FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2954096145> ?p ?o ?g. }

• W2954096145 abstract "Abstract The development of drugs targeting the brain still faces a high failure rate. One of the reasons is a lack of quantitative understanding of the complex processes that govern the pharmacokinetics (PK) of a drug within the brain. While a number of models on drug distribution into and within the brain is available, none of these addresses the combination of factors that affect local drug concentrations in brain extracellular fluid (brain ECF). Here, we develop a 3D brain unit model, which builds on our previous proof-of-concept 2D brain unit model, to understand the factors that govern local unbound and bound drug PK within the brain. The 3D brain unit is a cube, in which the brain capillaries surround the brain ECF. Drug concentration-time profiles are described in both a blood-plasma-domain and a brain-ECF-domain by a set of differential equations. The model includes descriptions of blood plasma PK, transport through the blood-brain barrier (BBB), by passive transport via paracellular and trancellular routes, and by active transport, and drug binding kinetics. The impact of all these factors on ultimate local brain ECF unbound and bound drug concentrations is assessed. In this article we show that all the above mentioned factors affect brain ECF PK in an interdependent manner. This indicates that for a quantitative understanding of local drug concentrations within the brain ECF, interdependencies of all transport and binding processes should be understood. To that end, the 3D brain unit model is an excellent tool, and can be used to build a larger network of 3D brain units, in which the properties for each unit can be defined independently to reflect local differences in characteristics of the brain. Author summary Insights on how a drug distributes within the brain over both time and space are still limited. Here, we develop a ‘3D brain unit model’ in order to understand the factors that control drug concentrations within a small piece of brain tissue, the 3D brain unit. In one 3D brain unit, the brain capillaries, which are the smallest blood vessels of the brain, surround the brain extracellular fluid (ECF). The blood-brain barrier (BBB) is located between the brain capillaries and the brain ECF. The model describes the impact of brain capillary blood flow, transport across the BBB, diffusion, flow and drug binding on the distribution of a drug within the brain ECF. We distinguish between free (unbound) drug and drug that is bound to binding sites within the brain. We show that all of the above mentioned factors affect drug concentrations within brain ECF in an interdependent manner. The 3D brain unit model that we have developed is an excellent tool to increase our understanding of how local drug concentrations within the brain ECF are affected by brain transport and binding processes." @default.
• W2954096145 created "2019-07-12" @default.
• W2954096145 creator A5006775121 @default.
• W2954096145 creator A5017208135 @default.
• W2954096145 creator A5053543520 @default.
• W2954096145 date "2019-07-01" @default.
• W2954096145 modified "2023-09-26" @default.
• W2954096145 title "A 3D brain unit model to further improve prediction of local drug distribution within the brain" @default.
• W2954096145 cites W1514359139 @default.
• W2954096145 cites W1526950891 @default.
• W2954096145 cites W1543016938 @default.
• W2954096145 cites W1580448123 @default.
• W2954096145 cites W1726768244 @default.
• W2954096145 cites W1895506743 @default.
• W2954096145 cites W1964549914 @default.
• W2954096145 cites W1966259921 @default.
• W2954096145 cites W1967908989 @default.
• W2954096145 cites W1968620174 @default.
• W2954096145 cites W1971423558 @default.
• W2954096145 cites W1974183119 @default.
• W2954096145 cites W1975732928 @default.
• W2954096145 cites W1982963770 @default.
• W2954096145 cites W1983223101 @default.
• W2954096145 cites W1985050254 @default.
• W2954096145 cites W1995735458 @default.
• W2954096145 cites W2002130713 @default.
• W2954096145 cites W2002541910 @default.
• W2954096145 cites W2004030603 @default.
• W2954096145 cites W2006669174 @default.
• W2954096145 cites W2013759477 @default.
• W2954096145 cites W2025475039 @default.
• W2954096145 cites W2028441136 @default.
• W2954096145 cites W2033285446 @default.
• W2954096145 cites W2037878590 @default.
• W2954096145 cites W2044644473 @default.
• W2954096145 cites W2047497650 @default.
• W2954096145 cites W2048709650 @default.
• W2954096145 cites W2052293404 @default.
• W2954096145 cites W2056475298 @default.
• W2954096145 cites W2058780134 @default.
• W2954096145 cites W2063770299 @default.
• W2954096145 cites W2075143031 @default.
• W2954096145 cites W2080894693 @default.
• W2954096145 cites W2082065600 @default.
• W2954096145 cites W2099718046 @default.
• W2954096145 cites W2101099825 @default.
• W2954096145 cites W2103111028 @default.
• W2954096145 cites W2103825400 @default.
• W2954096145 cites W2110935910 @default.
• W2954096145 cites W2117024164 @default.
• W2954096145 cites W2126205988 @default.
• W2954096145 cites W2135290713 @default.
• W2954096145 cites W2146345280 @default.
• W2954096145 cites W2150060563 @default.
• W2954096145 cites W2155837964 @default.
• W2954096145 cites W2156557177 @default.
• W2954096145 cites W2166269667 @default.
• W2954096145 cites W2171263166 @default.
• W2954096145 cites W2253346903 @default.
• W2954096145 cites W2293639413 @default.
• W2954096145 cites W2314371091 @default.
• W2954096145 cites W2329671310 @default.
• W2954096145 cites W2464012090 @default.
• W2954096145 cites W2465745915 @default.
• W2954096145 cites W2583981323 @default.
• W2954096145 cites W2767617726 @default.
• W2954096145 cites W2885117972 @default.
• W2954096145 cites W4254402395 @default.
• W2954096145 doi "https://doi.org/10.1101/688648" @default.
• W2954096145 hasPublicationYear "2019" @default.
• W2954096145 type Work @default.
• W2954096145 sameAs 2954096145 @default.
• W2954096145 citedByCount "0" @default.
• W2954096145 crossrefType "posted-content" @default.
• W2954096145 hasAuthorship W2954096145A5006775121 @default.
• W2954096145 hasAuthorship W2954096145A5017208135 @default.
• W2954096145 hasAuthorship W2954096145A5053543520 @default.
• W2954096145 hasBestOaLocation W29540961452 @default.
• W2954096145 hasConcept C110121322 @default.
• W2954096145 hasConcept C120843803 @default.
• W2954096145 hasConcept C134306372 @default.
• W2954096145 hasConcept C15744967 @default.
• W2954096145 hasConcept C169760540 @default.
• W2954096145 hasConcept C185592680 @default.
• W2954096145 hasConcept C2777670902 @default.
• W2954096145 hasConcept C2778402981 @default.
• W2954096145 hasConcept C2780035454 @default.
• W2954096145 hasConcept C33923547 @default.
• W2954096145 hasConcept C522805319 @default.
• W2954096145 hasConcept C529278444 @default.
• W2954096145 hasConcept C71924100 @default.
• W2954096145 hasConcept C98274493 @default.
• W2954096145 hasConceptScore W2954096145C110121322 @default.
• W2954096145 hasConceptScore W2954096145C120843803 @default.
• W2954096145 hasConceptScore W2954096145C134306372 @default.
• W2954096145 hasConceptScore W2954096145C15744967 @default.
• W2954096145 hasConceptScore W2954096145C169760540 @default.
• W2954096145 hasConceptScore W2954096145C185592680 @default.
• W2954096145 hasConceptScore W2954096145C2777670902 @default.
• W2954096145 hasConceptScore W2954096145C2778402981 @default.

• next