It is not a secret that the research and development in the pharmaceutical industry is very costly, time-consuming, risky and not particularly efficient. According to estimates bringing new drugs on the market could cost between $800 million and $2 billion. Failure is late stages – phase 2 and phase 3 – contribute significantly for the increased expenses. Some of the big challenges of phase 2 studies are that there is no enough information regarding dose-response relationship and the relevance of the biological target.
All these challenges were the reason for initiatives to change the way clinical trials are designed.
What kind of new approaches could be used?
- One of the new approaches involved combination of different types of modelling: biological modelling, pharmacological modelling and statistical modelling. This method allows to use external data in order to model and simulate your study. Later on simulation would allow selecting the best study design to achieve your goals.
- Bayesian modelling – this method combine external baseline data to improve efficacy and safety signal detection in early development. An example of such approach is to use small cohorts with dose-escalation to assess efficacy and safety at the end of each cohort. Dose will be increased until maximum therapeutic dose is achieved or unacceptable toxicity. Each new cohort will start only if all safety parameters are assessed for the different doses from the previous cohorts. This will allow detecting safety problems which are observed in large population of patients and only relatively strong efficacy signals will be detected.
- Adaptive designs – In this case interim data from a trial is used to modify and improve the study design, in a pre-planned manner and without undermining its validity or integrity. For example, a larger proportion of the enrolled patients can be assigned to the treatment arms that are performing well, drop arms that are performing poorly, and investigate a wider range of doses so as to more effectively select doses that are most likely to succeed. In the next stage this allows to identify early efficient treatment, drop poorly performing arms, stop the study early for meeting its primary endpoints or modify eligibility to include more patients.
- Seamless designs – in this case single trial has objectives that are traditionally addressed by separate trials.
- Sample size re-estimation methods – this allows to increase or decrease sample size at an interim point for the trial.
The way we run clinical trials is changing and while it becomes more flexible in drug development point of view it could also become more complex from research centres point of view.
Author: Olga Peycheva
Olga is a clinical research professional who has been working in clinical research since 2005. She has extensive experience in clinical research in Eastern and Western Europe.
Originally published on 6 Dec 2018