VIEWS AND ANALYSIS
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.
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PHARMA TECH
Article | April 20, 2021
Technological innovations disrupt many industries, but the speeds of their adoption in the pharma industry have become more rampant than ever. A report from global market advisory firm ABI Research predicts that by 2030, the pharma industry will have spent over $4.5 billion on digital transformation. This is due to many things, from the need to optimize production lines to patent protection.
A decade from the forecasted market peak there have already been many applications of these rising tech trends. So let’s have a look at some of them:
Digital monitoring system
Pharma businesses need to comply with certain regulatory requirements before their drugs can be sold on the consumer market. For example, they need to be stored at a certain temperature. The state of the drugs manufactured and used during clinical trials also needs to be monitored. Fortunately, digital monitoring systems created by companies like Aptar Pharma, Primex, and Monnit, have made it easier to provide the reports regulatory boards such as the Food and Drug Administration and Central require. Aptar Pharma, for instance, offers sensors that can monitor and record the patients’ adherence level during ophthalmic clinical trials. Meanwhile, Monnit’s freezer monitoring solution provides data logs that can be filed as proof of compliance.
It doesn’t matter what kind of pharma data you need — there will be a digital monitoring system that can help you collect it.
Extended reality
Extended reality (XR) is used to describe all real-like virtual environments that are generated by computer programs. The two most common types of XR are augmented reality (AR), where digital graphics are overlaid onto the real world, and virtual reality (VR) where the user is “transported” to a digital world through headsets. To create realistic projections, VR and AR technologies are built with complex and densely packed electrical PCB designs. From wiring the schematic to comparing physical validation rules, all of this is carefully done to ensure that the technology has all the 3D features it needs. Pharma has many uses for this kind of technology. For example, one of Augray’s solutions is to allow researchers to better visualize human models using XR.
XR can also be used in lab and manufacturing training. Before letting people train onsite, XR solution providers like SoftCover VR and Labrodex Studios can create simulations that let them familiarize themselves with the equipment virtually. This is very important in the pharma industry, as one error can easily contaminate the drugs.
Artificial intelligence
Whether it’s for drug discovery research or clinical trials, artificial intelligence (AI) can help accelerate the process. AI is a technology that “learns.” AI programs, after they’re made, are immediately trained to detect patterns and features in the data to help collect insights. British startup Pangaea Data helps global pharma companies identify patient cohorts and trials using AI algorithms.
AI can also be trained to perform mundane tasks more efficiently, like arrange clinical data for researchers or gather studies. An AI program called Atomwise does this by analyzing thousands of existing medicines and picking out the ones that can be repurposed to treat diseases it wasn’t initially made for. This was even the AI that identified two drugs that could mitigate Ebola’s effects in 2015, saving multiple lives. In the future, AI can be taught more things that will allow them to aid medical research.
Additive manufacturing
Additive manufacturing, commonly known as 3D printing, is an industrial production process that lets businesses create 3D products using successive layers of a specific material. Since 3D printers will literally print any object with the right blueprints, additive manufacturing has been a big help in the mass production of drugs. However, researchers are now finding more uses for additive manufacturing — one of which is in the field of precision medicine. Precision medicine takes into account the patient’s lifestyle, history record, and even genetics. Eventually, they're given medicine that’s specially tailored for their body. Since blueprints can easily be edited, combining drugs can be done faster and with more accuracy. Of course, additive manufacturing’s application in this field is still at its testing phases, but researchers are hopeful about the results.
New discoveries are made in the pharma industry thanks to technology, and more will continue to do so as long as breakthroughs are made. Businesses should always be updated on these emerging trends, lest they want to be left behind by the competition.
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Article | April 20, 2021
Global clinical guidelines have shifted the industry toward risk-based approaches for the planning and execution of clinical trials. The ICH’s guidelines for Good Clinical Practice state that sponsors should evaluate identified risks against existing risk controls by considering “the likelihood of errors occurring, the extent to which such errors would be detectable, and the impact of such errors on human subject protection and reliability of trial results” (ICH E6 R2).
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Article | April 20, 2021
Over the past half-century, clinical trials have grown increasingly complex. A flood of new data from novel sources combined with more elaborate study designs and tougher regulatory standards have lengthened timelines and hiked costs, contributing to a steady decline of efficiency. It all adds up: Nearly half of all drug launches underperform revenue expectations. And if that isn’t enough, the outbreak of COVID-19 worldwide is having an effect on how life sciences companies operate their businesses, including how clinical trials are conducted. Most clinical trial research organizations have tried to adapt by tweaking a platform here, adding a new app there, but this has only yielded patchwork systems of sites, spreadsheets and dashboards that further burden the process.
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