BioMed Research International

Volume 2014 (2014), Article ID 897653, 8 pages

http://dx.doi.org/10.1155/2014/897653

Research Article

Improved Candidate Drug Mining for Alzheimer’s Disease

Yu-Huei Cheng,¹ Li-Yeh Chuang,² Hsueh-Wei Chang,3,4,5,6 and Cheng-Hong Yang⁷

¹Department of Digital Content Design and Management, Toko University, Chiayi 613, Taiwan

²Department of Chemical Engineering, Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung 84001, Taiwan

³Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung 80708, Taiwan

⁴Translational Research Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan

⁵Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan

⁶Institute of Medical Science and Technology, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan

⁷Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 80778, Taiwan

Received 25 December 2013; Accepted 19 January 2014; Published 27 February 2014

Academic Editor: Wei Chiao Chang

Copyright © 2014 Yu-Huei Cheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Alzheimer's disease (AD) is the main cause of dementia for older people. Although several antidementia drugs such as donepezil, rivastigmine, galantamine, and memantine have been developed, the effectiveness of AD drug therapy is still far from satisfactory. Recently, the single nucleotide polymorphisms (SNPs) have been chosen as one of the personalized medicine markers. Many pharmacogenomics databases have been developed to provide comprehensive information by associating SNPs with drug responses, disease incidence, and genes that are critical in choosing personalized therapy. However, we found that some information from different sets of pharmacogenomics databases is not sufficient and this may limit the potential functions for pharmacogenomics. To address this problem, we used approximate string matching method and data mining approach to improve the searching of pharmacogenomics database. After computation, we can successfully identify more genes linked to AD and AD-related drugs than previous online searching. These improvements may help to improve the pharmacogenomics of AD for personalized medicine.

1. Introduction

Alzheimer’s disease (AD), the most common form of dementia, was first reported in 1906 [1]. In 2006, there were about 26.6 million AD patients worldwide and it was also common in southern Taiwan [2]. Although AD has been identified for a long time, most research progress was made in the recent 30 years [3]. However, no definitive cure is available for this disease and eventually it leads to death. Therefore, the drug discovery for Alzheimer’s disease remains challenging.

Single nucleotide polymorphisms (SNPs) are the most common variation in human genomes [4]. The importance of SNPs has been reviewed in genome-wide association studies for its association with disease susceptibility and drug metabolism [5, 6]. About 60–90% of the individual variation of drug response depends on pharmacogenomic factors. Therefore, SNP genotyping for candidate genes, pharmacological research, and drug discovery may play an increasingly important role in AD treatment. Meanwhile, increasing amounts of related information require the assistance of bioinformatics to construct the suitable databases and web servers.

Recently, PharmGKB (the Pharmacogenetics and Pharmacogenomics Knowledge Base) has been constructed to provide a comprehensive database for pharmacogenomic studies [7]. PharmGKB provides the pharmacogenetics research network in terms of SNP discovery and drug responses [8] with the fully curated knowledge for drug pathways, drug-related genes, and relationships among genes, drugs, and diseases. However, some information of different functions of PharmGKB is insufficient to allow convenient crosstalking between each other.

To solve this problem, we propose data mining method to improve the searching of pharmacogenomics of AD based on the download dataset of the PharmGKB resource.

2. Materials and Methods

The flowchart for pharmacogenomics in AD for personalized drug studies is shown in Figure 1. First of all, the AD-related drugs and genes are retrieved from PharmGKB download data using approximate string matching method and data mining approach. The genes associated with AD and the genes associated with a single Alzheimer’s drug are identified and compared with the online searching of PharmGKB. Then, numerous SNPs of genes associated with AD are identified. Through some SNP genotyping tools or assays, the association studies to AD-related drugs may be evaluated. Finally, the relevant information may be helpful for the personalized drug research.

Figure 1: The flowchart for PharmGKB-based pharmacogenomics of AD in this study.

2.1. AD-Related Drugs Using Approximate String Matching Based on PharmGKB Download Data

In order to study the pharmacogenomics of AD, we downloaded the known PharmGKB (the Pharmacogenetics and Pharmacogenomics Knowledge Base) (http://www.pharmgkb.org/downloads/) [9, 10] as source by the approximate string matching method [11] to find out all AD-related drug classes. The meaningful keywords associated with “Alzheimer’s disease” are shown in Table 1. Then, these found drug classes are used to find out associated genes by data mining approach. The description of the approximate string matching method for all AD-related drug classes gives a pattern string , that is, the meaningful keywords associated with “Alzheimer’s disease” and a text string , that is, the description for drug and disease retrieved from PharmGKB. Find a substring in that has the smallest edit distance [12] to the pattern . The pseudocode for the edit distance is shown in Algorithm 1.

Table 1: The meaningful keywords associated with “Alzheimer's disease” are retrieved from PharmGKB and they are applied to discover the drug classes*.

ID
Keywords

1
AD

2
Alzheimer's disease

3
AD—Alzheimer's disease

4
Acute Confusional Senile Dementia

5
Alzheimer Dementia, Presenile

6
Alzheimer Disease, Early Onset

7
Alzheimer Disease, Late Onset

8
Alzheimer Type Dementia

9
Alzheimer Type Senile Dementia

10
Alzheimer's Disease, Focal Onset

11
Alzheimer's disease, NOS

12
Dementia, Alzheimer Type

13
Dementia, Presenile

14
Dementia, Presenile Alzheimer

15
Dementia, Primary Senile Degenerative

16
Dementia, Senile

17
Dementias, Presenile

18
Dementias, Senile

19
Disease, Alzheimer

20
Disease, Alzheimer's

21
Early Onset Alzheimer Disease

22
Focal Onset Alzheimer's Disease

23
Late Onset Alzheimer Disease

24
Presenile Alzheimer Dementia

25
Presenile Dementia

26
Presenile Dementias

27
Primary Senile Degerative Dementia

28
Senile Dementia

29
Senile Dementia, Acute Confusional

30
Senile Dementia, Alzheimer Type

31
Senile Dementias

32
MeSH: D000544 (Alzheimer Disease)

33
MedDRA: 10001896 (Alzheimer's disease)

34
NDFRT: N0000000363 (Alzheimer Disease [Disease/Finding])

35
SnoMedCT: 26929004 (Alzheimer's disease)

36
UMLS: C0002395 (C0002395)

Drug class is one of the functions listed in the ParamGKB download data.

(1) // initialization

(2) for to do

(3)     

(4) end for

(5) for to do

(6)     

(7) end for

(8) // edit distance

(9) for to do

(10)     for to do

(11)          if(() = ()) then

(12)              

(13)          else

(14)              min  MIN[]

(15)                min + 1

(16)          end if

(17)     end for

(18) end for

(19) return

Algorithm 1: Pseudocode for the edit distance used for approximate string matching.

2.2. Data Mining Method for PharmGKB Download Data

In this study, we used a priori algorithm [13] for frequent item set mining and association rule learning over PharmGKB. The pseudocode for the a priori algorithm for data mining in PharmGKB is shown in Algorithm 2. At first, a priori algorithm has to find out the frequent gene in drug class for “Alzheimer’s disease.” A set of genes can be mined from each drug class. A priori algorithm is a “bottom up” approach, where frequent gene subsets are extended one item at a time (i.e., candidate generation) and groups of candidates are tested against the data. This algorithm is terminated when no further successful extensions are found.

(1) Apriori(PharmGKB, )

(2)   (frequent genes in drug class for Alzheimer’s disease)

(3) 

(4) while

(5)     

(6)     for each drug class PharmGKB

(7)         

(8)         for each candidate gene

(9)             countgene  countgene + 1

(10)          end for

(11)     end for

(12)     

(13)     

(14) end while

(15) return

Algorithm 2: Pseudocode for a priori algorithm for the data mining in PharmGKB, where is a support threshold, is the frequent gene subsets that satisfy the support threshold, is the number of current iterations, and is the candidate set, and countgene accesses a field of the data structure that represents gene candidate set.

2.3. SNP Searching for Genes Using the NCBI dbSNP

Every gene contains numerous SNPs. In order to find out SNPs of single gene for Alzheimer’s pharmacogenomics, NCBI dbSNP (http://www.ncbi.nlm.nih.gov/snp) is used to search in the study.

3. Results and Discussion

3.1. AD Information Based on PharmGKB Search

In PharmGKB online searching, the SNP variants, related genes, and drugs for AD are able to be retrieved. For example, the SNP information such as rs2066853 and rs6313 is provided (Figure 2). As shown in Figure 3, the AD-related genes such as ADRB1, AHR, HTR2A, MTHFR, and PTGS2 are identified and the related drugs such as olanzapine and risperidone are searched. This information may assist the researchers to study the pharmacogenomics of AD. Unfortunately, this PharmGKB online searching just provides limited information and it insufficiently copes with the complexity of the drug researches for Alzheimer’s personalized medicine.

Figure 2: PharmGKB-pharmacogenomics online query for the variant information (SNP rs#ID) of “Alzheimer’s disease.” Retrieval source: http://www.pharmgkb.org/disease/PA443319?previousQuery=Alzheimer’s%20disease.

Figure 3: Gene and drug related information of “Alzheimer’s disease” online query from PharmGKB. Retrieval source: http://www.pharmgkb.org/disease/PA443319?previousQuery=Alzheimer’s%20disease#tabview=table   3&subtab=33.

3.2. PharmGKB-Based Data Mining of AD Information of Drug Classes or Gene Symbols

In current study, our proposed method is used to perform data mining for PharmGKB download data in terms of the keyword “Alzheimer’s disease.” As shown in Table 2, 22 kinds of AD-related drug classes are identified from “drug classes” of PharmGKB. Their corresponding PharmGKB accession ID, PubMed PMID, and the number of genes that are associated with AD-related drug classes are also presented. In total, 495 genes are identified for AD information of drug classes (see Supplementary file 1: gene information includes PharmGKB Accession Id, gene symbol, and publications are providing in different classes; it is available online at http://dx.doi.org/10.1155/2014/897653). Alternatively, 99 genes associated with AD are identified from “gene symbols” of PharmGKB in terms of the keyword “Alzheimer’s disease.” These results suggest that the same keyword, for example, Alzheimer’s disease, may identify different numbers of AD-associated genes between “drug classes” or “gene symbols” of PharmGKB.

Table 2: PharmGKB-based data mining results in terms of the PharmGKB accession ID, drug class, publications, and the number of gene information of Alzheimer's disease.

No.
PharmGKB accession ID
Drug classes
Publications*1
Gene no.*2

1
PA164712423
Anticholinesterases
PMID: 20644562  20644562  14674789
6

2
PA164712308
Ace inhibitors, plain
PMID: 17362841
24

3
PA449515
Etanercept
PMID: 19027875
12

4
PA451262
Rivastigmine
PMID: 20644562  16323253  17082448 

20644562  15289797  17522596
2

5
PA450243
Lithium
PMID: 17082448
13

6
PA10384
Anti-inflammatory and antirheumatic products, nonsteroids
PMID: 17082448  17082448
11

7
PA449760
Glatiramer acetate
PMID: 17082448
4

8
PA133950441
Hmg coa reductase inhibitors
PMID: 17082448
39

9
PA151958596
Curcumin
PMID: 17082448
2

10
PA451898
Vitamin c
PMID: 17082448
16

11
PA451900
Vitamin e
PMID: 17082448
1

12
PA452229
Antidepressants
PMID: 17082448
43

13
PA452233
Antipsychotics
PMID: 17082448
46

14
PA449726
Galantamine
PMID: 20644562  16323253  17082448 

15853556  20644562  14674789  12177686
7

15
PA10364
Memantine
PMID: 17082448
0

16
PA451283
Rosiglitazone
PMID: 16770341
34

17
PA448031
Acetylcholine
PMID: 15695160
8

18
PA450626
Nicotine
PMID: 15695160
88

19
PA137179528
Nimesulide
PMID: 16331303  11810182
3

20
PA449394
Donepezil
PMID: 20859244  20644562  16323253 

16424819  17082448  20644562  1973817012142731
9

21
PA451576
Tacrine
PMID: 9521254  17082448  10801254 

9777427  18004213
6

22
PA448976
Choline
PMID: 8618881
122

PMID: PubMed article ID number.

*2The full gene names for each of the “drug classes” have been provided in the Supplementary file 1.

After detailed examination, 67 genes in the gene symbols searching (bold fonts of gene names as shown in Table 3) are absent from the genes in the drug class searching (Table 2). Furthermore, genes corresponding to the drug “memantine” listed in Table 2 (drug classes) are not found in Table 3 (gene symbols). Therefore, some current drugs have identified a small number of AD-related genes in the drug class searching; however, the remaining AD-related genes that may affect AD-related drugs may be partly discovered in the gene symbols searching. These novelly identified AD-related genes may be the potential candidates for further drug development of AD. These results demonstrated that our proposed data mining method may be an improved AD pharmacogenomics study.

Table 3: PharmGKB-based data mining results of gene symbols of Alzheimer's disease and NCBI dbSNP-based query results for SNP number for the genes of Alzheimer's disease.

No.
PharmGKB accession ID
Gene symbols*
SNP no.
No.
PharmGKB accession ID
Gene symbols*
SNP no.
No.
PharmGKB accession ID
Gene symbols*
SNP no.

1
PA20
ACHE
899
34
PA37597
ZNF225
813
67
PA125
CYP2C8
993

2
PA26490
CHRNA4
1518
35
PA38499
DEFB123
330
68
PA126
CYP2C9
1605

3
PA128
CYP2D6
482
36
PA134902026
SORCS2
19073
69
PA30864
MME
3323

4
PA130
CYP3A4
899
37
PA134949387
SORCS3
13969
70
PA142671271
NCSTN
741

5
PA26620
CLU
644
38
PA38274
TOMM40
462
71
PA36153
SST
120

6
PA26855
CR1
19859
39
PA162397694
NLRC5
2297
72
PA36457
TF
1501

7
PA33287
PICALM
3169
40
PA24641
AHR
991
73
PA31930
OPCML
28437

8
PA46
ALOX5
1992
41
PA134950706
DNMBP
3312
74
PA29561
HTR7
2623

9
PA293
PTGS2
579
42
PA24910
APP
9411
75
PA162393285
KIF20B
2109

10
PA108
CETP
1246
43
PA238
MAPT
4399
76
PA26971
CSRP3
907

11
PA32996
PCDH11X
15199
44
PA128394579
TMED10
1079
77
PA231
LMNA
1486

12
PA24507
ADAM12
10827
45
PA162397475
NGF
1286
78
PA27029
CTSD
460

13
PA25165
ATP8A1
5983
46
PA25232
BACE1
794
79
PA29629
IDE
2755

14
PA26243
CD86
1385
47
PA36022
SORL1
4394
80
PA31374
MYH7
1157

15
PA26935
CSF1
569
48
PA33796
PRNP
452
81
PA272
PLN
343

16
PA27342
DISC1
11813
49
PA37302
VEGFA
561
82
PA33855
PSEN1
2343

17
PA28597
GBP2
625
50
PA114
CHRNA7
3714
83
PA33856
PSEN2
959

18
PA220
KCNMA1
19081
51
PA37155
UBQLN1
1400
84
PA304
SCN5A
3380

19
PA25512
KCTD12
235
52
PA26123
CBS
924
85
PA36638
TNNT2
739

20
PA164724093
NOS2
1820
53
PA26976
CST3
233
86
PA139
ACE
1108

21
PA33614
PPP1R11
215
54
PA25623
C1QB
356
87
PA37935
SIRT1
1145

22
PA143485670
WWC1
5070
55
PA162380954
CALHM1
247
88
PA55
APOE
184

23
PA37596
ZNF224
490
56
PA30748
MEOX2
2140
89
PA24357
A2M
1385

24
PA162380963
CALHM2
192
57
PA26448
CHAT
2572
90
PA192
HTR1A
186

25
PA51
APOC1
243
58
PA38239
CLSTN2
15608
91
PA182
GSTM1
264

26
PA34958
ATXN1
11910
59
PA134952303
NMNAT3
39
92
PA183
GSTT1
200

27
PA26210
CD33
465
60
PA134904440
C1orf49
348
93
PA268
ABCB4
1915

28
PA28478
GAB2
5119
61
PA134864387
RALGPS2
3980
94
PA115
CHRNB2
698

29
PA34052
PVRL2
1344
62
PA134870196
RGSL1
3300
95
PA156
ESR1
10108

30
PA37754
ZNRD1
316
63
PA25294
BCHE
1796
96
PA134934259
GAPDHS
361

31
PA38114
TRIM15
466
64
PA120
CRP
977
97
PA245
MTHFR
790

32
PA134927803
MTHFD1L
7229
65
PA127
CYP2C18
1353
98
PA36458
TFAM
376

33
PA144596420
INTS1
1820
66
PA124
CYP2C19
2692
99
PA435
TNF
268

Gene names in bold fonts are not identified in Table 2.

3.3. SNP Information of AD-Related Genes

The SNP statuses for 99 AD-related genes are also provided in Table 3. This SNP status for each gene is calculated from the online NCBI dbSNP queries. In general, many SNPs are found in these AD-related genes. Some SNPs of these genes have been reported to be associated with AD. For example, the APOE gene is found in Table 3 and one of its SNPs, such as ApoE epsilon 4 allele, has been reported to be associated with AD [14]. With suitable tools for SNP genotyping, these SNP candidates are warranted for the pharmacogenomics research of AD.

Currently, there are many high throughput SNP genotyping methods developed (as shown in Figure 1), including PCR resequencing [15], TaqMan probes [16], SNP microarrays [17], Matrix Assisted Laser Desorption/Ionization-Time of Flight (MALDI-TOF) [18], and others [19, 20]. Furthermore, some SNP genotyping tools or databases are also developed, such as SNP-RFLPing2 for comprehensive PCR-RFLP information based on SNPs [21–24], algorithmic PCR-RFLP primer design and restriction enzymes for SNP genotyping [25, 26], and primer design for PCR-confronting two-pair primers (PCR-CTPP) [27, 28]. These tools and methods can provide useful and convenient information for SNP genotyping in the AD pharmacogenomics studies.

4. Conclusions

AD is the most common form of dementia for older people. The pharmacogenomics of AD still remains a challenge. In this study, we propose the pharmGKB-based data mining method to improve the gene discoveries for the potential AD-related drug candidates. With the assistance of bioinformatics, this improvement can help researchers to develop personal therapeutic drugs of AD.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This work is partly supported by the National Science Council (NSC) in Taiwan under Grant nos. NSC101-2622-E151-027-CC3, NSC101-2221-E-464-001, NSC101-2320-B-037-049, NSC102-2221-E151-024-MY3, NSC102-2221-E214-039, and NSC102-2221-E-464-004, by the National Sun Yat-Sen University-KMU Joint Research Project (no. NSYSU-KMU 103-p014), and by the Ministry of Health and Welfare, Taiwan (MOHW103-TD-B-111-05).

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