Michael Krams, Ph.D.
Pfizer Clinical Science CNS
Pfizer Global Research and Development
Innovative approaches to clinical trial designs
Developing new drug therapies can be expensive, high-risk operations. Much of the
cost of bringing a new compound to market (US $700M per successful drug candidate)
is accrued during late phase II/III development. Traditionally, phase II/III trials
do not formally look at a utility which accounts for cost of development and
eventual pay-off. However, cost-benefit considerations are intrinsically governing
the decision-making on go/no-go decision points for advancing drug development
programs. Improving the quality and speed of decision-making may translate into
higher cost efficiency in developing new drug therapies.
The failure to understand the dose-response of a new drug is one important reason for late attrition in drug development. We propose a decision-analytic approach to developing new therapies, and will use the example of developing a neuroprotectant for acute stroke to illustrate an adaptive design learning in real time about the dose-response and envisaging a seamless switch from a phase IIb dose-response finding phase to a confirmatory phase III trial. The design presented here is discussed in more detail by Berry et al (2002). We will present our experience in implementing this trial in an international stroke trial.
In a sequential adaptive dose-response study, two decision problems are formally and continuously assessed, using a Bayesian decision-analysis:
1. Which dose should the next patient be allocated to, in order to optimize learning about the minimal dose which will yield maximal efficacy (say ED95)?
2. Should the dose-response finding phase continue in order to learn more about the dose-response (and the ED95 in particular), or is there sufficient information to recommend stopping the trial, either for futility or to move into a confirmatory clinical trial, comparing the ED95 against control?
Using Bayesian statistics the probability of achieving a predefined overall utility in a future confirmatory trial can be calculated. The overall utility may integrate desired clinical effect, cost of running the development program, and estimated pay-off.
Reference:
Berry DA, Mueller P, Grieve AP,
Smith MK, Parke T, Krams M (2002) Bayesian designs
for dose-ranging drug trials. In: Case Studies in Bayesian Statistics Vol 5. Gatsonis C, Kass RE, CarlinB, Carriquiry A, Gelman A, Verdinelli I, and West M, editors.