Why do nine out of ten clinical trials fail, and how can the industry learn from these mistakes?

Date: 04. 28. 2022
Gen Li

Clinical trials are notoriously expensive, time-consuming, and often face huge setbacks. In fact, a recent report from the American Society for Biochemistry and Molecular Biology found that as many as 90% of drugs that make it to clinical trial ultimately fail – which is frustrating for sponsors, regulators, and patients.

A successful clinical trial starts at the design stage: rigorously designed and implemented clinical trials are at the heart of delivering innovative new treatments to patients. But how can the clinical development community overcome issues in planning and design trials that deliver results?

Falling at the first hurdle

Many clinical trials fail to pass testing and regulatory approval due to poor planning from the outset. At Phesi, we undertook a big data analysis of 330,000 trials from the past 15 years and found that the majority required substantial protocol amendments. The data revealed that one third of all trials could benefit from minor edits; one third require significant improvement; and the final third struggle from inception and are often on the brink of failure.

Not only do these protocol amendments slow down trials, they also add huge costs. Tufts University estimates that each trial amendment costs an average of $500,000 – making poorly designed trials a billion-dollar problem. Despite our scientific understanding and technology advances in recent years, the way that we design and implement trials has not kept pace.

The key to success is innovation

Reducing the risk of trial failure starts at the planning stage – and innovations in data science can help to address these underlying issues that have been plaguing clinical trials for years.

Harnessing the power of real-world data to guide clinical trial planning helps sponsors to design smarter trials, reduce development costs, and fast-track the delivery of treatments to patients. Data is a powerful tool for clinical researchers. Data from previous trials can help avoid common pitfalls– such as when setting inclusion/exclusion criteria, and primary endpoints – and minimize the number of costly protocol amendments.

Innovations in synthetic control arms and deployment of digital twins, which use data collated from similar or identical trials with real-world data, also help to reduce trial failure. Companies that take this data-driven approach to programs and trials will benefit from being able to accurately model comparator, including placebo-based outcomes. Synthetic patient profiles can also be used to understand how different protocol design elements impact the size and characteristics of a targeted patient population, which helps to optimize protocol design and minimize the risk of trial failure.

With a wealth of data at its fingertips, the clinical development industry has an opportunity to improve the success rate of trials and reduce the costs of medical development. Equipped with data, insights, and answers, clinical researchers can design safe and effective studies that accelerate the delivery of life-saving treatments to patients.

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