Chemotherapy resistance
Analyzing medicinal plants and their genes to design drugs that destroy cancer cells while protecting healthy cells
Project Contact: Jack Tuszynski, University of Alberta
The fundamental paradigm of chemotherapy is to find a unique target in the pathogenic cell that is not present in normal cells. Although this works well in bacterial infections (e.g., penicillin), its applicability to cancer cells is limited, since these are the patient’s own cells replicating out of control. Thus, for example, drugs that target DNA replication can be effective against tumor cells, but may also attack other tissues, such as bone marrow, that also undergo relatively rapid DNA replication. Hence, anti-cancer drugs have serious side effects and the hope is to find a “therapeutic window” whereby a dose regimen for the drug is sufficiently high to inhibit the growth of cancer cells but not high enough to cause lethal side effects. These “windows” are typically narrow and the highest tolerated dose is usually not high enough to eradicate the cancer completely. Obviously, the more the side effects are minimized, the greater the “window” would be and the chance of destroying all the cancer cells would be that much higher. Additionally, since cancer cells are programmed to mutate, they can relatively easily develop resistance to the drugs. The list of potential targets for anti-tumor drugs is very long and growing. They include topoisomerase, telomerase, transforming growth factors, kinase inhibitors, such as the monoclonal antibody trastuzemab, etc. Many of these drugs have been synthesized and tested. Often, although they show great promise in tissue culture and even against mouse tumors, they fail in humans. One notable exception to this pattern, however, is the group of drugs that target tubulin, the structural subunit of microtubules that constitute the mitotic spindle and other cellular structures. The tubulin molecule is the target of some of the most successful anti-tumor drugs. Among these are the taxanes (paclitaxel, docetaxol), epothilones and the Vinca alkaloids (vinblastine, vincristine, vindesine, navelbine). These drugs are natural products (mainly derived from various plants), with occasional minor chemical modifications. To cite a few examples of their success, vinblastine is able to essentially cure 80 % of the cases of Hodgkin disease, while vincristine is equally effective against 60 % of cases of acute lymphocytic leukemia. Taxotere is very effective in breast cancer and taxol in ovarian cancer. Our main initial target for drug design has been tubulin and it will continue to be so in the near future.
Medicinal Plant Genes and Optimization of Chemotherapy Agents
One of the important, untapped conceptual tools in the design of cancer chemotherapy agents is the understanding of the genes of the source plants from which many of the existing drugs have been derived. The plant expresses a given toxin as a mechanism of self-defense against various predators. While the toxin is fatal to the predator, it is mostly harmless to the host organism. Curiously, both organisms express the target protein but not in an identical manner. If we knew what types of differences exist in the host genes that confer resistance against the toxic effects of these compounds, we should be able to adopt the same strategy vis-à-vis the cancer cells (predator) and normal cells (host). In order to acquire this level of understanding we need to sequence the transcriptomes of a small number of medicinal plants. With the knowledge so obtained, we will be able to construct structural models of target proteins followed by combinatorial models of their inhibitors with binding affinity calculations guiding us in the optimization of the most potent toxin with the least amount of side effects. In a nutshell, the main objective of this proposal is to use computational biophysical models and predictive bioinformatics algorithms towards four types of specific applications leading to new improved cancer chemotherapy. We aim to find new improved drugs for cancer chemotherapy whose chemical structure is designed by taking clues from Mother Nature. Since the research and development for this project is being done at the Cross Cancer Institute (CCI) with major funding from the Alberta Cancer Foundation (ACF), further testing, pre-clinical and phase I and II clinical trials, as well as eventual therapy development will be streamlined within the framework of the Health Services of the Province of Alberta. Below, we list some of the key plants that are used to produce currently approved chemotherapy compounds and whose transcriptomes are suggested for sequencing:
1. Maytansine: Maytenus serrata (no English name)
2. Podophyllotoxin: Podophyllum pentatum (false mandrake).
3. Periwinkle: Vinca rosea
4. Yew: Taxus brevifolia
5. Crocus: Colchicum autumnale.
To support our hypothesis about the insensitivity of the plants to their own toxins, Gunning and Hardham (1982) refer to the insensitivity of Colchicum to colchicine and Vinca to vinblastine. Kramers and Stebbings (1977) document the insensitivity of Vinca microtubules to vinblastine. Plant tubulins in general do not bind well to colchicine or vinblastine. However, Colchicum tubulin is especially insensitive to colchicine. Vinca tubulin is especially insensitive to vinblastine although it binds OK to colchicine. Levan (1940) and Levan and Steinegger (1948) document that Colchicum is insensitive to colchicine. Finally, Santos Dias and Mesquita (1978) state that plant microtubules are only slightly sensitive to taxol.
We are studying the literature for additional plants that could be used in this project but the above five should already give us ample information for optimized drug design.
Literature Cited
1. B.E.S. Gunning and A.R. Hardham. (1982) Microtubules. Ann. Rev. Plant Physiology 33: 651-98.
2. M.R. Kramers and H. Stebbings. (1977) The Insensitivity of Hnca rosea to Vinblastine. Chromosoma (Berl) 61: 277-287.
3. A. Levan. (1940) The effect of acenaphthene and colchicine on mitosis of Allium and Colchicum. Hereditas (Lund) 26: 262-276.
4. A. Levan and E. Steinegger. (1948) The resistance of Colchicum and Bulbocodium to the c-mitotic action of colchicine. Hereditas (Lund) 33: 552-566.
5. J.D. Santos Dias and J.F. Mesquita. (1978) Comportement des microtubules descellules radiculaires de Colchicum multiflorum Brot. sous l'action de la colchicine exogene. Bol. Soc. Broteriana 52 (2.a. Ser.) 221-240.