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.

 
Nowadays, it’s difficult to pick up a mainstream newspaper or read an industry publication without seeing reference to Artificial Intelligence or AI and progress towards innovations like autonomous vehicles, or customer behavior prediction. For the biopharma industries specifically, AI represents an opportunity to avert the R&D productivity crisis with paradigm-shifting applications such as in-silico drug design, prediction of trial risks and big data analytics.
However, with every opportunity, there are risks and challenges, and in this blog, I will discuss how pharma needs to address the opacity of AI to ensure trust and credibility with all stakeholders.

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.