But what does “Lack of Efficacy” really mean? One question posed by Mike Rea, CEO of IDEA Pharma, is: “Did the drug fail the study, or did the study fail the drug? — that is, could a different question asked of the same drug have yielded a different answer?” 1

An FDA analysis published in 2017 detailed 22 case studies of drugs, vaccines, and medical devices that failed phase III trials. The report identified that the leading causes of failure in phase III were: use of biomarkers that did not adequately predict phase III outcomes, and untested mechanisms of action 2. Although not covered in the FDA report, another common reason for failure in phase III— particularly in studies of depression, schizophrenia, and pain— is a high placebo response, which doesn’t necessarily mean that there was a low drug response.3

A 2018 review by David B. Fogel referenced a study by Hwang et al. which assessed 640 phase III trials with novel therapeutics. The study reported that 54% failed in clinical development, and 57% of those that failed reported inadequate efficacy. This review provided some of the reasons that potentially efficacious drugs fail to demonstrate efficacy— these include: a flawed study design, an inappropriate statistical endpoint, or simply having an underpowered clinical trial, which may result from patient dropouts and insufficient enrollment. 4

This raises the following question: How can sponsoring companies de-risk some of the factors which lead to a considerable number of drug failures in phase III due to “lack of efficacy”? Companies like Pfizer, AstraZeneca and others have taken a deep, hard look at the drivers of clinical trial failure and have developed strategies to improve their R&D productivity. To improve phase II success rates, Pfizer has concentrated on three key areas: biology, modalities, and decision-making. The company is beginning to see early signs of a turnaround, and improvements in phase II success rates have increased from 19% to 53% (3-year rolling average). At the same time, it has been able to maintain and improve its Phase III through to approval success rates to 80%, which demonstrates that it has not simply pushed the issue from Phase II to Phase III.5

“Whereas, AstraZeneca established a framework based on the five most important technical determinants of project success and pipeline quality, which they have described as the five 'R's: the right target, the right patient, the right tissue, the right safety, and the right commercial potential. A sixth factor — the right culture — is also crucial in encouraging effective decision-making based on these technical determinants.”6

Another powerful innovative tool that Pharma companies have begun to leverage in various aspects of R&D is AI. The adoption of AI technologies for optimizing clinical trial design, patient recruitment and enrollment, investigator, and site selection, as well as patient monitoring is aimed at improving not only the efficiency, but also the “patient-centricity” of the clinical development process.7 Taken together, it seems intuitive that a more rigorous R&D process and testing in phase II, particularly for drugs with unproven mechanisms of action, along with optimized phase III clinical trial design, combined with a corporate culture that endorses effective decision-making will help to reduce the risk of phase III trial failures.8

References: 

1. https://www.linkedin.com/pulse/time-die-mike-rea/?trackingId=OK99BeacuF2M8PYzqp0y4Q%3D%3D

2. https://www.fda.gov/media/102332/download

3. https://ascpt.onlinelibrary.wiley.com/doi/epdf/10.1002/cpt.1632

4. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6092479/pdf/main.pdf

5. https://www.sciencedirect.com/science/article/pii/S1359644620304360

6. https://www.nature.com/articles/nrd4309

7. https://www2.deloitte.com/us/en/insights/industry/life-sciences/artificial-intelligence-in-clinical-trials.html

8. https://www.parexel.com/application/files_previous/5014/7274/5573/ACT_Article.pdf