The development of a novel drug is a time-consuming and expensive process. Innovative trial designs and optimal sequences of clinical trials aim to increase the efficiency of this process by improving flexibility and maximising the use of accumulated information throughout the trials while minimizing the number of patients that are exposed to unsafe or ineffective regimens. In Chapter 2 of this thesis, we focus on confirmatory trials that are one of the largest contributors to cost and time in later stages of the drug development process. We consider a clinical trial setting where multiple treatment arms are studied concurrently and an ‘order’ (i.e. a monotonic relationship) among the treatment effects can be assumed. We propose a novel design which incorporates the information about the order in the decision-making without assuming any parametric arm-response model and controlling error rates. We compare the performance of this novel approach with currently used trial designs and we describe its application to design an actual trial in tuberculosis in Chapter 3. In Chapter 4, we propose a Bayesian extension of the design described in Chapter 2 allowing to relax the order assumption and incorporate historical information. This is needed for settings where, for example, the increase in side effects or compliance with the treatment lead to a reduced efficacy of the treatment and, hence, violation of the order assumption. We compare this design with other competing approaches that do not consider uncertainty in the order. In Chapter 5, we focus on the whole drug development process. We consider an oncology trial setting and we compare two sequences of clinical trials, the first targeting the whole patient population and the second a molecularly defined subgroup within the population. We propose a metric to quantify the expected clinical benefit of these two strategies. In addition, for each strategy we measure the cost of development as the expected proportion of patients enrolled over the total common sample size. We illustrate a performance evaluation of the proposed metric in an actual trial.