Characterizing early drug resistance-related events using geometric ensembles from HIV protease dynamics:
- Amamuddy, Olivier S, Bishop, Nigel T, Tastan Bishop, Özlem
- Authors: Amamuddy, Olivier S , Bishop, Nigel T , Tastan Bishop, Özlem
- Date: 2018
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148126 , vital:38712 , DOI: 10.1038/s41598-018-36041-8
- Description: The use of antiretrovirals (ARVs) has drastically improved the life quality and expectancy of HIV patients since their introduction in health care. Several millions are still afflicted worldwide by HIV and ARV resistance is a constant concern for both healthcare practitioners and patients, as while treatment options are finite, the virus constantly adapts via complex mutation patterns to select for resistant strains under the pressure of drug treatment. The HIV protease is a crucial enzyme for viral maturation and has been a game changing drug target since the first application. Due to similarities in protease inhibitor designs, drug cross-resistance is not uncommon across ARVs of the same class.
- Full Text:
- Authors: Amamuddy, Olivier S , Bishop, Nigel T , Tastan Bishop, Özlem
- Date: 2018
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148126 , vital:38712 , DOI: 10.1038/s41598-018-36041-8
- Description: The use of antiretrovirals (ARVs) has drastically improved the life quality and expectancy of HIV patients since their introduction in health care. Several millions are still afflicted worldwide by HIV and ARV resistance is a constant concern for both healthcare practitioners and patients, as while treatment options are finite, the virus constantly adapts via complex mutation patterns to select for resistant strains under the pressure of drug treatment. The HIV protease is a crucial enzyme for viral maturation and has been a game changing drug target since the first application. Due to similarities in protease inhibitor designs, drug cross-resistance is not uncommon across ARVs of the same class.
- Full Text:
Improving fold resistance prediction of HIV-1 against protease and reverse transcriptase inhibitors using artificial neural networks:
- Amamuddy, Olivier S, Bishop, Nigel T, Tastan Bishop, Özlem
- Authors: Amamuddy, Olivier S , Bishop, Nigel T , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148261 , vital:38724 , https://0-doi.org.wam.seals.ac.za/10.1186/s12859-017-1782-x
- Description: Drug resistance in HIV treatment is still a worldwide problem. Predicting resistance to antiretrovirals (ARVs) before starting any treatment is important. Prediction accuracy is essential, as low-accuracy predictions increase the risk of prescribing sub-optimal drug regimens leading to patients developing resistance sooner. Artificial Neural Networks (ANNs) are a powerful tool that would be able to assist in drug resistance prediction. In this study, we constrained the dataset to subtype B, sacrificing generalizability for a higher predictive performance, and demonstrated that the predictive quality of the ANN regression models have definite improvement for most ARVs.
- Full Text:
- Authors: Amamuddy, Olivier S , Bishop, Nigel T , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148261 , vital:38724 , https://0-doi.org.wam.seals.ac.za/10.1186/s12859-017-1782-x
- Description: Drug resistance in HIV treatment is still a worldwide problem. Predicting resistance to antiretrovirals (ARVs) before starting any treatment is important. Prediction accuracy is essential, as low-accuracy predictions increase the risk of prescribing sub-optimal drug regimens leading to patients developing resistance sooner. Artificial Neural Networks (ANNs) are a powerful tool that would be able to assist in drug resistance prediction. In this study, we constrained the dataset to subtype B, sacrificing generalizability for a higher predictive performance, and demonstrated that the predictive quality of the ANN regression models have definite improvement for most ARVs.
- Full Text:
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