Prediction of Cancer Drugs by Chemical-Chemical Interactions
Citation: Lu J, Huang G, Li H-P, Feng K-Y, Chen L, et al. (
Prediction of Cancer Drugs by Chemical-Chemical Interactions
Jing Lu 0
Guohua Huang 0
Hai-Peng Li 0
Kai-Yan Feng 0
Lei Chen 0
Ming-Yue Zheng 0
Yu-Dong Cai 0
Lukasz Kurgan, University of Alberta, Canada
0 1 Department of Medicinal Chemistry, School of Pharmacy, Yantai University , Yantai, Shandong , People's Republic of China, 2 Institute of Systems Biology, Shanghai University , Shanghai , People's Republic of China, 3 Department of Mathematics, Shaoyang University , Shaoyang, Hunan , People's Republic of China, 4 CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai , People's Republic of China, 5 Beijing Genomics Institute, Shenzhen Beishan Industrial zone , Shenzhen , People's Republic of China, 6 College of Information Engineering, Shanghai Maritime University , Shanghai , People's Republic of China, 7 State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica , Shanghai , People's Republic of China
Cancer, which is a leading cause of death worldwide, places a big burden on health-care system. In this study, an orderprediction model was built to predict a series of cancer drug indications based on chemical-chemical interactions. According to the confidence scores of their interactions, the order from the most likely cancer to the least one was obtained for each query drug. The 1st order prediction accuracy of the training dataset was 55.93%, evaluated by Jackknife test, while it was 55.56% and 59.09% on a validation test dataset and an independent test dataset, respectively. The proposed method outperformed a popular method based on molecular descriptors. Moreover, it was verified that some drugs were effective to the 'wrong' predicted indications, indicating that some 'wrong' drug indications were actually correct indications. Encouraged by the promising results, the method may become a useful tool to the prediction of drugs indications.
Funding: This study was funded by the National Basic Research Program of China (2011CB510101, 2011CB510102), the National Science Foundation of China
(61202021, 31371335, 61373028), the Innovation Program of Shanghai Municipal Education Commission (12ZZ087, 12YZ120), the grant of The First-class
Discipline of Universities in Shanghai, Shanghai Educational Development Foundation (12CG55), the Scientific Research Fund of Hunan Provincial Science and
Technology Department (2011FJ3197), the Hunan National Science Foundation (Grand: 11JJ5001), and the Scientific Research Fund of Hunan Provincial Education
Department (Grant: 11C1125). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
. These authors contributed equally to this work.
Cancer is the main cause of death in both developed and
developing countries . In 2008 alone, there were 12.7 million
new cancer cases and 7.6 million cancer deaths worldwide .
Meanwhile, the quantity of newly approved drugs diminished
continually in spite of an increase of R&D investments . R&D
of a drug requires comprehensive experimental testing, which
often costs millions of dollars, involves several thousand animals,
and takes many years to complete. However, as a result, not many
chemicals have undergone the degree of testing needed to support
accurate health risk assessments or meet regulatory requirements
for drug approval. Thus, it is very attractive to develop quick,
reliable, and non-animal-involved prediction methods, e.g. using
structure-activity relationships (SARs), to predict the anticancer
activities of chemicals.
Some pioneer studies indicated that interactive proteins are
more likely to share the same functions than non-interactive ones
[3,4,5]. Likewise, interactive compounds are also more likely to
share common properties [6,7,8]. STITCH (Search Tool for
Interactions of Chemicals, http://stitch.embl.de/) is a well-known
database containing the interactions information of proteins and
chemicals [9,10]. It provides three categories of interactive
compounds: (1) those participating in the same reactions; (2) those
sharing similar structures or activities and (3) those with literature
associations, such as binding the same target . In this study, we
attempted to build a prediction model of drug-indication by
quantifying chemical-chemical interaction of every pair of
interactive compounds. Briefly, drugs and their corresponding
indications (i.e., 8 kinds of cancers) were extracted from KEGG
(Kyoto Encyclopedia of Genes and Genomes, http://www.
genome.jp/kegg/) , a well-known database dealing with
genomes, enzymatic pathways, and biological chemicals, and
Drugbank , another database containing detailed information
of drugs and their target information. Then, the score of each
indication of the query compound was obtained from the
confidence scores of interactions between the query compound
and its interactive compounds using the indications of the
interactive compounds. And the order from the most likely
indication to the least was obtained for each drug. Finally, the
prediction quality of the model was evaluated by Jackknife test and
some other parameters.
In addition to build an effective prediction model, another aim
of our study is to investigate the drug repositioning ability of our
model. Drug repositioning, i.e. finding novel uses of existing drugs,
is an alternative strategy towards drug development because it has
the potential to speed up the process of drug approvals. Several
drugs, such as thalidomide, sildenafil, bupropion and fluoxetine,
have been successfully repositioned to new indications [13,14].
Experimental approaches for drug repositioning usually employ
high throughput screening (HTS) to test the libraries of drugs
against biological targets of interest. More recently, several in silico
models were developed to address the issues of drug repositioning.
Iorio et al. predicted and validated new drug modes of action and
drug repositioning from transcriptional responses . Buttes
group reported two successful examples of drug repositioning
based on gene expression data from diseases and drugs [16,17].
Cheng et al. merged drug-based similarity inference (DBSI),
targetbased similarity inference (TBSI) and network-based inference
(NBI) methods for drug-target association and drug repositioning
. In our study, according to the assumption that interactive
drugs are more likely to target the same indication, we investigated
the repositioning possibility of some wrong predicted drugs by
retrieving references, and attempted to propose alternative
indications for some drugs.
Materials and Methods
The information of 98 drugs that can treat cancers was retrieved
from KEGG DISEASE in KEGG . These drugs can treat the
following 10 kinds of cancers:
(1) Cancers of the nervous system
(2) Cancers of the digestive system
(3) Cancers of haematopoietic and lymphoid tissues
(4) Cancers of the breast and female genital organs
(5) Cancers of soft tissues and bone
(6) Skin cancers
(7) Cancers of the urinary system and male genital organs
(8) Cancers of endocrine organs
(9) Head and neck cancers
(10) Cancers of the lung and pleura
Since some drugs have no information of chemical-chemical
interactions, we discarded these drugs, resulting in 68 drugs. After
that, we found that Skin cancers and Head and neck cancers
only contained 3 and 4 drugs, respectively. It is not sufficient to
establish an effective prediction model with only a few samples,
thus these two kinds of cancers were abandoned. As a result, 68
drugs were obtained, comprising the benchmark dataset S. These
68 drugs were classified into 8 categories in a way that drugs that
can treat one kind of cancers comprised one category. The codes
of the 68 drugs and their indications can be found in Table S1.
The number of drugs in each category is listed in column 5 of
Table 1. For convenience, we used tags C1, C2, . . . ,C8 to
represent each kind of cancers. Please see the column 1 and 2 of
Table 1 for the corresponding of tags and cancers. It is observed
from Table 1 that the sum of the number of drugs in each
category is much larger than the different drugs in S, indicating
that some drugs belong to more than one category, i.e. some drugs
can treat more than one kind of cancers. In details, 50 drugs can
treat only one kind of cancers, while 18 drugs can treat at least two
kinds of cancers. Please refer to Figure 1 for a plot of the number
of drugs against the number of cancers they can treat. Thus, it is a
multi-label classification problem which needs to assign each drug
to the aforementioned 8 categories in descending order. The
classifier only providing one candidate cancer that a query drug
can treat is not an optimal choice. Similar to the situation when
dealing with proteins and compounds with multiple attributions
[7,19], the proposed method also needs to provide a series of
candidate cancers, ranging from the most likely cancer to the least
To better evaluate the proposed method, the benchmark dataset
S was divided into one training dataset Str and one validation test
dataset Ste, i.e. S = Str<Ste and Str>Ste = , where drugs that can
only treat exact one kind of cancer and half of drugs that can treat
at least two kinds of cancers comprised Str, while Ste contained the
rest drugs in S. The number of drugs in each category for Str and
Ste is listed in column 3 and 4 of Table 1, respectively.
In addition, to test the generalization of the proposed method,
we extracted 59 drug compounds from Drugbank , which are
not in the benchmark dataset S. After excluding drug compounds
without information of chemical-chemical interactions, 44 drugs
were obtained, comprising the independent test dataset Site. The
number of drugs in each category of Site is listed in column 6 of
Table 1 and the detailed information of these drug compounds
including their codes and indications can be found in Table S2.
In recent years, the information of chemical-chemical
interactions is penetrating into the prediction of various attributions of
compounds [7,8,20]. The basic idea is that interactive compounds
are more likely to share common functions than non-interactive
ones. Compared with the information based on chemical
structure, it includes other essential properties of compounds,
such as compounds activities, reactions, and so on.
The information of interactive compounds was downloaded
from STITCH (chemical_chemical.links.detailed.v3.1.tsv.gz) .
In the obtained file, each interaction consists of two compounds
and five kinds of scores entitled Similarity, Experimental,
Database, Textmining and Combined_score. In details, the
first four kinds of scores are calculated based on the compound
structures, activities, reactions, and co-occurrence in literature,
respectively, while the last kind of score Combined_score
integrates the aforementioned four scores. Thus, it is used in this
study to indicate the interactivity of two compounds, i.e. two
compounds are interactive compounds if and only if the
combined_score of the interaction between them is greater than
zero. In fact, the value of combined_score also indicates the
strength of the interaction, i.e. the likelihood of the interactions
occurrence. Thus, it is termed as confidence score in this study.
For convenience, we denote the confidence score of the interaction
between c1 and c2 by S(c1,c2). In particular, if c1 and c2 are
noninteractive compounds, S(c1,c2) is set to zero.
112 drug compounds were investigated in this study as
described in Section Materials, and 1,393 chemical-chemical
interactions whose confidence scores were greater than zero were
obtained. Among the interactions which scores are greater than
zero, 50 of them belonged to the label Similarity, 4 belonged to
Experiment, 114 belonged to Database, and 1,352 belonged to
Textmining. It is necessary to point out that some drug
interactions had two or more than two kinds of scores. As far as
the quantity of chemical-chemical interactions is concerned, the
tag Textmining contributed most to the construction of the
prediction method described in Section The method based on
The method based on chemical-chemical
interactions. Systems biology has been applied extensively into
the predictions of properties of proteins and compounds and is
deemed to be more efficient than some conventional methods
[7,20,21,22]. In this study, we attempt to classify cancer drugs into
the aforementioned 8 categories based on chemical interactions.
Suppose there are n drugs in the training set S0, say
d1,d2, . . . ,dn. Cancers that dican treat is represented as follows:
Figure 1. The number of drugs plotted against the number of cancers they can treat in the benchmark dataset.
Number of drugs
test dataset Site
where T is the transpose operator and
For a query drug dq, which cancer it can treat can be determined
by its interactive compounds in S0. To evaluate the likelihood that
dq can treat cancer Cj, we calculated a score as follows:
j ~ 1, 2, 3, 4, 5, 6, 7, 8
Larger score of P(dq[Cj) indicates that it is more likely the
query drug can treat cancer Cj . And P(dq[Cj )~0 suggests that
the probability that the query drug can treat cancer Cj is zero,
because there are no interactive compounds in S0 that can treat
Cancers of the nervous system
Cancers of the digestive system
Cancers of haematopoietic and
Cancers of soft tissues and bone
Cancers of the urinary system and
male genital organs
Cancers of endocrine organs
Cancers of the lung and pleura
As mentioned in Section Materials, predicting which cancers
a drug can treat is a multi-label classification problem. A reliable
classifier should provide not only the most likely cancer but also a
series of candidate cancers, ranging from the most likely one to the
least likely one. According to the results of Eq. 3, it is easy to
arrange the candidate cancers using the decreasing order of the
corresponding scores. For example, if the results of Eq. 3 are:
it means that there are three candidate cancers of dq, where the
most likely cancer it can treat is C3, followed by C1 and C5.
Furthermore, C3 is called the 1st order prediction, and C1 is the
2nd order prediction, and so forth.
The Method Based on Molecular Descriptors
To compare our method with other methods, the method based
on molecular descriptors was constructed as follows. The structure
optimization of each drug compound was performed using the
AM1 semi-empirical method implemented in AMPAC 8.16 .
454 descriptors including constitutional, topological, geometrical,
electrostatic, and quantum-chemical descriptors were calculated
by Codessa 2.7.2 . To encode each drug compound
effectively, the descriptors with missing values were discarded,
resulting in 355 descriptors, i.e. each drug compound d can be
represented by a 355-D (dimension) vector which can be
formulated as follows:
where T is the transpose operator. Accordingly, the relationship of
two drugs d1 and d2 can be calculated by the following formula:
where D(d1):D(d2) is the dot product of D(d1) and D(d1), while
kD(d1)k and kD(d2)k is the modulus of D(d1) and D(d1),
Similar to the method based on chemical-chemical interactions,
the score that a query drug dq can treat cancer Cj can be
calculated by the following formula:
j ~ 1, 2, 3, 4, 5, 6, 7, 8
The rest procedure is the same as that of the method based on
chemical-chemical interactions, which also provides a series of
candidate cancers that dq can treat, ranging from the most likely
one to the least one.
Validation and Evaluation
Jackknife test is one of the most popular methods for evaluating
the performance of classifiers. During the test, each sample is
singled out one-by-one and predicted by the classifier trained by
the rest samples in the dataset. The test procedure is open, thereby
avoiding arbitrary problem . Therefore, the outcome obtained
by Jackknife test is always unique for a given dataset. In view of
where m represents the first m predictions that are taken into
consideration, Wi,m is the number of the correct predictions of the
i-th drug compound among its first m predictions, ni is the number
of cancers that the i-th drug compound can treat. It is easy to
deduce that Vm means the proportion of all true cancers that the
samples in the dataset can treat covered by the first m predictions
of each sample in it. It can be seen from Figure 1 that different
drug compounds may have different numbers of cancers they can
treat. In view of this, the parameter m in Eq. 10 usually takes the
value of the smallest but no less than the average number of
cancers that drug compounds in the dataset can treat. It can be
Results and Discussion
As described in Section Materials, the benchmark dataset S
was divided into a training dataset Str and a validation test dataset
this, many investigators have adopted it to evaluate the accuracies
of their classifiers in recent years [25,26,27,28,29].
As described in Section Prediction method, the methods in
this study can provide a series of candidate cancers for a given
query drug. The j-th order prediction accuracy is computed by the
following formula [7,8]:
where N is the total number of drugs in the dataset and hj is the
number of drugs such that their j-th predictions are the true
cancers that they can treat. It is obvious that ACCj measures the
quality of the j-th order prediction. If the true cancers that a query
drug can treat are positioned in low order, it is deemed as an
optimal predicted result. Thus, high ACCj with low order number
j and low ACCj with high order number j indicate a good
performance of the classifier. ACC1 is the most important
indicator of the performance of the classifier.
To evaluate the methods more thoroughly, we calculated the
prediction accuracy on cancer Cj for the i-th order prediction as
where Nj is the number of drugs that can treat cancer Cj in the
dataset and vi,j is the number of drugs such that its i-th order
prediction is correctly predicted to treating cancer Cj.
In addition, another measurement was taken, which was
adopted in some previous studies [6,7,8] and can be calculated
Ste, which contained 59 and 9 drugs, respectively. In addition, an
independent test dataset Site containing 44 drugs was constructed
to test the generalization of the method. The predicted method
introduced in Section The method based on chemical-chemical
interactions was used to make prediction. The detailed predicted
results are given as follows.
Performance of the Method Based on Chemical-chemical
Interactions on the Training Dataset
As for the 59 drugs in the training dataset Str, the predictor was
performed and evaluated by Jackknife test. Listed in column 2 of
Table 2 are the 8 prediction accuracies calculated by Eq. 8, from
which we can see that the 1st order prediction accuracy was
55.93%, while the 2nd order prediction accuracy was 22.73%. It is
also observed from column 2 of Table 2 that the prediction
accuracies generally followed a descending trend with the increase
of the order number, indicating that the proposed method
arranged the candidate cancers in the training dataset quite well.
In details, for each order prediction, we calculated the accuracies
of each kind of cancer according to Eq. 9, which were listed in
row 29 of Table 3. It can be seen that most of the 0.00%
accuracy occurred when the prediction order was high, indicating
that for each kind of cancer, it was better predicted with lower
order number of the predictions. The average number of cancers
which drugs in Str can treat was 1.31 (77/59), calculated by Eq.
11. It means that the average success rate would be only 16.38% if
ones make prediction by random guesses, i.e. randomly assign a
cancer indication to each sample, which is much lower than the 1st
order prediction accuracy obtained by our method. Because the
average number of cancers a drug can treat is 1.31, the first 2
order predictions of each sample in Str were taken to calculate the
proportion of true cancers that samples in Str can treat covered by
these predictions according to Eq. 10, obtaining a ratio of
Performance of the Method Based on Chemical-chemical
Interactions on the Validation Test Dataset
As for the 9 drugs in the validation test dataset Ste, their
candidate cancers were predicted by the method described in
Section The method based on chemical-chemical interactions
based on the information of the drugs in Str. 8 prediction
accuracies calculated by Eq. 8 were listed in column 3 of Table 2.
It can be seen that the 1st order prediction accuracy was 55.56%,
while the 2nd order one was 66.67%. It is also observed from
Table 2 that the prediction accuracies of this dataset were
generally higher than those of the training dataset, due to the fact
that drugs in Ste can treat two or more than two kinds of cancers,
while most drugs in Str can only treat one kind of cancers.
Similarly, we calculated the accuracies of each kind of cancer for
the 1st, 2nd, , 8th order prediction by Eq. 9. Row 1017 of
Table 3 listed them. The average number of cancers that drugs in
Ste can treat was 3.78 (34/9), indicating that if ones make
prediction by random guesses, the average success rate would be
47.22%, which is significantly lower than the 1st and 2nd order
accuracies listed in column 3 of Table 2. This suggests that the
performance of the method on the validation test dataset is fairly
good. Since the average number of cancers that drugs in Ste can
treat was 3.78, the first 4 order predictions of each sample in Ste
were considered. According to Eq. 10, 61.76% of true cancers
were correctly predicted by the first 4 order predictions.
Performance of the Method Based on Chemical-chemical
Interactions on the Independent Test Dataset
The candidate cancers of the 44 drugs in the independent test
dataset Site were also predicted by our predictor based on the drug
information in Str. 8 prediction accuracies were obtained and
listed in column 4 of Table 2, from which we can see that the 1st
order prediction accuracy was 59.09%, while the 2nd order
prediction accuracy was 29.55%. To better evaluate the method,
the prediction accuracies on each kind of cancer for the 8 order
predictions were calculated by Eq. 9 and listed in row 1825 in
Table 3. The average number of cancers that drugs in Site can
treat was 1.32 (58/44), suggesting that if ones make prediction by
random guesses, the average success rate would be 16.5%, which is
much lower than the 1st order prediction accuracy obtained by our
method. Because the average number of drug indications was
1.32, the first 2 order prediction of each sample in Site was
considered. According to Eq. 10, 67.24% of true cancers were
correctly predicted by the first 2 order predictions.
Comparison with other Methods
To indicate the effectiveness of our method for the prediction of
drugs cancer indications, some other methods were built to make
The method based on molecular descriptors described in
Section The method based on molecular descriptors was
conducted on Str with its performance evaluated by Jackknife
test. The 8 prediction accuracies calculated by Eq. 8 were listed in
column 2 of Table 4, from which we can see that the 1st order
prediction accuracy was 41.38%. It is much lower than the 1st
order prediction accuracy of 55.93% obtained by the method
based on chemical-chemical interactions. Also, for drugs in Ste and
Site, their cancer indications were predicted by molecular
descriptors on Str. The prediction accuracies were listed in column
3 and 4 in Table 4. In details, the 1st order prediction accuracy on
Ste and Site were 55.56% and 44.19%, respectively. Compared
with the prediction accuracies of 55.56% on Ste and 59.09% on
Site using chemical interactions, they performed at the same level
on Ste, and chemical interactions are much better than chemical
descriptors on Site. In addition, we considered the first 2-order,
4order and 2-order predictions on Str, Ste, and Site due to the
average number of cancers that drugs in these datasets can treat.
The proportion of true cancers that samples in Str, Ste, and Site can
treat covered by these predictions were 51.39%, 58.82% and
49.12%, respectively, which were all lower than the corresponding
proportions of 61.04%, 61.76% and 67.24%, respectively,
obtained by the method based on chemical-chemical interactions.
Therefore, the method based on chemical interactions was
superior to the method based on molecular descriptors.
As was described in the above three sections, the performance of
our method was much better than that of the random guesses,
which randomly assigned a cancer indication to a query drug.
Here, another random guesses method was applied to evaluate our
method from a different aspect. For any query drug dq, we
randomly selected a drug compound in the training set, say d, and
assigned true cancers that d can treat to dq, i.e. the predicted
cancers of dq were same as the true cancers that d can treat. Since
there is no order information in the predicted candidate cancers
for each sample, the measures provided by Section Validation
and evaluation cannot evaluate the performance of this method.
Thus, Recall and Precision [30,31] were employed to evaluate its
performance, which can be computed by.
where TPi is the number of correct predicted cancers for the i-th
drug compound, Ri represents the numbers of cancers which the
ith drug compound can treat, Pi represents the numbers of
predicted cancers for the i-th drug compound, and N is total
number of tested samples.
The random guess method described in the above paragraph
was conducted on Str with its performance evaluated by Jackknife
test. The Precision and Recall were 15.29% and 16.88%,
respectively. For the predicted results on Str by chemical-chemical
interactions, the 1st order prediction of each sample were picked,
obtaining Precision of 55.93% and Recall of 42.86%, which were
much higher than the random guess method.
It is easy to see that our method depend deeply on the
confidence scores of chemical-chemical interactions. To test the
importance of these scores, we randomly exchanged the
confidence scores of some interactions. Based on the random
permutations, the data were evaluated by Jackknife test on the
training dataset Str. The 1st order prediction accuracy was
23.73%, while the other prediction accuracies of 2nd, 3rd,,8th
order prediction were 18.64%, 11.86%, 18.64%, 20.34%,
15.25%, 13.56%, 8.47%, respectively. It is observed that the 1st
order prediction accuracy obtained by random permutation was
much lower than the 55.93% obtained by chemical interactions.
Furthermore, the 8 prediction accuracies were not followed a
descending trend with the increase of the order number, indicating
that the candidate cancers were not arranged well. This implicates
that confidence scores are very important to the predictions.
26 1st order predictions were wrong in the training dataset,
that is, the predicted cancer indications of these drugs were not
recorded in KEGG. These 26 drugs and their 1st order predictions
were available in Table S3. However, some references reported
that 23 of these 26 drugs were actually effective to their wrong
indications, and it was the same with 3 of the 4 drugs in the
validation test dataset (See Table S3 for the detailed 4 drugs and
their 1st order prediction) and 13 of the 18 drugs in the
independent test dataset (See Table S3 for detailed 18 drugs
and their 1st order prediction). Thus, we hope that our prediction
model can provide some information of drug repositioning. In the
following paragraphs, we cited some references to support our
Twenty-three Wrong Predicted Pairs of Drug and
Indication in the Training Dataset
Cisplatin-Cancers of haematopoietic and lymphoid
tissues. Cisplatin (KEGG ID: D00275), penicillin of cancer
drugs, is widely prescribed for many cancer treatments, such as
testicular, ovarian, bladder, lung, stomach cancers, and lymphoma
[32,33,34]. Prasad et al. investigated the effect of cisplatin on the
Daltons lymphoma, and concluded that cisplatin can induce
complete regression of ascites Daltons lymphoma in mice .
Ifosfamide-Cancers of haematopoietic and lymphoid
tissues. Ifosfamide (D00343) can be used to treat germ cell
testicular cancer, cervical cancer, small cell lung cancer,
nonHodgkins lymphoma, and so on . Extranodal natural killer/
T-cell lymphoma, nasal type (ENKL) is Epstein-Barr
virusassociated lymphoid malignancies, and patients with stage IV,
relapsed or refractory ENKL have dismal prognoses. Yamaguchi
et al. explored a new regimen SMILE, including the steroid
dexamethasone, methotrexate, ifosfamide, L-asparaginase, and
etoposide, and concluded that SMILE was effective for this kind of
Lomustine-Cancers of haematopoietic and lymphoid
tissues. Lomustine (D00363) is a component of the
combination chemotherapy for treating primary and metastatic brain
tumors, and also used as a secondary therapy for refractory or
relapsed Hodgkins disease . Moreover, previous studies
reported that lomustine can be considered for the treatment of
canine lymphoma in dogs [40,41,42,43], although it induced
common but not life-threatening toxicity .
Mitotane-Cancers of the urinary system and male genital
organs. Mitotane (D00420) is the first-line drug for metastatic
adrenocortical carcinoma [45,46,47], and also used for the
adjuvant therapy after removing the primary tumor .
However, mitotane treatment can induce some side effects, such
as adrenal insufficiency and male hypogonadism .
Procarbazine-Cancers of haematopoietic and lymphoid
tissues. Procarbazine (D00478) is used to treat human
leukemias . MOPP (mechlorethamine, oncovin, procarbazine, and
prednisone) is the first combination chemotherapy regimen for
treating Hodgkin lymphoma (HL) . And BACOPP regimen
(bleomycin, adriamycin, cyclophosphamide, vincristine,
procarbazine, and prednisone) improved both tolerability and efficacy of
older HLs, although it induced a high rate of toxic deaths .
Temozolomide-Cancers of haematopoietic and lymphoid
tissues. Temozolomide (D06067) is an oral alkylating agent
used for the treatment of anaplastic astrocytoma and glioblastoma
multiforme . Reni et al. reported that temozolomide was
effective for immunocompetent patients with recurrent primary
brain lymphoma, and its toxicity was negligible .
Thiotepa-Cancers of haematopoietic and lymphoid
tissues. Thiotepa (D00583) is an alkylating agent to treat
breast, ovarian, and bladder cancer . A regimen of
reducedintensity conditioning with thioteopa, fludarabine, and melphalan
produced remissions and a limited transplant mortality rate in
most multiple myeloma patients . Moreover, Kolb et al.
studied a phase II nonrandomized single-arm trial using TVTG
regimen (topotecan, vinorelbine, thiotepa, dexamethasone, and
gemcitabine) for relapsed or refractory leukemia, and reported
47% response rate of patients and acceptable toxicities .
Floxuridine-Cancers of the digestive system. Floxuridine
(D04197) is used to treat hepatic metastases of gastrointestinal
adenocarcinomas, and also used for palliation of cancers in the
liver and gastrointestinal tract . Moreover, hepatic arterial
infusion (HAI) can significantly enhance the antitumor activity of
floxuridine against colorectal liver metastases, as compared with
systemic infusion .
Carboplatin-Cancers of haematopoietic and lymphoid
tissues. Carboplatin (D01363) is approved with less side effects
compared with its parent compound cisplatin in the clinical
treatment, and mainly used to treat ovarian, lung, head cancers,
and so on . Through a phase II trial, Gopal et al. reported that
GCD (gemcitabine, carboplatin, dexamethasone, and rituximab)
was a safe and effective outpatient salvage regimen for relapsed
lymphoma . And Moskowitz et al. also reported that ICE
regimen (ifosfamide, carboplatin, and etoposide) was effective for
patients with non-Hodgkins lymphoma .
Epirubicin-Cancers of haematopoietic and lymphoid
tissues. Epirubicin (D02214) is a component of adjuvant
therapy in patients after resection of the primary breast cancer
. When used to treat chronic lymphocytic leukaemia, the
combination of fludarabine and epirubicin achieved a higher
response rate and a more rapid response, as compared with
fludarabine alone .
Gemcitabine- Cancers of haematopoietic and lymphoid
tissues. Gemcitabine (D01155) is a nucleoside analog that can
treat breast, non-small cell lung, and pancreatic cancer .
Moreover, a regimen including gemcitabine, carboplatin,
dexamethasone, and rituximab was reported to be effective for relapsed
Vinorelbine-Cancers of the breast and female genital
organs. Vinorelbine (D01935) is used to treat non-small cell
lung cancer . Aapro et al. explored the effects of vinorelbine on
metastatic breast cancer (MBC), and concluded that oral
vinorelbine was highly effective and well tolerated for patients
with MBC, no matter a single-agent or in combination with other
agents . Moreover, vinorelbine was also considered as a
promising alternative for older patients with advanced breast
cancers because of its clinical activity and low side effects .
Irinotecan-Cancers of the breast and female genital
organs. Irinotecan (D01061) is used to treat metastatic
colorectal cancer and extensive small cell lung cancer . Previous
studies reported that irinotecan was effective for the refractory
metastatic breast cancer after anthracyclines or taxanes treatment
[69,70]. Moreover, the combination of irinotecan and docetaxel
also achieved a high response rate in pre-treated advanced breast
cancer patients .
Capecitabine-Cancers of the breast and female genital
organs. Capecitabine (D01223) is an oral agent used for the
treatment of metastatic breast cancers, and toxicities are generally
Gefitinib-Cancers of the breast and female genital
organs. Gefitinib (D01977) is used for the continued treatment
of patients with locally advanced or metastatic non-small cell lung
cancer after failure of either platinum-based or docetaxel
chemotherapies . Moreover, gefitinib is the first selective
inhibitor of the epidermal growth factor receptor (EGFR) tyrosine
kinase, which controls cell proliferation by activating the Ras
signal transduction cascade . Thus, gefitinib may be a
promising agent used for the treatment of metaplastic breast
carcinoma with frequent expresses of EGFR .
Sorafenib-Cancers of the lung and pleura. Sorafenib
(D06272) is a multi-kinase inhibitor by targeting Raf/MEK/ER
pathway, and approved for the treatment of advanced renal cell
carcinoma and advanced hepatocellular carcinoma .
Blumenschein et al. reported that continuous treatment with sorafenib
400 mg twice daily helped disease stabilization of patients with
advanced non-small-cell-lung cancer, which is associated with
Paclitaxel-Cancers of the lung and pleura. Paclitaxel
(D05333) is used for the treatment of Kaposis sarcoma, lung
cancer, ovarian cancer, and breast cancer . Hensing et al.
explored the effects of carboplatin and paclitaxel (C/P) on elderly
patients with advanced non-small-cell-lung cancer, as compared
with younger patients. The study indicated that the survival rates
and quality-of-life of elderly and young groups are not different, so
C/P should be a reasonable regimen for elderly patients with this
kind of cancer .
Dacarbazine-Cancers of the breast and female genital
organs. Dacarbazine (D00288) is used to treat metastatic
malignant melanoma and Hodgkins disease . Moreover, the
regimen including cisplatin, adriamycin, and dacarbazine was
reported to be effective for patients with metastatic uterine and
ovarian mixed mesodermal sarcomas .
Sunitinib-Cancers of the breast and female genital
organs. Sunitinib (D06402) is an approved drug for the
treatment of renal cell carcinoma and imatinib-resistant
gastrointestinal stromal tumor . Moreover, previous study reported
that single-agent sunitinib achieved objective response rate of 11%
in MBC , and the combination of sunitinib and paclitaxel was
also well tolerated in patients with locally advanced or MBC .
Flutamide-Cancers of the breast and female genital
organs. Flutamide (D00586) is an antiandrogen for the
management of prostate carcinoma . Dimonaco et al. reported that
flutamide had an inhibitory effect on the growth of rat breast
Leucovorin-Cancers of the breast and female genital
organs. Leucovorin (D01211) is used to treat osteosarcoma
after high-dose methotrexate therapy . Moreover, a phase II
study showed that the regimen of weekly mitoxantrone,
5fluorouracil, and leucovorin (MFL) was well tolerated and
moderately effective to treat MBC . And a phase 3 trial of
eniluracil +5-fluorouracil+leucovorin in MBC is also ongoing .
Goserelin-Cancers of the breast and female genital
organs. Goserelin (D00573) is a luteinizing hormone blocker,
and reduces the oestrogen level. Thus, goserelin can improve the
long-term survival of premenopausal women with early breast
Fluorouracil-Cancers of haematopoietic and lymphoid
tissues. Fluorouracil (5-FU, D00584) is used to treat multiple
actinic and solar keratoses . Takeno et al. reported that a case
with advanced esophageal cancer accompanying multiple lymph
node metastases was successfully treated by the combination of
docetaxel, cisplatin, and fluorouracil .
Three Wrong Predicted Pairs of Drug and Indication in
the Validation Test Dataset
Dactinomycin-Cancers of haematopoietic and lymphoid
tissues. Dactinomycin (D00214) is an antineoplastic agent,
which can treat Wilms tumor and rhabdomyosarcoma .
However, it is reasonable to assume this compound for the
treatment of cancers of lymphoid tissues because it induced the
tumor regression of childhood lymphoma .
Mitomycin-Cancers of haematopoietic and lymphoid
tissues. Mitomycin (D00208) is an chemotherapy drug for
treating cancers of lip, oral cavity, digestive organ, and so on .
Mitomycin treated a case with localized conjunctival
mucosaassociated lymphoid tissue lymphoma, and had minimal local
controllable side effects . Moreover, mitomycin was about 5
times more potent than porfiromycin (methyl mitomycin) when
inhibiting the tumor growth in the lymphoma L1210 , but
M83 (7-N-(p-hydroxyphenyl)mitomycin) showed significantly higher
therapeutic activity than mitomycin in lymphoma EL4 .
Etoposide-Cancers of the breast and female genital
organs. Etoposide (D04107) is used to treat refractory testicular
tumors, small cell lung cancer, lymphoma, non-lymphocytic
leukemia, glioblastoma multiforme, and so on . Poplin et al.
reported that oral etoposide had a modest activity for chemonaive
patients with metastatic endometrial cancer, but the minimal
toxicity of this drug made it possible for the combination
chemotherapy . Moreover, etoposide was reported to be
one of the most effective agents for trophoblastic disease ,
and the combination of etoposide, ifosfamide/mesna, and cisplatin
(VIP) appeared to be active in advanced cervical cancer .
Thirteen Wrong Predicted Pairs of Drug and Indication in
the Independent Test Dataset
Diethylstilbestrol-Cancers of the breast and female
genital organs. Diethylstilbestrol (DrugBank ID: DB00255) is
used for the treatment of prostate cancer . Moreover,
Peethambaram et al. reported that diethylstilbestrol was more
effective than tamoxifen in postmenopausal women with MBC,
but this treatment was usually associated with toxicity such as
nausea, edema, vaginal bleeding, and cardiac problems .
Bleomycin-Cancers of the nervous system. Bleomycin
(DB00290) is a drug for the palliative treatment of malignant
neoplasm, such as lung cancers and lymphomas . Moreover,
Takeuchi et al. reported that bleomycin was effective for the
patients with gliomas, and the response rate was more than 50%
. And electrochemotherapy enhanced bleomycin uptake and
achieved 69% complete elimination of glial cell derived tumor cells
Bexarotene-Cancers of the lung and pleura. Bexarotene
(DB00307) is used orally to treat skin manifestations of cutaneous
T-cell lymphoma in patients after at least one prior systemic
therapy . Moreover, bexarotene was effective for preventing
the growth and progression of lung tumor in mice , and the
combination of bexarotene+paclitaxel or bexarotene+vinorelbine
had significantly greater antitumor effects than the single agent
Dexrazoxane-Cancers of haematopoietic and lymphoid
tissues. Dexrazoxane (DB00380) can reduce the incidence and
severity of cardiomyopathy associated with doxorubicin
administration in women with MBC . Moreover, dexrazoxane was
used as a cardioprotective agent that can attenuate the QT and
QTc dispersion associated with epirubicin-based chemotherapy in
patients with aggressive non-Hodgkin lymphoma , and
prevent or reduce cardiac injury associated with doxorubicin
administration for childhood acute lymphoblastic leukemia
Valrubicin-Cancers of haematopoietic and lymphoid
tissues. Valrubicin (DB00385) is used to treat bladder cancer
. Moreover, valrubicin was reported to inhibit the growth of
leukemia cells [117,118].
Zoledronate-Cancers of the breast and female genital
organs. Zoledronate (DB00399) is used for the treatment of
patients with multiple myeloma and bone metastases from solid
tumors when combining standard antitumor therapy .
Moreover, Steinman et al. reported that zoledronate increased
disease-free survival in postmenopausal and in premenopausal,
hormone-suppressed breast cancer patients, but had no antitumor
effect for premenopausal patients without ovarian suppression
Pemetrexed-Cancers of the digestive system. Pemetrexed
(DB00642) is used as a single agent to treat locally advanced or
metastatic NSCLC after a prior chemotherapy, and also used for
the treatment of adults malignant pleural mesothelioma in
combination with cisplatin . A phase II study reported that
pemetrexed disodium was effective for patients with advanced
gastric cancer, and the supplementation of folic acid decreased the
toxicity with no compromise in efficacy .
Fluoxymesterone-Cancers of haematopoietic and
lymphoid tissues. Fluoxymesterone (DB01185) is used for
the palliative treatment of androgenresponsive recurrent
mammary cancer in postmenopausal women with more than one year
but less than five years . Moreover, Bai et al. reported that
fluoxymesterone stimulated the proliferation and differentiation of
normal erythropoietic burst-forming units that are affected by
inhibitory factors produced by leukemic cells .
Genistein-Cancers of the lung and pleura. Genistein
(DB01645) is an experimental agent for the treatment of prostate
cancer . Moreover, Lian et al. reported that genistein may be
a promising agent to treat NSCLC because genistein induced
apoptosis of NSCLC cells by a p53-independent pathway .
Vorinostat-Cancers of the urinary system and male
genital organs. Vorinostat (DB02546) is used to treat skin
manifestations of cutaneous T-cell lymphoma patients with
progressive, persistent or recurrent disease on or after two systemic
therapies . Pratap et al. reported that vorinostat inhibited
tumor growth and associated osteolysis in the prostate cancer cells,
but increased normal bone loss .
Ixabepilone-Cancers of the digestive system. Ixabepilone
(DB04845) is investigated for the treatment of breast cancer, head
and neck cancer, lung cancer, and so on . Moreover,
ixabepilone was reported to be active against advanced or
metastatic gastric cancers [130,131].
Trabectedin-Cancers of the lung and pleura. Trabectedin
(DB05109) is used to treat soft tissue sarcoma and ovarian cancer,
and also investigated for the treatment of gastric cancer, and so on
. Moreover, Massuti et al. reported that trabectedin had
modest activity in NSCLC patients pretreated with platinum
Cabazitaxel-Cancers of the breast and female genital
organs. Cabazitaxel (DB06772) is used for the treatment of
hormone-refractory metastatic prostate cancer patients pretreated
with docetaxel . Moreover, Villanueva et al. reported that the
combination of cabazitaxel+capecitabine was active in patients
with MBC .
In this study, an order-prediction model for drugs and their
indications was built using the chemical-chemical interaction
information extracted from STITCH. The outstanding
performance of our model implicated that the model was feasible for
drug-indication prediction, i.e. it was more likely that interactive
chemicals would treat the same cancers than non-interactive ones.
Moreover, it was demonstrated that most of the wrong
predictions might actually right, which may help reposition drugs
to their new indications according to the prediction results.
List of drugs with wrong 1st order prediction.
Conceived and designed the experiments: LC MYZ YDC. Performed the
experiments: JL GH HPL KYF. Analyzed the data: JL HPL LC MYZ.
Contributed reagents/materials/analysis tools: GH KYF YDC. Wrote the
paper: JL GH KYF LC.
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