Citation : When referring to this article, please cite it as A. February 1, Agnes Shanley. Pretorius: While there can be various drivers that lead to the failure of drugs during Phase III, the following represent what we find to be the top five, based on presentations given at a summit organized by the European Center for Pharmaceutical Medicine ECPM : Basic science -Animal models that do not translate or are not entirely related to human disease, poor understanding of target disease biology, or drugs that are simply ineffective.
Inappropriate study design can undermine the ability to show efficacy or the sample size may be too small. This can happen when investigators or sponsors become overenthusiastic about Phase II results and rush to Phase III without fully exploring dose finding.
Inadequate therapeutic indices may also lead to suboptimal dosing. Data collection and analysis -False positive signals from Phase II and overly optimistic assumptions about variability and treatment differences may result in such problems as missing data, attrition bias, rater bias, errors in measurement methods, or inappropriate statistical methods.
In addition, unexpected variations in recruitment or dropouts can affect results, as can protocol variations, missing data, or unintentional unblinding of subjects. These include: Applying more rigor to the overall development process -Applying more rigor and discipline to the development process holds potential for weeding out likely failures earlier in the process, thereby reducing Phase III failure rates e. Not only does this proposed approach in Phase II make intuitive sense, but it also holds potential for reducing late-stage failures.
Optimizing Phase III study design -Strategies for success would include more disciplined protocol review and optimization, as well as use of modeling and simulation, adaptive trial designs, and biomarkers. New York, New York.
Dragalin, V. Adaptive designs: terminology and classification. Drug Information Journal. Marrer E, Dieterle F. Promises of biomarkers in drug development--a reality check. Chem Biol Drug Des.
Biomarkers, surrogate end points, and the acceleration of drug development for cancer prevention and treatment: an update prologue. Clin Cancer Res. Cancer biomarkers: selecting the right drug for the right patient. Mobile health technology evaluation: the mHealth evidence workshop. Am J Prev Med. August 1, Selected strategies to reduce the risk of late-stage failure Although no simple solution exists yet for preventing Phase III failures to confirm the efficacy of a new drug or biologic, several approaches have been and are currently being developed to reduce the risk of such failure.
Applying more rigor to the overall development process Over the past few years, most drug development companies have established and adopted more disciplined protocol, progress and portfolio review frameworks. To address this issue and improve the chance of NMEs being successful during Phase II testing, Pfizer performed an analysis of 44 Phase II programs during a four-year period to identify factors associated with success.
The next section aims to highlight a few of them: More rigorous protocol review and optimization is an approach that many companies are employing in varied degrees ranging from stage gate reviews and sign-offs by various internal committees to live, in-practice simulations of protocols to identify and resolve potential glitches proactively.
Typically, a number of trade-off decisions need to be made in the compilation of most Phase III protocols-e. Identifying and quantifying the impact of these trade-offs is helpful in designing better protocols.
Likewise, in the right capable hands, the vast amount of data available in public sources e. Modeling and simulation have contributed to regulatory approval and labeling decisions in recent years and are currently being employed as a potential solution for mitigating late-stage failure risk.
The FDA encourages sponsors to incorporate quantitative modeling and trial simulation in the drug development plan and to seek regulatory guidance on these strategies through participation in an end-of-phase 2A EOP2A meeting. Modelling and simulation is currently being used more broadly than just for the selection of an optimal dose.
Modeling and simulation of various clinical trial designs is one such example that is being used in an effort to design optimal Phase III studies. Adaptive designs are gaining momentum as a way to reduce failure risk. This is, in large part, due to the fact that these adaptive designs provide an opportunity to assess interim data and to sense-check some of the initial uncertainties or assumptions that were made at the outset of the trial.
This becomes even more important given that Phase 3 studies include sites around the world, many with varying degrees of access to health care.
In addition to the effect of patient-physician encounters, researchers from SIPS have identified an interaction between drug and placebo mechanisms. For example, a report f showed that brain imaging can be used to predict placebo responders and subjects with a higher propensity for response to active drug. Therefore, the implicit assumption made when evaluating the magnitude of a treatment effect i. The importance of genetic factors affecting placebo response is another important factor to be considered.
Thoughtful study design can be one way to reduce the placebo response. While several designs have been utilized in an attempt to reduce placebo response, the following study designs have generally proven ineffective for this purpose i. While intuitively these study designs might appear to be advantageous for reducing placebo response, they share a common flaw, which is that when placebo responders are eliminated, drug responders may also be eliminated.
Another strategy that can be used to address placebo response is to employ alternative methods of analysis, including:. Failures in Phase 3 clinical trials are costly and can be devastating for drug developers and for patients. An understanding of the root causes of the increased placebo response has the potential to reduce Phase 3 failures by identifying and mitigating placebo response risks.
Among the options for reducing placebo response risk are to employ alternative study designs and methods of analysis. Nuventra has a team of experienced drug development professionals and industry veterans who can provide critical insights into ways to reduce placebo response risk and give your clinical trial the best chance for success.
Clinical Development Success Rates Biotechnology Innovation Organization, Biomedtracker, Amplion. 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". Given the high failure rates and the increased costs of clinical trials, researchers need innovative design strategies to best optimize financial resources and reduce the risk to patients.
Adaptive designs are emerging as a way to reduce risk and cost associated with clinical trials. The FDA recently published new draft guidance on adaptive trials and are actively encouraging sponsors to use Adaptive trials.
Adaptive trials are a type of clinical trial design that allows adaptations or modifications to aspects of the trial while it is on-going, without undermining the validity and integrity of the trial. One common adaption is a sample size re-estimation, which increases the sample size in response to interim data. How to use nQuery. Why do phase III clinical trials fail? Written by nQuery Team. May 9, This previous study concluded that the three most common reasons for failure in Phase III development is: Lack of Efficacy — i.
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. Flaws in clinical trial design are a major driver of Phase III failures. Several strategies have been developed for optimizing trial design. The next section aims to highlight a few of them:. Review and optimization Typically, a number of trade-off decisions need to be made in the compilation of most Phase III protocols—e.
Identifying and quantifying the impact of these trade-offs is helpful in designing better protocols. Modeling and simulation Modeling and simulation is currently being used more broadly than just for the selection of an optimal dose.
Modeling and simulation of various clinical trial designs is one such example that is being used in an effort to design optimal Phase III studies.
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