Transforming trials: How the traditional clinical development model needs to change

Transforming Clinical Trials

The traditional clinical development model needs to change.

Current methods of drug and device development are time consuming, expensive, and prone to failure. Only a modest percentage of therapies successfully navigate the regulatory minefield from Phase I to final approval. The mounting costs and increasing complexity of the existing system means that numerous promising drug candidates are often abandoned for logistical or economic reasons rather than for issues related to their efficacy. Fundamental change is needed to overhaul the current pipeline system to shorten development cycles, increase return on investment, and efficiently bring medicines to those who need them most. Applying the following principles should be part of future development frameworks.

Adopt adaptive trial designs
Adaptive trials use accumulating information during the trial to alter the direction of the study and can be used in every phase. Encouraged by the FDA and EMA, adaptive design trials reduce risks for both patients and developers, particularly at difficult decision-points, such as in dose selection.

Furthermore, adaptive trials can reduce the total number of patients required to obtain reliable results, thereby relieving strain on the limited resources of sponsors, researchers, monitors, and trial sites. This increases the capacity of the entire clinical development system.

Minimize control arms
Assigning large numbers of patients to placebo or existing therapy arms in Phase III clinical trials means that fewer patients receive a potentially better treatment and reduces outcomes data generated for the trial drug.

Many control drugs and placebos have been studied in thousands of patients, with their effects well characterized. This historical data can make it possible to match the demographics, baseline conditions, and outcomes of current study patients with patients from previous studies of the control drug, thereby reducing the number of patients needed in control arms. 

Adopt EMR-driven patient recruitment
Patient recruitment is a primary reason that many clinical trials crawl rather than sprint from the starting blocks. Structural flaws in recruitment planning, unaudited enrolment estimates, unrealistic targets, and poor use of patient data all lead to widespread delays and budget overruns.

Big data in the form of electronic medical records (EMR) can help make patient recruiting more effective and efficient, drawing on real-time patient data and physicians’ notes to model various recruitment scenarios. This proactive method identifies not just patients who match the trial’s enrolment criteria, but also their proximity to participating sites. EMR data also enables direct outreach to patient candidates, helping to augment the patient pool and speed up recruitment.

Data-driven protocols
An estimated 80% of clinical trials start late, with amended trial protocols a major contributor to delays. One way to ensure trial protocols are feasible is to validate them using actual patient data. Proposed criteria can be screened against data from millions of EMRs available through commercial data aggregators. This enables realistic enrolment protocols to be developed more efficiently and with significant cost savings.

Integrate trials into clinical practice
EMR data can be used to integrate clinical trials into day-to-day clinical practice. Pre-matching patients with trial requirements enables physicians to identify strong candidates in their practices with little effort and to offer them trials as a treatment option. Enrolment rates are known to be higher when the invitation comes from a trusted physician.

The above list is an introduction to the possibilities of cultivating new approaches to trial design. Many other strategies exist to help manufacturers reimagine their clinical development pipeline and accelerate time to market. Read more on how ICON is transforming trials