By Esha Senchaudhuri

With thanks also to Jitendarreddy Seelam and Ramanatha Saralaya for their input.

The fact of the matter is that I now want to recall everything, every trifle, every little detail. I still want to collect my thoughts and - I can't, and now there are these little details, these little details...”
― Fyodor Dostoyevsky, The Meek One

Old Fyodor was hardly talking about clinical trials, but early phase trial sponsors can probably relate to a regulatory environment which requires systematic attention to details, the little details and all these little details. When conducting early phase studies, global regulators require submission of Non -Compartmental Analyses (NCAs) that measure factors such as extent and rate of exposure to a drug, without the complexity of strenuous assumptions or complex models. Through the use of rudimentary methods such as linear trapezoidal rules, NCAs make it relatively easy to measure the concentration of a drug in a body over time. They can capture length of exposure, and time of peak exposure, without the challenges of models that require independent validation [1]. While those other models are also becoming more common in quantitative pharmacometrics, ideally NCAs can complement these other methods.

It may be tempting to assume that due to the ease of measurement, it is unnecessary to invest in statistical expertise and reliable software for NCAs. While the calculations may not be as complex as other forms of pharmacometric modeling, taking shortcuts at this stage can prove problematic later on. 
Widely recognized for being ‘assumption-free’ [1] NCAs are a common subject of regulatory inquiries. Exposure and absorption data is obviously important for early phase trials, so NCAs are required for submission throughout the process. A strong data management system with reliable software can ensure that findings collected at this stage are streamlined across several early phase trials, making such information easy to access and ensuring a rapid response for regulators. Further, NCAs are often required to be submitted with early protocols making it useful to have statistical designers familiar with the NCA findings. As NCAs are an integral part of establishing an early phase audit trail, it is important to use NCA software that streamlines a detailed and complex workflow such as Phoenix WinNonlin. 

Accurate NCAs can combine with other forms of quantitative pharmacometric models like PK/PD analysis to build strong dose-response models for Phase 2. It is common knowledge that unreliable dose-response models in Phase 2 can create headaches for Phase 3 tests. Only 13.2% of Phase 3 trials that are accepted after initial rejection, are rejected on grounds of efficacy. More common reasons are dose selection, choice of endpoints, and other challenges that better Phase 2 modeling can prevent [2]. Working with statistical experts as early as Phase 1 can ensure that knowledge gleaned from NCAs can be employed to build stronger Phase 2 models, thus avoiding Phase 3 pitfalls.

Cytel has a dedicated team that has developed efficiencies and experience in early phase trials, including Non-Compartmental analyses. To learn more about our capabilities in this area, please click on the button below.

[1] Gabrielsson, J. and Weiner, D., 2012. Non-compartmental analysis. In Computational toxicology (pp. 377-389). Humana Press, Totowa, NJ.
[2] Sacks, L.V., Shamsuddin, H.H., Yasinskaya, Y.I., Bouri, K., Lanthier, M.L. and Sherman, R.E., 2014. Scientific and regulatory reasons for delay and denial of FDA approval of initial applications for new drugs, 2000-2012. Jama, 311(4), pp.378-384.

The ASCPT is the largest scientific and professional organization serving the disciplines of Clinical Pharmacology and Translational Medicine, and its annual conference is one of the most important events on the calendar for those involved in Quantitative Pharmacology and Pharmacometrics (QPP).  Cecilia Fosser, Nand Kishore Rawat and Tina Checchio represented Cytel’s expanding QPP team at this year’s event in Washington DC. In their experience, the meeting represents an excellent opportunity to keep up to speed with new trends and techniques within the space, and the quality of presentations is consistently high.  In this synopsis, we summarize some of the particular highlights from the sessions that our team members attended, along with other takeaways from the event.

 In this blog, Adam Hamm, PhD, Director Biostatistics at Cytel shares some of the most important knowledge he uses in  his day to day work as a biostatistician working extensively in oncology research. Adam has broad experience with statistical analysis and methodology over all phases (I-IV) of development, in particular working in the oncology arena. 

 As a Director of Biostatistics at Cytel, I work on design, statistical analysis and reporting projects for a range of biotechnology and pharmaceutical sponsors. During my career, I’ve developed a particular focus on oncology trials, so in this blog I’ll share some insights into the  knowledge which I have found particularly vital  as a biostatistician working in this area. This knowledge spans specific statistical methodologies and understanding of the clinical issues across the phases of clinical development. The summary is not exhaustive, but provides a glimpse into the broad exposure which is needed for a biostatistician to develop a fully rounded understanding in the area. To learn more, read on...

We were fortunate to welcome Björn Bornkamp of Novartis to the EUGM 2016 presenting work he has developed jointly with Marius Thomas (1) on methods of adjusting treatment effect estimates in subgroup analyses with a focus on early phase trials.   

FDA draft guidance on “Co development of two or more unmarketed investigational drugs for use in combination”  notes that:

“Combination therapy is an important treatment modality in many disease settings, including cancer, cardio-vascular disease, and infectious diseases. Recent scientific advances have increased our understanding of the pathophysiological processes that underlie these and other complex diseases. This increased understanding has provided further impetus for new therapeutic approaches using combinations of drugs directed at multiple therapeutic targets to improve treatment response or minimize development of resistance.” In this setting, it’s important to be able to design dose escalation studies which can identify the synergistic activity of compounds, and less toxic combinations.  

 Francois Beckers, Global Head of Biostatistics & Epidemiology at Merck KGaA joined us at the East User Group Meeting in March and presented case studies of Merck KGaA’s experiences with Blinded Sample Size Re-estimation in early phase studies, more specifically in the context of biosimilar studies.

On March 16th and 17th the 5th East User Group Meeting took place in London.  This very successful 2 days saw a variety of talks on aspects of clinical trial design innovation.  Over the next couple of weeks, we will be reviewing some of the key topics which were addressed during the meeting.

In this post, we'll take a look at Paul Frewer of Astrazeneca's presentation on Decision Making in Early Phase Clinical Development.  This talk was very well received by the delegates and prompted plenty of discussion afterwards. 

Last week Cytel joined forces with Sanofi/Genzyme to devote a full day of workshops and talks related to modern methods in early phase oncology. Patrick Mitchell, an Associate Director of Statistical Sciences at Astra Zeneca, gave this talk on Bayesian go/no-go decision-making in an early phase oncology event study. 

Astra Zeneca recently invited Cytel to take part in a collaborative initiative to develop software for go/no-go decision-making. Pat demonstrates the uses of this proprietary software in early phase oncology. 

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.

Our Client's Challenge:

Can knowledge of the relationship between biomarkers and clinical endpoints help us to optimize an early development program and improve the probability of selecting the right dose in Phase 3?

Our client approached us hoping to expedite dose-finding with biomarkers in Phase 1b, and to design an optimal Phase 2b clinical endpoint trial to maximize probability of correct Phase 3 dose selection.

If you’re in the practice of conducting early phase clinical trials, you’ve probably heard that modern trial designs include a number of new methodologies. There’s CRM and BLRM, model-based methods versus rule-based methods, and a number of other developments that might affect your clinical strategy. Each of these methods affects operational, financial and regulatory objectives in unique ways.

As a part of this year’s Joint Statistical Meeting, Cytel statisticians created a primer to go along with a workshop for early phase clinical trial design.

In the primer you will find: 

• 1. An overview of 3+3, CRM, BLRM and mTPI methods
• 2. A synopsis of basic concepts like Bayesian and frequentist statistics, models and rules, etc.
• 3. Reflections on regulatory considerations
• 4. Case studies and topical exercises with Cytel’s high-powered simulations
• 5. A solution guide for those with access to Cytel's East software  

 Click to download the Primer

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.

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.

Last week the Cytel Blog discussed the benefits of using the Adaptive Maximizing Design [AM Design] for dose-finding trials involving clinical utility limiting therapies. However, there are other ways that a dose-finding trial can make use of frequent-adaptation maximizing designs. Here we look at what happens to early phase clinical development when an AM Design combines with another adaptive design that is slowly gaining popularity, namely the Sequential Parallel Comparison Design. 

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The Journal of the American Medical Association recently published an article entitled ‘The Anatomy of Medical Research: US and International Comparisons.’ The stated objective of the study was to “quantify total public and private investment and personnel (economic inputs) and to evaluate resulting patents, publications, drug and device approvals, and value created (economic outputs)“ [1]

Amongst the many findings of this comprehensive study, a vital observation is the reduction of early phase spending by about 4% per year from 2004 to 2012. One attribution for this decline involves the financial constraints placed upon proof-of-concept trials, particularly when compared to the expected financial benefits of Phase 3 trials and medical devices. According to the authors, “Many new basic discoveries that have probable clinical value are stymied by financial constraints at the critical proof-of-concept stage, where utility in humans is demonstrated.” [1] They add that the number of new discoveries that will be underfunded at the proof-of-concept stage is expected to increase.

According to a recent Cytel Whitepaper on Adaptive Clinical Trials, 50% of Phase 3 trials eventually fail. This new Cytel Infographicoffers a breakdown of why so many drugs fail in Phase 3.

Earlier this week, we at Cytel enjoyed a riveting in-house discussion on the uses of Bayesian decision rules for Go/No-Go (GNG) decision-making. GNG rules establish the trajectory of a particular clinical program’s development by assessing whether or not a trial has met particular objects (e.g. target regions for PK, PD and safety endpoints.)

Traditionally, statisticians have used p-values and confidence intervals to construct GNG rules. However, moving to the Bayesian paradigm opens up exciting new possibilities for clinical development strategy. Our discussion earlier this week centered around three key benefits of using Bayesian statistics for GNG decision-making:

Statisticians and scientists at Novartis have been at the forefront of developing a new method in early phase oncology trials called the BLRM. Many believe that the BLRM, (short for the Bayesian Logistic Regression Method,) allows for the construction of clinical trials that have the dual benefit of improving treatment for patients participating in the trials, and allowing the trial to complete in a more timely and efficient manner.