Pharma R&D Today
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How Better Data is Reducing Drug Development Costs
Posted on July 1st, 2016 by Thibault Geoui in Pharma R&D
We’ve all heard how expensive it is to bring a drug to market. Depending on who you ask, the costs have risen somewhere between USD 1.6 and 2.6 billion.
The last generation of the “blockbusters” is going off patent and annual sales for approved drugs are dropping too. It all adds up to an extremely tough time for the pharmaceutical industry, with companies all looking for the best way to stop the dramatically increasing costs—or better yet, reduce them.
A major target for cost-cutting measures has to be the most expensive stage of preclinical development: lead optimization. Incredibly, this essential process of identifying promising new molecular entities and fine-tuning their potency and pharmacokinetics costs more than the combined costs of all other preclinical stages!
The reasons for this cost were always the complexity of the work. Lead optimization traditionally meant a lot of in vitro and in vivo studies. In silico work—using appropriate data to model the complex interactions between drugs and biological systems—was possible, but limited by the lack of high-quality data. Lead optimization took time, expertise and resources—always an expensive combination.
Thankfully for the future of the pharmaceutical industry, and indeed for the health of patients with conditions that have no treatment or non-optimal treatments, things have changed for the better. Expertly curated and normalized high-quality data about drug-like compounds and their bioactivity is more readily available. In fact, the quality of research solutions to access pharmacology and chemistry databases and analyze data is constantly improving, with each year bringing new possibilities for in silico profiling.
This is an era of incredible opportunity for the pharmaceutical industry. Rather than watching costs spiral completely out of control, there is finally a chance to reduce them—significantly, in fact. In silico modeling gives the drug developer the possibility to study very large numbers of compounds in a fraction of the time that in vitro and in vivo studies would have taken. These data-based methods also reduce the reliance on animal studies.
A great deal has been said about the grim future of the pharmaceutical industry, but this era of high-quality data has opened up the chance to prove those predictions wrong. We can have hope that the industry will turn its fate around and perhaps even apply data to find new treatments for previously unmanageable conditions.
Want to learn more about how in silico tools and high-quality data are improving the success of lead optimization? This article looks in greater detail at this topic and its potential benefits for the pharmaceutical industry.
All opinions shared in this post are the author’s own.
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