Adding Value to Translational Informatics through the Semantic

Management of Drug to Drug Interaction

Radmila Juric

University of South Eastern Norway, Kongsberg, Norway

Keywords: Translational Informatics, OWL/SWRL, Drug-to-Drug Interactions.

Abstract: Translational informatics, aimed at bridging the gap between biomedical scientific knowledge and clinical

practice has changed the way we use rapidly growing information from biomedical research and bring it closer

to clinical practice. Software technologies play an important role in this process, particularly if they help in

understanding and manipulating the meaning of data and information generated in biomedical research and

translate it into semantic suitable for clinical practice. In this paper, we propose software architectural and

conceptual computational models, which use semantic technologies in order to explore the meaning of the

relationships between drugs when they interact in clinical practice. The data about drug to drug interactions,

available from biomedical research, is reusable in instances where they are decisive factors in drug

administration in clinical practice. We explore the power of semantic web technologies and SWRL enabled

OWL ontologies to demonstrate the applicability and feasibility of our proposal in translational informatics.

1 INTRODUCTION

This research explores the personalization of patient

medication lists in terms of finding potential drug

interactions, because of the combination of prescribed

drugs. We are interested in drug to drug interactions

(DDI) which may appear if the therapeutic effect of

one drug changes because of the presence of another.

This problem is not easy to resolve for many reasons.

One of them is that the solution might require a

synergy of knowledge and expertise across the

disciplines of biomedical science, clinical practice

and computer science. Modern medicine strives for

personalization, hoping to include gender differences

and the clinical physiological effect drugs may have

on an individual patient, because every patient is

different. However, we still do not perform clinical

trials which take this personalization into account.

Furthermore, the advances of knowledge discoveries

in biomedical science, are not fast-forwarded to

clinical practice and the gap between the two is

widening. There are examples where translational

bioinformatics (Tsafnat et al., 2013) may address the

problem, but a long-term solution which addresses a)

data sharing between biomedical science and clinical

https://orcid.org/ 0000-0002-0441-0694

practice and b) personalization of patient medication

lists to avoid potential DDI, might be the only way

forward. However, a) and b) above are interwoven. If

we share the data between biomedical research and

clinical practice, we will find more about DDI. In

order to align personalized medication lists, for the

purpose of eliminating potential DDI between the

drugs in the list, we would need more than just data

sharing. We would need to understand the semantic

relationship between clinical recommendations, i.e.

prescribed drugs, drug therapeutic targets

(protein/genes) and related biological functions, in

the context relevant to a patient. If there is another

drug, which shares the same or similar therapeutic

target, intentionally or un-intentionally, then these

two drugs could have a variety of interactions, which

should be semantically explained. DDI would depend

on the exact involvement of each drug in their

therapeutic targets/biological functions of the patient.

Software engineering solutions, with data sharing

across disciplines, and reasoning upon the collected

data, in order to find potential DDI for the patient’s

medication list, would require a software architectural

(SA) model first, which specifies sources of shared

data and computational models for identifying

relevant DDI. A software application, created from

268

Juric, R.

Adding Value to Translational Informatics through the Semantic Management of Drug to Drug Interaction.

DOI: 10.5220/0009376002680275

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 3: BIOINFORMATICS, pages 268-275

ISBN: 978-989-758-398-8; ISSN: 2184-4305

the SA would work for patient/clinicians and secure

the best possible medication lists, personalized in a

particular context. The same application should allow

updating the semantics of the drug

-target-drug

relationship from biomedical research and thus fast

forward biomedical knowledge, on discovered DDI,

towards creating a personalized medication list.

Our proposal uses Semantic Web Technology

(SWT) and its languages OWL/SWRL

(OWL/SWRL) for defining the reasoning process

upon data shared from biomedical experiments, with

drug

-target-drug

pathways and patent clinical data,

i.e. medication lists. We infer DDI relationships

between the two drugs, from the patient medication

list, through semantic reasoning upon the data which

originate in biomedical research and clinical practice.

The paper is organized as follows. Related Work

lists examples of finding DDI, using software

solutions with Natural Language Processing (NLP),

semantic technologies and reasoning. Our proposal,

described in the sections, which follow, gives a

reusable SA model which hosts computations based

on reasoning upon shared biomedical and clinical

data. We illustrate the reasoning process and debate

the proposal in the Implementation and Conclusions.

2 RELATED WORK

Biomedical research has advanced significantly and it

is almost impossible to systemize results of research

advances and create an overall picture of new

knowledge which is emerging as we speak. In the

DDI field, we can go back through decades and find

a variety of research publications which highlighted

the problem. In this section we chose a selection of

interesting papers which either influenced us, or

illustrate new ideas to finding DDI.

The authors of (Herrero-Zazo et al., 2013) use

NLP, which is still very popular for retrieving textual

information from biomedical sources and finding

DDI. In order to improve NLP they created an

annotated corpus of pharmacological substances and

DDI, sourced at DrugBank database (Knox, 20|11)

and 233 Medline abstracts. The authors from (Aywaz

et al. 2015) created a complete data set of DDI

information from 14 public sources and merged them,

but found that there are inconsistencies and

overlapping between sources which disseminate

information on DDI. In (Segura-Bedmar et al.m

2010) linguistic extraction techniques and a hybrid

linguistic approach to DDI detection are used, that

combine shallow parsing, provided by UMLS

(UMLS, 2009) tools, and syntactic simplification

with pattern matching. Lexical patterns achieved

reasonable precision. In (Liu et al., 2019) the

detection of adverse drug events (ADE) from social

media is shown. This is not exactly DDI, but it is a

refreshing way of getting information fast and

exploring social media for learning differently about

DDI. Because of the type of sources used in this work,

a semi-supervised learning method, with weighted

features is used to distinguish between ADE and non-

ADE. (Kim et al., 2015) use text mining techniques

to identify DDI in the body of unstructured medical

text and consequently, a support vector machine with

a linear kernel is a good option for the task. The

authors of (Xhoua, et al., 2018) use a position aware

and multi-task deep learning to extract DDI from

unstructured medical texts. Deep neural networks,

which use words and their positions in the

unstructured text, for defining latent features and thus

avoiding explicit feature engineering, for finding

DDIs are in (Sahu and Anand, 2018).

All these examples show that NLP, information

extraction, text mining and statistical classifications,

with learning technologies, dominate the research

scene for one important reason. The Semantic of the

DDI from biomedical research is often buried in

unstructured biomedical texts.

Ontologies are not often used for detecting DDIs.

If they appear in research, they are mostly controlled

vocabularies for cumulating knowledge as results of

reasoning. Data retrievals are carried out with

SPARQL. Drug Interaction Ontology from

(Yoshikawa, 2004) is a very old, but formal ontology,

which accumulates reusable knowledge in molecular

pharmacology. Its information model is based on

fundamental concepts of biological interactions. The

paper was published before the standardization of the

SWT and its languages and therefore it can not be

compared with modern SWT solutions. Potential

DDI are results of retrievals upon ontological

concepts. The authors of (Alhaj et al., 2019) created

the ontology identifying DDI, but reasoning classifies

only DDI effects: reduction, synergism and toxicity.

In (Saleha et al, 2017) the DDI ontology helps in drug

discovery investigations, in (Sara et al., 2018) a drug

interaction ontology contains information about

ADE, and in (Grando et al., 2012) ontology is used

for safe and effective generic prescription principles.

DINTO ontology, which contains formal

representation of different types of DDI is available

in (Herrero-Zazo, 2015). It is very complex and

models DDIs as classes and properties. This may have

an impact on the OWL model’s efficiency and

reusability. It is interesting that they created a set of

inference rules for a variety of DDIs. They are

Adding Value to Translational Informatics through the Semantic Management of Drug to Drug Interaction

269

inferred on the basis of their pharmacological

mechanisms, which in turn depend on the biological

process leading to their occurrence. These interwoven

facts must be presented within the ontology, possibly

through chaining of object properties. This can lead

to inefficiency in the reasoning process, if the object

properties chaining were to be a part of any stand-

alone software application. If DINTO were used as a

formal vocabulary, then its efficiency in real life will

depend on the way SPARQL performs, and not on the

constraints, imposed by OWL object properties.

We could not find any solutions, which would

infer DDI from prescribed medication lists.

However, DINTO ontology could be used in our

proposal as a supporting source of information for

understanding the context within which we infer DDI

for a particular patient and his/her medication list.

There is only one publication which comes closer

to this research than any other. It uses GalenOWL

ontology for drug recommendation discovery

(Doluaverakis et al., 2003). It is an online-service,

based on queries aimed at drug-to-drug and drug-to-

disease discoveries. It offers conceptual reasoning

rules upon a set of domain ontologies for inferring

OWL properties and thus recommends a particular

drug to a patient. Its rule base engine evaluates

conflicts between drug therapeutic indications and

contraindications and thus indicates implicitly, but

not explicitly the problem of DDI.

3 THE PROPOSAL

The proposal consist of two parts. In the first part we

introduce the SA model which specifies the main

software components of the layered and component

based architectural style. The model is a prescription

on how to build software applications for delivering

the inference of potential DDIs.

In the second part, we look at the specificity of the

computational model from the software application

perspective and define OWL classes, with their

individuals and properties, and the reasoning process.

However, before we define the proposal, we

briefly debate the role of SWT in this problem

domain. This is important because we do NOT create

another formal ontology in biomedical science.

SWRL enabled OWL ontologies are here to (i)

become a part of a software engineering application

(ii) exploit the semantic relevant for the task of

discovering DDI and (iii) contribute towards a

computational model, which detects a potential DDI

and change the proposed medication list, if necessary.

3.1 Why SWT?

SWT and its layered cake has widely been used, since

its standardisation in 2004, for interpreting the

meaning of data available on the Web. It is perfect

for building common ontologies and controlled

vocabularies across domains, enriched with reasoning

rules in SWRL, and thus bringing inference and more

semantics to the Web. We can represent knowledge

with OWL/SWRL because of its powerful

representation through description logic.

There are numerous possibilities of using and

exploiting SWRL enabled OWL ontologies, but we

would like to emphasise its use outside the Web and

controlled vocabularies, i.e. knowledge-bases for

many reasons. The most important reason is that

computational models, which house SWRL enabled

reasoning upon OWL concepts, bring inference

without either having complex knowledge systems in

the background or using Artificial Intelligence (AI)

algorithms for creating inference. Therefore we talk

about software engineering applications of the SWT

technology. We can also create SWRL/OWL

inference on an ad-hoc basis and address constant

changes in environments we model. If we add that

SWRL reasoning upon OWL concepts can be

teamed-up with filtering, ranking, tagging, semantic

annotations, transactional and big data processing,

and performing prediction analytics (Juric 2016),

(Juric and Kim 2017) then our proposed software

engineering solution, outside formal ontologies and

knowledge-bases, is a promising start.

3.2 Software Architectural Model

The SA model is proposed in Figure 1. Its middle

vertical content houses a computational model which

performs reasoning upon SWRL enabled OWL

ontologies. This repository contains semantics of

both: drug(s) specifications, their therapeutic targets

(genes/proteins), and biological functions and

clinical-level of physiological effects, relevant to the

prescribed medication list (for a particular patient).

Therefore prescribed medication list originates in the

software application which Manipulates Clinical

Data (right vertical part of Fig 1.)

The left part of Fig. 1 illustrates repositories from

the biomedical (BM) field and may include BM

Databases {BMDB

, BMDB

,, …BMDBn}, existing

BM ontologies {BMOnt

, BMOnt

,, …BMOnt

}

containing results of BM research. Drug

specifications, explanations on their therapeutic

targets, and knowledge of existing DDI are all

BIOINFORMATICS 2020 - 11th International Conference on Bioinformatics Models, Methods and Algorithms

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available within BM software applications, which

Manipulates Biomedical Data, and their repositories.

The right side of Figure 1 (Manipulate Clinical

Data) contains data/software applications specific to

clinical data and patients Electronic Health Records

(EHR). Patient medication lists may be generated

there (not compulsory) and therefore the clinical level

of physiological effects of prescribed drugs to the

patient, might also be known within this environment.

The proposed computational model sits between

the two environments (left and right part of Figure 1)

and Creates Personal DDI. It bridges the gap

between the knowledge available from BM research

and data related to clinical practices. The data sharing

between these two environments, which are in reality

two separated worlds, is essential if we wish to bridge

the gap. DDI OWL Ontology with SWRL, can be

populated with the semantic of data from both sides

(Juric, 2016). This is the only way we can perform

inference where the reasoning creates a DDI on an ad-

hoc basis.

3.3 Semantic of DDI Relationship

The section on Related Work shows various ways of

describing the semantics of potential DDI. One

publication proved to be extremely beneficial for

performing reasoning upon biomedical concepts in

general. The relationship between drugs and their

therapeutic targets, described through semantic

predications, related to all medications, available in

Sem-MedDB (Kilicogly et al., 2012) is almost ideal

for being converted into OWL ontologies.

Semantic PREDICATES are defined in the

UMLS Manual (UMLS) (Ahlers et al., 2007), and the

authors of (Zhang et al., 2014) also use them as an

input into their own method of finding DDIs. These

predications are very attractive to software engineers

because they are TRIPLETS:

subject-PREDICATE-object

where subject is a particular drug and object is its

target (genes/enzymes). DDI between two drugs can

be identified, if we have the following logic:

subject

-PREDICATE

-object

-PREDCATE

subject

where subject

is NOT the same as subject

(these are

two different drugs) and therefore

subject

≠ subject

→ DDI

i,j

Figure 1: Software Architectural Model.

This means that if we have two drugs, both involved

in the same target (genes / enzymes / biological

function) through different predicates, and thus would

interfere with each other. The types and number of

PREDICATES, which can be extracted from the

SemMedDB and UMLS Manual are ideal for logic

reasoning. We can transfer scientifically agreed

predications directly into OWL (object properties).

This is one of the most important task when creating

a reasoning process using OWL/SWRL: semantically

rich object properties. There are many predicates in

SemMedDB, such as TREATS, AFFECTS,

INTERACTS_WITH, STIMULATES, INHIBITS,

which can be object properties in OWL. They can

connect individuals between drug and target concepts

in drug-tehrapeutic target pathways.

Figure 2: DDI

i,j

is defined between any pair of drugs.

Figure 2 shows a possible DDI between two drugs

Drug

and Drug

from the set of n drugs D= {Drug

Drug

,, ..Drug

}, where we allow up to n-1 potential

DDI between them. Some of DDI

j,k

might be

imported from repositories of the BM research (left

part of Fig. 1) and some might be known to clinicians

(right part of Fig 1). If there is no knowledge about

{BMOnti,… BMOntom}

OWL

DDI OWL Ontology Clinical

with SWRL Practice

Drug

Gene/

Target

Manipulate

Biomedical

Semantics

Create

Personal

DDI

Manipulate

Clinical

Data

OWL - API

HER

Med.

Lists

Report

{BMDB1,BMDB2,. BMDBn}

Adding Value to Translational Informatics through the Semantic Management of Drug to Drug Interaction

271

potential DDIi,j we have an opportunity to infer it

through reasoning if the semantics of subject

PREDICATE

-object

-PREDCATE

-subject

allows. Broken one directional lines in Fig. 2 indicate

therapeutic pathways for a drug, and the solid line

denotes (potential DDI). Abbreviation Pj and P

is for

two particular PREDICATES.

3.4 Semantic Relationship between

Drugs and Their Targets

We identify examples of predicates which can be

used for illustrating the semantic between Drugs and

their targets in order to define an OWL model.

Let us assume that the relationship between Drug

and gene is defined as a triplet

(Drug

-PREDICATE

-Gene)

where Drug

affects a particular Gene. However, the

same Gene might be involved, as a target, with

Drug

. This would require the definition of another

PREDICATE, between the same Gene and Drug

. If

we put these two triplets together,

Drug

-PREDICATE

-Gene-PREDCATE

-Drug

then the above construct can create Fig. 3, which

reads: Drug

AFFECTS a particular gene, but this

gene might be INVOLVED IN another Drug

Fig. 3 sends two messages. If there is a predicate

AFFECTS for an approved target for Drug

(black

arrow) we investigate if there is a potential predicate

INVOLVED-IN for the same target, but for a known

or approved Drug

(red arrow) The semantic of

predications is read in both directions. We may know

that a particular gene is involved in Drug

which

might require investigating is there is a drug Drug

which affects the same gene. From the computer

science perspective, we may allow multiple instances

of Drugi and Drugj, to appear in Fig. 3.

Figure 3: AFFECTS/INVOLVED-IN Predicates may be

used both ways.

3.5 Owl Model

One of the most important use of semantic

predication is in grasping semantic important for the

discovery of DDIs. From Figure 3, all

PREDICATIONS can be easily converted into OWL

constraints in ontological modelling. This means that

when building an ontological model we will be in a

position to use some PREDICATIONS as either

asserted OWL object properties or inferred.

In order to infer potential DDI between the two

drugs Drugi and Drugm, the most convenient way

would be to conceptualise them into two separate

OWL classes, which may change their role from

domain to range, to allow both way of reading form

Figure 3 (red and black arrows).

Figure 4: OWL Model derived from Figure 3.

In Fig 4. red and black arrows denote asserted

object properties (AFFECTS and INVOLVED-IN)

and the blue arrow is a new object property which is

inferred: It denotes a potential DDI

i,j..

Fig. 4 also shows a basic principle of conceptual

modelling of the OWL ontology: potential DDI

i,j

can

be inferred between Drug

and Drug

through

reasoning, if there existed a triplet Drug

-AFFECTS-

Gene-INVOLVED_IN-Drug

where Drug

is not the

same as Drug

. Fig. 5 shows a conceptual SWRL

rule with asserted PREDICATES are asserted from

SemMedDB for performing the reasoning.

Figure 5: Conceptual SWRL rule for Fig. 3 and 4.

The rule in Fig. 5 would work as long as semantic

predication is correct. Object properties affects (?x,

Gene and involved_in (?y,Drug

) may be replaced with any

other type of predicates available in SemMedDB or UMLS

and inferred using SWRL.

Drug

(?x) ˄ affects (?x, Gene) ˄ Gene(?y) ˄

involved_in (?y,Drug

) ˄ differentFrom (?x,?y)



Drug_Interaction (?x, ?,y)

Drug

Gene

Drug

AFFECTS

INVOLVED-IN

AFFECTS

Drug

Gen

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4 IMPLEMENTATION

The implementation of the middle part of the SA

model in Fig. 1 for Creating Personal DDI is based

on the reasoning process which conforms to Figures

3,4 and 5. The prototype was implemented as a Java

application, adopted from the research on biomedical

discoveries (Almami et al., 2016), (Almami et al.,

2017) and the modelling of semantic software

applications (Patadia et al., 2011), (Shojanoori, 203).

Due to space restrictions we show two important

aspects of the implementation.

Firstly, Fig. 6 illustrates a set of PREDICATES

extracted from public databases in order to test our

reasoning and the implementation.

Figure 6: Selection of Semantic Predications for the

Implementation.

We ran experiments for populating OWL classes

with individuals, extracted from the peer reviewed

papers on DDI discoveries. Figure 6 has data

extracted from Table 1 of (Zhang et al., 2014) and

thus we were able to define numerous triplets. We

also added a few other predicates available in some of

the sources described in the related work, which

shows that we can use any other set of biomedical

data (from the left part of the SA in Figure 1.) and

create more suitable triplets for our OWL model. In

cases where biomedical research results do not

generate knowledge in the format of predications, it

would not be difficult to create them either through

the software application from Figure 1 or by

exploring the nature of OWL object properties

defined Fig.4.

Secondly, an example of user interface for the

prototype is in Figure 1. From the patient medication

list, which initially contained Aspirin, Ibuprofen,

Warfarin, Diazepam, Lysinopril, Thyroxine, two

drugs have potentially known DDI: Ibuprofen and

Warfarin. However, our reasoning with SWRL

detected a new inferred DDI between Lisinopril,

Thyroxine.

Figure 7: Excerpt from Prototype UI.

However, our prototype, as an illustration of the

concept defined in Figure 1, which pushes forward

translational informatics, using efficient and light

weight software technologies, based on semantic

reasoning, shows only an excerpt of the overall

research. The comparison of two drugs, based on

semantic predications, uses bindings of drug

therapeutic compounds to intentional target genes.

There is much more semantic in biomedical science

which can enrich our OWL model and predications.

They include semantic of biological functions from

molecular to organism level to pathological function

related to diseases, to mention just a few.

5 CONCLUSIONS

Translational bioinformatics has come of age (Butte,

2008), (Machado et al., 2015), (Payne and Embi,

2015), and computational algorithms can be used for

assisting in experiments and analyzing results of

biomedical research at bio-molecular level.

However, we have not resolved all the problems we

identified more than a decade ago. The dissemination

Aspirin –INHIBITS -EGF-INTERACTS_WITH-

Ascorbic Acid

Lisinopril-INHIBITS-VIP-STIMULATES -

Thyroxine

Metformin -STIMULATES-Glu-

INTERACTS_WITH-Aspirin

Metformin-STIMULATES-THI-

INTERACTS_WITH -Pioglitazoone

Trastuzumab-INHIBITS-ld HER2 INHIBITS-

Pertuzumab

Ibuprofen-INHIBITS- COX-INTERACTS_WITH-

Warfarin

Nacfillin-INDUCES-CYP3A4-AFFECTS-Warfarin

Ibu

en-INHIBIT

TGS52-INTERACT

Personalised DD

Patient John Smith

Diag. Arthritis,

Knee replacement

Hypothyroidism

GP James Brown

Ibuprofen

Paracetamol

Warfarin

Diazepam

Lisinopril

Thyroxine

DDI Warnings in Yellow

Known DDI are blue lines

Check the dosage

Medication List

Adding Value to Translational Informatics through the Semantic Management of Drug to Drug Interaction

273

of and sharing viomedicla research/data in lcinicla

applications is not common therefore translational

bioinformatics is still evolving. This research shows

that we can enhance it with the manipulation of

semantics of and reasoning from the results of

biomedical research, which in turn derives new

knowledge and tools in/for clinical practice and

medicine in general. This is an opportunity for all of

us to allow the synergy between biomedical and

clinical data and secure that clinical practices

encompass results from biomedical science, because

the gap between the two has not been closed.

This paper just touches the top of the iceberg of

opportunities we may have in the field of adding

value to translational informatics. There are

opportunities of reusing the conceptual model from

this paper for the whole range of problem domains,

from predicting side effects from a drug to therapeutic

targets relationships, to looking at un-intentional

binding of drugs and therapeutic targets, which could

help to define drug repositioning, to mention just a

few (Juric, 2019) (Juric and Almami, 2019).

Therefore this work continues towards the

development of software application, from the same

proposed SA from Figure 1, with reasoning upon

SWRL enabled OWL ontologies as a part of new

computational models, but in different parts of

biomedical fields. The only prerequisite is that data

sharing and dissemination of biomedical research is

essential for progressing in medical science.

For readers interested in methods of populating

OWL ontologies from databases and structured

repositories, which exist in biomedical research, we

suggest reading a few publications (Juric, 2019),

(Saaidi et al., 2010). For readers interested in

understanding how the SA from Fig. 1 can be

implemented as a software application, which

involves accessing OWL ontologies and computing

with SWRL through OWL–API, we suggest reading

(Juric, 2016), (Juric and Kim, 2016), (Tarabi and

Juric, 2018), (Shojanoori,, 2013).

For the full deployment of the SA from Figure 1,

in terms of commercialising the prototype, we face

expected obstacles such as

(i) gaining access to a variety of data banks,

databases and knowledge repositories from

biomedicine and

(ii) the acceptance of this type of applications in

clinical practice.

Resolving (i) and (ii) would require changes in the

way clinical trials are conducted and biomedical

research financed. However, it would also require

changes in the way we manage differences between

interests of pharmaceuticals and biomedical

scientists.

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