Cytel Blog

The Cytel team made its annual trip to the PSI (Statisticians in the Pharmaceutical Industry) conference 2nd to 5th June. Taking place in London, UK, the theme of this year's meeting was Data-driven decision-making in medical research. As ever, the discussions both within the official conference agenda and during the networking breaks were engaging and productive.

In this blog, we share some of the particular highlights from the sessions that our team attended. We look forward to participating again in 2020 when the conference will return to Europe. 

This article was originally published as part of a series by pharmaphorum in association with Cytel and is reproduced with their permission. Scott Harris, a four-time biotech Chief Medical Officer, and principal at Middleburg Consultants, a pharmaceutical consulting organization, told pharmaphorum’s Richard Staines that using novel adaptive or seamless clinical trial models can help to cut development costs. In doing so they can reduce the risks of trial failure that can spell the end for those biotech companies without the deep pockets of big pharma behind them. 

In case you haven’t noticed, the traditional three-phase clinical development process is changing. While big late-stage trials are still pretty common, it’s also no longer a surprise to see sponsors refer to phase 1/2 trials, or phase 2/3, indicating that a smaller trial can be progressed to the next phase if an interim data readout supports further evaluation.

This is known as a “seamless” trial as the boundaries between each development stage have become less defined, and there are other options too.

Middleburg Consultants’ Scott Harris is a proponent of this new way of working and has personal experience of the approach after using it to steer a gastroenterology drug through the approval process.

In this blog, Alla Muchnik, Senior Clinical Data Manager at Cytel, discusses how specialist CROs can add value and streamline processes by providing oversight of data management services delivered by another CRO. This model helps to fulfill essential regulatory obligations for biopharma companies who may lack their own internal oversight resources.

In this blog, Jonathan Pritchard, Director Business Development at Cytel, draws on his experience in commercial, clinical and technology roles within the biopharmaceutical industry and shares his insights on the primary considerations for sponsors when implementing an ePRO solution.

 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. 

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.