Microdialysis: the Key to Physiologically Based Model Prediction of Human CNS Target Site Concentrations
Despite the enormous research efforts that have been put into the development of central nervous system (CNS) drugs, the success rate in this area is still disappointing. To increase the successful rate in the clinical trials, first the problem of predicting human CNS drug distribution should be sol...
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Veröffentlicht in: | The AAPS journal 2017-07, Vol.19 (4), p.891-909 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Despite the enormous research efforts that have been put into the development of central nervous system (CNS) drugs, the success rate in this area is still disappointing. To increase the successful rate in the clinical trials, first the problem of predicting human CNS drug distribution should be solved. As it is the unbound drug that equilibrates over membranes and is able to interact with targets, especially knowledge on unbound extracellular drug concentration-time profiles in different CNS compartments is important. The only technique able to provide such information
in vivo
is microdialysis. Also, obtaining CNS drug distribution data from human subjects is highly limited, and therefore, we have to rely on preclinical approaches combined with physiologically based pharmacokinetic (PBPK) modeling, taking unbound drug CNS concentrations into account. The next step is then to link local CNS pharmacokinetics to target interaction kinetics and CNS drug effects. In this review, system properties and small-molecule drug properties that together govern CNS drug distribution are summarized. Furthermore, the currently available approaches on prediction of CNS pharmacokinetics are discussed, including
in vitro
,
in vivo
,
ex vivo
, and
in silico
approaches, with special focus on the powerful combination of
in vivo
microdialysis and PBPK modeling. Also, sources of variability on drug kinetics in the CNS are discussed. Finally, remaining gaps and challenges are highlighted and future directions are suggested. |
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ISSN: | 1550-7416 1550-7416 |
DOI: | 10.1208/s12248-017-0050-3 |