Cytel Blog

By Esha Senchaudhuri

In response to its R&D productivity from 2005 – 2010, AstraZeneca took the initiative in 2011 to implement what it has called the 5R Framework to strengthen its capabilities. In a Perspectives article from Nature Reviews Drug Discovery [1], Paul Morgan and his team provided complex details about the success of this framework from the perspective of every stage of drug development. Between 2005 and 2010, AstraZeneca was behind industry averages in every phase of clinical development except Phase 1.  After the implementation of 5R, AstraZeneca success rates improved substantially. Indeed, it was announced by IDEA Pharma in March 2018 that AstraZeneca had topped its 2018 innovation index.  Here we examine to what Morgan et al, attribute AstraZeneca’s  success.  

At Cytel we believe that expert statistical input has the power to shape the future of clinical development: de-risking portfolios, accelerating timelines, and increasing the probability of success.

In this blog we talk to Benjamin who lives in France, to find out more about his career path, achievements, current role at Cytel and his interests outside of work. 

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.  

As part of Cytel's new Trial Innovations Webinar Series, Pat Mitchell, Statistical Science Director at AstraZeneca presented the October webinar "Formal Go/No-Go decisions are a key component of risk management in early clinical development."

It’s been hard to miss the prevalence of estimand-related discussions in the last year.  This is a topic which is very much at the forefront of statistics discussions right now.  We are lucky enough to welcome Mouna Akacha to the blog to give us the lowdown on estimands and the problems and opportunities they represent for the global biopharma industry. 

Mouna is a Consultant in the Statistical Methodology Group of Novartis Pharma AG, based in Basel, Switzerland. She has a wide range of research interests including topics on missing data, longitudinal data and recurrent event data and is an active participant in the current estimand discussions.

Read on to find out everything you ever wanted to know about estimands but were afraid to ask…..

Use of R is a hot topic among statisticians and programmers in the pharmaceutical industry.  At the recent PhUSE conference in Barcelona there was a clear uplift in interest in the  language and a number of sessions explored introductory principles and examples of how R can be used in practice.  Cytel's Namrata Deshpande presented on the use of R beyond Statistics through a case study of the development of a user friendly tool deploying non-statistical packages in R to enable clinical decision making. The talk won first prize in the Trends and Technology track at the PhUSE conference.  In this blog, we'll discuss some of the aspects presented and share Namrata's slides for download.

Statistical programmers play a key role in turning the data from clinical trials into knowledge and supporting the development of new medicines.  In a dynamic industry with demands such as CDISC compliance, data transparency initiatives, big data, and cost pressures the role is evolving to become ever more multi-dimensional. Statistical programmers now have the opportunity to follow their specific interests and specialize in a range of areas.

Drug development is an expensive and risky business.  To maximize a compound’s ultimate chances of commercial as well as regulatory success it’s imperative that sponsors are building up a strong understanding of its characteristics relative to competitors.  This knowledge can support critical decisions along the development path, such as optimizing dosing, and selecting the best active control. Importantly, it also ensures the sponsor can build a body of evidence and quantify the benefits of the compound in the context of other treatment options.  This knowledge can streamline the path to drug approval and support the drug’s chances of commercial success when it reaches the market.

 Model based meta- analysis (MBMA) is becoming an increasingly utilized strategy to conduct this competitive benchmarking. 

 Predicting the course of a clinical trial is something which people will always want to do-whether for statistical reasons, planning reasons or business reasons. In this blog we look at examples of where prediction goes off course, and how we can resolve these issues.  We also share valuable video and slidedeck resources from our VP Consulting and Software, Yannis Jemiai.