Jun 16, 2016

Could we change the way we design our clinical trials to minimize failures?

We all hear from time to time that clinical trials fail sometimes, but the question today is why do they fail? And why should everyone know when something fails? Could we change the way we design our studies to minimize failures if we know why they fail?

Any regulations set for the clinical trials have a common goal to harmonize the procedures for clinical trials while making sure of the safety of clinical trial volunteers, the ethicality of trials and the dependability and productive effect of data derived. It advocates increasing reliability with regard to clinical trials results, data and their result. For life science companies, clinical trial information is highly of discreet affair which permeates a wide cultural evolution given that they operate in a very systematic and competitive environment, with lots of risk and profit.

The last few years, increasingly noticeable numbers of life science organizations have adopted a more free and transparent policy with regards to the results of their clinical trials, regardless of the positive or negative results.

From the recent past (March 2016) Celldex Therapeutics conducted a clinical trial on a brain tumor vaccine Rintega but the results were fruitless crushing its stocks. The organization announced the failure of Rintega Phase III study known as ACT UV after an interim analysis conducted by independent data monitors. The studies enrolled patients with certain type of Gliblastoma Multiforme (GBM), an aggressive brain tumor. Rintega was found to reduce the risk of death by just 1% compared to the control arm. However, at the median, Rintega-treated patients fared worse, surviving 20.4 months compared to 21.1 months for the control arm. Celldex is discontinuing clinical development of Rintega. Obviously, the company's plans to seek approval for the product in the U.S. or Europe are also being shelved. Celldex has two other drugs being tested on different clinical trials.

Cancer vaccines, particularly those targeting GBM, have a dismal track record. The failure of Rintega follows negative study results for ImmunoCelluar Therapeutics' GBM vaccine ICT-107. Northwest Biotherapeutics is developing a GBM vaccine known as DCVax, but a phase III study has been stalled since August due to an unexplained patient enrollment halt. There are other clinical trial failures too: Chrimex`s stocks also plunged recently after the failure of the clinical trial of an anti-infection drug in Phase III. [Dec-2015]. A French company`s [Bial] drug trial leaves on Brain dead and two others with permanent damage, there is no known antidote as the drug was never used on human before this trial. There is a 50% probability for every clinical trial to fail.

There are three root causes of clinical trial failures:

A. Molecule issues
When the molecule, doesn’t have sufficient biological activity or doesn’t have manageable toxicity. A well-designed clinical program can pick up the side activity and the program can be redirected. But if the molecule has no biological activity when it enters clinical development, only little can be done to salvage it. Just as important are predictability and pervasiveness of the unmanageable toxicity. Every molecule has toxicity but it is important to design a clinical trial accordingly around them.

B. Logistic issues
Half of the published preclinical experiments may be unreproducible. In the hurry to get the clinical trial started, sometimes sponsors neglect to triple-check the randomization algorithm. Despite validation, mistakes in data analysis programming can occur. Many companies don’t double program (have two sets of independent programmers or program two full sets of analysis independently) and in that case, it is almost inevitable there will be mistakes somewhere.

C. Study design issues
Error in clinical trial design is perhaps the most common reason trials fail, other than the above two reasons. There are many variables in clinical trial design but of those, three are the most important when it comes to insuring a successful clinical trial:
  • Selecting the right patients
    • Patient selection can go awry if the selection blindly follow the conventional disease categories and definitions. There are many ways to define patient populations and diseases. It is not always optimal to define the patient population by a previously recognized disease category because disease categories are intellectual constructs
  • Selecting the right dosing
    • All the characteristics of the dose, including: the amount of an intervention administered, the route of administration (e.g., oral, IV, SC), the dosing interval, the rate and duration of administration.
    • Frequently seen error is using a dosing regime that is too undifferentiated, such as a flat dose. When great heterogeneity in patient response or a narrow therapeutic window exists, you may have to customize the dose.
  • Selecting the right endpoint
  • Clinically relevant
  • Closely and comprehensively reflects overall disease being treated
  • Rich in information
  • Responsive (sensitive, discriminating, and has good distribution)
  • Reliable (precise, low variability, and is reproducible) even across studies
  • Robust to dropouts and missing data
  • Does not influence treatment response or have biological effect in and of itself
  • Practical (implementable at different sites, measurable in all patients, economical, and reasonably noninvasive)

Often, a drug has biological activity but it is tested for the wrong indication. Or, it is tested in the right indication but in the wrong sub-populations. In other instances, the wrong dose or dose interval is selected. It is therefore kept in mind that sometimes the most expensive testing studies fail for various reasons leaving long lasting scientific and trading loss.

 http://www.celldex.com/pipeline/rindopepimut.php lhttps://clinicaltrialist.wordpress.com/clinical/why-clinical-trials-fail/

Written by Shalini P. Burra for The All Results Journals. 

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