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.

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.

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.

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.

Discovery of 1,2,3,4-tetrahydropyrimido[1,2-a]benzimidazoles as novel class of corticotropin releasing factor 1 receptor antagonists.

A new class of corticotropin releasing factor 1 (CRF1) receptor antagonists characterized by a tricyclic core ring was designed and synthesized. Novel tricyclic derivatives 2a-e were designed as CRF1 receptor antagonists based on conformation analysis of our original 2-anilinobenzimidazole CRF1 receptor antagonist. The synthesized tricyclic derivatives 2a-e showed CRF1 receptor binding activity with IC50 values of less than 400 nM, and the 1,2,3,4-tetrahydropyrimido-[1,2-a]benzimidazole derivative 2e was selected as a lead compound with potent in vitro CRF1 receptor binding activity (IC50 = 7.1 nM). To optimize the pharmacokinetic profiles of lead compound 2e, we explored suitable substituents on the 1-position and 6-position, leading to the identification of compound 42c-R, which exhibited potent CRF1 receptor binding activity (IC50 = 58 nM) with good oral bioavailability (F = 68% in rats). Compound 42c-R exhibited dose-dependent inhibition of [125I]-CRF binding in the frontal cortex (5 and 10 mg/kg, p.o.) as well as suppression of locomotor activation induced by intracerebroventricular administration of CRF in rats (10 mg/kg, p.o.). These results suggest that compound 42c-R successfully binds CRF1 receptors in the brain and exhibits the potential to be further examined for clinical studies.

Discovery of 4-chloro-2-(2,4-dichloro-6-methylphenoxy)-1-methyl-7-(pentan-3-yl)-1H-benzimidazole, a novel CRF1 receptor antagonist.

Compound 1 exhibits potent binding inhibition activity against a corticotropin-releasing factor 1 (CRF1) receptor (IC50=9.5nM) and in vitro antagonistic activity (IC50=88nM) but is rapidly metabolized by human hepatic microsomes (182µL/min/mg). Here we identified metabolically stable compounds with potent CRF binding inhibitory activity. Structure-activity relationship (SAR) studies considering in vitro metabolic stability revealed that 4-chloro-2-(2,4-dichloro-6-methylphenoxy)-1-methyl-7-(pentan-3-yl)-1H-benzimidazole 24d was more stable in human microsomes (87µL/min/mg) than compound 1. Compound 24d demonstrated potent CRF binding inhibitory activity (IC50=4.1nM), in vitro antagonistic activity (IC50=44nM), and slow dissociation from the CRF1 receptor. Orally administered compound 24d (6-24µmol/kg) showed ex vivo CRF1 receptor binding in the rat pituitary, olfactory bulb, and frontal cortex and suppressed stress-induced adrenocorticotropic hormone (ACTH) secretion. In this report, we discuss SAR studies on the metabolic stability as well as CRF binding inhibitory activity of the benzimidazole series as CRF1 receptor antagonists and the pharmacological profiles of compound 24d.

Species differences and substrate specificity of CYP3A heteroactivation by efavirenz.

1. The purpose of this study was to clarify species differences in the heteroactivation of CYP3A substrates by efavirenz, which is known from clinical studies to activate midazolam 1'-hydroxylation, and to assess the feasibility of an animal model. 2. In monkey and human liver microsomes, efavirenz activated CYP3A-mediated midazolam 1'-hydroxylation, but had no effect in rat liver microsomes. The activating effect of efavirenz was also observed with recombinant human CYP3A4 and CYP3A5. Midazolam 4-hydroxylation, testosterone 6ß-hydroxylation and the oxidation of nifedipine were not activated by efavirenz in any of the microsomes. 3. In an in vivo study using monkeys, the AUC ratio of midazolam/1'-hydroxymidazolam was reduced from 0.85 to 0.30 by efavirenz treatment, which was comparable to that obtained in clinical studies. However, the AUC changes of midazolam caused by efavirenz were smaller than those observed in clinical results, therefore the effect of efavirenz on monkeys was not completely consistent with that seen in humans. 4. In conclusion, this is the first report that efavirenz specifically activates midazolam 1'-hydroxylation only in monkey and human liver microsomes, revealing marked species differences and high substrate specificity in the heteroactivation. A further study is required to clarify whether this in vitro result reflects the in vivo situation.

Design, synthesis, and biological evaluation of a series of piperazine ureas as fatty acid amide hydrolase inhibitors.

A series of piperazine ureas were designed, synthesized, and evaluated for their potential as novel orally efficacious fatty acid amide hydrolase (FAAH) inhibitors for the treatment of neuropathic and inflammatory pain. We carried out an optimization study of compound 5 to improve its in vitro FAAH inhibitory activity, and identified the 2-pyrimidinylpiperazine derivative 21d with potent inhibitory activity, favorable DMPK profile and brain permeability. Compound 21d showed robust and dose-dependent analgesic efficacy in animal models of both neuropathic and inflammatory pain.

Risk assessment of drug-drug interactions using hepatocytes suspended in serum during the drug discovery process.

1. This study optimized the reported approach for the prediction of drug-drug interactions (DDIs) using hepatocytes suspended in serum (HHSS) and provided a practical usage of HHSS in the early and late phases of drug discovery. 2. First, the IC50 was determined using HHSS and evaluated as a qualitative index for DDI risks in the early phase. A retrospective study on clinical DDI cases revealed that inhibitors with IC50 < 100 µmol/L caused clinical DDIs while those with IC50 > 100 µmol/L showed weak or no potential for DDIs. Meanwhile, a pragmatic cutoff value could not be determined using previously reported Ki values of recombinant human cytochrome P450s. 3. Second, for a more substantial DDI risk assessment in the later phase, quantitative predictions of clinical DDI based on a static model were attempted by optimizing the most appropriate inhibitor concentration ([I]). The use of hepatic input plasma concentrations as a surrogate for [I] achieved the most successful predictions of the magnitude of increase in the AUC (within a 2-fold range of the observed values for 93.8% of inhibitors). 4. Through this study, we proposed the practical application of HHSS for an effective workflow to explore and profile candidates with less DDI liability.

Design, synthesis, and biological activities of novel hexahydropyrazino[1,2-a]indole derivatives as potent inhibitors of apoptosis (IAP) proteins antagonists with improved membrane permeability across MDR1 expressing cells.

We previously reported octahydropyrrolo[1,2-a]pyrazine derivative 2 (T-3256336) as a potent antagonist for inhibitors of apoptosis (IAP) proteins. Because compound 2 was susceptible to MDR1 mediated efflux, we developed another scaffold, hexahydropyrazino[1,2-a]indole, using structure-based drug design. The fused benzene ring of this scaffold was aimed at increasing the lipophilicity and decreasing the basicity of the scaffold to improve the membrane permeability across MDR1 expressing cells. We established a chiral pool synthetic route to yield the desired tricyclic chiral isomers. Chemical modification of the core scaffold led to a representative compound 50, which showed strong inhibition of IAP binding (X chromosome-linked IAP [XIAP]: IC50 23 nM and cellular IAP [cIAP]: IC50 1.1 nM) and cell growth inhibition (MDA-MB-231 cells: GI50 2.8 nM) with high permeability and low potential of MDR1 substrate.

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.

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.

Evaluation of cytochrome P450-mediated drug-drug interactions based on the strategies recommended by regulatory authorities.

Herein, we aimed to evaluate the recently proposed risk assessment strategies of a cytochrome P450 (CYP) mediated drug-drug interaction (DDI) according to the European Medicines Evaluation Agency (EMEA) draft guideline, and discuss the differences between this guideline and the Food and Drug Administration (FDA) draft guidance. A retrospective study on reported 35 clinical DDI cases revealed that the EMEA assessment successfully predicts moderate-to-strong DDIs, i.e. drugs that cause more than 2-fold increase in the area under the curve in the presence and absence of CYP inhibitor (AUC(i)/AUC); however, EMEA tends to overlook weak DDIs with AUC(i)/AUC ≤ 2 to > 1.25. For CYP3A4 inhibitors, even clinically insignificant DDIs were overemphasized if the intestinal DDI is considered. The differences between unbound fraction in plasma and microsomes account for the discrepancies in DDI risk assessment results between EMEA and FDA assessments. Comparing two assessment results for CYP2D6 and CYP2C9 inhibitors, the FDA assessment suggested potential DDI risks for sulphinpyrazone and amitriptyline, while the EMEA assessment indicated no potential risk for these drugs. Through a retrospective study, we showed practical differences in the DDI assessment strategies of EMEA and FDA and suggested improvements in their current strategies.

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.

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.