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Perception of the usefulness of drug/gene pairs and barriers for
pharmacogenomics in Latin America
Quiñones LA1, Lavanderos MA1, Cayún JP1, García-Martin E2, Agúndez JA3, Cáceres DD1,4, Roco AM1,5,
Morales JE6, Herrera L7, Encina G8, Isaza C9, Redal MA10, Laróvere LE11, Soria NW12, Eslava-Schmalbach
J13, Castañeda-Hernández G14, López-Cortés A15, Magno LA16, López M17, Chiurillo M18, Rodeiro I19, Castro
de Guerra D20, Terán E21, Estevez-Carrizo, F22, Lares-Assef I23.
1. Laboratory of Chemical Carcinogenesis and Pharmacogenetics, IFT, Molecular and Clinical
Pharmacology Program, ICBM, Faculty of Medicine, University of Chile, Santiago, Chile.
2. Department of Biochemistry, University of Extremadura, Cáceres, Spain.
3. Department of Pharmacology, University of Extremadura, Cáceres, Spain.
4. School of Public Health, Faculty of Medicine, University of Chile, Santiago, Chile.
5. Servicio Metropolitano de Salud Occidente, Santiago, Chile.
6. Dr. Luis Calvo Mackenna Hospital, Santiago, Chile.
7. Human Genetic Program, ICBM, Faculty of Medicine, University of Chile, Santiago, Chile.
8. Laboratorio de Oncología y Genética Molecular, Clínica Las Condes, Santiago, Chile.
9. Facultades de medicina de las universidades Tecnológica de Pereira y Autónoma de las Américas, Pereira,
Colombia.
10. Unidad de Medicina Molecular y Genómica-Hospital Italiano de Buenos Aires-CP1199, Argentina
11. CEMECO, Hospital de Niño, FCM, Universidad Nacional de Córdoba, Argentina.
12. Facultad de Ciencias Químicas - Universidad Católica de Córdoba - Campus Universitario, Córdoba -
Argentina.
13. Universidad Nacional de Colombia, Ciudad Universitaria, Facultad de Medicina, Bogotá, Colombia.
14. Centro de Investigación y de Estudios Avanzados del IPN, México, D.F., Mexico.
15. Instituto de Investigaciones Biomédicas. Universidad de las Américas, Quito, Ecuador.
16. INCT de Medicina Molecular, Faculdade de Medicina, Universidade Federal de Minas Gerais, Brazil.
17. Departamento de Sistemas Biológicos, División Ciencias Biológicas y de la Salud, Universidad Autónoma
Metropolitana-Xochimilco, México.
18. Universidad Centroccidental Lisandro Alvarado, Decanato de Ciencias de la Salud, Barquisimeto,
Venezuela.
19. Centro de Bioproductos Marinos (CEBIMAR), La Habana, Cuba.
20. Instituto Venezolano de Investigaciones Científicas, Centro de Medicina Experimental, Laboratorio de
genética Humana, Caracas, Venezuela.
21. Colegio de Ciencias de la Salud, Universidad San Francisco de Quito. Quito, Ecuador.
22. Center for Clinical Pharmacology Research, Italian Hospital. pc 11600 Montevideo. Uruguay
23. Instituto Politécnico Nacional, CIIDIR. Academia de Farmacogenómica y Biomedicina Molecular.
Durango, México.
Running Title: Drug/gene pairs and barriers for pharmacogenomics in Latin America
Key Words: biomarkers, adverse drug reactions, pharmacogenomics, clinical recommendations,
clinical relevance.
Corresponding author: Dr. Luis Quiñones S., Laboratory of Chemical Carcinogenesis and
Pharmacogenetics, IFT, Molecular and Clinical Pharmacology Program, ICBM, Faculty of Medicine,
University of Chile. E-mail: lquinone@med.uchile.cl, Phone number: 56-2-6817756, Fax number: 56-2-
6822406, P.O. Box 70111. Carlos Schachtebeck 299, Quinta Normal, Santiago, Chile.
ABSTRACT
Pharmacogenetics and Pharmacogenomics areas are currently emerging fields focused to manage
pharmacotherapy that may prevent undertreatment wh ile avoiding associated drug toxicity in patients. Large
international differences in the awareness and in the use of pharmacogenomic testing are presumed, but not
well assessed to date. In the pr esent study we review the awareness of Latin American scientific community
about pharmacogenomic testing and the perceived barriers for their clinical application. In order to that, we
have compiled information from 9 countries of the region using a structured survey which is compared with
surveys previously performed in USA and Spain.
The most relevant group of barriers was related to the need for clear guidelines for the use of
pharmacogenomics in clinical practice, followed by insufficient awareness about pharmacogenomics among
clinicians and the absence of regulatory institutions that facilitate the use of pharmacogenetic tests.
The higher ranked pairs were TPMT/thioguanine, TPMT/azathioprine, CYP2C9/warfarin,
UGT1A1/irinotecan, CYP2D6/amitriptiline, CYP2C19/citalopram and CYP2D6/clozapine. The lower ranked
pairs were SLCO1B1/simvastatin, CYP2D6/metoprolol and GP6D/chloroquine. Compared with USA and
Spanish surveys, 25 pairs were of lower importance for Latin American respondents. Only
CYP2C19/esomeprazole, CYP2C19/omeprazole, CYP2C19/celecoxib and G6PD/dapsone were ranked higher
or similarly to the USA and Spanish surveys.
Integration of pharmacogenomics in clinical practice needs training of healthcare professionals and
citizens, but in addition legal and regulatory guidelines and safeguards will be needed. We propose that the
approach offered by pharmacogenomics should be incorporated into the decision-making plans in Latin
America.
RATIONALE FOR THIS STUDY
It is well known that the efficacy and safety of drug therapy show substantial inter-individual
variability which is based on genetic variations affecting pharmacokinetic and/or pharmacodynamic factors
[1]. But it is also known that there are non-genetic factors affecting drug response, for example age, sex,
organ function, concomitant therapies, drug interactions, evolution of disease, nutritional factors, smoking,
alcohol, the presence of virus, among others. We see that the ineffectiveness or toxicity of drug therapy is due
to the interaction of genes with environmental factors. A drug that is well tolerated and causes a strong
response in some patients may be ineffective, toxic or may cause adverse drug reactions in other patients. In
fact, it has been reported that 1 in 15 hospital admissions in the United Kingdom are due to adverse drug
reactions [2] and that adverse drug effects in hospitalized patients are the fifth leading cause of death in the
United States [3]. It has been reported that approximately 2 million adverse drug reactions lead to a spending
of U$100 billion annually [4].
Variability in drug metabolism and response is not a new idea. In 1892 Sir William Osler pointed out
“If it were not for the great variability among individuals, medicine might as well be a science, not an art" and
in 1895 Dr. Claude Bernard said “the excessive complexity of the physiological processes and organisms
wher e are observed commands respect by saying that no two patients are alike”. Both ancient sentences
reinforce the idea that it is necessary not to treat illnesses but ill people.
In this sense, it is known that pharmacokinetic factors affecting absorption, distribution,
biotransformation and excretion influence the plasma and tissue concentration reached by drugs. Therefore,
polymorphism of genes encoding drug transporters, biotransformation enzymes and drug targets will
influence drug efficacy and safety. Accordingly, the current practices for the dosing of therapeutic agents
should be improved through the understanding of gene variation associated with “drug life” inside the body.
Therefore, in order to be able to predict patient’s predispositions to treatment complications and poor outcome
it is essential to examine all candidate loci influencing response to drugs. We should also investigate
metabolic pathways for activation or inactivation of drugs, the interaction between drugs, age and gender
sensitivities, the impact of ethnicity and environmental factors to understand the individual and population
variability in drug response.
Pharmacogenetics and Pharmacogenomics areas are currently emerging fields focused to manage
pharmacotherapy that may prevent undertreatment while avoiding associated drug toxicity in patients. Large
international differences in the awareness and in the use of pharmacogenomic testing are presumed, but not
well assessed to date. In the present study we review the awareness of Latin American scientific community
about pharmacogenomic testing and the perceived barriers for their clinical application. A major focus of
current research entails clinical evaluation of polymorphisms on their impact on gene function.
There has been an increase in the number of research articles and clinical trials of
pharmacogenomics/pharmacogenetics studies since 1961, just after the German pharmacologist Friedrich
Vogel (1959) [5] coined the term pharmacogenetics. As it is observed in Figure 1 from Vogel’s definition the
number of publications has constantly increased, especially in the last 15 years, concomitantly the
development of pharmacogenomics has evolved.
While the most conservative use of pharmacogenomics aims to stratify patient populations into poor,
extensive, intermediate and rapid/ultrarapid metabolizers, which leads to a selection criteria for those who
should or should not receive a given drug [6], other researchers promote guidelines intended to adjust drug
dosage based on pharmacogenomics tests [7]. Of course, in both cases, pharmacogenomic testing could be
more useful in outlier patients.
In the present work we review and analyze the knowledge of Latin American scientific and clinical
community about pharmacogenomic testing and the barriers for their clinical application. In order to that, we
have compiled the information of a number of countries of the region (Figure 2) using a structured survey
which is compared to the USA and Spanish surveys previously performed [8, 9].
The survey was structurated into two items, first, a list of 15 potential barriers to the clinical
application of biomarkers were evaluated in terms of their importance on the scale of 1 to 10 (10 being the
highest) (Figure 3). Second, a list of 51 gene/drug pairs were evaluated on the scale of 1-5 (5 being the
highest) to ascribe association between biomarkers and their response to genomic medicine (Figure 4).
Using the Scopus database and several key words related to pharmacogenics/pharmacogenetics we
searched for potential respondents of the survey in Latin America. This search was based on papers published
between 1990 to 2013. The used keywords were: polymorphisms, pharmacogenetics, pharmacogenomics,
biomarkers, adverse drug reactions, clinical pharmacology. We searched for clinicians and biomedical
researchers from all Latin American countries. Our search yielded 44 potential respondents from 13 countries.
We did not find any potential respondent from Antigua and Barbuda, Belize, Bolivia, Dominican Republic, El
Salvador, Guiana, Guatemala, Nicaragua, Panama, Paraguay, Peru, Suriname and Trinidad and Tobago, and
even though we contact researchers from Bolivia, Costa Rica, Haiti and Honduras they did not respondr the
survey. Therefore, the results were obtained from 20 respondents from 9 countries, Argentina, Brazil, Chile,
Colombia, Cuba, Ecuador, Mexico, Uruguay and Venezuela. The response rate was 45.45%.
The profile of the participants was as follows: 60% medical doctors, 20% biochemists, and a
miscellaneous group comprising one pharmacist, one veterinary doctor, one biologist and one anthropologist,
all of them working in the field of pharmacology. 20% of the participants were affiliated to clinical centers,
45% were affiliated to universities, 30% were affiliated to clinical centers associated with academic activities
in universities and 15% were affiliated to research centers. We were not able to find any participants affiliated
to the pharmaceutical industry.
Figure 3 summarizes the results about the perceived importance of barriers for implementing the use
of pharmacogenomics testing in clinical practice. Our results showed three major groups of barriers. The most
commom group was related to the need for clear guidelines for the use of pharmacogenomics in clinical
practice (8.76 points on a scale of 1 to 10). The second was the insufficient awareness about
pharmacogenomics among clinicians (8.52) and the third group was the absence of a regulatory institution
that facilitates the use of pharmacogenetic tests (8.47). As expected, all three barriers are closely related.
(8.33 and 8.14, respectively). We believe that these are the barriers specific to Latin America, taking account
that in the Spanish survey these are lower ranked barriers. Moreover, both, Spanish and Latin America
surveys consider that ethical, legal and social implication are not important barriers as they were evaluated
with the lowest importance. We believe that the insufficient awareness of the clinicians leads to the lack of
regulatory institutions and guidelines.
Because most of the main barriers were related to lack of clinical guidelines and protocols, we
included in our survey the same 29 gene/drug pairings a criteria listed in two previous studies [8,9] which
were obtained from members of the Spanish Societies of Pharmacology and Clinical Pharmacology, the
Clinical Pharmacogenetics Implementation Consortium (CPIC; see http://www.pharmgkb.org/page/cpic) and
members of the American Society for Clinical Pharmacology and Therapeutics. This was in order to obtain
comparative results. However we also include 22 additional gene/drug pairs selected from the recently
published list of pharmacogenomic biomarkers by FDA (2013) [10] (Figure 4).
Data related to the percentages of respondents (frequencies) shown in figure 4 were defined as
respondents who ranked the gene/drug pairs as 3, 4 or 5 in relation to the total responses (on a scale of 1–5)
are plotted along the y-axis. We used scale of 1 to 5 for gene/drug pair evaluation to make results comparable
to those from the Spanish and US survey [8,9].
Based on the survey results, the perceived importance of the data linking the drug to the gene
variation the higher ranked pairs were TPMT/thioguanine, TPMT/azathioprine and CYP2C9/warfarin, with a
very close ranking for UGT1A1/irinotecan (Figure 4A and 4B). From the additional 22 gene/drug pairs
CYP2D6/amitriptiline, CYP2C19/citalopram and CYP2D6/clozapine wer e the higher ranked pairs (Figure
4C), wh ile the lower ranked pairs were SLCO1B1/simvastatin, CYP2D6/metoprolol and GP6D/chloroquine.
In comparison with USA and Spanish surveys from the 29 pairs, 25 were of lower importance to the Latin
American and the Caribbean respondents. Only CYP2C19/esomeprazole, CYP2C19/omeprazole,
CYP2C19/celecoxib and G6PD/dapsone were ranked higher or similar to the USA and Spanish surveys. We
believe this is mainly due to insufficient scientific information available in our countries giving rise to an
underestimation of the importance of gene/drug pairs. The higher ranked pairs in the US study were
CYP2C9/warfarin, UGT1A1/irinotecan, VKORC1/warfarin, and for the Spanish study were HLA-B/abacavir,
UGT1A1/irinotecan and CYP2D6/tamoxifen. Therefore, UGT1A1/irinotecan seems to be very important in
all analyzed countries and the importance of CYP2C9/warfarin seems to be ratified. On the other hand
CYP2D6/amitriptiline, CYP2C19/citalopram and CYP2D6/clozapine, the higher ranked additional pairs are
related to psychiatric drugs giving rise to the idea that in the Latin American countries the variability in the
response to these drugs (e.g. antidepressants) is fairly important. Dramatically lower ranked pairs in this study
(LAC) in comparison with the US and Spain studies (less than half the importance in the evaluation) were
SLCO1B1/simvastatin and DPYD/5-fluorouracil (gene/drug pairs). We have no an explanation for this result
and neither for the low(er) ranking of SLCO1B1/simvastatin, CYP2D6/metoprolol and GP6D/chloroquine in
this study.
Some limitations of this survey must be pointed. First, the poor developed pharmacogenomic research in this
region does not allow us to have a large number of participants, thus not all Latin American countries
participated in the survey. This can question the representativeness of the survey. However, we are confident
enough that the study participants from each country are the most knowledgeable people in the field of
pharmacogenomics. Second, the importance of the gene/drug pairings in different countries could be
evaluated differently due to the absence of some drugs in the market according to drug acquisition policies of
each Ministry of Health.
DICUSSION AND FUTURE PERSPECTIVES
Since the completion of the human genome project and its potential ability to change the practice of
medicine, great expectations and enthusiasm regarding possible applications were positioned in the scientific
community. Together, millions of SNPs have been identified [11] and the effects of each specific SNPs are
still under study [12]. However, human genome sequence defined how similar people are (99.9%) and not
how different they are. Thus, actually many researchers believe that pharmacogenomics can be one of the first
successes in the study of individual differences, at least in relation to drug response.
The ultimate goal of pharmacogenetic research is to predict individual’s responses to drug therapy
and subsequently to adapt the therapeutic strategy. In this regard, it is estimated that gene polymorphisms
account for 20 to 95% of the variability in therapeutic response and toxicity [13]. Of all the known drugs
involved in adverse reactions about 80% are metabolized by polymorphic enzymes [14]. Since 2004, several
drugs refer to this study in the labeling information, some of them considered sufficient to guide decision-
treatment decisions [15]. In 2005 the FDA issued a guidance document for the industry about the referral data
for genotyping drug metabolizing enzymes. Some authors estimate that in the next 5-10 years, 10 to 20% of
new drugs approved will include genetic study. In 2005 the FDA approved the marketing of the first
laboratory test system based on cytochrome P450 genotypes [16], which allows the use of genetic information
to select appropriate doses of drugs and drugs for a wide variety of common conditions. However, in Latin
America the test seems to have suboptimal results, which could be due to ethnic differences between Latin
Americans and other human populations.
Currently, the FDA recommends more than 100 drugs for pharmacogenomic monitoring to improve
prescription dosage including antivirals, antibiotics, psychiatry drugs, analgesic and anticancer agents (FDA,
2013)[10]. In some cases this information has been incorporated into the dataset long after the drug was
approved by regulatory agencies. In other cases, pharmacogenetic data has been obtained during the process
of drug development and has been taken into account for approval.
Therefore, the current challenge for personalized therapy is to define genetic profiles to predict the
response to drugs and the progression of the diseases [9, 17, 18, 19, 20, 21]. Information to address this
challenge can only be obtained from case-control and prospective studies with a pharmacogenomics basis.
Despite the enormous amount of known information about the genetic basis of variable response to
drugs, it has little influence on its application to the current clinical practice. Thus, acceptance of
pharmacogenomic studies in medical practice is gradual. Several issues have prevented its rapid
implementation, such as, a) lack of readily available clinical laboratories which can perform these tests
quickly and cost-effectively, b) shortage of health care professionals who can interpret the test data and
associated clinical pharmacology and c) doubts whether insurance companies will pay for thie study. In
addition, many ethical questions pose continuing challenges. However, the number of drugs approved with a
reference to the genetic study in labeling information is increasing.
Of course pharmacogenomics has several limitations to its application in clinical practice which
should be addressed, some of them have been analyzed previously by Agúndez et al [20]. These limitations
include the lack of sufficient evidence for cost–efficiency, the need for the identification of new biomarkers
for drug toxicity and response, technical limitations and ethnicity questions. Together, we know that inter-
individual variability to drug response exists, even in individuals with identical pharmacogenomic profile,
giving rise to the idea that pharmacogenomics is only one of the several factors to be considered in dose
adjustment. Therefore algorithms including anthropometric, lifestyle and environmental factors appear to be
the best approach.
Another restriction for the use of pharmacogenomics is the poor information about pharmagenes in
Latin America populations, which prevents direct extrapolation of the dosage of drugs with clinical studies
performed in other ethnic groups. Since profound variation in the effect of drugs have been described to be
associated to the genetic polymorphisms in diverse populations, ethnicity appears to be an important issue in
Latin America. In this sense, in order to have a first approach, particularly in American Hispanic populations,
we have previously discussed the implications of interethnic and intraethnic genetic variability [22, 23, 24, 25,
26]. In this respect, it is clear that there is a need for developing more and well designed studies in Latin
Americanpopulations to better address the issue that the introduction of pharmacogenomics in clinical
practice. These studies should include ethnic comparison of pharmacogenomic profiles, the impact of
polymorphism on phenotype, gene expression and regulation, metabolic profiles of patients with a given drug
and relevant environmental factors that influence drug response.
Clinical practice guidelines and protocols may help to overtake the major groups of barriers shown in
Figure 3 and, in consequence, this will hopefully lessen the impact of the first ranked and the most
determinant barrier. Similarly, we believe that governmental support and promotion for the use of
pharmacogenomics biomarkers in the countries of this region will greatly influence the relevance of the other
barriers.
The healthcare professionals (prescribers, insurers and regulators) will want to know if there is a
substantial impact of pharmacogenomics on the safety and efficacy of the drug on an individual. Of course,
before use in the clinical routine selected pharmagenes must demonstrate, in retrospective and prospective
studies, a value sufficient to have good cost-effectiveness.
Pharmacology of the future intends to conduct individualized pharmacotherapeutic treatment for the
manifestation of a disease and the appropriate dose for the therapeutic effect in a given patient, minimizing
the risk of adverse reactions. Therefore the main idea is the accomplishment of the five “R” for drug therapy
“the Right dose of the Right drug for the Right indication in the Right patient at the Right time”. For instance,
nowadays the individualized treatments are a pressing need. The current formula of standard
pharmacotherapy is not ideal according to the great variability between patients.
The rapidly evolving field of pharmacogenetics holds great promise for assisting the selection of
patient-individualized treatment regimens and dosages. A vast number of single nucleotide polymorphisms
have been discovered in genes thought to be involved in the regulation of drug metabolism; however,
relatively few studies have been conducted that establish a link between genotype, efficacy and safety of
drugs.
In short, integration of pharmacogenomics in clinical practice needs training of healthcare
professionals and citizens, moreover legal and regulatory guidelines and safeguards will be needed. The
answers to the question of which patient should receive which drug and dose will be not easy, but we believe
that the approach offered by pharmacogenomics should be incorporated into the decision-making process. A
more rational use of expensive treatment drugs together with actions to minimize patient toxic events and its
consequences, would dramatically reduce medical costs, as an added benefit.
Financial Support:
The work in the author’s laboratory have been financed by Grants FONDECYT 3020043, DI, U. de Chile nº
1102-002, Chile; PS09/00943, PS09/00469, PI12/00241, PI12/00324 and RETICS RD12/0013/0002 from
Fondo de Investigación Sanitaria, Instituto de Salud Carlos III, Spain, and GR10068 from Junta de
Extremadura, Spain. Financed in part with FEDER funds from the European Union.
Acknowledgements: The authors wish to thank to Dr. Marcia Llacuachaqui, from the Women’s College
Research Institute, Women's College Research Hospital , Canada, for her assistance in language editing.
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Conflict of Interest Statement: The authors declare that do not have any potential conflict of interest.
FIGURE 1: Variation in number of publications [Scopus] and clinical trials [27] including pharmacogenomics/pharmacogenetics studies from
1961.
12410 8 8 30 51 30 39 43 31 57 75 58 45 48 82 64 51 38 62 58 54 59 93 70 43 69 55 56 60 61 55 76 71 96 148
262
445
637
846
1045
1154
1427
1521
1608
1891
1768
1928
1890
1985
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
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1977
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1980
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1983
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1987
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1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Publications
22487
70 61 64
95 95
123
92 83
Clinical trials
Figure 2: Latin American countries participating in the survey (in gray).
Figure 3. Highest-ranking barriers for implementing the use of pharmacogenomics testing, based on a survey in Latin American Scientific and
Clinical Researchers. Data related to average importance on a scale of 1 (low) to 10 (high) + standard deviation are plotted along the X-axis.
Figure 4. Highest- ranking gene/drug pairs, based on the survey of Latin American scientific and
clinical community, compared to the published survey of Spanish Societies for Pharmacology
and Clinical Pharmacology members in 2012 and the American Society for Clinical Pharmacology
and Therapeutics (ASCPT) members conducted by CPIC. Data related to the percentages of
respondents who ranked the gene/drug pairs as 3, 4, and 5 in relation to the total evaluations
(on a scale of 1(low)–5(high)) are plotted along the Y-axis.
A
B
Percentage of Respondents
C
36
27
13
93
46 46 50
20
36
70 72
80
92
59
41 46
35
82
69
57 59
26
39
69
83
65
90 89
40
19 25
39 33 39 43
25
15
47 43 50 46 43
40
50
27
36
54
67
54
42 42 38
25
67
82
63 63
25
79
21 28 25
90
65
42
55
43
27
42
87
57
48
23
39
29 32 27
47 42
29 31 35
17
30
42
25
35
Span ish surv ey ASCPT survey LAC survey
42
10 13
21 25
31
42
29
25
29
15 19
41
10
32 28
23 23 23
38
27
20
