Prediction of Total Drug Clearance in Humans Using Animal Data: Proposal of a Multimodal Learning Method Based on Deep Learning

Research into pharmacokinetics plays an important role in the development process of new drugs. Accurately predicting human pharmacokinetic parameters from preclinical data can increase the success rate of clinical trials. Since clearance (CL) which indicates the capacity of the entire body to process a drug is one of the most important parameters, many methods have been developed. However, there are still rooms to be improved for practical use in drug discovery research; "improving CL prediction accuracy" and "understanding the chemical structure of compounds in terms of pharmacokinetics". To improve those, this research proposes a multimodal learning method based on deep learning that takes not only the chemical structure of a drug but also rat CL as inputs. Good results were obtained compared with the conventional animal scale-up method; the geometric mean fold error was 2.68 and the proportion of compounds with prediction errors of 2-fold or less was 48.5%. Furthermore, it was found to be possible to infer the partial structure useful for CL prediction by a structure contributing factor inference method. The validity of these results of structural interpretation of metabolic stability was confirmed by chemists

Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data

Pharmacokinetic research plays an important role in the development of new drugs. Accurate predictions of human pharmacokinetic parameters are essential for the success of clinical trials. Clearance (CL) and volume of distribution (Vd) are important factors for evaluating pharmacokinetic properties, and many previous studies have attempted to use computational methods to extrapolate these values from nonclinical laboratory animal models to human subjects. However, it is difficult to obtain sufficient, comprehensive experimental data from these animal models, and many studies are missing critical values. This means that studies using nonclinical data as explanatory variables can only apply a small number of compounds to their model training. In this study, we perform missing-value imputation and feature selection on nonclinical data to increase the number of training compounds and nonclinical datasets available for these kinds of studies. We could obtain novel models for total body clearance (CLtot) and steady-state Vd (Vdss) (CLtot: geometric mean fold error [GMFE], 1.92; percentage within 2-fold error, 66.5%; Vdss: GMFE, 1.64; percentage within 2-fold error, 71.1%). These accuracies were comparable to the conventional animal scale-up models. Then, this method differs from animal scale-up methods because it does not require animal experiments, which continue to become more strictly regulated as time passes

Development and evaluation of automated ultrasonographic detection of bladder diameter for estimation of bladder urine volume

Bladder urine volume has been estimated using an ellipsoid method based on triaxial measurements of the bladder extrapolated from two-dimensional ultrasound images. This study aimed to automate this process and to determine the accuracy of the automated estimation method for normal and small amounts of urine. A training set of 81 pairs of transverse and longitudinal ultrasound images were collected from healthy volunteers on a tablet-type ultrasound device, and an automatic detection tool was developed using them. The tool was evaluated using paired transverse/longitudinal ultrasound images from 27 other healthy volunteers. After imaging, the participants voided and their urine volume was measured. For determining accuracy, regression coefficients were calculated between estimated bladder volume and urine volume. Further, sensitivity and specificity for 50 and 100 ml bladder volume thresholds were evaluated. Data from 50 procedures were included. The regression coefficient was very similar between the automatic estimation (β = 0.99, R2 = 0.96) and manual estimation (β = 1.05, R2 = 0.97) methods. The sensitivity and specificity of the automatic estimation method were 88.5% and 100.0%, respectively, for 100 ml and were 94.1% and 100.0%, respectively, for 50 ml. The newly-developed automated tool accurately and reliably estimated bladder volume at two different volume thresholds of approximately 50 ml and 100 ml

Neuromuscular synaptic transmission in aged ganglioside-deficient mice

Gangliosides are sialylated glycosphingolipids that are present in high density on neuronal membranes, especially at synapses, where they are assumed to play functional or modulating roles. Mice lacking GM2/GD2-synthase express only the simple gangliosides GD3 and GM3 and develop progressive motor behaviour deficits upon ageing, apparently due to failing complex ganglioside-dependent maintenance and/or repair processes or, alternatively, toxic GM3/GD3 accumulation. We investigated the function of neuromuscular junctions (NMJs) of aged (>9 month-old) GM2/GD2-synthase null-mutant mice, because synaptic dysfunction might develop with age and could potentially contribute to the late-onset motor phenotype. In addition, we studied NMJs of old mice lacking GD3-synthase (expressing only O- and a-series gangliosides), which do not show an overt neurological phenotype but may develop subclinical synaptic deficits. Detailed electrophysiological analyses showed subtle changes in presynaptic neurotransmitter release. Acetylcholine release at 40 Hz nerve stimulation at aged GM2/GD2-synthase null-mutant NMJs ran down slightly more pronounced than at wild-type NMJs, and spontaneous acetylcholine release rate at GD3-synthase null-mutant NMJs was somewhat higher than at wild-type, selectively at 25 degrees C bath temperature. Interestingly, we observed faster kinetics of postsynaptic electrophysiological responses at aged GD3-synthase null-mutant NMJs, not previously seen by us at NMJs of young GD3-synthase null-mutants or other types of (aged or young) ganglioside-deficient mice. These kinetic changes might reflect a change in postsynaptic acetylcholine receptor behaviour. Our data indicate that it is highly unlikely that transmission failure at NMJs contributes to the progressive motor defects of aged GM2/GD2-synthase null-mutants and that, despite some kinetic changes of synaptic signals, neuromuscular transmission remains successful in aged GD3-synthase null-mutant mice. Apparently, mutual redundancy of the different gangliosides in supporting presynaptic function, as observed previously by us in young mice, remains adequate upon ageing or, alternatively, gangliosides have only relatively little direct impact on neuromuscular synaptic function, even in aged mice. (C) 2009 Elsevier Inc. All rights reserve

Nationwide retrospective observational study of idiopathic dendriform pulmonary ossification : clinical features with a progressive phenotype

Background: Diffuse pulmonary ossification is a specific lung condition that is accompanied by underlying diseases. However, idiopathic dendriform pulmonary ossification (IDPO) is extremely rare, and the clinical features remain unclear. In this study, we aimed to report the clinical characteristics of IDPO. Methods: We conducted a nationwide survey of patients with IDPO from 2017 to 2019 in Japan and evaluated the clinical, radiological, and histopathological findings of patients diagnosed with IDPO. Results: Twenty-two cases of IDPO were identified. Most subjects (82%) were male, aged 22-56 years (mean (SD), 37.9 (9.1)) at diagnosis. Nearly 80% of the subjects were asymptomatic, and the condition was discovered during a medical check-up. However, 36% of the subjects showed a decline in forced vital capacity (%FVC) predicted <80% at diagnosis. The typical radiological features of high-resolution CT (HRCT) are calcified branching structures that are predominantly distributed in the lower lung fields without any other conspicuous finding. Histopathological analysis also showed dendriform ossified lesions from the intraluminal areas to interstitial areas. Notably, during the follow-up period of 20 years, disease progression was found in 88% on HRCT and more than 50% on pulmonary function tests (FVC and/or forced expiratory volume in 1s). Two cases with rapid decline of 10% /year in %FVC predicted were observed.)) at diagnosis. Nearly 80% of the subjects were asymptomatic, and the condition was discovered during a medical check-up. However, 36% of the subjects showed a decline in forced vital capacity (%FVC) predicted <80% at diagnosis. The typical radiological features of high-resolution CT (HRCT) are calcified branching structures that are predominantly distributed in the lower lung fields without any other conspicuous finding. Histopathological analysis also showed dendriform ossified lesions from the intraluminal areas to interstitial areas. Notably, during the follow-up period of 20 years, disease progression was found in 88% on HRCT and more than 50% on pulmonary function tests (FVC and/or forced expiratory volume in 1s). Two cases with rapid decline of 10% /year in %FVC predicted were observed. )) at diagnosis. Nearly 80% of the subjects wereasymptomatic, and the condition was discovered during a medical check-up. However, 36% of the subjects showed a decline in forced vital capacity (%FVC) predicted <80% at diagnosis. The typical radiological features of high-resolution CT (HRCT) are calcified branching structures that are predominantly distributed in the lower lung fields without any other conspicuous finding. Histopathological analysis also showed dendriform ossified lesions from the intraluminal areas to interstitial areas. Notably, during the follow-up period of 20 years, disease progression was found in 88% on HRCT and more than 50% on pulmonary function tests (FVC and/or forced expiratory volume in 1s). Two cases with rapid decline of 10% /year in %FVC predicted were observed. Conclusions: IDPO develops at a young age with gradually progressive phenotype. Further research and long-term (>20 years) follow-up are required to clarify the pathogenesis and clinical findings in IDPO

Combined Data-driven and Mechanism-based Approaches for Human-Intestinal-Absorption Prediction in Early Drug-Discovery Stage

It is important to precisely predict the intestinal absorption ratio (Fa) at an early stage in the discovery of orally available drugs because it directly influences drug efficacy. Gastrointestinal unified theoretical framework (GUTFW) and machine learning (ML) are commonly used to predict the percentage of Fa. In GUTFW, the Fa of a drug is estimated using an equation based on the mechanism of human intestinal absorption, dose, solubility, membrane permeability, and dispersion of the drug. The experimental values of these in vitro parameters are required to accurately predict Fa. However, most of these values are unavailable at early stages of development. ML uses a dataset of the observed Fa values of many drugs in humans. In most previously published ML approaches, the dose information for each drug has been ignored. However, Fa can vary in a dose-dependent manner through changes in solubility, membrane permeability, and dispersion. To overcome these problems, we combined GUTFW and ML to compensate for each defect. We collected published data on the chemical structures of 460 drugs, including Fa and dose amounts. The key parameters of the GUTFW (Do, dose number; Dn, dispersion number; Pn, permeation number), solubility, membrane permeability, and structural descriptors were calculated and used as explanatory variables for ML. ML algorithms, namely, the random forest (RF) and message-passing neural network (MPNN; Chemprop), were investigated. The GUTFW model was compared to the conventional ML method, which uses only structural descriptors, and combined ML method, which uses both structural descriptors and GUTFW parameters. In addition, using the Chemprop framework, we investigated important substructures of Fa. Our result suggested that combinational ML produced higher predictivity than the GUTFW model and conventional ML model in the test dataset (20% of the dataset) [R2 value and RMSE in combinational ML method: 0.611 and 19.7 (RF), 0.520 and 21.6 (Chemprop); in conventional ML: 0.339 and 25.4 (RF), 0.497 and 22.1 (Chemprop); in GUTFW: 0.353 and 31.9]. Additionally, most of the substructures indicated by the Chemprop framework were consistent with the common knowledge of medicinal chemistry. We developed an accurate prediction method for human Fa using a combination of data-driven ML and mechanism-based GUTFW, where the parameters could be calculated without experimental data, enabling the model to efficiently promote early drug discovery. Furthermore, some of the important substructures identified here were previously unknown and require further investigation