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Re: Minutes from Clinical Decision Support teleconference

From: Matthias Samwald <matthias.samwald@meduniwien.ac.at>
Date: Fri, 27 Apr 2012 12:40:12 +0200
Message-ID: <DBACB49587DF4FA488966C0CD6186141@zetsu>
To: <rdb20@pitt.edu>, <public-semweb-lifesci@w3.org>
Hi Richard,

>Were the entries blank in "Germ-line genetic marker (curated SNPs)" because the 2C9 variants were not present in a curated resource?

We discussed this a bit after you had left. Some entries for curated SNPs in [1] are blank because the warfarin drug label there refers to CPY2C9*1 alleles, i.e., the wild-type alleles. These are a bit tricky, because these wild-type alleles can be defined by not having any SNP that would turn it into a non-wildtype allele (i.e., you would need to know the status of a great number of SNPs to make a wild-type call, while you only need to know one or two SNPs to make a non-wildtype call). 

Generally, it seems a bit problematic to use alleles denoted with the * notation in drug labels without referring to the concrete SNPs or genetic sequences, because the definition of alleles can change when new variants are discovered.

> What is the meaning of the evidence column? 

I think for the time being it is sufficient to capture from which source the rule was derived in a short text and a URL that points to that resource, if available online. I added such a text to the table now. In general, I think we won't derive these rules from primary datasets, but mostly from already curated (albeit unstructured) and authoritative sources.

> It might also be interesting to link to the evidence that motivated that decision for which one would have to go to a different source e.g., <http://www.ncbi.nlm.nih.gov/pubmed/19228618> or <http://www.ncbi.nlm.nih.gov/pubmed/17906972>

I don't understand -- could you elaborate?

> It might help communicate the rules more clearly if you provide a label for VKORC1 variants in the column "Germ-line genetic marker (as appearing in source)" 

Done.

> The "level of evidence" and "clinical significance" columns are very challenging things to assign. The level of evidence could be some evidence-based ranking (e.g.,<http://www.fda.gov/ohrms/dockets/dailys/03/Aug03/080103/03n-0069-rpt0001-04-Attachment-b-vol4.pdf> ) or a consensus-based scale (e.g., SORT, <http://www.aafp.org/afp/2004/0201/p548.html>). I think that there is no standard rating scale but that most will agree some kind of rating is important. So, it might make sense to include a triple that has the rating and metadata describing the scale used and who did the rating. 

There exist some evidence classification schemes in the pharmacogenomics domain (e.g. from PharmGKB, of course). I documented the PharmGKB schemes at https://docs.google.com/spreadsheet/pub?hl=en_US&key=0AiGT-vnkGcoLdFFVMEdqcFdYaDFqS0xHTnlUT0N3cEE&hl=en_US&gid=1 (values in the decision table are auto-completed based on the entities there). I guess for this project we should standardize to using a single scale, rather than mixing different scales. 

TODO: Would you (or someone else) be interested in looking into these classification schemes to see what would fit best? Are some of these schemes available as ontologies, e.g. via BioPortal? However, I guess we should at least retain some compatibility with PharmGKB, CPIC and other such initiatives!


Cheers,
Matthias

[1] https://docs.google.com/spreadsheet/pub?hl=en_US&hl=en_US&key=0AiGT-vnkGcoLdFFVMEdqcFdYaDFqS0xHTnlUT0N3cEE&single=true&gid=3&output=html







From: Richard Boyce 
Sent: Thursday, April 26, 2012 8:26 PM
To: public-semweb-lifesci@w3.org 
Subject: Re: Minutes from Clinical Decision Support teleconference


I wanted to first thank Matthias and the CDS group for doing a very nice job on some initial OWL and SPARQL rules for pharmacogenomic clinical decision support [1]. I had a few comments and questions regarding the rules...

Questions:

- Were the entries blank in "Germ-line genetic marker (curated SNPs)" because the 2C9 variants were not present in a curated resource?

- What is the meaning of the evidence column? I could imagine assigning and evidence type, an OBI description of the study from which the evidence was derived, or a citation. Thinking about the big picture, it would be neat if the evidence was a URI or SPARQL query to an annotation of the text (human or computer) complete with metadata on when the annotation was created and the expertise of the group providing the annotation. Perhaps there should be both an entry for the annotation and metadata about the scientific provenance of the data from which the rule was derived. In this case, the scientific provenance would be that the statement came from a decision made by an authoritative source (the FDA). It might also be interesting to link to the evidence that motivated that decision for which one would have to go to a different source e.g., <http://www.ncbi.nlm.nih.gov/pubmed/19228618> or <http://www.ncbi.nlm.nih.gov/pubmed/17906972>

Comments:

- It might help communicate the rules more clearly if you provide a label for VKORC1 variants in the column "Germ-line genetic marker (as appearing in source)" 

- I think that the key to really making this example strong will be to include rules that factor in both clinical and genetic factors. Genetic variability is important but it is only part of the picture. Perhaps some rules could be derived from clinical dosing algorithms that factor in clinical and genetic factors such as described in <http://www.ncbi.nlm.nih.gov/pubmed/19228618>

- You might find it interesting that one can get a SPARQL query to the actual text and table from which you grabbed this variant information using the linkedSPLs endpoint: http://dbmi-icode-01.dbmi.pitt.edu/linkedSPLs/sparql:

PREFIX dailymed: <http://dbmi-icode-01.dbmi.pitt.edu/linkedSPLs/vocab/resource/>SELECT ?dosage WHERE {
  ?s dailymed:setId "d91934a0-902e-c26c-23ca-d5accc4151b6".
  ?s dailymed:dosage ?dosage.  
}


- The "level of evidence" and "clinical significance" columns are very challenging things to assign. The level of evidence could be some evidence-based ranking (e.g.,<http://www.fda.gov/ohrms/dockets/dailys/03/Aug03/080103/03n-0069-rpt0001-04-Attachment-b-vol4.pdf> ) or a consensus-based scale (e.g., SORT, <http://www.aafp.org/afp/2004/0201/p548.html>). I think that there is no standard rating scale but that most will agree some kind of rating is important. So, it might make sense to include a triple that has the rating and metadata describing the scale used and who did the rating. 

Clinical significance is even more challenging because no one can agree on what this means. In drug-drug interactions, all of the major drug compendia have very poor agreement on the clinical severity of an interaction and local sites will customize the severity rankings to fit the populations they work with. Some groups avoid classifying severity and go for an operational classification such as "avoid", "minimize risk", or "no special precaution" (<www.ncbi.nlm.nih.gov/pubmed/11297327>. Here again, it might make sense to include a triple that has the clinical significance (or operational) rating and metadata describing the scale used and who did the rating. 

thanks, 
-Rich


[1] <https://docs.google.com/spreadsheet/pub?hl=en_US&hl=en_US&key=0AiGT-vnkGcoLdFFVMEdqcFdYaDFqS0xHTnlUT0N3cEE&single=true&gid=3&output=html> 

On 04/26/2012 12:19 PM, M. Scott Marshall wrote: 
You will find the minutes for today's Clinical Decision Support
teleconference here:
http://www.w3.org/2012/04/26-HCLS-minutes.html

Matthias showed us an interested demonstration of his SPARQL rule
approach to pharmacogenomic data (example: matching on certain SNPs to
produce personalized drug advice). Very nice! Many participants are
hoping to look deeper into this in a followup call.

Cheers,
Scott




-- 
Richard Boyce, PhD
Assistant Professor of Biomedical Informatics
Scholar, Comparative Effectiveness Research Program
University of Pittsburgh
rdb20@pitt.edu
412-648-6768
Received on Friday, 27 April 2012 10:41:29 GMT

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