Study of pharmacogenomic information in FDA-approved drug labeling to facilitate application of precision medicine

Text summarization with ChatGPT for drug labeling documents

Text summarization is crucial in scientific research, drug discovery and development, regulatory review, and more. This task demands domain expertise, language proficiency, semantic prowess, and conceptual skill. The recent advent of large language models (LLMs), such as ChatGPT, offers unprecedented opportunities to automate this process. We compared ChatGPT-generated summaries with those produced by human experts using FDA drug labeling documents. The labeling contains summaries of key labeling sections, making them an ideal human benchmark to evaluate ChatGPT's summarization capabilities. Analyzing >14000 summaries, we observed that ChatGPT-generated summaries closely resembled those generated by human experts. Importantly, ChatGPT exhibited even greater similarity when summarizing drug safety information. These findings highlight ChatGPT's potential to accelerate work in critical areas, including drug safety.

Immunohistochemical Expression Levels of Epidermal Growth Factor Receptor, Cyclooxygenase-2, and Ki-67 in Canine Cutaneous Squamous Cell Carcinomas

Squamous cell carcinoma (SCC) stands as the second most prevalent skin cancer in dogs, primarily attributed to UV radiation exposure. Affected areas typically include regions with sparse hair and pale or depigmented skin. The significance of spontaneous canine cutaneous SCC as a model for its human counterpart is underscored by its resemblance. This study assesses the expression of key markers-Epidermal Growth Factor Receptor (EGFR), Cyclooxygenase-2 (Cox-2), and Ki-67-in canine cutaneous SCC. Our objective is to investigate the association between their expression levels and classical clinicopathological parameters, unraveling the intricate relationships among these molecular markers. In our retrospective analysis of 37 cases, EGFR overexpression manifested in 43.2% of cases, while Cox-2 exhibited overexpression in 97.3%. The EGFR, Cox-2 overexpression, and Ki-67 proliferation indices, estimated through immunohistochemistry, displayed a significant association with the histological grade, but only EGFR labeling is associated with the presence of lymphovascular emboli. The Ki-67 labeling index expression exhibited an association with EGFR and Cox-2. These findings propose that EGFR, Cox-2, and Ki-67 hold promise as valuable markers in canine SCC. EGFR, Cox-2, and Ki-67 may serve as indicators of disease progression, offering insights into the malignancy of a lesion. The implications extend to the potential therapeutic targeting of EGFR and Cox-2 in managing canine SCC. Further exploration of these insights is warranted due to their translational relevance and the development of targeted interventions in the context of canine SCC.

Polygenic scores for cardiovascular risk factors improve estimation of clinical outcomes in CCB treatment compared to pharmacogenetic variants alone

Pharmacogenetic variants are associated with clinical outcomes during Calcium Channel Blocker (CCB) treatment, yet whether the effects are modified by genetically predicted clinical risk factors is unknown. We analyzed 32,000 UK Biobank participants treated with dihydropiridine CCBs (mean 5.9 years), including 23 pharmacogenetic variants, and calculated polygenic scores for systolic and diastolic blood pressures, body fat mass, and other patient characteristics. Outcomes included treatment discontinuation and heart failure. Pharmacogenetic variant rs10898815-A (NUMA1) increased discontinuation rates, highest in those with high polygenic scores for fat mass. The RYR3 variant rs877087 T-allele alone modestly increased heart failure risks versus non-carriers (HR:1.13, p = 0.02); in patients with high polygenic scores for fat mass, lean mass, and lipoprotein A, risks were substantially elevated (HR:1.55, p = 4 × 10-5). Incorporating polygenic scores for adiposity and lipoprotein A may improve risk estimates of key clinical outcomes in CCB treatment such as treatment discontinuation and heart failure, compared to pharmacogenetic variants alone.

Pharmacogenomic biomarker information on drug labels of the Spanish Agency of Medicines and Sanitary products: evaluation and comparison with other regulatory agencies

This work aimed to analyse the pharmacogenetic information in the Spanish Drug Regulatory Agency (AEMPS) Summary of Products Characteristics (SmPC), evaluating the presence of pharmacogenetic biomarkers, as well as the associated recommendations. A total of 55.4% of the 1891 drug labels reviewed included information on pharmacogenetic biomarker(s). Pharmacogenomic information appears most frequently in the "antineoplastic and immunomodulating agents", "nervous system", and "cardiovascular system" Anatomical Therapeutic Chemical groups. A total of 509 different pharmacogenetic biomarkers were found, of which CYP450 enzymes accounted for almost 34% of the total drug-biomarker associations evaluated. A total of 3679 drug-biomarker pairs were identified, 102 of which were at the 1A level (PharmGKB® classification system), and 33.33% of these drug-pharmacogenetic biomarker pairs were assigned to "actionable PGx", 12.75% to "informative PGx", 4.9% to "testing recommended", and 4.9% to "testing required". The rate of coincidence in the assigned PGx level of recommendation between the AEMPS and regulatory agencies included in the PharmGKB® Drug Label Annotations database (i.e., the FDA, EMA, SWISS Medic, PMDA, and HCSC) ranged from 45% to 65%, being 'actionable level' the most frequent. On the other hand, discrepancies between agencies did not exceed 35%. This study highlights the presence of relevant pharmacogenetic information on Spanish drug labels, which would help avoid interactions, toxicity, or lack of treatment efficacy.

Analytical validation of GenoPharm a clinical genotyping open array panel of 46 pharmacogenes inclusive of variants unique to people of African ancestry

Pharmacogenomic testing may be used to improve treatment outcomes and reduce the frequency of adverse drug reactions (ADRs). Population specific, targeted pharmacogenetics (PGx) panel-based testing methods enable sensitive, accurate and economical implementation of precision medicine. We evaluated the analytical performance of the GenoPharm® custom open array platform which evaluates 120 SNPs across 46 pharmacogenes. Using commercially available reference samples (Coriell Biorepository) and in-house extracted DNA, we assessed accuracy, precision, and linearity of GenoPharm®. We then used GenoPharm® on 218 samples from two Southern African black populations and determined allele and genotype frequencies for selected actionable variants. Across all assays, the GenoPharm® panel demonstrated 99.5% concordance with the Coriell reference samples, with 98.9% reproducibility. We observed high frequencies of key genetic variants in people of African ancestry: CYP2B6*6 (0.35), CYP2C9*8, *11 (0.13, 0.03), CYP2D6*17 (0.21) and *29 (0.11). GenoPharm® open array is therefore an accurate, reproducible and sensitive test that can be used for clinical pharmacogenetic testing and is inclusive of variants specific to the people of African ancestry.

Pharmacogenomics: Driving Personalized Medicine

Personalized medicine tailors therapies, disease prevention, and health maintenance to the individual, with pharmacogenomics serving as a key tool to improve outcomes and prevent adverse effects. Advances in genomics have transformed pharmacogenetics, traditionally focused on single gene-drug pairs, into pharmacogenomics, encompassing all "-omics" fields (e.g., proteomics, transcriptomics, metabolomics, and metagenomics). This review summarizes basic genomics principles relevant to translation into therapies, assessing pharmacogenomics' central role in converging diverse elements of personalized medicine. We discuss genetic variations in pharmacogenes (drug-metabolizing enzymes, drug transporters, and receptors), their clinical relevance as biomarkers, and the legacy of decades of research in pharmacogenetics. All types of therapies, including proteins, nucleic acids, viruses, cells, genes, and irradiation, can benefit from genomics, expanding the role of pharmacogenomics across medicine. Food and Drug Administration approvals of personalized therapeutics involving biomarkers increase rapidly, demonstrating the growing impact of pharmacogenomics. A beacon for all therapeutic approaches, molecularly targeted cancer therapies highlight trends in drug discovery and clinical applications. To account for human complexity, multicomponent biomarker panels encompassing genetic, personal, and environmental factors can guide diagnosis and therapies, increasingly involving artificial intelligence to cope with extreme data complexities. However, clinical application encounters substantial hurdles, such as unknown validity across ethnic groups, underlying bias in health care, and real-world validation. This review address the underlying science and technologies germane to pharmacogenomics and personalized medicine, integrated with economic, ethical, and regulatory issues, providing insights into the current status and future direction of health care. SIGNIFICANCE STATEMENT: Personalized medicine aims to optimize health care for the individual patients with use of predictive biomarkers to improve outcomes and prevent adverse effects. Pharmacogenomics drives biomarker discovery and guides the development of targeted therapeutics. This review addresses basic principles and current trends in pharmacogenomics, with large-scale data repositories accelerating medical advances. The impact of pharmacogenomics is discussed, along with hurdles impeding broad clinical implementation, in the context of clinical care, ethics, economics, and regulatory affairs.

Cardiovascular precision medicine - A pharmacogenomic perspective

Precision medicine envisages the integration of an individual's clinical and biological features obtained from laboratory tests, imaging, high-throughput omics and health records, to drive a personalised approach to diagnosis and treatment with a higher chance of success. As only up to half of patients respond to medication prescribed following the current one-size-fits-all treatment strategy, the need for a more personalised approach is evident. One of the routes to transforming healthcare through precision medicine is pharmacogenomics (PGx). Around 95% of the population is estimated to carry one or more actionable pharmacogenetic variants and over 75% of adults over 50 years old are on a prescription with a known PGx association. Whilst there are compelling examples of pharmacogenomic implementation in clinical practice, the case for cardiovascular PGx is still evolving. In this review, we shall summarise the current status of PGx in cardiovascular diseases and look at the key enablers and barriers to PGx implementation in clinical practice.

Comparison of pharmacogenomic information for drug approvals provided by the national regulatory agencies in Korea, Europe, Japan, and the United States

Pharmacogenomics, which is defined as the study of changes in the properties of DNA and RNA associated with drug response, enables the prediction of the efficacy and adverse effects of drugs based on patients' specific genetic mutations. For the safe and effective use of drugs, it is important that pharmacogenomic information is easily accessible to clinical experts and patients. Therefore, we examined the pharmacogenomic information provided on drug labels in Korea, Europe, Japan, and the United States (US). The selection of drugs that include pharmacogenomic information was based on the drug list that includes genetic information from the Korea Ministry of Food and Drug Safety (MFDS) and US Food and Drug Administration (FDA) websites. Drug labels were retrieved from the sites of MFDS, FDA, European Medicines Agency, and Japanese Pharmaceuticals and Medical Devices Agency. Drugs were classified as per the Anatomical Therapeutic Chemical code, and the biomarkers, labeling sections, and necessity of genetic tests were determined. In total, 348 drugs were selected from 380 drugs with available pharmacogenomic information in Korea and the US after applying the inclusion and exclusion criteria. Of these drugs, 137, 324, 169, and 126 were with pharmacogenomics information in Korea, the US, Europe, and Japan, respectively. The most commonly represented drug class was antineoplastic and immunomodulating agents. Regarding the classification as per the mentioned biomarkers, the cytochrome P450 enzyme was the most frequently mentioned information, and the targeted anticancer drugs most commonly required genetic biomarker testing. The reasons for differences in drug labeling information based on country include differences in mutant alleles according to ethnicity, frequencies at which drug lists are updated, and pharmacogenomics-related guidelines. Clinical experts must continuously strive to identify and report mutations that can explain drug efficacy or side effects for safe drug use.

Pharmacogenomics in practice: a review and implementation guide

Considerable efforts have been exerted to implement Pharmacogenomics (PGx), the study of interindividual variations in DNA sequence related to drug response, into routine clinical practice. In this article, we first briefly describe PGx and its role in improving treatment outcomes. We then propose an approach to initiate clinical PGx in the hospital setting. One should first evaluate the available PGx evidence, review the most relevant drugs, and narrow down to the most actionable drug-gene pairs and related variant alleles. This is done based on data curated and evaluated by experts such as the pharmacogenomics knowledge implementation (PharmGKB) and the Clinical Pharmacogenetics Implementation Consortium (CPIC), as well as drug regulatory authorities such as the US Food and Drug Administration (FDA) and European Medicinal Agency (EMA). The next step is to differentiate reactive point of care from preemptive testing and decide on the genotyping strategy being a candidate or panel testing, each of which has its pros and cons, then work out the best way to interpret and report PGx test results with the option of integration into electronic health records and clinical decision support systems. After test authorization or testing requirements by the government or drug regulators, putting the plan into action involves several stakeholders, with the hospital leadership supporting the process and communicating with payers, the pharmacy and therapeutics committee leading the process in collaboration with the hospital laboratory and information technology department, and healthcare providers (HCPs) ordering the test, understanding the results, making the appropriate therapeutic decisions, and explaining them to the patient. We conclude by recommending some strategies to further advance the implementation of PGx in practice, such as the need to educate HCPs and patients, and to push for more tests' reimbursement. We also guide the reader to available PGx resources and examples of PGx implementation programs and initiatives.

Precision medicine from a citizen perspective: a survey of public attitudes towards pharmacogenomics in Flanders

BACKGROUND

Personalized medicine is an emerging field, aiming to improve the safety and efficacy of pharmacotherapy. The field's implementation in clinical care is steadily increasing. Pharmacogenomics are one example of personalized approaches in the clinic and direct-to-consumer (DTC) pharmacogenomic tests have become publicly available. We aimed to assess public opinion on pharmacogenomic research and testing to foster integration within Belgian health care.

METHODS

A cross-sectional survey was created and disseminated online, focusing on the citizen perspective. Participants' willingness to engage in pharmacogenomic research was the primary outcome. In addition, their awareness, understanding, expectations and overall acceptance towards pharmacogenomic testing was investigated.

RESULTS

A total of 156 participants (54.5% aged between 18 and 30 years, 45.5% > 30 years; 73.1% females) completed the survey. Half ever experienced side effects (46.2%) and treatment failure (52.6%). Up to 45.5% (n = 71) were willing to participate in pharmacogenomics research, and the majority (78.8%) were convinced that pharmacogenomic tests could help doctors to prescribe them the right medications. Additionally, 76.3% (n = 118) supported a partial reimbursement of pharmacogenomics tests. A minority (5.1%, n = 8) of participants showed interest in DTC tests, and 15.4% (n = 24) expressed privacy concerns regarding pharmacogenomics testing. Participants preferred their healthcare professionals' to perform the test and access their data, but refused commercial providers.

CONCLUSION

Overall, participants showed a positive attitude towards precision medicine and pharmacogenomics research. Our findings may help guiding future pharmacogenomic implementation initiatives to optimize drug use by using pharmacogenomic information integrated within health care.

Population genetic polymorphisms of pharmacogenes in Zimbabwe, a potential guide for the safe and efficacious use of medicines in people of African ancestry

OBJECTIVE

Pharmacogenomics (PGx) is a clinically significant factor in the safe and efficacious use of medicines. While PGx knowledge is abundant for other populations, there are scarce PGx data on African populations and is little knowledge on drug-gene interactions for medicines used to treat diseases common in Africa. The aim of this study was to use a custom-designed open array to genotype clinically actionable variants in a Zimbabwean population. This study also identified some of the commonly used drugs in Zimbabwe and the associated genes involved in their metabolism.

METHODS

A custom-designed open array that covers 120 genetic variants was used to genotype 522 black Zimbabwean healthy volunteers using TaqMan-based single nucleotide polymorphism genotyping. Data were also accessed from Essential Drugs' List in Zimbabwe (EDLIZ), and the medicines were grouped into the associated biomarker groups based on their metabolism. We also estimated the national drug procurement levels for medicines that could benefit from PGx-guided use based on the data obtained from the national authorities in Zimbabwe.

RESULTS

The results demonstrate the applicability of an open-array chip in simultaneously determining multiple genetic variants in an individual, thus significantly reducing cost and time to generate PGx data. There were significantly high frequencies of African-specific variants, such as the CYP2D6*17 and *29 variants and the CYP2B6*18 variant. The data obtained showed that the Zimbabwean population exhibits PGx variations in genes important for the safe and efficacious use of drugs approved by the EDLIZ and are procured at significantly large amounts annually. The study has established a cohort of genotyped healthy volunteers that can be accessed and used in the conduct of clinical pharmacogenetic studies for drugs entering a market of people of predominantly African ancestry.

CONCLUSION

Our study demonstrated the potential benefit of integrating PGx in Zimbabwe for the safe and efficacious use of drugs that are commonly used.

Analysis of a panel-based pharmacogenomics testing program among members of a commercial and Medicare client of a pharmacy benefits manager

BACKGROUND: Although the field of pharmacogenomics (PGx) has existed for decades, use of pharmacogenomic information by providers to optimize medication therapy for patients has had relatively slow adoption. There are many factors that have contributed to the slow adoption of PGx testing, but it is partially due to a lack of coverage by payers. If PGx testing is covered by payers, frequently only testing of a specific gene is covered, rather than a panel of many genes. As a result, little is known about how coverage of a panel-based PGx test will affect a member's medication therapy. OBJECTIVES: To determine how giving providers specific medication optimization recommendations, based on results of a panel-based PGx test, impacted members' medication regimens. METHODS: Pharmacy claims data were retrospectively reviewed for this exploratory study. Members who participated in PGx testing were in the intervention group and members who chose not to participate in the PGx testing, but who were eligible to participate, were in the control group. PGx test results, including suggested medication changes, were mailed to providers. To determine if providers adopted the suggested medication changes, pharmacy claims data were analyzed retrospectively for the 4-month period preceding and following the date from which recommendations were provided to prescribers. RESULTS: Of the 101 members included in the analysis, 50 were in the intervention group and 51 were in the control group. In the intervention group, members were taking in a total of 352 medications; 165 of the medications had PGx guidance. Based on the PGx test results, 62 of these medications (37.6%) had recommendations. Of members who received PGx testing, 76% had at least 1 recommended change. When pharmacist recommendations were made, a change was made to the medication 27% of the time. There was a statistically significant difference between the number of medication changes in the PGx group and the control group (P = 0.024). CONCLUSIONS: Recommendations based on PGx testing can lead to changes in medications and an optimized medication regimen for members. DISCLOSURES: The authors have no conflicts to disclose that may present a potential conflict of interest.

Clinical Application of Pharmacogenetic Markers in the Treatment of Dermatologic Pathologies

Dermatologic pathologies are the fourth most common cause of non-fatal disease worldwide; however, they produce a psychosocial, economic, and occupational impact equal to or greater than other chronic conditions. The most prevalent are actinic keratosis, followed by basal-cell carcinoma, in a lesser proportion acne vulgaris, psoriasis, and hidradenitis suppurativa, among others, and more rarely dermatitis herpetiformis. To treat actinic keratosis and basal-cell carcinoma, 5-fluorouracil (5-FU) 0.5% is administered topically with good results, although in certain patients it produces severe toxicity. On the other hand, dapsone is a drug commonly used in inflammatory skin conditions such as dermatitis herpetiformis; however, it occasionally causes hemolytic anemia. Additionally, biologic drugs indicated for the treatment of moderate-to-severe psoriasis and hidradenitis suppurativa have proved to be effective and safe; nevertheless, a small percentage of patients do not respond to treatment with biologics in the long term or they are ineffective. This interindividual variability in response may be due to alterations in genes that encode proteins involved in the pathologic environment of the disease or the mechanism of action of the medication. Pharmacogenetics studies the relationship between genetic variations and drug response, which is useful for the early identification of non-responsive patients and those with a higher risk of developing toxicity upon treatment. This review describes the pharmacogenetic recommendations with the strongest evidence at present for the treatments used in dermatology, highlighting those included in clinical practice guides. Currently, we could only find pharmacogenetic clinical guidelines for 5-FU. However, the summary of product characteristics for dapsone contains a pharmacogenetic recommendation from the United States Food and Drug Administration. Finally, there is an enormous amount of information from pharmacogenetic studies in patients with dermatologic pathologies (mainly psoriasis) treated with biologic therapies, but they need to be validated in order to be included in clinical practice guides.

DICE: A Drug Indication Classification and Encyclopedia for AI-Based Indication Extraction

Drug labeling contains an ‘INDICATIONS AND USAGE’ that provides vital information to support clinical decision making and regulatory management. Effective extraction of drug indication information from free-text based resources could facilitate drug repositioning projects and help collect real-world evidence in support of secondary use of approved medicines. To enable AI-powered language models for the extraction of drug indication information, we used manual reading and curation to develop a Drug Indication Classification and Encyclopedia (DICE) based on FDA approved human prescription drug labeling. A DICE scheme with 7,231 sentences categorized into five classes (indications, contradictions, side effects, usage instructions, and clinical observations) was developed. To further elucidate the utility of the DICE, we developed nine different AI-based classifiers for the prediction of indications based on the developed DICE to comprehensively assess their performance. We found that the transformer-based language models yielded an average MCC of 0.887, outperforming the word embedding-based Bidirectional long short-term memory (BiLSTM) models (0.862) with a 2.82% improvement on the test set. The best classifiers were also used to extract drug indication information in DrugBank and achieved a high enrichment rate (>0.930) for this task. We found that domain-specific training could provide more explainable models without performance sacrifices and better generalization for external validation datasets. Altogether, the proposed DICE could be a standard resource for the development and evaluation of task-specific AI-powered, natural language processing (NLP) models.

Towards population-specific pharmacogenomics in the era of next-generation sequencing

Pharmacogenomics (PGx) has essential roles in identifying optimal drug responders, optimizing dosage regimens and avoiding adverse events. Population-specific therapeutic interventions that tackle the genetic root causes of clinical outcomes are an important precision medicine strategy. In this perspective, we discuss next-generation sequencing genotyping and its significance for population-specific PGx applications. We emphasize the potential of NGS for preemptive pharmacogenotyping, which is crucial to population-specific clinical studies and patient care. We also provide examples that use publicly available population-based genomics data for population-specific PGx studies. Last, we discuss the remaining challenges and regulatory efforts towards improvements in this field.

Pharmacogenomic Biomarkers in US FDA-Approved Drug Labels (2000-2020)

Pharmacogenomics (PGx) is a key subset of precision medicine that relates genomic variation to individual response to pharmacotherapy. We assessed longitudinal trends in US FDA approval of new drugs labeled with PGx information. Drug labels containing PGx information were obtained from Drugs@FDA and guidelines from PharmGKB were used to compare the actionability of PGx information in drug labels across therapeutic areas. The annual proportion of new drug approvals with PGx labeling has increased by nearly threefold from 10.3% (n = 3) in 2000 to 28.2% (n = 11) in 2020. Inclusion of PGx information in drug labels has increased for all clinical areas over the last two decades but most prominently for cancer therapies, which comprise the largest proportion (75.5%) of biomarker–drug pairs for which PGx testing is required. Clinically actionable information was more frequently observed in biomarker–drug pairs associated with cancer drugs compared to those for other therapeutic areas (n = 92 (59.7%) vs. n = 62 (40.3%), p < 0.0051). These results suggest that further evidence is needed to support the clinical adoption of pharmacogenomics in non-cancer therapeutic areas.

Characterization of Pharmacogenetic Information in Food and Drug Administration Drug Labeling and the Table of Pharmacogenetic Associations

BACKGROUND

The US Food and Drug Administration (FDA) recommends using only FDA-reviewed pharmacogenetic information to make prescribing decisions based on genetic test results. Such information is available in drug labeling and in the Table of Pharmacogenetic Associations ("Associations table").

OBJECTIVE

To compile a list of drug-gene pairs from drug labeling and the Associations table and categorize the pharmacogenetic information and clinical outcome associated with each drug-gene pair.

METHODS

This was a cross-sectional analysis of pharmacogenetic information in the Associations table and individual drug labeling in March 2020. We used the Table of Pharmacogenomic Biomarkers in Drug Labeling to identify drug labels to review. We categorized the pharmacogenetic information for each drug-gene pair according to whether the purpose was to describe (1) polymorphisms affecting drug disposition (metabolism or transport), (2) polymorphisms affecting a direct drug target, (3) variants associated with adverse drug reaction (ADR) susceptibility, (4) variants associated with therapeutic failure, (5) a biomarker-defined indication, or (6) a biomarker-defined ADR. We also categorized the clinical outcome-efficacy, safety, or unknown-associated with each drug-gene pair. We reported counts and proportions of drug-gene pairs in each pharmacogenetic information and clinical outcome category.

RESULTS

We identified 308 drug-gene pairs, of which 36% were associated with a biomarker-defined drug indication, 33% with polymorphic drug metabolism, and 28% with ADR susceptibility. Most drug-gene pairs (n = 267, 87%) were associated with an efficacy or safety-related outcome.

CONCLUSION AND RELEVANCE

FDA-reviewed pharmacogenetic information is available for more than 300 drug-gene pairs and can help guide prescribing decisions.

Unraveling heterogeneity of the clinical pharmacogenomic guidelines in oncology practice among major regulatory bodies

Pharmacogenomics (PGx) implementation in clinical practice is steadily increasing. PGx uses genetic information to personalize medication use, which increases medication efficacy and decreases side effects. The availability of clinical PGx guidelines is essential for its implementation in clinical settings. Currently, there are few organizations/associations responsible for releasing those guidelines, including the Clinical Pharmacogenetics Implementation Consortium, Dutch Pharmacogenetics Working Group, the Canadian Pharmacogenomics Network for Drug Safety and the French National Network of Pharmacogenetics. According to the US FDA, oncology medications are highly correlated to PGx biomarkers. Therefore, summarizing the PGx guidelines for oncology drugs will positively impact the clinical decisions for cancer patients. This review aims to scrutinize side-by-side available clinical PGx guidelines in oncology.