Pharmacogenomic Biomarkers Research Articles

Clinical application of companion diagnostics

Optimally, any prescription should rely on an in-depth understanding of the disease biology and the mechanism of action of the drug. However, despite the hype about precision medicine in recent years, the prescription of most drugs is still, except for a few anticancer drugs, largely based on 'trial and error' and not on solid pharmacogenomic biomarker data.

FDA's draft guidance on laboratory-developed tests increases clinical and economic risk to adoption of pharmacogenetic testing.

The US Food and Drug Administration (FDA) notified Congress July 31, 2014,1 of its intent to regulate laboratory developed tests. These encompass thousands of clinical assays currently used in medical practice including most pharmacogenetic tests. This guidance has the potential to impact the innovation and sustainability of pharmacogenetic research and its clinical implementation.

Actionable clinical decisions based on comprehensive genomic evaluation in asymptomatic adults.

Whole-exome sequencing (WES) arises as a new approach in diagnosing individuals affected by multigenic and complex phenotypes. Herein, we aim to examine whether WES is useful in screening asymptomatic individuals for actionable interventions, which has not yet been established. Twenty-five healthy adults underwent WES, bioinformatics, and manual curation of their exomes. Six participants (24%) harbored significant, management-changing variants in cancer predisposition genes, American College of Medical Genetics, and genomics reportable cardiac diseases and pharmacogenomic biomarkers that have led to clinical recommendations and interventions. Furthermore, more than 80% of the participants (21) carried 1–3 genetic variants with an associated clinical guideline for an altered drug dosing or administration based on the FDA’s table of pharmacogenomics. These results support WES potential not only to answer specific diagnostic questions presented by the relevant personal and/or family history but also to uncover clinically important genetic findings unrelated to the primary indication for sequencing.

PCN172 - Quality of evidence supporting inclusion of Pharmacogenomic biomarkers in product labels of fda approved Oncology Therapies

PCN172 - Quality of evidence supporting inclusion of Pharmacogenomic biomarkers in product labels of fda approved Oncology Therapies

Pharmacogenomics and personalized medicine: a review focused on their application in the Chinese population.

The field of pharmacogenomics was initiated in the 1950s and began to thrive after the completion of the human genome project 10 years ago. Thus far, more than 100 drug labels and clinical guidelines referring to pharmacogenomic biomarkers have been published, and several key pharmacogenomic markers for either drug safety or efficacy have been identified and subsequently adopted in clinical practice as pre-treatment genetic tests. However, a tremendous variation of genetic backgrounds exists between different ethnic groups. The application of pharmacogenomics in the Chinese population is still a long way off, since the published guidelines issued by the organizations such as US Food and Drug Administration require further confirmation in the Chinese population. This review highlights important pharmacogenomic discoveries in the Chinese population and compares the Chinese population with other nations regarding the pharmacogenomics of five most commonly used drugs, ie, tacrolimus, cyclosporine A, warfarin, cyclophosphamide and azathioprine.

Mode of action and pharmacogenomic biomarkers for exceptional responders to didemnin B.

Modern cancer treatment employs many effective chemotherapeutic agents originally discovered from natural sources. However, a significant challenge currently confronting clinical application is balancing systemic toxicity risk with therapeutic benefit. The cyclic depsipeptide didemnin B has demonstrated impressive anti-cancer activity in preclinical models. Clinical use has been approved but is limited by sparse patient responses combined with toxicity risk and an unclear mechanism of action. From a broad-scale effort to match antineoplastic natural products to their cellular activities, we found that didemnin B selectively induces rapid and wholesale apoptosis through dual inhibition of PPT1 and EEF1A1. Furthermore, empirical discovery of a small panel of exceptional responders to didemnin B allowed generation of a regularized regression model to extract a sparse-feature genetic biomarker capable of predicting sensitivity to didemnin B. This may facilitate patient selection that could enhance and expand therapeutic application of didemnin B against neoplastic disease.

Clinical association between pharmacogenomics and adverse drug reactions.

Adverse drug reactions (ADRs) are a major public health concern and cause significant patient morbidity and mortality. Pharmacogenomics is the study of how genetic polymorphisms affect an individual's response to pharmacotherapy at the level of a whole genome. This article updates our knowledge on how genetic polymorphisms of important genes alter the risk of ADR occurrence after an extensive literature search. To date, at least 244 pharmacogenes identified have been associated with ADRs of 176 clinically used drugs based on PharmGKB. At least 28 genes associated with the risk of ADRs have been listed by the Food and Drug Administration as pharmacogenomic biomarkers. With the availability of affordable and reliable testing tools, pharmacogenomics looks promising to predict, reduce, and minimize ADRs in selected populations.

How Can Pharmacogenomics Biomarkers Be Translated into Patient Benefit

Treatment resistant schizophrenia (TRS) is one of the most disabling of psychiatric disorders, affecting about 1/3 of patients. First-line treatments include both atypical and typical antipsychotics. The original atypical, clozapine, is a final option, and although it has been shown to be the only effective treatment for TRS, many patients do not respond well to clozapine. Clozapine use is related to adverse events, most notably agranulocytosis, a potentially fatal blood disorder which affects about 1% of those prescribed clozapine and requires regular blood monitoring. This as a barrier to prescription and there is a long delay in access for TRS patients, of five or more years, from first antipsychotic prescription. Better tools to predict treatment resistance and to identify risk of adverse events would allow faster and safer access to clozapine for patients who are likely to benefit from it. The CRESTAR project (www.crestar-project.eu) is a European Framework 7 collaborative project that aims to develop tools to predict i) treatment response, particularly patients who are less likely to respond to usual antipsychotics, indicating treatment with clozapine as early as possible, ii) patients who are at high or low risk of adverse events and side effects, iii) extreme TRS patients so that they can be stratified in clinical trials for novel treatments. CRESTAR has addressed these questions by examining genome-wide association data, genome sequence, epigenetic biomarkers and epidemiological data in European patient cohorts characterized for treatment response, and adverse drug reaction using data from clozapine therapeutic drug monitoring and linked National population medical and pharmacy databases, to identify predictive factors. In parallel CRESTAR will perform health economic research on potential benefits, and ethics and patient-centred research with stakeholders.

Pharmacogenomic biomarkers for personalized cancer treatment

Personalized medicine involves the selection of the safest and most effective pharmacological treatment based on the molecular characteristics of the patient. In the case of anticancer drugs, tumour cell alterations can have a great impact on drug activity and, in fact, most biomarkers predicting response originate from these cells. On the other hand, the risk of developing severe toxicity may be related to the genetic background of the patient. Thus, understanding the molecular characteristics of both the tumour and the patient, and establishing their relation with drug outcomes will be critical for the identification of predictive biomarkers and to provide the basis for individualized treatments. This is a complex scenario where multiple genes as well as pathophysiological and environmental factors are important; in addition, tumours exhibit large inter- and intraindividual variability in space and time. Against this background, the huge amounts of biological and genetic data generated by the high-throughput technologies will facilitate pharmacogenomic progress, suggest novel druggable molecules and support the design of future strategies aimed at disease control. Here, we will review the current challenges and opportunities for pharmacogenomic studies in oncology, as well as the clinically established biomarkers. Lung and renal cancer, two areas in which huge progress has been made in the last decade, will be used to illustrate advances in personalized cancer treatment; we will review EGFR mutation as the paradigm of targeted therapies in lung cancer, and discuss the dissection of lung cancer into clinically relevant molecular subsets and novel advances that suggest an important role of single nucleotide polymorphisms in the response to antiangiogenic agents, as well as the challenges that remain in these fields. Finally, we will present new approaches and future prospects for personalizing medicine in oncology.

Complementary versus companion diagnostics: apples and oranges?

There have been several major problems that have plagued biopharmaceutical development since the end of the 1990s, but two in particular have reached the point where they are impacting the economic viability of the industry: the lack of efficacy of new drugs and increasing competition among therapeutics that broadly attack certain common diseases and disease areas. The US FDA has noted that the era of one-size-fits-all treatment may well be reaching its end days as companies increasingly adopt approaches that involve biomarkers (there are now commercial databases that purport to track over 11,000 of them). Pharmacogenomic biomarkers in particular are used to create diagnostics that help to differentiate or stratify the likely outcomes a patient will experience with a drug, which can now be said to be targeted or tailored to patients with particular traits (i.e., personalized), leading to an era of so-called precision medicine. As more is understood about diseases and the why and how of their effects on people through advances in biomarkers and genomics, personalized medicine is becoming a natural result of biomedical science and a natural trajectory for the innovation-based biopharmaceutical industry. The focus of this article is to examine an apparent divergence in that trajectory engendered by a growing differentiation in the approaches to personalized medicines in terms of their accompanying diagnostics: companion diagnostics are typically linked to a specific drug within its approved label, while complementary diagnostics are associated more broadly, usually not with a specific drug but with a class of drugs, and not confined to specific uses by labeling, with consequent ramifications for economic, regulatory and strategic considerations.

Pharmacogenomic biomarkers for colorectal cancer treatment

Abstract An important part of personalized medicine in colorectal cancer (CRC) relies on using the most effective treatment based on molecular biomarkers, which define groups of patients according to specific tumor alterations. Therefore, anti-tumoral drugs are administered selectively to a subgroup of patients whose genetic alterations in the tumors indicate that they have an increased probability of recurrence without treatment or to those patients who will most likely respond to the treatment. Currently, pharmaceutical companies use targeted drugs with biomarkers during the early stages of drug development. Then, the companion diagnostics that are developed based on response-specific biomarkers allow for the administration of the right drug to the right patient. Because CRC has become one of the most common neoplasias, personalized medicine has changed the oncologists' and pathologists' daily routines. In fact, KRAS mutations represented a revolution in targeted therapies and had clinical relevance for patients, clinicians and pharmaceutical companies. However, the new biomarkers, including microsatellite instability (MSI) and both NRAS and BRAF mutations, are well established molecular markers that determine CRC subgroups and should be considered separately when debating treatment options. However, despite the scientific evidence, these biomarkers have not yet been incorporated into practice. More clinical facts and cost-effectiveness analysis may be needed for their uniform implementation.

A Reachable Goal: Application of Pharmacogenomics Biomarkers to Improve Drug Efficacy and Safety

A Reachable Goal: Application of Pharmacogenomics Biomarkers to Improve Drug Efficacy and Safety

Roadblocks preventing personalized medicine from reaching its potential.

Biomarkers in MedicineVol. 9, No. 1 CommentaryRoadblocks preventing personalized medicine from reaching its potentialJoshua P CohenJoshua P CohenTufts Center for the Study of Drug Development, 75 Kneeland Street, Suite 1100, Boston, MA 02111, USA E-mail Address: joshua.cohen@tufts.eduSearch for more papers by this authorPublished Online:21 Jan 2015https://doi.org/10.2217/bmm.14.66AboutSectionsView ArticleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInReddit View articleKeywords: biomarkersclinical utilitypersonalized medicinereimbursementReferences1 President's Council of Advisors on Science and Technology. Priorities for personalized medicine (2008). www.whitehouse.gov Google Scholar2 US FDA. Guidance for Industry and Food and Drug Administration Staff: in vitro companion diagnostic devices (2011). www.fda.gov Google Scholar3 US FDA. Paving the way for personalized medicine: FDA's role in a new era of medical product development (2013). www.fda.gov Google Scholar4 Cohen J. Overcoming regulatory and economic challenges facing pharmacogenomics. N. Biotechnol. 29(6), 751–756 (2012).Crossref, Medline, CAS, Google Scholar5 US FDA. Table of pharmacogenomic biomarkers in drug labeling (2014). www.fda.gov Google Scholar6 Reinke T. Targeted medications: new focus on companion tests. Manag. Care 21(2), 35–38 (2012).Medline, Google Scholar7 Milne C-P, Garafalo S, Bryan C et al. Trial watch: Personalized medicines in late-stage development. Nat. Rev. Drug Discov. 13, 324–325 (2014).Crossref, Medline, CAS, Google Scholar8 Cohen J, Felix A. Personalized medicine's bottleneck: diagnostic test evidence and reimbursement. J. Pers. Med. 4, 163–175 (2014).Crossref, Medline, Google Scholar9 Ansari M. The regulation of companion diagnostics: a global perspective. Ther. Innovat. Reg. Sci. 47, 405–415 (2013).Crossref, Medline, Google Scholar10 US FDA. FDA approves Xalkori with companion diagnostic for a type of late-stage lung cancer (2011). www.fda.gov Google Scholar11 Centers for Medicare and Medicaid Services. Pharmacogenomic testing to predict warfarin responsiveness. www.cms.gov Google Scholar12 Eisenberg A. Variations on a gene, and tools to find them. New York Times 27 April 2013.Google Scholar13 Tunis S, Pearson S. Coverage options for promising technologies: Medicare's ‘coverage with evidence development’. Health Aff. 25(5), 1218–1230 (2006).Crossref, Medline, Google Scholar14 NIH. Reimbursement models to promote evidence generation and innovation for genomic tests (2012). www.genome.gov Google Scholar15 Twachtman G. With personalized medicine, payers want a role in early development. The Pink Sheet (2013). www.pharmamedtechbi.comGoogle Scholar16 Edlin M. Drug manufacturers seek payer feedback. Managed Care Executive (2013). http://managedhealthcareexecutive.modernmedicine.comlGoogle Scholar17 Palmetto's Molecular Diagnostic Services Program (MolDx) program. www.palmettogba.com Google Scholar18 Usdin S. Coding for utility. BioCentury 15 July 2013.Google Scholar19 Quinn B, Hoag F. Current issues and options: coverage and reimbursement for molecular diagnostics: Department of Health and Human Services (HHS) ASPE Workgroup (2011). http://aspe.hhs.govGoogle ScholarFiguresReferencesRelatedDetailsCited ByFundamentally low reproducibility in molecular genetic cancer research30 July 2016 | Russian Journal of Genetics, Vol. 52, No. 7Companion diagnostics at the intersection of personalized medicine and healthcare deliveryScott A Waldman & Andre Terzic21 January 2015 | Biomarkers in Medicine, Vol. 9, No. 1 Vol. 9, No. 1 Follow us on social media for the latest updates Metrics Downloaded 81 times History Published online 21 January 2015 Published in print January 2015 Information© Future Medicine LtdKeywordsbiomarkersclinical utilitypersonalized medicinereimbursementFinancial & competing interests disclosureThe author has no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.No writing assistance was utilized in the production of this manuscript.PDF download

Guidance for pharmacogenomic biomarker testing in labels of FDA-approved drugs.

The aim of this study was to compare guidance for genetic testing in US Food and Drug Administration (FDA)-approved drug labels in oncology to those of drugs for other therapeutic areas. We reviewed labels of all the FDA-approved drugs with labels containing pharmacogenomic information. We assessed whether genetic testing was required or recommended before prescription and, if not, the reason for pharmacogenomic labeling. We included 140 drugs corresponding to 158 drug-biomarker pairs. Overall, 46 (29%) of 158 pairs stated a requirement or recommendation for genetic biomarker testing in the label. This proportion was higher in oncology than in other areas (62 vs. 12%; P < 0.001). For the 112 drug-biomarker pairs (including 20 in oncology) without recommendation or requirement for genetic testing, the main reasons for pharmacogenomic labeling were change in pharmacologic end points (32%) and higher risk of toxicity (30%). For 11 (10%) pairs (including 1 in oncology), a genetic biomarker was mentioned only to inform that it was not relevant. In oncology, the main reasons for pharmacogenomic labeling were higher risk of toxicity (55%) and definition of the mechanism of action (25%). Inclusion of biomarkers in drug labels does not always correspond to required or recommended genetic testing, especially outside oncology.Genet Med 17 9, 733-738.

Clinical evidence supporting pharmacogenomic biomarker testing provided in US Food and Drug Administration drug labels.

Genetic biomarkers that predict a drug's efficacy or likelihood of toxicity are assuming increasingly important roles in the personalization of pharmacotherapy, but concern exists that evidence that links use of some biomarkers to clinical benefit is insufficient. Nevertheless, information about the use of biomarkers appears in the labels of many prescription drugs, which may add confusion to the clinical decision-making process. To evaluate the evidence that supports pharmacogenomic biomarker testing in drug labels and how frequently testing is recommended. Publicly available US Food and Drug Administration databases. We identified drug labels that described the use of a biomarker and evaluated whether the label contained or referenced convincing evidence of its clinical validity (ie, the ability to predict phenotype) and clinical utility (ie, the ability to improve clinical outcomes) using guidelines published by the Evaluation of Genomic Applications in Practice and Prevention Working Group. We graded the completeness of the citation of supporting studies and determined whether the label recommended incorporation of biomarker test results in therapeutic decision making. Of the 119 drug-biomarker combinations, only 43 (36.1%) had labels that provided convincing clinical validity evidence, whereas 18 (15.1%) provided convincing evidence of clinical utility. Sixty-one labels (51.3%) made recommendations about how clinical decisions should be based on the results of a biomarker test; 36 (30.3%) of these contained convincing clinical utility data. A full description of supporting studies was included in 13 labels (10.9%). Fewer than one-sixth of drug labels contained or referenced convincing evidence of clinical utility of biomarker testing, whereas more than half made recommendations based on biomarker test results. It may be premature to include biomarker testing recommendations in drug labels when convincing data that link testing to patient outcomes do not exist.

A call for accurate pharmacogenetic labeling: telling it like it is.

A call for accurate pharmacogenetic labeling: telling it like it is.

Pharmacogenomic Biomarker Information in FDA‐approved Paediatric Drug Labels

Gene maturation differs between paediatric and adult populations, and the extrapolation of adult pharmacogenomic information to paediatrics is not always appropriate. We sought to determine the extent of paediatric pharmacogenomic trial translation into US FDA-approved labels and to evaluate needs for biomarker studies. Using FDA's Table of Genomic Biomarkers and Drugs@FDA website, 38 pharmacogenomic biomarkers in 56 drug labels were identified with possible application in paediatrics. Of these 56 drugs, biomarker comparison against 'Very Important Pharmacogenes (VIPs)' defined in PharmGKB's database revealed a total of eight VIPs labelled among 41 drugs. One hundred and thirty-nine product reviews posted on the FDA website under the Best Pharmaceuticals for Children Act and Paediatric Research Equity Act between October 2007 and July 2014 were examined. Review screening identified 43 drugs with 'pharmacogenomic' content, of which only three were true genotyping study reviews for proton pump inhibitors, all evaluating CYP2C19 polymorphisms. Pantoprazole was the sole drug labelled with pharmacogenomic information obtained specifically from paediatric trials. Clinicaltrials.gov was searched to further evaluate the current availability of pharmacogenomic studies in the paediatric population. Of the 33,132 trials registered on Clinicaltrials.gov, 137 were labelled as paediatric pharmacogenetic and pharmacogenomic studies. Pharmacogenomic studies directly conducted in paediatric patients are lacking, and thus, pharmacogenomic biomarker information based on adult studies is commonly presented in FDA-approved labels for use in paediatric patients. Considering differences in gene expression and physiological maturation between paediatric and adult populations, studies investigating pharmacogenomic effects specifically in paediatric patients should be conducted whenever significant biomarkers are available.

Liver safety assessment: required data elements and best practices for data collection and standardization in clinical trials.

A workshop was convened to discuss best practices for the assessment of drug-induced liver injury (DILI) in clinical trials. In a breakout session, workshop attendees discussed necessary data elements and standards for the accurate measurement of DILI risk associated with new therapeutic agents in clinical trials. There was agreement that in order to achieve this goal the systematic acquisition of protocol-specified clinical measures and lab specimens from all study subjects is crucial. In addition, standard DILI terms that address the diverse clinical and pathologic signatures of DILI were considered essential. There was a strong consensus that clinical and lab analyses necessary for the evaluation of cases of acute liver injury should be consistent with the US Food and Drug Administration (FDA) guidance on pre-marketing risk assessment of DILI in clinical trials issued in 2009. A recommendation that liver injury case review and management be guided by clinicians with hepatologic expertise was made. Of note, there was agreement that emerging DILI signals should prompt the systematic collection of candidate pharmacogenomic, proteomic and/or metabonomic biomarkers from all study subjects. The use of emerging standardized clinical terminology, CRFs and graphic tools for data review to enable harmonization across clinical trials was strongly encouraged. Many of the recommendations made in the breakout session are in alignment with those made in the other parallel sessions on methodology to assess clinical liver safety data, causality assessment for suspected DILI, and liver safety assessment in special populations (hepatitis B, C, and oncology trials). Nonetheless, a few outstanding issues remain for future consideration.

Pharmacogenetic information for patients on drug labels.

Advances in pharmacogenetic research have improved our understanding of adverse drug responses and have led to the development of pharmacogenetic tests and targeted drugs. However, the extent of the communication process and provision of information to patients about pharmacogenetics is unclear. Pharmacogenetic information may be included in sections of a drug’s package insert intended for patients, which is provided directly to patients or communicated via the health provider. To determine what pharmacogenetic information, if any, is included in patient-targeted sections of the drug label, we reviewed the labels listed in the US Food and Drug Administration’s Table of Pharmacogenomic Biomarkers in Drug Labels. To date, 140 drugs include pharmacogenetic-related information in the approved label. Our analysis revealed that pharmacogenetic information is included in patient-targeted sections for a minority (n=29; 21%) of drug labels, with no obvious pattern associated with the inclusion of pharmacogenetic information. Therefore, patients are unlikely to learn about pharmacogenetics through written materials dispensed with the drug. Given that there are also inconsistencies with regard to inclusion of pharmacogenetic information in the patient counseling information section, it is also unlikely that patients are receiving adequate pharmacogenetic information from their provider. The inconsistent presence of pharmacogenetic information in patient-targeted sections of drug labels suggests a need to review the criteria for inclusion of information in patient-targeted sections in order to increase consistency and patient knowledge of pharmacogenetic information.

Pharmacogenetics: can genes determine treatment efficacy and safety in JIA?

Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic condition in childhood, with many children requiring immunomodulatory therapies for many years following diagnosis. A considerable proportion of children experience therapeutic inefficacy or substantial adverse effects, or both, but a lack of reliable clinical indicators and biomarkers to predict treatment response prevents optimization of existing therapies. The identification of valid candidate gene variants involved in the pathways of methotrexate and etanercept, the most commonly used medications in JIA, has seen little success to date. The limited success of these studies is possibly due to the presence of confounding variables in the study populations, the heterogeneity of outcome parameters used to determine treatment response and the small number of candidate gene variants analysed. The first genome-wide pharmacogenetic study in JIA has identified gene regions of particular biological interest, but these findings require validation. Moreover, epigenetic mechanisms as well as ontogeny processes might be additional factors influencing drug responses. Access to large, well-documented JIA cohorts and the rapid development of advanced genome analytics is ushering in a personalized approach to treatment. The discovery of new pharmacogenomic biomarkers and systems pathways can provide new drug targets and predictive tools for improved drug response and fewer adverse drug reactions in JIA.