The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
Quantitative pharmacology encompasses the many strategic advantages of using complex mathematical models to understand biochemical relationships that ultimately improve clinical decision-making. This includes pharmacometric modeling, familiar to those who have used pharmacokinetic/pharmacodynamic (PK/PD) modeling to improve dosage decisions, and the extension of such models to the performance of meta-analyses, the construction of decision rules, and other uses involving a broad array of cases. In this blog we summarize some key areas of opportunity.
Career Perspectives: Interview with Tina Checchio, Associate Director, Quantitative Pharmacology & Pharmacometrics
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.
Measuring lots of little details: Non-Compartmental Analysis and the Early Phase Regulatory Environment.
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 . 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’  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 . 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.
 Gabrielsson, J. and Weiner, D., 2012. Non-compartmental analysis. In Computational toxicology (pp. 377-389). Humana Press, Totowa, NJ.
 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.
By Esha Senchaudhuri
An important trend in clinical development involves integrating strategic pharmacometric analysis with program level decision-making, to make the most use of available data. This can occur in various forms, from leveraging preclinical data for go-no-go decision making , to the need for improved comparative effectiveness frameworks .
Here we have five reasons why you should consider utilizing model-based meta-analyses ( MBMAs) for your program or portfolio development.
Last month was the eighth American Conference on Pharmacometrics (ACoP8) in Florida, a key event on the calendar for Cytel’s Quantitative Pharmacology and Pharmacometrics subject matter experts.
Cytel was delighted to contribute to the event this year and present two posters. This was excellent opportunity to share our knowledge and innovative research, alongside networking with likeminded industry professionals.
Model-informed drug development has been defined by Richard Lalonde ( Lalonde, 2007) (1) as “Development and application of pharmaco-statistical models of drug efficacy and safety from preclinical and clinical data to improve drug development knowledge management and decision-making”. It has been identified by the FDA as an important way to help reduce attrition and uncertainty in drug development.
In a recent FDA Voice article,(2) FDA Commissioner Scott Gottlieb noted the critical role which modeling and simulation can play in making clinical development more efficient.
He commented that:
“FDA’s Center for Drug Evaluation and Research (CDER) is currently using modeling and simulation to predict clinical outcomes, inform clinical trial designs, support evidence of effectiveness, optimize dosing, predict product safety, and evaluate potential adverse event mechanisms. We’ll be putting out additional, updated guidance on how aspects of these in silico tools can be advanced and incorporated into different aspects of drug development.”
In this blog, we share a new Cytel infographic highlighting 4 key questions a sponsor can explore to apply these approaches within their development programs.
The Population Approach Group in Europe (PAGE) represents a community with a shared interest in data analysis using the population approach. Each June, a meeting of the community is held at a different European location. At this year's meeting in Budapest, Hungary, Cytel's Director of Quantitative Pharmacology and Pharmacometrics, Cecilia Fosser, showcased innovative work on creating model based predictions of pharmacodynamic responses in ulcerative colitis patients. Fosser presented a poster Model based predictions of the PTG-100 pharmacodynamic responses in ulcerative colitis patients created in collaboration with colleagues from Protagonist Therapeutics.
In this blog we share the abstract and link to access an electronic copy of the poster .
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.
Nonlinear Mixed Effects Modeling (NONMEM) is a type of population pharmacokinetics/pharmacodynamics (popPK/PD) analysis used in Clinical Pharmacology research. The population PK approach combined with pharmacodynamics modeling, allows integrated analysis, interpretation, and prediction of the drug’s safety, efficacy, dose-concentration relationship, and dosing strategy.