the emergence of viral strains with reduced susceptibility to oseltamivir. These factors, set alongside increased capacity for oseltamivir production, should be carefully considered before an oseltamivir-probenecid combination is used.
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| 33. |
- Ribbing, Jakob, et al.
(författare)
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Non-Bayesian Knowledge Propagation using Model Model-Based Analysis of Data from Multiple Clinical Studies
- 2008
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 35:1, s. 117-137
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Tidskriftsartikel (refereegranskat)abstract
- The ultimate goal in drug development is to establish the manner of safe and efficacious administration to patients. To achieve this in an efficient way the information contained in the clinical studies should contribute to the increasing pool of accumulated knowledge. The aim of this simulation study is to investigate different knowledge-propagation strategies when the data is analysed using a model-based approach in NONMEM. Pharmacokinetic studies were simulated according to several scenarios of the underlying model and study design, including a population-optimal design based on analysis of a previous study. Five approaches with different degrees of knowledge propagation were investigated: analysing the studies pooled into one dataset, merging the results from analysing the studies separately, fitting a pre-specified model that has been selected from a previous study on either the most recent study or on the pooled dataset, or naively analysing the most recent study without any regards to any previous study. The approaches were evaluated on what model was selected (qualitative knowledge, investigated by stepwise covariate selection within NONMEM) as well as parameter precision (quantitative knowledge) and predictive performance of the model. Pooling all studies into one dataset is the best approach for identifying the correct model and obtaining good predictive performance and merging the results of separate analyses may perform almost as well. Fitting a pre-specified model on new data is fast, without selection bias, and sanctioned for model-based confirmatory analyses. However, fitting the same pre-specified model to all available data is still fast and can be expected to perform better in terms of predictive performance than the unbiased alternative. Using ED-optimal design of sample times and stratification of subjects from different subgroups is a successful strategy which allows sparse sampling and handles prior parameter uncertainty.
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| 34. |
- Ribbing, Jakob, et al.
(författare)
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Power, Selection Bias and Predictive Performance of the Population Pharmacokinetic Covariate Model
- 2004
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - 1567-567X .- 1573-8744. ; 31:2, s. 109-134
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Tidskriftsartikel (refereegranskat)abstract
- Identification and quantification of covariate relations is often an important part of population pharmacokinetic/pharmacodynamic (PK/PD) modelling. The covariate model is regularly built in a stepwise manner. With such methods, selection bias may be a problem if only statistically significant covariates are accepted into the model. Competition between multiple covariates may further increase selection bias, especially when there is a moderate to high correlation between the covariates. This can also result in a loss of power to find the true covariates. The aim of this simulation study was to investigate the effect on power, selection bias and predictive performance of the covariate model, when altering study design and system-related quantities. Data sets with 20-1000 subjects were investigated. Five covariates were created by sampling from a multivariate standard normal distribution. The true covariate was set up to have no, low, moderate and high correlation to the other four covariates, respectively. Data sets, in which each individual had two or three PK observations, were simulated using a one-compartment i.v. bolus model. The true covariate influenced clearance according to one of several magnitudes. Different magnitudes of residual error and inter-individual variability in the structural model parameters were also introduced to the simulation model. A total of 7400 replicate data sets were simulated independently for each combination of the above conditions. Models with one of the five simulated covariates influencing clearance and the model without any covariate were fitted to the data. The probability of selecting (according to a pre-specified P-value) the different covariates, along with the estimated covariate coefficient, was recorded. The results show that selection bias is very high for small data sets (< or = 50 subjects) simulated with a weak covariate effect. If selected under these circumstances, the covariate coefficient is on average estimated to be more than twice its true value, making the covariate model useless for predictive purposes. Surprisingly, even though competition from false covariates caused substantial loss in the power of selecting the true covariate, the already high selection bias increased only marginally. This means that the bias due to competition is negligible if statistical significance is also required for covariate selection. Bias and predictive performance are direct functions of power, only indirectly affected by study design and system-related quantities. Mainly because of selection bias, low-powered covariates can be expected to harm the predictive performance when selected. For the same reason these low-powered covariates may falsely appear to be clinically relevant when selected. If the aim of an analysis is predictive modelling, we do not recommend stepwise selection or significance testing of covariates to be performed on small or moderately sized data sets (<50-100 subjects).
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| 35. |
- Ribbing, Jakob, et al.
(författare)
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The Lasso – A Novel Method for Predictive Covariate Model Building in Nonlinear Mixed Effects Models
- 2007
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 34:4, s. 485-517
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Tidskriftsartikel (refereegranskat)abstract
- Covariate models for population pharmacokinetics and pharmacodynamics are often built with a stepwise covariate modelling procedure (SCM). When analysing a small dataset this method may produce a covariate model that suffers from selection bias and poor predictive performance. The lasso is a method suggested to remedy these problems. It may also be faster than SCM and provide a validation of the covariate model. The aim of this study was to implement the lasso for covariate selection within NONMEM and to compare this method to SCM. In the lasso all covariates must be standardised to have zero mean and standard deviation one. Subsequently, the model containing all potential covariate–parameter relations is fitted with a restriction: the sum of the absolute covariate coefficients must be smaller than a value, t. The restriction will force some coefficients towards zero while the others are estimated with shrinkage. This means in practice that when fitting the model the covariate relations are tested for inclusion at the same time as the included relations are estimated. For a given SCM analysis, the model size depends on the P-value required for selection. In the lasso the model size instead depends on the value of t which can be estimated using cross-validation. The lasso was implemented as an automated tool using PsN. The method was compared to SCM in 16 scenarios with different dataset sizes, number of investigated covariates and starting models for the covariate analysis. Hundred replicate datasets were created by resampling from a PK-dataset consisting of 721 stroke patients. The two methods were compared primarily on the ability to predict external data, estimate their own predictive performance (external validation), and on the computer run-time. In all 16 scenarios the lasso predicted external data better than SCM with any of the studied P-values (5%, 1% and 0.1%), but the benefit was negligible for large datasets. The lasso cross-validation provided a precise and nearly unbiased estimate of the actual prediction error. On a single processor, the lasso was faster than SCM. Further, the lasso could run completely in parallel whereas SCM must run in steps. In conclusion, the lasso is superior to SCM in obtaining a predictive covariate model on a small dataset or on small subgroups (e.g. rare genotype). Run in parallel the lasso could be much faster than SCM. Using cross-validation, the lasso provides a validation of the covariate model and does not require the user to specify a P-value for selection.
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| 36. |
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| 37. |
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| 38. |
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| 39. |
- Tornøe, Christoffer W., et al.
(författare)
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Population pharmacokinetic/pharmacodynamic (PK/PD) modelling of the hypothalamic-pituitary-gonadal axis following treatment with GnRH analogues
- 2007
-
Ingår i: British Journal of Clinical Pharmacology. - : Wiley. - 0306-5251 .- 1365-2125. ; 63:6, s. 648-664
-
Tidskriftsartikel (refereegranskat)abstract
- Aims:To develop a population pharmacokinetic/pharmacodynamic (PK/PD) model of the hypothalamic–pituitary–gonadal (HPG) axis describing the changes in luteinizing hormone (LH) and testosterone concentrations following treatment with the gonadotropin-releasing hormone (GnRH) agonist triptorelin and the GnRH receptor blocker degarelix.MethodsFifty-eight healthy subjects received single subcutaneous or intramuscular injections of 3.75 mg of triptorelin and 170 prostate cancer patients received multiple subcutaneous doses of degarelix of between 120 and 320 mg. All subjects were pooled for the population PK/PD data analysis. A systematic population PK/PD model-building framework using stochastic differential equations was applied to the data to identify nonlinear dynamic dependencies and to deconvolve the functional feedback interactions of the HPG axis.ResultsIn our final PK/PD model of the HPG axis, the half-life of LH was estimated to be 1.3 h and that of testosterone 7.69 h, which corresponds well with literature values. The estimated potency of LH with respect to testosterone secretion was 5.18 IU l−1, with a maximal stimulation of 77.5 times basal testosterone production. The estimated maximal triptorelin stimulation of the basal LH pool release was 1330 times above basal concentrations, with a potency of 0.047 ng ml−1. The LH pool release was decreased by a maximum of 94.2% by degarelix with an estimated potency of 1.49 ng ml−1.ConclusionsOur model of the HPG axis was able to account for the different dynamic responses observed after administration of both GnRH agonists and GnRH receptor blockers, suggesting that the model adequately characterizes the underlying physiology of the endocrine system.
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| 40. |
- Tunblad, Karin, et al.
(författare)
-
An integrated model for the analysis of pharmacokinetic data from microdialysis experiments
- 2004
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Ingår i: Pharmaceutical research. - 0724-8741 .- 1573-904X. ; 21:9, s. 1698-1707
-
Tidskriftsartikel (refereegranskat)abstract
- PURPOSE: To develop an integrated model for microdialysis data that incorporates all data including the recovery measurements in one model, and to compare this model to a previous model and the results from a noncompartmental analysis. METHODS: The models were developed in NONMEM. The modes of analysis were compared with respect to parameter estimates, model structures, gained mechanistic insight, and practical aspects. RESULTS: Both modeling approaches resulted in similar model structures. The parameter estimates in blood and brain from the models and the results from the noncompartmental analysis were comparable. Using the integrated model all data, that is, the total arterial concentrations, the venous and brain dialysate concentrations, and the recovery measurements, were analyzed simultaneously. CONCLUSION: The theoretical benefits of the integrated model are related to the inclusion of the recovery in the model and the use of all collected data as it was observed. Thus, all data are described in a single model, corrections for the recovery and the protein binding are done within the model, and the dialysate observations are described by the integral over each collection interval. Thereby, the variability and the uncertainty in the model parameters are handled correctly to give more reliable parameter estimates.
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| 41. |
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| 42. |
- Tunblad, Karin, et al.
(författare)
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Influence of probenecid on the delivery of morphine-6-glucuronide to the brain
- 2005
-
Ingår i: European Journal of Pharmaceutical Sciences. - : Elsevier BV. - 0928-0987 .- 1879-0720. ; 24:1, s. 49-57
-
Tidskriftsartikel (refereegranskat)abstract
- The objective was to evaluate the influence of probenecid on the blood-brain barrier (BBB) transport of morphine-6-glucuronide (M6G). Microdialysis probes were placed in the striatum and into the jugular vein of Sprague-Dawley rats. Each probe was calibrated in vivo using retrodialysis by drug. M6G was administered as a 4-h exponential i.v. infusion, and the experiment was repeated the following day with the addition of probenecid. The data were analysed using NONMEM. An integrated model including the total arterial concentrations, the dialysate concentrations in brain and blood, and the recovery measurements, was developed. The extent of BBB transport, expressed as the ratio between clearance into the brain and clearance out of the brain (CL(in)/CL(out)), was estimated as 0.29 on both days, indicating that efflux transporters act on M6G at the BBB. However, the probenecid-sensitive transporters are not involved in the brain efflux, as the ratio was unaltered although probenecid was co-administered. In contrast, the systemic elimination of M6G decreased by 22% (p<0.05) upon probenecid co-administration. The half-life of M6G was longer in the brain than in blood on both experimental days (p<0.05). In conclusion, probenecid decreased the systemic elimination of M6G, but had no effect on the BBB transport of M6G.
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| 43. |
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| 44. |
- Tunblad, Karin, et al.
(författare)
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Morphine blood-brain barrier transport is influenced by probenecid co-administration
- 2003
-
Ingår i: Pharmaceutical research. - 0724-8741 .- 1573-904X. ; 20:4, s. 618-623
-
Tidskriftsartikel (refereegranskat)abstract
- PURPOSE: The objective of this study was to investigate the possible influence of probenecid on morphine transport across the blood-brain barrier (BBB) in rats. METHODS: Microdialysis probes, calibrated using retrodialysis by drug, were placed into the striatum and jugular vein of seven Sprague-Dawley rats. Morphine was administered as a 4-h exponential infusion. The experiment was repeated the next day with the addition of probenecid, administered as a bolus dose (20 mg/kg) followed by a constant infusion (20 mg/kg/h). Models for BBB transport were built using the computer program NONMEM. RESULTS: The steady-state ratio of 0.29 +/- 0.07 of unbound morphine concentration in brain to that in blood indicates that morphine is actively effluxed at the BBB. Probenecid co-administration increased the ratio to 0.39 +/- 0.04 (p < 0.05). Models in which probenecid influenced the brain efflux clearance rather than the influx clearance, well described the data. The half-life in brain increased from 58 +/- 9 min to 115 +/- 25 min when probenecid was co-administered. Systemic clearance of morphine also decreased upon probenecid co-administration, and M3G formation was decreased. CONCLUSION: This study indicates that morphine is a substrate for the probenecid-sensitive transporters at the BBB. Co-administration of probenecid decreased the brain efflux clearance of morphine.
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| 45. |
- Tunblad, Karin, et al.
(författare)
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The use of clinical irrelevance criteria in covariate model building with application to dofetilide pharmacokinetic data
- 2008
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 35:5, s. 503-526
-
Tidskriftsartikel (refereegranskat)abstract
- To characterise the pharmacokinetics of dofetilide in patients and to identify clinically relevant parameter-covariate relationships. To investigate three different modelling strategies in covariate model building using dofetilide as an example: (1) using statistical criteria only or in combination with clinical irrelevance criteria for covariate selection, (2) applying covariate effects on total clearance or separately on non-renal and renal clearances and (3) using separate data sets for covariate selection and parameter estimation. Pooled concentration-time data (1,445 patients, 10,133 observations) from phase III clinical trials was used. A population pharmacokinetic model was developed using NONMEM. Stepwise covariate model building was applied to identify important covariates using the strategies described above. Inclusion and exclusion of covariates using clinical irrelevance was based on reduction in interindividual variability and changes in parameters at the extremes of the covariate distribution. Parametric separation of the elimination pathways was accomplished using creatinine clearance as an indicator of renal function. The pooled data was split in three parts which were used for covariate selection, parameter estimation and evaluation of predictive performance. Parameter estimations were done using the first-order (FO) and the first-order conditional estimation (FOCE) methods. A one-compartment model with first order absorption adequately described the data. Using clinical irrelevance criteria resulted in models containing less parameter-covariate relationships with a minor loss in predictive power. A larger number of covariates were found significant when the elimination was divided into a renal part and a non-renal part, but no gain in predictive power could be seen with this data set. The FO and FOCE estimation methods gave almost identical final covariate model structures with similar predictive performance. Clinical irrelevance criteria may be valuable for practical reasons since stricter inclusion/exclusion criteria shortens the run times of the covariate model building procedure and because only the covariates important for the predictive performance are included in the model.
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| 46. |
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| 47. |
- Yngman, Gunnar, et al.
(författare)
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An introduction of the full random effects model
- 2022
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Ingår i: CPT. - : John Wiley & Sons. - 2163-8306. ; 11:2, s. 149-160
-
Tidskriftsartikel (refereegranskat)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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| 48. |
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| 49. |
- Zingmark, Per-Henrik, 1972-
(författare)
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Models for Ordered Categorical Pharmacodynamic Data
- 2005
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In drug development clinical trials are designed to investigate whether a new treatment is safe and has the desired effect on the disease in the target patient population. Categorical endpoints, for example different ranking scales or grading of adverse events, are commonly used to measure effects in the trials. Pharmacokinetic/Pharmacodynamic (PK/PD) models are used to describe the plasma concentration of a drug over time and its relationship to the effect studied. The models are utilized both in drug development and in discussions with drug regulating authorities. Methods for incorporation of ordered categorical data in PK/PD models were studied using a non-linear mixed effects modelling approach as implemented in the software NONMEM. The traditionally used proportional odds model was used for analysis of a 6-grade sedation scale in acute stroke patients and for analysis of a T-cell receptor expression in patients with Multiple Sclerosis, where the results also were compared with an analysis of the data on a continuous scale. Modifications of the proportional odds model were developed to enable analysis of a spontaneously reported side-effect and to analyze situations where the scale used is heterogeneous or where the drug affects the different scores in the scale in a non-proportional way. The new models were compared with the proportional odds model and were shown to give better predictive performances in the analyzed situations. The results in this thesis show that categorical data obtained in clinical trials with different design and different categorical endpoints successfully can be incorporated in PK/PD models. The models developed can also be applied to analyses of other ordered categorical scales than those presented.
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| 50. |
- Zingmark, Per-Henrik, et al.
(författare)
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Population pharmacokinetics of clomethiazole and its effect on the natural course of sedation in acute stroke patients
- 2003
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Ingår i: British Journal of Clinical Pharmacology. - : Wiley. - 0306-5251 .- 1365-2125. ; 56:2, s. 173-183
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Tidskriftsartikel (refereegranskat)abstract
- AIMS: This analysis was performed to investigate the population pharmacokinetics of clomethiazole and its effect on the natural course of sedation in acute stroke patients using a nonlinear mixed effects modelling approach. METHODS: One thousand five hundred and forty-six acute stroke patients (774 on active treatment) from 166 centres were included in three randomized, double-blind, placebo-controlled phase III efficacy and safety studies. A total dose of 68 mg kg(-1) clomethiazole edisilate was given as a three-phase i.v.-infusion over 24 h. Three blood samples were drawn from all patients to characterize the pharmacokinetics. Sedation was monitored throughout the entire treatment period and the degree of sedation was measured on a discrete ordinal scale with six levels. Models were fitted to the data using the software NONMEM. RESULTS: Clomethiazole was characterized by a two-compartment pharmacokinetic model with interindividual variability in all structural parameters. For a patient weighing 75 kg, the average CL, V1, Q, and V2 was estimated to be 52.7 l h(-1), 82.5 l, 167 l h(-1) and 335 l, respectively. The interindividual variability in CL, V1, Q and V2 was estimated to be 48%, 53%, 42% and 54%, respectively. Increasing body weight and concomitant administration of liver enzyme inducing drugs were found to increase clearance (by 0.5 l h(-1) kg(-1) and 40%, respectively). Increasing weight also increased the volume of distribution (1.1 l kg(-1) for V1 and 4.7 l kg(-1) for V2). A six-category proportional odds model with a component including the natural course of sedation following placebo administration, a drug component (present or absent) and an interindividual variability component described the degree of sedation. Stroke severity as measured on the NIH-stroke scale on admission and drug treatment were the most important predictors of sedation, but a nonlinear increase in sedation with increasing age was also found. Increasing body weight increased the sedative drug effect. CONCLUSIONS: The pharmacokinetics of clomethiazole were characterized in acute stroke patients and the analysis excluded several possible covariates of interest in drug development. The time course of sedation could be quantitatively described during the first 24 h following an acute stroke in the presence or absence of clomethiazole treatment.
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