scholarly journals An In Silico Classification Model for Putative ABCC2 Substrates

2012 ◽  
Vol 31 (8) ◽  
pp. 547-553 ◽  
Author(s):  
Marta Pinto ◽  
Michael Trauner ◽  
Gerhard F. Ecker
Keyword(s):  
In Silico ◽  
Classification Model

scholarly journals Development of an in silico prediction system of human renal excretion and clearance from chemical structure information incorporating fraction unbound in plasma as a descriptor

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Reiko Watanabe ◽  
Rikiya Ohashi ◽  
Tsuyoshi Esaki ◽  
Hitoshi Kawashima ◽  
Yayoi Natsume-Kitatani ◽  
...  
Keyword(s):  
Renal Clearance ◽  
In Silico ◽  
Regression Models ◽  
Chemical Structure ◽  
Prediction Models ◽  
Renal Excretion ◽  
Early Stage ◽  
Classification Model ◽  
Prediction System ◽  
Structure Information

AbstractPrediction of pharmacokinetic profiles of new chemical entities is essential in drug development to minimize the risks of potential withdrawals. The excretion of unchanged compounds by the kidney constitutes a major route in drug elimination and plays an important role in pharmacokinetics. Herein, we created in silico prediction models of the fraction of drug excreted unchanged in the urine (fe) and renal clearance (CLr), with datasets of 411 and 401 compounds using freely available software; notably, all models require chemical structure information alone. The binary classification model for fe demonstrated a balanced accuracy of 0.74. The two-step prediction system for CLr was generated using a combination of the classification model to predict excretion-type compounds and regression models to predict the CLr value for each excretion type. The accuracies of the regression models increased upon adding a descriptor, which was the observed and predicted fraction unbound in plasma (fu,p); 78.6% of the samples in the higher range of renal clearance fell within 2-fold error with predicted fu,p value. Our prediction system for renal excretion is freely available to the public and can be used as a practical tool for prioritization and optimization of compound synthesis in the early stage of drug discovery.

scholarly journals Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds

Molecules ◽  
2019 ◽  
Vol 24 (10) ◽  
pp. 2006 ◽  
Author(s):  
Liadys Mora Lagares ◽  
Nikola Minovski ◽  
Marjana Novič
Keyword(s):  
In Silico ◽  
Transmembrane Protein ◽  
External Validation ◽  
Assessment Process ◽  
Classification Model ◽  
Training Set ◽  
Test Set ◽  
Active Compounds ◽  
P Glycoprotein ◽  
Validation Set

P-glycoprotein (P-gp) is a transmembrane protein that actively transports a wide variety of chemically diverse compounds out of the cell. It is highly associated with the ADMET (absorption, distribution, metabolism, excretion and toxicity) properties of drugs/drug candidates and contributes to decreasing toxicity by eliminating compounds from cells, thereby preventing intracellular accumulation. Therefore, in the drug discovery and toxicological assessment process it is advisable to pay attention to whether a compound under development could be transported by P-gp or not. In this study, an in silico multiclass classification model capable of predicting the probability of a compound to interact with P-gp was developed using a counter-propagation artificial neural network (CP ANN) based on a set of 2D molecular descriptors, as well as an extensive dataset of 2512 compounds (1178 P-gp inhibitors, 477 P-gp substrates and 857 P-gp non-active compounds). The model provided a good classification performance, producing non error rate (NER) values of 0.93 for the training set and 0.85 for the test set, while the average precision (AvPr) was 0.93 for the training set and 0.87 for the test set. An external validation set of 385 compounds was used to challenge the model’s performance. On the external validation set the NER and AvPr values were 0.70 for both indices. We believe that this in silico classifier could be effectively used as a reliable virtual screening tool for identifying potential P-gp ligands.

scholarly journals Virtual Screening of DrugBank Reveals Two Drugs as New BCRP Inhibitors

2016 ◽  
Vol 22 (1) ◽  
pp. 86-93 ◽  
Author(s):  
Floriane Montanari ◽  
Anna Cseke ◽  
Katrin Wlcek ◽  
Gerhard F. Ecker
Keyword(s):  
Virtual Screening ◽  
Small Molecules ◽  
In Silico ◽  
Breast Cancer Resistance Protein ◽  
Classification Model ◽  
Efflux Transporter ◽  
Cancer Resistance ◽  
Resistance Protein ◽  
Inhibitory Potential ◽  
Potential Inhibitors

The breast cancer resistance protein (BCRP) is an ABC transporter playing a crucial role in the pharmacokinetics of drugs. The early identification of substrates and inhibitors of this efflux transporter can help to prevent or foresee drug-drug interactions. In this work, we built a ligand-based in silico classification model to predict the inhibitory potential of drugs toward BCRP. The model was applied as a virtual screening technique to identify potential inhibitors among the small-molecules subset of DrugBank. Ten compounds were selected and tested for their capacity to inhibit mitoxantrone efflux in BCRP-expressing PLB985 cells. Results identified cisapride (IC50 = 0.4 µM) and roflumilast (IC50 = 0.9 µM) as two new BCRP inhibitors. The in silico strategy proved useful to prefilter potential drug-drug interaction perpetrators among a database of small molecules and can reduce the amount of compounds to test.

scholarly journals Machine Learning-Based Adherence Detection of Type 2 Diabetes Patients on Once-Daily Basal Insulin Injections

2020 ◽  
Vol 15 (1) ◽  
pp. 98-108
Author(s):  
Daniel N. Thyde ◽  
Ali Mohebbi ◽  
Henrik Bengtsson ◽  
Morten Lind Jensen ◽  
Morten Mørup
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
In Silico ◽  
Basal Insulin ◽  
Classification Model ◽  
Detection Accuracy ◽  
Classification Models ◽  
Once Daily ◽  
Learned Features

Background: Lack of treatment adherence can lead to life-threatening health complications for people with type 2 diabetes (T2D). Recent improvements and availability in continuous glucose monitoring (CGM) technology have enabled various possibilities to monitor diabetes treatment. Detection of missed once-daily basal insulin injections can be used to provide feedback to patients, thus improving their diabetes management. In this study, we explore how machine learning (ML) based on CGM data can be used for detecting adherence to once-daily basal insulin injections. Methods: In-silico CGM data were generated to simulate a cohort of T2D patients on once-daily insulin injection (Tresiba®). Deep learning methods within ML based on automatic feature extraction including convolutional neural networks were explored and compared with simple feature-engineered ML classification models for adherence detection. It was further investigated whether fused expert-dependent and automatically learned features could improve performance, resulting in a comparison of six different detection models. Adherence was detected throughout each day with an increasing amount of CGM data available. Results: The adherence detection accuracy improved as more CGM data became available on the day of classification. The three classification models based on expert-engineered features obtained mean accuracies of 78.6%, 78.2%, and 78.3%. The classification model based purely on learned features obtained a mean accuracy of 79.7%. The two classification models fusing expert-engineered and learned features obtained mean accuracies of 79.7% and 79.8%. All the mentioned results were obtained 16 hours after time of injection. Conclusion: The results suggest that adherence detection based on CGM data is feasible. Even though our study based on in-silico data indicates only slightly improved performance of more complex models, the question remains whether advanced models would outperform the simple in a real-world setting. Thus, future studies on adherence monitoring using real CGM data are relevant.

A new in silico classification model for ready biodegradability, based on molecular fragments

Chemosphere ◽  
2014 ◽  
Vol 108 ◽  
pp. 10-16 ◽  
Author(s):  
Anna Lombardo ◽  
Fabiola Pizzo ◽  
Emilio Benfenati ◽  
Alberto Manganaro ◽  
Thomas Ferrari ◽  
...  
Keyword(s):  
In Silico ◽  
Classification Model ◽  
Molecular Fragments ◽  
Ready Biodegradability

A systematic review with in silico analysis on transcriptomic profile of gallbladder carcinoma

2020 ◽  
Vol 47 (6) ◽  
pp. 398-408
Author(s):  
Sonam Tulsyan ◽  
Showket Hussain ◽  
Balraj Mittal ◽  
Sundeep Singh Saluja ◽  
Pranay Tanwar ◽  
...  
Keyword(s):  
Systematic Review ◽  
Gallbladder Carcinoma ◽  
In Silico ◽  
In Silico Analysis ◽  
Transcriptomic Profile ◽  
Silico Analysis

Common and well-documented Allele List: unverzichtbares Hilfsmittel für die moderne HLA-Typisierung

2019 ◽  
Vol 9 (04) ◽  
pp. 241-245
Author(s):  
Nils Lachmann ◽  
Diana Stauch ◽  
Axel Pruß
Keyword(s):  
In Silico ◽  
Regionale Unterschiede

ZusammenfassungDie Typisierung der humanen Leukozytenantigene (HLA) vor Organ- und hämatopoetischer Stammzelltransplantation zur Beurteilung der Kompatibilität von Spender und Empfänger wird heutzutage in der Regel molekulargenetisch mittels Amplifikation, Hybridisierung oder Sequenzierung durchgeführt. Durch die exponentiell steigende Anzahl an neu entdeckten HLA-Allelen treten vermehrt Mehrdeutigkeiten, sogenannte Ambiguitäten, in der HLA-Typisierung auf, die aufgelöst werden müssen, um zu einem eindeutigen Ergebnis zu gelangen. Mithilfe kategorisierter Allelfrequenzen (häufig, gut dokumentiert und selten) in Form von CWD-Allellisten (CWD: common and well-documented) ist die In-silico-Auflösung von Ambiguitäten durch den Ausschluss seltener Allele als mögliches Ergebnis realisierbar. Ausgehend von einer amerikanischen CWD-Liste existieren derzeit auch eine europäische, deutsche und chinesische CWD-Liste, die jeweils regionale Unterschiede in den Allelfrequenzen erkennbar werden lassen. Durch die Anwendung von CWD-Allelfiltern in der klinischen HLA-Typisierung können Zeit, Kosten und Arbeitskraft eingespart werden.

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