PredMS: a random Forest model for predicting metabolic stability of drug candidates in human liver microsomes

Author(s):  
Jae Yong Ryu ◽  
Jeong Hyun Lee ◽  
Byung Ho Lee ◽  
Jin Sook Song ◽  
Sunjoo Ahn ◽  
...  

Abstract Motivation Poor metabolic stability leads to drug development failure. Therefore, it is essential to evaluate the metabolic stability of small compounds for successful drug discovery and development. However, evaluating metabolic stability in vitro and in vivo is expensive, time-consuming, and laborious. Additionally, only a few free software programs are available for metabolic stability data and prediction. Therefore, in this study, we aimed to develop a prediction model that predicts the metabolic stability of small compounds. Results We developed a computational model, PredMS, which predicts the metabolic stability of small compounds as stable or unstable in human liver microsomes. PredMS is based on a random forest model using an in-house database of metabolic stability data of 1,917 compounds. To validate the prediction performance of PredMS, we generated external test data of 61 compounds. PredMS achieved an accuracy of 0.74, Matthew’s correlation coefficient of 0.48, sensitivity of 0.70, specificity of 0.86, positive predictive value of 0.94, and negative predictive value of 0.46 on the external test dataset. PredMS will be a useful tool to predict the metabolic stability of small compounds in the early stages of drug discovery and development. Availability and implementation The source code for PredMS is available at https://bitbucket.org/krictai/predms, and the PredMS web server is available at https://predms.netlify.app. Supplementary information Supplementary data are available at Bioinformatics online.

2003 ◽  
Vol 42 (6) ◽  
pp. 515-528 ◽  
Author(s):  
Collen M Masimirembwa ◽  
Ulf Bredberg ◽  
Tommy B Andersson

Author(s):  
Maharshi Thalla ◽  
Aishwarya Jala ◽  
Roshan M. Borkar ◽  
Subham Banerjee

AbstractPyrazinamide (PZA), a medication for tuberculosis, has high aqueous solubility and low permeability, undergoes extensive liver metabolism, and exhibits liver toxicity through its metabolites. To avoid this, PZA in lipid core-shell nanoarchitectonics has been formulated to target lymphatic uptake and provide metabolic stability to the incorporated drug. The UPLC-MS/MS method for reliable in vitro quantitative analysis of pyrazinamide (PZA) in lipid core-shell nanoarchitectonics as per ICH guidance was developed and validated using the HILIC column. The developed UPLC-MS/MS method is a simple, precise, accurate, reproducible, and sensitive method for the estimation of PZA in PZA-loaded lipid core-shell nanoarchitectonics for the in vitro determination of % entrapment efficiency, % loading of pyrazinamide, and microsomal stability of lipid core-shell nanoarchitectonics in human liver microsomes. The % entrapment efficiency was found to be 42.72% (±12.60). Lipid nanoarchitectonics was found to be stable in human liver microsomes, where %QH was found to be 6.20%, that is, low clearance. Thus, this formulation is suitable for preventing PZA-mediated extensive liver metabolism. These findings are relevant for the development of other lipid-mediated, suitable, stable nanoformulations containing PZA through various in vitro methods.


2021 ◽  
Author(s):  
Minghui Wang ◽  
Hanqiao Zhang ◽  
Li Dong ◽  
Yang Li ◽  
Zhijia Hou ◽  
...  

Abstract Objective: The aim of this study is to establish a random forest model to detect active and quiescent phases of patients with Thyroid-associated ophthalmopathy (TAO) and to evaluate its diagnostic performance.Methods:A total of 146 patients (292 eyes) who were diagnosed with TAO and were treated in the Ophthalmology Outpatient Clinic of Beijing TongRen hospital were retrospectively included in the study. We took the clinical activity score of TAO as the target; took gender, age, smoking status, I-131 treatment history, thyroid nodules, thyromegaly, thyroid hormone and TSH-receptor antibodies (TRAb) as predictive characteristic variables to establish a random forest model. The proportion of the training group to the testing group was 7:3. We analyzed the model’s accuracy, precision, sensitivity, specificity, positive predictive value (PPV), negative predictive value (PPV), F1 score and out-of-bag (OOB) error, with the accuracy, the brier loss and the area under the receiver operating characteristic curve compared with logistic regression model.Results:Our model has an accuracy of 0.93, a sensitivity of 0.88, a specificity of 0.96, a positive predictive value of 0.94, a negative predictive value of 0.93, an F1 score of 0.91 and an OOB error of 0.12. The accuracy of the random forest model and the logistic regression model were 0.93 and 0.79, respectively, the brier loss were 0.06 and 0.20, and the area under the receiver operating characteristic curve were 0.95 and 0.86.Conclusion:By integrating these high-risk factors, the random forest algorithm can be used as a complementary diagnostic method to determine the activity of TAO, showing prominent diagnostic performance.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Vishal B. Siramshetty ◽  
Pranav Shah ◽  
Edward Kerns ◽  
Kimloan Nguyen ◽  
Kyeong Ri Yu ◽  
...  

AbstractHepatic metabolic stability is a key pharmacokinetic parameter in drug discovery. Metabolic stability is usually assessed in microsomal fractions and only the best compounds progress in the drug discovery process. A high-throughput single time point substrate depletion assay in rat liver microsomes (RLM) is employed at the National Center for Advancing Translational Sciences. Between 2012 and 2020, RLM stability data was generated for ~ 24,000 compounds from more than 250 projects that cover a wide range of pharmacological targets and cellular pathways. Although a crucial endpoint, little or no data exists in the public domain. In this study, computational models were developed for predicting RLM stability using different machine learning methods. In addition, a retrospective time-split validation was performed, and local models were built for projects that performed poorly with global models. Further analysis revealed inherent medicinal chemistry knowledge potentially useful to chemists in the pursuit of synthesizing metabolically stable compounds. In addition, we deposited experimental data for ~ 2500 compounds in the PubChem bioassay database (AID: 1508591). The global prediction models are made publicly accessible (https://opendata.ncats.nih.gov/adme). This is to the best of our knowledge, the first publicly available RLM prediction model built using high-quality data generated at a single laboratory.


2015 ◽  
Vol 6 (2) ◽  
pp. 48-51 ◽  
Author(s):  
Shaheed Ur Rehman ◽  
n Sook Kim ◽  
Min Sun Choi ◽  
Zengwei Luo ◽  
Guangming Yao ◽  
...  

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