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Computer-aided discovery of biological activity spectra for anti-aging and anti-cancer olive oil oleuropeins.

Corominas-Faja B, Santangelo E, Cuyàs E, Micol V, Joven J, Ariza X, Segura-Carretero A, García J, Menendez JA - Aging (Albany NY) (2014)

Bottom Line: We have recently postulated that the secoiridoids oleuropein aglycone (OA) and decarboxymethyl oleuropein aglycone (DOA), two complex polyphenols present in health-promoting extra virgin olive oil (EVOO), might constitute a new family of plant-produced gerosuppressant agents.PASS can predict thousands of biological activities, as the BAS of a compound is an intrinsic property that is largely dependent on the compound's structure and reflects pharmacological effects, physiological and biochemical mechanisms of action, and specific toxicities.Using Pharmaexpert, a tool that analyzes the PASS-predicted BAS of substances based on thousands of "mechanism-effect" and "effect-mechanism" relationships, we illuminate hypothesis-generating pharmacological effects, mechanisms of action, and targets that might underlie the anti-aging/anti-cancer activities of the gerosuppressant EVOO oleuropeins.

View Article: PubMed Central - PubMed

Affiliation: Metabolism and Cancer Group, Translational Research Laboratory, Catalan Institute of Oncology (ICO), Girona, Spain. Girona Biomedical Research Institute (IDIBGI), Girona, Spain.

ABSTRACT
Aging is associated with common conditions, including cancer, diabetes, cardiovascular disease, and Alzheimer's disease. The type of multi-targeted pharmacological approach necessary to address a complex multifaceted disease such as aging might take advantage of pleiotropic natural polyphenols affecting a wide variety of biological processes. We have recently postulated that the secoiridoids oleuropein aglycone (OA) and decarboxymethyl oleuropein aglycone (DOA), two complex polyphenols present in health-promoting extra virgin olive oil (EVOO), might constitute a new family of plant-produced gerosuppressant agents. This paper describes an analysis of the biological activity spectra (BAS) of OA and DOA using PASS (Prediction of Activity Spectra for Substances) software. PASS can predict thousands of biological activities, as the BAS of a compound is an intrinsic property that is largely dependent on the compound's structure and reflects pharmacological effects, physiological and biochemical mechanisms of action, and specific toxicities. Using Pharmaexpert, a tool that analyzes the PASS-predicted BAS of substances based on thousands of "mechanism-effect" and "effect-mechanism" relationships, we illuminate hypothesis-generating pharmacological effects, mechanisms of action, and targets that might underlie the anti-aging/anti-cancer activities of the gerosuppressant EVOO oleuropeins.

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Related in: MedlinePlus

Biological activity spectra of the gerosuppressant olive oil oleuropein OAThe results of predicted activity spectra generated by PASS are presented as a bar graph of biological activities with the probabilities “to be active” (Pa) and “to be inactive” (Pi) calculated for each activity. The values vary from 0.000 to 1.000; the higher a Pa value is the lower is the predicted probability of obtaining false positives in biological testing. The lists are arranged in descending order of Pa-Pi; therefore, more probable biological activities are at the top of the list. The list can be shortened at any desirable cutoff value, but PASS uses the criteria Pa=Pi as the as the default threshold, i.e., only biological activities with Pa > Pi are considered as probable for a particular compound. If we choose to use rather high value of Pa as cutoff for selection of probable activities, the chance to confirm the predicted activities is high too, but many existing activities will be lost. For instance, if one selects for consideration particular biological activities predicted with Pa > 0.9, then about 90% of actual activities will be lost (i.e., the expected probability to find inactive compounds in the selected set is very low but about 90% of active compounds will be missed). If one lowers the Pa threshold to 0.8, the probability to find inactive compounds is still low, but about 80% of active compounds will be missed, etc. Another important aspect of PASS predictions is the compounds' novelty. If one limits to high Pa values, one may find close analogues of known biologically active substances among the tested compounds. For instance, for Pa > 0.7, the chance to experimentally find the biological activity is high, but some of the activities may be close analogue of known pharmaceutical agents. If one chooses 0.5<Pa<0.7 values, the chances of obtaining activity in the experiment are lower, but the compound may be less similar to known pharmaceutical agents. For Pa < 0.5, the chances of obtaining activity in the experiment are even lower, but if activity is found, the compound might happen to be a new chemical entity. Nevertheless, it is important to keep in mind that the probability Pa reflects the similarity of a molecule under prediction with the structures of molecules, which are the most typical in a sub-set of “actives” in the training set. Therefore, there is no usually direct correlation between the Pa vales and quantitative characteristics of biological activities.
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Figure 2: Biological activity spectra of the gerosuppressant olive oil oleuropein OAThe results of predicted activity spectra generated by PASS are presented as a bar graph of biological activities with the probabilities “to be active” (Pa) and “to be inactive” (Pi) calculated for each activity. The values vary from 0.000 to 1.000; the higher a Pa value is the lower is the predicted probability of obtaining false positives in biological testing. The lists are arranged in descending order of Pa-Pi; therefore, more probable biological activities are at the top of the list. The list can be shortened at any desirable cutoff value, but PASS uses the criteria Pa=Pi as the as the default threshold, i.e., only biological activities with Pa > Pi are considered as probable for a particular compound. If we choose to use rather high value of Pa as cutoff for selection of probable activities, the chance to confirm the predicted activities is high too, but many existing activities will be lost. For instance, if one selects for consideration particular biological activities predicted with Pa > 0.9, then about 90% of actual activities will be lost (i.e., the expected probability to find inactive compounds in the selected set is very low but about 90% of active compounds will be missed). If one lowers the Pa threshold to 0.8, the probability to find inactive compounds is still low, but about 80% of active compounds will be missed, etc. Another important aspect of PASS predictions is the compounds' novelty. If one limits to high Pa values, one may find close analogues of known biologically active substances among the tested compounds. For instance, for Pa > 0.7, the chance to experimentally find the biological activity is high, but some of the activities may be close analogue of known pharmaceutical agents. If one chooses 0.5<Pa<0.7 values, the chances of obtaining activity in the experiment are lower, but the compound may be less similar to known pharmaceutical agents. For Pa < 0.5, the chances of obtaining activity in the experiment are even lower, but if activity is found, the compound might happen to be a new chemical entity. Nevertheless, it is important to keep in mind that the probability Pa reflects the similarity of a molecule under prediction with the structures of molecules, which are the most typical in a sub-set of “actives” in the training set. Therefore, there is no usually direct correlation between the Pa vales and quantitative characteristics of biological activities.

Mentions: Figs. 2 and 3 show only the activities predicted at a Pa (probability “to be active”) > 0.200 for the hemiacetalic and dialdehidic forms of OA (Fig. 2) and DOA (Fig. 3), grouped using PharmaExpert (www.genexplain.com/pharmaexpert), a tool that was developed to analyze the biological activity spectra of substances predicted by PASS. PharmaExpert software analyzes the relationships between biological activities (“mechanism-effect(s)” and “effect-mechanism(s)”), identifies probable drug-drug interactions, and searches for compounds acting on multiple targets [33-43].


Computer-aided discovery of biological activity spectra for anti-aging and anti-cancer olive oil oleuropeins.

Corominas-Faja B, Santangelo E, Cuyàs E, Micol V, Joven J, Ariza X, Segura-Carretero A, García J, Menendez JA - Aging (Albany NY) (2014)

Biological activity spectra of the gerosuppressant olive oil oleuropein OAThe results of predicted activity spectra generated by PASS are presented as a bar graph of biological activities with the probabilities “to be active” (Pa) and “to be inactive” (Pi) calculated for each activity. The values vary from 0.000 to 1.000; the higher a Pa value is the lower is the predicted probability of obtaining false positives in biological testing. The lists are arranged in descending order of Pa-Pi; therefore, more probable biological activities are at the top of the list. The list can be shortened at any desirable cutoff value, but PASS uses the criteria Pa=Pi as the as the default threshold, i.e., only biological activities with Pa > Pi are considered as probable for a particular compound. If we choose to use rather high value of Pa as cutoff for selection of probable activities, the chance to confirm the predicted activities is high too, but many existing activities will be lost. For instance, if one selects for consideration particular biological activities predicted with Pa > 0.9, then about 90% of actual activities will be lost (i.e., the expected probability to find inactive compounds in the selected set is very low but about 90% of active compounds will be missed). If one lowers the Pa threshold to 0.8, the probability to find inactive compounds is still low, but about 80% of active compounds will be missed, etc. Another important aspect of PASS predictions is the compounds' novelty. If one limits to high Pa values, one may find close analogues of known biologically active substances among the tested compounds. For instance, for Pa > 0.7, the chance to experimentally find the biological activity is high, but some of the activities may be close analogue of known pharmaceutical agents. If one chooses 0.5<Pa<0.7 values, the chances of obtaining activity in the experiment are lower, but the compound may be less similar to known pharmaceutical agents. For Pa < 0.5, the chances of obtaining activity in the experiment are even lower, but if activity is found, the compound might happen to be a new chemical entity. Nevertheless, it is important to keep in mind that the probability Pa reflects the similarity of a molecule under prediction with the structures of molecules, which are the most typical in a sub-set of “actives” in the training set. Therefore, there is no usually direct correlation between the Pa vales and quantitative characteristics of biological activities.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4221918&req=5

Figure 2: Biological activity spectra of the gerosuppressant olive oil oleuropein OAThe results of predicted activity spectra generated by PASS are presented as a bar graph of biological activities with the probabilities “to be active” (Pa) and “to be inactive” (Pi) calculated for each activity. The values vary from 0.000 to 1.000; the higher a Pa value is the lower is the predicted probability of obtaining false positives in biological testing. The lists are arranged in descending order of Pa-Pi; therefore, more probable biological activities are at the top of the list. The list can be shortened at any desirable cutoff value, but PASS uses the criteria Pa=Pi as the as the default threshold, i.e., only biological activities with Pa > Pi are considered as probable for a particular compound. If we choose to use rather high value of Pa as cutoff for selection of probable activities, the chance to confirm the predicted activities is high too, but many existing activities will be lost. For instance, if one selects for consideration particular biological activities predicted with Pa > 0.9, then about 90% of actual activities will be lost (i.e., the expected probability to find inactive compounds in the selected set is very low but about 90% of active compounds will be missed). If one lowers the Pa threshold to 0.8, the probability to find inactive compounds is still low, but about 80% of active compounds will be missed, etc. Another important aspect of PASS predictions is the compounds' novelty. If one limits to high Pa values, one may find close analogues of known biologically active substances among the tested compounds. For instance, for Pa > 0.7, the chance to experimentally find the biological activity is high, but some of the activities may be close analogue of known pharmaceutical agents. If one chooses 0.5<Pa<0.7 values, the chances of obtaining activity in the experiment are lower, but the compound may be less similar to known pharmaceutical agents. For Pa < 0.5, the chances of obtaining activity in the experiment are even lower, but if activity is found, the compound might happen to be a new chemical entity. Nevertheless, it is important to keep in mind that the probability Pa reflects the similarity of a molecule under prediction with the structures of molecules, which are the most typical in a sub-set of “actives” in the training set. Therefore, there is no usually direct correlation between the Pa vales and quantitative characteristics of biological activities.
Mentions: Figs. 2 and 3 show only the activities predicted at a Pa (probability “to be active”) > 0.200 for the hemiacetalic and dialdehidic forms of OA (Fig. 2) and DOA (Fig. 3), grouped using PharmaExpert (www.genexplain.com/pharmaexpert), a tool that was developed to analyze the biological activity spectra of substances predicted by PASS. PharmaExpert software analyzes the relationships between biological activities (“mechanism-effect(s)” and “effect-mechanism(s)”), identifies probable drug-drug interactions, and searches for compounds acting on multiple targets [33-43].

Bottom Line: We have recently postulated that the secoiridoids oleuropein aglycone (OA) and decarboxymethyl oleuropein aglycone (DOA), two complex polyphenols present in health-promoting extra virgin olive oil (EVOO), might constitute a new family of plant-produced gerosuppressant agents.PASS can predict thousands of biological activities, as the BAS of a compound is an intrinsic property that is largely dependent on the compound's structure and reflects pharmacological effects, physiological and biochemical mechanisms of action, and specific toxicities.Using Pharmaexpert, a tool that analyzes the PASS-predicted BAS of substances based on thousands of "mechanism-effect" and "effect-mechanism" relationships, we illuminate hypothesis-generating pharmacological effects, mechanisms of action, and targets that might underlie the anti-aging/anti-cancer activities of the gerosuppressant EVOO oleuropeins.

View Article: PubMed Central - PubMed

Affiliation: Metabolism and Cancer Group, Translational Research Laboratory, Catalan Institute of Oncology (ICO), Girona, Spain. Girona Biomedical Research Institute (IDIBGI), Girona, Spain.

ABSTRACT
Aging is associated with common conditions, including cancer, diabetes, cardiovascular disease, and Alzheimer's disease. The type of multi-targeted pharmacological approach necessary to address a complex multifaceted disease such as aging might take advantage of pleiotropic natural polyphenols affecting a wide variety of biological processes. We have recently postulated that the secoiridoids oleuropein aglycone (OA) and decarboxymethyl oleuropein aglycone (DOA), two complex polyphenols present in health-promoting extra virgin olive oil (EVOO), might constitute a new family of plant-produced gerosuppressant agents. This paper describes an analysis of the biological activity spectra (BAS) of OA and DOA using PASS (Prediction of Activity Spectra for Substances) software. PASS can predict thousands of biological activities, as the BAS of a compound is an intrinsic property that is largely dependent on the compound's structure and reflects pharmacological effects, physiological and biochemical mechanisms of action, and specific toxicities. Using Pharmaexpert, a tool that analyzes the PASS-predicted BAS of substances based on thousands of "mechanism-effect" and "effect-mechanism" relationships, we illuminate hypothesis-generating pharmacological effects, mechanisms of action, and targets that might underlie the anti-aging/anti-cancer activities of the gerosuppressant EVOO oleuropeins.

Show MeSH
Related in: MedlinePlus