SIP is a knowledgebase of toxicological and pathological adverse effects of drugs and other compounds.  It contains deep coverage of biomedical observations (BMOs) occurring in many different tissues (e.g. liver, heart), and we can now add skin (including appendages such as hair and nail) to that list.

The skin is an important organ, being the largest in the body, and is affected by a wide variety of different drugs, including those administered topically and subcutaneously as well as via other routes.

Urticaria - A common skin reaction caused by drugs (4)

Skin reactions are among the most common types of adverse reaction to drugs¹. Almost any medicine can induce skin reactions, ranging from minor skin toxicities,  such as rash (although this could also be indicative of a more serious reaction), to severe adverse events that cause drugs to be withdrawn from the market.  Valdecoxib was withdrawn in 2005 as it causes serious skin reactions, including Stevens-Johnson syndrome, a life-threatening condition in which the epidermis separates from the dermis². Antibiotics are the most common cause of Stevens-Johnson syndrome, followed by analgesics, psychoepileptics, and antigout drugs³.

To support drug risk assessments across the drug discovery & development process, in-depth intelligence around the tox/path effects of a wide range of drugs and chemicals in skin has been added to the Safety Intelligence Program, including:

• Over 44,000 compound-to-observation assertions affecting many different species (Figure 1)
• Over 2,500 different skin observations (Figure 2), of which 2,200 are adverse pathological effects
• Over 8,300 compounds affecting the skin (Figure 3), including active pharmaceutical ingredients, environmental chemicals, natural products, agrochemicals, and other test compounds, covering a broad chemical landscape

There is a breadth of non-clinical and clinical data for the skin in SIP, covering 14 species, with the majority of the intelligence for humans and rodents.

The effects of compounds on the skin include pathological observations such as urticaria (also known as hives), pruritus, rash, and dermatitis. Moreover, compounds can mediate physiological processes, i.e. normal skin function. For example, skin cell growth is inhibited by Fluorouracil and enhanced by Retinol. These data were extracted and summarised from key public domain data sources, including Medline, FDA MedWatch, FDA AERS,  DailyMed and the Electronic Medicines Compendium.

There are over 8000 compounds in SIP that affect the skin.  Those inducing the greatest number of skin conditions are shown in Figure 3, which includes 7,12-dimethylbenzanthacene (causing skin cancer and cell death);  Tretinoin (inducing acne, urticaria and various other conditions); and Phenytoin, which causes Stevens-johnson syndrome, alopecia and cleft lip.

SIP also features intelligence on the skin-related effects of around 2,000 active pharmaceutical ingredients, with some of the most prominent summarised in Figure 4.

References:

1. Lee, Anne. 2006. Adverse Drug Reactions.  http://www.pharmpress.com/files/docs/ADRe2Ch05.pdf
2. http://emedicine.medscape.com/article/1197450-overview#aw2aab6b2b3aa
3. http://www.fda.gov/Drugs/DrugSafety/PostmarketDrugSafetyInformationforPatientsandProviders/ucm124649.htm
4. http://en.wikipedia.org/wiki/File:EMminor2010.JPG

For more information on the Safety Intelligence Program, please see https://sip.biowisdom.com or contact  sip@instem.com to understand how you can access this important resource for skin tox/path knowledge.

As of January 2012, DailyMed (http://dailymed.nlm.nih.gov/) featured labels for over 32,000 drug products submitted to the FDA. These labels are a rich source of drug safety intelligence, but their free text format and inconsistent terminology use make them difficult to interrogate or use with other applications.

BioWisdom’s Metawise enabled the identification of key medical terms as well as the creation of assertional metadata. Approximately 380,000 assertions were curated from 5000 DailyMed labels. They describe the side effects, black box warnings and laboratory tests for over 700 medicinal products, including highly-prescribed and black box warning-containing drugs. The data were originally built as a decision support tool for a US government agency and the approach was presented at the 2011 OpenTox conference in Munich (click for poster and presentation).

These data have now been integrated into SIP, allowing complex questions to be answered such as:

• Which laboratory tests should be considered when prescribing ACE inhibitors?
• Which black box warnings occur in medicines that list haemolytic anaemia as an adverse event?

The DailyMed data further enhances SIP’s content, particularly in the area of clinical adverse drug effects. The drug label information also benefits hugely from the SIP search interface, with a wide range of summary and dashboard views, querying against a rich collection of synonyms and taxonomic “tags” and chemical structure searches. Example summary views for the questions listed above are presented in Figure 1.

Figure 1: A list of the most common laboratory tests for drugs of the ACE inhibitor pharmacological class (left image), and the most frequent boxed warnings in drugs for which haemolytic anaemia is an adverse event (right image). Data were generated in approximately 5 minutes.

As always, please contact us if you need more information, or would like to comment on BioWisdom’s Safety Intelligence Program content and application.

A new update release for SRS has been made available – it includes a number of usability improvements to the web interface, additional tools to facilitate administration, as well as a range of performance and stability enhancements. The range of supported databanks has been growing steadily; recently support has been added for integration of the iRefIndex, MINT, BioGRID and DIP databanks on protein interaction data. More tools are now supported as well including GLSearch, GGSearc, FastF, FastS and FastM tools (part of the FASTA package) and Clustal Omega. Support for the latest version of the Hmmer package has been updated as well.

You can read the full release notes as well as download the software itself in the SRS 8.3 Downloads area.

The kidney is inherently prone to toxicity. It is highly vascularised, receiving ~25% of cardiac output, and as a consequence is especially vulnerable to drug-induced injury. Moreover, it is a primary site for the elimination of drugs, which further increases exposure of the kidney to potentially toxic substances. Drug-induced nephrotoxicity is common in clinical practice; drugs account for ~20% of acquired cases of acute renal failure and even more in at-risk populations, such as the elderly or those with underlying chronic kidney disease. A number of widely-used drug classes have been implicated in nephrotoxicity, including the NSAIDs, antibiotics and anticancer agents such as cisplatin.

Drug-induced renal injury is also a major cause of drug attrition in clinical development, accounting for 2% of candidate failures during preclinical studies and 19% during Phase III There is therefore a pressing need to improve prediction of renal toxicity, through the development of biomarkers, in vitro experimental models and better use of legacy information.

With this in mind, the Safety Intelligence Program now includes a rich set of assertions around renal toxicity to complement the existing in-depth intelligence for drug induced liver, muscle, cardiac and vascular injury, carcinogenicity and genotoxicity. This new intelligence includes:

• Over 35,000 assertions for compounds causing kidney effects, curated and integrated from a variety of public data sources, with intelligence for 6,500 compounds including over 1000 drug molecules.
• More than 1300 renal observations, including major pathophysiological events such as interstitial nephritis, kidney tubular necrosis and altered glomerular filtration rate.
• 6,000 assertions describing the effects of 1899 compounds on 73 key body fluid markers of renal function, such as creatinine level and blood urea nitrogen.

Read more about SIP Update: Drug-induced Renal Injury »

In August this year I joined around 100 other delegates to attend the OpenTox 2011 meeting in the pleasant surroundings of the Technical University of Munich (TUM). The meeting was kindly organised by Barry Hardy from Douglas Connect and Stefan Kramer of the TUM and further information can be found at http://www.opentox.org/meet/opentox2011. The program was spread over 4 days, starting with a pre-conference workshop, followed by a 3 day conference comprising presentations, poster sessions and social activities.

OpenTox is a collaborative project, with contributions from academia, government research groups, industry and individual experts. Its aim is to establish an interoperable predictive toxicology framework that can enable the creation of predictive toxicology applications. The project received funding from the seventh framework programme (FP7) and is coming to a close, and the conference provided an opportunity for those affiliated with the project as well as non-participants, such as myself, to talk about their work in predictive toxicology.

Download OpenTox 2011 Poster

There were too many presentations in the conference program to single any out in particular but, not surprisingly, quantitative structure-activity relationship (QSAR) modelling was a common theme. Something that struck me from speaking to some of the delegates that work in this area is the sparseness of information in the domain. Consequently, the correlations that come from QSAR experiments are often based on highly curated decisions from relatively small amounts of data. This contrasts with the approach we typically take at BioWisdom, where decisions are usually based on large quantities of data harvested from a variety of data sources.

Read more about OpenTox 2011 Conference Report »

In this Webinar, Paul Bradley presents Metawise, a novel approach to terminology management that provides flexible and controllable terminology for search and identification of life science concepts from text sources. View the Webinar to understand how the Metawise approach can enable you to turn intractable legacy data into actionable decision support intelligence.

Metawise Webinar - Turning Legacy Data into Actionable Intelligence

	Metawise - Turning Legacy Data into Actionable Intelligence (Slides)

Update:

BioWisdom’s poster “The Power of MetaData” at the BioIT conference in October 2010 was one of four nominees for the quarterly Drug Safety Executive Council (DSEC) Technology Innovation Award. All the nominated posters can be seen under the “Technology Innovation Award Nominees” tab, at the DSEC Virtual Poster Hall (http://www.chicorporate.com/dsec_poster_list.aspx).

Our poster was nominated based upon the following criteria:

Click to Download the Poster

• Innovation in technology or approach to methodology
• Meets a testing need in drug development or addresses a gap in testing
• Reflects a new paradigm in testing or is potentially transformational
• Endpoints can be translational across species and humans

You can visit the poster gallery here.

The Designing Safer Medicines in Discovery conference and exhibition (http://www.soci.org/General-Pages/Display-Event?EventCode=FCHEM310) brought together over 100 delegates from Pharma, Academia and CROs to discuss the emerging opportunities for reducing drug attrition. In the 1990s, issues with pharmacokinetics and drug metabolism were the major cause of drug failure in clinical development, but the industry has since put in place successful strategies to reduce the impact of ADME/DMPK. Now, drug toxicity has come to the fore, leading to an increased focus on drug safety and the development of early screening procedures to detect and mitigate its effects.

Read more about Safety First: Tackling the Major Cause of Drug Attrition »

We are very pleased to announce that at Instem Life Science Systems (http://www.instem-lss.com) has completed the acquisition of Biowisdom Limited.  More details are available in the download below.

Instem BioWisdom Acquisition