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Why do Phase III clinical trials fail and how can we improve them? Dr. Grignolo, PhD, Corporate Vice President of PAREXEL International, the multinational life sciences consulting firm, recently examined this issue in a paper titled: Phase III Trial Failures: Costly, But Preventable.
We have reviewed this journal and brought you the primary findings as they appeared in Applied Clinical Trials Volume 25, Issue 8 and also on appliedclinicaltrialsonline.com.
Dr. Alberto Grignolo of Parexel undertook a study to answer this question.
Grignolo and his researchers selected 38 failed Phase III trials spanning mid 2012 to 2015, where the data was publicly available and then evaluated these in a variety of methods to see what data and lessons could be extracted. It is worth noting that the 38 phase III trials that failed, had a significant enrollment total of 150,000 patients.
Previous studies have examined this important and costly question before. Dr. Grignolo's paper first references a previous study by the Tufts Center for the Study of Drug Development (CSDD) evaluating clinical trials from 2000 to 2009. This previous study concluded that the three most common reasons for failure in Phase III development is:
Parexel expanded on this research and conducted their own analysis into phase III failures due to efficacy. The results of their findings are listed in the table below:
So after establishing efficacy as the primary driving factor for phase III clinical trial failures Dr. Grignolo then examines the failures by a number of criteria including new molecular entities, therapeutic area and molecule type.
The rest of the report is split into six parts. To go directly to a particular entity just click on the name below. Otherwise, please read on.
A study by BioMedTracker (BMT) and the Biotechnology Industry Organization (BIO) evaluated R&D projects involving more than 9,500 different drug and biological products from 2004 to 2014.
The high failure rate of Phase II trials reported in that analysis (62% and 67%, respectively) is not unexpected for exploratory trials.
Among the Phase III trials evaluated, not surprisingly oncology and cardiovascular trials had the highest failure rates (data not shown).
The higher failure rate for oncology trials might be due to the inclusion of survival endpoints and the need to show efficacy by an improvement in overall survival.
Failure rates differ by type of molecule. A study by the Tufts CSDD found that the probability of success for clinical trials of small molecules is lower than for trials of large molecule.
In September 2014, The European Center for Pharmaceutical Medicine (ECPM) organized a seminar titled “Why Clinical Trials Fail”. Insufficient sample size was highlighted as a factor that leads to study failure.
However, an insufficient sample size calculation is a risk factor that can be greatly controlled by using nQuery sample size and power calculation software for successful clinical trials.
nQuery is sample size software that is specifically designed for clinical trials and regulatory approval. In 2017, 90% of organizations with FDA approved clinical trials used nQuery for sample size calculation. Below is a brief snap shot of the core tables in the current version of nQuery.
We encourage you to examine our applied example page. This provides all researchers with guided sample size examples in nQuery that are divided by both statistical and therapeutic method. In addition, to increase your understanding of sample size, our free Professional Development Sample Size Training is open to both current nQuery users and non nQuery users.
Confirming the efficacy of a new drug or biologic is not a task that can be simplified, it can be improved though. Due to the nature of Parexel being an international CRO, Grignolo states that
“Given our unique perspective in working with hundreds of companies across thousands of clinical trials and compounds, we and numerous colleagues at PAREXEL are exposed to these approaches on a daily basis”.
In 2011, AstraZeneca aimed to overhaul its R&D process to improve the health of the organization and increase the chance of success of its Phase III trials.
By evaluating its small-molecule drug projects over a 5 year period AstraZeneca identified the factors associated with project success and developed a framework that now drives its development process. These strategies can be implemented during the entire development process, in specific phases of development, and/or during clinical trial design.
The 5R framework guides R&D teams in identifying the right target, the right tissue, the right safety, the right patients and the right commercial potential.
Review and optimization
Modeling and simulation
De-risking study execution through data
Reducing the risk of insufficient sample size calculation
The paper lists examples of what other areas that are currently being explored and could hold potential in reducing Phase III failure. They include:
Dr. Grignolo concludes his publishing with the following
“We believe that the current failure rate in Phase III studies is unacceptably high, and that industry is keen on reducing this failure risk, although some in industry may believe that failure is the price to be paid for innovation.
As a first step, it is important to understand the reasons and root causes driving these failures. Our research identified recently failed Phase III studies that have enrolled nearly 150,000 patients. Based on data from our analysis and others, we have listed the main reasons why Phase III trials fail. In addition, and given our unique perspective in working with hundreds of sponsors across thousands of trials, we have highlighted some of the approaches that pharmaceutical companies are implementing in an effort to reduce these costly late-stage failures.
Along with our colleagues in the pharmaceutical industry, we are optimistic about the potential of some or all of these approaches to improve the Phase III success rate".
The full link to both articles of Phase III Trial Failures: Costly, But Preventable can be accessed here:
Here is some other content from Statsols you may interest you. Common Clinical Trial Design & Sample Size Calculation Mistakes to Avoid is a helpful white paper that highlights some common clinical trial design and sample size calculation mistakes to avoid. Referenced from the E9 Statistical Principles for Clinical Trials found in the FDA Guidance for industry.