Interactions of Bioactive Plant Metabolites: Synergism, Antagonism, and Additivity
Drugs are commonly used in mixtures, also called cocktails, to treat disease, particularly cancer and viral infections. Any two or more drugs, or for that matter, two or more bioactive plant compounds, will either interact in some way or fail to interact. If an interaction produces an effect greater than that expected for each individual drug, the interaction is termed synergistic. If the effect is less than expected, it is termed antagonistic. If the effect is equal to the expected effect (i.e., there is no interaction), the interaction is termed additive (see Greco et al., 1995; Spelman, 2007, in Cseke et al., 2006). In most therapeutic situations, the hope is that mixtures will produce a synergistic effect, but additivity can also be useful and should not be neglected.
Our focus in this chapter is on interactions between bioactive plant compounds used in food and medicine. In particular, we are interested in plant compounds that have potential therapeutic effects, but also exhibit low systemic toxicity, and thus do not pose a high risk of producing adverse effects. Thousands of such compounds are known to exist, and more are being discovered each year. Even a single plant can contain dozens of bioactive compounds. With such a large pool to draw from, there is nearly an unlimited number of ways in which compounds can be combined, either with each other or with market-approved drugs. Clearly many opportunities exist to find mixtures that exhibit synergism or additivity.
In the following sections we explore physical models of drug interaction, discuss a mathematical model that can be used to assess interactions, and provide a number of examples of plant compounds that have been shown to interact in a synergistic fashion. In particular, we look at ways by which mixtures of plant compounds may bind to proteins and affect signaling pathways, as well as ways by which plant compounds could alter receptors indirectly by affecting the plasma membrane. The mathematical model discussed provides an accurate method to estimate interaction indices as well as to construct confidence intervals of the indices. An interaction index is of little use if it is not accompanied by confidence intervals. Technical aspects of the model are presented in order to provide a full description, but publically available software for the model can be used without a complete understanding of the mathematics involved.
- Adachi, S., Nagao, T., Ingolfsson, H.I., Maxfield, F.R., Andersen, O.S., Kopelovich, L., Weinstein, I.B. 2007. The inhibitory effect of (–)-epigallocatechin gallate on activation of the epidermal growth factor receptor is associated with altered lipid order in HT29 colon cancer cells. Cancer Res. 67: 6493–6501.Google Scholar
- Aggarwal, B.B., Harikumar, K.B. 2009. Potential therapeutic effects of curcumin, the anti-inflammatory agent, against neurodegenerative, cardiovascular, pulmonary, metabolic, autoimmune and neoplastic diseases. Int J Biochem Cell Biol 41: 40–59.Google Scholar
- Boik, J.C., Narasimhan, B. 2008. An R package for Assessing Drug. Synergism/Antagonism (submitted).Google Scholar
- Chan, T.A., Glockner, S., Yi, J.M., Chen, W., Van Neste, L., Cope, L., Herman, J.G., Velculescu, V., Schuebel, K.E., Ahuja, N., Baylin, S.B. 2008. Convergence of mutation and epigenetic alterations identifies common genes in cancer that predict for poor prognosis. PLoS Med 5: e114.PubMedCrossRefGoogle Scholar
- Cseke, L., Kirakosyan, A., Kaufman, P., Warber, S., Duke, J., Brielmann H. 2006. “Natural Products From Plants”. Second Edition, CRC Press/Taylor & Francis Group: Boca Raton, FL.Google Scholar
- Csermely, P., Agoston, V., Pongor, S. 2005. The efficiency of multi-target drugs: the network approach might help drug design. Trends Pharmacol Sci 26: 178–182.Google Scholar
- Fuxe, K., Marcellino, D., Rivera, A., Diaz-Cabiale, Z., Filip, M., Gago, B., Roberts, D.C., Langel, U., Genedani, S., Ferraro, L., de la Calle, A., Narvaez, J., Tanganelli, S., Woods, A., Agnati, L.F. 2008. Receptor–receptor interactions within receptor mosaics. Impact on neuropsychopharmacology. Brain Res Rev 58: 415–452.PubMedCrossRefGoogle Scholar
- Greco, W.R., Unkelbach, H.D., Pöch, G., Sühnel, J., Kundi, M., Bödeker, W. 1992. Consensus on concepts and terminology for combined action assessment: The Saariselka Agreement. ACES 4: 65–69.Google Scholar
- Patra, S.K. 2008. Dissecting lipid raft facilitated cell signaling pathways in cancer. Biochim Biophys Acta 1785: 182–206.Google Scholar
- Perillo, M. 2002. The drug–membrane interaction: Its modulation at the supramolecular level. In Recent Research Developments in Biophysical Chemistry. (C.A. Condat, Baruzzi, A., editors). Research Signpost: Kerala, India. 2: 105–121.Google Scholar
- Spelman, K. 2007. Ecopharmacology: molecular details. Unified Energetics 3: 58–62.Google Scholar
- Spinella, M. 2002. The importance of pharmacological synergy in psychoactive herbal medicines. Alternative Medicine Review 7: 130–137.Google Scholar