Drug-drug interaction discovery and demystification using Semantic Web technologies

Knowledge Graphs in Pharmacovigilance: A Scoping Review

PURPOSE

To critically assess the role and added value of knowledge graphs in pharmacovigilance, focusing on their ability to predict adverse drug reactions.

METHODS

A systematic scoping review was conducted in which detailed information, including objectives, technology, data sources, methodology, and performance metrics, were extracted from a set of peer-reviewed publications reporting the use of knowledge graphs to support pharmacovigilance signal detection.

FINDINGS

The review, which included 47 peer-reviewed articles, found knowledge graphs were utilized for detecting/predicting single-drug adverse reactions and drug-drug interactions, with variable reported performance and sparse comparisons to legacy methods.

IMPLICATIONS

Research to date suggests that knowledge graphs have the potential to augment predictive signal detection in pharmacovigilance, but further research using more reliable reference sets of adverse drug reactions and comparison with legacy pharmacovigilance methods are needed to more clearly define best practices and to establish their place in holistic pharmacovigilance systems.

A Rule-Based Inference Framework to Explore and Explain the Biological Related Mechanisms of Potential Drug-Drug Interactions

As more drugs are developed and the incidence of polypharmacy increases, it is becoming critically important to anticipate potential DDIs before they occur in the clinic, along with those for which effects might go unobserved. However, traditional methods for DDI identification are unable to coalesce interaction mechanisms out of vast lists of potential or known DDIs, much less study them accurately. Computational methods have great promise but have realized only limited clinical utility. This work develops a rule-based inference framework to predict DDI mechanisms and support determination of their clinical relevance. Given a drug pair, our framework interrogates and describes DDI mechanisms based on a knowledge graph that integrates extensive available biomedical resources through semantic web technologies and backward chaining inference, effectively identifying facts within the graph that prove and explain the mechanisms of the drugs' interaction. The framework was evaluated through a case study combining a chemotherapy agent, irinotecan, and a widely used antibiotic, levofloxacin. The mutual interactions identified indicate that our framework can effectively explore and explain the mechanisms of potential DDIs. This approach has the potential to improve drug discovery and design and to support rapid and cost-effective identification of DDIs along with their putative mechanisms, a key step in determining clinical relevance and supporting clinical decision-making.

Recent Progress in Traditional Chinese Medicines and Their Mechanism in the Treatment of Allergic Rhinitis

Objective

To conduct a systematic review on the mechanism of action and use of traditional Chinese medicines (TCM) in allergic rhinitis treatment.

Background

Allergic rhinitis (AR) is a type I allergic disease of the immune system induced by immunoglobulin E mediated inflammation and is characterized by sneezing, nasal itching, paroxysmal nasal obstruction, mucosal edema, cough, and rhinorrhea. More than 500 million people have been affected by rhinitis worldwide in the past 20 years, leading to negative effects on health, quality of life, and social relationships. Currently, the trending medicines used in the case of AR include intranasal corticosteroids and oral H1 antihistamines, which are given as combinatorial medicines supplemented with immune therapy. These medications have been found to be very effective in either the short term or long term; however, they have been found to possess some serious side effects. Search Methodology. The information in this article on classical and traditional Chinese medications used to treat AR was derived from original papers and reviews published in Chinese and English language journals. Two Chinese databases (Wanfang and CNKI) and three English databases (Cochrane Library, PubMed, and Embase) were utilized for data gathering.

Results

Traditional Chinese remedies have been identified to influence the production of cytokines such as IL-5 and IL-6, which are key mediators of eosinophilic inflammation, TNF-α, which stimulates TH2 cells at the site of inflammation, and NF-кB, which is required for cytokine and IgE antibody production. TCM has also been shown to be successful in lowering histamine levels, preserving histological changes by decreasing the thickness of the lamina propria, and downregulating the expression of Orai1, STIM1, and TRYC1, showing low expression of Ca⁺² channel proteins.

Conclusion

In this review, we discussed a series of classical, traditional Chinese medications, including Centipeda minima, Scutellaria baicalensis, licorice root (Glycyrrhiza uralensis), and others, as potential antiallergic agents and investigate their in vivo effect upon the production of cytokines and release of histamines for allergic rhinitis treatment.

Anti-DDI Resource: A Dataset for Potential Negative Reported Interaction Combinations to Improve Medical Research and Decision-Making

Potential drug-drug interactions (DDIs) are a core concern across medical decision support systems. Among healthcare practitioners, the common practice for screening these interactions is via computer software. However, as real-world negative reporting is missing, counterexamples that serve as contradictory evidence may exist. In this study, we have developed an anti-DDI resource, a set of drug combinations having negative reported interactions. This resource was created from a set of the top 200 most-used drugs, resulting in 14365 prospective negative reported DDI pairs. During analysis and filtering, 2110 DDIs (14.69%) were found in publicly free DDI resources, another 11130 (77.48%) were filtered by a rule-based inference engine incorporating ten mechanisms of interaction, and 208 were identified through commercial resources. Additionally, 90 pairs were removed due to recent FDA approvals or being unapplicable in clinical use. The final set of 827 drug pairs represents combinations potentially having negative reported interactions. The anti-DDI resource is intended to provide a distinctly different direction from the state of the art and establish a ground focus more centered on the evaluation and utilization of existing knowledge for performing thorough assessments. Our negative reported DDIs resource shall provide healthcare practitioners with a level of certainty on DDIs that is worth investigating.

A Data-Driven Medical Decision Framework for Associating Adverse Drug Events with Drug-Drug Interaction Mechanisms

Adverse drug events (ADEs) occur when multiple drugs interact within an individual, thus causing effects that were not initially predicted. Such toxic interactions lead to morbidity and mortality. Contemporary research surrounding ADEs has tended to focus on the detection of potential ADEs without great concern for elucidating the associations of drug-drug interaction (DDI) mechanisms that can predict potential adverse drug reactions (ADRs). Such associations are of great practical importance for everyday pharmacovigilance efforts. This study presents a data-driven framework for conducting knowledge-driven data analysis that combines a semantic inference system and enrichment analysis in order to identify potential ADE mechanisms. The framework was used to rank mechanisms according to their relevance for DDIs and also to categorize ADEs based on the number of DDI mechanism associations identified through enrichment analysis. Its validity is demonstrated through using both commercial and publicly available DDI resources. The results of this study solidly prove the framework's effectiveness and highlight potential for future research by way of incorporating additional and broader data to deepen and expand its capabilities.

Improving bioinformatics software quality through incorporation of software engineering practices

BACKGROUND

Bioinformatics software is developed for collecting, analyzing, integrating, and interpreting life science datasets that are often enormous. Bioinformatics engineers often lack the software engineering skills necessary for developing robust, maintainable, reusable software. This study presents review and discussion of the findings and efforts made to improve the quality of bioinformatics software.

METHODOLOGY

A systematic review was conducted of related literature that identifies core software engineering concepts for improving bioinformatics software development: requirements gathering, documentation, testing, and integration. The findings are presented with the aim of illuminating trends within the research that could lead to viable solutions to the struggles faced by bioinformatics engineers when developing scientific software.

RESULTS

The findings suggest that bioinformatics engineers could significantly benefit from the incorporation of software engineering principles into their development efforts. This leads to suggestion of both cultural changes within bioinformatics research communities as well as adoption of software engineering disciplines into the formal education of bioinformatics engineers. Open management of scientific bioinformatics development projects can result in improved software quality through collaboration amongst both bioinformatics engineers and software engineers.

CONCLUSIONS

While strides have been made both in identification and solution of issues of particular import to bioinformatics software development, there is still room for improvement in terms of shifts in both the formal education of bioinformatics engineers as well as the culture and approaches of managing scientific bioinformatics research and development efforts.

Health informatics publication trends in Saudi Arabia: a bibliometric analysis over the last twenty-four years

OBJECTIVE

Understanding health informatics (HI) publication trends in Saudi Arabia may serve as a framework for future research efforts and contribute toward meeting national "e-Health" goals. The authors' intention was to understand the state of the HI field in Saudi Arabia by exploring publication trends and their alignment with national goals.

METHODS

A scoping review was performed to identify HI publications from Saudi Arabia in PubMed, Embase, and Web of Science. We analyzed publication trends based on topics, keywords, and how they align with the Ministry of Health's (MOH's) "digital health journey" framework.

RESULTS

The total number of publications included was 242. We found 1 (0.4%) publication in 1995-1999, 11 (4.5%) publications in 2000-2009, and 230 (95.0%) publications in 2010-2019. We categorized publications into 3 main HI fields and 4 subfields: 73.1% (n=177) of publications were in clinical informatics (85.1%, n=151 medical informatics; 5.6%, n=10 pharmacy informatics; 6.8%, n=12 nursing informatics; 2.3%, n=4 dental informatics); 22.3% (n=54) were in consumer health informatics; and 4.5% (n=11) were in public health informatics. The most common keyword was "medical informatics" (21.5%, n=52). MOH framework-based analysis showed that most publications were categorized as "digitally enabled care" and "digital health foundations."

CONCLUSIONS

The years of 2000-2009 may be seen as an infancy stage of the HI field in Saudi Arabia. Exploring how the Saudi Arabian MOH's e-Health initiatives may influence research is valuable for advancing the field. Data exchange and interoperability, artificial intelligence, and intelligent health enterprises might be future research directions in Saudi Arabia.

A computational approach to predict multi-pathway drug-drug interactions: A case study of irinotecan, a colon cancer medication

Drug-drug interactions (DDIs) are a potentially distressing corollary of drug interventions, and may result in discomfort, debilitating illness, or even death. Existing research predominantly considers only a single level of interaction; however, serious health complications may result from multi-pathway DDIs, and so new methods are needed to enable predicting and preventing complex DDIs. This article introduces a novel method for the prediction of DDIs at two pharmacological levels (metabolic and transporter interactions) by means of a rule-based model implemented with Semantic Web technologies. The chemotherapy agent irinotecan is used as a case study for demonstrating the validity of this approach. Mechanistic and interaction data were mined from available sources and then used to predict interactors of irinotecan, including potential DDIs mediated by previously unidentified mechanisms. The findings also draw attention to the profound variation between DDI resources, indicating that clinical practice would see significant value from the development of an evidence-based resource to support DDI identification.

Prediction of Drug-Drug Interactions by Using Profile Fingerprint Vectors and Protein Similarities

Objectives

Drug-drug interaction (DDI) is a vital problem that threatens people's health. However, the prediction of DDIs through in-vivo experiments is not only extremely costly but also difficult as many serious side effects are hard to detect in in-vivo and in-vitro settings. The aim of this study was to assess the effectiveness of similarity-based in-silico computational DDI prediction approaches and to provide a cost effective and scalable solution to predict potential DDIs.

Methods

In this study, widely known similarity-based computational DDI prediction methods were utilized to discover novel potential DDIs. More specifically, known interactions, drug targets, adverse effects, and protein similarities of drug pairs were used to construct drug fingerprints for the prediction of DDIs.

Results

Using the drug interaction profile, our approach achieved an area under the curve (AUC) of 0.975 in the prediction of a potential DDI. The drug adverse effect profile and protein profile similarity-based methods resulted in AUC values of 0.685 and 0.895, respectively, in the prediction of DDIs.

Conclusions

In this study, we developed a computational approach to the prediction of potential drug interactions. The performance of the similarity-based computational methods was comparatively evaluated using a comprehensive real-world DDI dataset. The evaluations showed that the drug interaction profile information is a better predictor of DDIs compared to drug adverse effects and protein similarities among DDI pairs.

Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings

BACKGROUND

Current approaches to identifying drug-drug interactions (DDIs), include safety studies during drug development and post-marketing surveillance after approval, offer important opportunities to identify potential safety issues, but are unable to provide complete set of all possible DDIs. Thus, the drug discovery researchers and healthcare professionals might not be fully aware of potentially dangerous DDIs. Predicting potential drug-drug interaction helps reduce unanticipated drug interactions and drug development costs and optimizes the drug design process. Methods for prediction of DDIs have the tendency to report high accuracy but still have little impact on translational research due to systematic biases induced by networked/paired data. In this work, we aimed to present realistic evaluation settings to predict DDIs using knowledge graph embeddings. We propose a simple disjoint cross-validation scheme to evaluate drug-drug interaction predictions for the scenarios where the drugs have no known DDIs.

RESULTS

We designed different evaluation settings to accurately assess the performance for predicting DDIs. The settings for disjoint cross-validation produced lower performance scores, as expected, but still were good at predicting the drug interactions. We have applied Logistic Regression, Naive Bayes and Random Forest on DrugBank knowledge graph with the 10-fold traditional cross validation using RDF2Vec, TransE and TransD. RDF2Vec with Skip-Gram generally surpasses other embedding methods. We also tested RDF2Vec on various drug knowledge graphs such as DrugBank, PharmGKB and KEGG to predict unknown drug-drug interactions. The performance was not enhanced significantly when an integrated knowledge graph including these three datasets was used.

CONCLUSION

We showed that the knowledge embeddings are powerful predictors and comparable to current state-of-the-art methods for inferring new DDIs. We addressed the evaluation biases by introducing drug-wise and pairwise disjoint test classes. Although the performance scores for drug-wise and pairwise disjoint seem to be low, the results can be considered to be realistic in predicting the interactions for drugs with limited interaction information.

Enabling Web-scale data integration in biomedicine through Linked Open Data

The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web. Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge. Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine. The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases. We will discuss some of the major challenges that hinder the wide-spread use and consumption of LSLOD by the biomedical research community. Finally, we provide a few technical solutions and insights that can address these challenges. Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems.

Enhancing Clinical Data and Clinical Research Data with Biomedical Ontologies - Insights from the Knowledge Representation Perspective

OBJECTIVES

There exists a communication gap between the biomedical informatics community on one side and the computer science/artificial intelligence community on the other side regarding the meaning of the terms "semantic integration" and "knowledge representation". This gap leads to approaches that attempt to provide one-to-one mappings between data elements and biomedical ontologies. Our aim is to clarify the representational differences between traditional data management and semantic-web-based data management by providing use cases of clinical data and clinical research data re-representation. We discuss how and why one-to-one mappings limit the advantages of using Semantic Web Technologies (SWTs).

METHODS

We employ commonly used SWTs, such as Resource Description Framework (RDF) and Ontology Web Language (OWL). We reuse pre-existing ontologies and ensure shared ontological commitment by selecting ontologies from a framework that fosters community-driven collaborative ontology development for biomedicine following the same set of principles.

RESULTS

We demonstrate the results of providing SWT-compliant re-representation of data elements from two independent projects managing clinical data and clinical research data. Our results show how one-to-one mappings would hinder the exploitation of the advantages provided by using SWT.

CONCLUSIONS

We conclude that SWT-compliant re-representation is an indispensable step, if using the full potential of SWT is the goal. Rather than providing one-to-one mappings, developers should provide documentation that links data elements to graph structures to specify the re-representation.

Clopidogrel Drug Interactions and Serious Bleeding: Generating Real-World Evidence via Automated High-Throughput Pharmacoepidemiologic Screening

Few population-based studies have examined bleeding associated with clopidogrel drug-drug interactions (DDIs). We sought to identify precipitant drugs taken concomitantly with clopidogrel (an object drug) that increased serious bleeding rates. We screened 2000-2015 Optum commercial health insurance claims to identify DDI signals. We performed self-controlled case series studies for clopidogrel plus precipitant pairs, examining associations with gastrointestinal bleeding or intracranial hemorrhage. To distinguish native bleeding effects of a precipitant, we reexamined associations using pravastatin as a negative control object drug. Among 431 analyses, 28 clopidogrel plus precipitant pairs were statistically significantly positively associated with serious bleeding. Ratios of rate ratios ranged from 1.13-3.94. Among these pairs, 13 were expected given precipitant drugs alone increased and/or were harbingers of serious bleeding. The remaining 15 pairs constituted new DDI signals, none of which are currently listed in two major DDI knowledge bases.

Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system.

Identifying Common Methods Used by Drug Interaction Experts for Finding Evidence About Potential Drug-Drug Interactions: Web-Based Survey

Background

Preventing drug interactions is an important goal to maximize patient benefit from medications. Summarizing potential drug-drug interactions (PDDIs) for clinical decision support is challenging, and there is no single repository for PDDI evidence. Additionally, inconsistencies across compendia and other sources have been well documented. Standard search strategies for complete and current evidence about PDDIs have not heretofore been developed or validated.

Objective

This study aimed to identify common methods for conducting PDDI literature searches used by experts who routinely evaluate such evidence.

Methods

We invited a convenience sample of 70 drug information experts, including compendia editors, knowledge-base vendors, and clinicians, via emails to complete a survey on identifying PDDI evidence. We created a Web-based survey that included questions regarding the (1) development and conduct of searches; (2) resources used, for example, databases, compendia, search engines, etc; (3) types of keywords used to search for the specific PDDI information; (4) study types included and excluded in searches; and (5) search terms used. Search strategy questions focused on 6 topics of the PDDI information—(1) that a PDDI exists; (2) seriousness; (3) clinical consequences; (4) management options; (5) mechanism; and (6) health outcomes.

Results

Twenty participants (response rate, 20/70, 29%) completed the survey. The majority (17/20, 85%) were drug information specialists, drug interaction researchers, compendia editors, or clinical pharmacists, with 60% (12/20) having >10 years’ experience. Over half (11/20, 55%) worked for clinical solutions vendors or knowledge-base vendors. Most participants developed (18/20, 90%) and conducted (19/20, 95%) search strategies without librarian assistance. PubMed (20/20, 100%) and Google Scholar (11/20, 55%) were most commonly searched for papers, followed by Google Web Search (7/20, 35%) and EMBASE (3/20, 15%). No respondents reported using Scopus. A variety of subscription and open-access databases were used, most commonly Lexicomp (9/20, 45%), Micromedex (8/20, 40%), Drugs@FDA (17/20, 85%), and DailyMed (13/20, 65%). Facts and Comparisons was the most commonly used compendia (8/20, 40%). Across the 6 attributes of interest, generic drug name was the most common keyword used. Respondents reported using more types of keywords when searching to identify the existence of PDDIs and determine their mechanism than when searching for the other 4 attributes (seriousness, consequences, management, and health outcomes). Regarding the types of evidence useful for evaluating a PDDI, clinical trials, case reports, and systematic reviews were considered relevant, while animal and in vitro data studies were not.

Conclusions

This study suggests that drug interaction experts use various keyword strategies and various database and Web resources depending on the PDDI evidence they are seeking. Greater automation and standardization across search strategies could improve one’s ability to identify PDDI evidence. Hence, future research focused on enhancing the existing search tools and designing recommended standards is needed.

Mechanism-based Pharmacovigilance over the Life Sciences Linked Open Data Cloud

Adverse drug reactions (ADR) result in significant morbidity and mortality in patients, and a substantial proportion of these ADRs are caused by drug-drug interactions (DDIs). Pharmacovigilance methods are used to detect unanticipated DDIs and ADRs by mining Spontaneous Reporting Systems, such as the US FDA Adverse Event Reporting System (FAERS). However, these methods do not provide mechanistic explanations for the discovered drug-ADR associations in a systematic manner. In this paper, we present a systems pharmacology-based approach to perform mechanism-based pharmacovigilance. We integrate data and knowledge from four different sources using Semantic Web Technologies and Linked Data principles to generate a systems network. We present a network-based Apriori algorithm for association mining in FAERS reports. We evaluate our method against existing pharmacovigilance methods for three different validation sets. Our method has AUROC statistics of 0.7-0.8, similar to current methods, and event-specific thresholds generate AUROC statistics greater than 0.75 for certain ADRs. Finally, we discuss the benefits of using Semantic Web technologies to attain the objectives for mechanism-based pharmacovigilance.

Data-driven prediction of adverse drug reactions induced by drug-drug interactions

Background

The expanded use of multiple drugs has increased the occurrence of adverse drug reactions (ADRs) induced by drug-drug interactions (DDIs). However, such reactions are typically not observed in clinical drug-development studies because most of them focus on single-drug therapies. ADR reporting systems collect information on adverse health effects caused by both single drugs and DDIs. A major challenge is to unambiguously identify the effects caused by DDIs and to attribute them to specific drug interactions. A computational method that provides prospective predictions of potential DDI-induced ADRs will help to identify and mitigate these adverse health effects.

Method

We hypothesize that drug-protein interactions can be used as independent variables in predicting ADRs. We constructed drug pair-protein interaction profiles for ~800 drugs using drug-protein interaction information in the public domain. We then constructed statistical models to score drug pairs for their potential to induce ADRs based on drug pair-protein interaction profiles.

Results

We used extensive clinical database information to construct categorical prediction models for drug pairs that are likely to induce ADRs via synergistic DDIs and showed that model performance deteriorated only slightly, with a moderate amount of false positives and false negatives in the training samples, as evaluated by our cross-validation analysis. The cross validation calculations showed an average prediction accuracy of 89% across 1,096 ADR models that captured the deleterious effects of synergistic DDIs. Because the models rely on drug-protein interactions, we made predictions for pairwise combinations of 764 drugs that are currently on the market and for which drug-protein interaction information is available. These predictions are publicly accessible at http://avoid-db.bhsai.org. We used the predictive models to analyze broader aspects of DDI-induced ADRs, showing that ~10% of all combinations have the potential to induce ADRs via DDIs. This allowed us to identify potential DDI-induced ADRs not yet clinically reported. The ability of the models to quantify adverse effects between drug classes also suggests that we may be able to select drug combinations that minimize the risk of ADRs.

Conclusion

Almost all information on DDI-induced ADRs is generated after drug approval. This situation poses significant health risks for vulnerable patient populations with comorbidities. To help mitigate the risks, we developed a robust probabilistic approach to prospectively predict DDI-induced ADRs. Based on this approach, we developed prediction models for 1,096 ADRs and used them to predict the propensity of all pairwise combinations of nearly 800 drugs to be associated with these ADRs via DDIs. We made the predictions publicly available via internet access.

Electronic supplementary material

The online version of this article (doi:10.1186/s40360-017-0153-6) contains supplementary material, which is available to authorized users.