| 1. |
- Dahl, Svein G., et al.
(author)
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Incorporating Physiological and Biochemical Mechanisms into Pharmacokinetic-Pharmacodynamic Models : A Conceptual Framework
- 2010
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In: Basic & Clinical Pharmacology & Toxicology. - : Wiley. - 1742-7835 .- 1742-7843. ; 106:1, s. 2-12
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Research review (peer-reviewed)abstract
- The aim of this conceptual framework paper is to contribute to the further development of the modelling of effects of drugs or toxic agents by an approach which is based on the underlying physiology and pathology of the biological processes. In general, modelling of data has the purpose (1) to describe experimental data, (2a) to reduce the amount of data resulting from an experiment, e.g. a clinical trial and (2b) to obtain the most relevant parameters, (3) to test hypotheses and (4) to make predictions within the boundaries of experimental conditions, e.g. range of doses tested (interpolation) and out of the boundaries of the experimental conditions, e.g. to extrapolate from animal data to the situation in man. Describing the drug/xenobiotic-target interaction and the chain of biological events following the interaction is the first step to build a biologically based model. This is an approach to represent the underlying biological mechanisms in qualitative and also quantitative terms thus being inherently connected in many aspects to systems biology. As the systems biology models may contain variables in the order of hundreds connected with differential equations, it is obvious that it is in most cases not possible to assign values to the variables resulting from experimental data. Reduction techniques may be used to create a manageable model which, however, captures the biologically meaningful events in qualitative and quantitative terms. Until now, some success has been obtained by applying empirical pharmacokinetic/pharmacodynamic models which describe direct and indirect relationships between the xenobiotic molecule and the effect, including tolerance. Some of the models may have physiological components built in the structure of the model and use parameter estimates from published data. In recent years, some progress toward semi-mechanistic models has been made, examples being chemotherapy-induced myelosuppression and glucose-endogenous insulin-antidiabetic drug interactions. We see a way forward by employing approaches to bridge the gap between systems biology and physiologically based kinetic and dynamic models. To be useful for decision making, the 'bridging' model should have a well founded mechanistic basis, but being reduced to the extent that its parameters can be deduced from experimental data, however capturing the biological/clinical essential details so that meaningful predictions and extrapolations can be made.
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| 2. |
- Henningsson, Anja, 1973-
(author)
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Mechanism-Based Pharmacokinetic and Pharmacodynamic Modelling of Paclitaxel
- 2005
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Doctoral thesis (other academic/artistic)abstract
- Paclitaxel (Taxol®) is now widely used against breast, ovarian and non-small-cell lung cancer. Anticancer agents generally have narrow therapeutic indices, often with myelosuppression (mainly neutropenia) as dose-limiting side effect. A further complicating factor is that paclitaxel when given as Taxol® has a nonlinear pharmacokinetic (PK) behaviour in plasma. Identifying risk groups more sensitive to chemotherapy due to either a PK or pharmacodynamic (PD) interindividual variability is of importance. The aim of the thesis was to develop predictive mechanism-based PK and PD models applicable for paclitaxel. PK and PK/PD models were developed for patient data from studies with relatively frequent sampling or sparse sampling schedules. Population analyses were performed using the software NONMEM. A pharmacokinetic model describing unbound, total plasma and blood concentrations of paclitaxel from known binding mechanisms was developed and validated. The nonlinear PK in plasma could to a large extent be explained by the micelle forming vehicle Cremophor EL (CrEL) and the unbound drug showed linear PK. Besides a binding component directly proportional to concentrations of CrEL, the model included both linear and nonlinear binding components in plasma and blood. Further, relations between the PK parameters and different demographic factors, including polymorphisms in the cytochrome P450s involved in paclitaxel metabolism, were investigated. A semi-physiological PD model for chemotherapy-induced myelosuppression was developed and applied to different anticancer drugs. The model included a self-renewal for proliferating cells, transit compartments describing the delay in observed myelosuppression and a feedback parameter reflecting the effect on the bone marrow from growth factors that can result in an overshoot in white blood cells. The system-related parameters estimated showed consistency across drugs and the difference in the drug-related parameter reflected the relative bone marrow toxicity of the drugs. Relations between demographic factors and the PD parameters were identified. The developed mechanism-based models promote a better understanding of paclitaxel PK and PD and may be used as tools in dosing individualisation and in development of dosing strategies for new administration forms and new drugs in the same area.
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| 3. |
- Plan, Elodie L., et al.
(author)
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Performance in population models for count data, part I: maximum likelihood approximations.
- 2009
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In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 36:4, s. 353-366
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Journal article (peer-reviewed)abstract
- There has been little evaluation of maximum likelihood approximation methods for non-linear mixed effects modelling of count data. The aim of this study was to explore the estimation accuracy of population parameters from six count models, using two different methods and programs. Simulations of 100 data sets were performed in NONMEM for each probability distribution with parameter values derived from a real case study on 551 epileptic patients. Models investigated were: Poisson (PS), Poisson with Markov elements (PMAK), Poisson with a mixture distribution for individual observations (PMIX), Zero Inflated Poisson (ZIP), Generalized Poisson (GP) and Negative Binomial (NB). Estimations of simulated datasets were completed with Laplacian approximation (LAPLACE) in NONMEM and LAPLACE/Gaussian Quadrature (GQ) in SAS. With LAPLACE, the average absolute value of the bias (AVB) in all models was 1.02% for fixed effects, and ranged 0.32-8.24% for the estimation of the random effect of the mean count (lambda). The random effect of the overdispersion parameter present in ZIP, GP and NB was underestimated (-25.87, -15.73 and -21.93% of relative bias, respectively). Analysis with GQ 9 points resulted in an improvement in these parameters (3.80% average AVB). Methods implemented in SAS had a lower fraction of successful minimizations, and GQ 9 points was considerably slower than 1 point. Simulations showed that parameter estimates, even when biased, resulted in data that were only marginally different from data simulated from the true model. Thus all methods investigated appear to provide useful results for the investigated count data models.
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| 4. |
- Soto, Elena, et al.
(author)
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Predictive ability of a semi-mechanistic model for neutropenia in the development of novel anti-cancer agents : two case studies
- 2011
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In: Investigational new drugs. - : Springer Science and Business Media LLC. - 0167-6997 .- 1573-0646. ; 29:5, s. 984-995
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Journal article (peer-reviewed)abstract
- In cancer chemotherapy neutropenia is a common dose-limiting toxicity. An ability to predict the neutropenic effects of cytotoxic agents based on proposed trial designs and models conditioned on previous studies would be valuable. The aim of this study was to evaluate the ability of a semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model for myelosuppression to predict the neutropenia observed in Phase I clinical studies, based on parameter estimates obtained from prior trials. Pharmacokinetic and neutropenia data from 5 clinical trials for diflomotecan and from 4 clinical trials for indisulam were used. Data were analyzed and simulations were performed using the population approach with NONMEM VI. Parameter sets were estimated under the following scenarios: (a) data from each trial independently, (b) pooled data from all clinical trials and (c) pooled data from trials performed before the tested trial. Model performance in each of the scenarios was evaluated by means of predictive (visual and numerical) checks. The semi-mechanistic PK/PD model for neutropenia showed adequate predictive ability for both anti-cancer agents. For diflomotecan, similar predictions were obtained for the three scenarios. For indisulam predictions were better when based on data from the specific study, however when the model parameters were conditioned on data from trials performed prior to a specific study, similar predictions of the drug related-neutropenia profiles and descriptors were obtained as when all data were used. This work provides further indication that modeling and simulation tools can be applied in the early stages of drug development to optimize future trials.
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| 5. |
- Steimer, Jean-Louis, et al.
(author)
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Modelling the genesis and treatment of cancer : the potential role of physiologically based pharmacodynamics
- 2010
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In: European Journal of Cancer. - : Elsevier BV. - 0959-8049 .- 1879-0852. ; 46:1, s. 21-32
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Journal article (peer-reviewed)abstract
- Physiologically based modelling of pharmacodynamics/toxicodynamics requires an a priori knowledge on the underlying mechanisms causing toxicity or causing the disease. In the context of cancer, the objective of the expert meeting was to discuss the molecular understanding of the disease, modelling approaches used so far to describe the process, preclinical models of cancer treatment and to evaluate modelling approaches developed based on improved knowledge. Molecular events in cancerogenesis can be detected using 'omics' technology, a tool applied in experimental carcinogenesis, but also for diagnostics and prognosis. The molecular understanding forms the basis for new drugs, for example targeting protein kinases specifically expressed in cancer. At present, empirical preclinical models of tumour growth are in great use as the development of physiological models is cost and resource intensive. Although a major challenge in PKPD modelling in oncology patients is the complexity of the system, based in part on preclinical models, successful models have been constructed describing the mechanism of action and providing a tool to establish levels of biomarker associated with efficacy and assisting in defining biologically effective dose range selection for first dose in man. To follow the concentration in the tumour compartment enables to link kinetics and dynamics. in order to obtain a reliable model of tumour growth dynamics and drug effects, specific aspects of the modelling of the concentration-effect relationship in cancer treatment that need to be accounted for include: the physiological/circadian rhythms of the cell cycle; the treatment with combinations and the need to optimally choose appropriate combinations of the multiple agents to study; and the schedule dependence of the response in the clinical situation.
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| 6. |
- Trocóniz, Iñaki F, et al.
(author)
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Modelling overdispersion and Markovian features in count data.
- 2009
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In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 36:5, s. 461-477
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Journal article (peer-reviewed)abstract
- The number of counts (events) per unit of time is a discrete response variable that is generally analyzed with the Poisson distribution (PS) model. The PS model makes two assumptions: the mean number of counts (lambda) is assumed equal to the variance, and counts occurring in non-overlapping intervals are assumed independent. However, many counting outcomes show greater variability than predicted by the PS model, a phenomenon called overdispersion. The purpose of this study was to implement and explore, in the population context, different distribution models accounting for overdispersion and Markov patterns in the analysis of count data. Daily seizures count data obtained from 551 subjects during the 12-week screening phase of a double-blind, placebo-controlled, parallel-group multicenter study performed in epileptic patients with medically refractory partial seizures, were used in the current investigation. The following distribution models were fitted to the data: PS, Zero-Inflated PS (ZIP), Negative Binomial (NB), and Zero-Inflated Negative Binomial (ZINB) models. Markovian features were introduced estimating different lambdas and overdispersion parameters depending on whether the previous day was a seizure or a non-seizure day. All analyses were performed with NONMEM VI. All models were successfully implemented and all overdispersed models improved the fit with respect to the PS model. The NB model resulted in the best description of the data. The inclusion of Markovian features in lambda and in the overdispersion parameter improved the fit significantly (P < 0.001). The plot of the variance versus mean daily seizure count profiles, and the number of transitions, are suggested as model performance tools reflecting the capability to handle overdispersion and Markovian features, respectively.
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