Polarized P-glycoprotein expression by the immortalised human brain endothelial cell line, hCMEC/D3, restricts apical-to-basolateral permeability to rhodamine 123

P-glycoprotein (P-gp) expression at the blood-brain barrier prevents unwanted blood-borne toxins and signalling molecules from entering the brain. Primary and immortalised human brain endothelial cells (BECs) represent two suitable options for studying P-gp function in vitro. The limited supply of primary human BECs and their instability over passage number makes this choice unattractive for medium/high throughput studies. The aim of this study was to further characterise the expression of P-gp by an immortalised human BEC line, hCMEC/D3, in order to evaluate their use as an in vitro human blood-brain barrier model. P-gp expression was stable over a high passage number (up to passage 38) and was polarised on the apical plasma membrane, consistent with human BECs in vivo. In addition, hCMEC/D3 cell P-gp expression was comparable, albeit slightly lower to that observed in primary isolated human BECs although P-gp function was similar in both cell lines. The P-gp inhibitors tariquidar and vinblastine prevented the efflux of rhodamine 123 (rh123) from hCMEC/D3 cells, indicative of functional P-gp expression. hCMEC/D3 cells also displayed polarised P-gp transport, since both tariquidar and vinblasine selectively increased the apical-to-basolateral permeability of hCMEC/D3 cells to rh123. The results presented here demonstrate that hCMEC/D3 cells are a suitable model to investigate substrate specificity of P-gp in BECs of human origin

Qualitative prediction of blood–brain barrier permeability on a large and refined dataset

The prediction of blood–brain barrier permeation is vitally important for the optimization of drugs targeting the central nervous system as well as for avoiding side effects of peripheral drugs. Following a previously proposed model on blood–brain barrier penetration, we calculated the cross-sectional area perpendicular to the amphiphilic axis. We obtained a high correlation between calculated and experimental cross-sectional area (r = 0.898, n = 32). Based on these results, we examined a correlation of the calculated cross-sectional area with blood–brain barrier penetration given by logBB values. We combined various literature data sets to form a large-scale logBB dataset with 362 experimental logBB values. Quantitative models were calculated using bootstrap validated multiple linear regression. Qualitative models were built by a bootstrapped random forest algorithm. Both methods found similar descriptors such as polar surface area, pKa, logP, charges and number of positive ionisable groups to be predictive for logBB. In contrast to our initial assumption, we were not able to obtain models with the cross-sectional area chosen as relevant parameter for both approaches. Comparing those two different techniques, qualitative random forest models are better suited for blood-brain barrier permeability prediction, especially when reducing the number of descriptors and using a large dataset. A random forest prediction system (ntrees = 5) based on only four descriptors yields a validated accuracy of 88%

Validacija topokemijskih modela za predviđanje permeabilnosti kroz krvno-moždanu barijeru

Recently published topochemical models for permeability through the blood-brain barrier were validated and cross-validated in the present study. Five models based on three topochemical indices, Wiener’s topochemical index - a distance-based topochemical descriptor, molecular connectivity topochemical index - an adjacency-based topochemical descriptor and eccentric connectivity topochemical index - an adjacency-cum-distance based topochemical descriptor, for permeability of structurally and chemically diverse molecules through blood-brain barrier were used in the present investigation. A data set comprising 62 structurally and chemically diverse compounds was selected. This data set was divided into two sets of 31 compounds each - one to serve as the validation set and other as the cross-validation set. The values of all the three-topochemical indices in the original as well as in the normalized form for each of the 31 compounds of the validation set were computed using an in house computer program. Resultant data was analyzed and each compound was assigned a permeability characteristic using topochemical models, which was then compared with the reported permeability through the blood-brain barrier. Accuracy of prediction of these models was calculated. The same procedure was similarly followed for the cross-validation set. Studies revealed accuracy of prediction of the order of 7080% during validation. Surprisingly, very high predictability of the order of 7791% was observed during cross-validation. High predictability observed during validation as well as cross-validation authenticates topochemical models for prediction of permeability through the blood-brain barrier.U ovom radu su validirani i unakrsno validirani nedavno objavljeni topokemijski modeli za permeabilnost kroz krvno-moždanu barijeru. Predviđanje prolaska kroz krvno-moždanu barijeru strukturno i kemijski različitih molekula provedeno je na pet modela koji se temelje na tri topološka indeksa, Wienerovom topološkom indeksu, topološkom indeksu molekularne povezanosti i topološkom indeksu ekscentrične povezanosti. Ukupno 62 spoja podijeljena su u dva seta koji su sadržavali 31 spoj. Jedan set upotrebljen je za validaciju, a drugi za unakrsnu validaciju. Vrijednosti svih triju topoloških indeksa u početnom setu i u normaliziranom setu su računate pomoću kompjutorskog programa. Rezultati su analizirani i svakom spoju je pridružena teorijska vrijednost permeabilnosti, koja je zatim uspoređivana s objavljenim eksperimentalnim podacima za permeabilnost kroz krvno-moždanu barijeru. Točnost predviđanja bila je između 70 i 80%. Isti postupak je proveden za unakrsno validacijski set, a točnost je bila iznenađujeće velika (7791%), što ukazuje da se upotrebljeni topokemijski modeli mogu upotrijebiti za predviđanje permeabilnsot kroz krvno-moždanu barijeru

PDE 7 Inhibitors: New Potential Drugs for the Therapy of Spinal Cord Injury

BACKGROUND: Primary traumatic mechanical injury to the spinal cord (SCI) causes the death of a number of neurons that to date can neither be recovered nor regenerated. During the last years our group has been involved in the design, synthesis and evaluation of PDE7 inhibitors as new innovative drugs for several neurological disorders. Our working hypothesis is based on two different facts. Firstly, neuroinflammation is modulated by cAMP levels, thus the key role for phosphodiesterases (PDEs), which hydrolyze cAMP, is undoubtedly demonstrated. On the other hand, PDE7 is expressed simultaneously on leukocytes and on the brain, highlighting the potential crucial role of PDE7 as drug target for neuroinflammation. METHODOLOGY/PRINCIPAL FINDINGS: Here we present two chemically diverse families of PDE7 inhibitors, designed using computational techniques such as virtual screening and neuronal networks. We report their biological profile and their efficacy in an experimental SCI model induced by the application of vascular clips (force of 24 g) to the dura via a four-level T5-T8 laminectomy. We have selected two candidates, namely S14 and VP1.15, as PDE7 inhibitors. These compounds increase cAMP production both in macrophage and neuronal cell lines. Regarding drug-like properties, compounds were able to cross the blood brain barrier using parallel artificial membranes (PAMPA) methodology. SCI in mice resulted in severe trauma characterized by edema, neutrophil infiltration, and production of a range of inflammatory mediators, tissue damage, and apoptosis. Treatment of the mice with S14 and VP1.15, two PDE7 inhibitors, significantly reduced the degree of spinal cord inflammation, tissue injury (histological score), and TNF-α, IL-6, COX-2 and iNOS expression. CONCLUSIONS/SIGNIFICANCE: All these data together led us to propose PDE7 inhibitors, and specifically S14 and VP1.15, as potential drug candidates to be further studied for the treatment of SCI

Current Industrial Practices in Assessing CYP450 Enzyme Induction: Preclinical and Clinical

Induction of drug metabolizing enzymes, such as the cytochromes P450 (CYP) is known to cause drug-drug interactions due to increased elimination of co-administered drugs. This increased elimination may lead to significant reduction or complete loss of efficacy of the co-administered drug. Due to the significance of such drug interactions, many pharmaceutical companies employ screening and characterization models which predict CYP enzyme induction to avoid or attenuate the potential for drug interactions with new drug candidates. The most common mechanism of CYP induction is transcriptional gene activation. Activation is mediated by nuclear receptors, such as AhR, CAR, and PXR that function as transcription factors. Early high throughput screening models utilize these nuclear hormone receptors in ligand binding or cell-based transactivation/reporter assays. In addition, immortalized hepatocyte cell lines can be used to assess enzyme induction of specific drug metabolizing enzymes. Cultured primary human hepatocytes, the best established in vitro model for predicting enzyme induction and most accepted by regulatory agencies, is the predominant assay used to evaluate induction of a wide variety of drug metabolizing enzymes. These in vitro models are able to appropriately predict enzyme induction in patients when compared to clinical drug-drug interactions. Finally, transgenic animal models and the cynomolgus monkey have also been shown to recapitulate human enzyme induction and may be appropriate in vivo animal models for predicting human drug interactions

Natural and Synthetic Polymers as Inhibitors of Drug Efflux Pumps

Inhibition of efflux pumps is an emerging approach in cancer therapy and drug delivery. Since it has been discovered that polymeric pharmaceutical excipients such as Tweens® or Pluronics® can inhibit efflux pumps, various other polymers have been investigated regarding their potential efflux pump inhibitory activity. Among them are polysaccharides, polyethylene glycols and derivatives, amphiphilic block copolymers, dendrimers and thiolated polymers. In the current review article, natural and synthetic polymers that are capable of inhibiting efflux pumps as well as their application in cancer therapy and drug delivery are discussed

Predicting blood-brain barrier permeation from three-dimensional molecular structure.

Predicting blood-brain barrier (BBB) permeation remains a challenge in drug design. Since it is impossible to determine experimentally the BBB partitioning of large numbers of preclinical candidates, alternative evaluation methods based on computerized models are desirable. The present study was conducted to demonstrate the value of descriptors derived from 3D molecular fields in estimating the BBB permeation of a large set of compounds and to produce a simple mathematical model suitable for external prediction. The method used (VolSurf) transforms 3D fields into descriptors and correlates them to the experimental permeation by a discriminant partial least squares procedure. The model obtained here correctly predicts more than 90% of the BBB permeation data. By quantifying the favorable and unfavorable contributions of physicochemical and structural properties, it also offers valuable insights for drug design, pharmacological profiling, and screening. The computational procedure is fully automated and quite fast. The method thus appears as a valuable new tool in virtual screening where selection or prioritization of candidates is required from large collections of compounds

Predicting blood-brain barrier permeation from three-dimensional molecular structure

Predicting blood-brain barrier (BBB) permeation remains a challenge in drug design. Since it is impossible to determine experimentally the BBB partitioning of large numbers of preclinical candidates, alternative evaluation methods based on computerized models are desirable. The present study was conducted to demonstrate the value of descriptors derived from 3D molecular fields in estimating the BBB permeation of a large set of compounds and to produce a simple mathematical model suitable for external prediction. The method used (VolSurf) transforms 3D fields into descriptors and correlates them to the experimental permeation by a discriminant partial least squares procedure. The model obtained here correctly predicts more than 90% of the BBB permeation data. By quantifying the favorable and unfavorable contributions of physicochemical and structural properties, it also offers valuable insights for drug design, pharmacological profiling, and screening. The computational procedure is fully automated and quite fast. The method thus appears as a valuable new tool in virtual screening where selection or prioritization of candidates is required from large collections of compounds