Like drugs and medical devices, approval of personalized medicines for marketing by regulatory authorities requires clinical trials to be conducted. Traditional clinical trials lack flexibility and are consist of a fixed protocol, interim analysis at predefined points, and interpretation of results based on a predefined statistical analysis plan. Personalized medicine lends way to the use of adaptive trial designs. Adaptive trials allow for prospectively planned modifications to one or more aspects of the design based on accumulation of information as the trial proceeds. Various aspects of the trial can be adapted such as sample size, patient population, treatment arm selection, endpoint selection, and allocation ratio. Adopting an adaptive design can make trials more flexible, efficient, ethical, and informative allowing for the use of fewer patients and therefore saving companies a considerable amount of money. Basket and umbrella designs in which either a targeted therapy is tested on different diseases with the same molecular alteration or multiple targeted therapies are evaluated on a single disease that is stratified into subgroups based on genetic markers are commonly used for personalized medicine trials and are amenable to adaptive trial formats. Alterations in allocation ratios, doses, and participant subgroups depending upon their responses to treatment can be made during the trial allowing for selection of a patient subpopulation that show the maximal response to a treatment. Another type of trial design most suited for personalized medicine is the N-of-1 trial design which focuses on a single patient. Identification of the patient as a responder or non-responder is based on data collection over a prolonged period. Integration of results from several N-of-1 trials will allow determination of certain subpopulations that will benefit from the treatment.
Despite the advantages of adaptive trials for personalized medicine there are several drawbacks resulting in their limited adoption. The limitations include cost associated with
N-of-1 trials, prolonged collection of patient characteristics that can be cumbersome, identification of suitable biomarkers, and most important hesitancy of regulatory agencies to shift their thinking from conventional trials.
Recognizing the specialized needs for personalized medicine clinical trials is essential to ensure success. Challenges such as patient recruitment due to subpopulations being studied, access to key opinion leaders (KOL) that have an adequate understanding of the disease, trial designs and statistical analysis, and availability of genetic testing facilities need to be overcome.
