Cytel Blog

QPP 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. Cytel’s expert Quantitative Pharmacology and Pharmacometrics group delivers high quality solutions to help our customers get those decisions right.

In this blog we talk to Tina who lives in Stonington, Connecticut, to find out more about her career path, current role at Cytel, and her interests outside of work.

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.