Why poor target validation is costing pharmaceutical businesses millions

Richard Harrison
published on August 17, 2022

It has been well documented that only 1 out of 10 compounds that enter clinical trials makes it to market¹. That is an astonishing 90% failure rate. It is hard to imagine hiring a lawyer or doctor or any professional who fails so often. Yet in our industry, it remains the norm.

Furthermore, we know why compounds are not working: more than half of Phase 2 and 3 failures are due to efficacy. Or in other words, the compounds are simply not working as they were intended.

A publication from AstraZeneca² links the high failure rates to poor target selection. In fact, by employing more stringent validation criteria, they have demonstrated a four-fold increase in Phase 2 and Phase 3 success rates³.

The cost of failure

There are other reasons linked to high attrition. Poor drug properties and inappropriate animal models that do not represent human disease are a few. However, it is the connection between the level of target validation and late-stage success that appears most correlated.

In many cases, the data showing poor target disease correlation exists in the literature but is missed or ignored. The volume of literature is outpacing researchers’ ability to keep up and can result in expensive oversights.

According to the NIH, 90% of drugs fail before ever being tested on humans. Each one of these studies takes up to one year, and costs on average $1 million. Bringing a better candidate from nomination to the end of Phase 3 and avoiding failure would save, on average, over $100MM for each failed program.

While the business case is clear, perhaps more important is the moral case.

Our industry is dedicated to developing drugs that have an impact on real lives. By unclogging R&D pipelines and investing time and money into viable targets, drug companies will be much more effective in their mission to alleviate suffering and save lives.

Fortunately, tools do exist that can help researchers by quickly extracting information and guiding target selection. In my view, Causaly Cloud is by far the most advanced. Below you can find a video demo I recently recorded showing you just how Causaly can aid your research.

Introducing Causaly

Causaly Cloud enables researchers to find novel insights, qualify scientific evidence from millions of documents and make predictions in biomedical sciences.

It uses AI to machine read and digest all published biomedical documents. It understands sentences as a human would and can extract precise answers to complex research questions. This helps researchers not just save time, but also discover new insights and connections that would otherwise go undiscovered.

Let me illustrate the point with an example. Elafibranor is a PPAR alpha agonist that entered clinical trials in 2015 for NASH, and failed in Phase 3 because of poor efficacy. The program was stopped after 6 years and cost about $100 million.

Using Causaly Cloud to review the literature up to 2015, there appears a weak, if any correlation between PPARa agonists and NASH. If they had this information at the time, the company could have questioned the decision to advance. Not moving forward would have saved a huge amount of time and money – and importantly let them proceed with a more viable nomination.

Research redefined

The pharmaceutical industry has a big responsibility. There are few missions more important than alleviating pain and saving lives. Yet as you can see, researchers need a platform that can help them find the answers to their questions. Causaly Cloud enables you to not only rapidly speed up research, but to make completely new discoveries that would otherwise go unnoticed.

We would love you to give it a try. Our experts are available to answer your scientific questions and demonstrate the power of the platform. Please click here to get in contact and find out more.


  1. S. Galson et al., The failure to fail smartly. Nat Rev Drug Discov 20, 259-260 (2021).
  2. D. Cook et al., Lessons learned from the fate of AstraZeneca's drug pipeline: a five-dimensional framework. Nat Rev Drug Discov 13, 419-431 (2014).
  3. P. Morgan et al., Impact of a five-dimensional framework on R&D productivity at AstraZeneca. Nat Rev Drug Discov 17, 167-181 (2018).
  4. O. Wouters et al., Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018, JAMA 9, 844-853, (2020).

About the author


Richard Harrison
Chief Scientist at Causaly

Richard comes to Causaly from Accenture, where he was responsible for IT solutions and platform innovations for pharmaceutical clients. In addition, Richard served as Chief Scientific Officer at Thomson Reuters (later Clarivate Analytics) pioneering the use of Knowledge-Based Drug Discovery. Richard spent 30 years in pre-clinical drug discovery in companies such as Merck, Novartis and Aventis, and also was a founding scientist in two biotech companies. He holds a PhD in Physical Organic Chemistry from Rutgers University and has authored more than 50 peer reviewed publications in top tier journals.

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