Structure−Brain Exposure Relationships in Rat and Human Using a Novel Data Set of Unbound Drug Concentrations in Brain Interstitial and Cerebrospinal Fluids

Abstract

New experimental methodologies were applied to measure the unbound brain-to-plasma concentration ratio (K(p,uu,brain)) and the unbound CSF-to-plasma concentration ratio (K(p,uu,CSF)) in rats for 43 structurally diverse drugs. The relationship between chemical structure and K(p,uu,brain) was dominated by hydrogen bonding. Contrary to popular understanding based on the total brain-to-plasma concentration ratio (logBB), lipophilicity was not a determinant of unbound brain exposure. Although changing the number of hydrogen bond acceptors is a useful design strategy for optimizing K(p,uu,brain), future improvement of in silico prediction models is dependent on the accommodation of active drug transport. The structure-brain exposure relationships found in the rat also hold for humans, since the rank order of the drugs was similar for human and rat K(p,uu,CSF). This cross-species comparison was supported by K(p,uu,CSF) being within 3-fold of K(p,uu,brain) in the rat for 33 of 39 drugs. It was, however, also observed that K(p,uu,CSF) overpredicts K(p,uu,brain) for highly effluxed drugs, indicating lower efflux capacity of the blood-cerebrospinal fluid barrier compared to the blood-brain barrier.

Species Independence in Brain Tissue Binding Using Brain Homogenates

“…For neuroscience therapeutic targets, accurate understanding of total and free drug concentrations in plasma and brain, particularly brain interstitial fluid, is critical for developing PK/PD relationships, projecting doses, and designing clinical studies. Several techniques have been developed to obtain free drug concentrations in the brain directly or indirectly Liu et al, 2008;Di and Kerns, 2011), including in vivo microdialysis (direct) (Elmquist and Sawchuk, 1997;Hammarlund-Udenaes et al, 1997;de Lange et al, 1997;Hammarlund-Udenaes, 2000;Watson et al, 2006), CSF sampling (indirect) (Shen et al, 2004;Lin, 2008;Fridén et al, 2009b), and a combination of brain distribution through measuring plasma and brain concentration time courses ) and brain tissue binding (quasi-direct) (Kalvass and Maurer, 2002;Mano et al, 2002;Maurer et al, 2005;Summerfield et al, 2006;Liu et al, 2009). The latter approach of measuring brain distribution and brain tissue binding is one of the most common strategies in the pharmaceutical industry to elucidate total and free drug PK relationships in brain and plasma.…”

Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics

“…As discussed earlier, BBB permeability, which is the rate of BBB transport, was often confused with the extent of brain distribution, which confounded the search for good predictors of brain exposure. Other in silico approaches have focused on quantitative structure-property relationships (QSPR), using the physicochemical properties of a drug as predictors of the rate and extent of BBB transport and brain distribution [25][26][27][28][29].…”

“…The better we will be able to develop predictive models in preclinical studies, the more the number of often extremely costly clinical studies can be reduced. The focus should therefore be on the design of quantitative in vivo animal studies such that translational pharmacology approaches can be applied [25,36,37,61,89], which will be discussed below. In refined animal models, the biomarkers of the effect that can be measured in both animals and human will be particularly useful.…”

P2X receptor antagonists for pain management: examination of binding and physicochemical properties

“…Lipinski found that 90% of the drugs from the World Drug Index violated not more than one of these rules [14]. Since then, a number of investigators have generated more quantitatively correlated subsets of molecular physicochemical properties related to the absorption, solubility, permeability, oral bioavailability, target specificity, and toxicity of drugs and Additional information on the properties of drug-like molecules can be found in [74][75][76] clinically evaluated molecules [15][16][17][18][19][20][21][22][23]. These parameters have been used to assess the relative probability of success of clinical lead optimization research for specific drug-like compounds that have demonstrated proof of concept in subsequent clinical testing [15][16][17][18][19][20][21][22][23].…”