Continuing from our introduction in part 1 where we established the “what” – i.e. Zika Virus – we will address now the “how”.
(disclaimer for the nit-picky ones: this is a casually written blog, not a full-fledged scientific article)
First – which target do we need to dig into? There is of course the virus itself. This is somewhat “easily” done by creating a vaccine against it. As far as we can tell, efforts for this are ongoing, though require still many years to deliver any positive or negative clinical outcome. It won’t be the focus of this little report – we are small-molecule people after all!
Thus, the alternative is to look into the biological pathways affected within the host by the Zika-Virus. There are currently a multitude of targets that are of interest. Generally speaking, all drugs that are developed affect a target within such a pathway (or several) – if such pathway(s) are known. In the case of Zika, several dozens of targets are under investigation which seem to show some impact on inhibiting/stopping the disease. Some targets are more promising than others.
After some digging around on the Web and reported literature, there are two sources we will be using:
ZikaVR: http://bioinfo.imtech.res.in/manojk/zikavr/ a large database dedicated to this particular virus. It has been described in detail in this research article: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5025660/ (you can access it for free).
This database has done a lot of the leg-work in collecting relevant information at this single resource. E.g., results of the Zika genome compared to known viruses which allows for quick identification of known drugs. These known drugs affect certain targets, therefore allowing the assumption, that the Zika virus affects said targets in the host as well. Of course, this is still to be proven in animal models/clinical trials. But, it reduces the number of possible targets to allow for a more focused plan of attack until more details are known (e.g. new targets may emerge from the genome comparisons or certain known drug targets being less/more attractive for whatever reason, etc).
There are numerous ways and strategies to use as starting point to find new leads. Strategies can be at times a question of personal vs company taste, experience and even “philosophy”. The overall (certainly non-exhaustive) gist in our case is a structural analysis with associated data.
The second resource we will be using is a recent article by Guo-Li Ming and coworkers who describe in more details some of the most advanced candidate molecules and discuss possible combination therapies: http://www.nature.com/nm/journal/v22/n10/full/nm.4184.html
Actually, there are two more general resources which we use:
Pubchem (https://pubchem.ncbi.nlm.nih.gov/) and DrugBank (https://www.drugbank.ca/) – public accessible databases with a wealth of information. They can be searched via a web interface, downloaded via FTP or accessed via their APIs. The latter is the way we access especially Pubchem, with the help of Knime . If you are wondering at this point about Knime – please see some of the previous blog entries on this site.
Analysis Part 1
To identify known drugs (pre-clinical-, clinical-, as well as marketed ones) and (related) drug targets. From a small molecule perspective, the most interesting section of ZikaVR is the “Drug Target” section, as of October 2017, containing 464 targets: http://bioinfo.imtech.res.in/manojk/zikavr/drug_target1.php, the list of compounds is over 500 structures long! For easier and faster analysis, we exported this list containing Drugbank IDs and used Knime to continue working with these. We need to find the structures, check for duplicates, and do a clustering – if at all possible.
With the help of Knime, we end up with a list of only 14 compounds! (We will give details on the Knime workflows in a subsequent part of this blog series).
And looking more closely at the targets of these, we see that all of them belong to three main biological classes:
We will at this point not discuss a potential bias stemming from compounds entered into the ZikaVR database – we refer to the database itself on details on curation.
The next step in part one is to analyze the structures and check for any similarities. Fingerprint & graph-based clustering (in simple terms: strip all attached groups from a molecule to keep the core, then replace all heteroatoms with Carbon and finally make all bonds to single bonds; again, see upcoming part 3) and end up with only three distinct cores, whereas six of the DrugDB compounds share one common core (subgraph), all corresponding to KIF11 inhibitors:
Analysis Part 2
Looking into the above mentioned article by Ming et al. we payed particular attention to the PHA-690509 compound. It is a CDK (see e.g. this WIKI entry) type of inhibitor known to have antiviral activity. The authors disclose further structurally unrelated CDKi compounds which inhibit ZIKA replication as well.
In the supplementary material one finds a list of 27 CDKi compounds which we used as basis for further analysis. If we do a subgraph analysis of these compounds we find three such graphs for all of these (with some overlap):
We have one structural “coincidence” based on KIF 11 (plus two other target classes which we will not use here) and ten structurally unrelated CDKi compounds (different CDK targets). Thus we intended the following:
- Search for bio-actives based on the subgraphs shown above
- Search for KIF11 inhibitors (target/structure related)
- Search for CDK inhibitors (target/structure related)
We searched through Pubchem either by target-name or via the subgraphs (substructure type of search) – also, we used an activity cut-off in the case of CDKi’s of pI50 > 6 (i.e. sub-micromolar activity). For target names we also had to do a detour to check the Assay IDs (AIDs) based on related structure IDs (CIDs) – if there is a better way that you happen to know of, please contact us, we would be delighted to hear how.
In the case of CDKi’s we found 257 structurally unrelated CDKi’s based on the subgraph search, such as
In total we found over 1450 CDKi’s (hitting the whole range of CDK1-CDK20).
In the case of KIF11 it wasn’t equally straightforward. One of the reasons is that for KIF11 the alternative name KIF1 is used… Absolutely not confusing, right? So initially we get 0 hits when we do targetname/assay related searches. With the graph based search though, we do find over 1100 analogues. If we filter by fingerprint similarity (Tanimoto >0.7, or even 0.8) we end up with a bit over 730 compounds, whereas 18 of those are reported as KIF1 compounds (meaning KIF11).
We were able to systematically identify compounds that are not necessarily structurally related (which is good to since it removes some of the starting bias), many of them are active on the same targets. There is assay data available as well, thanks to Pubchem, something we haven’t really used so far other than a generic potency cut-off in one case.
For a simple exercise as we have done here, we would claim it is acceptable if one neglects some details, e.g. the at times varying curation quality of Pubchem. You might need to drill down further into things like target names and make sure you search through all alternative names, just in case. Sometimes structures aren’t 100% correct, etc. etc. Don’t get us wrong, in general curation is very good, but you cannot trust it blindly since “the devil at times lies in the details”.
And now what?
Now? Only now one would really get started! We have some starting points and would want to make of them. For this, there are “gazillion” things to do further with these findings. Property analysis, pharmacophore modelling, 3d-shape-modelling, etc. etc. etc. Perhaps we will revisit this in the future.
Comparison with commercial products
On a final note, if you have the possibility to search through commercial databases, you can do basically the same thing. It might even be a good idea to combine these two types of searches. A quick test with e.g. Reaxys revealed many more CDKi structures, but less KIF11 inhibitors. In the end, you have roughly an equal amount of data and would probably end up with the same conclusion in terms of what you want to focus on. For someone doing Science@home though, Pubchem and other public databases are the go-to-place.
Stay tuned for some practical examples from the above mentioned Knime work-flows in Part 3.
PS: Part of this blog will be (have been) presented at the “ICIC 2017“, the International Conference on Trends for Scientific Information Professionals, Heidelberg, October 23-24, by Dr. Fernando Huerta from InOutScience .