The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Iftekhar Mahmood - One of the best experts on this subject based on the ideXlab platform.
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Center specificity in the limited Sampling Model (LSM): can the LSM developed from healthy subjects be extended to disease states?
International journal of clinical pharmacology and therapeutics, 2003Co-Authors: Iftekhar MahmoodAbstract:BACKGROUND AND OBJECTIVES: Area under the curve (AUC) can be related to the therapeutic or toxic effect of a drug. In order to accurately measure AUC, multiple blood samples are required, but in a clinical setting, frequent blood Sampling from the patients is time-consuming and expensive. The limited Sampling Model (LSM) is one of the approaches that is gaining popularity due to its simplicity for the estimation of AUC using 1 - 3 samples. Despite its simplicity, the LSM has some shortcomings. One of the major drawbacks of the LSM is that the LSM developed under a given condition may not be extended to other conditions. For example, the LSM developed from healthy subjects may not be extended to disease states such as renal or hepatic impairment or vice versa. This characteristic of the LSM can be referred to as "center-specific". In this investigation, the LSM developed from the healthy subjects was used to predict AUC in patients with renal or hepatic impairment. METHODS: Two sets of simulated plasma concentration versus time data for 2 antihypertensive drugs and measured plasma concentration versus time data for 2 representative drugs (A and B) were used in the analysis. RESULTS AND CONCLUSION: The results of the study indicate that the LSM developed from healthy subjects is inadequate to predict AUC in patients with hepatic or renal impairment, indicating center specificity of the LSM.
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Center specificity in the limited Sampling Model (LSM): can the LSM developed from healthy subjects be extended to disease states?
International journal of clinical pharmacology and therapeutics, 2003Co-Authors: Iftekhar MahmoodAbstract:BACKGROUND AND OBJECTIVES: Area under the curve (AUC) can be related to the therapeutic or toxic effect of a drug. In order to accurately measure AUC, multiple blood samples are required, but in a clinical setting, frequent blood Sampling from the patients is time-consuming and expensive. The limited Sampling Model (LSM) is one of the approaches that is gaining popularity due to its simplicity for the estimation of AUC using 1 - 3 samples. Despite its simplicity, the LSM has some shortcomings. One of the major drawbacks of the LSM is that the LSM developed under a given condition may not be extended to other conditions. For example, the LSM developed from healthy subjects may not be extended to disease states such as renal or hepatic impairment or vice versa. This characteristic of the LSM can be referred to as "center-specific". In this investigation, the LSM developed from the healthy subjects was used to predict AUC in patients with renal or hepatic impairment. METHODS: Two sets of simulated plasma concentration versus time data for 2 antihypertensive drugs and measured plasma concentration versus time data for 2 representative drugs (A and B) were used in the analysis. RESULTS AND CONCLUSION: The results of the study indicate that the LSM developed from healthy subjects is inadequate to predict AUC in patients with hepatic or renal impairment, indicating center specificity of the LSM.
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Limited Sampling Model for the estimation of pharmacokinetic parameters in children.
Therapeutic drug monitoring, 2000Co-Authors: Iftekhar MahmoodAbstract:Summary:A limited Sampling Model (LSM) is proposed for the first-time assessment of pharmacokinetic parameters (area under the concentration–time curve (AUC), Cmax, and T½) in children after a single oral dose of drug. Three drugs were evaluated in this study. The LSM was developed for each drug fro
Clinton F. Stewart - One of the best experts on this subject based on the ideXlab platform.
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Development of a pharmacokinetic limited Sampling Model for temozolomide and its active metabolite MTIC
Cancer Chemotherapy and Pharmacology, 2005Co-Authors: Mark N. Kirstein, John C. Panetta, Amar Gajjar, Geeta Nair, Lisa C. Iacono, Burgess B. Freeman, Clinton F. StewartAbstract:Purpose To develop a pharmacokinetic limited Sampling Model (LSM) for temozolomide and its metabolite MTIC in infants and children. Methods LSMs consisting of either two or four samples were determined using a modification of the D-optimality algorithm. This accounted for prior distribution of temozolomide and MTIC pharmacokinetic parameters based on full pharmacokinetic Sampling from 38 patients with 120 pharmacokinetic studies (dosage range 145–200 mg/m^2 per day orally). Accuracy and bias of each LSM were determined relative to the full Sampling method. We also assessed the predictive performance of the LSMs using Monte-Carlo simulations. Results The four strategies generated from the D-optimality algorithm were as follows: LSM 1=0.25, 1.25, and 3 h; LSM 2=0.25, 1.25, and 6 h; LSM 3=0.25, 0.5, 1.25, and 3 h; LSM 4=0.25, 0.5, 1.25, and 6 h. LSM 2 demonstrated the best combination of low bias [0.1% (−8.9%, 11%) and 11% (4.3%, 15%)] and high accuracy [−1.0% (−12%, 24%) and 14% (7.9%, 37%)] for temozolomide clearance and MTIC AUC, respectively. Furthermore, adding a fourth sample (e.g., LSM 4) did not substantially decrease the bias or increase the accuracy for temozolomide clearance or MTIC AUC. Results from Monte-Carlo simulations also revealed that LSM 2 had the best combination of lowest bias (0.1±6.1% and −0.8±6.5%), and the highest accuracy (4.5±4.1% and 5.0±4.3%) for temozolomide clearance and MTIC apparent clearance, respectively. Conclusions Using data derived from our population analysis, the Sampling times for a limited sample pharmacokinetic Model for temozolomide and MTIC in children are prior to the temozolomide dose, and 15 min, 1.25 h and 6 h after the dose.
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Development of a pharmacokinetic limited Sampling Model for temozolomide and its active metabolite MTIC
Cancer chemotherapy and pharmacology, 2005Co-Authors: Mark N. Kirstein, John C. Panetta, Amar Gajjar, Geeta Nair, Lisa C. Iacono, B. Freeman, Clinton F. StewartAbstract:Purpose To develop a pharmacokinetic limited Sampling Model (LSM) for temozolomide and its metabolite MTIC in infants and children.
Lee Hsiang Liow - One of the best experts on this subject based on the ideXlab platform.
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how many dinosaur species were there fossil bias and true richness estimated using a poisson Sampling Model
Philosophical Transactions of the Royal Society B, 2016Co-Authors: Jostein Starrfelt, Lee Hsiang LiowAbstract:The fossil record is a rich source of information about biological diversity in the past. However, the fossil record is not only incomplete but has also inherent biases due to geological, physical, chemical and biological factors. Our knowledge of past life is also biased because of differences in academic and amateur interests and Sampling efforts. As a result, not all individuals or species that lived in the past are equally likely to be discovered at any point in time or space. To reconstruct temporal dynamics of diversity using the fossil record, biased Sampling must be explicitly taken into account. Here, we introduce an approach that uses the variation in the number of times each species is observed in the fossil record to estimate both Sampling bias and true richness. We term our technique TRiPS (True Richness estimated using a Poisson Sampling Model) and explore its robustness to violation of its assumptions via simulations. We then venture to estimate Sampling bias and absolute species richness of dinosaurs in the geological stages of the Mesozoic. Using TRiPS, we estimate that 1936 (1543–2468) species of dinosaurs roamed the Earth during the Mesozoic. We also present improved estimates of species richness trajectories of the three major dinosaur clades: the sauropodomorphs, ornithischians and theropods, casting doubt on the Jurassic–Cretaceous extinction event and demonstrating that all dinosaur groups are subject to considerable Sampling bias throughout the Mesozoic.
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how many dinosaurs were there true richness estimated using a poisson Sampling Model trips
bioRxiv, 2015Co-Authors: Jostein Starrfelt, Lee Hsiang LiowAbstract:The fossil record is a rich source of information about biological diversity in the past. However, the fossil record is not only incomplete but has inherent biases due to geological, physical, chemical and biological factors such that not all individuals or species are equally likely to be discovered at any point in time or space. In order to use the fossil record to reconstruct temporal dynamics of diversity, biased Sampling must be explicitly taken into account. Here, we introduce an approach that utilizes the variation in the number of times each species is observed in the fossil record to estimate both Sampling bias and true richness. We term our technique TRiPS (True Richness estimated using a Poisson Sampling Model) and explore its robustness to violation of its assumptions via simulations before applying it to an empirical dataset. We then venture to estimate Sampling bias and absolute species richness of dinosaurs in the geological stages of the Mesozoic. Using TRiPS, we present new estimates of species richness trajectories of the three major dinosaur clades; the sauropods, ornithischians and theropods, casting doubt on the Jurassic-Cretaceous extinction event and demonstrating that all dinosaur groups are subject to considerable Sampling bias throughout the Mesozoic.
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how many dinosaur species were there true richness estimated using a poisson Sampling Model trips
bioRxiv, 2015Co-Authors: Jostein Starrfelt, Lee Hsiang LiowAbstract:Abstract The fossil record is a rich source of information about biological diversity in the past. However, the fossil record is not only incomplete but has inherent biases due to geological, physical, chemical and biological factors such that not all individuals or species are equally likely to be discovered at any point in time or space. In order to use the fossil record to reconstruct temporal dynamics of diversity, biased Sampling must be explicitly taken into account. Here, we introduce an approach that utilizes the variation in the number of times each species is observed in the fossil record to estimate both Sampling bias and true richness. We term our technique TRiPS (True Richness estimated using a Poisson Sampling Model) and explore its robustness to violation of its assumptions via simulations before applying it to an empirical dataset. We then venture to estimate Sampling bias and absolute species richness of dinosaurs in the geological stages of the Mesozoic. Using TRiPS, we present new estimates of species richness trajectories of the three major dinosaur clades; the sauropods, ornithischians and theropods, casting doubt on the Jurassic-Cretaceous extinction event and demonstrating that all dinosaur groups are subject to considerable Sampling bias throughout the Mesozoic.
Jos H. Beijnen - One of the best experts on this subject based on the ideXlab platform.
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A limited-Sampling Model for the pharmacokinetics of carboplatin administered in combination with paclitaxel.
Journal of cancer research and clinical oncology, 1999Co-Authors: Vinodh R. Nannan Panday, Laurence J. C. Van Warmerdam, M. T. Huizing, Wim W. Ten Bokkel Huinink, Jan H.m. Schellens, Jos H. BeijnenAbstract:Purpose: Carboplatin doses are often determined by using modified Calvert formulas. It has been observed that the area under the concentration versus time curve (AUC) for free carboplatin is lower than expected when modified formulas are used for carboplatin/paclitaxel chemotherapy combination regimens. By using limited-Sampling Models, the carboplatin AUC actually reached can easily be verified, and the dose adjusted accordingly. Methods: In this report, we describe the development and validation of a limited-Sampling Model for carboplatin from 77 pharmacokinetic curves, when carboplatin is used in combination with paclitaxel. Results: The following single-point Model was selected as optimal: AUC carboplatin (min mg−1 ml−1) = 418 · c2.5 h(mg/ml) + 0.43 (min mg−1 ml−1), where c2.5 h is the concentration (mg/ml) of carboplatin 2.5 h after the start of a 30-min infusion. This Model proved to be unbiased (mean prediction error = 3.4 ± 1.6%) and precise (root mean square error = 10.1 ± 1.5%). Conclusions: The proposed Model can be very useful for ongoing and future carboplatin/paclitaxel studies aimed to optimise and individualise treatment.
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Validation of a limited Sampling Model for carboplatin in a high-dose chemotherapy combination
Cancer chemotherapy and pharmacology, 1994Co-Authors: Laurence J. C. Van Warmerdam, Sjoerd Rodenhuis, Olaf Van Tellingen, Robert A. A. Maes, Jos H. BeijnenAbstract:A limited Sampling Model for the estimation of the carboplatin area under the concentration versus time curve (AUC), as developed by Sorensen et al., was validated prospectively for the use in a high-dose combination chemotherapy schedule. The Model allows an estimation of the AUC on the basis of only one timed plasma drug concentration, sampled at exactly 2.75 h after a 1-h carboplatin infusion. Pharmacokinetic curves were obtained from nine patients receiving carboplatin (400 mg/m2 per day) combined with cyclophosphamide (1500 mg/m2 per day), thiotepa (120 mg/m2 per day), and mesna (3 g/day) for 4 consecutive days. Peripheral blood stem-cell transplantation (PBSCT) was performed 3 days later to restore hematopoiesis. Using this combination of high doses, the Model proved to be unbiased (MPE −3.40%; SE, 1.22%) and highly precise [root mean squared prediction error (RMSE), 5.15%; SE, 0.17%] for estimation of the AUC during 4 consecutive days. The validated limited Sampling Model provides a starting point for future pharmacokinetic studies in a larger population of patients, which might lead to more insight into the relationships with the pharmacodynamic outcome of carboplatin and may help in achieving more rational dosing of patients on the basis of an AUC determination.
Mark N. Kirstein - One of the best experts on this subject based on the ideXlab platform.
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Development of a pharmacokinetic limited Sampling Model for temozolomide and its active metabolite MTIC
Cancer Chemotherapy and Pharmacology, 2005Co-Authors: Mark N. Kirstein, John C. Panetta, Amar Gajjar, Geeta Nair, Lisa C. Iacono, Burgess B. Freeman, Clinton F. StewartAbstract:Purpose To develop a pharmacokinetic limited Sampling Model (LSM) for temozolomide and its metabolite MTIC in infants and children. Methods LSMs consisting of either two or four samples were determined using a modification of the D-optimality algorithm. This accounted for prior distribution of temozolomide and MTIC pharmacokinetic parameters based on full pharmacokinetic Sampling from 38 patients with 120 pharmacokinetic studies (dosage range 145–200 mg/m^2 per day orally). Accuracy and bias of each LSM were determined relative to the full Sampling method. We also assessed the predictive performance of the LSMs using Monte-Carlo simulations. Results The four strategies generated from the D-optimality algorithm were as follows: LSM 1=0.25, 1.25, and 3 h; LSM 2=0.25, 1.25, and 6 h; LSM 3=0.25, 0.5, 1.25, and 3 h; LSM 4=0.25, 0.5, 1.25, and 6 h. LSM 2 demonstrated the best combination of low bias [0.1% (−8.9%, 11%) and 11% (4.3%, 15%)] and high accuracy [−1.0% (−12%, 24%) and 14% (7.9%, 37%)] for temozolomide clearance and MTIC AUC, respectively. Furthermore, adding a fourth sample (e.g., LSM 4) did not substantially decrease the bias or increase the accuracy for temozolomide clearance or MTIC AUC. Results from Monte-Carlo simulations also revealed that LSM 2 had the best combination of lowest bias (0.1±6.1% and −0.8±6.5%), and the highest accuracy (4.5±4.1% and 5.0±4.3%) for temozolomide clearance and MTIC apparent clearance, respectively. Conclusions Using data derived from our population analysis, the Sampling times for a limited sample pharmacokinetic Model for temozolomide and MTIC in children are prior to the temozolomide dose, and 15 min, 1.25 h and 6 h after the dose.
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Development of a pharmacokinetic limited Sampling Model for temozolomide and its active metabolite MTIC
Cancer chemotherapy and pharmacology, 2005Co-Authors: Mark N. Kirstein, John C. Panetta, Amar Gajjar, Geeta Nair, Lisa C. Iacono, B. Freeman, Clinton F. StewartAbstract:Purpose To develop a pharmacokinetic limited Sampling Model (LSM) for temozolomide and its metabolite MTIC in infants and children.
