In Vitro-In Vivo Correlation of Blood-Brain Barrier Permeability of Drugs: A Feasibility Study Towards Development of Prediction Methods for Brain Drug Concentration in Humans.

PURPOSE: In vitro human blood-brain barrier (BBB) models in combination with central nervous system-physiologically based pharmacokinetic (CNS-PBPK) modeling, hereafter referred to as the "BBB/PBPK" method, are expected to contribute to prediction of brain drug concentration profiles in humans. As part of our ongoing effort to develop a BBB/PBPK method, we tried to clarify the relationship of in vivo BBB permeability data to those in vitro obtained from a human immortalized cell-based tri-culture BBB model (hiBBB), which we have recently created. METHODS: The hiBBB models were developed and functionally characterized as previously described. The in vitro BBB permeabilities (Pe, × 10-6 cm/s) of seventeen compounds were determined by permeability assays, and in vivo BBB permeabilities (QECF) for eight drugs were estimated by CNS-PBPK modeling. The correlation of the Pe values with the QECF values was analyzed by linear regression analysis. RESULTS: The hiBBB models showed intercellular barrier properties and several BBB transporter functions, which were enough to provide a wide dynamic range of Pe values from 5.7 ± 0.7 (rhodamine 123) to 2580.4 ± 781.9 (rivastigmine). Furthermore, the in vitro Pe values of the eight drugs showed a good correlation (R2 = 0.96) with their in vivo QECF values estimated from human clinical data. CONCLUSION: We show that in vitro human BBB models provide clinically relevant BBB permeability that can be used as input for CNS-PBPK modeling. Therefore, our findings will encourage the development of a BBB/PBPK method as a promising approach for predicting brain drug concentration profiles in humans.

E7386, a Selective Inhibitor of the Interaction between ß-Catenin and CBP, Exerts Antitumor Activity in Tumor Models with Activated Canonical Wnt Signaling.

The Wnt/ß-catenin signaling pathway plays crucial roles in embryonic development and the development of multiple types of cancer, and its aberrant activation provides cancer cells with escape mechanisms from immune checkpoint inhibitors. E7386, an orally active selective inhibitor of the interaction between ß-catenin and CREB binding protein, which is part of the Wnt/ß-catenin signaling pathway, disrupts the Wnt/ß-catenin signaling pathway in HEK293 and adenomatous polyposis coli (APC)-mutated human gastric cancer ECC10 cells. It also inhibited tumor growth in an ECC10 xenograft model and suppressed polyp formation in the intestinal tract of ApcMin /+ mice, in which mutation of Apc activates the Wnt/ß-catenin signaling pathway. E7386 demonstrated antitumor activity against mouse mammary tumors developed in mouse mammary tumor virus (MMTV)-Wnt1 transgenic mice. Gene expression profiling using RNA sequencing data of MMTV-Wnt1 tumor tissue from mice treated with E7386 showed that E7386 downregulated genes in the hypoxia signaling pathway and immune responses related to the CCL2, and IHC analysis showed that E7386 induced infiltration of CD8+ cells into tumor tissues. Furthermore, E7386 showed synergistic antitumor activity against MMTV-Wnt1 tumor in combination with anti-PD-1 antibody. In conclusion, E7386 demonstrates clear antitumor activity via modulation of the Wnt/ß-catenin signaling pathway and alteration of the tumor and immune microenvironments, and its antitumor activity can be enhanced in combination with anti-PD-1 antibody. SIGNIFICANCE: These findings demonstrate that the novel anticancer agent, E7386, modulates Wnt/ß-catenin signaling, altering the tumor immune microenvironment and exhibiting synergistic antitumor activity in combination with anti-PD-1 antibody.

A Human Immortalized Cell-Based Blood-Brain Barrier Triculture Model: Development and Characterization as a Promising Tool for Drug-Brain Permeability Studies.

Brain microvascular endothelial cells (BMEC), together with astrocytes and pericytes, form the blood-brain barrier (BBB) that strictly restricts drug penetration into the brain. Therefore, in central nervous system drug development, the establishment of an in vitro human BBB model for use in studies estimating the in vivo human BBB permeability of drug candidates has long been awaited. The current study developed and characterized a human immortalized cell-based BBB triculture model, termed the "hiBBB" model. To set up the hiBBB model, human immortalized BMEC (HBMEC/ci18) were cocultured with human immortalized astrocytes (HASTR/ci35) and brain pericytes (HBPC/ci37) in a transwell system. The trans-endothelial electrical resistance of the hiBBB model was 134.4 ± 5.5 (Ω × cm2), and the efflux ratios of rhodamine123 and dantrolene were 1.72 ± 0.11 and 1.72 ± 0.45, respectively, suggesting that the hiBBB model possesses essential cellular junction and efflux transporter functions. In BBB permeability assays, the mean value of the permeability coefficients (Pe) of BBB permeable compounds (propranolol, pyrilamine, memantine, and diphenhydramine) was 960 × 10-6 cm/s, which was clearly distinguishable from that of BBB nonpermeable compounds (sodium fluorescein and Lucifer yellow, 18 × 10-6 cm/s). Collectively, this study successfully developed the hiBBB model, which exhibits essential BBB functionality. Taking into consideration the high availability of the immortalized cells used in the hiBBB model, our results are expected to become an initial step toward the establishment of a useful human BBB model to investigate drug penetration into the human brain.

In Silico Prediction of Major Clearance Pathways of Drugs among 9 Routes with Two-Step Support Vector Machines.

PURPOSE: The clearance pathways of drugs are critical elements for understanding the pharmacokinetics of drugs. We previously developed in silico systems to predict the five clearance pathway using a rectangular method and a support vector machine (SVM). In this study, we improved our classification system by increasing the number of clearance pathways available for our prediction (CYP1A2, CYP2C8, CYP2C19, and UDP-glucuronosyl transferases (UGTs)) and by accepting multiple major pathways. METHODS: Using the four default descriptors (charge, molecular weight, logD at pH 7.0, and unbound fraction in plasma), three kinds of SVM-based predictors based on traditional single-step approach or two-step focusing approaches with subset or partition clustering were developed. The two-step approach with subset clustering resulted in the highest prediction performance. The feature-selection of additional descriptors based on a greedy algorithm was employed to further improve the predictability. RESULTS: The prediction accuracy for each pathway was increased to more than 0.83 with the exception of CYP2C19 and UGTs pathways, whose accuracies were below 0.7. Prediction performance of CYP1A2, CYP3A4 and renal excretion pathways were found to be acceptable using external dataset. CONCLUSIONS: We successfully constructed a novel SVM-based predictor for the multiple major clearance pathways based on chemical structures.

In silico prediction of major drug clearance pathways by support vector machines with feature-selected descriptors.

We have previously established an in silico classification method ("CPathPred") to predict the major clearance pathways of drugs based on an empirical decision with only four physicochemical descriptors-charge, molecular weight, octanol-water distribution coefficient, and protein unbound fraction in plasma-using a rectangular method. In this study, we attempted to improve the prediction performance of the method by introducing a support vector machine (SVM) and increasing the number of descriptors. The data set consisted of 141 approved drugs whose major clearance pathways were classified into metabolism by CYP3A4, CYP2C9, or CYP2D6; organic anion transporting polypeptide-mediated hepatic uptake; or renal excretion. With the same four default descriptors as used in CPathPred, the SVM-based predictor (named "default descriptor SVM") resulted in higher prediction performance compared with a rectangular-based predictor judged by 10-fold cross-validation. Two SVM-based predictors were also established by adding some descriptors as follows: 1) 881 descriptors predicted in silico from the chemical structures of drugs in addition to 4 default descriptors ("885 descriptor SVM"); and 2) selected descriptors extracted by a feature selection based on a greedy algorithm with default descriptors ("feature selection SVM"). The prediction accuracies of the rectangular-based predictor, default descriptor SVM, 885 descriptor SVM, and feature selection SVM were 0.49, 0.60, 0.72, and 0.91, respectively, and the overall precision values for these four methods were 0.72, 0.77, 0.86, and 0.98, respectively. In conclusion, we successfully constructed SVM-based predictors with limited numbers of descriptors to classify the major clearance pathways of drugs in humans with high prediction performance.