Ethnobotanical Leaflets 12: 801-10. 2001.

 

 

Prediction of Biological Activity Spectra for Few Anticancer Drugs Derived from Plant Sources

 

A. John De Britto*,  T. Leon Stephan Raj and D. Abiya Chelliah

 

PG & Research Department of Plant Biology and Biotechnology

St.Xavier’s College (Autonomous) Tirunelveli – 627 002, Tamilnadu, India

*E-mail: bjohnde@yahoo.co.in

Tel: 0462-2560813; Fax: 0462-2561765

 

Issued 30 October 2008

 

ABSTRACT

            Over the past decade plants have become an interesting source of new classes of pharmacologically active natural products. Some secondary metabolites are also well known for their effectiveness on living species. The PASS (Prediction of Activity Spectra for Substances) computer program, which is able to simultaneously predict more than one thousand biological and toxicological activities from only the structural formulas of the chemicals, was used to predict the biological activity profile of 7 secondary metabolites. PASS predictions were successfully compared to the available information on the pharmacological and toxicological activity of these compounds.

 

INTRODUCTION

             Cancer is a group of diseases characterized by uncontrolled growth and spread of abnormal cells. If the spread is not controlled, it can result in death. Cancer is caused by both external factors (tobacco, chemicals, radiation, and infectious organisms) and internal factors (inherited mutations, hormones, immune conditions, and mutations that occur from metabolism). These causal factors may act together or in sequence to initiate or promote carcinogenesis. When the control signals in one of the cell goes wrong, and its life cycle becomes disturbed, it divides and divides. It continues multiplying uncontrollably, and the result of this accumulation of abnormal cells is a mass of cells called a "tumor". A tumor can be either benign or malignant.

            Benign tumors are non-cancerous and are rarely life-threatening. They do not spread (metastasize) to other parts of the body. Many breast lumps, for example, are benign tumors.

Malignant tumors are cancerous and can spread to other parts of the body. When a malignant tumor spreads, the malignant cells break off and travel through the blood lymph system to other places in the body to settle and multiply; or metastasize, resulting in a new tumor called a secondary tumor, or metastasis. The name given to the cancer, however, is reflective of the origin of the cancer, even if it has spread to other areas of the body. For example, if prostate cancer has spread to the liver it is called metastatic prostate cancer.

 Ten or more years often pass between exposure to external factors and detectable cancer. Cancer is treated with surgery, radiation, chemotherapy, hormone therapy, biological therapy, and targeted therapy. As it is difficult to differentiate between the cancerous and normal cells in-vivo the innovation of a potential drug against the disease it yet a menace. With the known therapeutic knowledge and indigenous crude custom scientists have been thriving their excellence in the field of pharmacology to design a potential drug that is suited to kill the cancerous cells alone.

            Most anti-cancer drugs act by inhibiting DNA synthesis or some other process in the cell growth cycle. Because anti-cancer drugs generally affect rapidly dividing cells, other non-cancerous cells will also be affected. The way in which the other cells are affected determines the side-effects of the individual drugs. Other cells affected include blood cells, which fight infection, help the blood to clot, and carry oxygen to all parts of the body. When blood cells are affected, patients are more likely to get infections, may bruise or bleed easily, and may feel unusually weak and very tired. Rapidly dividing cells in hair roots, and cells that line the digestive tract, may also be affected. As a result, side effects may include loss of hair, poor appetite, nausea and vomiting, diarrhea, or mouth and lip sores. Many of these side effects can now be controlled, thanks to new or improved drugs. Side effects generally are short-term and gradually go away. Hair grows back, but it may be different in color and texture. With the known ethno botanical knowledge and phytochemical interpretations there are a number of drugs that could be effective against the cancer. The outcome of insilico knowledge on the designing of drug is yet another boom for the designers to minimize the work, PASS (Prediction of Activity Spectra for Substances) is one such a server that predicts the possibility of a drug to be active against a target based on the physico- chemical methods using comparisons and various algorithms.

            PASS predicts simultaneously several hundreds of biological activities (pharmacological main and side effects, mechanisms of action, mutagenicity, carcinogenicity, teratogenicity and embryotoxicity). The biological activity spectrum of a compound presents all compounds’ actions despite the difference in essential conditions of its experimental determination. If the difference in species, sex, age, dose, route, etc. is neglected, the biological activity can be identified only qualitatively. Thus, "the biological activity spectrum" is defined as the "intrinsic" property of a compound depending only on its structure and physico-chemical characteristics.

            PASS is the product of ideas originated more than 25 years ago within the framework of the National Registration System of New Chemical Compounds organized in the USSR in 1972 (Burov et al., 1990). It was V.Avidon who suggested that many kinds of biological activity could be predicted on the basis of structural formulae of chemical compounds (Avidon, 1974). Similar approach was under development by V.Golender and A.Rozenblit (Golender, and Rosenblit, 1983). PASS  has already found new leads with antiulcer, antitumor and antiamnestic activity, and discovered new mechanism of action for some compounds with known effect (Filimonov and Poroikov, 1996, Filimonov et al., 1995, Poroikov and Staraya Kupavna, 1995, Poroikov et al., 1996, Poroikov et al., 1994). Experimental determination of drug efficacy and safety is time- and cost-consuming procedure. There exist standard tests for drug safety assessment (Maggon et. al., 1992) and different strategies for search of new lead compounds (Walker, 1994). Biological testing is organised taking into account “similarity/dissimilarity” of new compound to the other known biologically active substances. Several similarity/dissimilarity suggestions are used both in drug design and screening to determine if particular tests are necessary and sufficient for comprehensive estimation of new compound activity.  PASS 4.20 training set includes 9314 biologically active substances. PASS 4.20 predicts the probabilities of presence/absence for 114 biological actions simultaneously (main and side pharmacological effects, mechanisms, specific toxicity). It is shown that the approach, used in PASS, can be applied to the other biological activities [Filimonov et al.,1995, Shilova et al.,, Filimonov et al.,1996].In the past years Computer Aided Drug Design (CADD) is widely used in new drug R & D [Franke and Herrmann, 1994]. Recently, we developed the computerised system PASS (Prediction of Activity Spectra for Substance) that estimates simultaneously the probability of more than 100 pharmacological effects and mechanisms [Filimonov et al, 1995, Filimonov and Poroikov (1996)]. The effectiveness of this computer aided approach application in screening has been shown to be 800% more than the random guess-work [Filimonov and Poroikov (1996)] and 300% more than the estimation by skilled experts [Poroikov et al., 1993]. However, the antiulcer action has not been covered by the initial version of PASS. Therefore, in this work we extend PASS prediction’s area on antiulcer activities and use this specialised system to discover some new antiulcer agents.

            Prediction of this spectrum by PASS is based on SAR analysis of the training set containing more than 35,000 compounds which have more than 500 kinds of biological activity (http://www.ibmh.msk.su/PASS). Therefore, PASS once trained is able to predict simultaneously all biological activities which are included in the training set. To provide the best quality of prediction new information about biologically active compounds is collected permanently from papers and electronic sources and, after the experts' evaluation, is regularly added to the training set.

MATERIALS AND METHODS

            The following chemical compounds were selected for the study, the predicted structures are obtained from the Pub-Chem database of National Center for Biotechnology Iinformation.

1. Taxol

2. Vinblastine

3. Vincristine

4. Topotecan (a camptothecin derivative)

5. Irinotecan

6. Etoposide

7. Teniposide

Belongs to the family of drugs called mitotic inhibitors. Teniposide is a semisynthetic podophyllotoxin derived from the root of Podophyllum peltatum (the May apple or mandrake). It is used for treating Acute lymphocytic leukemia and neuroblastoma

 

CHEMICAL STRUCTURE OF ANTICANCER DRUGS

Taxol

Vinblastine

 

      Vincristine

Topotecan

Irinotecan

Etoposide

 

 

Teniposide

 

 

            Biological activity is the result of chemical compound's interaction with biological entity. In clinical study biological entity is represented by human organism. In preclinical testing it is the experimental animals (in vivo) and experimental models (in vitro). Biological activity depends on peculiarities of compound (structure and physico-chemical properties), biological entity (species, sex, age, etc.), mode of treatment (dose, route, etc.). Any biologically active compound reveals wide spectrum of different effects. Some of them are useful in treatment of definite diseases but the others cause various side and toxic effects. Total complex of activities caused by the compound in biological entities is called the "biological activity spectrum of the substance".Biological activity spectrum of a compound presents every its activity despite of the difference in essential conditions of its experimental determination. The biological activity spectrum of PASS is designed according to the algorithm specified below:

            For the compound under prediction structural descriptors are generated. For each activity the following values are calculated:

uj = a i ArcSin{ri(2pij-1)}, u0j = a i ArcSin{ri(2pj-1)}

sj = Sin(uj/m), s0j = Sin(u0j/m)

Prj = (1+(sj-s0j)/(1-sjs0j))/2

            Then the results are validated and predicted., In case when the probabilities for more than 400 different activities are estimated simultaneously, and the ideal training set should include all referenced biologically active compounds from literature, the best estimate of prediction's quality can be calculated by leave one out cross validation. Each of the compounds is subsequently removed from the training set and the prediction of its activity spectrum is carried out on the basis of the remaining part of the training set. The result is compared to the known activity of a compound, and the maximal error of prediction (MEP) is calculated through the all compounds and activities.

RESULTS

BIOLOGICAL ACTIVITY SPECTRUM OF THE ANTICANCER DRUGS

TAXOL

76 Substructure descriptors; 0 new.

45 Possible activities at Pa > 30%

Pa Pi for Activity:

0,991   0,001    Antimitotic 

0,982   0,001    Microtubule formation inhibitor 

0,957   0,000    Antimitotic Taxane-like 

0,945   0,001    Microtubule formation stimulant 

0,850   0,004    Protein-arginine deiminase inhibitor 

0,829   0,008    Antineoplastic 

0,707   0,006    Antineoplastic enhancer 

0,691   0,006    Radiosensitizer 

0,652   0,014    CYP2C8 substrate 

0,629   0,022    CYP3A substrate 

0,661   0,106    Phosphatase inhibitor 

0,529   0,001    Beta tubulin antagonist 

 

VINBLASTIN

87 Substructure descriptors; 0 new.

45 Possible activities at Pa > 30%

Pa Pi for Activity:

0,949   0,002    Xenobiotic-transporting ATPase inhibitor 

0,931   0,001    Antineoplastic alkaloid 

0,912   0,003    Lactose synthase inhibitor 

0,896   0,007    Antineoplastic 

0,871   0,001    Tubulin antagonist 

0,870   0,001    Antineoplastic (multiple myeloma

0,713   0,027    Teratogen 

0,692   0,007    Cytostatic 

0,659   0,017    Embryotoxic 

0,637   0,029    Toxic 

VINCRISTINE

88 Substructure descriptors; 0 new. 

31 Possible activities at Pa > 30%

Pa Pi for Activity:

0,941   0,001    Antineoplastic alkaloid 

0,917   0,007    Antineoplastic 

0,879   0,001    Tubulin antagonist 

0,853   0,001    Antineoplastic (multiple myeloma

0,816   0,003    Leukopoiesis inhibitor 

0,802   0,006    Cytostatic 

0,742   0,021    Teratogen 

0,693   0,014    Embryotoxic 

0,675   0,022    Toxic 

0,579   0,003    Microtubule formation inhibitor 

TOPOTECAN

57 Substructure descriptors; 0 new.

64 Possible activities at Pa > 30%

Pa Pi for Activity:

0,877   0,002    DNA intercalator 

0,870   0,002    Topoisomerase I inhibitor 

0,875   0,007    Antineoplastic 

0,689   0,043    CYP2D16 substrate 

0,626   0,010    Antiviral (Influenza) 

0,614   0,002    Antineoplastic alkaloid 

0,617   0,046    Apoptosis agonist 

0,537   0,082    Magnesium protoporphyrin IX monomethyl ester (oxidative) cyclase inhibitor 

0,523   0,071    Immunomodulator 

0,548   0,107    CYP3A1 substrate 

IRINOTECAN

66 Substructure descriptors; 0 new.

43 Possible activities at Pa > 30%

Pa Pi for Activity:

0,894   0,007    Antineoplastic 

0,840   0,002    DNA intercalator 

0,810   0,002    Topoisomerase I inhibitor 

0,796   0,002    Antineoplastic alkaloid 

0,454   0,035    Antiviral (Influenza) 

0,437   0,047    Alzheimer’s disease treatment 

0,533   0,176    Nerve growth factor agonist 

0,447   0,112    ®-Pantolactone dehydrogenase (flavin) inhibitor 

0,484   0,157    Transplant rejection treatment 

0,359   0,034    Tetrahydroxynaphthalene reductase inhibitor 

ETOPOSIDE

61 Substructure descriptors; 0 new.

72 Possible activities at Pa > 30%

Pa Pi for Activity:

0,976   0,005    Hematotoxic 

0,930   0,007    Antineoplastic 

0,907   0,000    Antimitotic Podophyllotoxin-like 

0,819   0,012    Pulmonary hypertension treatment 

0,803   0,003    Topoisomerase I inhibitor 

0,756   0,006    Carcinogenic, group 2A 

0,725   0,008    CYP3A5 substrate 

0,696   0,015    Emetic 

0,683   0,003    DNA intercalator 

0,666   0,002    Antimitotic 

TENIPOSIDE

67 Substructure descriptors; 0 new.

63 Possible activities at Pa > 30%

Pa Pi for Activity:

0,942   0,006    Antineoplastic 

0,902   0,004    Xenobiotic-transporting ATPase inhibitor 

0,823   0,010    Cardiotoxic 

0,782   0,016    Pulmonary hypertension treatment 

0,726   0,004    Topoisomerase I inhibitor 

0,717   0,007    Carcinogenic, group 2A 

0,657   0,013    CYP2B6 substrate 

0,606   0,012    CYP3A5 substrate 

0,610   0,017    Beta-amylase inhibitor 

0,598   0,027    Emetic 

 

DISCUSSION

            Pa and Pi are the estimates of probability for the compound to be active and inactive respectively for each type of activity from the biological activity spectrum. Their values vary from 0.000 to 1.000. It is reasonably that only those types of activities may be revealed by the compound, which Pa > Pi and so they are put into the biological activity spectrum.

            Planning experiments and choosing the activities on which the compound has to be tested, one should have in mind the necessity of balancing between the novelty of pharmacological action and the risk to obtain negative result in experimental testing. Certainly, one will also take into account the particular interest in some kinds of activity, experimental facilities, etc.

The accuracy of prediction is about 90%.

 

CONCLUSION

            All the seven drugs estimated show a good tendency to fight against the neoplastic cancer, also the drug Irinotecan is against Alzimers disease. Teniposide is not as worthy as others as it is a cardiotoxic. Topotecan, Etaposide and Irinotecan are DNA intercalators. Now a bandwith of drugs for the cancer treatment is analysed,it must be improved and lot of studies have to be continued to ensure its activity invitro and invivo.

 

 

REFERENCES

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            Burov, Yu.V., Poroikov, V.V., Korolchenko, L.V. National system for registration and biological testing of chemical compounds: facilities for new drugs search. Bull. Natl. Center for Biologically Active Compounds (Rus.) 1990, 1: 4-25.

            Filimonov D.A, Poroikov V.V, Boudunova A.P, Rudnitskih A.V, Yu.V. Burov.  (1995), Computerized Prediction of Antiamnestic Activity for Chemical Compounds: PASS Possibilities Extending. Abstr. SCI Conference “Design of Bioactive Compounds”, 4-7 September, Potsdam, Germany, p.26.

            Filimonov D.A., Trapkov V.A., Boudunova A.P., Burova O.A., Poroikov V.V. (1996), Discovery of New Chemical Entry with Antiulcer Activity by Using Computer Aided Prediction. Abstr. XIVth International Symposium on Medicinal Chemistry, Maastricht, the Netherlands, P-1.12.

            Filimonov, D. A., Poroikov, V.V. PASS: Computerized prediction of biological activity spectra for chemical substances. In: Bioactive Compound Design: Possibilities for Industrial Use, Oxford: BIOS Scientific Publishers 1996, 47-56.

            Filimonov, D.A., Poroikov, V.V., Karaicheva ,E.I., Kazaryan, R.K., Boudunova, A.P., Mikhailovsky, E.M., Rudnitskih, A.V., Goncharenko, L.V., Burov, Yu.V. (1995) mputer-aided prediction of biological activity spectra of chemical substances on the basis of their structural formulae: computerized system PASS. Experimental and Clinical Pharmacology (Rus), 8(2): 56-62.

            Franke R., Herrmann E.C. 1994.   Eds. // Computer Aided Drug Design in Industrial Research Berlin: Springer-Verlag, - 252 pp.

            Golender, V.E., Rosenblit, A.B. Logical and Combinatorial Algorithms for Drug Design. Wiley&Sons: L., a.o. 1983.

            Maggon, K.K., Mechkovski, A. (1992) Total Quality Management. Drug News and Perspectives 5: 261-270.

             Poroikov V.V, Boudunova A.P, Shamshin V.P, Suhanova S.A, Trapkova A.A, Yu.V. Burov. (1994),   Bull. Natl. Res. Center for Biologically Active Compounds (Rus), Revealing of New Mechanism for Barbiturates Activity Potentiation on the Basis of Computer Aided Prediction of Biological Activity Spectra. No 1, p.39-45.

             Poroikov V.V, Filimonov D.A, Stepanchikova A.V., Chim.-Pharm. J. (Rus), (1996), Opimization of Synthesis and Pharmacological Testing of New Compounds Based on Computerized Prediction of Their Biological Activity Spectra. v.30, N 9, p.20-23.

            Poroikov V.V, Sc. D. in Pharmacology Thesis (Rus). Staraya Kupavna (1995):  (Moscow Region) National Research Center for Biologically Active Compounds, Computer Aided Prediction in New Drugs Research and Development. pp348.

            Poroikov V.V., Filimonov D.A., Boudunova A.P.. Comparison of Prediction Results for the Chemical Compounds Spectrum of Biological Activity by Experts and the PASS System. // Automatic Documentation and Mathematical Linguistics. Allerton Press, Inc. - 1993 - V.27 - No 3 - P.40-43.

            Shilova E.V, Filimonov D.A, Poroikov  V.V PASS: Prediction of Activity Spectra for Substance. Adrenergic Compounds Set. ibid, p.27.

            Walker, S. (1994) The future of the Japanese pharmaceutical industry. - How can R&D be really international Pharma Japan, No 1424, pp 7-10.

            Computer Aided Drug Design in Industrial Research. / Franke R., Herrmann E.C. Eds. // Berlin: Springer-Verlag, 1996, 252 pp.

            195.178.207.233/PASS/index.html

            www.akosgmbh.de/pass/

            http://www.drugs.com/cancer.html