Why we need Collaborative Drug Discovery

Lets get this out of the way before I start. Collaborative Drug Discovery is something that is very important to me. I am passionate about it. Predominantly because it has collaboration at the core and if we could all do more of this I think we could impact finding cures for so many diseases, neglected and rare. It is also a company and I have been consulting / working for them since about 2008. I also am very fortunate to have supportive collaborators that provide their software to me namely Accelrys, Molecular Materials Informatics and in return I use it in my work and publish about it. So there we go in the interests of full disclosure.

I plan on talking about collaborative drug discovery and why its important for so many groups but most importantly pharma. Yes I have worked for pharmas (Lilly and Pfizer, more full disclosure) and saw first hand the patent cliffs that were looming a decade or so off in the late 1990s. Those cliffs have been happening with regularity over the past years and every company has been affected. Some companies for all their advance planning could not stave it off. If ever there was a time for more collaborative drug discovery it is now. Instead what do we see, a toe in the water. And a slow moving one at that. While increasingly they outsource most things in early R&D to China and India and beyond, they probably should look closer to home for the people that can help them most.

No one can do it all. You might have the best scientists in the world inside your firewall, but there will always be groups elsewhere that have the missing pieces you need. Some of it can be accessed by crowdsourcing using things like Innocentive etc..Some of it can be accessed from academia, but increasingly I am seeing the growth of public efforts as patient and parent advocates with something to teach us about collaboration and doing drug discovery in a new way.  This will be expanded on in future blogs, but lets get the justification for collaborative drug discovery out of the way.

I think I can illustrate this by some of the projects I have been most recently involved in. I should acknowledge what are essential tools in doing what I do. So here goes. (Please see figure).

some of the tools & technologies I use frequently






Here is a start.

1. Getting drugs in the brain and cerebrospinal fluid has been a challenge for anyone trying to target many diseases, Depression, Alzheimers, etc. and yet until recently we did not even understand our own physiology. A study by a long time collaborator Erin Schuetz and her colleagues at St Jude Children’s Research Hospital has shown that arachnoid barrier cells that form part of the blood-cerebrospinal fluid  barrier contain many drug transporters and some drug metabolizing enzymes. So this fundamentally changes our understanding of the barriers to CNS and CSF entry. We have so many other pharmacophores to consider when targeting the brain we have to avoid the transporters and enzymes present in these cells as well. To add insult to injury, none of the mathematical models used increasingly in the pharma industry have taken this into account, until now.

2. We are in the early days of understanding what proteins drugs target besides their intended target/s. A great example here are the drug transporters being characterized with collaborators at The University of Maryland led by Jim Polli, we are developing an understanding of which drug transporters may be interfered with by FDA drugs. Again this is information the industry is neither funding or contributing to.

3. While some scientists are aware of difficulties that may be observed in simply dispensing liquid in high throughput screening this is far from well understood and its not universal. Some pharmas have changed their dispensing habits in recent years, others have not. It may not solve all of their problems but it can help. This is an example where a vendor is leading the way to improve the quality of data generated. Its definitely self serving, but it shows that there needs to be better education on how some of the techniques such as pipetting, which we take for granted can introduce experimental anomalies. I think this calls for collaboration and pharmas should donate compounds and fund these efforts to increase the quality of data generated.

4. While there are efforts from the NIH to bring in big pharma and repurpose compounds via NCATS. These efforts could benefit from more community engagement and be enhanced by using some of the computational technologies available. There are certainly many more interesting molecules sitting on the pharma storage shelves and these need exposure for repurposing. Who is going to facilitate this?

5. There are attempts to foster big pharma collaborations around neglected diseases, but there are also open source spirited community efforts that could benefit from sponsorship and support. Too often the complexities with working with big organizations get in the way. When they do put data out into the community they should also make use of some of the analyses performed. A recent example with TB suggests that predictions made 3 years ago using public data and compounds from GSK active against malaria, could have identified selected compounds with activity against TB.  The key here is to get over the hurdles of the industry and make better use of the data that is in the public domain and generated at great public cost.

6. While the Pharma industry would like to think it is running the rare disease show, there are thousands of diseases and the reality is that most of that research is happening in academia. Academia is even trying to think of better ways to speed up finding cures and approaches that are more generalizable. There is a huge opportunity here for pharma collaboration and using tools to securely share data may be very useful.

7. The quality of data in databases needs help and this will depend not only on funding bodies like NIH, academics but also pharmas.

8. The concerns of pharmas about using the cloud for data sharing seem to be shifting, but seriously Its amazing how banks have made good use of the cloud and come up with standards and yet pharma and the informatics industry has lagged behind.

9. Interesting to see that both topics in 7 and 8 above are getting lots of interest based on my Impact Story analysis below (Thank you Heather Piwowar and Jason Priem).

impact story

10. Collaborative drug discovery can be facilitated by computational tools and there are many described above that are free. Depending on whether the collaboration is open, closed or somewhere in between will point you to which tools to use. The examples above are just a tiny snapshot and I am sure you could think of other science that would justify more collaboration in drug discovery. This at least gets us started.






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