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At present, the development of new drugs for the treatment of diseases such as cancer, is slow and expensive. It is estimated that of every ten drugs that enter clinical trials, only one will reach the marketplace. A dropout rate of this magnitude, with its associated human and financial costs, would not be tolerated in any other industry. This dropout rate is directly attributable to present technology's inability to quickly determine how effectively a drug is working, to accurately predict what will happen in the human body based on results of tests in the laboratory, and to determine variations in drug response from one person to another. Two projects in which mathematical modelling is being being used to develop effective anti-cancer drug therapies are in progress:

1) The modelling and analysis of the pharmacodynamics of anti-cancer drugs (with Cyclacel Ltd.) All disease processes and all drug effects, leave a "fingerprint" in the complex pattern of proteins in the blood. If these fingerprints could be accurately interpreted, then a simple and quick blood test would provide clear and precise information about the progression of disease and the effectiveness of drugs.

All cells in the body have a switch, which if activated, causes the cell to die. Apoptosis or "programmed cell-death" is very important in maintaining a proper balance of healthy cells. Anti-cancer drugs often work by triggering one of the cell-death switches. This involves the activation of a family of molecules in the cell called caspases, which in turn, activate irreversible cellular damage and finally cell death. The by-products of this cell death process are released into the blood. The normal levels of these by-products are very low, and increases are seen when tumour cells are being destroyed by drugs. Hence, provided a detailed understanding can be obtained of how the concentration of these bi-products in the blood relates to cell death, then a simple blood test could quickly and easily determine whether a given anti-cancer drug is killing cancer cells in the tested patient. (At present, the patient often has to wait weeks or even months to see if a drug is working. If it is not, it is unfortunately often too late to change the treatment).

The classic approach to testing drugs in humans is based on pharmacokinetics i.e. how widely a drug is distributed in the body, its peak concentration in the blood and how rapidly is it eliminated. What pharmacokinetic measurements do not provide, however, is any indication of how effectively the drug is working: this is what is called pharmacodynamics. In some cases it is possible to infer drug effect from drug concentration. When these conditions are not met, then the relationship is not clear and therefore the usefulness of traditional pharmacokinetic modelling is limited. In these cases pharmacodynamic data and modelling is vital. Cyclacel Ltd. are developing a range of range anti-cancer drugs that act by perturbing the cell cycle (the sequence of events leading to cell division). To fully understand how these drugs work, requires quantitative models of this complex interactive system, an understanding of how the drug dose (concentration and timing) relates to drug effects and how these effects can be quickly and simply measured in a given patient. We are working closely with Cyclacel to develop novel mathematical models to obtain a better understanding of the link between anti-cancer drugs, cell-death and by-product concentration in the blood. This work will assist in the development of a simple and quick diagnostic tool with which to test the effectiveness of anti-cancer drugs. This diagnostic tool could help to devise useful drug combinations, to individualise treatment for a specific patient and in the long term, could be used to predict the efficacy of an experimental treatment without the need to wait many months for a measurable outcome.