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Inferring Cuisine - Drug Interactions Using
the Linked Data Approach
Milos Jovanovik, Aleksandra Bogojeska, Dimitar Trajanov & Ljupco Kocarev
Faculty of Computer Science and Engineering, Rugjer Boshkovikj 16, 1000, Skopje, Macedonia.
Food - drug interactions are well studied, however much less is known about cuisine - drug interactions.
Non-native cuisines are becoming increasingly more popular as they are available in (almost) all regions in
the world. Here we address the problem of how known negative food - drug interactions are spread in
different cuisines. We show that different drug categories have different distribution of the negative effects
in different parts of the world. The effects certain ingredients have on different drug categories and in
different cuisines are also analyzed. This analysis is aimed towards stressing out the importance of cuisine -
drug interactions for patients which are being administered drugs with known negative food interactions. A
patient being under a treatment with one such drug should be advised not only about the possible negative
food - drug interactions, but also about the cuisines that could be avoided from the patient’s diet.
The task of identifying and obtaining foods which contribute to the overall human health, satisfy nutritional
and energy needs and at the same time not inducing food poisoning, has been around as long as humans
have. Human diet, which provides essential nutrients for the organism health, is influenced by different
factors, including cultural habits, socio-economic status, and weather/climate. For example, spice consumption in
the warm regions is tightly connected to the need for keeping the food resistant to bacteria for a longer period of
time
1
.
It is a well known fact that certain foods can influence the effect of a drug in an organism
2–5
. The food-induced
changes in the bioavailability (the degree and rate at which a drug is absorbed into someone’s system) of the drug
modify the clinical effect of the drug. Generally, food - drug interactions can result in significant reduction of the
drug bioavailability, either by direct interaction between a substance from the food and a chemical component of a
drug, or by the physiological response of food intake (e.g. gastric acid secretion). This can often result in treatment
failure. Additionally, food - drug interactions can result in an increase in drug bioavailability, either by increased
drug solubility as a direct result from a substance from the food, or indirectly, by food-initiated secretion of gastric
acid or bile. Even though this leads to an increase in the effect of the drug, it can often result in serious toxicity
2
.
The highest selling drugs in the world include antineoplastic and immunomodulating agents, respiratory
system drugs, alimentary tract and metabolism drugs, cardiovascular system drugs, and nervous system drugs
6
.
Recent statistics report that nearly 70% of the American population consumes at least one prescription drug, a
number which was only 48% in 2010. Twenty percent of them are on five or more drugs
7
. According to Ref. 8, an
alarming number of 1.5 million people are harmed by medications, including the errors due to the lack of
information provided by the pharmacist or the unawareness of the patient to read and follow the patient drug
information.
Different world regions use diverse ingredients and foods as part of their cuisines and therefore the negative
food-induced interactions with drugs vary from one part of the world to another. In addition, as non-native
cuisines are becoming increasingly popular (due to travel and due to their appearance in almost every corner of
the planet as a result of global cultural exchange), the effect different cuisines have on certain drugs or categories of
drugs is becoming very important.
Although food - drug interactions are well studied, much less is known about cuisine - drug interactions. The
food - drug interactions are clinically proven interactions between a drug and a given ingredient (ex. milk,
avocado, garlic), and with cuisine - drug interactions we define the interactions between a drug and a world
cuisine, using information extracted from the food - drug interactions and the usage of the interacting ingredients
in a specific cuisine. The aim of the analysis presented here is to address the distribution of the known negative
food - drug interactions in the world cuisines. The influence that world cuisines have on different drug categories
is analysed by transforming and connecting two datasets (drug data and recipes data) into a novel structure
empowered with the concept of Linked Data
9
. The results are striking: North American cuisine has the most
OPEN
SUBJECT AREAS:
NUTRITION
DATA INTEGRATION
Received
14 September 2014
Accepted
2 March 2015
Published
20 March 2015
Correspondence and
requests for materials
should be addressed to
L.K. (lkocarev@ucsd.
edu)
SCIENTIFIC REPORTS | 5 : 9346 | DOI: 10.1038/srep09346 1
negative interactions (10.242%) with drugs from the category
‘Antiinfectives for systemic use’. In other words, 10 out of 1,000
patients could possibly have negative effects when being adminis-
tered this category of drugs. Similarly, European cuisines (from
Western Europe, Northern Europe and Eastern Europe) have the
most negative interactions with drugs from the same category
(‘Antiinfectives for systemic use’). On the other hand, the cuisines
from Southern Europe, Asia, Latin America and Africa negatively
interact mostly with drugs from the categories ‘Blood and blood
forming organs’ and ‘Various’.
Our main message from this work could be summarized as guid-
ance in the form: ‘‘if you are being administered drugs from a certain
drug category, be aware of what cuisines you should be consuming’’.
Results
Two different aspects of food - drug interactions are considered: (1)
negative interactions between drugs from a given category and recipes
from a given cuisine, and (2) ingredients’ impact on the negative food
- drug interactions in different parts of the world.
Cuisine - drug category interactions.For the analysis of negative
interactions between a particular drug category, i.e. a category of drugs
grouped by their Anatomical Therapeutic Chemical (ATC) classifica-
tion codes (Table 6), and a cuisine, we calculate the permils of existing
interactions between them (Table 1) using Equation 1.
This ratio represents the probability of a negative food interaction
to occur when a patient using a drug from a given category consumes
a meal from a given cuisine. We use the measurement of permils for
the ratio in order to show the number of patients, out of 1,000, which
can have a negative food interaction when combining the drug cat-
egory with the cuisine. The aim of this analysis aspect is to identify
the negative impact of consuming foods from specific cuisine while
taking a prescribed drug.
The results (Table 1) show that some of the most intensive nega-
tive food - drug interaction occur between drugs from category B and
recipes from Asian, African and Latin American cuisines, drugs from
category J and recipes from North America and Europe, as well as
drugs from category V and recipes from almost the entire world.
Also, we can note that drugs from categories H, P and R have a very
rare occurrence of negative interactions with food, and drugs from
category M have no interactions at all.
The results from Table 1 help us distinguish three different pat-
terns of food - drug interactions from cuisine and drug category
points of view.
Pattern 1.The first pattern consists of drugs from categories B, C, N
and V. As shown in Table 1, the drugs from these four categories have
more negative food - drug interactions with recipes from South
Europe, Middle East, South Asia, Southeast Asia, East Asia, Latin
America and Africa, as opposed to other cuisines. This pattern is
depicted on Fig. 1a.
The reason behind this pattern of influence is the fact that the drugs
from these four categories have negative food interactions with garlic
and ginger. These two ingredients are largely present and directly
responsible for the negative food - drug interactions in these geo-
graphical regions (Table 3, Table 4). Additionally, the negative food
interactions these drugs have with avocado, licorice and grapefruit,
Table 1
|
Permils of existing Interactions between Drugs from an ATC Category and Recipes from a Cuisine
NAm WEu NEu EEu SEu MEa SAs SEAs EAs LAm Af
A 5.288%5.263%4.462%4.930%2.547%1.531%2.085%1.080%0.408%2.356%1.238%
B 5.271%4.345%1.643%4.687%9.663%6.894%10.640%12.699%10.756%11.013%10.045%
C 5.821%5.207%3.640%5.083%6.790%4.815%7.123%8.371%6.645%8.003%6.554%
D 7.081%7.011%5.983%6.573%3.401%1.928%2.717%1.351%0.543%3.130%1.655%
G 7.240%7.504%6.326%6.836%3.789%2.848%3.520%1.781%0.528%3.237%1.652%
H 0.129%0.111%0.000%0.000%0.056%0.091%0.000%0.386%0.023%0.242%0.000%
J 10.242%10.532%8.468%9.793%6.276%2.965%3.682%2.035%1.234%5.641%2.642%
L 4.430%4.673%3.818%4.156%2.744%1.644%1.720%1.276%0.844%2.137%1.442%
M 0.000%0.000%0.000%0.000%0.000%0.000%0.000%0.000%0.000%0.000%0.000%
N 4.829%5.510%3.397%4.706%5.935%4.157%4.588%4.923%3.555%6.032%3.514%
P 0.078%0.067%0.000%0.000%0.034%0.055%0.000%0.234%0.014%0.147%0.000%
R 0.193%0.237%0.155%0.153%0.139%0.211%0.172%0.234%0.015%0.160%0.028%
S 5.117%5.074%4.302%4.779%2.437%1.433%1.975%1.012%0.398%2.270%1.206%
V 9.022%8.023%5.123%8.150%10.912%7.453%11.498%12.822%10.888%12.582%10.815%
Figure 1
|
Negative food - drug interactions between drugs from a category and foods from a cuisine, expressed in permils. Figure (a) depicts the
interactions of drugs from categories B, C, N and V. The drugs from these four categories have more negative food - drug interactions with recipes from
South Europe, the Middle East, South Asia, Southeast Asia, East Asia, Latin America and Africa, as opposed to other cuisines. Figure (b) depicts the
interactions of drugs from categories A, D, G, J, L and S. These drugs have more intensive negative food - drug interactions with recipes from North
America, Western Europe, Northern Europe and Eastern Europe, as opposed to other cuisines. Figure (c)depicts the interactions of drugs from categories
H, P and R. These drugs have a significantly smaller ratio of interactions compared to those from (a) and (b).
www.nature.com/scientificreports
SCIENTIFIC REPORTS | 5 : 9346 | DOI: 10.1038/srep09346 2
add up to the difference between these drug categories and the rest.
The interactions with coffee are also present within these categories,
but they are present in other categories as well, so the effect of coffee is
not much evident in the specifics of this pattern.
Fig. 2a depicts the intensity of negative food - drug interactions for
drugs from the B category, shown in a color scale. The figure shows
the number of patients out of 1,000, which are being administered a
drug from category B, which can have a negative interaction with a
recipe from a cuisine. The white areas on the map represent the
cuisines we don’t have any data on.
Since category B drugs belong to the first pattern, Fig. 2a shows the
same negative food - drug interaction intensity we see in Fig. 1a, i.e.
drugs from category B have significantly more interactions with
recipes from South Europe, Middle East, South Asia, Southeast
Asia, East Asia, Latin America and Africa.
Pattern 2.The second pattern consists of drugs from categories A, D,
G, J, L and S. These drugs have a significant ratio of negative food -
drug interactions with recipes from North America, Western
Europe, Northern Europe and Eastern Europe, as opposed to other
cuisines. This pattern is depicted on Fig. 1b.
The emergence of this pattern is mainly due to the negative food
interactions which drugs from these categories have with milk. As we
can see from Table 2, milk is the number one cause for negative food -
drug interactions globally, and Table 3 and Table 4 show that it is the
primary source of negative interaction in these regions of the world.
Since these cuisines use milk in a large portion of their recipes, unlike
the rest of the cuisines, this pattern is expected.
Fig. 2b depicts the intensity of negative food - drug interactions for
drugs from the J category. Since category J drugs belong to the second
pattern, this figure shows the same negative food - drug interaction
intensity we see in Fig. 1b, i.e. drugs from category J have significantly
more interactions with recipes from North America, Western
Europe, Northern Europe and Eastern Europe.
Pattern 3.The remaining drug categories, H, P and R, have signifi-
cantly smaller negative food - drug interactions ratio compared to
other categories. They form a different pattern, depicted on Fig. 1c,
but due to the very small ratio of interactions, this pattern is not as
compact as the other two. The drugs from these categories have nega-
tive effects when interacting with coffee, tea and grapefruit (Table 5),
and not with other ingredients. As we see from Table 3 and Table 4,
coffee is a top three interacting ingredient in North America, Europe,
the Middle East and in Latin America, which corresponds with the
pattern on Fig. 1c. The high use of grapefruit and tea in recipes from
Southeast Asia (Table 4) is responsible for the high interactions of
drugs from these categories with this cuisine.
Ingredient analysis.The second aspect of the analysis aims to detect
the main ingredients involved in the negative food - drug interactions.
Table 2 shows the percentage of negative food - drug interactions in
which an ingredient is involved and is responsible for the negative
interaction. The percentage is calculated out of the total number of
negative food - drug interactions we discovered in the analysis, which
is 298,762 interactions.
Figure 2
|
Number of patients (per 1,000) with possible negative food - drug interactions while being administered a category B or category J drug,
in different cuisines, globally. Figure (a) depicts the global distribution of negative interactions involving a category B drug. These drugs have significantly
more interactions with recipes from South Europe, the Middle East, South Asia, Southeast Asia, East Asia, Latin America and Africa. Figure (b) depicts
the global distribution of negative interactions involving a category J drug. These drugs have significantly more interactions with recipes from North
America, Western Europe, Northern Europe and Eastern Europe. The maps were generated using the d3.js library (http://d3js.org).
Table 2
|
Percentage of Interactions involving the Ingredient, for all
Cuisines
Ingredient % of Interactions involving the Ingredient
milk 56.110%
garlic 22.617%
coffee 8.388%
ginger 5.109%
cheese 2.197%
bacon 2.165%
red wine 1.865%
grapefruit 1.684%
ham 1.296%
wine 1.174%
tea 1.149%
avocado 0.869%
beer 0.304%
licorice 0.120%
Table 3
|
Top 3 interacting Ingredients per Cuisine
Cuisine Top 3 interacting Ingredients
North American milk, garlic, coffee
Western European milk, garlic, coffee
Northern European milk, coffee, ginger
Eastern European milk, garlic, coffee
Southern European garlic, milk, coffee
Middle Eastern garlic, milk, coffee
South Asian garlic, ginger, milk
Southeast Asian garlic, ginger, milk
East Asian garlic, ginger, milk
Latin American garlic, milk, avocado
African garlic, ginger, milk
www.nature.com/scientificreports
SCIENTIFIC REPORTS | 5 : 9346 | DOI: 10.1038/srep09346 3
Table 2 clearly shows that two ingredients are most common in the
negative interactions: milk, responsible for with over 56% of the
negative interactions, and garlic with over 22%.
These two ingredients have different effects on the food - drug
interactions in the parts of the world, and within distinct drug cat-
egories. Table 3 shows the top three ingredients per cuisine which are
involved in the negative interactions. As we can see, milk is the ingre-
dient which causes most of the negative interaction in North America,
Western, Northern and Eastern Europe. On the other hand, the most
problematic ingredient in recipes from South Europe, Middle East,
Asia, Latin America and Africa, is garlic.
Fig. 3 shows this trend on a world map. Fig. 3a illustrates the
percentage of occurrences of milk in the total number of negative food
- drug interaction within a cuisine, while Fig. 3b depicts the same
impact of garlic.
A more complete overview of the impact some ingredients have in
different parts of the world is presented in Table 4. We see here that
the most common ingredients responsible for the negative food -
drug interactions are milk, garlic, coffee and ginger, while the other
ingredients have significantly lower impact. However, out of these
four ingredients, milk and garlic have the most notable impact
(Table 2, Table 3, Table 4), so the discussion will be focused on them.
The impact of milk.Milk has been proved to have negative effect
generally on antibiotics
2,5
, and antibiotics belong to categories A,
C, D, G, J, L and S
10
. Milk reduces the bioavailability and even pre-
vents the absorption of some of these drugs. This corresponds with
our results in Table 5.
The reasons for such high occurrence of milk in negative food -
drug interactions in the western culture (Fig. 3a, Table 3, Table 4)
may be a result of the high use of milk and dairy products in general
in this part of the world. The consumption of milk in the western
culture, and especially in Northern Europe where the percentage of
occurrence of milk in the interactions is the largest, is probably a
direct consequence of the high lactose tolerance the population from
these regions has. Countries from Europe, and especially Northern
Europe, are known to have the highest percentage of lactose tolerant
population in the world
11–13
. On the other hand, regions such as
Southeast Asia, East Asia and South Africa, are known as regions
with high percentage of lactose intolerant population
12
. This prob-
ably has a direct cause on the consumption levels of milk in those
regions, leading to a decrease in the negative effects milk has in food -
drug interactions in this parts of the world.
The impact of garlic.The reason garlic is part of more than 22% of all
negative food - drug interactions we detect (Table 2), is its negative
interactions with anticoagulant drugs
14,15
, which belong to categories
B, C and S. Table 3 and Table 4 clearly show that garlic is largely
responsible for the negative food - drug interactions in South Europe,
the Middle East, Asia, Latin America and Africa. Its impact is evident
in the rest of the world, but with much less intensity. This pattern of
garlic use in various cuisines probably has some cultural and historical
background, with garlic being used in Egypt, Greece, Rome, China and
India since ancient times, for prevention and treatment of disease, for
providing strength and increasing work capacity of laborers, and even
as a performance enhancing agent for Olympic athletes
16
.
Table 4
|
Percentage of Ingredient participation in negative food - drug interactions, per Cuisine
NAm WEu NEu EEu SEu MEa SAs SEAs EAs LAm Af
milk 62.288% 60.132% 70.955% 62.030% 32.665% 26.331% 30.302% 13.634% 8.637% 29.196% 23.833%
garlic 17.606% 13.616% 3.119% 17.868% 41.754% 39.943% 45.090% 59.570% 67.848% 47.122% 53.333%
coffee 8.662% 11.213% 10.234% 8.883% 8.711% 14.837% 4.012% 3.671% 0.456% 5.648% 2.917%
ginger 3.929% 2.670% 4.678% 1.218% 0.689% 6.971% 40.352% 32.092% 42.626% 1.399% 28.000%
cheese 1.631% 2.203% 0.780% 2.132% 6.241% 0.754% 0.382% 0.315% 0.182% 7.302% 0.500%
bacon 2.244% 3.344% 0.877% 4.416% 1.795% 0.000% 0.115% 0.000% 0.899% 1.815% 0.250%
grapefruit 1.705% 1.435% 0.000% 0.000% 0.823% 2.025% 0.000% 6.765% 0.560% 3.470% 0.000%
red wine 1.434% 3.337% 3.119% 2.843% 5.303% 5.652% 0.611% 0.210% 1.303% 1.829% 6.000%
ham 1.234% 1.602% 1.462% 0.457% 2.757% 0.000% 0.000% 0.157% 1.133% 0.746% 0.000%
wine 0.847% 1.575% 1.170% 0.203% 2.431% 1.507% 0.611% 4.195% 7.817% 0.403% 1.333%
tea 1.141% 1.589% 3.314% 1.726% 0.000% 4.805% 10.394% 5.349% 0.443% 0.000% 0.000%
avocado 0.554% 0.133% 0.780% 0.000% 0.211% 0.565% 0.000% 0.210% 1.042% 7.746% 0.333%
beer 0.299% 0.601% 0.292% 0.152% 0.101% 0.141% 0.000% 0.000% 0.156% 0.605% 0.000%
licorice 0.141% 0.000% 1.754% 0.000% 0.000% 0.000% 0.000% 0.000% 0.234% 0.000% 0.000%
Table 5
|
Percentage of drugs, per ATC Category, which have negative food interaction with an Ingredient
ABCDGHJ LMNPR SV
milk 1.923% 0.000% 1.058% 2.609% 2.326% 0.000% 3.509% 1.515% 0.000% 0.431% 0.000% 0.000% 1.887% 1.754%
garlic 0.000% 1.786% 1.058% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.431% 0.000% 0.000% 0.000% 1.754%
coffee 1.282% 1.786% 1.058% 0.870% 6.977% 0.000% 1.170% 0.758% 0.000% 8.621% 0.000% 0.971% 0.943% 0.000%
ginger 0.000% 1.786% 1.058% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.431% 0.000% 0.000% 0.000% 1.754%
cheese 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.585% 0.000% 0.000% 0.431% 0.000% 0.000% 0.000% 0.000%
bacon 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.585% 0.000% 0.000% 0.862% 0.000% 0.000% 0.000% 0.000%
red wine 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.585% 0.758% 0.000% 0.862% 0.000% 0.000% 0.000% 0.000%
grapefruit 1.923% 0.000% 9.524% 1.739% 0.000% 5.882% 1.754% 3.030% 0.000% 5.172% 3.571% 1.942% 1.887% 0.000%
ham 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.585% 0.000% 0.000% 0.862% 0.000% 0.000% 0.000% 0.000%
wine
a
0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.585% 0.758% 0.000% 0.862% 0.000% 0.000% 0.000% 0.000%
tea 1.282% 1.786% 1.058% 0.870% 6.977% 0.000% 0.585% 0.758% 0.000% 8.621% 0.000% 0.971% 0.943% 0.000%
avocado 0.000% 0.000% 1.058% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.862% 0.000% 0.000% 0.000% 1.754%
beer 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.585% 0.000% 0.000% 0.862% 0.000% 0.000% 0.000% 0.000%
licorice 0.000% 0.000% 9.524% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000%
a
There is a significant number of recipes which contain ‘wine’ as an ingredient. The negative interactions of drugs only contain ‘red wine’ explicitly while ‘white wine’ is considered safe. Despite the
ambiguous nature of the ingredient listed simply as ‘wine’ we decided to use it in the analysis since it can still be a cause of negative cuisine - drug interactions in cases when red wine is used in the preparation
of the recipe. As we can see from the table the ‘wine’ ingredient has negative interactions with the drugs which react negatively with ‘red wine’.
www.nature.com/scientificreports
SCIENTIFIC REPORTS | 5 : 9346 | DOI: 10.1038/srep09346 4
Discussion
By transforming and connecting two datasets (drug data and recipes
data) we have generated an open semantic structure (dataset), avail-
able on the Web, which has provided a basis for a cuisine - drug
category interactions analysis, showing how drugs from different
categories interact with recipes from different cuisines. Two patterns
of negative interactions could be stressed: drugs from categories B, C,
N and V have a negative impact on foods from South Europe, Asia,
Latin America and Africa, whereas drugs from categories A, D, G, J,
L and S negatively interact with foods from North America and
Europe (Western Europe, Northern Europe and Eastern Europe).
These patterns arise from the diverse ingredients used in the world
cuisines, with milk being mostly responsible for the first pattern, and
garlic and ginger for the second. The impact of the milk and garlic
varies in different parts of the world, mainly because of the cultural,
historical and biological reasons of their presence (or lack thereof) in
the recipes in a given cuisine.
Our work is aimed towards stressing out the importance of pro-
fessional guidance of patients which are on drugs with known nega-
tive food interactions. A patient being under a treatment with one
such drug should be advised by a pharmacist or a doctor about the
foods, the ingredients and the cuisines that should be avoided (or
even excluded) from his/her diet. Our analysis of the global distri-
bution of negative interactions between different drugs and cuisines
can provide a general overview and a general guide for the dangers of
making bad cuisine - drug combinations.
Methods
In order for the analysis to be relevant, we acquire real-world data about drugs and
recipes. Various factors influenced our decision on which sources to use to obtain the
data: the validity of the data, its volume and how up-to-date they were.
Here we describe the process of choosing, obtaining and transforming the two
datasets used in the analysis. In order to bring the datasets to a common repres-
entational model, we transform and publish them into datasets which follow the
Linked Data principles
9
.
The drugs dataset.Over the past years, various databases with drug data have become
freely available on the Web. The Semantic Web Health Care and Life Sciences (HCLS)
Interest Group (http://www.w3.org/blog/hcls/), a W3C group focused on using
Semantic Web technologies in the fields of health care, life sciences, clinical research
and translational medicine, has been working on transforming a range of health data
from the Web into a comperhensive dataset based on Semantic Web technologies and
Linked Data principles. As a result of this work, the HCLS Interest Group has
deployed the Linked Open Drug Data (LODD) Cloud
17
, which contains over 380
million RDF triples (http://www.w3.org/wiki/HCLSIG/LODD/Data).
Part of the LODD Cloud is the DrugBank dataset, a Semantic Web version of the
DrugBank database. The DrugBank database is a free database which provides
chemical, pharmacological and pharmaceutical data for over 6,800 drugs. Since its
release in 2006, the DrugBank database has been widely used for research and edu-
cational purposes by pharmacists, medical chemists, pharmaceutical researchers,
clinicians, educators and the general public
18
. Because of this, it has been selected by
the HCLS and included into their efforts. The DrugBank dataset in the LODD Cloud
holds the same data for over 4,700 drugs in RDF format and provides a SPARQL
endpoint (http://wifo5-03.informatik.uni-mannheim.de/drugbank/) for remote data
access.
Among other data, the DrugBank dataset contains information about the food
interactions of the drugs. There are 968 food - drug interactions in the dataset, which
connect 525 different drugs with various food indications. These indications contain a
reference to one or more ingredients and are mostly negative, such as ‘‘Avoid alco-
hol.’’ or ‘‘Do not take with milk.’’ However, there are cases where the food interaction
is neutral (‘‘Take without regard to meals.’’) as well as cases where the interaction is
actually positive (‘‘Increase dietary intake of magnesium, folate, vitamin B6, B12, and/
or consider taking a multivitamin.’’)
Therefore, we need to precisely denote the sentiment of each food - drug inter-
action. In order to achieve this, we obtain a local copy of the parts of the DrugBank
dataset which we need for the analysis and create new RDF properties for the dataset,
which denote the different types of food interactions: negative, neutral and positive.
We do the sentiment analysis of the food interactions of all drugs from the drugs
dataset in a semi-automatic fashion. Then, for each drug from the dataset we analyze
the negative food interactions, detect the ingredients mentioned, and locate those
ingredients in the recipes from the recipes dataset. For each such drug - recipe pair we
add a new relation in the drugs dataset, denoting that the drug has a negative food
interaction with the recipe. This interlinking of datasets is enabled by the use of
Linked Data principles and the RDF data model. As a tool for storing, interlinking and
querying the datasets we use a Virtuoso Universal Server instance (http://linkeddata.
finki.ukim.mk/). Using the SPARQL query language over the SPARQL endpoint of
the Virtuoso instance, we are then able to query both datasets and extract the
necessary data needed for the analysis.
Our extended version of the DrugBank dataset is published following the Linked
Data principles and is available via a public SPARQL endpoint (http://linkeddata.
finki.ukim.mk/sparql/).
The recipes dataset.With the expansion of the Web and the presence of mobile and
web applications in everyday life, there is a significant increase of the availability of
Table 6
|
ATC Code List
Code Contents
A Alimentary tract and metabolism
B Blood and blood forming organs
C Cardiovascular system
D Dermatologicals
G Genito-urinary system and sex hormones
H Systemic hormonal preparations
J Antiinfectives for systemic use
L Antineoplastic and immunomodulating agents
M Musculo-skeletal system
N Nervous system
P Antiparasitic products, insecticides and repellents
R Respiratory system
S Sensory organs
V Various
Figure 3
|
Percentage of occurrence of milk and garlic in negative food - drug interactions in different cuisines, globally. Figure (a) depicts the
percentage of occurrence of milk, which causes most of the negative interaction in North America, Western, Northern and Eastern Europe. Figure (b)
depicts the percentage of occurance of garlic, which is the most problematic ingredient in recipes from South Europe, the Middle East, Asia, Latin America
and Africa. The maps were generated using the d3.js library (http://d3js.org).
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SCIENTIFIC REPORTS | 5 : 9346 | DOI: 10.1038/srep09346 5
online recipes and recipe datasets, which provide easy and quick access to millions of
recipes from various cuisines around the world. The general intent of these datasets is
merely to provide the users with everyday ideas for preparing meals and with useful
information for ingredients combinations. Some of them provide even personalized
ingredient shopping lists, menu planers, user ratings, etc.
Some of the recipes are available as commercial datasets and are intended for usage
from mobile applications: Yammly (https://developer.yummly.com/), Food2Fork
(http://food2fork.com/about/api), BigOven (http://api.bigoven.com/), and others are
available on websites and are free for use: Allrecipes.com (http://allrecipes.com),
Epicurious (http://www.epicurious.com/), Taste.com.au (http://www.taste.com.au/),
Foodnetwork.com (http://www.foodnetwork.com/), etc.
For the purposes of this work we use the available recipes dataset provided in Ref.
19. The dataset is created using recipes from three different sources: allrecipes.com,
epicurious.com,andmenupan.com. The data file contains information for a total of
56,458 recipes, their ingredients and the cuisine of origin. The recipes are divided in 11
cuisines: North American (41,525), Western European (2,659), Eastern European
(381), Southern European (4,180), Northern European (250), Middle Eastern (645),
South Asian (621), Southeast Asian (457), East Asian (2,512), Latin American (2,917),
African (352). In order to enable interoperability between the datasets, we transform
the cleaned data from the CSV file into an RDF dataset. For the CSV-to-RDF trans-
formation we use the Food Ontology (http://data.lirmm.fr/ontologies/food), which
allows us to denote the cuisine and ingredients for all the recipes from the dataset. We
do not extend the dataset further, since we already create new relations in the drugs
dataset pointing from a drug to a specific recipe in therecipes dataset. We then publish
the recipes dataset in Linked Data format, in the same manner as the drugs dataset.
The recipes dataset is publicly available via the same SPARQL endpoint, as well.
Analysis.After collecting and refining the two datasets, we load them into a Virtuoso
Universal Server instance which is publicly available (http://linkeddata.finki.ukim.
mk/) and provides endpoint-based web service access to the data from both datasets,
in RDF.
The analysis is done by using the SPARQL endpoint for querying our drugs and
recipes datasets. We use SPARQL queries which make use of the relations in the drugs
dataset which connect the recipes a drug has negative food interactions with, and
analyze various aspects of the domain and the results which arise.
The measurement of permils used to assess the ratio of interactions between a drug
category and cuisine is given with:
EI=PI
ðÞ1000 ð1Þ
where E
I
is the number of existing interactions found from the datasets, and P
I
is the
number of possible interactions between the drug category and the cuisine, calculated
as the number of drugs in the category multiplied by the number of recipes in the
cuisine. We use the measurement of permils for the ratio in order to show the number
of patients, out of 1,000 patients treated with a drug from a given category, which can
have a negative food interaction when consuming a meal from the cuisine. To cal-
culate E
I
, the number of existing interactions between a drug category and a cuisine,
we count the existing negative food interactions a drug from a given drug category has
with recipes from a given cuisine.
To illustrate this analysis, we can use a specific example: the interaction between
Oxazepam and tea. Oxazepam is a benzodiazepine used for the treatment of anxiety
disorders, alcohol withdrawal, and insomnia. According to DrugBank, it has three
clinically proven food - drug interactions: (a) avoid alcohol, (b) avoid excessive
quantities of coffee or tea (caffeine) and (c) take with food. In our analysis, we
conclude that (a) and (b) are negative food interaction of Oxazepam with alcohol,
coffee, tea and caffeine. The (c) interaction is considered a positive food interaction of
Oxazepam and is not taken into account for our analysis. Oxazepam is a drug cate-
gorized in the ATC category N; its ATC code is N05BA04.
On the other hand, tea is an ingredient in 102 distinct recipes from 8 cuisines in our
dataset. One of the 102 recipes is recipe #9966, ‘‘Kumquat-Cardamom Tea Bread’’,
which belongs to the North American cuisine and has the following ingredients:
cardamom, egg, vegetable oil, butter, wheat, lemon juice, vanilla, walnut, corn,
kumquat, pineapple and tea.
What we do in our analysis is we conclude that in this case tea is responsible for a
negative cuisine - drug interaction between the North American cuisine and category
N drugs (Fig. 4). We then count this cuisine - drug category interaction as one existing
negative interaction.
If this was a case in which recipe #9966, ‘‘Kumquat-Cardamom Tea Bread’’, con-
tained another ingredient which has a negative interaction with Oxazepam, such as
alcohol or coffee, we would still count the interaction between the recipe (and the
North American cuisine) and Oxazepam (and the N drug category) as a single
negative interaction. We do this because we base our analysis on the ratio between
existing (E
I
) and possible (P
I
) negative interactions, and we calculate the possible
negative interactions on a drug - recipe level. Therefore, we have to calculate the
number of existing negative interactions on that level, as well.
Maps.Maps similar to Fig. 2 and Fig. 3 for the other drug categories and ingredients
can also be viewed,using our visualization web application (http://viz.linkeddata.finki.
ukim.mk/). Thesemaps use data and results from the analysis presented in this paper.
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Figure 4
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The cuisine - drug interaction concluded from the clinically confirmed negative interaction between Oxazepam and tea. We conclude
that Oxazepam, and N drugs in general, have one negative interaction with the North American cuisine, based on this existing connection denoted on the
figure with gray elements.
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Acknowledgments
The work in this paper was partially financed by the Faculty of Computer Science and
Engineering, Ss. Cyril and Methodius University in Skopje, as part of the ‘‘Modelling and
Analysis of Dynamical Processes in Composite and Multiplex Networks’’ project.
Author contributions
M.J., A.B., D.T. and L.K. designed the research. M.J. and A.B. developed tools for the
analysis and performed the analysis. M.J., A.B., D.T. and L.K. discussed the results, wrote
the paper, and reviewed the manuscript. M.J. and A.B. equally contributed to the work.
Additional information
Competing financial interests: The authors declare no competing financial interests.
How to cite this article: Jovanovik, M., Bogojeska, A., Trajanov, D. & Kocarev, L. Inferring
Cuisine - Drug Interactions Using the Linked Data Approach. Sci. Rep. 5, 9346;
DOI:10.1038/srep09346 (2015).
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