Leaders in Pharmaceutical Business Intelligence (LPBI) Group

Pharmacogenomics – A New Method for Druggability

 

Author and Curator: Demet Sag, PhD

 

Living organisms have three main needs transfer of information, food and adopt to survive.  As a result translational medicine utilizes the “genetic information”, to correct health problems that are acquired or earned at birth.

Translational Pharmacogenomics relates to durable genome against diseases, complex, congenital, orphan- uncommon and infectious diseases.  Yet, there are caveats that need to be completed.

Hence, in this series I like to discuss clinical genomics, metabolomics and regulation of drugs by FDA to adopt what we need to what we can make.   This is the new terminology trio of life replacing genetics, food and adoption to survive.  Thus, genetics, genomics, bioinformatics, clinical research, clinical genomics and drugs make up part of the translational medicine in short this field classified under pharmacogenomics.

In 2003 there were two reviews on pharmacogenomics but there is a great jump in this area.

There are differences and similarities in genomics.  When drug-ability is combined we can have a “pharmacogenetics” that directly uses genetics information to diagnose or fix the disease. Thus, “intellectual” screening may involves sequencing the whole genome or using a sensor to detect the specific piece of genome.  This brings out the personalized medicine since each one of us has a unique genetic make- up.

Inconsistencies are part of the connection details where curation is the key to identify the real targets and eliminate the false targets for development of targets. There are inconsistencies that need to be identified.  This can be observed in detail at Haibe-Kains et al, Nature reported “Inconsistency in large pharmacogenomic studies (PMID 24284626) or recent

Anticoagulant therapy is a game of tight balancing for the sake of patient as there are not many drugs to control blood coagulation mechanism properly during the course of drug treatment. As a result, anticoagulant therapy is a plausible area to discover new drug by pharmacokinetics to replace well known warfarin.

Coumarins have a wide range of use in clinics with  a narrow therapeutic index drug with frequent hemorrhagic complications regardless of its dose adjustment because there are many clinical variables including age, gender, weight, nutritional factors, dietary vitamin K intake and interactive medications.

The ratio between cost and health outcomes is very important since the cost of warfarin adverse drug reactions is high and is estimated to exceed $180 billion dollars annually.

On the other hand, in average about 18-22 million dollar warfarin prescribed per year, in 2003 23 million, in 2006 19 million dollars etc.  However, coagulation, and consequently, warfarin dose, is influenced by many other factors both physiological and genetic.

There has been a clinical trial on warfarin treatment based on genotype guided  dosing but this study fail to present any improvement on anticoagulation control at the first 4 weeks of therapy,  NCT00839657. However, neither patients nor doctors knew about the warfarin doses in the study that included 1015 patients to receive doses of warfarin during the first 5 days of therapy or 28 days. The doses determined by reported clinical outcomes and genotypes.

On the other hand, a total of 455 patients were recruited, with 227 randomly assigned to the genotype-guided group and 228 assigned to the control group. The mean percentage of time in the therapeutic range was 67.4% in the genotype-guided group as compared with 60.3% in the control group (adjusted difference, 7.0 percentage points; 95% confidence interval, 3.3 to 10.6; P<0.001). There were significantly fewer incidences of excessive anticoagulation (INR ≥4.0) in the genotype-guided group. The median time to reach a therapeutic INR was 21 days in the genotype-guided group as compared with 29 days in the control group (P<0.001).

Higashi et al also reported that the CYP2C9*2 and CYP2C9*3 polymorphisms may increase over anticoagulation and of bleeding thus it is plausible to suggest that screening for CYP2C9 polymorphisms may help clinicians in two ways.  First improve dosing protocols.  Second, prevent the risk of adverse drug reactions in patients receiving warfarin.

On the other hand, vitamin K epoxide reductase complex 1 (VKORC1) at transcriptional gene regulation level may gauge warfarin doses. They suggest that if one has certain variants the warfarin dose changes low, medium and high based on transcriptional level VKORC1 gene expression.

Thus, Tuan study showed that VKORC1 promoter mutation to identify if this results in any changes for warfarin dosing among population. . They found that Chinese population requires smaller dose than the Caucasians because patients with the −1639 promoter polymorphism AA genotype had lower dose requirements, whereas the AG/GG genotypes had higher dose requirements.

Yet, another study tried to relate warfarin dosing based on genetic mutations,  CYP2C9 and  VKORC1.  However, the missing link of this study is excluding the SNPs and variations in other genes in the coagulation cascade that is affected by VKORC1 specially since VKORC1 play a role in vitamin K recycling and posttranslation that insures proper attachment of coagulation factors prothrombin, HFVII, HFIX and HFX.

Demographic variables	N = 495
SD, standard deviation; VKORC1, vitamin K epoxide reductase complex 1; CYP, cytochrome P 450. †The VKORC1 A haplotype can be detected by −1639 G > A.
Age, mean (SD), years	55 (13)
Gender
Female, n (%)	234 (47%)
Male, n (%)	261 (53%)
Race
Caucasian, n (%)	434 (88%)
African-American, n (%)	47 (9%)
Other, n (%)	14 (3%)
Hispanic ethnicity, n (%)	6 (1%)
Genetic variables
VKORC1 A haplotype† frequency	37.5%
CYP2C9*2 allele frequency	12.2%
CYP2C9*3 allele frequency	6.4%
Clinical variables
Geometric mean warfarin dose, mg per day, (SD)	4.4 (1.5)
Body surface area, in m2 mean (SD)	2.05 (0.27)
Smoker, n (%)	57 (12%)
Takes statin, n (%)	53 (11%)
Takes amiodarone, n (%)	0 (0%)
Takes aspirin, n (%)	97 (20%)
Table 1.   Demographic, genetic and clinical characteristics of participants

 

Figure 1.  Percentage of dose variation explained at weekly time points.

	Day 0 (%)	Day 7 (%)	Day 14 (%)	Day 21 (%)
Genetic	42.8	12.1	3.9	1.4
Clinical	10.8	6.4	2.2	1.9
INR	0	31.7	19.1	5.1
Prior dose	0	18.0	50.3	68.6
TOTAL	53.6	68.1	75.4	77.0
Table 2.   Percentage of dose variation explained (partial R2) at weekly time points

 

In summary,

we found that SNPs causing slower warfarin metabolism and increased warfarin sensitivity account for significant variability of therapeutic warfarin dose. These SNPs

are associated with increased risk of supratherapeutic INRs up to 28 days after initiation. However, the importance of genotype wanes over the initial weeks of therapy. Our findings

should prompt future studies to develop and assess the clinical utility of a day 7 pharmacogenetic dosing algorithm.

There are controversial studies or conflicting reports that needs to be elucidated with good bioinformatics tools as well as well done curation of available data.  After all the work CYP2C9 and VKORC1 genotypes defined as  key factors and about 30 to 40% of the total variation in the final warfarin dose.  Patients for variations in CYP2C9 and VKORC1 provide information to enhance clinical algorithms currently.

Clinicians should apply genomics tools for maintain anticoagulant therapy for their patients.

 

SOURCE

A pharmacogenetic versus a clinical algorithm for warfarin dosing.

Kimmel SE, French B, Kasner SE, Johnson JA, Anderson JL, Gage BF, Rosenberg YD, Eby CS, Madigan RA, McBane RB, Abdel-Rahman SZ, Stevens SM, Yale S, Mohler ER 3rd, Fang MC, Shah V, Horenstein RB, Limdi NA, Muldowney JA 3rd, Gujral J, Delafontaine P, Desnick RJ, Ortel TL, Billett HH, Pendleton RC, Geller NL, Halperin JL, Goldhaber SZ, Caldwell MD, Califf RM, Ellenberg JH; COAG Investigators.

N Engl J Med. 2013 Dec 12;369(24):2283-93. doi: 10.1056/NEJMoa1310669. Epub 2013 Nov 19. PMID:24251361

http://www.ncbi.nlm.nih.gov/pubmed/24251361

A randomized trial of genotype-guided dosing of warfarin

Pirmohamed M¹, Burnside G, Eriksson N, Jorgensen AL, Toh CH, Nicholson T, Kesteven P, Christersson C, Wahlström B, Stafberg C, Zhang JE, Leathart JB,Kohnke H, Maitland-van der Zee AH, Williamson PR, Daly AK, Avery P, Kamali F, Wadelius M; EU-PACT Group.

N Engl J Med. 2013 Dec 12;369(24):2294-303. doi: 10.1056/NEJMoa1311386. Epub 2013 Nov 19.

A novel functional VKORC1 promoter polymorphism is associated with inter-individual and inter-ethnic differences in warfarin sensitivity.

Yuan HY, Chen JJ, Lee MT, Wung JC, Chen YF, Charng MJ, Lu MJ, Hung CR, Wei CY, Chen CH, Wu JY, Chen YTHum Mol Genet 2005; 14: 1745–51.

Genotypes of the cytochrome p450 isoform, CYP2C9, and the vitamin K epoxide reductase complex subunit 1 conjointly determine stable warfarin dose: a prospective study.

Carlquist JF, Horne BD, Muhlestein JB, Lappe DL, Whiting BM, Kolek MJ, Clarke JL, James BC, Anderson JL.  J Thromb Thrombolysis 2006; 22: 191–7.

Further reading :

Abouzaid, S., Couto, J. E., & Royo, M. B. (2009). 58th annual meeting american society of human genetics, 2008. P T, 34(2), 92-94.

Bernhardt, B. A., Zayac, C., Gordon, E. S., Wawak, L., Pyeritz, R. E., & Gollust, S. E. (2012). Incorporating direct-to-consumer genomic information into patient care: attitudes and experiences of primary care physicians. Per Med, 9(7), 683-692. doi: 10.2217/pme.12.80

Chouchane, L., Mamtani, R., Dallol, A., & Sheikh, J. I. (2011). Personalized medicine: a patient-centered paradigm. J Transl Med, 9, 206. doi: 10.1186/1479-5876-9-206

Cooper, G. M., Johnson, J. A., Langaee, T. Y., Feng, H., Stanaway, I. B., Schwarz, U. I., . . . Rieder, M. J. (2008). A genome-wide scan for common genetic variants with a large influence on warfarin maintenance dose. Blood, 112(4), 1022-1027. doi: 10.1182/blood-2008-01-134247

Ensor, C. R., Cahoon, W. D., Crouch, M. A., Katlaps, G. J., Hess, M. L., Cooke, R. H., . . . Kasirajan, V. (2010). Antithrombotic therapy for the CardioWest temporary total artificial heart. Tex Heart Inst J, 37(2), 149-158.

LaSala, A., Bower, B., Windemuth, A., White, C. M., Kocherla, M., Seip, R., . . . Ruano, G. (2008). Integrating genomic based information into clinical warfarin (Coumadin) management: an illustrative case report. Conn Med, 72(7), 399-403.

Lewis, D. A., Stashenko, G. J., Akay, O. M., Price, L. I., Owzar, K., Ginsburg, G. S., . . . Ortel, T. L. (2011). Whole blood gene expression analyses in patients with single versus recurrent venous thromboembolism. Thromb Res, 128(6), 536-540. doi: 10.1016/j.thromres.2011.06.003

Ozdemir, V., Suarez-Kurtz, G., Stenne, R., Somogyi, A. A., Someya, T., Kayaalp, S. O., & Kolker, E. (2009). Risk assessment and communication tools for genotype associations with multifactorial phenotypes: the concept of “edge effect” and cultivating an ethical bridge between omics innovations and society. OMICS, 13(1), 43-61. doi: 10.1089/omi.2009.0011

Roth, J. A., Garrison, L. P., Jr., Burke, W., Ramsey, S. D., Carlson, R., & Veenstra, D. L. (2011). Stakeholder perspectives on a risk-benefit framework for genetic testing. Public Health Genomics, 14(2), 59-67. doi: 10.1159/000290452

Veenstra, D. L., Roth, J. A., Garrison, L. P., Jr., Ramsey, S. D., & Burke, W. (2010). A formal risk-benefit framework for genomic tests: facilitating the appropriate translation of genomics into clinical practice. Genet Med, 12(11), 686-693. doi: 10.1097/GIM.0b013e3181eff533

Wang, L., McLeod, H. L., & Weinshilboum, R. M. (2011). Genomics and drug response. N Engl J Med, 364(12), 1144-1153. doi: 10.1056/NEJMra1010600

Woo, K. T., Lau, Y. K., Yap, H. K., Lee, G. S., Choong, H. L., Vathsala, A., . . . Lim, C. H. (2006). 3rd College of Physicians’ lecture–translational research: From bench to bedside and from bedside to bench; incorporating a clinical research journey in IgA nephritis (1976 to 2006). Ann Acad Med Singapore, 35(10), 735-741.

 

Other articles on Pharmacogenomics published in this Open Access Online Scientific Journal include the following:

Pharmacogenomics for Cardiovascular Diseases

Blood Pressure Response to Antihypertensives: Hypertension Susceptibility Loci Study

Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2014/01/02/blood-pressure-response-to-antihypertensives-atenolol-and-hydrochlorothiazide-hypertension-susceptibility-loci-study/ 

Statin-Induced Low-Density Lipoprotein Cholesterol Reduction: Genetic Determinants in the Response to Rosuvastatin

Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2014/01/02/statin-induced-low-density-lipoprotein-cholesterol-reduction-genetic-determinants-in-the-response-to-rosuvastatin/

SNPs in apoE are found to influence statin response significantly. Less frequent variants in PCSK9 and smaller effect sizes in SNPs in HMGCR

Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2014/01/02/snps-in-apoe-are-found-to-influence-statin-response-significantly-less-frequent-variants-in-pcsk9-and-smaller-effect-sizes-in-snps-in-hmgcr/

Voltage-Gated Calcium Channel and Pharmacogenetic Association with Adverse Cardiovascular Outcomes: Hypertension Treatment with Verapamil SR (CCB) vs Atenolol (BB) or Trandolapril (ACE)

Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2014/01/02/voltage-gated-calcium-channel-and-pharmacogenetic-association-with-adverse-cardiovascular-outcomes-hypertension-treatment-with-verapamil-sr-ccb-vs-atenolol-bb-or-trandolapril-ace/

Response to Rosuvastatin in Patients With Acute Myocardial Infarction: Hepatic Metabolism and Transporter Gene Variants Effect

Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2014/01/02/response-to-rosuvastatin-in-patients-with-acute-myocardial-infarction-hepatic-metabolism-and-transporter-gene-variants-effect/

Helping Physicians identify Gene-Drug Interactions for Treatment Decisions: New ‘CLIPMERGE’ program – Personalized Medicine @ The Mount Sinai Medical Center

Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2013/04/15/helping-physicians-identify-gene-drug-interactions-for-treatment-decisions-new-clipmerge-program-personalized-medicine-the-mount-sinai-medical-center/

Leveraging Mathematical Models to Understand Population Variability in Response to Cardiac Drugs: Eric Sobie, PhD

Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2013/04/22/leveraging-mathematical-mod

els-to-understand-population-variability-in-response-to-cardiac-drugs-eric-s

obie-phd/ 

Is Pharmacogenetic-based Dosing of Warfarin Superior for Anticoagulation Control?

Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2014/02/04/is-pharmacogenetic-based-dosing-of-warfarin-superior-for-anticoagulation-control/