BioMed Research International

BioMed Research International / 2014 / Article
Special Issue

Pharmacogenomics in Personalized Medicine and Drug Metabolism

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Research Article | Open Access

Volume 2014 |Article ID 897653 | 8 pages | https://doi.org/10.1155/2014/897653

Improved Candidate Drug Mining for Alzheimer’s Disease

Academic Editor: Wei Chiao Chang
Received25 Dec 2013
Accepted19 Jan 2014
Published27 Feb 2014

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.

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.


IDKeywords

1AD
2Alzheimer's disease
3AD—Alzheimer's disease
4Acute Confusional Senile Dementia
5Alzheimer Dementia, Presenile
6Alzheimer Disease, Early Onset
7Alzheimer Disease, Late Onset
8Alzheimer Type Dementia
9Alzheimer Type Senile Dementia
10Alzheimer's Disease, Focal Onset
11Alzheimer's disease, NOS
12Dementia, Alzheimer Type
13Dementia, Presenile
14Dementia, Presenile Alzheimer
15Dementia, Primary Senile Degenerative
16Dementia, Senile
17Dementias, Presenile
18Dementias, Senile
19Disease, Alzheimer
20Disease, Alzheimer's
21Early Onset Alzheimer Disease
22Focal Onset Alzheimer's Disease
23Late Onset Alzheimer Disease
24Presenile Alzheimer Dementia
25Presenile Dementia
26Presenile Dementias
27Primary Senile Degerative Dementia
28Senile Dementia
29Senile Dementia, Acute Confusional
30Senile Dementia, Alzheimer Type
31Senile Dementias
32MeSH: D000544 (Alzheimer Disease)
33MedDRA: 10001896 (Alzheimer's disease)
34NDFRT: N0000000363 (Alzheimer Disease [Disease/Finding])
35SnoMedCT: 26929004 (Alzheimer's disease)
36UMLS: 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

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)   count gene  count gene  + 1
(10)   end for
(11) end for
(12)
(13)
(14)end while
(15)return

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.

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.


No.PharmGKB accession IDDrug classesPublications*1Gene no.*2

1PA164712423AnticholinesterasesPMID: 20644562  20644562  146747896
2PA164712308Ace inhibitors, plainPMID: 1736284124
3PA449515EtanerceptPMID: 1902787512
4PA451262RivastigminePMID: 20644562  16323253  17082448
20644562  15289797  17522596
2
5PA450243LithiumPMID: 1708244813
6PA10384Anti-inflammatory and antirheumatic products, nonsteroidsPMID: 17082448  1708244811
7PA449760Glatiramer acetatePMID: 170824484
8PA133950441Hmg coa reductase inhibitorsPMID: 1708244839
9PA151958596CurcuminPMID: 170824482
10PA451898Vitamin cPMID: 1708244816
11PA451900Vitamin ePMID: 170824481
12PA452229AntidepressantsPMID: 1708244843
13PA452233AntipsychoticsPMID: 1708244846
14PA449726GalantaminePMID: 20644562  16323253  17082448
15853556  20644562  14674789  12177686
7
15PA10364MemantinePMID: 170824480
16PA451283RosiglitazonePMID: 1677034134
17PA448031AcetylcholinePMID: 156951608
18PA450626NicotinePMID: 1569516088
19PA137179528NimesulidePMID: 16331303  118101823
20PA449394DonepezilPMID: 20859244  20644562  16323253
16424819  17082448  20644562  1973817012142731
9
21PA451576TacrinePMID: 9521254  17082448  10801254
9777427  18004213
6
22PA448976CholinePMID: 8618881122

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.


No.PharmGKB accession IDGene symbols*SNP no.No.PharmGKB accession IDGene symbols*SNP no.No.PharmGKB accession IDGene symbols*SNP no.

1PA20ACHE89934PA37597ZNF22581367PA125CYP2C8993
2PA26490CHRNA4151835PA38499DEFB12333068PA126CYP2C91605
3PA128CYP2D648236PA134902026SORCS21907369PA30864MME3323
4PA130CYP3A489937PA134949387SORCS31396970PA142671271NCSTN741
5PA26620CLU64438PA38274TOMM4046271PA36153SST120
6PA26855CR11985939PA162397694NLRC5229772PA36457TF1501
7PA33287PICALM316940PA24641AHR99173PA31930OPCML28437
8PA46ALOX5199241PA134950706DNMBP331274PA29561HTR72623
9PA293PTGS257942PA24910APP941175PA162393285KIF20B2109
10PA108CETP124643PA238MAPT439976PA26971CSRP3907
11PA32996PCDH11X1519944PA128394579TMED10107977PA231LMNA1486
12PA24507ADAM121082745PA162397475NGF128678PA27029CTSD460
13PA25165ATP8A1598346PA25232BACE179479PA29629IDE2755
14PA26243CD86138547PA36022SORL1439480PA31374MYH71157
15PA26935CSF156948PA33796PRNP45281PA272PLN343
16PA27342DISC11181349PA37302VEGFA56182PA33855PSEN12343
17PA28597GBP262550PA114CHRNA7371483PA33856PSEN2959
18PA220KCNMA11908151PA37155UBQLN1140084PA304SCN5A3380
19PA25512KCTD1223552PA26123CBS92485PA36638TNNT2739
20PA164724093NOS2182053PA26976CST323386PA139ACE1108
21PA33614PPP1R1121554PA25623C1QB35687PA37935SIRT11145
22PA143485670WWC1507055PA162380954CALHM124788PA55APOE184
23PA37596ZNF22449056PA30748MEOX2214089PA24357A2M1385
24PA162380963CALHM219257PA26448CHAT257290PA192HTR1A186
25PA51APOC124358PA38239CLSTN21560891PA182GSTM1264
26PA34958ATXN11191059PA134952303NMNAT33992PA183GSTT1200
27PA26210CD3346560PA134904440C1orf4934893PA268ABCB41915
28PA28478GAB2511961PA134864387RALGPS2398094PA115CHRNB2698
29PA34052PVRL2134462PA134870196RGSL1330095PA156ESR110108
30PA37754ZNRD131663PA25294BCHE179696PA134934259GAPDHS361
31PA38114TRIM1546664PA120CRP97797PA245MTHFR790
32PA134927803MTHFD1L722965PA127CYP2C18135398PA36458TFAM376
33PA144596420INTS1182066PA124CYP2C19269299PA435TNF268

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 [2124], 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).

Supplementary Materials

Gene information includes PharmGKB Accession Id, gene symbol, and publications are providing in different classes.

  1. Supplementary Tables

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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.


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