| 1. |
- Liesenfeld, K. H., et al.
(author)
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Effects of the direct thrombin inhibitor dabigatran on ex vivo coagulation time in orthopaedic surgery patients: a population model analysis
- 2006
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In: Br J Clin Pharmacol. - 0306-5251. ; 62:5, s. 527-37
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Journal article (peer-reviewed)abstract
- AIMS: To describe the pharmacokinetic-pharmacodynamic (PK-PD) characteristics of the direct thrombin inhibitor dabigatran in hip replacement patients by assessing coagulation parameters activated partial thromboplastin time (aPTT) and ecarin clotting time (ECT), interindividual variability and factors affecting PD responses. METHODS: BISTRO I patients received oral dabigatran etexilate postsurgery for 6-10 days. Dabigatran plasma concentrations and aPTT/ECT were measured on the day of surgery, on subsequent days and at steady state. PK-PD characteristics of the dabigatran-aPTT/ECT relationships were evaluated using NONMEM V. RESULTS: The dabigatran concentration-aPTT relationship was described combining a linear and an E(max) model. Mean baseline aPTT was 33.4 s and E(max) (maximum increase in aPTT contributed by the E(max) model) was 26.9 s. The dabigatran concentration needed to attain 50% of maximum effect (EC(50)) was 94.7 ng ml(-1) and the mean slope of the linear concentration-response relationship (SLOP) was 0.0509 s ng(-1) ml(-1). Baseline aPTT and E(max) were highest following surgery and declined with time. The dabigatran concentration-ECT relationship fitted a linear model. Mean baseline ECT was 28 s and decreased with time; 50% of the maximum effect was observed after 2.9 days. SLOP decreased from 0.38 to 0.27 s ng(-1) ml(-1) with a half-life of 1.1 day, indicating greater PD effects on the day of surgery. Interindividual and residual variability was low. Covariates could not explain variability of this model. CONCLUSIONS: aPTT and ECT prolongation were directly correlated with dabigatran concentrations. Blood coagulation prolongation was most pronounced following surgery. Data suggest that ECT provides a more precise description of the anticoagulant effect than aPTT.
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| 2. |
- Ribba, B, et al.
(author)
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A review of mixed-effects models of tumor growth and effects of anticancer drug treatment used in population analysis
- 2014
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In: CPT. - : Wiley. - 2163-8306. ; 3:5, s. 1-10
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Journal article (peer-reviewed)abstract
- Population modeling of tumor size dynamics has recently emerged as an important tool in pharmacometric research. A series of new mixed-effects models have been reported recently, and we present herein a synthetic view of models with published mathematical equations aimed at describing the dynamics of tumor size in cancer patients following anticancer drug treatment. This selection of models will constitute the basis for the Drug Disease Model Resources (DDMoRe) repository for models on oncology.
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| 3. |
- Swat, M. J., et al.
(author)
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Pharmacometrics Markup Language (PharmML) : Opening New Perspectives for Model Exchange in Drug Development
- 2015
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In: CPT. - : American Society for Clinical Pharmacology & Therapeutics. - 2163-8306. ; 4:6, s. 316-319
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Journal article (peer-reviewed)abstract
- The lack of a common exchange format for mathematical models in pharmacometrics has been a long-standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps.
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| 4. |
- Venkatakrishnan, K., et al.
(author)
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Optimizing Oncology Therapeutics Through Quantitative Translational and Clinical Pharmacology : Challenges and Opportunities
- 2015
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In: Clinical Pharmacology and Therapeutics. - : Wiley. - 0009-9236 .- 1532-6535. ; 97:1, s. 37-54
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Journal article (peer-reviewed)abstract
- Despite advances in biomedical research that have deepened our understanding of cancer hallmarks, resulting in the discovery and development of targeted therapies, the success rates of oncology drug development remain low. Opportunities remain for objective dose selection informed by exposure-response understanding to optimize the benefit-risk balance of novel therapies for cancer patients. This review article discusses the principles and applications of modeling and simulation approaches across the lifecycle of development of oncology therapeutics. Illustrative examples are used to convey the value gained from integration of quantitative clinical pharmacology strategies from the preclinical-translational phase through confirmatory clinical evaluation of efficacy and safety.
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