Article | March 11, 2020
The pharmaceutical industry is set to greatly benefit from the use of artificial intelligence (AI), due to its wide range of applications. Sydney Tierney discusses how machine learning can enhance marketing, manufacturing and drug trials. Artificial intelligence (AI) can be applied to nearly every aspect of the pharmaceutical and healthcare industry, to enhance data processing. Adopting the technology will reveal the astonishing potential of the healthcare sector, with success rates flying higher than ever before – especially in the research and development of crucial, life-changing drugs.
Article | March 20, 2020
One predominant and common element within our pharmaceutical industry, is our devotions to patients. Within supply chain there is always a focus on ensuring the right product is delivered to the right place at the right time in order to ensure patient safety and the continuity of medicinal supplies. With the spread of COVID-19 across 117 countries and counting, every supply chain needs to evaluate their global footprint and develop contingency plans within their end to end operations.
Article | April 20, 2021
For much of the past three decades, even as methodologies for clinical trial design have advanced and refined, the idea of the optimized clinical trial has centered on optimal patient samples, target enrollment rates, and generally the most efficient uses of scarce resources in the form of patients. Yet anyone who has had to design and optimize a clinical trial, knows that trial optimization occurs within an ecosystem of choices; a series of choices that stretch from the time it takes to implement a clinical trial and submit clinical data for analysis, to general concerns about the cost and power of a clinical trial. A true clinical trial optimization process would try to unify a number of these choices into a single framework for trial optimization.
The complexity of clinical trial optimization comes from the need to align priorities on the one hand, and to understand opportunities on the other. We know that at a very general level, clinical operations specialists benefit from simplicity in clinical trial design, and that commercial teams prefer shorter clinical trials to longer ones. We also know that the statistical design of a clinical trial can influence both simplicity and duration. Yet how many sponsors have their clinical operations and commercial teams, sit with their R&D teams to review various statistically nuanced design options?
For many sponsors, the reason this process does not occur as often as it should, is because the nuanced statistical parameters of a clinical trial design are very difficult to communicate to non-statisticians. Yet a trial optimization tool like Solara, equipped with data visualizations and the ability to see tradeoffs intuitively, can overcome this challenge. The real challenge is often convincing the non-statistician that they have a stake in clinical trial design.
Cytel recently had a client that thought it needed a sample size re-estimation design, because it had a very strict limit on the number of patients it could enroll. After a few hours of working with Solara, though, a statistician discovered that a much simpler Group Sequential Design would deliver comparable power using about the same number of patients. The gains from the more complex design were minimal from the optimization perspective, when understood as the eco-system of choices.
Similarly, most commercial teams pressure their clinical trial designers to have the most accelerated clinical trial imaginable, but as we all know, the longer the clinical trial the more likely there will be a higher number of events that demonstrate the effectiveness of a new medicine. So commercialization teams have a stake in longer clinical trials, even when their rule of thumb is to shorten them.
Therefore, it is absolutely essential to communicate the benefits of various statistical designs to multiple stakeholders in a way that makes tradeoffs clear. Aligning on priorities early during the clinical trial design process is essential to selecting the optimal clinical trial. Yet for this statisticians need to be equipped for both a strategic and communicative role in the R&D process.
Article | March 26, 2020
If you’re part of a clinical study team racing a new product to commercialization, you likely live by these two simple rules: time is money, and the first one to market wins. But just because it’s simple doesn’t mean it’s easy. That ticking clock is background noise to the responsibilities of regulations, study protocols, supply chains, and patient recruitment — all the details that must be worked out before a study can even begin. The pressure is always there. The longer it takes for a study to start, the longer it takes to complete.