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Potential Compounds for Oral Cancer Treatment: Resveratrol, Nimbolide, Lovastatin, Bortezomib, Vorinostat, Berberine, Pterostilbene, Deguelin, Andrographolide, and Colchicine.

Bundela S, Sharma A, Bisen PS - PLoS ONE (2015)

Bottom Line: The molecular targets of the predicted anti-cancer compounds were mined from reliable sources like experimental bioassays studies associated with the compound, and from protein-compound interaction databases.The majority of the compounds in this list are natural products, which are well-tolerated and have minimal side-effects compared to the synthetic counterparts.Some of the potential therapeutic compounds identified in the current study are resveratrol, nimbolide, lovastatin, bortezomib, vorinostat, berberine, pterostilbene, deguelin, andrographolide, and colchicine.

View Article: PubMed Central - PubMed

Affiliation: Defence Research Development Establishment, Defence Research Development Organization, Ministry of Defence, Govt. of India, Gwalior, Madhya Pradesh, India.

ABSTRACT
Oral cancer is one of the main causes of cancer-related deaths in South-Asian countries. There are very limited treatment options available for oral cancer. Research endeavors focused on discovery and development of novel therapies for oral cancer, is necessary to control the ever rising oral cancer related mortalities. We mined the large pool of compounds from the publicly available compound databases, to identify potential therapeutic compounds for oral cancer. Over 84 million compounds were screened for the possible anti-cancer activity by custom build SVM classifier. The molecular targets of the predicted anti-cancer compounds were mined from reliable sources like experimental bioassays studies associated with the compound, and from protein-compound interaction databases. Therapeutic compounds from DrugBank, and a list of natural anti-cancer compounds derived from literature mining of published studies, were used for building partial least squares regression model. The regression model thus built, was used for the estimation of oral cancer specific weights based on the molecular targets. These weights were used to compute scores for screening the predicted anti-cancer compounds for their potential to treat oral cancer. The list of potential compounds was annotated with corresponding physicochemical properties, cancer specific bioactivity evidences, and literature evidences. In all, 288 compounds with the potential to treat oral cancer were identified in the current study. The majority of the compounds in this list are natural products, which are well-tolerated and have minimal side-effects compared to the synthetic counterparts. Some of the potential therapeutic compounds identified in the current study are resveratrol, nimbolide, lovastatin, bortezomib, vorinostat, berberine, pterostilbene, deguelin, andrographolide, and colchicine.

No MeSH data available.


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Algorithm for finding duplicates in the list of compounds.
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pone.0141719.g006: Algorithm for finding duplicates in the list of compounds.

Mentions: The target weights obtained from the partial least squares regression modelling was used to compute oral cancer specific statistic ‘OC_Score’, for the merged list of predicted anti-cancer compounds. The OC_Scores thus obtained for the merged list of predicted anti-cancer compounds can be found by uncompressing zipped file ‘Ch_Pub_consolidate_list.zip’ (see S20 Text) available online as supplementary material. A total of 311 compounds were selected, based on OC_Score cutoff of ≥ 0.67, and were regarded as compounds with potential to treat oral cancer. Quality check of this list, of 311 compounds was conducted and it was observed that quite a few duplicate compounds from STITCH database were still present in this list. Few compounds in STITCH database are represented by two records which correspond to its stero-specific and flat structure (refer to: http://stitch.embl.de/download/README). Such duplicate compounds were identified and removed based on their structural similarity to retrieve the list of unique compounds (Fig 6). After removal of duplicates among this list, we got 218 compounds (80 compounds from ChEMBL, and 138 compounds from PubChem/STITCH database). The compounds used for building partial least squares regression model were also interrogated to check their potential to treat oral cancer (see S18 Text). A total of 100 compounds with an OC_Score cutoff of ≥ 0.67 were collected, these consisted of compounds from DrugBank database [3] and manually curated list of plant based natural products with anticancer activity. The list of potential compounds derived from different sources (ChEMBL, STITCH / PubChem compounds, DrugBank and manually curated anticancer natural products) were consolidated, and compound duplicates, if any, were removed from this list. The consolidated list has 288 potential compounds with OC_Scores ranging from 0.67 to 3.14 (Fig 7). These compounds were annotated with associated attributes related with their bioactivity against cancer cells and physicochemical parameters (see section Annotation of List of Potential Compounds). The detailed information about active BioAssays associated with these compounds can be found in ‘GI50_BioAssays.xlsx’ (see S21 Text), available as online supplementary material. The possible false positives were identified by CDRUG tool [17]; the result of this analysis can be found in ‘Lead_cmpds_cdrug_result.txt’ (see S22 Text), available as online supplementary material.


Potential Compounds for Oral Cancer Treatment: Resveratrol, Nimbolide, Lovastatin, Bortezomib, Vorinostat, Berberine, Pterostilbene, Deguelin, Andrographolide, and Colchicine.

Bundela S, Sharma A, Bisen PS - PLoS ONE (2015)

Algorithm for finding duplicates in the list of compounds.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4633227&req=5

pone.0141719.g006: Algorithm for finding duplicates in the list of compounds.
Mentions: The target weights obtained from the partial least squares regression modelling was used to compute oral cancer specific statistic ‘OC_Score’, for the merged list of predicted anti-cancer compounds. The OC_Scores thus obtained for the merged list of predicted anti-cancer compounds can be found by uncompressing zipped file ‘Ch_Pub_consolidate_list.zip’ (see S20 Text) available online as supplementary material. A total of 311 compounds were selected, based on OC_Score cutoff of ≥ 0.67, and were regarded as compounds with potential to treat oral cancer. Quality check of this list, of 311 compounds was conducted and it was observed that quite a few duplicate compounds from STITCH database were still present in this list. Few compounds in STITCH database are represented by two records which correspond to its stero-specific and flat structure (refer to: http://stitch.embl.de/download/README). Such duplicate compounds were identified and removed based on their structural similarity to retrieve the list of unique compounds (Fig 6). After removal of duplicates among this list, we got 218 compounds (80 compounds from ChEMBL, and 138 compounds from PubChem/STITCH database). The compounds used for building partial least squares regression model were also interrogated to check their potential to treat oral cancer (see S18 Text). A total of 100 compounds with an OC_Score cutoff of ≥ 0.67 were collected, these consisted of compounds from DrugBank database [3] and manually curated list of plant based natural products with anticancer activity. The list of potential compounds derived from different sources (ChEMBL, STITCH / PubChem compounds, DrugBank and manually curated anticancer natural products) were consolidated, and compound duplicates, if any, were removed from this list. The consolidated list has 288 potential compounds with OC_Scores ranging from 0.67 to 3.14 (Fig 7). These compounds were annotated with associated attributes related with their bioactivity against cancer cells and physicochemical parameters (see section Annotation of List of Potential Compounds). The detailed information about active BioAssays associated with these compounds can be found in ‘GI50_BioAssays.xlsx’ (see S21 Text), available as online supplementary material. The possible false positives were identified by CDRUG tool [17]; the result of this analysis can be found in ‘Lead_cmpds_cdrug_result.txt’ (see S22 Text), available as online supplementary material.

Bottom Line: The molecular targets of the predicted anti-cancer compounds were mined from reliable sources like experimental bioassays studies associated with the compound, and from protein-compound interaction databases.The majority of the compounds in this list are natural products, which are well-tolerated and have minimal side-effects compared to the synthetic counterparts.Some of the potential therapeutic compounds identified in the current study are resveratrol, nimbolide, lovastatin, bortezomib, vorinostat, berberine, pterostilbene, deguelin, andrographolide, and colchicine.

View Article: PubMed Central - PubMed

Affiliation: Defence Research Development Establishment, Defence Research Development Organization, Ministry of Defence, Govt. of India, Gwalior, Madhya Pradesh, India.

ABSTRACT
Oral cancer is one of the main causes of cancer-related deaths in South-Asian countries. There are very limited treatment options available for oral cancer. Research endeavors focused on discovery and development of novel therapies for oral cancer, is necessary to control the ever rising oral cancer related mortalities. We mined the large pool of compounds from the publicly available compound databases, to identify potential therapeutic compounds for oral cancer. Over 84 million compounds were screened for the possible anti-cancer activity by custom build SVM classifier. The molecular targets of the predicted anti-cancer compounds were mined from reliable sources like experimental bioassays studies associated with the compound, and from protein-compound interaction databases. Therapeutic compounds from DrugBank, and a list of natural anti-cancer compounds derived from literature mining of published studies, were used for building partial least squares regression model. The regression model thus built, was used for the estimation of oral cancer specific weights based on the molecular targets. These weights were used to compute scores for screening the predicted anti-cancer compounds for their potential to treat oral cancer. The list of potential compounds was annotated with corresponding physicochemical properties, cancer specific bioactivity evidences, and literature evidences. In all, 288 compounds with the potential to treat oral cancer were identified in the current study. The majority of the compounds in this list are natural products, which are well-tolerated and have minimal side-effects compared to the synthetic counterparts. Some of the potential therapeutic compounds identified in the current study are resveratrol, nimbolide, lovastatin, bortezomib, vorinostat, berberine, pterostilbene, deguelin, andrographolide, and colchicine.

No MeSH data available.


Related in: MedlinePlus