Prediction of hepatitis B virus lamivudine resistance based on YMDD sequence data using an artificial neural network model

AUTHORS

Mehrdad Ravanshad 1 , * , Farzaneh Sabahi 2 , Shahab Falahi 3 , Azra kenarkoohi 2 , Samad Amini Bavil Olaee 4 , Seyed Younes 5 , Hossein Riahi 6 , Sayad Khanizade 2

1 Department of Virology, Faculty of Medical Sciences, Tarbiat Modares University, [email protected], IR.IRan

2 Department of Virology, Faculty of Medical Sciences, Tarbiat Modares University, IR.IRan

3 Department of Microbiology, School of Medicine, Ilam University of Medical Sciences, IR.IRan

4 Department of Biotechnology, Pasteur Institute of Iran, IR Iran

5 Hosseini, Department of Virology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran

6 Madvar, Water Structures and Engineering Department, Tarbiat Modares University, Tehran

How to Cite: Ravanshad M, Sabahi F, Falahi S, kenarkoohi A, Olaee S A, et al. Prediction of hepatitis B virus lamivudine resistance based on YMDD sequence data using an artificial neural network model, Hepat Mon. Online ahead of Print ; 11(2):108-113.

ARTICLE INFORMATION

Hepatitis Monthly: 11 (2); 108-113
Article Type: Research Article
Received: April 24, 2010
Accepted: December 17, 2010

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Abstract

Background: Hepatitis B virus (HBV) infection is an important health problem worldwide with critical outcomes. The nucleoside analog lamivudine (LMV) is a potent inhibitor of HBV polymerase and impedes HBV replication in patients with chronic hepatitis B. Treatment with LMV for long periods causes the appearance and reproduction of drug-resistant strains, rising to more than 40% after 2 years and to over 50% and 70% after 3 and 4 years, respectively.
Objectives: Artificial neural networks (ANNs) were used to make predictions with regard to resistance phenotypes using biochemical and biophysical features of the YMDD sequence.
Patients and Methods: The study population comprised patients who were intended for surgery in various hospitals in Tehran-Iran. An ACRS-PCR method was performed to distinguish mutations in the YMDD motif of HBV polymerase. In the training and testing stages, these parameters were used to identify the most promising optimal network. The ideal values of RMSE and MAE are zero, and a value near zero indicates better performance. The selection was performed using statistical accuracy measures, such as root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). The main purpose of this paper was to develop a new method based on ANNs to simulate HBV drug resistance using the physiochemical properties of the YMDD motif and compare its results with multiple regression models.
Results: The results of the MLP in the training stage were 0.8834, 0.07, and 0.09 and 0.8465, 0.160.04 in the testing stage; for the total data, the values were 0.8549, 0.115, and 0.065, respectively. The MLP model predicts lamivudine resistance in HBV better than the MLR model.
Conclusions: The ANN model can be used as an alternative method of predicting the outcome of HBV therapy. In a case study, the proposed model showed vigorous clusterization of predicted and observed drug responses. The current study was designed to develop an algorithm for predicting drug resistance using chemiophysical data with artificially created neural networks. To this end, an intelligent and multidisciplinary program should be developed on the basis of the information to be gained on the essentials of different applications by similar investigations. This program will help design expert neural network architectures for each application automatically.


  • Implication for health policy/practice/research/medical education:
    One of the main obstacles of effective oral antiviral treatment regimens for patients with HBV is viral resistance against oral medications. We suggest reader’s attentions in the field of gastroenterology and liver diseases to the conclusion of this article.
  • Please cite this paper as:
    Ravanshad M, Sabahi F, Falahi S, kenarkoohi A, Amini-Bavil-Olyaee S, Hosseini SY, et al. Prediction of hepatitis B virus lamivudine resistance based on YMDD sequence data using an artificial neural network model. Hepat Mon. 2011;11(2):108-113.

Keywords

Drug resistance Lamivudine Neural network models

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