scholarly journals Predicting blood-to-plasma concentration ratios of drugs from chemical structures and volumes of distribution in humans

2021 ◽  
Author(s):  
Hideaki Mamada ◽  
Kazuhiko Iwamoto ◽  
Yukihiro Nomura ◽  
Yoshihiro Uesawa
Keyword(s):  
Plasma Concentration ◽  
Prediction Model ◽  
Correlation Coefficient ◽  
Prediction Models ◽  
Molecular Descriptors ◽  
Volume Of Distribution ◽  
Human Pharmacokinetics ◽  
Chemical Structures ◽  
Drug Concentrations ◽  
Concentration Ratios

Abstract Despite their importance in determining the dosing regimen of drugs in the clinic, only a few studies have investigated methods for predicting blood-to-plasma concentration ratios (Rb). This study established an Rb prediction model incorporating typical human pharmacokinetics (PK) parameters. Experimental Rb values were compiled for 289 compounds, offering reliable predictions by expanding the applicability domain. Notably, it is the largest list of Rb values reported so far. Subsequently, human PK parameters calculated from plasma drug concentrations, including the volume of distribution (Vd), clearance, mean residence time, and plasma protein binding rate, as well as 2702 kinds of molecular descriptors, were used to construct quantitative structure–PK relationship models for Rb. Among the evaluated PK parameters, logVd correlated best with Rb (correlation coefficient of 0.47). Thus, in addition to molecular descriptors selected by XGBoost, logVd was employed to construct the prediction models. Among the analyzed algorithms, artificial neural networks gave the best results. Following optimization using six molecular descriptors and logVd, the model exhibited a correlation coefficient of 0.64 and a root-mean-square error of 0.205, which were superior to those previously reported for other Rb prediction methods. Since Vd values and chemical structures are known for most medications, the Rb prediction model described herein is expected to be valuable in clinical settings. Graphical abstract

scholarly journals Construction of a prediction model for drug removal rate in hemodialysis based on chemical structures

2022 ◽  
Author(s):  
Kousuke Nishikiori ◽  
Kentaro Tanaka ◽  
Yoshihiro Uesawa
Keyword(s):  
Prediction Model ◽  
Model Validation ◽  
Prediction Models ◽  
Removal Rate ◽  
Structural Characteristics ◽  
Estimation Method ◽  
Quantitative Structure Activity Relationship ◽  
Pharmacokinetic Parameters ◽  
Qsar Analysis ◽  
Chemical Structures

Abstract In designing drug dosing for hemodialysis patients, the removal rate (RR) of the drug by hemodialysis is important. However, acquiring the RR is difficult, and there is a need for an estimation method that can be used in clinical settings. In this study, the RR predictive model was constructed using the RR of known drugs by quantitative structure–activity relationship (QSAR) analysis. Drugs were divided into a model construction drug set (75%) and a model validation drug set (25%). The RR was collected from 143 medicines. The objective variable (RR) and chemical structural characteristics (descriptors) of the drug (explanatory variable) were used to construct a prediction model using partial least squares (PLS) regression and artificial neural network (ANN) analyses. The determination coefficients in the PLS and ANN methods were 0.586 and 0.721 for the model validation drug set, respectively. QSAR analysis successfully constructed dialysis RR prediction models that were comparable or superior to those using pharmacokinetic parameters. Considering that the RR dataset contains potential errors, we believe that this study has achieved the most reliable RR prediction accuracy currently available. These predictive RR models can be achieved using only the chemical structure of the drug. This model is expected to be applied at the time of hemodialysis. Graphic Abstract

scholarly journals kGCN: A Graph-Based Deep Learning Framework for Chemical Structures

2020 ◽  
Author(s):  
Ryosuke Kojima ◽  
Shoichi Ishida ◽  
Masateru Ohta ◽  
Hiroaki Iwata ◽  
Teruki Honma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Prediction Models ◽  
Bayesian Optimization ◽  
Factors Affecting ◽  
Learning Framework ◽  
Chemical Structures ◽  
Programming Skills ◽  
Model Tuning ◽  
Interpretation Of Results

<div>Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of this resource for obtaining reasonable and reliable prediction results requires thorough understanding of GCN and programming. To leverage the power of GCN to benefit various users from chemists to cheminformaticians, an open-source GCN tool, kGCN, is introduced. To support the users with various levels of programming skills, kGCN includes three interfaces: a graphical user interface (GUI) employing KNIME for users with limited programming skills such as chemists, as well as command-line and Python library interfaces for users with advanced programming skills such as cheminformaticians. To support the three steps required for building a prediction model, i.e., pre-processing, model tuning, and interpretation of results, kGCN includes functions of typical pre-processing, Bayesian optimization for automatic model tuning, and visualization of the atomic contribution to prediction for interpretation of results. kGCN supports three types of approaches, single-task, multi-task, and multimodal predictions. The prediction of compound-protein interaction for four matrixmetalloproteases, MMP-3, -9, -12 and -13, in the inhibition assays is performed as a representative case study using kGCN. Additionally, kGCN provides the visualization of atomic contributions to the prediction. Such visualization is useful for the validation of the prediction models and the design of molecules based on the prediction model, realizing “explainable AI” for understanding the factors affecting AI prediction. kGCN is available at https://github.com/clinfo/kGCN.</div>

scholarly journals kGCN: A Graph-Based Deep Learning Framework for Chemical Structures

2020 ◽  
Author(s):  
Ryosuke Kojima ◽  
Shoichi Ishida ◽  
Masateru Ohta ◽  
Hiroaki Iwata ◽  
Teruki Honma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Prediction Models ◽  
Bayesian Optimization ◽  
Factors Affecting ◽  
Learning Framework ◽  
Chemical Structures ◽  
Programming Skills ◽  
Model Tuning ◽  
Interpretation Of Results

<div>Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of this resource for obtaining reasonable and reliable prediction results requires thorough understanding of GCN and programming. To leverage the power of GCN to benefit various users from chemists to cheminformaticians, an open-source GCN tool, kGCN, is introduced. To support the users with various levels of programming skills, kGCN includes three interfaces: a graphical user interface (GUI) employing KNIME for users with limited programming skills such as chemists, as well as command-line and Python library interfaces for users with advanced programming skills such as cheminformaticians. To support the three steps required for building a prediction model, i.e., pre-processing, model tuning, and interpretation of results, kGCN includes functions of typical pre-processing, Bayesian optimization for automatic model tuning, and visualization of the atomic contribution to prediction for interpretation of results. kGCN supports three types of approaches, single-task, multi-task, and multimodal predictions. The prediction of compound-protein interaction for four matrixmetalloproteases, MMP-3, -9, -12 and -13, in the inhibition assays is performed as a representative case study using kGCN. Additionally, kGCN provides the visualization of atomic contributions to the prediction. Such visualization is useful for the validation of the prediction models and the design of molecules based on the prediction model, realizing “explainable AI” for understanding the factors affecting AI prediction. kGCN is available at https://github.com/clinfo/kGCN.</div>

scholarly journals kGCN: A graph-based deep learning framework for chemical structures

2020 ◽  
Author(s):  
Ryosuke Kojima ◽  
Shoichi Ishida ◽  
Masateru Ohta ◽  
Hiroaki Iwata ◽  
Teruki Honma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Prediction Models ◽  
Bayesian Optimization ◽  
Factors Affecting ◽  
Learning Framework ◽  
Chemical Structures ◽  
Programming Skills ◽  
Model Tuning ◽  
Interpretation Of Results

Abstract Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of this resource for obtaining reasonable and reliable prediction results requires thorough understanding of GCN and programming. To leverage the power of GCN to benefit various users from chemists to cheminformaticians, an open-source GCN tool, kGCN, is introduced. To support the users with various levels of programming skills, kGCN includes three interfaces: a graphical user interface (GUI) employing KNIME for users with limited programming skills such as chemists, as well as command-line and Python library interfaces for users with advanced programming skills such as cheminformaticians. To support the three steps required for building a prediction model, i.e., pre-processing, model tuning, and interpretation of results, kGCN includes functions of typical pre-processing, Bayesian optimization for automatic model tuning, and visualization of the atomic contribution to prediction for interpretation of results. kGCN supports three types of approaches, single-task, multi-task, and multi-modal predictions. The prediction of compound-protein interaction for four matrixmetalloproteases, MMP-3, -9, -12 and -13, in the inhibition assays is performed as a representative case study using kGCN. Additionally, kGCN provides the visualization of atomic contributions to the prediction. Such visualization is useful for the validation of the prediction models and the design of molecules based on the prediction model, realizing “explainable AI” for understanding the factors affecting AI prediction. kGCN is available at https://github.com/clinfo/kGCN.

Study on E-Commerce Trust Prediction Mode Based on Combination Parameters

2012 ◽  
Vol 241-244 ◽  
pp. 2944-2952
Author(s):  
Shao Zhong Zhang ◽  
Hai Dong Zhong
Keyword(s):  
Prediction Model ◽  
Correlation Coefficient ◽  
Prediction Models ◽  
High Accuracy ◽  
Trust Evaluation ◽  
Individual Parameter ◽  
Single Item ◽  
Trust Prediction ◽  
Item Parameters ◽  
Prediction Mode

Based on the prediction of individual parameter and the theory of the correlation coefficient, it proposes a combination parameter trust prediction model to solve the parameter weights. The model takes correlation coefficient as the weight of the single parameter, and trust evaluation and prediction analysis is conducted in the form of combined indictors. The method of taking the single item parameters as the foundation of forecasting can increase a variety of items in the forecast parameters dynamically. At the same time, using correlation coefficient as the form of weight can maximize the weights of some parameters with high accuracy and reduce the weight of some parameters with poor accuracy. At last, an optimization algorithm of combination parameters prediction model is designed, and experiments show that the proposed combination parameters trust prediction model of e-commerce has a better accuracy compared to several other typical prediction models.

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

2020 ◽  
Vol 26 (33) ◽  
pp. 4195-4205
Author(s):  
Xiaoyu Ding ◽  
Chen Cui ◽  
Dingyan Wang ◽  
Jihui Zhao ◽  
Mingyue Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Lead Optimization ◽  
Consensus Method ◽  
Molecular Pair ◽  
Bioactivity Prediction ◽  
Compound Synthesis ◽  
Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

Clinical Impact of Co-medication of Levetiracetam and Clobazam with Proton Pump Inhibitors: A Drug Interaction Study

2020 ◽  
Vol 21 (2) ◽  
pp. 126-131
Author(s):  
Bhuvanachandra Pasupuleti ◽  
Vamshikrishna Gone ◽  
Ravali Baddam ◽  
Raj Kumar Venisetty ◽  
Om Prakash Prasad
Keyword(s):  
Plasma Concentration ◽  
Proton Pump Inhibitors ◽  
Proton Pump ◽  
Plasma Concentrations ◽  
Control Group ◽  
Drug Concentrations ◽  
Significant Difference ◽  
Post Hoc ◽  
Hydrolysis Of ◽  
Cytochrome P 450

Background: Clobazam (CLBZ) metabolized primarily by Cytochrome P-450 isoenzyme CYP3A4 than with CYP2C19, Whereas Levetiracetam (LEV) is metabolized by hydrolysis of the acetamide group. Few CYP enzymes are inhibited by Proton Pump Inhibitors (PPIs) Pantoprazole, Esomeprazole, and Rabeprazole in different extents that could affect drug concentrations in blood. The aim of the present study was to evaluate the effect of these PPIs on the plasma concentrations of LEV and CLBZ. Methods: Blood samples from 542 patients were included out of which 343 were male and 199 were female patients and were categorized as control and test. Plasma samples analyzed using an HPLC-UV method. Plasma concentrations were measured and compared to those treated and those not treated with PPIs. One way ANOVA and games Howell post hoc test used by SPSS 20 software. Results: CLBZ concentrations were significantly 10 folds higher in patients treated with Pantoprazole (P=0.000) and 07 folds higher in patients treated with Esmoprazole and Rabeprazole (P=0.00). Whereas plasma concentration of LEV control group has no statistical and significant difference when compared to pantoprazole (P=0.546) and with rabeprazole and esomeprazole was P=0.999. Conclusion: The effect of comedication with PPIs on the plasma concentration of clobazam is more pronounced for pantoprazole to a greater extent when compared to esomeprazole and rabeprazole. When pantoprazole is used in combination with clobazam, dose reduction of clobazam should be considered, or significance of PPIs is seen to avoid adverse effects.

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