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Is the Pharma Industry Missing the Big Picture?

Posted on September 22nd, 2017 by in Pharma R&D


There is a disconnect between how drugs are developed and how they are used. In the traditional method of target-oriented development, each new candidate drug is scrutinized in a handful of interactions with desirable and undesirable targets. However, if it is used in an organism, it will encounter countless other interactions with enzymes, transporters, receptors and other drugs.

Target-oriented development approaches that isolate lead discovery, design and optimization from the full range of potential interactions that would occur in a biological system are not just a “small picture” approach for these biological reasons. It also means that studies tend to focus on the same areas, perhaps because scientists already have enough information to feel there is a marginally higher chance of success; or perhaps because new targets are so challenging to validate.

Looking at the high costs (~$50 billion in the U.S. in 2014), high attrition rates (80–90% in the same period), and lack of any upward trend in number of approvals, it is clear that pharmaceutical developers cannot continue to develop solely on the assumption that a compound that decommissions one molecule in a molecular network can taper or eliminate a disease.

Therefore, researchers are working to narrow this disconnect by leveraging the big data of biology and chemistry: everything known about the various interactions that a compound could have in a physiological environment is a matter for consideration in drug discovery, design, and optimization. The idea is that building the context of a disease and viewing data within that context will reveal more about the compound and better inform decisions at each stage of drug development.

Injecting this knowledge into early drug development means investing in information and applying it early. The current paradigm of drug development is already more expensive than it has ever been and investing in information systems, modeling tools and databases might seem like additional costs. But their promise is high and certainly worth it.

Evaluating information about the larger picture during drug candidate vetting could prevent late-stage failures, increasing chances for approval and most importantly, reducing costs. The time to implement this data-driven approach is now before costs rise again and approved drugs can no longer bear the burden of that expense.

Our new white paper “Drug Attrition in Check: Shifting Information Input to Where it Matters” examines drug development, attrition rates, and the potential of data-driven approaches in more detail.

See the full paper

All opinions shared in this post are the author’s own.

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