Abstract
The proneness of diseases and susceptibility towards drugs vary from person to person. At present, there is a strong demand for the personalization of drugs. The genetic signature behind proneness of the disease has been studied through a comprehensive ‘octopodial approach’. All the genetic variants included in the approach have been introduced. The breast cancer associated with BRCA1 mutation has been taken as the illustrative example to introduce all these factors. The genetic variants associated with the drug action of tamoxifen have been fully illustrated in the manuscript. The design of a new personalized anti-breast cancer drug has been explained in the third phase. For the design of new personalized drugs, a metabolite of anti-cancer drug chlorambucil has been taken as the template. The design of drug has been made with respect to the protein 1T15 of BRCA1 gene corresponding to the genetic signature of rs28897696.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aguilera O, Fernandez AF, Munoz A, Fraga MF (2010) Epigenetics and environment: a complex relationship. J Appl Physiol 109:243–251. doi:10.1152/japplphysiol.00068.2010
Anusooya NJ, Krishnapriya AS, Deepak OM et al (2014) Individual variation in p53 responsiveness: a pharmacogenomic approach. World J Pharm Pharm Sci 3:2059–2069
Beck T, Hastings RK, Gollapudi S et al (2014) GWAS central: a comprehensive resource for the comparison and interrogation of genome-wide association studies. Eur J Hum Genet 22:949–952. doi:10.1038/ejhg.2013.274
Bock C EpiGRAPH: searching genomes and epigenomes with machine learning technology.
Bock C, Walter J, Paulsen M, Lengauer T (2007) CpG island mapping by epigenome prediction. PLoS Comput Biol 3:e110. doi:10.1371/journal.pcbi.0030110
Butts C, Kamel Reid S, Batist G et al (2013) Benefits, issues, and recommendations for personalized medicine in oncology in Canada. Curr Oncol 20:475. doi:10.3747/co.20.1253
Chelala C, Khan A, Lemoine NR (2009) SNPnexus: a web database for functional annotation of newly discovered and public domain single nucleotide polymorphisms. Bioinformatics 25:655–661. doi:10.1093/bioinformatics/btn653
Davis AP, Grondin CJ, Lennon-Hopkins K et al (2015) The comparative toxicogenomics database’s 10th year anniversary: update 2015. Nucleic Acids Res 43:D914–D920. doi:10.1093/nar/gku935
Dennis J, Krewski D, Côté F-S et al (2011) Breast cancer risk in relation to alcohol consumption and BRCA gene mutations—a case-only study of gene-environment interaction: alcohol and breast cancer among BRCA Gene carriers. Breast J 17:477–484. doi:10.1111/j.1524-4741.2011.01133.x
Douville C, Carter H, Kim R et al (2013) CRAVAT: cancer-related analysis of variants toolkit. Bioinforma Oxf Engl 29:647–648. doi:10.1093/bioinformatics/btt017
Han X, Zheng T, Foss FM et al (2009) Genetic polymorphisms in the metabolic pathway and non-Hodgkin lymphoma survival. Am J Hematol NA–NA. doi:10.1002/ajh.21580
Hilakivi L, Clarke (2001) Estrogens, BRCA1, and breast cancer. Cancer Res 60:4993–5001
Hou L, Zhang X, Wang D, Baccarelli A (2012) Environmental chemical exposures and human epigenetics. Int J Epidemiol 41:79–105. doi:10.1093/ije/dyr154
Jain KK (2009) Molecular diagnostics as basis of personalized medicine. In: Textbook of personalized medicine. Springer New York, New York, pp 29–58
Jehan T, Lakhanpaul S (2006) Single nucleotide polymorphism (SNP)—methods and applications in plant genetics: a review. Indian J Biotechnol 5:435–459
Kozakov D, Grove LE, Hall DR et al (2015) The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins. Nat Protoc 10:733–755. doi:10.1038/nprot.2015.043
Kuo H-C, Lin P-Y, Chung T-C et al (2011) DBCAT: database of CpG Islands and analytical tools for identifying comprehensive methylation profiles in cancer cells. J Comput Biol 18:1013–1017. doi:10.1089/cmb.2010.0038
Lagunin A, Stepanchikova A, Filimonov D, Poroikov V (2000) PASS: prediction of activity spectra for biologically active substances. Bioinformatics 16:747–748. doi:10.1093/bioinformatics/16.8.747
Laing RE, Hess P, Shen Y et al (2011) The role and impact of SNPs in pharmacogenomics and personalized medicine. Curr Drug Metab 12:460–486
Lim E, Pon A, Djoumbou Y et al (2010) T3DB: a comprehensively annotated database of common toxins and their targets. Nucleic Acids Res 38:D781–D786. doi:10.1093/nar/gkp934
Liu X, Vogt I, Haque T, Campillos M (2013) HitPick: a web server for hit identification and target prediction of chemical screenings. Bioinformatics 29:1910–1912. doi:10.1093/bioinformatics/btt303
Madian AG, Wheeler HE, Jones RB, Dolan ME (2012) Relating human genetic variation to variation in drug responses. Trends Genet 28:487–495. doi:10.1016/j.tig.2012.06.008
Maryah Safi (2013) Computational modeling of drug resistance: structural and evolutionary models. University of Toronto
Namboori PKK, Vineeth KV, Rohith V et al (2011) The ApoE gene of Alzheimer’s disease (AD). Funct Integr Genomics 11:519–522. doi:10.1007/s10142-011-0238-z
Pavelka A, Chovancova E, Damborsky J (2009) HotSpot wizard: a web server for identification of hot spots in protein engineering. Nucleic Acids Res 37:W376–W383. doi:10.1093/nar/gkp410
Pruitt KD, Brown GR, Hiatt SM et al (2014) RefSeq: an update on mammalian reference sequences. Nucleic Acids Res 42:D756–D763. doi:10.1093/nar/gkt1114
Ryan M, Diekhans M, Lien S et al (2009) LS-SNP/PDB: annotated non-synonymous SNPs mapped to protein data Bank structures. Bioinformatics 25:1431–1432. doi:10.1093/bioinformatics/btp242
Sharma VK, Kumar N, Prakash T, Taylor TD (2012) Fast and accurate taxonomic assignments of metagenomic sequences using MetaBin. PLoS One 7:e34030. doi:10.1371/journal.pone.0034030
Stelzer G, Rosen R, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, Iny Stein T, Nudel R, Lieder I, Mazor Y, Kaplan S, Dahary, D, Warshawsky D, Guan-Golan Y, Kohn A, Rappaport N, Safran M, Lancet D (2016) The GeneCards suite: from gene data mining to disease genome sequence analysis. Curr Protoc Bioinformatics 54:1.30.1–1.30.33
Stenson PD, Mort M, Ball EV et al (2014) The human gene mutation database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Hum Genet 133:1–9. doi:10.1007/s00439-013-1358-4
Urbaniak C, Cummins J, Brackstone M et al (2014) Microbiota of human breast tissue. Appl Environ Microbiol 80:3007–3014. doi:10.1128/AEM.00242-14
Wu G, Robertson DH, Brooks CL, Vieth M (2003) Detailed analysis of grid-based molecular docking: a case study of CDOCKER-A CHARMm-based MD docking algorithm. J Comput Chem 24:1549–1562. doi:10.1002/jcc.10306
Acknowledgments
The author would like to express their deepest gratitude to Computational Chemistry Research Group of Amrita University for their valuable support. They would also express their sincere thankfulness to the Coconut Development Board (CDB), for funding.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflicting interest.
Rights and permissions
About this article
Cite this article
Iyer, P.M., Karthikeyan, S., Sanjay Kumar, P. et al. Comprehensive strategy for the design of precision drugs and identification of genetic signature behind proneness of the disease—a pharmacogenomic approach. Funct Integr Genomics 17, 375–385 (2017). https://doi.org/10.1007/s10142-017-0559-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10142-017-0559-7