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. 2017 Nov 9;12(11):e0187925.
doi: 10.1371/journal.pone.0187925. eCollection 2017.

Nature is the best source of anticancer drugs: Indexing natural products for their anticancer bioactivity

Anwar Rayan  1   2 , Jamal Raiyn  1 , Mizied Falah  3   4
Affiliations

Nature is the best source of anticancer drugs: Indexing natural products for their anticancer bioactivity

Anwar Rayan et al. PLoS One. .

Abstract

Cancer is considered one of the primary diseases that cause morbidity and mortality in millions of people worldwide and due to its prevalence, there is undoubtedly an unmet need to discover novel anticancer drugs. However, the traditional process of drug discovery and development is lengthy and expensive, so the application of in silico techniques and optimization algorithms in drug discovery projects can provide a solution, saving time and costs. A set of 617 approved anticancer drugs, constituting the active domain, and a set of 2,892 natural products, constituting the inactive domain, were employed to build predictive models and to index natural products for their anticancer bioactivity. Using the iterative stochastic elimination optimization technique, we obtained a highly discriminative and robust model, with an area under the curve of 0.95. Twelve natural products that scored highly as potential anticancer drug candidates are disclosed. Searching the scientific literature revealed that few of those molecules (Neoechinulin, Colchicine, and Piperolactam) have already been experimentally screened for their anticancer activity and found active. The other phytochemicals await evaluation for their anticancerous activity in wet lab.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Diversity within anticancer drugs (A, left side) and diversity within natural products database (B, right side).
Fig 2
Fig 2. Flowcharts for the modeling process (2a), and the ISE engine (2b).
Fig 3
Fig 3. Physicochemical properties distribution of anticancer drugs (A) Molecular weight distribution, (B) Log P values, (C) Number of H-bond acceptors [lip_acc], (D) Number of H-bond donors [lip_don], (E) Number of rigid bonds, (F) number of rotatable bonds, (G) Number of aromatic atoms.
Fig 4
Fig 4. Violation distribution of anticancer drugs to Lipinski rule of 5 for drug-likeness (left side) and Oprea rule for lead-likeness (right side).
Fig 5
Fig 5. Redundancy of descriptors in the 29 filters used to produce the anticancer indexing model.
The picture was constructed by using WORDLE module.
Fig 6
Fig 6. Indexing model for anticancer potential activity: True/false positives percentage (left Y-axis) and Matthews's correlation coefficient (MCC, right Y-axis) illustrated against molecular bioactivity index threshold (MBI, X-axis).
Fig 7
Fig 7. Enrichment plot of the anticancer potential activity-indexing model of natural products.
Fig 8
Fig 8. A receiver operating characteristic (ROC) curve showing the performance of the anticancer bioactivity-indexing model.
Fig 9
Fig 9. Twelve of the natural products that are scored highly as potential anticancer drug candidates according to our ISE-based anticancer indexing model.
See this image and copyright information in PMC

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