Data Analytics Clinical Studies • 3 min read

What are the different stages of the clinical trial process, and how can data analytics help?


To get technical, depending on how you perceive this process, there are either three or six stages to bringing a new drug to market. Most medical professionals recognise Phases I-III as the standard development procedure, but a growing number are taking note of a few extra parts to this process.

First things first: conducting preclinical studies

During the development of any new medication or treatment, preclinical studies are crucial starting point. This means that the product candidate – another term for the relevant drug, vaccine, medical device, or diagnostic assay – goes through extensive tests to determine how worthwhile a full clinical trial might be. The product will likely undergo both in vitro (cell culture or test tube) and in vivo (animal model) experiments. If the research team decide that there’s enough scientific merit in this candidate to investigate further, then they’ll move on to the next stage in the process – also known as Phase 0.

Phase 0 is a relatively new designation within the drug development process, and involves human microdosing of the product candidate in order to establish very early on if the drug behaves as was expected in preclinical studies. These tests can’t really give any concrete indication as to whether or not the treatment is safe for use in humans, nor can they really suggest whether or not it is effective.

Demonstrate proof of concept and compound safety

Phase I is, generally speaking, the first “in-human” test, involving around 20-100 healthy volunteers. As these volunteers aren’t selected randomly, this stage is particularly vulnerable to selection bias. Phase I trials normally involve dose escalation studies – a stage in the clinical trial process which determines the point at which a compound is unsafe to administer. This stage determines whether or not the product candidate is safe to check for efficacy, before moving on to Phase II: the testing of the drug for side effects and overall capability.

The goal of this phase is to evaluate if the product candidate has any biological activity or effect – essentially, this stage is designed to demonstrate clinical efficacy, or “proof of concept”. This part of the clinical trial process determines the optimal dose at which the product candidate shows the desired biological activity, but with minimal potential side effects. It’s a tricky balancing act – meaning that Phase II is the stage where most drugs trials fail, and researchers have to go back to the drawing board.

Establish real-world efficacy and actual value

Phase III is designed to assess the effectiveness of this new drug, as well as its actual value in terms of clinical practice. Because of the sheer number of participants and and comparatively long duration, this is often the most expensive, time-consuming and difficult stage of the clinical trial. It’s also the stage with the lowest success rate: roughly only 25-30% of drugs in development will make it past this point – and that’s if they’ve been able to make it to this stage in the first place. This drops even lower when considering vaccines, with an average of only 7% being approved after Phase III trials. This is widely seen as the final stage leading to approval.

Lastly, Phase IV consists of comprehensive post-marketing surveillance in order to ensure long-term safety of the product candidate. As the bulk of the work has already been done by the time a drug reaches this stage, efforts here are mainly focused on general monitoring after it receives regulatory approval to be sold, in addition to highlighting any as of yet unnoticed safety concerns. Additionally, this phase considers and analyses how patients are responding to the treatment, gathering information on their opinions and reactions and tweaking the drug or therapy accordingly.

Using data-driven insights to boost success rates

Ensuring that these tests are trialed successfully and safely involves huge amounts of effort, time, money – and data. With only one in ten clinical trials actually resulting in a drug that can be safely and confidently sent to market, pharmaceutical companies are taking a big risk when considering particular compounds. This is where the power of data-driven insights steps in.

By only focusing on therapies that are likely to be successful, data analytics and artificial intelligence are dramatically reducing potential investment. Embracing the real-world evidence and insights provided these technologies and opting for a data-driven approach to the clinical trial process will ensure that the future of healthcare is more solutions-oriented than existing practices allow - resulting in better patient outcomes.

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