The Importance of Innovation in Trial Design
Clinical development is expensive, invariably high risk and at times enormously inefficient, which is most likely the reason why productivity is sliding so rapidly in the pharmaceutical industry. While drug companies continually ramp up their R&D investments, the number of new products reaching the market has stalled.
The growing rate at which high-profile Phase III failures occur has put even greater emphasis on the need to make good decisions from the start. A recent Novartis report outlined the ways in which novel approaches to clinical development and trial design could boast potential in overcoming some of these challenges by improving efficiency and bringing down attrition rates. The authors John J Orloff and Donald Stanski suggested that trial outcomes could be improved through the adoption of a more integrated model that increases flexibility and maximises the use of accumulated knowledge. Central to this approach are innovative tools, including modelling and simulation, and adaptive designs.
When developers embrace an integrated approach to drug development, there are three types of modelling that should be used throughout the process to maximise their synergies, the report noted. Firstly, biological modelling is used to gain understanding of genetic, biochemical and physiological networks, as well as the pathways and processes behind disease and pharmacotherapy. Secondly, pharmacological modelling is what guides trial design, dose selection and development strategies. Finally, statistical modelling allows research teams to assess the effectiveness of designs and strategies in populations.
One of the key goals of a drug development programme is the selection of a dosing regimen that achieves the target clinical benefit while minimising adverse effects. Biological and pharmacological modelling can prove very useful in this context, the Novartis authors explained. "For example, we have used such modelling in the dose selection for canakinumab, a monoclonal antibody that has recently been approved for the treatment of the rare genetic disease Muckle Wells syndrome. Clinical data on the relationship between activity of the therapeutic target, markers of inflammation and remission of symptoms were captured in a mathematical model that was continuously adjusted to fit emerging data.
"Simulation was then used to propose a suitable dose and dosing regimen to achieve the desired response for the majority of patients – in this instance, an 80 per cent probability that 90 per cent of patients would remain flare-free for two months," they wrote. Data derived from this particular modelling exercise enabled the selection of a dosing regimen that was later investigated and confirmed in a Phase III trial, the authors added.
Also central to the adoption of a more integrated model for drug development are adaptive trial designs. The core concept in this instance is that accumulating data is used to make decisions on how to modify aspects of the study once the trial is underway, without undermining its integrity. "Possible adaptations include adjustments to sample size, allocation of treatments, the addition or deletion of treatment arms, inclusion and exclusion criteria for the study population, adjusting statistical hypotheses and combining trials or treatment phases," the report suggested.
It is increasingly being recognised by developers that adaptive trials have the potential to translate into more efficient drug development and better use of available resources. According to the Novartis report, there are several potential advantages of seamless adaptive designs. They can reduce the programme duration, by eliminating time lags that traditionally occur between phases. Adaptive designs also promote greater efficiency from the use of data from multiple stages of the process, meaning fewer patients are required to obtain information of the same quality.
However, the authors noted: "There are a number of requirements for successfully implementation of adaptive trial designs. Drug responses should be rapidly observable relative to accrual rate. Adaptive trials also necessitate more up-front statistical work to model dose response curves and to perform simulations – and many simulations are required to find the best combinations of sample size, the randomisation ratio between placebo and drug, starting dose and number of doses. This in turn demands efficient programming to develop complex algorithms and programs, and fast computing platforms."
While innovative approaches to clinical development and trial design have the potential to boost efficiency and improve outcomes, there are clearly distinct challenges that must first be understood and overcome. In order for the adoption of these modern tools to increase, awareness of their advantages to developers, sponsors and indeed regulators will crucially need to improve.