Quantitation and characterization of glucosylsphingosine in cerebrospinal fluid (CSF), plasma, and brain of monkey model with Gaucher disease.

Treatment with conduritol-ß-epoxide (CBE) in preclinical species is expected to be a powerful approach to generate animal models of Gaucher disease (GD) and Parkinson's disease associated with heterozygous mutations in Glucocerebrosidase (GBA-PD). However, it is not fully elucidated how quantitatively the change in glucosylsphingosine (GlcSph) levels in cerebrospinal fluid (CSF) correlates with that in the brain, which is expected to be clinically informative. Herein, we aimed to investigate the correlation with successfully quantified GlcSph in monkey CSF by developing highly sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods. The GlcSph in normal monkey CSF was 0.635 ± 0.177 pg/mL at baseline and increased by CBE treatment at 3 mg/kg daily for five days up to a moderate level, comparable to that in GD patients. The balance between GlcSph and galactosylsphingosine (GalSph) in the CSF matched that in the brain rather than plasma. In addition, GlcSph in the CSF was increased, accompanied by that in the brain at a dose of 3 mg/kg daily. These results indicate that GlcSph in the CSF is worth evaluating for concentration changes in the brain. Thus, this model can be useful for evaluating GBA-related diseases such as GD and GBA-PD.

Ensemble Machine Learning Approaches Based on Molecular Descriptors and Graph Convolutional Networks for Predicting the Efflux Activities of MDR1 and BCRP Transporters.

Multidrug resistance (MDR1) and breast cancer resistance protein (BCRP) play important roles in drug absorption and distribution. Computational prediction of substrates for both transporters can help reduce time in drug discovery. This study aimed to predict the efflux activity of MDR1 and BCRP using multiple machine learning approaches with molecular descriptors and graph convolutional networks (GCNs). In vitro efflux activity was determined using MDR1- and BCRP-expressing cells. Predictive performance was assessed using an in-house dataset with a chronological split and an external dataset. CatBoost and support vector regression showed the best predictive performance for MDR1 and BCRP efflux activities, respectively, of the 25 descriptor-based machine learning methods based on the coefficient of determination (R2). The single-task GCN showed a slightly lower performance than descriptor-based prediction in the in-house dataset. In both approaches, the percentage of compounds predicted within twofold of the observed values in the external dataset was lower than that in the in-house dataset. Multi-task GCN did not show any improvements, whereas multimodal GCN increased the predictive performance of BCRP efflux activity compared with single-task GCN. Furthermore, the ensemble approach of descriptor-based machine learning and GCN achieved the highest predictive performance with R2 values of 0.706 and 0.587 in MDR1 and BCRP, respectively, in time-split test sets. This result suggests that two different approaches to represent molecular structures complement each other in terms of molecular characteristics. Our study demonstrated that predictive models using advanced machine learning approaches are beneficial for identifying potential substrate liability of both MDR1 and BCRP.

Human Brain Penetration Prediction Using Scaling Approach from Animal Machine Learning Models.

Machine learning (ML) approaches have been applied to predicting drug pharmacokinetic properties. Previously, we predicted rat unbound brain-to-plasma ratio (Kpuu,brain) by ML models. In this study, we aimed to predict human Kpuu,brain through animal ML models. First, we re-evaluated ML models for rat Kpuu,brain prediction by using trendy open-source packages. We then developed ML models for monkey Kpuu,brain prediction. Leave-one-out cross validation was utilized to rationally build models using a relatively small dataset. After establishing the monkey and rat ML models, human Kpuu,brain prediction was achieved by implementing the animal models considering appropriate scaling methods. Mechanistic NeuroPK models for the identical monkey and human dataset were treated as the criteria for comparison. Results showed that rat Kpuu,brain predictivity was successfully replicated. The optimal ML model for monkey Kpuu,brain prediction was superior to the NeuroPK model, where accuracy within 2-fold error was 78% (R2 = 0.76). For human Kpuu,brain prediction, rat model using relative expression factor (REF), scaled transporter efflux ratios (ERs), and monkey model using in vitro ERs can provide comparable predictivity to the NeuroPK model, where accuracy within 2-fold error was 71% and 64% (R2 = 0.30 and 0.52), respectively. We demonstrated that ML models can deliver promising Kpuu,brain prediction with several advantages: (1) predict reasonable animal Kpuu,brain; (2) prospectively predict human Kpuu,brain from animal models; and (3) can skip expensive monkey studies for human prediction by using the rat model. As a result, ML models can be a powerful tool for drug Kpuu,brain prediction in the discovery stage.

Quantitative assessment of coat-hanger ribs detected on three-dimensional ultrasound for prenatal diagnosis of Kagami-Ogata syndrome.

Kagami-Ogata syndrome (KOS14) is a rare disease characterized by omphalocele, polyhydramnios and a bell-shaped thorax. Although the coat-hanger appearance of the ribs on postnatal X-rays is a key diagnostic finding of KOS14, its prenatal diagnosis remains challenging. We encountered a case of KOS14 diagnosed prenatally that showed omphalocele, polyhydramnios, and a bell-shaped narrow thorax. The coat-hanger angle (CHA) measured at the sixth thoracic vertebrae and the ribs using three-dimensional (3D) ultrasonography was 39°, reflecting the coat-hanger appearance of the ribs. Segmental uniparental disomy chromosome 14 (UPD(14)pat) was confirmed by a methylation analysis and microsatellite analysis after birth. The median CHA (minimum, maximum) in 25 normal fetuses was 19 (9, 26) degrees, and a sonographic CHA of 30° may be a border value for diagnosing KOS14. When the combination of omphalocele and polyhydramnios is found prenatally, 3D ultrasonography for CHA might aid in the differential diagnosis of KOS14.

Highly-sensitive simultaneous quantitation of glucosylsphingosine and galactosylsphingosine in human cerebrospinal fluid by liquid chromatography/tandem mass spectrometry.

Mutations in the GBA gene, encoding glucocerebrosidase (GCase), are linked to Gaucher disease (GD) and are the most common risk factors for Parkinson's disease (PD). The glucosylsphingosine (GlcSph) in cerebrospinal fluid (CSF) is used as a pharmacodynamic marker for GCase functionalizing therapy in GD patients. Its isobaric structural isomer, galactosylsphingosine (GalSph, psychosine), is also used as a diagnostic blood marker in Krabbe disease (KD) which is caused by a deficiency in ß-galactocerebrosidase (GALC). However, there are no reports of GlcSph quantification in the CSF of GBA-PD patients and normal healthy humans due to low concentrations. In this study, we successfully quantified GlcSph in healthy human CSF using a highly sensitive LC-MS/MS method with separation of GalSph. The lower limit of quantitation (LLOQ) was 0.1 pg/mL. Additionally, GlcSph and GalSph concentrations in the plasma and brain were determined using different LC-MS/MS methods. The mean concentrations of GlcSph and GalSph in normal human CSF were 1.07 and 9.44 pg/mL, respectively. The GalSph level in the CSF and brain was higher than that of GlcSph, whereas plasma GalSph was lower than GlcSph. Because GCase and GALC are expressed in the brain and the peripheral tissues, GlcSph and GalSph in CSF would be a good surrogate of concentration change in the brain by targeted therapies. This method measures normal levels of GlcSph and GalSph in healthy human CSF without accumulation of sphingolipids, and confirms whether abnormal CSF concentrations can be reduced to normal levels by therapy.

Strictly regulated agonist-dependent activation of AMPA-R is the key characteristic of TAK-653 for robust synaptic responses and cognitive improvement.

Agonistic profiles of AMPA receptor (AMPA-R) potentiators may be associated with seizure risk and bell-shaped dose-response effects. Here, we report the pharmacological characteristics of a novel AMPA-R potentiator, TAK-653, which exhibits minimal agonistic properties. TAK-653 bound to the ligand binding domain of recombinant AMPA-R in a glutamate-dependent manner. TAK-653 strictly potentiated a glutamate-induced Ca2+ influx in hGluA1i-expressing CHO cells through structural interference at Ser743 in GluA1. In primary neurons, TAK-653 augmented AMPA-induced Ca2+ influx and AMPA-elicited currents via physiological AMPA-R with little agonistic effects. Interestingly, TAK-653 enhanced electrically evoked AMPA-R-mediated EPSPs more potently than AMPA (agonist) or LY451646 (AMPA-R potentiator with a prominent agonistic effect) in brain slices. Moreover, TAK-653 improved cognition for both working memory and recognition memory, while LY451646 did so only for recognition memory, and AMPA did not improve either. These data suggest that the facilitation of phasic AMPA-R activation by physiologically-released glutamate is the key to enhancing synaptic and cognitive functions, and nonselective activation of resting AMPA-Rs may negatively affect this process. Importantly, TAK-653 had a wide safety margin against convulsion; TAK-653 showed a 419-fold (plasma Cmax) and 1017-fold (AUC plasma) margin in rats. These findings provide insight into a therapeutically important aspect of AMPA-R potentiation.

Translational CNS Steady-State Drug Disposition Model in Rats, Monkeys, and Humans for Quantitative Prediction of Brain-to-Plasma and Cerebrospinal Fluid-to-Plasma Unbound Concentration Ratios.

Capturing unbound drug exposure in the brain is crucial to evaluate pharmacological effects for drugs acting on the central nervous system. However, to date, there are no reports of validated prediction models to determine the brain-to-plasma unbound concentration ratio (Kp,uu,brain) as well as the cerebrospinal fluid (CSF)-to-plasma unbound concentration ratio (Kp,uu,CSF) between humans and other species. Here, we developed a translational CNS steady-state drug disposition model to predict Kp,uu,brain and Kp,uu,CSF across rats, monkeys, and humans by estimating the relative activity factors (RAF) for MDR1 and BCRP in addition to scaling factors (γ and σ) using the molecular weight, logD, CSF bulk flow, and in vitro transport activities of these transporters. In this study, 68, 26, and 28 compounds were tested in the rat, monkey, and human models, respectively. Both the predicted Kp,uu,brain and Kp,uu,CSF values were within the 3-fold range of the observed values (71, 73, and 79%; 79, 88, and 78% of the compounds, respectively), indicating successful prediction of Kp,uu,brain and Kp,uu,CSF in the three species. The overall predictivity of the RAF approach is consistent with that of the relative expression factor (REF) approach. As the established model can predict Kp,uu,brain and Kp,uu,CSF using only in vitro and physicochemical data, this model would help avoid ethical issues related to animal use and improve CNS drug discovery workflow.

Direct Comparison of the Prediction of the Unbound Brain-to-Plasma Partitioning Utilizing Machine Learning Approach and Mechanistic Neuropharmacokinetic Model.

The mechanistic neuropharmacokinetic (neuroPK) model was established to predict unbound brain-to-plasma partitioning (Kp,uu,brain) by considering in vitro efflux activities of multiple drug resistance 1 (MDR1) and breast cancer resistance protein (BCRP). Herein, we directly compare this model to a computational machine learning approach utilizing physicochemical descriptors and efflux ratios of MDR1 and BCRP-expressing cells for predicting Kp,uu,brain in rats. Two different types of machine learning techniques, Gaussian processes (GP) and random forest regression (RF), were assessed by the time and cluster-split validation methods using 640 internal compounds. The predictivity of machine learning models based on only molecular descriptors in the time-split dataset performed worse than the cluster-split dataset, whereas the models incorporating MDR1 and BCRP efflux ratios showed similar predictivity between time and cluster-split datasets. The GP incorporating MDR1 and BCRP in the time-split dataset achieved the highest correlation (R2 = 0.602). These results suggested that incorporation of MDR1 and BCRP in machine learning is beneficial for robust and accurate prediction. Kp,uu,brain prediction utilizing the neuroPK model was significantly worse compared to machine learning approaches for the same dataset. We also investigated the predictivity of Kp,uu,brain using an external independent test set of 34 marketed drugs. Compared to machine learning models, the neuroPK model showed better predictive performance with R2 of 0.577. This work demonstrates that the machine learning model for Kp,uu,brain achieves maximum predictive performance within the chemical applicability domain, whereas the neuroPK model is applicable more widely beyond the chemical space covered in the training dataset.

Discovery of a Potent and Orally Bioavailable Melatonin Receptor Agonist.

To develop potent and orally bioavailable melatonin receptor (MT1 and MT2) agonists, a novel series of 5-6-5 tricyclic derivatives was designed, synthesized, and evaluated. The synthesized indeno[5,4-d][1,3]oxazole, cyclopenta[c]pyrazolo[1,5-a]pyridine, indeno[5,4-d][1,3]thiazole, and cyclopenta[e]indazole derivatives showed potent binding affinities for MT1/MT2 receptors. Further optimization of these derivatives based on their metabolic stability in human hepatic microsomes revealed that (S)-3b ((S)-N-[2-(2-methyl-7,8-dihydro-6H-indeno[5,4-d][1,3]oxazol-8-yl)ethyl]acetamide) was a potent MT1 and MT2 ligand (MT1, Ki = 0.031 nM; MT2, Ki = 0.070 nM) with good metabolic stability in human hepatic microsomes. Moreover, compound (S)-3b showed good BBB permeability in rats, and its in vivo pharmacological effects were confirmed by its sleep-promotion ability in cats.

Discovery of a Novel and Brain-Penetrant O-GlcNAcase Inhibitor via Virtual Screening, Structure-Based Analysis, and Rational Lead Optimization.

O-GlcNAcase (OGA) has received increasing attention as an attractive therapeutic target for tau-mediated neurodegenerative disorders; however, its role in these pathologies remains unclear. Therefore, potent chemical tools with favorable pharmacokinetic profiles are desirable to characterize this enzyme. Herein, we report the discovery of a potent and novel OGA inhibitor, compound 5i, comprising an aminopyrimidine scaffold, identified by virtual screening based on multiple methodologies combining structure-based and ligand-based approaches, followed by sequential optimization with a focus on ligand lipophilicity efficiency. This compound was observed to increase the level of O-GlcNAcylated protein in cells and display suitable pharmacokinetic properties and brain permeability. Crystallographic analysis revealed that the chemical series bind to OGA via characteristic hydrophobic interactions, which resulted in a high affinity for OGA with moderate lipophilicity. Compound 5i could serve as a useful chemical probe to help establish a proof-of-concept of OGA inhibition as a therapeutic target for the treatment of tauopathies.

Prediction of Oral Pharmacokinetics Using a Combination of In Silico Descriptors and In Vitro ADME Properties.

Accurate prediction of oral pharmacokinetics remains challenging. This study investigated quantitative approaches for the prediction of the area under the plasma concentration-time curve after oral administration (AUCp,oral) to rats using the in vitro-in vivo extrapolation (IVIVE), in silico model using machine learning approaches and the combination of the in silico model and in vitro data. A set of 595 structurally diverse compounds with determined AUCp,oral at 1 mg/kg, in vitro intrinsic clearance (CLint), an unbound fraction in plasma (fu,p) in rats, and kinetic solubility at pH 6.8 was used for this assessment. Prediction models developed by two different types of machine learning techniques (i.e., random forest regression and Gaussian processes) were evaluated using three validation methods implementing the time and cluster-split training and test set and fivefold cross-validation. The developed machine learning models have a square of correlation coefficient (R2) in the range of 0.381-0.685 with 33-45% of the compounds being predicted within 2-fold of the observed AUCp,oral value. The predictivity was improved by incorporating CLint, fu,p, and solubility as explanatory variables with R2 = 0.554-0.743. In cases where extraction by the liver is the main elimination pathway and intestinal extraction is negligible, AUCp,oral can be expressed by dose, CLint, and fu,p based on a well-stirred model. By using this conventional IVIVE approach, only 1.7-5.0% of compounds were predicted within the 2-fold error with R2 = 0.354-0.487. Two empirical scaling factors (ESFs) determined by linear regression analysis and machine learning approaches improved the predictivity of AUCp,oral with 33-44% predicted within twofold variability. The IVIVE using ESF predicted by random forest regression showed better predictivity of AUCp,oral with R2 = 0.471-0.618, while it still showed lower predictivity than machine learning approaches applied directly to AUCp,oral prediction. This study demonstrated that the combination of in silico and in vitro parameters is useful to improve the predictivity of the machine learning model for rat AUCp,oral and supports consideration for predicting AUCp,oral for human and other non-clinical species in a similar manner.

Characterization of plasma protein binding in two mouse models of humanized liver, PXB mouse and humanized TK-NOG mouse.

The unbound fractions in plasma (f up) in two mouse models of humanized liver mice, PXB and humanized TK-NOG mice, were compared with human f up values using equilibrium dialysis method. A good relationship between f up values obtained from PXB mice and humans was observed; the f up of 34/39 compounds (87.2%) in PXB mice were within 3-fold of human f up. In contrast, a weak correlation was observed between human and humanized TK-NOG mouse f up values; the f up of 15/24 compounds (62.5%) in humanized TK-NOG mice were within 3-fold of human f up. As different profiles of plasma protein binding (PPB) profiles were observed between PXB and humanized TK-NOG mice, f up evaluation is necessary in each mouse model to utilize these humanized liver mice for pharmacological, drug-drug interaction (DDI), and toxicity studies. The unbound fraction in the mixed plasma of human and SCID mouse plasma (85:15) was well correlated with f up in PXB mice (38/39 compounds within a 3-fold). Thus, this artificial PXB mouse plasma could be used to evaluate PPB.

Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-Up Approach Using In Vitro Assay.

The in vitro-in vivo extrapolation (IVIVE) approach for predicting total plasma clearance (CLtot) has been widely used to rank order compounds early in discovery. More recently, a computational machine learning approach utilizing physicochemical descriptors and fingerprints calculated from chemical structure information has emerged, enabling virtual predictions even earlier in discovery. Previously, this approach focused more on in vitro intrinsic clearance (CLint) prediction. Herein, we directly compare these two approaches for predicting CLtot in rats. A structurally diverse set of 1114 compounds with known in vivo CLtot, in vitro CLint, and plasma protein binding was used as the basis for this evaluation. The machine learning models were assessed by validation approaches using the time- and cluster-split training and test sets, and five-fold cross validation. Assessed by five-fold validation, the random forest regression (RF) and radial basis function (RBF) models demonstrated better prediction performance in eight attempted machine learning models. The CLtot values predicted by the RF and RBF models were within two-fold of the observed values for 67.7 and 71.9% of cluster-split test set compounds, respectively, while the predictivity was worse in the time-split dataset. The predictivity of both models tended to be improved by incorporating in vitro parameters, unbound fraction in plasma (fu,p), and CLint. CLtot prediction utilizing in vitro CLint and the well-stirred model, correcting for the fraction unbound in blood, was substantially worse compared to machine learning approaches for the same cluster-split test set. The reason that CLtot is underestimated by IVIVE is not fully explained by considering the calculated microsomal unbound fraction (cfu,mic), extended clearance classification system (ECCS), and omitting high clearance compounds in excess of hepatic blood flow. The analysis suggests that in silico machine learning models may have the power to reduce reliance on or replace in vitro and in vivo studies for chemical structure optimization in early drug discovery.

Investigation of MDR1-overexpressing cell lines to derive a quantitative prediction approach for brain disposition using in vitro efflux activities.

MDR1-overexpressing Lilly Laboratories cell porcine kidney 1 cells (LLC-PK1-MDR1) and Madin-Darby canine kidney cells (MDCK-MDR1) are widely used in drug discovery to evaluate the in vivo relevance of MDR1-mediated efflux. However, as the in vitro efflux ratio (ER) of these cell lines are variable among research facilities, the in vitro ER of these cell lines that would affect quantitative predictivity of brain disposition has not been fully clarified. The aim of this study was to examine the effect of ER on the quantitative predictivity of brain disposition toward compounds with MDR1 and/or breast cancer resistant protein (BCRP) liabilities. Test compounds including internal molecules and five typical substrates of MDR1 and/or BCRP were assessed via an in vitro transporter assay to determine the corrected flux ratio (CFR) and an in vivo animal study using wild-type (WT) and Mdr1a (-/-)/Bcrp(-/-) (dual KO) rats. To assess the in vivo ER for MDR1, the two cell lines LLC-PK1-MDR1 and MDCK-MDR1 were used. After intravenously administering 29 test compounds to rats, the Kp,brain ratio (ratio of Kp,brain,WT to Kp,brain,dual KO), which is considered to be the unbound plasma-to-brain ratio (Kp,uu,brain) that does not require correction for protein binding in both plasma and brain, was determined by measuring their concentrations in the plasma and brain. The Kp,brain ratio of these compounds was predicted by fitting scaling factor that was extrapolated from the in vitro to in vivo ER for MDR1 and BCRP, defined as α and ß, respectively. Kp,brain ratio values of 83% and 68% of compounds were predicted by using MDCK-MDR1 and LLC-PK1-MDR1, respectively, within a 2-fold range of the actual corresponding values. The α predicted from CFRs of MDCK-MDR1 was 47-fold smaller than that of LLC-PK1-MDR1; however, a dramatic change in ß was not observed. This result appears to be consistent with the data of in vitro transport activity of MDR1, which was estimated to be ~28-fold higher in MDCK-MDR1 than in LLC-PK1-MDR1 by correlation analysis with CFR. Through this study, we revealed that 1) brain disposition in rats was well-predicted by considering the in vitro efflux activities for both MDR1 and BCRP, and 2) MDCK-MDR1 was the superior cell line for the quantitative prediction of brain disposition.

Direct Drug Delivery of Low-Permeable Compounds to the Central Nervous System Via Intranasal Administration in Rats and Monkeys.

PURPOSE: Intranasal administration enhances drug delivery to the brain by allowing targeted-drug delivery. Here, we investigated the properties that render a compound suitable for intranasal administration, and the differences between rodents and non-human primates in delivery to the brain. METHODS: The delivery of 10 low-permeable compounds to the brain, including substrates of efflux drug transporters expressed in the blood-brain barrier (didanosine, metformin, zolmitriptan, cimetidine, methotrexate, talinolol, ranitidine, atenolol, furosemide, and sulpiride) and two high-permeable compounds (ropinirole and midazolam) was evaluated following intranasal and intravenous administration in rats. Six of the 12 compounds (metformin, cimetidine, methotrexate, talinolol, sulpiride, and ropinirole) were also evaluated in monkeys, which have a similar nasal cavity anatomical structure to humans. RESULTS: In rats, most of the low-permeable compounds displayed an obvious increase in the brain/plasma concentration ratio (Kp) by intranasal administration (despite their substrate liability for efflux drug transporters); this was not observed with the high-permeable compounds. Similarly, intranasal administration increased Kp for all low-permeable compounds in monkeys. CONCLUSIONS: Compound permeability is a key determinant of Kp increase by intranasal administration. This route of administration is more beneficial for low-permeable compounds and enhances their delivery to the brain in rodents and non-human primates.

Prediction of human pharmacokinetics of long half-life compounds using chimeric mice with humanised liver.

1. The prediction of human pharmacokinetic (PK) parameters is an important theme to select drug candidates from preclinical studies. It is essential to improve the prediction accuracy of compound half-life (t1/2) in humans. In this study, the predictability of t1/2 in humans using PXB mice®, chimeric mice with humanised liver, was assessed using 14 compounds showing long t1/2 in humans. 2. After intravenous administration of the compounds to PXB mice, the plasma concentration-time profiles were fitted using one- or two-compartment models and the human clearance (CLt) and distribution volume (Vdss) were predicted from single-species scaling. Using the obtained parameters, the t1/2 in humans was predicted. Using PXB mice, the predicted t1/2 values of 71.4% of the compounds were within two-fold of the actual values. Meanwhile, based on predictions using SCID mice, the host strain of the PXB mice, only 7.1% of tested compounds were within two-fold. 3. In conclusion, we demonstrated the novel utility of PXB mice for human PK predictions of compounds having long t1/2 in humans.

TAK-137, an AMPA-R potentiator with little agonistic effect, has a wide therapeutic window.

Activation of α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid receptor (AMPA-R) is a promising strategy to treat psychiatric and neurological diseases if issues of bell-shaped response and narrow safety margin against seizure can be overcome. Here, we show that structural interference at Ser743 in AMPA-R is a key to lower the agonistic effect of AMPA-R potentiators containing dihydropyridothiadiazine 2,2-dioxides skeleton. With this structural insight, TAK-137, 9-(4-phenoxyphenyl)-3,4-dihydropyrido[2,1-c][1,2,4]thiadiazine 2,2-dioxide, was discovered as a novel AMPA-R potentiator with a lower agonistic effect than an AMPA-R potentiator LY451646 ((R)-N-(2-(4'-cyanobiphenyl-4-yl)propyl)propane-2-sulfonamide) in rat primary neurons. TAK-137 induced brain-derived neurotrophic factor in neurons in rodents and potently improved cognition in both rats and monkeys. Compared to LY451646, TAK-137 had a wider safety margin against seizure in rats. TAK-137 enhanced neural progenitor proliferation over a broader range of doses in rodents. Thus, TAK-137 is a promising AMPA-R potentiator with potent procognitive effects and lower risks of bell-shaped response and seizure. These data may open the door for the development of AMPA-R potentiators as therapeutic drugs for psychiatric and neurological diseases.

Application of unbound liver-to-plasma concentration ratio to quantitative projection of cytochrome P450-mediated drug-drug interactions using physiologically based pharmacokinetic modelling approach.

1. This study evaluated the prediction accuracy of cytochrome P450 (CYP)-mediated drug-drug interaction (DDI) using minimal physiologically-based pharmacokinetic (PBPK) modelling incorporating the hepatic accumulation factor of an inhibitor (i.e. unbound liver/unbound plasma concentration ratio [Kp,uu,liver]) based on 22 clinical DDI studies. 2. Kp,uu,liver values were estimated using three methods: (1) ratio of cell-to-medium ratio in human cryopreserved hepatocytes (C/Mu) at 37 °C to that on ice (Kp,uu,C/M), (2) multiplication of total liver/unbound plasma concentration ratio (Kp,u,liver) estimated from C/Mu at 37 °C with unbound fraction in human liver homogenate (Kp,uu,cell) and (3) observed Kp,uu,liver in rats after intravenous infusion (Kp,uu,rat). 3. PBPK model using each Kp,uu,liver projected the area under the curve (AUC) increase of substrates more accurately than the model assuming a Kp,uu,liver of 1 for the average fold error and root mean square error did. Particularly, the model with a Kp,uu,liver of 1 underestimated the AUC increase of triazolam following co-administration with CYP3A4 inhibitor itraconazole by five-fold, whereas the AUC increase projected using the model incorporating the Kp,uu,C/M, Kp,uu,cell, or Kp,uu,rat of itraconazole and hydroxyitraconazole was within approximately two-fold of the actual value. 4. The results indicated that incorporating Kp,uu,liver into the PBPK model improved the accuracy of DDI projection.

Impact of P-Glycoprotein on Intestinal Absorption of an Inhibitor of Apoptosis Protein Antagonist in Rats: Mechanisms of Nonlinear Pharmacokinetics and Food Effects.

PURPOSE: This study was designed to investigate the effects of P-glycoprotein (P-gp) expressed in the intestine on the nonlinear pharmacokinetics (PK) of T-3256336, an inhibitor of apoptosis protein inhibitor, and food effects on its bioavailability in rats. METHODS: To investigate the factors that contribute to nonlinear PK of T-3256336 in the intestine and liver, rats double-cannulated in the portal vein and femoral artery (PS rats) were used. FaFg (Fa, absorption ratio; Fg, intestinal availability) and hepatic availability (Fh) were simultaneously evaluated based on the difference between the portal and systemic blood area under the concentration-time curve (AUC). Elacridar was used as a P-gp inhibitor to assess the impact of P-gp on the intestinal absorption. RESULTS: After oral administration of T-3256336 to PS rats at 3 and 30 mg/kg, FaFg value increased with dose escalation, whereas Fh value was nearly constant. Moreover, co-administration of elacridar resulted in a 5-fold increase in the FaFg value at 3 mg/kg. The AUC value of T-3256336 under fed conditions was 3-fold lower than that under fasted conditions. This food effect on the oral bioavailability (BA) was reduced by concomitant administration of elacridar. CONCLUSION: P-gp expressed in the intestine would cause nonlinear PK and a food effect on BA of T-3256336 in rats.