Validation of Quantitative Structure-Activity Relationship (QSAR) Model for Photosensitizer Activity Prediction

Photodynamic therapy is a relatively new treatment method for cancer which utilizes a combination of oxygen, a photosensitizer and light to generate reactive singlet oxygen that eradicates tumors via direct cell-killing, vasculature damage and engagement of the immune system. Most of photosensitizer...

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Main Authors: Sharifuddin M. Zain, Noorsaadah Abd. Rahman, Rozana Othman, Mun Li Yam, Hong Boon Lee, Neni Frimayanti
Format: Article
Language:English
Published: MDPI AG 2011-11-01
Series:International Journal of Molecular Sciences
Subjects:
Online Access:http://www.mdpi.com/1422-0067/12/12/8626/
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spelling doaj-d3bfd84cb999461d97c31d203129dbce2020-11-24T21:41:21ZengMDPI AGInternational Journal of Molecular Sciences1422-00672011-11-0112128626864410.3390/ijms12128626Validation of Quantitative Structure-Activity Relationship (QSAR) Model for Photosensitizer Activity PredictionSharifuddin M. ZainNoorsaadah Abd. RahmanRozana OthmanMun Li YamHong Boon LeeNeni FrimayantiPhotodynamic therapy is a relatively new treatment method for cancer which utilizes a combination of oxygen, a photosensitizer and light to generate reactive singlet oxygen that eradicates tumors via direct cell-killing, vasculature damage and engagement of the immune system. Most of photosensitizers that are in clinical and pre-clinical assessments, or those that are already approved for clinical use, are mainly based on cyclic tetrapyrroles. In an attempt to discover new effective photosensitizers, we report the use of the quantitative structure-activity relationship (QSAR) method to develop a model that could correlate the structural features of cyclic tetrapyrrole-based compounds with their photodynamic therapy (PDT) activity. In this study, a set of 36 porphyrin derivatives was used in the model development where 24 of these compounds were in the training set and the remaining 12 compounds were in the test set. The development of the QSAR model involved the use of the multiple linear regression analysis (MLRA) method. Based on the method, r2 value, r2 (CV) value and r2 prediction value of 0.87, 0.71 and 0.70 were obtained. The QSAR model was also employed to predict the experimental compounds in an external test set. This external test set comprises 20 porphyrin-based compounds with experimental IC50 values ranging from 0.39 µM to 7.04 µM. Thus the model showed good correlative and predictive ability, with a predictive correlation coefficient (r2 prediction for external test set) of 0.52. The developed QSAR model was used to discover some compounds as new lead photosensitizers from this external test set.http://www.mdpi.com/1422-0067/12/12/8626/QSARphotodynamic therapyphotosensitizerporphyrinIC50 half maximal inhibitory concentration
collection DOAJ
language English
format Article
sources DOAJ
author Sharifuddin M. Zain
Noorsaadah Abd. Rahman
Rozana Othman
Mun Li Yam
Hong Boon Lee
Neni Frimayanti
spellingShingle Sharifuddin M. Zain
Noorsaadah Abd. Rahman
Rozana Othman
Mun Li Yam
Hong Boon Lee
Neni Frimayanti
Validation of Quantitative Structure-Activity Relationship (QSAR) Model for Photosensitizer Activity Prediction
International Journal of Molecular Sciences
QSAR
photodynamic therapy
photosensitizer
porphyrin
IC50 half maximal inhibitory concentration
author_facet Sharifuddin M. Zain
Noorsaadah Abd. Rahman
Rozana Othman
Mun Li Yam
Hong Boon Lee
Neni Frimayanti
author_sort Sharifuddin M. Zain
title Validation of Quantitative Structure-Activity Relationship (QSAR) Model for Photosensitizer Activity Prediction
title_short Validation of Quantitative Structure-Activity Relationship (QSAR) Model for Photosensitizer Activity Prediction
title_full Validation of Quantitative Structure-Activity Relationship (QSAR) Model for Photosensitizer Activity Prediction
title_fullStr Validation of Quantitative Structure-Activity Relationship (QSAR) Model for Photosensitizer Activity Prediction
title_full_unstemmed Validation of Quantitative Structure-Activity Relationship (QSAR) Model for Photosensitizer Activity Prediction
title_sort validation of quantitative structure-activity relationship (qsar) model for photosensitizer activity prediction
publisher MDPI AG
series International Journal of Molecular Sciences
issn 1422-0067
publishDate 2011-11-01
description Photodynamic therapy is a relatively new treatment method for cancer which utilizes a combination of oxygen, a photosensitizer and light to generate reactive singlet oxygen that eradicates tumors via direct cell-killing, vasculature damage and engagement of the immune system. Most of photosensitizers that are in clinical and pre-clinical assessments, or those that are already approved for clinical use, are mainly based on cyclic tetrapyrroles. In an attempt to discover new effective photosensitizers, we report the use of the quantitative structure-activity relationship (QSAR) method to develop a model that could correlate the structural features of cyclic tetrapyrrole-based compounds with their photodynamic therapy (PDT) activity. In this study, a set of 36 porphyrin derivatives was used in the model development where 24 of these compounds were in the training set and the remaining 12 compounds were in the test set. The development of the QSAR model involved the use of the multiple linear regression analysis (MLRA) method. Based on the method, r2 value, r2 (CV) value and r2 prediction value of 0.87, 0.71 and 0.70 were obtained. The QSAR model was also employed to predict the experimental compounds in an external test set. This external test set comprises 20 porphyrin-based compounds with experimental IC50 values ranging from 0.39 µM to 7.04 µM. Thus the model showed good correlative and predictive ability, with a predictive correlation coefficient (r2 prediction for external test set) of 0.52. The developed QSAR model was used to discover some compounds as new lead photosensitizers from this external test set.
topic QSAR
photodynamic therapy
photosensitizer
porphyrin
IC50 half maximal inhibitory concentration
url http://www.mdpi.com/1422-0067/12/12/8626/
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