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The Importance of Selecting The Most Promising Drug Target

Posted on September 27th, 2017 by in Pharma R&D

The growing economic pressure due to late-stage drug approval failures has been forcing drug discovery and development scientists to adapt their strategies in order to increase their chances of success. While it’s important to optimize drug candidates with respect to pharmacokinetics, efficacy and toxicity, perhaps even more critical to a drug’s ultimate success are ensuring that it modulates the right target in the first place.

Many drugs end up failing due to poor efficacy in large populations or problems with toxicity. Certainly, small tweaks can make incremental improvements. But no amount of drug candidate optimization can completely overcome these issues if they stem from modulating a target with limited prevalence in the patient population, or with a high probability of side effects.

Choosing the best target early in the discovery process is, therefore, the key to increasing the chances of regulatory approval success. The most promising targets are tightly connected to the disease of interest, have a proven function in the underlying pathology and are observed with high frequency in the patient population. Optimal target modulation should affect the desired disease pathway more potently than any other pathway in which the target is involved so that the predictable side effects are few or none. Thoroughly evaluating potential drug targets with respect to these properties requires scientists to develop a complete picture of the underlying disease biology and to understand a large number of intricate interactions involved in the related pathways.

Here we describe an approach that uses Pathway Studio’s powerful tools to identify and prioritize drug targets related to insulin resistance based on the targets’ potency in the insulin signaling pathway and experimental data from type 2 diabetic patients. This approach can potentially save weeks or months of research time in understanding disease biology, and that is critically important to novel target discovery.

Pathway Studio enables scientists to quickly and easily gather this information and unravel these numerous and complex biological interactions by allowing them to construct pathways based on literature data, imported experimental data or a combination of both. Derived using Elsevier’s proprietary deep reading natural language processing technology, this ever-growing knowledge base contains molecular relationships from 4.3 million full-text scientific articles, 26 million PubMed abstracts, and more than 325,000 clinical trials. These are among a list of source documents that provide a comprehensive dataset which helps enable more informed hypotheses generation and research decisions.

Read the customer story to learn more: Solution Story: The importance of selecting the most promising drug target.


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

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