Amy Cotterill

Using established biomarkers to inform clinical trials design for new patient populations or conditions

In recent years the number of registrations of truly new drugs has been about 30-40 each year (eg DiMasi, et al., 2003; Schmid & Smith, 2004) yet many more trials have been conducted successfully. This is because most trials establish benefit of a known compound in a new population, different dose or a different disease rather then investigate a novel compound. An example of such a trial could seek to show benefit of a compound in children when the product has already been shown to be beneficial in adults. From the example it is clear that, although some similarities between the findings can be expected, factors such as dose are likely going to be rather different. In practice many such trials are therefore designed using little or no information from the original trial. While this strategy is certainly sometimes justified, one would expect influential biomarkers such as single nucleotide polymorphisms (SNPs) to be stable across certain populations. Inclusion of these biomarkers in the design of trials, such as the one given in the example, would result in more efficient and safer trials.

The problem of inclusion of biomarkers in the design of clinical trials for identification of subpopulations benefiting from the treatment has been addressed by various authors (eg Brannath, et al., 2009; Chen & Beckman, 2009). Instead of claiming benefit for only a sub-population, an alternative strategy is to adjust dosage based on biomarkers. The potential for this approach has been recognised for some compounds such as the anticoagulant warfarin (eg Loebstein, et al., 2001), but no trial design has been developed to include a possible dose adjustment based on likely influential biomarkers. The objective of this project is therefore to develop a adaptive Phase I/II trials that exploit existing knowledge about the test item during candidate dose selection.

DiMasi, J. A., Hansen, R. W. & Grabowski, H. G., 2003. The price of innovation: New estimates of drug development costs. Journal of Health Economics, 22 (6), 151-185.

Schmid, E.F. & Smith D.A., 2004. Is pharmaceutical R&D just a game of chance or can strategy make a difference? Drug Discovery Today, 9 (1), pp. 18-26.

Brannath, W., Zuber, E., Branson, M., Bretz, F., Gallo, P., Posch, M. & Racine-Poon, A., 2009. Confirmatory adaptive designs with Bayesian decision tools for a targeted therapy in oncology. Statistics in Medicine, 28 (10), pp. 1445-1463.

Chen, C. & Beckman R.A., 2009. Hypothesis Testing in a Confirmatory Phase III Trial With a Possible Subset Effect. Statistics in Biopharmaceutical Research, 1 (4), pp. 431-440.

Loebstein, R., Yonath, H., Peleg, D., Almog, S., Rotenberg, M., Lubetsky, A., Roitelman, J., Harats, D., Halkin, H. & Ezra, D., 2001. Interindividual variability in sensitivity to warfarin-Nature or nurture? Clinical Pharmacology & Therapeutics, 70 (2), pp. 159-164.

Amy Cotterill
The MPS Research Unit
Department of Mathematics and Statistics
Fylde College; Lancaster University Lancaster, Lancashire LA1 4YF

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