Nanomedicine and personalized medicine toward the application of pharmacotyping in clinical practice to improve drug-delivery outcomes

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Nanomedicine: Nanotechnology, Biology, and Medicine 7 (2011) 11 – 17 www.nanomedjournal.com

Perspective

Nanomedicine and personalized medicine toward the application of pharmacotyping in clinical practice to improve drug-delivery outcomes, Ioannis S. Vizirianakis, PhD⁎ Laboratory of Pharmacology, Department of Pharmaceutical Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece Received 20 September 2010; accepted 2 November 2010

Abstract Recent technological advances in nanomedicine and nanotechnology in parallel with knowledge accumulated from the clinical translation of disease- and drug-related genomic data have created fertile ground for personalized medicine to emerge as the new direction in diagnosis and drug therapy. To this end, the development of sophisticated nano-based systems for targeted drug delivery, along with the advent of pharmacogenomics, moves the drug-prescription process toward pharmacotyping, e.g., the individualized adjustment of drug selection and dosage. However, the clinical validity and utility of pharmacogenomic testing must be demonstrated by cost-effectiveness analysis and establishment of clinical-practice reimbursement codes. Within this framework, and to achieve major benefits for all patients worldwide, a multidisciplinary scientific and technological infrastructure has to be organized in the healthcare system to address better the issues affecting regulatory environment, clinical pharmacology guidelines, education, bioethics and genomics data dissemination. From the Clinical Editor: Individualized pharmacotyping, patient and disease-specific delivery of drugs, combining nanotechnology and pharmagenomics-based approaches would result in much more specific and efficient treatment of a variety of illnesses. While this clearly is one of the main cornerstones of individualized medicine; the cost effective integration of this complex technology is far from trivial, as discussed in details in this opinion paper. © 2011 Elsevier Inc. All rights reserved. Key words: Nanomedicine; Personalized medicine; Pharmacogenomics; Pharmacotyping; Drug prescription

Until recently, medical practitioners had only their own clinical experience and knowledge for patient diagnosis and drug prescription. However, in recent years, physicians faced an information explosion from advances in genomic medicine and pharmacogenomics. Such genomic knowledge needs to be translated into clinically relevant forms readily applicable in clinical practice.1-13 Unfortunately this plethora of information has had a limited impact on pharmacotherapy and routine patient care due mainly to the lack of suitable drug-delivery information-based infrastructure in healthcare and due to the limited number of convenient tools available to make this knowledge broadly accessible for all patients. On the other

NOTE: The topic of this invited contribution has been presented at the 7th International Conference on Nanosciences & Nanotechnologies (NN10), 10-17 July, 2010, Ouranoupolis, Halkidiki, Greece. No funding was received for conducting the article and/or preparation of the paper. There are no conflicts of interest that are relevant to the content of the manuscript. ⁎Corresponding author. E-mail address: [email protected].

hand, the progress achieved and the experience gained in clinical pharmacogenomics have shown that an interdisciplinary approach could better enable personalized medicine treatment options to enter successfully and become efficiently established in the clinical setting.8,14

Factors contributing to drug response heterogeneity and the emergence of ADRs in clinical practice It has long been recognized by healthcare practitioners that individual patients respond differently to the same drug in terms of efficacy and safety.15,16 Drug pharmacological response involves two interrelated dynamic processes: pharmacodynamics (PD) and pharmacokinetics (PK). PD concerns effects of drugs on the body (e.g., drug-receptor interactions), whereas PK involves actions by an organism on a drug (e.g., the processes of drug absorption, distribution, metabolism and elimination [ADME]). The factors contributing to inter-individual heterogeneity of drug actions in groups of patients and the incidence of adverse drug reactions (ADRs) include several potential risk

1549-9634/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.nano.2010.11.002 Please cite this article as: I.S. Vizirianakis, Nanomedicine and personalized medicine toward the application of pharmacotyping in clinical practice to improve drug-delivery outcomes. Nanomedicine: NBM 2011;7:11-17, doi:10.1016/j.nano.2010.11.002

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Table 1 Estimated response rate variability for marketed drugs⁎ Disease

Inefficiency

Asthma Alzheimer’s disease Cancer (brain, breast, lung) Cardiac arrhythmias Depression Diabetes Duodenal ulcer Incontinence Hepatitis C (HCV) Hyperlipidemia Hypertension Migraine Osteoporosis Osteo/rheumatoid arthritis Schizophrenia

40–75% 70% 70–100% 40% 20–40% 50–75% 20–70% 60% 53% 30–75% 10–70% 30–60% 52% 20–50% 25–75%

⁎ See Silber et al18 and Spear et al.19

parameters, including disease pathophysiology, disease severity, potential drug or nutrient interactions, concomitant illnesses, specific organ function, age, lifestyle, behavior and/or genetic factors.13,17 It has been estimated that the response rate among patients for marketed drugs varies greatly as shown in Table 1.18,19 Of equal importance are findings regarding the incidence and the severity of ADRs in clinical practice. In a meta-analysis of 39 prospective studies from United States (U.S.) hospitals, it has been estimated that 6.7% of hospitalized patients developed ADRs during pharmacotherapy and 0.32% experienced fatal ADRs, the latter causing approximately 100,000 deaths per year in U.S.20 These findings are supported by data from the Institute of Medicine showing that nearly 98,000 deaths in U.S. annually are attributed to medication errors, including ADRs.21 Furthermore, 10–17% of patient hospitalizations are directly related to the emergence of ADRs, an incidence that makes drug safety a fundamental issue in healthcare.22 In economic terms, the cost attributed to such ADRs was estimated to reach $100 billion, a cost that further stresses the need for the improvement of drug-delivery practices.23 The incidence of ADRs in hospitalized patients was further confirmed in a European study conducted in a Norwegian hospital. In that study, of the 732 deaths reported among the 13,993 in-patients in a 2-year period, 133 deaths were directly attributed to ADRs.24 Such data indicates almost 10 deaths per 1,000 hospitalized patients, a number that urgently demands the improvement of drug delivery by strengthening the efficacy and minimizing the toxicity of drugs. Such a need is further supported by the fact that 7% of all hospital admissions in the United Kingdom and Sweden are due to serious ADRs developed in clinical practice.23

Pharmacogenomics, ADRs and the development of biomarkers to predict drug response The genetic background, or the actions of genes, involved in the response of an organism to delivered drugs is complex. This

interplay between genes and drugs also suggests that their interaction can modulate the pharmacotherapeutic outcome through either genetic variation or drug-regulated gene expression. This effect leads to phenotypic variation in pharmacotherapy, e.g., to differential pharmacological response.6,8 Detailed analysis of the molecular actions of drugs has clearly shown that medicines exert their effects via specific “molecular networks” involving several genes and proteins.25 Recently, researchers established the notion that epigenetic phenomena (DNA methylation, histone methylation and acetylation), as well as microRNAs (miRNAs) and RNA interference (RNAi) mechanisms, contribute to establishing specific gene expression patterns in both normal physiology and disease pathophysiology.26-28 This finding means that both genetic and epigenetic factors must be considered in the efficient clinical translation of pharmacogenomics data to enhance their clinical validity and utility. Although the prospects for basic research in pharmacogenomics and the generation of considerable amounts of data look very promising, their incorporation into clinical practice is challenging.29 However, the progress achieved thus far in biomolecular and genomic technologies facilitate the analysis and exploitation of genetic variations for therapeutic intervention. By incorporating the findings of the field of clinical PK, it has been revealed that drug-metabolizing enzymes and transporters play important roles in disposition, therapeutic efficacy and ADRs of various drug molecules.23,30 Indeed, several drug interaction studies have suggested that transporters often work together with drug-metabolizing enzymes to affect drug absorption and elimination processes. The latter is of clinical relevance both in new drug development, as well as when trying to understand drug response heterogeneity, drug interactions and the emergence of ADRs.30,31 In general, cytochrome P450 (CYP) enzymes belonging to families 1, 2 and 3 mediate 70-80% of all phase I-dependent metabolism of clinically used drugs. By evaluating the incidence of ADRs, researchers have shown that ∼56% of drugs cited in ADR-related studies and databases are metabolized by polymorphic enzymes of phase I, in which 86% account for CYP-mediated metabolism. Importantly, the polymorphic CYP2C9, CYP2C19 and CYP2D6 enzymes mediate ∼40% of CYP-mediated drug metabolism and are responsible for a significant number of ADRs, a result that makes determining correct drug dosages problematical.23 The cost of treating patients who possess polymorphic forms of CYPs is much higher than the cost of treating patients with nonpolymorphic alleles. In contrast, only 20% of drugs that are substrates of non-polymorphic enzymes are cited in the ADR reports deposited in pharmacovigilance data banks.23,32 Thus, the need for the development and clinical validation of specific pharmacogenomics biomarkers to predict and evaluate drug response is of crucial importance for strengthening the new drug development process and for improving drug efficacy and safety profiles. Until now, several such pharmacogenomics biomarkers have been approved and are included in drug labeling by regulatory agencies (the U.S. Food and Drug Administration [FDA] and the European Medicines Agency [EMA]) (Table 2).

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Table 2 Clinically relevant predictive pharmacogenomic biomarkers for improved drug delivery and therapy outcomes Gene / Specific allele

Drug

Efficacy / safety outcome

ABCC2 C-24T CYP2D6

Diclofenac Atomoxetine Thioridazine Codeine Tamoxifene Voriconazole Warfarin Maraviroc Imatinib Rasburicase Capecitabine Erlotinib Cetuximab Panitumumab Anastozole Exemestane Letrozole Tamoxifene NSAIDs Tacrine Trastuzumab Carbamazepine

Lower safety; ADRs; Increased risk for hepatotoxicity Lower safety; ADRs; Dose reduction for PM patients Lower safety; ADRs; QT prolongation, torsade de points Lower safety; ADRs; Overdose symptoms for UM patients Lower efficacy; Loss of therapeutic benefit for PMs and/or upon co-administration with CYP2D6 inhibitors Lower safety; ADRs; Hepatotoxicity Lower safety; ADRs; Risk of bleeding Lower efficacy; No response for CCR5-negative patients Lower efficacy; No response in cancer patients with absence of tumor activating c-Kit mutations Lower safety; ADRs; Hemolysis in G6PD-deficient patients Lower safety; ADRs; Orodigestive neutropenia Lower efficacy; No response in cancer patients with tumor EGFR-negative expression Lower efficacy; No response in cancer patients with tumor EGFR-negative expression Lower efficacy; No response in cancer patients with tumor EGFR-negative expression Lower efficacy; No response in cancer patients with tumor ER-negative expression Lower efficacy; No response in cancer patients with tumor ER-negative expression Lower efficacy; No response in cancer patients with tumor ER-negative expression Lower efficacy; No response in cancer patients with tumor ER-negative expression Lower safety; ADRs; Increased risk for cytolytic hepatitis Lower safety; ADRs; Increased risk for cytolytic hepatitis Lower efficacy; No response in cancer patients with tumor HER2-negative expression Lower safety; ADRs; Severe cutaneous immunoallergic reaction; Increased risk for Stevens-Johnson syndrome (Asian) Lower safety; ADRs; Increased risk for hypersensitivity reactions Lower safety; ADRs; Increased risk for hypersensitivity reactions Lower safety; ADRs; Increased risk for hypersensitivity reactions Lower safety; ADRs; Increased risk for cytolytic hepatitis Lower safety; ADRs; Increased risk for cholestatic hepatitis Lower safety; ADRs; Increased risk for Stevens-Johnson syndrome Lower efficacy; No response in cancer patients with tumor specific K-RAS mutations Lower efficacy; No response in cancer patients with tumor specific K-RAS mutations Lower safety; ADRs; Increased risk for cytolytic hepatitis Lower safety; ADRs; Increased risk for myopathy Lower safety; ADRs; Increased risk for cholestatic hepatitis Lower safety; ADRs; Neutropenia Lower safety; ADRs; Neutropenia Lower safety; ADRs; Neutropenia Lower safety; ADRs; Increased risk for hepatotoxicity Lower safety; ADRs; Diarrhea, Increased risk for severe neutropenia in high doses of irinotecan Lower safety; Risk of bleeding

CYP2C19 CYP2C9 CCR5 c-kit G6PD DPD EGFR

ER

GSTM1 / GSTT1 HER2 HLA-B*1502 HLA-B*5701 HLA Cw8-B14 HLA-DRB1*0101 HLA-DRB1*0701 HLA-DRB1*DQB1 HLA-B*5801 K-RAS NAT2 SLC01B1*5 TNF-α -238G/A TPMT

UGT2B7*2 UGT1A1*28 VCORC1

Abacavir Nevirapine Nevirapine Ximelagatran Flucloxacillin Allopurinol Cetuximab Panitumumab Isoniazid Simvastatin Flucloxacillin Azathioprine 6-Mercaptopurine Thioguanine Diclofenac Irinotecan Warfarin

Abbreviations: ADRs: adverse drug reactions; NSAIDs: non-steroid anti-inflammatory drugs; PM: poor metabolizers; UM: ultra-rapid metabolizers.

Personalized medicine and pharmacotyping in drug prescription for everyday clinical practice By integrating in healthcare genomic drug-related data, pharmacogenomics is moving toward the application of pharmacotyping in drug prescription, i.e., individualized drug selection and dosage scheme profiling. Such direction will enable physicians to incorporate patients' genotyping and haplotyping data for genes involved in PD- and PK-related effects of drugs when prescribing drugs.3,33 This means that the drug-delivery environment is changing to a drug-selection process in which physicians use their own clinical experience in a more highly integrated, information-based and computer-aided process than was possible in the past. Importantly, these changes ensure personalized medicine by making drug delivery digitized, more efficient and safer. However, major changes in a costeffective way must be clearly addressed and demonstrated to

allow a) the broader clinical translation of genomic data for routine drug prescription, b) the suitable training of future healthcare professionals; and c) the integration of genomic medicine into clinical trials.33 In addition, extensive and thoughtful discussions about ethical, societal, and economic impacts arising from the clinical application of pharmacogenomic testing are both a necessity and a major challenge. The advent of specialized genotyping techniques in conjunction with functional genomics knowledge positively influences the application of genomic information to laboratory and clinical practice. This means that unified platforms must be developed to permit compatibility in handling different data gathered from unrelated sources, like those of drug databases, clinical trial reports, genotyping and functional genomics data. By building such infrastructure, our scientific skills in pharmacological molecular target exploitation of specific cellular molecules involved in PK and PD effects of drugs in

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the body would be greatly enhanced. Indeed, such an approach is beneficial in developing modern therapies with targeted molecules to exert specific pharmacological profiles in diseased tissues.34 This task is also facilitated through the development of a functional mapping framework in pharmacology to address more effectively the smooth integration of pharmacogenomics data into PD/PK environment, as has been recently proposed.35 Thus, the mathematical models currently used to study the PD and PK processes can be efficiently coupled with pharmacogenomics data applied to specific molecular networks and pharmacological pathways.

Nanomedicine to advance personalized medicine and pharmacotyping Due to the advancements of nanotechnology, nanomedicine formulations (nanovehicles) of therapeutics are now developed with proper PK/PD behavior to specifically accumulate in their site of action within the body and to achieve better pharmacotherapeutic outcomes. In fact, a balance between maximum therapeutic efficacy with lower toxicity can be attained. Nanodrugs and nanodiagnostics often lead to greater bioavailability profiles with lower doses, thus decreasing the rate of ADRs in clinical practice and increasing patients' compliance.36 Important nanotechnological applications for personalized medicine are now considered for the development of targeted drug formulations that achieve maximum efficacy and optimal safety profiles. For example, through conjugation of nanovehicles with an antibody, the guided accumulation of the carrying drug in its target antigen located in diseased specific tissue (e.g., cancer) can be directed efficiently.37,38 Furthermore, such therapeutic advantages of nanovehicles are also used for imaging applications and theranostic approaches, i.e., for systems and strategies in which both disease diagnosis and therapy are combined to benefit personalized medicine.39 At the same time, specific nanomaterials, including nanotubes, dendrimers, liposomes, and quantum dots, are being developed as molecular diagnostic probes to target in vivo specific tissues or cells, thus implementing imaging techniques and improving clinical outcomes.40 Last, the development of various nanotechnologyenabled miniature tools and machines is now feasible and cost effective, which will make it possible to produce lab-on-chip approaches (including sample mixing, transport, integration, detection and data processing) that can substitute for testing in a clinical laboratory.41 In addition, the capability to generate cheaper high-throughput DNA sequencing and other genomic technologies permits the application of personal genome analysis in clinical practice for each patient, which will facilitate the movement of pharmacogenomics and personalized medicine toward pharmacotyping in drug prescription.

The economic basis for applying pharmacogenomics in clinical practice Although there is widespread interest in personalized medicine, the broad application of pharmacogenomic testing

Figure 1. Diagrammatic outline of the multidisciplinary environment proposed for nanomedicine and personalized medicine to ensure major benefits for disease diagnosis and drug development and delivery to improve clinical outcomes (see text for details).

implies that cost-effective validation of clinical improvement outcomes must be demonstrated. However, only a few studies have addressed cost-effectiveness of pharmacogenomics applications in clinical practice. In one case, for example, thiopurine methyltransferase (TPMT) genotyping for children with acute lymphoblastic leukemia (ALL) who took thiopurine was assessed in 4 European countries (Germany, Ireland, The Netherlands and the United Kingdom). Interestingly, the practice of prescribing thiopurine to juvenile ALL patients based on their TPMT genotype has shown a favorable cost-effectiveness ratio.42 Further analysis of data collected from the U.K. and Spain suggests that key pharmacovigilance data should include evidence for azathioprine prescription in favor of routine TPMT testing by reducing the emergence of azathioprine-related ADRs.43 On the contrary, the lack of validated economic models on a unified basis to examine the costs and benefits of pharmacogenomic testing has been clearly identified in the case of CYP polymorphisms testing in antipsychotics prescription.44 Furthermore, the economic evaluation of pharmacogenomic testing is now considered a main barrier to the implementation of clinical practice with pharmacogenomics knowledge.45,46 Importantly, before implementing routine application of pharmacotyping concepts in the healthcare system, the demonstration of economic benefits must accompany the validation of clinical effectiveness of pharmacogenomic testing. Such a direction will allow cost-effectiveness analysis to verify the relative costs and benefits of pharmacogenomic interventions in comparison with current practice and create the framework for healthcare providers to make reimbursement decisions.47,48 Importantly, this need is further emphasized by the fact that, in a recent study to assess the clinical effectiveness of pharmacogenomics, only two biomarkers had demonstrated clinical utility, although most biomarkers had demonstrated clinical validity.49

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Figure 2. Presentation of the proposed multidisciplinary research groups collaboration to achieve personalized drug therapy and pharmacotyping in clinical practice and pharmacotherapy. Research groups working in the areas of therapeutic drug monitoring and population-based PK/PD models (regarding the assessment of drug levels and behavior in the body) can efficiently collaborate with genomic medicine and pharmacogenetics/pharmacogenomics groups conducting the clinical translation of genomics data; such collaboration will build a multidisciplinary infrastructure and knowledge base that may enhance personalized medicine implementation and pharmacotyping born out (see text for details).

Multidisciplinary approach to support personalized medicine and pharmacotyping infrastructure Specific scientific steps based on a multidisciplinary infrastructure and representing painstaking and time-consuming processes are needed to achieve a better understanding of the mechanisms implicated in disease pathophysiology and basic biological processes of pharmacological interest, including drug response heterogeneity. The development of clinically validated, specific genetic biomarkers for their role to improve diagnosis and to allow the prediction of drug response must be achieved. Researchers must ensure that the broader application of genome-wide linkage analysis, genotyping, gene array, proteomics, transcriptomics and metabolomics profiling can be efficiently integrated in the clinic and prove valuable for all patients.50-53 Moving toward personalized medicine and pharmacotyping also requires the development of tools and molecular diagnostics capable of assessing genome-related clinical information in laboratory medicine that will enable the physician to apply the extracted information (Figure 1). This is considered, however, a very difficult task, because before techniques used in genomic-related research laboratories can be transferred to diagnostic laboratories that analyze clinical samples, these methods and techniques must be also assessed for their ethical, social and cost-benefit consequences.13,29,54 Although there is no single model for pharmacogenomics research to achieve the major benefits in clinical practice and ensure a secure roadmap for pharmacotyping, it is evident that this can be achieved by building a multidisciplinary structure to integrate expertise from pharmacology, genomics, bioinformatics and clinical sciences (Figure 1). This direction is obviously supported through the development of specialized

pharmacogenomics databases to support the collection, assessment, organization and finally dissemination of relevant drugrelated data including pharmacological, pharmacogenomics and clinical ones. Such an example represents the “Pharmacogenetics and Pharmacogenomics Knowledge Base” (PharmGKB), which is a public database that focuses on genotype and phenotype data relevant to pharmacogenomics.55,56 The organization of this knowledge base is being achieved by capturing the relationships among drugs, diseases/phenotypes and genes involved in PK and PD through literature annotations, primary data sets, PK and PD pathways, as well as expert-generated summaries of PK/PD relationships among drugs, diseases/phenotypes and genes. By building such a multidisciplinary infrastructure, where the current clinical approaches of therapeutic drug monitoring and population-based PK/PD models assessing drug levels and behavior in the body could be integrated with pharmacogenomics and genomic medicine concepts, this approach paves the way for pharmacotyping to be greatly facilitated and implemented in routine clinical practice (Figure 2). In addition, by establishing such an environment, the education of healthcare practitioners in molecular/clinical pharmacology, drug interactions, pharmacogenomics and translational medicine should be finally achieved.57,58

The example of tamoxifen delivery and the need for the conduction of large prospective pharmacogenomics studies Among the major challenges facing clinicians and researchers engaged in the clinical translation of pharmacogenomics research data has been the integration of such data into the clinical pharmacology guidelines for most drugs and for each

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medical specialty. The successful application of pharmacotyping concepts in pharmacotherapy is predicated on the practical integration of such guidelines for specific drug-dosage scheme recommendations in everyday drug prescription processes.33 This task is expected to be advanced through the development of workflow information-based operating systems in healthcare to support the utilization, assessment and outcome of clinical and genomic information. Such a direction would also promote the revision and adjustment of clinical regulatory requirements, the improved design of clinical trials, and the registry and evaluation of pharmacovigilance data.33 Recently, the delivery of the anti-estrogen drug tamoxifen to hormone-dependent breast cancer patients has clearly demonstrated the usefulness of the CYP2D6 metabolism system in terms of drug interactions and pharmacogenomics. Tamoxifen is a pro-drug that needs to be metabolized mainly by CYP2D6 into the pharmacologically active metabolites 4-hydroxytamoxifen and endoxifen that mediate the anti-estrogen action. It has been postulated that knowledge of the CYP2D6 system could efficiently allow physicians to coprescribe tamoxifen with other medications (e.g., antidepressants) in individualized dosage schemes to improve its clinical outcome.59-63 It is now evident that CYP2D6 poor-matabolizer (PM) patients (due to polymorphic null-activity CYP2D6 alleles), and women undergoing tamoxifen therapy coprescribed with potent CYP2D6 inhibitors (e.g., the antidepressant drugs fluoxetine and paroxetine), exhibit an increased risk of breast cancer recurrence and mortality due to decreased levels of active tamoxifen metabolites formed in the body (see also Table 2). Thus, the individualized tamoxifen-dosage scheme can be achieved for CYP2D6 PM breast cancer patients by not prescribing tamoxifen with CYP2D6 inhibitors, by adjustment of tamoxifen dose, and/or by switching hormonal therapy into another drug class (e.g., aromatase inhibitors). This individualized tamoxifen-dosage scheme can also be achieved for breast cancer patients with CYP2D6 extensive-metabolizers phenotype (exhibiting normal CYP2D6 metabolism) by choosing an antidepressant with no CYP2D6-inhibitory effect (e.g., venlafaxine). This is an example of pharmacotyping according to how drug-interaction information extracted from clinical and molecular pharmacology can be successfully combined with pharmacogenomics clinicians' knowledge of genes involved in PK/PD drug effects to advance drug-prescription proficiency and improve clinical outcomes. In such a case, well-educated healthcare practitioners, development and adjustment of clinical pharmacology/pharmacogenomics guidelines, and organization of infrastructure in healthcare equipped with the proper clinically validated technological methodologies is now, more than ever, essential. Finally, to this end, well-designed clinical pharmacogenomics studies to confirm utility and determine whether personalized medicine and pharmacogenomics are mature enough to be of practical value for most drugs in everyday clinical practice are needed. An example of such an attempt is the “Coriell Personalized Medicine Collaborative,” which is a large prospective observational study designed to address the clinical utility of personal genome data in routine patient care.64 In such well-organized pharmacogenomics models that assemble large cohort studies assessed by multidisciplinary research teams, the utility of personal genetic

information for disease risk assessment and prevention, as well as for pharmacotyping-based drug prescription, will be better outlined and elucidated. By achieving that task, major benefits for both the healthcare system and society are delineated and more efficiently implemented.

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