| 1. |
- Bergstrand, Martin, 1977-, et al.
(author)
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A semi-mechanistic modeling strategy for characterization of regional absorption properties and prospective prediction of plasma concentrations following administration of new modified release formulations
- 2012
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In: Pharmaceutical research. - : Springer Science+Business Media B.V.. - 0724-8741 .- 1573-904X. ; 29:2, s. 574-584
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Journal article (peer-reviewed)abstract
- PURPOSE To outline and test a new modeling approach for prospective predictions of absorption from newly developed modified release formulations based on in vivo studies of gastro intestinal (GI) transit, drug release and regional absorption for the investigational drug AZD0837. METHODS This work was a natural extension to the companion article "A semi-mechanistic model to link in vitro and in vivo drug release for modified release formulations". The drug release model governed the amount of substance released in distinct GI regions over time. GI distribution of released drug substance, region specific rate and extent of absorption and the influence of food intake were estimated. The model was informed by magnetic marker monitoring data and data from an intubation study with local administration in colon. RESULTS Distinctly different absorption properties were characterized for different GI regions. Bioavailability over the gut-wall was estimated to be high in duodenum (70%) compared to the small intestine (25%). Colon was primarily characterized by a very slow rate of absorption. CONCLUSIONS The established model was largely successful in predicting plasma concentration following administration of three newly developed formulations for which no clinical data had been applied during model building.
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| 2. |
- Bergstrand, Martin, 1977-, et al.
(author)
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A Semi-mechanistic Modeling Strategy to Link In Vitro and In Vivo Drug Release for Modified Release Formulations
- 2012
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In: Pharmaceutical research. - : Springer Science and Business Media LLC. - 0724-8741 .- 1573-904X. ; 29:3, s. 695-706
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Journal article (peer-reviewed)abstract
- PURPOSE: To develop a semi-mechanistic model linking in vitro to in vivo drug release. METHODS: A nonlinear mixed-effects model describing the in vitro drug release for 6 hydrophilic matrix based modified release formulations across different experimental conditions (pH, rotation speed and ionic strength) was developed. It was applied to in vivo observations of drug release and tablet gastro intestinal (GI) position assessed with magnetic marker monitoring (MMM). By combining the MMM observations with literature information on pH and ionic strength along the GI tract, the mechanical stress in different parts of the GI tract could be estimated in units equivalent to rotation speed in the in vitro USP 2 apparatus. RESULTS: The mechanical stress in the upper and lower stomach was estimated to 94 and 134 rpm, respectively. For the small intestine and colon the estimates of mechanical stress was 93 and 38 rpm. Predictions of in vivo drug release including between subject/tablet variability was made for other newly developed formulations based on the drug release model and a model describing tablet GI transit. CONCLUSION: The paper outlines a modeling approach for predicting in vivo behavior from standard in vitro experiments and support formulation development and quality control.
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| 3. |
- Bergstrom, Christel A. S., et al.
(author)
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Computational Prediction of CNS Drug Exposure Based on a Novel In Vivo Dataset
- 2012
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In: Pharmaceutical research. - : Springer Science and Business Media LLC. - 0724-8741 .- 1573-904X. ; 29:11, s. 3131-3142
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Journal article (peer-reviewed)abstract
- To develop a computational model for predicting CNS drug exposure using a novel in vivo dataset. The brain-to-plasma (B:P) ratio of 43 diverse compounds was assessed following intravenous administration to Swiss Outbred mice. B:P ratios were subjected to PLS modeling using calculated molecular descriptors. The obtained results were transferred to a qualitative setting in which compounds predicted to have a B:P ratio > 0.3 were sorted as high CNS exposure compounds and those below this value were sorted as low CNS exposure compounds. The model was challenged with an external test set consisting of 251 compounds for which semi-quantitative values of CNS exposure were available in the literature. The dataset ranged more than 1700-fold in B:P ratio, with 16 and 27 compounds being sorted as low and high CNS exposure drugs, respectively. The model was a one principal component model based on five descriptors reflecting molecular shape, electronegativity, polarisability and charge transfer, and allowed 74% of the compounds in the training set and 76% of the test set to be predicted correctly. A qualitative computational model has been developed which accurately classifies compounds as being high or low CNS exposure drugs based on rapidly calculated molecular descriptors.
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| 4. |
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| 5. |
- Carlert, Sara, et al.
(author)
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Predicting intestinal precipitation : a case example for a basic BCS class II drug
- 2010
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In: Pharmaceutical research. - : Springer Science and Business Media LLC. - 0724-8741 .- 1573-904X. ; 27:10, s. 2119-2130
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Journal article (peer-reviewed)abstract
- PURPOSE: To investigate the prediction accuracy of in vitro and in vitro/in silico methods for in vivo intestinal precipitation of basic BCS class II drugs in humans. METHODS: Precipitation rate of a model drug substance, AZD0865 (pKa = 6.1; log K(D) = 4.2), was investigated in vitro using simulated intestinal media, and calculations of the crystallization rates were made with a theoretical model. Human intestinal precipitation was estimated by analysis of pharmacokinetic data from clinical studies at different doses. RESULTS: All in vitro models predicted rapid drug precipitation, where the intestinal concentration of dissolved AZD0865 at the highest dose tested was expected to decrease to half after less than 20 min. However, there was no indication of precipitation in vivo in humans as there was a dose proportional increase in drug plasma exposure. The theoretical model predicted no significant precipitation within the range of expected in vivo intestinal concentrations. CONCLUSIONS: This study indicated that simple in vitro methods of in vivo precipitation of orally administered bases overpredict the intestinal crystalline precipitation in vivo in humans. Hydrodynamic conditions were identified as one important factor that needs to be better addressed in future in vivo predictive methods.
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| 6. |
- Chapman, Colin D, et al.
(author)
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Intranasal Treatment of Central Nervous System Dysfunction in Humans
- 2013
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In: Pharmaceutical research. - : Springer Science and Business Media LLC. - 0724-8741 .- 1573-904X. ; 30:10, s. 2475-2484
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Research review (peer-reviewed)abstract
- One of the most challenging problems facing modern medicine is how to deliver a given drug to a specific target at the exclusion of other regions. For example, a variety of compounds have beneficial effects within the central nervous system (CNS), but unwanted side effects in the periphery. For such compounds, traditional oral or intravenous drug delivery fails to provide benefit without cost. However, intranasal delivery is emerging as a noninvasive option for delivering drugs to the CNS with minimal peripheral exposure. Additionally, this method facilitates the delivery of large and/or charged therapeutics, which fail to effectively cross the blood-brain barrier (BBB). Thus, for a variety of growth factors, hormones, neuropeptides and therapeutics including insulin, oxytocin, orexin, and even stem cells, intranasal delivery is emerging as an efficient method of administration, and represents a promising therapeutic strategy for the treatment of diseases with CNS involvement, such as obesity, Alzheimer's disease, Parkinson's disease, Huntington's disease, depression, anxiety, autism spectrum disorders, seizures, drug addiction, eating disorders, and stroke.
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| 7. |
- Chen, Mei-Ling, et al.
(author)
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The BCS, BDDCS, and regulatory guidances
- 2011
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In: Pharmaceutical research. - : Springer Science and Business Media LLC. - 0724-8741 .- 1573-904X. ; 28:7, s. 1774-1778
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Journal article (peer-reviewed)
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| 8. |
- De Cock, Roosmarijn F W, et al.
(author)
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A Neonatal Amikacin Covariate Model Can Be Used to Predict Ontogeny of Other Drugs Eliminated Through Glomerular Filtration in Neonates
- 2014
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In: Pharmaceutical research. - : Springer Science and Business Media LLC. - 0724-8741 .- 1573-904X. ; 31:3, s. 754-767
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Journal article (peer-reviewed)abstract
- PURPOSERecently, a covariate model characterizing developmental changes in clearance of amikacin in neonates has been developed using birth bodyweight and postnatal age. The aim of this study was to evaluate whether this covariate model can be used to predict maturation in clearance of other renally excreted drugs.METHODSFive different neonatal datasets were available on netilmicin, vancomycin, tobramycin and gentamicin. The extensively validated covariate model for amikacin clearance was used to predict clearance of these drugs. In addition, independent reference models were developed based on a systematic covariate analysis.RESULTSThe descriptive and predictive properties of the models developed using the amikacin covariate model were good, and fairly similar to the independent reference models (goodness-of-fit plots, NPDE). Moreover, similar clearance values were obtained for both approaches. Finally, the same covariates as in the covariate model of amikacin, i.e. birth bodyweight and postnatal age, were identified on clearance in the independent reference models.CONCLUSIONSThis study shows that pediatric covariate models may contain physiological information since information derived from one drug can be used to describe other drugs. This semi-physiological approach may be used to optimize sparse data analysis and to derive individualized dosing algorithms for drugs in children.
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| 9. |
- Ernest, C. Steven, II, et al.
(author)
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Methodological Comparison of In Vitro Binding Parameter Estimation : Sequential vs. Simultaneous Non-linear Regression
- 2010
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In: Pharmaceutical research. - : Springer Science and Business Media LLC. - 0724-8741 .- 1573-904X. ; 27:5, s. 866-877
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Journal article (peer-reviewed)abstract
- Analysis of simulated data was compared using sequential (NLR) and simultaneous non-linear regression (SNLR) to evaluate precision and accuracy of ligand binding parameter estimation. Commonly encountered experimental error, specifically residual error of binding measurements (RE), experiment-to-experiment variability (BEV) and non-specific binding (B-NS), were examined for impact of parameter estimation using both methods. Data from equilibrium, dissociation, association and non-specific binding experiments were fit simultaneously (SNLR) using NONMEM VI compared to the common practice of analyzing data from each experiment separately and assigning these as exact values (NLR) for estimation of the subsequent parameters. The greatest contributing factor to bias and variability in parameter estimation was RE of the measured concentrations of ligand bound; however, SNLR provided more accurate and less bias estimates. Subtraction of B-NS from total ligand binding data provided poor estimation of specific ligand binding parameters using both NLR and SNLR. Additional methods examined demonstrated that the use of SNLR provided better estimation of specific binding parameters, whereas there was considerable bias using NLR. NLR cannot account for BEV, whereas SNLR can provide approximate estimates of BEV. SNLR provided superior resolution of parameter estimation in both precision and accuracy compared to NLR.
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