New OmniViz version offers improved exporting and large-scale annotation of data sets

Posted on May 29, 2013 by Paul Bradley

On 28th May, Instem released version 6.1.3 of its data mining software, OmniViz. In this new version, data exporting has been enhanced to allow the export of group information.

Within OmniViz, groups can be created for any collection of records and according to any criteria: date of publication, occurrence of specific words in text fields, categorical values, a numerical feature, or the presence of an experimental flag such as a missing value or high standard deviation. Grouping makes it easy to select a large number of records, speeds up querying and lets you use set operations to examine the union, difference, and intersection of record sets.

Prior to the release of version 6.1.3, the use of groups was confined to within the user interface. With this new release, group information can be exported alongside selected column sets (Figure 1, attached). This provides a new and rapid method of adding annotations to selected records in a plain text format.

Figure 1: Creation of MedDRA annotations for a corpus of Medline abstracts using the group export feature. A CoMet plot (top left) is created for clusters using a MedDRA term list and groups are created from this view (top right). Distribution of these groups can be viewed in the Galaxy (bottom left). Records are exported, together with group annotations in the form of a pivot chart, and can be viewed in tools such as Excel (bottom right). The first record, which focuses on treatment of endometriosis, is annotated with the MedDRA classification reproductive system and breast disorders (column W).

This new feature complements the existing facility to export user annotations, or Notes, which are added on an individual record or cluster basis. A number of new applications are now possible, e.g. to quickly create bespoke subject headings for a collection of literature references or to filter and triage large collections of exported data within text editing tools.

Strength in numbers: more is better for drug safety/risk prediction

Posted on April 26, 2013 by Beatriz Bustillo

The re-use of data is an ever present issue within the pharmaceutical industry. Publicly available data is an important resource for companies looking to develop new drugs, and find new uses for current ones. The search has just become easier with the publication of the DrugMatrix database by the National Toxicology Program.

DrugMatrix is a large molecular toxicology reference database and informatics system. It contains data on the effects of more than 600 therapeutic, industrial and environment chemicals at a variety of doses and exposure times. For each of these compounds, relevant data curated from the literature is available, as well as assay results for inhibition of 132 protein targets. These have been chosen for their importance in drug development, and so among them, we can find drug-metabolizing enzymes or proteins involved in important toxicities.

The core of DrugMatrix is a set of highly standardised toxicological experiments performed in male, Sprague-Dawley rats, resulting in a wealth of data regarding histopathology, clinical chemistry and gene expression responses elicited by 638 compounds. The main strength of this database is that it provides the basis to linking macroscopic observations to alterations in genetic pathways. And the great news is, Safety Intelligence Program (SIP) users can now access this fantastic resource, with the addition of ~88,000 curated assertions with DrugMatrix evidence. The inclusion of the DrugMatrix data in SIP makes it possible to answer questions that were currently not addressable through the DrugMatrix interface, as the data is now integrated with knowledge extracted from several other relevant sources such as Medline, DailyMed and FDA NDAs.

DrugMatrix interface showing the results of an experiment where the administration of 2mg/kg Cisplatin for 3 days caused a 1.4-fold increase in blood urea nitrogen.

To give an example, let’s look at the effects of Cisplatin in rats. According to DrugMatrix, this compound increases blood urea nitrogen level, which is an important safety signal because it is an indicator of renal health. If the kidneys are not working properly and the glomerular filtration rate decreases, blood urea nitrogen will increase. This compound level can also be associated with heart failure, dehydration, fever or high-protein diet.

SIP ToxPath knowledgebase summary matrix showing that Cisplatin-induced increases in blood urea nitrogen occur in different species and relevant datasources

The next thing we might be interested in knowing is whether the effect is replicated in other animal species. While DrugMatrix only includes rat information, a quick search in SIP will point to the answer. Firstly, we will see that apart from DrugMatrix, there are Medline records describing the same observation in Sprague-Dawley and Fischer 344 strains. Increases in blood urea nitrogen are also described in mouse and rabbit. More importantly, this finding is also seen in humans according to DailyMed and the Electronic Medicines Compendium.

But should we worry about kidney function because of this increase? We can search SIP to see if Cisplatin is known to be associated with kidney dysfunction in patients. Again, we can see that Cisplatin is linked to liver disorder-related biomedical observations in 43 assertions from 6 different datasources. This makes sense, as Cisplatin is a well-known nephrotoxicant and kidney toxicity is dose-limiting in this type of chemotherapy.

Summary matrix of associations between Cisplatin and kidney disorder in humans, and the sources where the data has been obtained from.

Finally, we might also be interested in assessing whether compounds that cause an increase in blood urea nitrogen share a similar structure or protein target. While DrugMatrix is an excellent tool for this task, it only queries the 600+ compounds that are included in the dataset. Conversely, at present, SIP contains 85,996 compounds and 22,036 proteins, which are part of 2,574,454 assertions, and this allows the users to expand the search and increases the likelihood of finding meaningful results.

This is a very good example that when it comes to toxicology and pathology data, there is certainly strength in numbers, and when two powerful tools such as these are put together, their usefulness is greatly enhanced.

A small subset of curated SIP assertions linking Cisplatin to kidney disorder in humans.

Genome Browsing via SRS

Posted on April 3, 2013 by Mark Miller

From time to time, we are asked how genome browsing is supported in SRS. It’s a very good question, because SRS’ foundation is the integration of life sciences searching, browsing, and launching of bioinformatics analyses.

I have prepared a screencast that illustrates one approach for genome browsing in SRS, as developed by Martin Hilbers. Even if you don’t require genome browsing, this video could serve as a review of SRS searches and analyses, or as an introduction to how web applications can interface with SRS.

The importance of sharing data

Posted on March 28, 2013 by Stephanie Berry

I was concerned to read about the recent conviction of a former CRO researcher under the U.K’s Good Laboratory Practice law. Steven Eaton has been charged with manipulating animal study data to show drugs were a success, when in fact they failed. The Medicines and Healthcare Products Regulatory Agency says that he selectively reported drug concentration results, and an internal review found that Eaton had been meddling with data since 2003.

This caused a number of drugs to be delayed on their way to market, as hundreds of safety studies were reviewed, taking up valuable time and resources. Human lives may also have been at risk, as these drugs go to clinical trials following successful animal studies.

An Omniviz screenshot of pre-clinical data - animals have been clustered according to clinical chemistry parameters. The intermediate dose group is shown in white. If more people looked at this data, there would be more chance of realising any significance behind the outlying points.

An Omniviz plot of pre-clinical data – animals have been clustered according to clinical chemistry profile. The intermediate dose group is shown in white, and a clear outlier is visible at the top of the image. Tools such as this give greater visibility over data, and make it easy to spot out of place results.

I guess what this goes to show, is the importance of data visibility and sharing. If more people see data from a study, it takes the onus off one person to analyse the data and report results, and means there is a greater chance of discrepancies being noticed. In order to get to human trials, a drug must go through a bare minimum of 9 regulatory toxicology studies, including repeat dose toxicity studies in 2 different species, chronic toxicity studies in 2 species, and a 24 month carcinogenicity study in 2 species. And that’s just if no safety issues are flagged, and doesn’t account for non-regulatory studies. If lots of people are allowed access to that data, for one thing it would mean that no one person would be able to manipulate it, but it might also open the door to new uses for a drug.

SEND is the FDA’s preferred way to present non-clinical data, it is completely electronic so the sharing potential is huge. In a few years’ time, all detailed, tabulated, non-clinical study data submitted to the FDA will need to be in this format, and lots of solutions are available to do this. This means that not only will data be highly sharable within a particular company, but any studies that are outsourced or otherwise done by someone else will be in the same format, and be accessible to lots of different eyes. Instem is a provider of key laboratory information management software (LIMS) and also of tools for integration and visualisation of non-clinical data.

How do you define a New Chemical Entity?

Posted on February 7, 2013 by Jane Reed

What’s the definition of a New Chemical Entity? A new chemical entity (NCE) is, according to the U.S. Food and Drug Administration, a drug that contains no active moiety that has been approved by the FDA in any other application submitted under section 505(b) of the Federal Food, Drug, and Cosmetic Act. This might seem pretty straightforward, but Keryx are currently finding that there are issues. Keryx (as you may have read in the news feeds) are having worries over the potential exclusivity of their new drug, Zerenex, as the active ingredient may not be distinct enough from Otsuka’s FerriSeltz, approved more than 15 years ago (according to IPD Analytics) – and this piqued my curiosity.

So, what is the definition of an “active moiety”? Again, from the FDA website: “An active moiety means the molecule or ion, excluding those appended portions of the molecule that cause the drug to be an ester, salt (including a salt with hydrogen or coordination bonds), or other noncovalent derivative (such as a complex, chelate, or clathrate) of the molecule, responsible for the physiological or pharmacological action of the drug substance.” Let’s look at the two compounds that are under scrutiny.

Fig 1: 2D chemical images of Zerenex and FerriSeltz active ingredients

Fig 2: Summary matrix from SIP ToxPath knowledgebase, of associations between either FerriSeltz (ferric ammonium citrate) or Zerenex (ferric citrate) and biomedical observations, systematically extracted from Medline, regulatory documents, and public domain toxicology databases, and formatted into assertional metadata, for rapid search and analysis.

If you look at the structures (Fig.1), you can see the potential similarity. However, the two indications are very different – FerriSeltz was approved for upper gastrointestinal enhancement during MRI; Zerenex aims to lower blood levels of phosphorous in patients undergoing kidney dialysis. The two compounds also have quite different biological “fingerprints” (Fig 2), looking at the range of effects on various biomedical observations, reported across current and historic key public domain data sources. Looking at the tissue effects of the two active ingredients again gives a different summary matrix (data not shown). For example, from the biological effects matrix, it can be seen that there are only a few effects that both compounds have in common (effects on ferritin biosynthesis, iron level, oxidative stress) and many effects where there is no overlap (e.g. ALT and AST levels). These data provide more insight into the two different compounds, and hint to possible paths for further investigations.

Meanwhile, I await with interest the outcome of the FDA deliberations – as I am sure, so do Keryx.