Oftentimes people perceive a tradeoff between speed and quality. The faster you do something the more likely you are to make mistakes. Then again, checking everything carefully means things take a longer time.

Right?  

What if you could promise a team that could work 24-7?

QPP (sometimes called QP2) remains at the heart of model based drug development. Short for Quantitative Pharmacology & Pharmacometrics, it refers to several types of quantitative modeling including meta-analysis, PK/PD , statistical modeling and the modeling of go-no-go decision rules.

Here at Cytel we have enjoyed following the debates on the p-value controversy currently taking place on the ASA website, and indeed debating the issue ourselves. While we plan to post our views on the topic in the weeks to come, we would like to invite your thoughts on the issue as well. 

It is often necessary to pool safety data from late phase studies, in preparation for regulatory submission. Some of our clients have also begun to add Phase 1 safety data to this pool. On some occasions this is required by regulators. In many cases, however, these Phase 1 data simply provide further evidence that a new therapeutic lives up to the promise of safety across patient populations.

This week marks the sixth annual American Conference on Pharmacometrics, held this year in Crystal City, VA. Situated at the intersection of mathematical modeling, simulation and big data, the field of pharmacometrics is delivering on its promise to revolutionize clinical research and by extension clinical development.

No wonder then that biostatisticians are now fully engaged with the enterprise.  

Earlier this week, Patti Arsenault, Cytel’s Global Head of Clinical Data Management, sat on an SCDM panel with members of Gilead and Westat. The panel partook in an interactive discussion on both the opportunities and challenges which arise from managing virtual teams.

As teams become more global in nature – optimizing delivery by around the clock work hours –many have weighed in on the best way to manage virtual teams. Unfortunately, there seems to be very little agreement on such best practices. The panel provided insights from industry leaders in clinical data management, and then invited audience members to share their experiences.

Most people familiar with adaptive clinical trial designs are familiar with those statistical designs that reject the null hypothesis. These include now familiar designs like the promising zone design and the adaptive switch design. 

A newer breed of adaptive designs, however, aims to apply adaptation techniques to confidence intervals.

Full service or specialized? Full service or specialized?

For many looking to hire a CRO, the answer is obvious.

When approaching a Phase 3 clinical trial, the need to ‘de-risk’ the massive investment often leads sponsors on a quest for the perfect risk mitigating adaptation. While a strategically planned clinical trial design can be an important step in giving a new medicine its best possible chance of success, there are a number of other ways that a trial sponsor can minimize study risk.

We were saddened to learn earlier this year, of the passing of Professor David Sackett. Widely recognized as the father of evidence based medicine, Professor Sackett confronted tough criticism in advancing the cause of evidence based medicine during the early nineties. During his four years at the Centre for Evidence Based Medicine at Oxford, Sackett’s team produced an array of books, articles, curricular and pedagogical practices, and software techniques which remain foundational to EBM's teaching and learning.

Evidence based medicine refers to the practice of incorporating “current best evidence” when determining care for individual patients [1]. Clinicians use their clinical expertise to specify the problem and the evidence necessary to solve it; the evidence itself, however, makes reference to biostatistics and epidemiology [2].More generally, evidence based medicine defends the view that clinicians should use both their clinical expertise and the findings of general clinical research in their practices, and that neither alone is sufficient to provide an appropriate level of care [1].

In a widely-cited paper, Sackett explains the irony of having to combat criticisms of championing a practice that was simultaneously ‘too old hat’ and yet also ‘too revolutionary’ [1]. In celebration of David Sackett, we consider Evidence Based Medicine in the early 1990s, and consider new developments twenty-five years later.  

MCP-Mod methodology for dose-ranging clinical trials has been gaining popularity since the 2013 publication of the qualification opinion by the European Medicines Agency Committee for Medical Products for Human Use. Since its development at Novartis, MCP-Mod promises to devise proof-of-concept and dose-ranging trials which generate superior statistical evidence for dose-selection, while providing safety and efficacy data that can prove critical data for Phase III clinical trial design. 

Although the general framework of the MCP-Mod method are becoming more familiar, its added complexity raises the question of whether it is a necessary supplement (or even substitute) for traditional dose-ranging trials. Here are a few shortfalls of the traditional approach that MCP-Mod is equipped to handle. 

Powering a trial for superiority can be financially risky. In some instances it may also prove unnecessary.

In August 2006 AstraZeneca completed the ARISE trial, which aimed to determine whether AGI-1067 was effective in reducing atherosclerosis in patients with acute coronary artery disease [1][2]. A double-blind, placebo controlled, Phase 3 trial, the primary efficacy endpoint was a composite endpoint which included major adverse cardiovascular events (MACE) like cardiovascular death, resuscitated cardiac arrest, non-fatal myocardial infarction and non-fatal stroke. However, it also included two less serious but more frequently observed events, namely hospitalization due to coronary revascularization and hospitalization due to unstable angina with evidence of ischemia [2]. 

When the FDA first began to require pharmaceuticals to perform cardiovascular outcome trials to establish the safety of certain new drugs, many worried that this new regulatory requirement would diminish investments in therapeutic areas like diabetes and obesity [1]. CVOTs are typically designed as massive time-to-event trials that need to enroll several thousands of patients to establish safety [2][3]. The sheer number of patients and size of the trial increases the time that it takes for these drugs to get to market.

So what is it the makes US and EU regulatory strategy different for diabetes and weightloss drugs, and how should this effect clinical development strategy?

Not long ago, one of our clients submitted Phase 2 and Phase 3 data for a new rare disease drug which had received priority review status. Following submission, regulators requested that our client also submit pooled data from its Phase 1 studies along with a safety analysis. This analysis had the potential to clinch NDA approval for the new product.

Risks in drug development range from taking the wrong drugs forward to Phase 3 to investing in a drug development program at a time when regulatory standards are evolving, or competition is catching up with comparable products.

There is a particular source of risk, however, which deserves special attention. Currently, the pharmaceutical and biologics industry outsources approximately $25 billion dollars worth of clinical development projects. That is over a quarter of the $98 billion dollars currently devoted to clinical drug development.

This naturally raises several questions:

Seamless adaptive clinical trials have gained popularity for reducing the projected time it takes to complete the process of drug development.  However, a study by Cuffe et al., shows that despite a tremendous amount of statistical knowledge about seamless trials, sponsors remain unsure about how to calculate the financial and operational costs of a seamless clinical development program [1]. This in turn results in many unnecessary risks and missed opportunities. This post offers advice on what you need to keep in mind in order to implement a successful seamless adaptive clinical study.

Imagine that it’s been three years since the completion of a trial, and that suddenly a regulatory body calls into question the findings:

• Was a particular trial site operating properly?
• Can you clarify an aspect of the results? 
• Why did you make a particular decision at an interim look?

Suddenly, your somewhat old data needs to be able to reproduce your initial findings. In such a case, how long would it take you to satisfy the regulatory body?

Ten years ago, in May 2005, world-renowned biostatistician Marvin Zelen was asked to deliver a keynote address before the Eastern Mediterranean Region of the Biometric Society. His address, entitled ‘Biostatisticians, Biostatistical Science and the Future,’ [1] offered a reflection on the direction which the field at large would have to take, given the anticipated leaps in software and the rise of big data.

This included eight predictions on how short and longer term advances in technology would shape the field.

As we prepare to celebrate the annual Marvin Zelen Leadership Award in Statistical Science, this year awarded to Professor Nan Laird (Harvard University) [2] [3] we can take a moment to re-examine the predictions and insights of Cytel's beloved friend and Board Member. 

Where will the field of biostatistical sciences be ten years from now? Thirty years from now? How about one hundred years from now? It is perhaps a guessing game full of imprecise probabilities and chances for Bayesian updating.

Adaptive financing (not to be confused with adaptive licensing) explores how biotechs, pharmaceuticals and potential investors, can use adaptive designs for more strategic financial decision-making, as well as for efficient drug development.  

“For example,” writes Zoran Antonijevic, Senior Director at Cytel Consulting, “Reduction in costs and development time can be accomplished by combining stages of development, or by early stopping for efficacy or futility. Investment risks at late stage of development can be reduced by incorporating a sample size reassessment at an interim analysis, or simply by incorporating stopping rules for efficacy and/or futility."

Zoran has just edited a volume of Clinical Investigation featuring articles on the financial and developmental benefits of adaptive clinical trial design. The volume contains a broad spectrum of issues beginning with a couple of articles on adaptive financing by Tessella’s Tom Parke, and Christopher Weir of the Health Services Research Unit at the University of Edinburgh. 

The volume also stresses the pivotal nature of dose-finding for efficient development, with contributions on Phase 2 oncology trials from Anastasia Ivanova, Guochen Song, Olga Marchenko and Stergios Moschos; as well as Meinhard Kieser, Stefan Englert and Geraldine Rauch. Thomas Jaki also explains the use of multi-arm trials in treatment selection with a look at an Alzheimer’s case.