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A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations.

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Τίτλος: A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations.
Συγγραφείς: Yamamoto, Yumi1, Välitalo, Pyry1, Berg, Dirk-Jan1, Hartman, Robin1, Brink, Willem1, Wong, Yin1, Huntjens, Dymphy2, Proost, Johannes3, Vermeulen, An2, Krauwinkel, Walter4, Bakshi, Suruchi1, Aranzana-Climent, Vincent5, Marchand, Sandrine5, Dahyot-Fizelier, Claire6, Couet, William5, Danhof, Meindert1, Hasselt, Johan1, Lange, Elizabeth ecmdelange@lacdr.leidenuniv.nl
Πηγή: Pharmaceutical Research. Feb2017, Vol. 34 Issue 2, p333-351. 19p. 3 Diagrams, 4 Charts, 3 Graphs.
Θεματικοί όροι: *Brain -- Mathematical models, *Central nervous system, *Targeted drug delivery, *Pharmacokinetics, *Acetaminophen
Περίληψη: Purpose: Predicting target site drug concentration in the brain is of key importance for the successful development of drugs acting on the central nervous system. We propose a generic mathematical model to describe the pharmacokinetics in brain compartments, and apply this model to predict human brain disposition. Methods: A mathematical model consisting of several physiological brain compartments in the rat was developed using rich concentration-time profiles from nine structurally diverse drugs in plasma, brain extracellular fluid, and two cerebrospinal fluid compartments. The effect of active drug transporters was also accounted for. Subsequently, the model was translated to predict human concentration-time profiles for acetaminophen and morphine, by scaling or replacing system- and drug-specific parameters in the model. Results: A common model structure was identified that adequately described the rat pharmacokinetic profiles for each of the nine drugs across brain compartments, with good precision of structural model parameters (relative standard error <37.5%). The model predicted the human concentration-time profiles in different brain compartments well (symmetric mean absolute percentage error <90%). Conclusions: A multi-compartmental brain pharmacokinetic model was developed and its structure could adequately describe data across nine different drugs. The model could be successfully translated to predict human brain concentrations. [ABSTRACT FROM AUTHOR]
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  – Url: https://dx.doi.org/doi:10.1007/s11095-016-2065-3
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  Data: A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations.
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  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22Pharmaceutical+Research%22&quot;&gt;Pharmaceutical Research&lt;/searchLink&gt;. Feb2017, Vol. 34 Issue 2, p333-351. 19p. 3 Diagrams, 4 Charts, 3 Graphs.
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  Data: Purpose: Predicting target site drug concentration in the brain is of key importance for the successful development of drugs acting on the central nervous system. We propose a generic mathematical model to describe the pharmacokinetics in brain compartments, and apply this model to predict human brain disposition. Methods: A mathematical model consisting of several physiological brain compartments in the rat was developed using rich concentration-time profiles from nine structurally diverse drugs in plasma, brain extracellular fluid, and two cerebrospinal fluid compartments. The effect of active drug transporters was also accounted for. Subsequently, the model was translated to predict human concentration-time profiles for acetaminophen and morphine, by scaling or replacing system- and drug-specific parameters in the model. Results: A common model structure was identified that adequately described the rat pharmacokinetic profiles for each of the nine drugs across brain compartments, with good precision of structural model parameters (relative standard error &lt;37.5%). The model predicted the human concentration-time profiles in different brain compartments well (symmetric mean absolute percentage error &lt;90%). Conclusions: A multi-compartmental brain pharmacokinetic model was developed and its structure could adequately describe data across nine different drugs. The model could be successfully translated to predict human brain concentrations. [ABSTRACT FROM AUTHOR]
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        Value: 10.1007/s11095-016-2065-3
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        Text: English
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