**UPDATE As of 21st Jan 2016 please note the actual structure of BIA 10-2474 changed to this.
My “Armchair science” / speculative contributions on this blog for the clinical trial disaster with BIA 10-2474 got a bit of notice over the past few days at Forbes and C&E News. There have also been discussions with reporters at the Boston Globe and Nature, so lets see where they lead, if anywhere. But what is interesting from my perspective is each time I chat with someone outside it puts a slightly different perspective on what motivated a role in this crowdsourced evaluation. From one side its what I do anyway. Try to understand what molecules are doing computationally. Normally I would use models built for a specific target or an activity measured in whole cells. Then I would use the model to find new molecules that might be worth testing. I also have a nearly 20 year interest in predicting toxicity so its not as if I am new to this. But I do not think any of the reporters even Google me or search in PubMed, or even know what computational toxicology is. People are perhaps too busy.
The challenge here is that BIA 10-2474 is a black box, we do not even have 100% proof its the compound or some human error that caused the disaster. The timeline on figuring this out is fluid, what can be done in the meantime?
So to me this is not armchair science, its gone beyond that, it is my attempt at trying to contribute to identify what this molecule can do, what it might have done in the human clinical trial subjects. I think the foundation for making predictions using published data is real science. Some might argue that drawing a structure or a SMILES string into a computer and pressing a ‘button’ to make predictions is science or a career but I would beg to differ.
Some of the models I have highlighted make use of ChEMBL which has carefully compiled molecules and their binding data to targets for > 1 M molecules. This is big data that s been used to create machine learning models. Leveraging data like this seems obvious to try to figure out what a novel compound might ‘hit’. There are many other tools that members of the scientific community have developed and could apply, so it will be interesting to see some of the papers that come out in the future.
Other questions arose during discussions and after, like why should I do this? I don’t work for the company involved. For my own personal interest in using the various tools and models that are out there I want to see what they come up with. When push comes to shove can any of these tools even work?
Also the way this unfolded over a holiday weekend in the US, made me think that the traditional way of peer reviewed science would not cope in this instance until weeks/months after the event. As the various scientists were trying to identify the molecule and then we run it through different apps and online tools prior to making the data available, moves this outside of the traditional paper / publication scenario. Its like an open lab notebook in that the blog posts are showing you the thinking, results and ideas as they happen (well at least after they are typed). The others in the community have free access to this information which they can link to or not as they come up with their own posts.
So all in all, its a definite change from what one would normally do in keeping your ideas private or sharing with close collaborators, building up the results and experimental verification and then doing the analysis, write up and publishing. In this case its flipped. Once the structure of the candidate compound was postulated and put in Wikipedia and on Chris Southan’s blog it was open season.
The closest I have come to this scenario was the work on trying to understand the targets for the FDA drugs that were active against Ebola, as the initial work was posted on FigShare etc. and ultimately resulted in a publication. In the case of BIA 10-2474, unless there is compound that is made available or synthesized we cannot do the experiments to verify the predictions. As of today I do not see a clear path to publishing these experiences in a ‘journal’ but then this might change.
So where will BIA 10-2474 lead? For one it might make people think about what other tools can be used to get insights on a molecule before human testing. Perhaps we should be exploiting Big Data to predict toxicity, off targets as well as potential metabolites that might not have been predicted in liver microsomes. I will be giving a talk next Monday at SLAS 2016 in San Diego and this experience fits nicely into the title ‘Ensuring Chemical Structure, Biological Data and Computational Model Quality’. Between now and then perhaps there will be other information that will become available. In addition there are certainly many more computational models and tools that could be evaluated. The question is will this tragedy persist in the news cycle or will it fade away until it happens again with another trial. What can we learn in the interim? Because that is were our efforts will have most value, preventing it from happening again for a very long time.