Universidad Autónoma de Madrid Faculty of Medicine
Department of Pharmacology and Therapeutics
Research for clinical pharmacogenetics implementation: from candidate gene studies to physiologically based
pharmacokinetic modelling.
Doctoral Thesis Pablo Zubiaur Precioso
Madrid, 2020
Universidad Autónoma de Madrid Facultad de Medicina
Dr. Francisco Abad Santos, jefe del Servicio de Farmacología Clínica del Hospital Universitario de La Princesa y Profesor Titular del Departamento de Farmacología y Terapéutica de la Facultad de Medicina de la Universidad Autónoma de Madrid y Dra.
Miriam Saiz Rodríguez, investigadora posdoctoral de la Fundación Burgos por la Investigación de la Salud y Profesora Asociada del Departamento de Fisiología de la Universidad de Burgos certifican:
Que Don Pablo Zubiaur Precioso ha realizado la presente Tesis Doctoral “Research for clinical pharmacogenetics implementation: from candidate gene studies to physiologically based pharmacokinetic modelling” con objeto de obtener el Grado de Doctor.
Como directores del trabajo hacemos constar que ha sido realizado con todas las garantías técnicas y metodológicas, y las conclusiones obtenidas son plenamente válidas, siendo considerado, por tanto, apto para ser presentado como Tesis Doctoral.
En Madrid, a 11 de noviembre de 2020.
Fdo. Dr. Francisco Abad Santos Fdo. Dra. Miriam Saiz Rodríguez
Universidad Autónoma de Madrid Facultad de Medicina
Dr. Francisco Abad Santos (PhD, MD), head of the Clinical Pharmacology Department, Hospital Universitario de La Princesa and Professor at the Department of Pharmacology and Therapeutics, Faculty of Medicine, Universidad Autónoma de Madrid and Dr.
Miriam Saiz Rodriguez (PhD), postdoctoral researcher at Fundación Burgos por la Investigación de la Salud and Associate Professor at the Department of Physiology, Universidad de Burgos, herby certify:
That Mr. Pablo Zubiaur Precioso has completed the present Doctoral Thesis “Research for the implementation of precision medicine: from clinical pharmacogenetics to physiologically based pharmacokinetic modelling” with the purpose of obtaining the degree of Doctor of Philosophy (Ph.D.).
We, in the capacity of directors of the thesis, confirm that it has been conducted with all technical and methodological requirements, and that the conclusions obtained are fully valid, and are therefore considered suitable for presentation as a doctoral thesis.
In Madrid, November 11th, 2020.
Sig. Dr. Francisco Abad Santos Sig. Dr. Miriam Saiz Rodríguez
A mi familia,
de sangre y elegida.
Pablo Zubiaur Precioso benefited from a pre-doctoral fellowship during his first two years of doctoral studies (2018 and 2019), financed by the Consejería de Educación, Juventud y Deporte of Comunidad de Madrid and by the European Social Fund (scholarship number: PEJD-2017-PRE/BMD-4164).
Pablo Zubiaur Precioso disfrutó de un contrato predoctoral durante sus dos primeros años de doctorado (2018 y 2019) financiado por la Consejería de Educación, Juventud y Deporte de la Comunidad de Madrid y por el Fondo Social Europeo (número de beca:
PEJD-2017-PRE/BMD-4164).
13
Table of contents
List of tables ... 15
List of figures ... 17
Abbreviations ... 19
Abstract ... 25
Introduction ... 33
1. Precision medicine ... 34
1.1. Factors determining the response to drugs. ... 35
1.1.1. Biopharmaceutical and pharmacokinetic factors... 35
1.1.2. Pharmacodynamics. ... 38
1.2. Elements modifying the response to drugs... 39
1.2.1. Demographic characteristics. ... 39
1.2.2. Drug interactions. ... 40
1.2.3. Disease ... 41
1.3. Pharmacogenetics ... 41
1.3.1. Genetic variants. ... 43
1.3.2. Pharmacogenetic studies. ... 46
1.3.3. Clinical pharmacogenetics. ... 47
1.3.4. Pharmacogenetic guidelines. ... 50
1.4. Therapeutic drug monitoring. ... 64
2. Pharmacokinetic analyses. ... 65
2.1. Non-compartmental approaches. ... 65
2.2. Compartmental approaches. ... 66
2.3. Physiologically based pharmacokinetic modelling. ... 71
2.4. The PK-SIM Software. ... 72
2.4.1. Model optimization. ... 73
2.4.2. Model evaluation. ... 75
2.4.3. Model extrapolation. ... 76
2.4.4. Integration of Clinical Pharmacogenetics in PBPK modelling. ... 77
14
3.1. Efavirenz. ... 78
3.2. Progesterone. ... 80
3.3. Dabigatran. ... 82
3.4. Voriconazole. ... 83
4. HCP5 rs2395029 and the abacavir hypersensitivity reaction. ... 85
5. The Covid-19 pandemic. ... 87
Hypotheses and objectives. ... 93
Compendium of articles... 97
Discussion. ... 193
1. Efavirenz. ... 193
2. Progesterone. ... 197
3. Dabigatran. ... 202
4. Voriconazole. ... 205
5. HCP5 rs2395029 and the abacavir hypersensitivity reaction. ... 208
6. The Covid-19 pandemic. ... 209
7. Clinical pharmacogenetics implementation... 210
Conclusions. ... 223
References. ... 229
Other articles. ... 245
Acknowledgements. ... 269
15
Table 1. List of genes recognized as very important pharmacogenes (VIP) by
PharmGKB ... 49 Table 2. Summary of recommendations for voriconazole and clopidogrel prescription based on CYP2C19 phenotype according to CPIC guidelines. ... 54 Table 3. Summary of recommendations for paroxetine and fluvoxamine prescription based on CYP2C19 phenotype according to CPIC guidelines. ... 55 Table 4. Summary of prescription recommendations for efavirenz based on CYP2B6 phenotype according to the CPIC guideline. ... 56 Table 5. Summary of prescription recommendations for tacrolimus based on CYP3A5 phenotype according to the CPIC guideline. ... 56 Table 6. Summary of prescription recommendations for nonsteroidal-anti-inflammatory drugs based on CYP2C9 phenotype according to the CPIC guideline... 58 Table 7. Summary of dose adjustment recommendations for thiopurines based on TPMT and NUDT15 phenotype according to the CPIC guideline. ... 59 Table 8. Summary of dose adjustment recommendations for capecitabine and 5-
fluoruracil (5-FU) based on DPYD phenotype according to the CPIC guideline. ... 60 Table 9. Summary of dose adjustment recommendations for atazanavir based on
UGT1A1 phenotype according to the CPIC guideline. ... 61 Table 10. Summary of dose adjustment recommendations for simvastatin based on SLCO1B1 phenotype according to the CPIC guideline. ... 62 Table 11. Allele definition table of CYP2B6*1, *4, *6, *7 and *9. ... 194 Table 12. Frequency of CYP2B6 alleles with functional impact according to races based on the CPIC guideline on CYP2B6 and Efavirenz-Containing Antiretroviral Therapy.
... 195 Table 13. Frequency of CYP2C9 alleles with functional impact according to races based on the CPIC guideline on CYP2C9 and NSAIDs. ... 199 Table 14. Frequency of CYP2C19 alleles with functional impact according to races based on the CPIC guideline on CYP2C19 and voriconazole. ... 200 Table 15. Design of the Open Array ® custom plate used for the dabigatran study. .... 204 Table 16. Proposed dose adjustments based on CYP2C19 phenotype... 207
16
17
Figure 1. Diagram of classical medicine pharmacotherapy. ... 33
Figure 2. Diagram of precision medicine pharmacotherapy. ... 34
Figure 3. Concentration-time curve of multiple-dose drug administrations. ... 37
Figure 4. Dose-response and log dose-response relationship diagrams. ... 38
Figure 5. Dose-response relationship diagrams for three drugs with three different potencies (ED50). ... 39
Figure 6. Representation of CYP2C9 and CYP2C19 loci in chromosome 10. ... 44
Figure 7. Nomenclature of the different members, subfamilies and families of the cytochrome P450. ... 51
Figure 8. Involvement of the main Cytochrome P450 isoforms in drug metabolism. .. 52
Figure 9. Inference of CYP isoform phenotype according to the presence of alleles (CYP2C19, CYP3A5, CYP2B6) or to the enzyme activity score (CYP2D6 and CYP2C9). ... 53
Figure 10. Examples of a) one-compartment and b) two-compartment pharmacokinetic models. ... 68
Figure 11. Expected log concentration-time curves for a) one-compartment and b) two- compartment pharmacokinetic models. ... 69
Figure 12. Hydraulic pharmacokinetic model for the study of inhaled anesthetics. ... 70
Figure 13. Schematic representation of a PBPK model. ... 72
Figure 14. Goodness of fit plot comparing observed and predicted concentrations of a drug. ... 76
Figure 15. Results per year for the search in PubMed for the term "PBPK" and “physiologically based pharmacokinetic modelling”. ... 77
Figure 16. The pharmacokinetic pathway of efavirenz. ... 79
Figure 17. The pharmacokinetic pathway of progesterone. ... 81
Figure 18. The pharmacokinetic pathway of voriconazole. ... 84
Figure 19. Molecular mechanism of the abacavir hypersensitivity reaction (ABC-HSR). ... 86
18
updated on October 19, 2020. ... 89 Figure 21. Proposed metabolic pathway for dabigatran and effects of CYP2D6
phenotype, sex and pantoprazole intake on dabigatran pharmacokinetics and safety. . 203 Figure 22. Cost evolution for a complete human genome sequencing since 2001. ... 208 Figure 23. Schematic representation of the research that promotes clinical
pharmacogenetics and its implementation in clinical practice. ... 210 Figure 24. Location and institutions comprising the Multidisciplinary Action for
Pharmacogenetics Implementation. ... 218 Figure 25. Evidence-driven process of the Multidisciplinary Action for
Pharmacogenetics Implementation initiative in Madrid, Spain... 219
19
ABCB1 ATP Binding Cassette, family B, member 1.
ABC Abacavir.
ABC-HSR Hypersensitivity reaction to abacavir.
ADME Absorption, distribution, metabolism and excretion.
ADR Adverse drug reaction.
AKR1D1 5β-reductase.
ARDS Acute respiratory distress syndrome.
AS Activity score.
AUC Area under the concentration-time curve.
BMI Body mass index.
CACNA1S Calcium voltage-gated channel subunit α-1.
cART Combination antiretroviral therapy.
CDSS Cleveland Clinic’s Personalized Medication Program.
CeGen-ISCIII Spanish National Genotyping Centre.
CES Carboxylesterase.
Cl Clearance.
Cmax Peak or maximum concentration.
Cmin Trough or minimum concentration.
CNV Copy number variation.
Covid-19 Coronavirus disease 2019.
CPIC Clinical Pharmacogenetics Implementation Consortium.
Ct Last observed concentration.
CYP Cytochrome P450.
CYP21A2 21-hydroxylase.
DAB Dabigatran.
DABE Dabigatran etexilate.
DOAC Direct oral anticuagulant.
DF Decreased function.
DPWG Dutch Pharmacogenetics Working Group.
DPYD Dihydropyrimidine dehydrogenase.
Emax Maximum effect.
ED50 Effective dose 50.
EMA European Medicines Agency.
ERDF European Regional Development Fund
F Bioavailability.
FDA U.S. Food and Drug Administration.
FMO Flavin-containing monooxygenase.
GOF Goodness of fit.
GWAS Genome-wide association study.
HIV Human immunodeficiency virus.
HLA Human leukocyte antigen.
20 mass spectrometry
HRT Hormone replacement therapy.
ICH International council of harmonization.
IV Intravenous.
IM Intermediate metabolizer.
ke Terminal rate constant.
LD Linkage disequilibrium.
MEC Minimum effective concentration.
MSC Maximum safe concentration.
NCBI U.S. National Center for Biotechnology Information.
NGS High-throughput next-generation sequencing.
NIH US National Institutes of Health.
NRTI Nucleoside analogue reverse-transcriptase inhibitor.
NM Normal metabolizer.
NSAIDs Nonsteroidal anti-inflammatory drugs.
Nudix Nucleoside diphosphate linked moiety X.
NUDT15 Nudix hydrolase 15.
PBPK Physiologically based pharmacokinetic modelling.
PF Poor function.
PGRN Pharmacogenomics Research Network.
PGx Pharmacogenetics.
PharmVar The Pharmacogene Variation Consortium, PharmGKB The Pharmacogenomics Knowledgebase.
PM Poor metabolizer.
popPK Population pharmacokinietics.
PrM Precision medicine.
qPCR Real time polymerase chain reaction.
RM Rapid metabolizer.
RYR1 Ryanodine receptor 1.
SLC Solute carrier.
SLC22A1 Solute carrier family 22 member 1.
SLCO1B1 Solute carrier organic anion transporter 1B1.
SNP Single nucleotide polymorphism.
SRD5A2 5α-reductase.
SEFF Spanish Society of Pharmacogenetics and Pharmacogenomics
SSOP Sequence Specific Oligonucleotide Probes.
SSRI Selective serotonin reuptake inhibitors.
t1/2 Elimination half-life.
TDM Therapeutic drug monitoring.
tmax Time to reach the maximum concentration.
TPMT Thiopurine S-methyltransferase.
UGT UDP glucuronosyltransferase.
21
Vd Volume of distribution.
VIP Very important pharmacogene.
These abbreviations correspond to the text of this doctoral thesis. The abbreviations used in the articles included in this doctoral thesis are described in the enclosed manuscripts.
ABSTRACT
RESUMEN
25
Abstract
Precision medicine (PrM) is an approach of contemporary medicine in which treatments are adjusted to the patient’s individual characteristics. PrM considers all possible aspects that determine the response to drugs, such as pharmacokinetic and biopharmaceutical factors or drug pharmacodynamics, also known as the mechanism of action of the drug.
Furthermore, certain factors modify the response to drugs, such as demographic characteristics, health status or drug interactions. The response to drugs depends on all these elements and, additionally, on genetic variants located in genes related to their pharmacokinetics and pharmacodynamics. This field of medicine is known as clinical pharmacogenetics, an essential part of PrM. For the progression of clinical pharmacogenetics and PrM, it is necessary to conduct association studies in which the impact of genetic polymorphisms on clinical events is investigated. These clinical events include pharmacokinetic parameters, safety or effectiveness of drugs.
Eventually, these associations are validated and clinical pharmacogenetic guidelines are elaborated. As an example, some polymorphisms reduce metabolizing enzyme activity, therefore drugs may accumulate and the patient may be overexposed to the drug.
Consequently, dose reductions or treatment change are warranted. However, it is not enough to find a pharmacogenetic association to directly implement prescribing modifications in the clinical practice. A process of clinical validation is required, which may last many years, as it often requires clinical trials or numerous observational studies.
An alternative methodology to evaluate the effect of dose adjustments in patients is the use of computerized predictive pharmacokinetic models, namely Physiologically-Based Pharmacokinetic (PBPK) models. Even after the publication of clinical guidelines, the implementation in clinical practice is slow due to the lack of awareness of many physicians and pharmacists about pharmacogenetics. In view of the relative youth of clinical pharmacogenetics, it is therefore necessary to devote effort to teaching and convincing physicians and pharmacists to make use of this tool.
26
The aim of this doctoral thesis was to contribute to the implementation of clinical pharmacogenetics. For this respect, different methodologies were used: firstly, the development of three candidate gene pharmacogenetic studies for three drugs with different levels of previous pharmacogenetic information available: efavirenz, progesterone and dabigatran; secondly, the evaluation of CYP2C19 phenotype-guided dose adjustments for voriconazole by means of PBPK modelling; thirdly, to contribute to the implementation of clinical pharmacogenetics through various methodologies, namely the publication of pharmacogenetic reviews or the validation of new laboratory genotyping techniques.
The efavirenz study was timely and contemporary to the publication of the Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline on CYP2B6 and efavirenz. A predictive model of efavirenz pharmacokinetics was optimized based on G516T and rs4803419 variants which improved the predictive power of G516T alone.
Concerning progesterone work, it was the first study to date to evaluate pharmacogenetic determinants of the exposure to exogenously administered progesterone, orally and vaginally. The key finding was the relationship observed between CYP2C19 impaired phenotypes and progesterone overexposure.
Regarding the study with dabigatran, previous works had reported CES1 and ABCB1 variants to alter dabigatran exposure, however, in this work, these relationships were not observed. Alternatively, CYP2D6 functional impairment was related to drug accumulation, suggesting that dabigatran is metabolized by this enzyme.
Moreover, voriconazole PBPK models were able to excellently predict voriconazole disposition based on CYP2C19 phenotype. The ultrarapid (UM) and poor (PM) metabolizer phenotypes required sharper dose adjustments than the 50% dose increase or decrease proposed by the Dutch Pharmacogenomics Working Group (DPWG), respectively. Indeed, no phenotype was adequately exposed to voriconazole therapeutic window after receiving the standard administration protocol, which justifies dosage individualization.
27
In addition, the validation of an HLA-B*57:01 surrogate biomarker, HCP5 rs2395029, for the prediction of abacavir hypersensitivity reaction (ABC-HSR), was conducted and published. This is a cheaper and faster alternative method to the one traditionally used in our laboratory.
Finally, due to the SARS-CoV-2 infection, or coronavirus disease 2019 (Covid-19), a review about the pharmacogenetics of the drugs used for the management of the disease was published. Until a vaccine for the prevention of the disease is available, the off-label use of repurposed drugs will continue, therefore this document will be a valid pharmacogenetic reference.
Ultimately, this doctoral thesis represents an advance in the implementation of clinical pharmacogenetics through the application of a number of different strategies. Moreover, the current landscape of Spanish and international pharmacogenetic implementation initiatives was described.
28
Resumen
La medicina de precisión (PrM) es un enfoque de la medicina contemporánea en el que los tratamientos se ajustan a las características individuales del paciente. La PrM considera todos los posibles factores que determinan la respuesta a los fármacos, como los factores farmacocinéticos y biofarmacéuticos o los factores farmacodinámicos, también conocidos como el mecanismo de acción del fármaco. Además, ciertos factores modifican la respuesta a fármacos, como las características demográficas, el estado de salud o las interacciones farmacológicas. La respuesta a los fármacos depende de todos estos factores y, además, de las variantes genéticas localizadas en los genes relacionados con su farmacocinética y farmacodinamia. Este campo de la medicina se conoce como farmacogenética clínica, una parte esencial de la PrM. Para la implementación de la farmacogenética clínica y la PrM, es necesario realizar estudios de asociación en los que se investigue el impacto de los polimorfismos genéticos en acontecimientos clínicos.
Estos incluyen la farmacocinética, la seguridad o la eficacia de los medicamentos.
Con el tiempo, estas asociaciones se validan hasta que se elaboran guías clínicas farmacogenéticas. Por ejemplo, algunos polimorfismos reducen la actividad de enzimas metabolizadoras, por lo que los fármacos se eliminan en menor medida y el paciente puede estar sobreexpuesto. Por consiguiente, se recomienda la reducción de la dosis. Sin embargo, no basta con encontrar una asociación farmacogenética para modificar directamente los hábitos de prescripción en la práctica clínica. Se requiere un proceso de validación clínica, que puede durar muchos años, ya que a menudo requiere ensayos clínicos o numerosos estudios observacionales. Una metodología alternativa para evaluar el efecto de los ajustes de dosis en los pacientes es el uso de modelos predictivos computacionales, como por ejemplo los modelos farmacocinéticos basados en la fisiológia (PBPK). Incluso después de la publicación de las guías clínicas, la implementación en la práctica clínica es lenta debido a la falta de concienciación de médicos prescriptores y farmacéuticos sobre la farmacogenética. En vista de la relativa juventud de la farmacogenética clínica, es necesario, por lo tanto, dedicar esfuerzos a enseñar y convencer a los médicos y farmacéuticos de que hagan uso de esta herramienta.
29
El objetivo de esta tesis doctoral es contribuir a la implementación de la farmacogenética clínica. Para ello, se utilizaron diferentes metodologías: en primer lugar, el desarrollo de tres estudios farmacogenéticos de genes candidatos para tres fármacos con diferentes niveles de información farmacogenética previa disponible: efavirenz, progesterona y dabigatran; en segundo lugar, la evaluación de los ajustes de dosis basados en el fenotipo de CYP2C19 para voriconazol mediante modelado PBPK; en tercer lugar, contribuir a la implementación de la farmacogenética clínica mediante diversos mecanismos, incluyendo la publicación de revisiones farmacogenéticas o la validación de nuevas técnicas de genotipado.
El estudio sobre el efavirenz fue oportuno y contemporáneo a la publicación de la guía clínica del Consorcio para la Implementación de la Farmacogenética Clínica (CPIC) sobre CYP2B6 y efavirenz. Se optimizó un modelo predictivo de la farmacocinética de efavirenz basado en las variantes G516T y rs4803419, que mejoró el poder predictivo de G516T solo.
En cuanto al trabajo con progesterona, fue el primer estudio hasta la fecha en evaluar los determinantes farmacogenéticos de la exposición a la progesterona administrada exógenamente, por vía oral y vaginal. El hallazgo clave fue la relación observada entre los fenotipos de pérdida de función de CYP2C19 y la sobreexposición a la progesterona.
En cuanto al estudio con dabigatran, trabajos previos encontraron asociación entre variantes de CES1 y ABCB1 y variabilidad en la exposición al fármaco; sin embargo, en este trabajo, estas relaciones no se observaron. Por otra parte, la pérdida de función de CYP2D6 se relacionó en nuestro trabajo con la acumulación del fármaco, lo que sugiere que dabigatran es metabolizado a otros metabolitos por esta enzima.
Además, los modelos PBPK para voriconazol pudieron predecir de manera excelente su biodisponibilidad basándose en fenotipo de CYP2C19. Los fenotipos ultrarrápido (UM) y lento (PM) requirieron ajustes de dosis más pronunciados que el aumento o disminución de la dosis del 50% propuesto por el Grupo de Trabajo de Farmacogenómica de los Países Bajos (DPWG), respectivamente. De hecho, ningún fenotipo se encontró dentro de la
30
ventana terapéutica de voriconazol al recibir la pauta estándar, lo que justifica la individualización de la dosis.
Además, se llevó a cabo y se publicó la validación de un biomarcador alternativo a HLA- B*57:01, HCP5 rs2395029, para la predicción de la reacción de hipersensibilidad a abacavir (ABC-HSR). Se trata de un método más barato y rápido que el utilizado tradicionalmente en nuestro laboratorio.
Por último, debido a la infección por SARS-CoV-2, o enfermedad por coronavirus de 2019 (Covid-19), se publicó una revisión sobre la farmacogenética de los medicamentos utilizados para el tratamiento de la enfermedad. Hasta que se disponga de una vacuna para la prevención de la infección, se seguirán utilizando medicamentos fuera de indicación, por lo que este documento será una referencia farmacogenética válida.
En última instancia, esta tesis doctoral representa un avance en la implementación de la farmacogenética clínica mediante la aplicación de una serie de estrategias diferentes.
Además, se describió el panorama actual de las iniciativas para la implementación de la farmacogenética españolas e internacionales.
INTRODUCTION
33
Introduction
Medicine is defined as the science dealing with the preserving of health and with preventing and treating disease or injury 1. The documented use of substances for the treatment of diseases dates back to more than five millennia ago in the Sumerian civilization 2. The first drugs used were obtained from raw materials. Their use was completely based on the appearance of symptoms e.g., the use of opium for pain management 3. The development of modern pharmacotherapy led to a vast range of drugs with well described mechanisms of action and specific for the treatment of several diseases. However, despite the extraordinary progress of pharmacology and the increased specificity, effectiveness and safety of treatments, drugs continue to be prescribed according to groups or levels of disease, on a trial and error basis. In other words, in most cases, the physician makes a diagnosis, based on empirical knowledge or diagnostic tests, and assigns the corresponding treatment (Figure 1). With this approach, some patients respond positively to treatment and others respond negatively, or do not respond at all.
Figure 1. Diagram of classical medicine pharmacotherapy.
34
1. Precision medicine
Precision medicine (PrM) is an approach of contemporary medicine in which treatments are adjusted to patient’s individual characteristics, including genetic information, lifestyle, demographic characteristics, drug interactions and any other factors that may influence therapy outcomes. This discipline assumes that the variability in response to medication is specific to each patient, refusing to classify patients into groups of illness, but rather each patient is examined individually. For that purpose, it considers all the factors related to drug response variability and attempts to assign the most appropriate drug to a safe and effective dose (Figure 2) 4. Compared to traditional medicine, a higher percentage of patients respond positively and more safely as the treatments are adjusted to individual patient characteristics 5.
Figure 2. Diagram of precision medicine pharmacotherapy.
35 1.1. Factors determining the response to drugs.
Factors related to the variability of the response to drugs should be addressed for a better understanding of the key elements of PrM, i.e., genotyping or therapeutic drug monitoring. These factors can be grouped into a) biopharmaceutical and pharmacokinetic factors and b) pharmacodynamic factors. In the following subsections, they are further described.
1.1.1. Biopharmaceutical and pharmacokinetic factors.
Pharmaceutical products must be released in order to dispense the drug, which must be absorbed and distributed so that it can exert its mechanism of action. The absorption process does not occur in intravenous formulations since the drug accesses directly to systemic circulation 6. Once systemic circulation is reached, three simultaneous processes occur. Firstly, the drug is distributed to the different tissues of the body. Secondly, it is metabolized after reaching the corresponding organs (e.g., the liver). Thirdly, the parent drug and its metabolites are excreted, for instance, through urine or faeces. Although these processes may occur simultaneously, one process or another may be predominant at certain times 6. Thus, after drug intake, absorption and distribution processes prevail;
afterwards, these processes end and metabolism and elimination predominate until the drug is fully eliminated. The "ADME" series stands for the pharmacokinetic processes of absorption, distribution, metabolism and excretion. Together with release or biopharmaceutical factors, they determine drug exposure 6.
Moreover, the pharmaceutical formulation has a significant impact on the pharmacological effect of drugs, since it conditions their release and absorption 7. The main biopharmaceutical determinants are: a) the physicochemical properties of the drug, e.g. its solubility or its crystalline polymorphism, b) biorelevant properties of the pharmaceutical formulation, such as the porosity of the tablet, c) physiological factors, such as gastrointestinal motility or the effect of pH and food, and d) physicochemical interactions. The latter, physicochemical interactions, may be of three types: drug- excipient, such as the effect of cyclodextrins on the solubility of active ingredients;
36
excipient-excipient, such the combinations of excipients to modify drug release; finally, the excipients themselves can have an effect on physiological parameters, for example, they can modify the gastric pH 7.
The performance of biopharmaceutical and pharmacokinetic processes leads to recognisable plasma concentrations. Drugs need to reach a minimum concentration to be effective (minimum effective concentration, MEC) and shall not exceed an upper threshold to avoid toxicity (maximum safe concentration, MSC) 6. Plasma concentrations within the MEC and MSC are acknowledged as the therapeutic range or window of a drug; when plasma levels exceed the MSC, the range is considered toxic and is related to the occurrence of adverse reactions; when plasma levels fail to exceed the MEC, the range is considered subtherapeutic and is related to the lack of effectiveness of the therapy.
(Figure 3). Therapeutic drug monitoring (TDM) aims to maintain drug levels within the therapeutic window, to ensure drug effectiveness and safety, as it will be explained in sections below.
37
Figure 3. Concentration-time curve of multiple-dose drug administrations.
Above, a patient within the therapeutic range; in the middle, a patient exceeding the MSC, therefore within the toxic range; below, a patient under the MEC, therefore within the subtherapeutic range.
38 1.1.2. Pharmacodynamics.
Drug exposure is necessary for a drug to exert its mechanism of action, but not enough, since the interaction with a pharmacological target is also necessary. Depending on the type of interaction with the target, there are different types of drugs: a) agonists: drugs that bind to receptors and activate their transduction pathway, b) antagonists: drugs that bind to receptors and block their activation, i.e. they prevent the activation of the receptors by natural ligands and c) reverse agonists: drugs that bind to receptors with constitutive activity but induce a pharmacological response opposite to that of the agonist 6.
The pharmacological effect of a drug is dose-dependent. This relationship is measured with dose-response curves (Figure 4). The higher the dose, the greater therapeutic effect the drug will have, until an asymptote is reached, known as Emax, which determines the upper limit of effectiveness of a drug 6.
Figure 4. Dose-response and log dose-response relationship diagrams.
Moreover, the effective dose 50 (ED50) is the dose that produces the desired effect in half the population. It is a meter of drug potency. Figure 5 shows the variation in the potency of three drugs according to their ED50. The lower the ED50 is, the higher the potency of the drug will be. In other words, it is understood that the less amount of drug needed to produce a pharmacological effect, the greater its potency will be 6.
39
Figure 5. Dose-response relationship diagrams for three drugs with three different potencies (ED50).
In this example, ED501, is the most potent drug, while ED503 is the less potent; that is, in order to achieve the same therapeutic effect, higher doses of ED503 would be required compared to ED501.
1.2. Elements modifying the response to drugs.
The factors determining the response to drugs were described in the previous sections. In summary, the pharmacological effect of a drug can be related to its bioavailability and its mechanism of action. However, there are other factors that can alter drug exposure or pharmacodynamics and, consequently, the pharmacological effect. The main factors modifying the drug response are described below.
1.2.1. Demographic characteristics.
Sex, weight or height condition the bioavailability of drugs. In general, the weight and height of men are higher than that of women. Consequently, drugs are distributed in a higher body volume and plasma disposition may be diminished. However, women tend to have higher body fat levels, which conditions the distribution of lipophilic drugs; renal processes of filtration and tubular secretion and reabsorption, hepatic phase I, phase II and conjugation metabolism occur faster in men compared to women 8. Furthermore,
40
aging is related to the reduction of renal and hepatic clearance and to an increase of the volume of distribution of lipophilic drugs, while pharmacodynamic sensitivity to anticoagulants, cardiovascular or psychotropic drugs increases 9. Race or ethnicity likewise influences drug pharmacokinetics, as the following processes are different according to different ethnic groups: first-pass metabolism, protein binding, volume of distribution, hepatic metabolism and renal tubular secretion 10.
1.2.2. Drug interactions.
Concomitant use of drugs (drug-drug interactions), dietary elements (drug-food interactions) or exposure to other substances (e.g. xenobiotics, tobacco, alcohol, caffeine) may cause pharmacokinetic or pharmacodynamic alterations 11. Several interactions occur at the absorption stage. For instance, fluoroquinolones bind to antacids if administered simultaneously, leading to little or no systemic absorption of the antibiotic11. Moreover, the intake of certain food, namely fruit juices, may alter gastric pH and, consequently, drug absorption 12. Regarding distribution, phenytoin and valproic acid or salicylates compete for the same binding sites at plasma proteins, what alters the free unbound fraction 11. Concerning metabolism, patients treated with dasatinib for the management of chronic myeloid leukaemia must not use dexamethasone. This drug is a potent CYP3A4 inducer, an enzyme that metabolizes dasatinib, therefore the concomitant use of both drugs leads to subtherapeutic dasatinib plasma levels 13. A good example of a food-drug interaction is coffee with drug metabolism, as caffeine induces the metabolism of numerous drugs, which results in a reduction of their plasma levels 11. Finally, the use of omeprazole and methotrexate is a classical drug-drug interaction concerning drug elimination. Omeprazole inhibits methotrexate elimination in renal tubules as it inhibits hydrogen (protons) efflux and methotrexate is actively secreted in the distal tubule with hydrogen ions 11. In addition, other drug-drug or food-drug interactions can be due to alteration of pharmacodynamics. For instance, in patients on anticoagulant therapy, the intake of green leafy vegetables, which have high levels of vitamin K, results in a decrease in the anticoagulant effect of warfarin or acenocoumarol 11.
41
In summary, drug pharmacokinetics is not invariable, but can be altered by numerous substances to which patients are constantly – sometimes involuntarily – exposed.
Occasionally, these interactions are desirable from a pharmacological perspective. For instance, tramadol and paracetamol exert different mechanisms of action for the treatment of pain. When they are administered simultaneously, the effect is superior to that of the sum of their effect when administrated separately, a phenomenon known as synergy 14. In addition, for the treatment of drug-resistant bacteraemia, combinations of antibiotics are often used (e.g. ampicillin/sulbactam). They similarly exert their mechanism of action synergistically 15.
1.2.3. Disease.
Health status conditions the response to drugs by altering pharmacokinetic, but also pharmacodynamic processes. Malnutrition, causes alterations in the absorption of drugs (as a result of alterations in stomach and intestinal pH) as well as in the distribution of drugs (as a result of alterations in plasma proteins such as albumin) 16. Furthermore, acute and chronic liver disease and digestive pathologies can lead to alterations in the metabolism and absorption of drugs, respectively 16. Furthermore, allergic reactions caused by drugs are a very well described phenomenon 17 caused by an exacerbated reaction of the organism to the drug which behaves as a xenobiotic. It is therefore a pathological response of the immune system.
1.3. Pharmacogenetics.
The previous sections describe how a drug exerts its mechanism of action and how the processes necessary for this to occur can be affected by patient idiosyncrasies, or by situations unrelated to patients. PrM, in addition, studies the effect of DNA variations in genes that code for enzymes, transporters, receptors and channels on the pharmacokinetics and pharmacodynamics of drugs. These genes are acknowledged as
“pharmacogenes” for their involvement in drug response 18. This discipline is known as pharmacogenetics and its objective is to establish associations between genetic polymorphisms and clinical events. Thus, applied in clinical practice, this information
42
allows for the avoidance of the use of ineffective or toxic drugs; alternatively, it facilitates the adjustment of drug dose to guarantee therapeutic concentrations while maintaining their safety. Pharmacogenetic associations can be established between polymorphisms and pharmacokinetic or pharmacodynamic processes 19.
On the one hand, associations between polymorphisms and pharmacokinetic parameters provide information on drug bioavailability. Polymorphism of enzyme or transporter genes can lead to a reduction or an increase of protein activity or expression levels.
Consequently, an alteration of pharmacokinetic processes may occur. For instance, no function variants in metabolizing enzyme genes relate to significant drug accumulation;
polymorphism of transporter genes (namely ABCB1 or SLCO1B1) may have a significant effect on drug absorption or hepatic uptake 20(p1),21(p1). These circumstances relate to ineffective or toxic treatments. PrM attempts to genotype patients before the drug is prescribed so that, if necessary, a dose adjustment or change in treatment is applied.
On the other hand, pharmacogenetics studies direct associations between polymorphisms and alterations in drug efficacy and toxicity independent of plasma drug levels.
Polymorphisms in genes that code for drug targets may result in ineffectiveness, because of a worse drug-target interaction. Moreover, hypersensitivity reactions may occur because of the formation of immunogenic epitopes 22,23. The first of them and one of the most important pharmacogenetic biomarkers is HLA-B*57:01 for its relationship with abacavir hypersensitivity reaction (ABC-HSR) 24,25. The implementation of these associations in clinical practice is significantly lower than that of pharmacokinetics.
Pharmacogenetics, along with other variables such as age or weight, does not explain 100% of the variability in response to drugs. Only the integration of all this information permits the provision of a personalised therapy for each patient. However, the genome is stable throughout the lifetime of an individual, making it a factor of variability that can be controlled from birth.
43 1.3.1. Genetic variants.
The different locations of DNA are known as loci (singular, locus). Genomic loci always occur in duplicate on homologous chromosomes, except for those located in male sex chromosomes. This is due to the diploid nature of humans, i.e., for each chromosome we inherit two haploid genetic copies from each parent (22 somatic chromosomes + 1 sex chromosome).
There are different types of variations in the DNA sequence that cause functional changes in proteins, altering the efficacy and safety of many drugs. These include insertions and deletions (known as indels), copy number variations (CNVs), tandem repetitions, structural variations and single nucleotide polymorphisms (SNPs). The latter are the most studied ones in pharmacogenetics. They differ from single nucleotide mutations in that their prevalence exceeds 1% of the population. In addition, they occur in one out of every three hundred base pairs, contributing up to 90% of the DNA variability between individuals 26. Notwithstanding this definition, SNPs or mutations may be referred generically in this text as “variants” or “polymorphisms” regardless their prevalence.
By SNP, variant, mutation or polymorphism, we refer to a locus of the genome with the two nucleotides or alleles (Figure 6). The nucleotide that appeared most frequently in the past is called "ancestral", while the other nucleotides are considered "mutated" or
"variations". The presence of one nucleotide or another can lead to functional consequences for the protein encoded by the gene. Oftentimes, the ancestral variety is associated with a functionally normal or wild-type protein and is more prevalent.
Conversely, the mutated variant is associated with a functional alteration (gain or loss) and is less prevalent. However, this definition is imprecise, since variants can also have higher functionality and prevalence than ancestral ones.
Combinations of polymorphisms in a gene determine particular alleles, with each of these being functionally different from the others. For a SNP, a subject with the same nucleotide in both chromosomes is known as "homozygote", while a subject with different nucleotides is considered "heterozygote". Normally, for a SNP, an individual can be wild-
44
type if they are homozygous for the wild-type nucleotide, heterozygous, with one wild- type and one mutated nucleotide, or mutated homozygous, with both mutated nucleotides.
An example of two genomic loci is represented in Figure 6. Each individual has two copies of chromosome 10, which contains the genes encoding for CYP2C9 and CYP2C19, both located in a CYP cluster. Half of the protein pool of the organism is encoded by the maternal chromosome and half by the paternal chromosome. In the example, the patient carries, for CYP2C9, one *2 allele (maternal chromosome) and one
*1 allele (paternal chromosome), which confers a *1/*2 genotype or diplotype; for CYP2C19, the patient shows a *1/*17 genotype. In other words, we could say that the patient is heterozygote for CYP2C9*2 and for CYP2C19*17.
Figure 6. Representation of CYP2C9 and CYP2C19 loci in chromosome 10.
There are numerous databases that list the SNPs that comprise alleles 27. In pharmacogenetics, a reference database is the Pharmacogene Variation Consortium (PharmVar) database (available at https://www.pharmvar.org/). Each allele groups a unique combination of SNPs. However, one SNP may appear in different alleles. The
45
most widely accepted nomenclature to refer to alleles is an asterisk, "*", followed by a number and sometimes a letter. Thus, the *1 or *1a allele refers to the wild-type or reference allele. Each subsequent allele (*2, *3, *4...) corresponds to a combination of SNPs that determine a particular functionality. There are four allele types based on their functional impact: wild-type (which always receives the *1 number), no function, decreased and increased function alleles.
The growing number of pharmacogenetic studies motivated the scientific community to find a consensus on the nomenclature of SNPs and alleles 27. Traditionally, SNPs were identified by their position in the gene followed by a description of the mutation.
Substitutions are recognized by the ">" symbol, which indicates the direction of the substitution. For example, the SNP c. 2345 C>T (or C2345T) of a gene would indicate a C (ancestral) for T (mutated) substitution at the 2345 coding position; deletions are identified by the letters "del", before which the position of the DNA is specified and after which the deleted nucleotide is specified. For example, c. 351delA refers to a deletion of an A at coding position 351 of the gene; insertions are recognized by the "ins" letters.
They are acknowledged by the positions adjacent to the insert separated by a "_" symbol followed by "ins" followed by the inserted nucleotide. For example, c. 234_235insT indicates an insertion of a thymine between coding positions 234 and 235; finally, duplications are recognized by "dup"; before the dup letters, the affected position is specified and after them, the duplicated nucleotide is indicated. For example, 123dupT means a duplicate of a thymine at coding position 123. The disadvantage of this nomenclature is that the position of the gene depends on the reference DNA sequence.
Each gene is composed of several introns and exons, and it is necessary to know their numbering in order to refer to the position. For this reason, the Reference SNP ID number (rs) tends to be used. This number is assigned by the NCBI (National Center for Biotechnology Information, United States) and refers to a unique position in the genome.
Thanks to the sequences that flank each SNP, the rs number is unique for each SNP 28. For instance, the ABCB1 C3435T or c. 3535 C>T substitution is assigned rs1045642 ID29.
46
SNPs located in coding regions result in two types of changes. First, synonymous SNPs, which do not alter the structure of the protein. This is due to the universal nature of the genetic code, by which the same amino acid can be encoded by different combinations of nucleotides. These nucleotide variations may not produce functional alterations or may produce them because of alterations in the expression, splicing or stability of the protein30. Secondly, non-synonymous SNPs, in which the nucleotide changes and so does the aminoacid. Consequently, functional alterations may occur. Sometimes, variation causes a STOP codon, what truncates the protein, which loses all or part of its activity.
Occasionally, the SNP occurs in interaction pockets with substrates, resulting in an increase or decrease in substrate specificity 30.
On the other hand, SNPs can be located in non-coding regions. They may produce no effect or defects in expression when they are located in promoter regions. Other intronic SNPs may cause splicing errors, therefore the protein may show altered functionality 30. 1.3.2. Pharmacogenetic studies.
Pharmacogenetics is still a relatively young developing discipline. The first reference to the “occurrence of unusual reactions to drugs on the basis of biochemical individuality”
dates back to the 1930’s. However, it was not until late 1950’s that the discipline became a recognized science 31. In the early 2020s, there is still a great demand for research seeking new genetic markers or SNPs to be associated with clinical events. These studies help to advance clinical pharmacogenetics by generating knowledge. However, nowadays, research is faster and much more cost-effective due to technological advances.
There are two main types of pharmacogenetic studies:
First, genome-wide association studies (GWAS) are analyses of variants across the entire genome. They are generally exploratory studies and help to identify possible variants associated with clinical events. The number of SNPs studied may reach millions.
Different platforms are available for DNA genotyping, typically based on high- throughput next-generation sequencing (NGS) or microarray technologies.
47
Secondly, candidate-gene studies involve selecting a few genes that are relevant to drug pharmacokinetics or pharmacodynamics and genotyping variants with known effect on gene expression or protein function. The number of SNPs is variable and considerably lower than in GWAS studies. Different platforms are available for DNA genotyping, e.g.
traditional real time thermal cyclers for studies with a few SNPs or more powerful genotyping platforms, for instance genotyping arrays for studies with hundreds of SNPs.
Unlike GWAS, candidate gene studies are based on: a) knowledge of pharmacogenes important in drug pharmacokinetics and pharmacodynamics and b) selection of genetic variants with known impact on transporter function. In the early days of the discipline, these studies were difficult because no variants with impact were known. Nowadays, as the variants affecting the activity of proteins encoded by pharmacogenes are reasonably well known, this resource is highly useful.
Nevertheless, the progress of pharmacogenetics must be based on a balance of both techniques: with GWAS new variants are identified and with candidate gene studies they are validated. Both have advantages and disadvantages and are appropriate in certain circumstances. GWAS are exploratory, new associations can be established, while with candidate gene studies this is not possible. However, the level of significance in a GWAS is sometimes difficult to reach and important associations can be missed. The advantage of candidate gene studies is that it is easier to establish significant associations, yet they only increase the evidence for previously described associations, as no new ones can be established.
The clinical events to be studied depend on the design of each study. Usually, associations are established between polymorphisms and variables of efficacy, safety or tolerability and exposure to drugs. Any clinical event may be explored through a GWAS or a candidate-gene association study.
1.3.3. Clinical pharmacogenetics.
The Clinical Pharmacogenetics Implementation Consortium (CPIC) is an international group that seeks to promote the implementation of pharmacogenetics. To this end, it
48
gathers and filters quality scientific works to increase the level of evidence of associations between polymorphisms and clinical events. The PharmGKB (The Pharmacogenomics Knowledgebase) is a public database managed by Stanford University that compiles information on genetic variants, clinical notes, levels of evidence and prescription information. PharmGKB is a partner in the Pharmacogenomics Research Network (PGRN) of the US National Institutes of Health (NIH). Both institutions promoted CPIC in 2009. The levels of evidence described in PharmGKB are now widely accepted 32,33, and are described below:
❖ Level 1A: “variant-drug combination in a CPIC or medical society endorsed pharmacogenetic (PGx) guideline, or implemented at a PGRN site or in another major health system”.
❖ Level 1B: “variant-drug combination where the preponderance of evidence shows an association. The association must be replicated in more than one cohort with significant p-values, and preferably will have a strong effect size”.
❖ Level 2A “variant-drug combination that qualifies for level 2B where the variant is within a VIP (Very Important Pharmacogene) as defined by PharmGKB. The variants in level 2A are in known pharmacogenes, so functional significance is more likely”. There are 68 VIP genes in total recognized by PharmGKB.
❖ Level 2B: “variant-drug combination with moderate evidence of an association.
The association must be replicated but there may be some studies that do not show statistical significance, and/or the effect size may be small”.
❖ Level 3: “variant-drug combination based on a single significant (not yet replicated) study or annotation for a variant-drug combination evaluated in multiple studies but lacking clear evidence of an association”.
❖ Level 4: associations “based on a case report, non-significant study or in vitro, molecular or functional assay evidence only”.
These levels of evidence are frequently updated. Periodically, PharmGKB reviews all the information available about VIP genes and publishes them, typically, in the Pharmacogenetics and Genomics Journal. The preparation of these reviews and the
49
assignment of test levels are two interrelated processes. Until June 2020, there was a single list of VIP genes, based on several sources, including CPIC, PGRN or the FDA biomarker list. Recently, an evidence assessment of these VIP genes was conducted by PharmGKB, which classified VIP genes in three tiers (Table 1):
❖ Tier 1: “genes with substantial evidence to support their importance in pharmacogenomics. New genes will be added to Tier 1 as and when they are linked to recommendations from CPIC clinical guidelines”.
❖ Tier 2: “genes with limited evidence to support their importance in pharmacogenomics”.
❖ Cancer Genome: “genes which are important in tumor pharmacogenomics. New genes may be added to the Cancer Genome list as new evidence and cancer treatments become available”
Table 1. List of genes recognized as very important pharmacogenes (VIP) by PharmGKB.
TIER 1 TIER 2 Cancer Genome
ABCB1 CYP2C9 MT-RNR1 ADH1A KCNH2 ABL1
ABCG2 CYP2D6 NAT2 ADH1B KCNJ11 ALK
ACE CYP3A4 NUDT15 ADH1C NQO1 BCR
ADRB1 CYP3A5 RYR1 AHR NR1I2 BRAF
ADRB2 CYP4F2 SLC19A1 ALDH1A1 P2RY1 EGFR
CACNA1S DPYD SLCO1B1 ALOX5 P2RY12 ERBB2
CFTR DRD2 TPMT BRCA1 PTGIS KIT
COMT F5 TYMS CYP1A2 PTGS2 KRAS
CYP2A6 G6PD UGT1A1 CYP2A13 SCN5A NRAS
CYP2B6 GSTP1 VKORC1 CYP2E1 SLC22A1
CYP2C19 HLA-B CYP2J2 SULT1A1
CYP2C8 MTHFR GSTT1 VDR
HMGCR
Ultimately, when a gene is defined as a VIP gene and evidence of pharmacogenetic associations is very high, the CPIC publishes a clinical pharmacogenetic guideline between a VIP gene and a drug or group of drugs which contains therapeutic recommendations. All information on VIP genes can be found at
50
https://www.pharmgkb.org/vips. In the following section, a summary of the most important VIP genes, along with pharmacogenetic guidelines is described.
1.3.4. Pharmacogenetic guidelines.
CPIC guidelines are developed based on an evidence-driven process, hence the recommendations are reliable and rigorous. These guidelines are intended to orient pharmacogenetic specialists in selecting the variants of interest, translating them into interpretable phenotypic information and providing a therapeutic recommendation to the physician requesting the test. At the Department of Clinical Pharmacology of Hospital Universitario La Princesa we comply with CPIC guidelines, which we complement with the guidelines of the Dutch Pharmacogenetics Working Group (DPWG) 34. Other societies publish pharmacogenetic guidelines, namely the Canadian Pharmacogenomics Network for Drug Safety 35.
Metabolizing enzymes.
CYP is an enzymatic complex that metabolizes a huge variety of xenobiotic substances.
Over 18 families of CYP are encoded by 57 genes. The rules for naming the different members and families of CYP are shown in Figure 7. Most CYP genes code for three families: CYP2, and CYP3 and CYP4 36. Many of the members of these families are highly inducible and redundant, that is, they are expressed repeatedly and in response to certain stimuli. The rest of the families usually contain only one member and are not very inducible or redundant 36.
51
Figure 7. Nomenclature of the different members, subfamilies and families of the cytochrome P450.
The following members of CYP are considered VIP genes: CYP1A2, CYP2A6, CYP2B6, CYP2C19, CYP2C8, CYP2C9, CYP2D6, CYP2E1, CYP2J2, CYP3A4, CYP3A5, CYP4F2.
They are responsible for metabolizing the majority of drugs (Figure 8) 37. Numerous polymorphisms located in these genes were associated with variability in response to multiple drugs 36,38. The presence of no function or decreased function alleles in CYP enzymes is associated with reduced drug metabolism, resulting in elevated plasma levels and increased toxicity. In contrast, increased function alleles have the opposite effect:
plasma levels are reduced, reducing the effectiveness of the drugs. This relationship between increased or decreased function alleles and toxicity or efficacy is reversed for drugs that need to be metabolized to obtain the active moiety, i.e. prodrugs.
52
Figure 8. Involvement of the main Cytochrome P450 isoforms in drug metabolism.
The percentage indicates the relative amount of drugs metabolized per isoform.
Based on the number of increased, decreased or no function alleles, the metabolizing phenotypes are assigned. CPIC guidelines are available for CYP2D6, CYP2C19, CYP2C9, CYP2B6 and CYP3A5; the inference of the phenotype is validated through the calculation of activity scores or directly through the presence of decreased or increased function alleles (Figure 9).
CYP3A4/5 (30.2%) CYP2D6 (20,0%) CYP2C9 (12.8%) CYP1A2 (8.9%) CYP2B6 (7.2%) CYP2C19 (6.8%) CYP2C8 (4.7%) CYP2A6 (3.4%) CYP2J2 (3.0%) CYP2E1 (3.0%)
53
Figure 9. Inference of CYP isoform phenotype according to the presence of alleles (CYP2C19, CYP3A5, CYP2B6) or to the enzyme activity score (CYP2D6 and CYP2C9).
UM: Ultrarapid metabolizer; RM: rapid metabolizer; NM: Normal metabolizer; IM:
Intermediate metabolizer; PM: Poor metabolizer.
Some examples of clinical pharmacogenetic guidelines for CYP enzymes are described as follows. Firstly, voriconazole is a drug prescribed for the management of severe fungal infections. It is mainly metabolized by CYP3A4 and CYP2C19 enzymes. It is well known that CYP2C19 ultrarapid (UMs) and poor (PMs) metabolizers may not respond appropriately to the treatment as they are under- or overexposed to voriconazole, respectively. Therefore, the drug may not be effective, or it may produce toxicity.
Consequently, the CPIC published a pharmacogenetic guideline recommending not to prescribe voriconazole to UMs or PMs and advocating for an alternative drug that is not metabolized by CYP2C19, e.g. posaconazole 39. On the contrary, clopidogrel is a prodrug that requires to me metabolized to exert its mechanism of action. CYP2C19 PMs and intermediate metabolizers (IMs) are related to reduced platelet inhibition and to an increased risk for life-threatening cardiovascular adverse events. Hence, an alternative treatment not metabolized by CYP2C19 is recommended, namely ticagrelor or prasugrel (Table 2) 40.
54
Table 2. Summary of recommendations for voriconazole and clopidogrel prescription based on CYP2C19 phenotype according to CPIC guidelines.
CYP2C19 Phenotype
Genotype (example)
Recommendation
Voriconazole Clopidogrel
Ultrarapid
metabolizer *17/*17
Use an alternative agent not dependent on CYP2C19 metabolism
Label recommended dosage and administration
Rapid
metabolizer *1/*17
Use an alternative agent not dependent on CYP2C19 metabolism
(adults) or label recommended dosage
and administration (paediatric)
Label recommended dosage and administration
Normal
metabolizer *1/*1
Label recommended dosage and administration
Label recommended dosage and administration Intermediate
metabolizer *1/*2
Label recommended dosage and administration
Alternative antiplatelet therapy
Poor metabolizer *2/*3
Use an alternative agent not dependant on CYP2C19 metabolism.
Alternative antiplatelet therapy
Regarding selective serotonin reuptake inhibitors (SSRI), paroxetine and fluvoxamine are significantly accumulated by CYP2D6 PMs, hence the use of a drug not metabolized by CYP2D6 is warranted, or a 25 – 50% initial dose reduction followed by dose titration is recommended. In the case of paroxetine, an alternative treatment is recommended for UMs. Similarly, sertraline and citalopram CYP2C19 PMs require a 50% initial dose reduction followed by titration or a change or treatment. For CYP2C19 UMs, a change of drug is necessary (Table 3) 41.
