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- Ahlström, Christine, et al.
(författare)
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Feedback modeling of non-esterified fatty acids in rats after nicotinic acid infusions
- 2011
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Ingår i: JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS. - 1567-567X. ; 38:1, s. 1-24
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Tidskriftsartikel (refereegranskat)abstract
- A feedback model was developed to describe the tolerance and oscillatory rebound seen in non-esterified fatty acid (NEFA) plasma concentrations following intravenous infusions of nicotinic acid (NiAc) to male Sprague-Dawley rats. NiAc was administered as an intravenous infusion over 30 min (0, 1, 5 or 20 μmol kg(-1) of body weight) or over 300 min (0, 5, 10 or 51 μmol kg(-1) of body weight), to healthy rats (n = 63), and serial arterial blood samples were taken for measurement of NiAc and NEFA plasma concentrations. Data were analyzed using nonlinear mixed effects modeling (NONMEM). The disposition of NiAc was described by a two-compartment model with endogenous turnover rate and two parallel capacity-limited elimination processes. The plasma concentration of NiAc was driving NEFA (R) turnover via an inhibitory drug-mechanism function acting on the formation of NEFA. The NEFA turnover was described by a feedback model with a moderator distributed over a series of transit compartments, where the first compartment (M (1)) inhibited the formation of R and the last compartment (M ( N )) stimulated the loss of R. All processes regulating plasma NEFA concentrations were assumed to be captured by the moderator function. The potency, IC (50), of NiAc was 45 nmol L(-1), the fractional turnover rate k ( out ) was 0.41 L mmol(-1) min(-1) and the turnover rate of moderator k ( tol ) was 0.027 min(-1). A lower physiological limit of NEFA was modeled as a NiAc-independent release (k ( cap )) of NEFA into plasma and was estimated to 0.032 mmol L(-1) min(-1). This model can be used to provide information about factors that determine the time-course of NEFA response following different modes, rates and routes of administration of NiAc. The proposed model may also serve as a preclinical tool for analyzing and simulating drug-induced changes in plasma NEFA concentrations after treatment with NiAc or NiAc analogues.
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| 2. |
- Ahlström, Christine
(författare)
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Modelling of tolerance and rebound in normal and diseased rats
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The development of rebound and tolerance is an important consideration when optimizing medical therapy, both with respect to drug dosing and adverse effects. By using quantitative approaches to study these processes, potential risks can be minimized. In this thesis nicotinic acid (NiAc)-induced changes in non-esterified fatty acids (NEFA) were used as a tool to investigate key determinants of tolerance and rebound in normal Sprague Dawley and in obese Zucker rats, a disease model of dyslipidaemia. The aim of the studies was to develop and challenge a model that described tolerance and rebound following different durations, rates and routes of NiAc administration. In normal rats, administration of NiAc resulted in a rapid decrease in NEFA plasma concentration, followed by rebound, the extent of which depended on both the level and duration of drug exposure. Rebound oscillations followed long duration of NiAc exposure. During constant drug exposure, increasing NEFA concentrations indicated tolerance development. The pharmacodynamic characteristics of NiAc-induced changes in NEFA differed in normal and diseased rats, with NEFA baseline concentrations being increased, rebound diminished, and tolerance develop¬ment more pronounced in the diseased animals. The non-intuitive pattern of NiAc-induced changes in NEFA was captured by a feedback model with a moderator distributed over a series of transit compartments, where the first compartment inhibited the formation of response and the last stimulated the loss of response. The model was based on mechanistic principles, mimicking the dual actions of insulin in inhibiting the hydrolysis of triglycerides to NEFA and glycerol, and stimulating the re-esterification of NEFA. In both the normal and diseased rats, the model described the pharmacodynamic characteristics adequately. The concentration-response relationship at steady state was shifted upwards and to the right, and was shallower, in diseased rats compared to normal rats. The extent of such shifts demonstrates the impact of disease at equilibrium in the system. These studies have shown that by eliciting different exposure patterns and taking into account both the washout dynamics of the administered drug and the pharmacodynamic characteristics of normal and diseased animals, a mechanistically-based feedback model was able to tease out important information about tolerance and rebound.
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| 4. |
- Almquist, Joachim, 1980, et al.
(författare)
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Overexpressing cell systems are a competitive option to primary adipocytes when predicting in vivo potency of dual GPR81/GPR109A agonists
- 2018
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Ingår i: European Journal of Pharmaceutical Sciences. - : Elsevier BV. - 0928-0987 .- 1879-0720. ; 114, s. 155-165
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Tidskriftsartikel (refereegranskat)abstract
- Mathematical models predicting in vivo pharmacodynamic effects from in vitro data can accelerate drug discovery, and reduce costs and animal use. However, data integration and modeling is non-trivial when more than one drug-target receptor is involved in the biological response. We modeled the inhibition of non-esterified fatty acid release by dual G-protein-coupled receptor 81/109A (GPR81/GPR109A) agonists in vivo in the rat, to estimate the in vivo EC50 values for 12 different compounds. We subsequently predicted those potency estimates using EC 50 values obtained from concentration-response data in isolated primary adipocytes and cell systems overexpressing GPR81 or GPR109A in vitro. A simple linear regression model based on data from primary adipocytes predicted the in vivo EC50 better than simple linear regression models based on in vitro data from either of the cell systems. Three models combining the data from the overexpressing cell systems were also evaluated: two piecewise linear models defining logical OR- and AND-circuits, and a multivariate linear regression model. All three models performed better than the simple linear regression model based on data from primary adipocytes. The OR-model was favored since it is likely that activation of either GPR81 or GPR109A is sufficient to deactivate the cAMP pathway, and thereby inhibit non-esterified fatty acid release. The OR-model was also able to predict the in vivo selectivity between the two receptors. Finally, the OR-model was used to predict the in vivo potency of 1651 new compounds. This work suggests that data from the overexpressing cell systems are sufficient to predict in vivo potency of GPR81/GPR109A agonists, an approach contributing to faster and leaner drug discovery.
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| 5. |
- Dong, Jin, et al.
(författare)
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Understanding Statin-Roxadustat Drug–Drug-Disease Interaction Using Physiologically-Based Pharmacokinetic Modeling
- 2023
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Ingår i: Clinical Pharmacology and Therapeutics. - : John Wiley & Sons. - 0009-9236 .- 1532-6535. ; 114:4, s. 825-835
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Tidskriftsartikel (refereegranskat)abstract
- A different drug–drug interaction (DDI) scenario may exist in patients with chronic kidney disease (CKD) compared with healthy volunteers (HVs), depending on the interplay between drug–drug and disease (drug-drug-disease interaction (DDDI)). Physiologically-based pharmacokinetic (PBPK) modeling, in lieu of a clinical trial, is a promising tool for evaluating these complex DDDIs in patients. However, the prediction confidence of PBPK modeling in the severe CKD population is still low when nonrenal pathways are involved. More mechanistic virtual disease population and robust validation cases are needed. To this end, we aimed to: (i) understand the implications of severe CKD on statins (atorvastatin, simvastatin, and rosuvastatin) pharmacokinetics (PK) and DDI; and (ii) predict untested clinical scenarios of statin-roxadustat DDI risks in patients to guide suitable dose regimens. A novel virtual severe CKD population was developed incorporating the disease effect on both renal and nonrenal pathways. Drug and disease PBPK models underwent a four-way validation. The verified PBPK models successfully predicted the altered PKs in patients for substrates and inhibitors and recovered the observed statin-rifampicin DDIs in patients and the statin-roxadustat DDIs in HVs within 1.25- and 2-fold error. Further sensitivity analysis revealed that the severe CKD effect on statins PK is mainly mediated by hepatic BCRP for rosuvastatin and OATP1B1/3 for atorvastatin. The magnitude of statin-roxadustat DDI in patients with severe CKD was predicted to be similar to that in HVs. PBPK-guided suitable dose regimens were identified to minimize the risk of side effects or therapeutic failure of statins when co-administered with roxadustat.
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| 9. |
- Leander, Jacob, 1987, et al.
(författare)
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Mixed-effects modeling using stochastic differential equations - Applications to pharmacokinetic modeling
- 2014
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Ingår i: Population Approach Group Europe (PAGE) 2014.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Objectives: The model dynamics is often assumed to be deterministic in traditional mixed-effects modeling. We want to extend the non-linear mixed-effects model to a so called stochastic differential mixed-effects model, to account for model deficiencies and uncertainty in the dynamics [1-4]. In extension to previous results, interactions between the output covariance and the random effects, together with correlation between random effects are considered. Moreover, we aim for a robust calculation of the gradient of the objective function by using sensitivity equations. Methods: The ordinary non-linear mixed-effects modeling framework is extended by considering stochastic differential equations. The population likelihood is approximated using Laplace's approximation together with the First Order Conditional Estimation with Interaction (FOCEI) method. The state variables of system (e.g., drug concentration) is estimated using the extended Kalman filter on an individual level. In contrast to the commonly used finite difference approximation of the gradient we utilize the so called sensitivity equations. These equations provide a robust and efficient evaluation of the objective function and its gradient. They are obtained by differentiating the update and prediction equations in the extended Kalman filter. Results: An algorithm for parameter estimation in stochastic differential mixed-effects models has been developed. It features sensitivity equations for a robust and efficient calculation of the gradient in both the outer and inner optimization problem. The stochastic differential mixed-effects framework is illustrated by using a pharmacokinetic model of nicotinic acid (NiAc) turnover in obese rats [5-7]. The analysis shows that the total error consists of pure measurement error together with a significant uncertainty in model dynamics. The smoothed state variables estimates are used to provide a visualization of uncertainty in variables after the parameter estimation has been completed. Conclusions: We account for three sources of variability by considering stochastic differential mixed-effects models. We are able to account for uncertainty in the dynamics, in addition to measurement noise and interindividual variability. The new model structure is able to handle interaction effects and correlation between random parameters. The uncertainty plots derived from smoothing serve as an illustrative way to understand output variability. References: [1] R. Overgaard, E.Jonsson, C. Tornøe, H. Madsen, Non-linear mixed-effects models with stochastic differential equations: Implementation of an estimation algorithm, Journal of Pharmacokinetics and Pharmacodynamics 32(1), 85-107 (2005) [2] S. Mortensen, S. Klim, B. Dammann, N. Kristensen, H. Madsen, R. Overgaard, A Matlab framework for estimation of NLME models using stochastic differential equations, Journal of Pharmacokinetics and Pharmacodynamics 34(5), 623-642 (2007) [3] M. Delattre, P. Del Moral, M. Lavielle - The SAEM algorithm in MONOLIX for Non-Linear Mixed Effects Models with Stochastic Differential Equations. PAGE 19 (2010) Abstract 1733 [4] M. Lavielle, M.Delattre - On the use of stochastic differential mixed effects models for modeling inter occasion variability. Models and methods. PAGE 21 (2012) Abstract2372 [5] C. Ahlström, L. Peletier, J. Gabrielsson, Challenges of a mechanistic feedback model describing nicotinic acid-induced changes in non-esterified fatty acids in rats. Journal of Pharmacokinetics and Pharmacodynamics 40(4), 497-512 (2013) [6] C. Ahlström, T. Kroon, L. Peletier, J. Gabrielsson, Feedback modeling of non-esterified fatty acids in obese Zucker rats after nicotinic acid infusions. Journal of Pharmacokinetics and Pharmacodynamics 40(6), 623-638 (2013) [7] C. Ahlström, Modelling of tolerance and rebound in normal diseased rats, Thesis for the degree of Doctor of Medicine, University of Gothenburg, 2011
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| 10. |
- Leander, Jacob, 1987, et al.
(författare)
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Mixed Effects Modeling Using Stochastic Differential Equations: Illustrated by Pharmacokinetic Data of Nicotinic Acid in Obese Zucker Rats
- 2015
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Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 17:3, s. 586-596
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Tidskriftsartikel (refereegranskat)abstract
- Inclusion of stochastic differential equations in mixed effects models provides means to quantify and distinguish three sources of variability in data. In addition to the two commonly encountered sources, measurement error and interindividual variability, we also consider uncertainty in the dynamical model itself. To this end, we extend the ordinary differential equation setting used in nonlinear mixed effects models to include stochastic differential equations. The approximate population likelihood is derived using the first-order conditional estimation with interaction method and extended Kalman filtering. To illustrate the application of the stochastic differential mixed effects model, two pharmacokinetic models are considered. First, we use a stochastic one-compartmental model with first-order input and nonlinear elimination to generate synthetic data in a simulated study. We show that by using the proposed method, the three sources of variability can be successfully separated. If the stochastic part is neglected, the parameter estimates become biased, and the measurement error variance is significantly overestimated. Second, we consider an extension to a stochastic pharmacokinetic model in a preclinical study of nicotinic acid kinetics in obese Zucker rats. The parameter estimates are compared between a deterministic and a stochastic NiAc disposition model, respectively. Discrepancies between model predictions and observations, previously described as measurement noise only, are now separated into a comparatively lower level of measurement noise and a significant uncertainty in model dynamics. These examples demonstrate that stochastic differential mixed effects models are useful tools for identifying incomplete or inaccurate model dynamics and for reducing potential bias in parameter estimates due to such model deficiencies.
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