Pharmacogenetic Testing: A Tool for Personalized Drug Therapy Optimization

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

Precision, integrative medicine for pain management in sickle cell disease

Sickle cell disease (SCD) is a prevalent and complex inherited pain disorder that can manifest as acute vaso-occlusive crises (VOC) and/or chronic pain. Despite their known risks, opioids are often prescribed routinely and indiscriminately in managing SCD pain, because it is so often severe and debilitating. Integrative medicine strategies, particularly non-opioid therapies, hold promise in safe and effective management of SCD pain. However, the lack of evidence-based methods for managing SCD pain hinders the widespread implementation of non-opioid therapies. In this review, we acknowledge that implementing personalized pain treatment strategies in SCD, which is a guideline-recommended strategy, is currently fraught with limitations. The full implementation of pharmacological and biobehavioral pain approaches targeting mechanistic pain pathways faces challenges due to limited knowledge and limited financial and personnel support. We recommend personalized medicine, pharmacogenomics, and integrative medicine as aspirational strategies for improving pain care in SCD. As an organizing model that is a comprehensive framework for classifying pain subphenotypes and mechanisms in SCD, and for guiding selection of specific strategies, we present evidence updating pain research pioneer Richard Melzack's neuromatrix theory of pain. We advocate for using the updated neuromatrix model to subphenotype individuals with SCD, to better select personalized multimodal treatment strategies, and to identify research gaps fruitful for exploration. We present a fairly complete list of currently used pharmacologic and non-pharmacologic SCD pain therapies, classified by their mechanism of action and by their hypothesized targets in the updated neuromatrix model.

Effect of CYP2D6 pharmacogenetic phenotype and phenoconversion on serum concentrations of antidepressants and antipsychotics: a retrospective cohort study

BACKGROUND

Pharmacogenetics (PGx), especially in regard to CYP2D6, is gaining more importance in routine clinical settings. Including phenoconversion effects (PC) in result interpretation could maximize its potential benefits. However, studies on genetics of pharmacokinetic genes including the functional enzyme status are lacking.

AIM

The retrospective analyses of clinical routine data aimed to investigating how the CYP2D6 functional enzyme status affects serum concentrations and metabolite-to-parent ratios of seven common psychotropic drugs and allows an evaluation of the relevance of this information for patient care.

METHOD

Two patient cohorts (total n = 316; 44.2 ± 15.4 years) were investigated for the CYP2D6 functional enzyme status and its associations with drug exposure and metabolism of venlafaxine, amitriptyline, mirtazapine, sertraline, escitalopram, risperidone and quetiapine.

RESULTS

We found an increase in intermediate and poor metabolizers, as well as a decrease in normal metabolizers of CYP2D6 when including PC. Moreover, we found associations between amitriptyline exposure with the phenoconversion-corrected activity score of CYP2D6 (Spearman correlation; p = 0.03), and risperidone exposure with CYP2D6 functional enzyme status (Kruskal-Wallis test; p = 0.01), as well as between metabolite-to-parent ratio of venlafaxine and risperidone with CYP2D6 functional enzyme status (Kruskal-Wallis test; p < 0.001; p = 0.05).

CONCLUSION

The data stress the relevance of PC-informed PGx in psychopharmacological treatment and suggest that PC should be included in PGx result interpretation when PGx is implemented in routine clinical care, especially before initiating amitriptyline- or risperidone-treatment, to start with a dose adequate to the respective CYP2D6 functional enzyme status. Moreover, PGx and therapeutic drug monitoring should be used complementary but not alternatively.

Unexpected High Need for Genetic Testing in Rheumatology: A Cross-Sectional Cohort Study

BACKGROUND

Genetic testing may provide information for diagnostic, prognostic and pharmacogenetic purposes. The PREPARE study recently showed that the number of clinically relevant adverse drug reactions could be reduced via genotype-guided treatment. The aim of this work was to assess the relevance of genetic testing and its actual use in consecutive rheumatic outpatients.

METHODS

A retrospective cross-sectional analysis was performed with data from a prospectively designed observational project with outpatients consecutively recruited from a university clinic of rheumatology.

RESULTS

In this cohort of 2490 patients, the potential need for genetic testing is immense, with 57.3% of patients having the potential to benefit from genetic testing according to their diagnosis and treatment and 53.3% of patients with actually performed genetic testing for diagnostic, prognostic or pharmacogenetic purposes. In detail, patients would potentially benefit from genetic testing especially for therapeutic (28.0%) and diagnostic (26.9%) purposes. Genetic testing was performed for diagnostic purposes in 51.6% of subjects, for pharmacogenetic purposes in 3.7% and for prognostic purposes in 0.1%. The ratio between the number of patients who had had tests performed to those with a potential need for genetic testing decreased with age, from 127.1% for 20 to <30-year-old patients to 46.1% for 80 to <90-year-old patients. Pharmacogenetic testing was only performed for disease-related medications.

CONCLUSIONS

Genetic testing is frequently needed in patients with rheumatic diseases. The value of pharmacogenetic testing is certainly underestimated, especially in case of medications for comorbidities.

Lack of exposure to pharmacogenomics education among the health care providing students in the West Bank of Palestine

OBJECTIVES

Evaluating the knowledge in pharmacogenomics (PGx) is the first step toward the implementation of PGx testing in clinical practice. This survey aimed to evaluate the knowledge of PGx testing among healthcare providing students at the top-ranked university in the West Bank of Palestine.

METHODS

First an online questionnaire consisting of 30 questions regarding the demographic, knowledge, and attitude toward pharmacogenomics testing was structured and validated. Then the questionnaire was distributed to 1,000 current students from different fields.

RESULTS

696 responses was received. The results showed that almost half of the participants (n=355, 51.1%) have never took any courses about PGx during their university training. Only 81 (11.7%) of the students who took the PGx course stated that it helped them understanding how genetic variations affect drug response. The majority of the students were uncertain (n=352, 50.6%) or disagreed (n=143, 20.6%) that the lectures during university education described the effects of genetic variants on drug response. Although most of the students (70-80%) answered that genetic variants can indeed affect the drug's response, only 162 students (23.3%) responded that VKORC1 and CYP2C9 genotypes influence the response to warfarin. In addition, only 94 (13.5%) students were aware that many medicine labels include clinical information about PGx testing provided by the FDA.

CONCLUSIONS

It is concluded from the results of this survey that there is a lack of exposure to PGx education associated with poor knowledge of PGx testing among the healthcare providing students in the West Bank of Palestine. It is recommended to include and improve the lectures and courses regarding PGx as this will have a major impact on precision medicine.

Approach to the Patient With a Suboptimal Statin Response: Causes and Algorithm for Clinical Management

CONTEXT

Statins are the lipid-lowering therapy of choice for the prevention of atherosclerotic cardiovascular disease (ASCVD) but their effectiveness in lowering low-density lipoprotein cholesterol (LDL-C) can substantially differ between individuals. In this mini-review, we describe the different causes for a suboptimal statin response and an algorithm for the diagnosis and clinical management of these patients.

EVIDENCE ACQUISITION

A PubMed search using the terms "statin resistance," "statin sensitivity," "statin pharmacokinetics," "cardiovascular disease," and "lipid-lowering therapies" was performed. Published papers in the past 10 years that were relevant to the topic were examined to provide content for this mini-review.

EVIDENCE SYNTHESIS

Suboptimal lowering of LDL-C by statins is a major problem in the clinical management of patients and limits the value of this therapeutic approach. There are multiple causes of statin hyporesponsiveness with compliance being the most common explanation. Other causes, such as analytical issues with LDL-C measurement and the presence of common lipid disorders (familial hypercholesterolemia, elevated lipoprotein[a] and secondary dyslipidemias) should be excluded before considering primary statin resistance from rare genetic variants in lipoprotein-related or drug-metabolism genes. A wide variety of nonstatin lipid-lowering drugs are now available and can be added to statins to achieve more effective LDL-C lowering.

CONCLUSIONS

The evaluation of statin hyporesponsiveness is a multistep process that can lead to the optimization of lipid-lowering therapy for the prevention of ASCVD. It may also lead to the identification of distinct types of dyslipidemias that require specific therapies and/or the genetic screening of family members.

Economic evaluations of predictive genetic testing: A scoping review

Predictive genetic testing can provide information about whether or not someone will develop or is likely to develop a specific condition at a later stage in life. Economic evaluation can assess the value of money for such testing. Studies on the economic evaluation of predictive genetic testing have been carried out in a variety of settings, and this research aims to conduct a scoping review of findings from these studies. We searched the PubMed, Web of Science, Embase, and Cochrane databases with combined search terms, from 2019 to 2022. Relevant studies from 2013 to 2019 in a previous systematic review were also included. The study followed the recommended stages for undertaking a scoping review. A total of 53 studies were included, including 33 studies from the previous review and 20 studies from the search of databases. A significant number of studies focused on the US, UK, and Australia (34%, 23%, and 11%). The most frequently included health conditions were cancer and cardiovascular diseases (68% and 19%). Over half of the studies compared predictive genetic testing with no genetic testing, and the majority of them concluded that at least some type of genetic testing was cost-effective compared to no testing (94%). Some studies stated that predictive genetic testing is becoming more cost-effective with the trend of lowering genetic testing costs. Studies on predictive genetic testing covered various health conditions, particularly cancer and cardiovascular diseases. Most studies indicated that predictive genetic testing is cost-effective compared to no testing.

Prescription medications with actionable pharmacogenomic recommendations in Veterans Health Administration patients

Aim: To evaluate the prevalence of medications with actionable pharmacogenomic (PGx) safety and efficacy recommendations in patients receiving care from the Veterans Health Administration. Materials & methods: Outpatient prescription data from 2011 to 2021 and any documented adverse drug reactions (ADRs) were reviewed for those who received PGx testing at one Veterans Administration location between November 2019 and October 2021. Results: Among the reviewed prescriptions, 381 (32.8%) were associated with an actionable recommendation based on the Clinical Pharmacogenetics Implementation Consortium (CPIC) prescribing guidelines, with 205 (17.7%) for efficacy concerns and 176 (15.2%) for safety concerns. Among those with a documented ADR for a PGx-impacted medication, 39.1% had PGx results that aligned with CPIC recommendations. Conclusion: Medications with actionable PGx recommendations for safety and efficacy concerns are received with similar frequency, and most patients who have undergone PGx testing at the Phoenix Veterans Administration have received medications that may be impacted by PGx testing.

Meta-analysis of pharmacogenetic clinical decision support systems for the treatment of major depressive disorder

The study aimed to conduct a meta-analysis of studies comparing pharmacogenetically guided dosing of antidepressants with empiric standard of care. Publications referring to genotype-guided antidepressant therapy were identified via PubMed, Google Scholar, Scopus, Web of Science, Embase, and Cochrane databases from the inception of the databases to 2021. In addition, bibliographies of all articles were manually searched for additional references not identified in primary searches. Studies comparing clinical outcomes between two groups of patients who received antidepressant treatment were included in meta-analysis. Analysis of the data revealed statistically significant differences between the experimental group receiving pharmacogenetically guided dosing and the empirically treated controls. Specifically, genotype-guided treatment significantly improved response and remission of patients after both eight and twelve weeks of therapy, whereas no effect on the development of adverse drug reactions was observed. This meta-analysis indicates that the use of preemptive genotyping to guide dosing of antidepressants might increase treatment efficacy.

Effects of Pharmacokinetic Gene Variation on Therapeutic Drug Levels and Antidepressant Treatment Response

Introduction Pharmacogenetic testing is proposed to minimize adverse effects when considered in combination with pharmacological knowledge of the drug. As yet, limited studies in clinical settings have investigated the predictive value of pharmacokinetic (pk) gene variation on therapeutic drug levels as a probable mechanism of adverse effects, nor considered the combined effect of pk gene variation and drug level on antidepressant treatment response.

Methods Two depression cohorts were investigated for the relationship between pk gene variation and antidepressant serum concentrations of amitriptyline, venlafaxine, mirtazapine and quetiapine, as well as treatment response. For the analysis, 519 patients (49% females; 46.6±14.1 years) were included.

Results Serum concentration of amitriptyline was associated with CYP2D6 (higher concentrations in poor metabolizers compared to normal metabolizers), of venlafaxine with CYP2C19 (higher concentrations in intermediate metabolizers compared to rapid/ultrarapid metabolizers) and CYP2D6 (lower metabolite-to-parent ratio in poor compared to intermediate and normal metabolizers, and intermediate compared to normal and ultrarapid metabolizers). Pk gene variation did not affect treatment response.

Discussion The present data support previous recommendations to reduce starting doses of amitriptyline and to guide dose-adjustments via therapeutic drug monitoring in CYP2D6 poor metabolizers. In addition, we propose including CYP2C19 in routine testing in venlafaxine-treated patients to improve therapy by raising awareness of the risk of low serum concentrations in CYP2C19 rapid/ultrarapid metabolizers. In summary, pk gene variation can predict serum concentrations, and thus the combination of pharmacogenetic testing and therapeutic drug monitoring is a useful tool in a personalized therapy approach for depression.

The Value of Pharmacogenetics to Reduce Drug-Related Toxicity in Cancer Patients

Many anticancer drugs cause adverse drug reactions (ADRs) that negatively impact safety and reduce quality of life. The typical narrow therapeutic range and exposure-response relationships described for anticancer drugs make precision dosing critical to ensure safe and effective drug exposure. Germline mutations in pharmacogenes contribute to inter-patient variability in pharmacokinetics and pharmacodynamics of anticancer drugs. Patients carrying reduced-activity or loss-of-function alleles are at increased risk for ADRs. Pretreatment genotyping offers a proactive approach to identify these high-risk patients, administer an individualized dose, and minimize the risk of ADRs. In the field of oncology, the most well-studied gene-drug pairs for which pharmacogenetic dosing recommendations have been published to improve safety are DPYD-fluoropyrimidines, TPMT/NUDT15-thiopurines, and UGT1A1-irinotecan. Despite the presence of these guidelines, the scientific evidence showing the benefits of pharmacogenetic testing (e.g., improved safety and cost-effectiveness) and the development of efficient multi-gene genotyping panels, routine pretreatment testing for these gene-drug pairs has not been implemented widely in the clinic. Important considerations required for widespread clinical implementation include pharmacogenetic education of physicians, availability or allocation of institutional resources to build an efficient clinical infrastructure, international standardization of guidelines, uniform adoption of guidelines by regulatory agencies leading to genotyping requirements in drug labels, and development of cohesive reimbursement policies for pretreatment genotyping. Without clinical implementation, the potential of pharmacogenetics to improve patient safety remains unfulfilled.

Utilizing a Human-Computer Interaction Approach to Evaluate the Design of Current Pharmacogenomics Clinical Decision Support

A formal assessment of pharmacogenomics clinical decision support (PGx-CDS) by providers is lacking in the literature. The objective of this study was to evaluate the usability of PGx-CDS tools that have been implemented in a healthcare setting. We enrolled ten prescribing healthcare providers and had them complete a 60-min usability session, which included interacting with two PGx-CDS scenarios using the “Think Aloud” technique, as well as completing the Computer System Usability Questionnaire (CSUQ). Providers reported positive comments, negative comments, and suggestions for the two PGx-CDS during the usability testing. Most provider comments were in favor of the current PGx-CDS design, with the exception of how the genotype and phenotype information is displayed. The mean CSUQ score for the PGx-CDS overall satisfaction was 6.3 ± 0.95, with seven strongly agreeing and one strongly disagreeing for overall satisfaction. The implemented PGx-CDS at our institution was well received by prescribing healthcare providers. The feedback collected from the session will guide future PGx-CDS designs for our healthcare system and provide a framework for other institutions implementing PGx-CDS.

Validation of a multi-gene qPCR-based pharmacogenomics panel across major ethnic groups in Singapore and Indonesia

Aim: The clinical utility of pharmacogenomics (PGx) has been gaining traction alongside growing evidence that adverse drug reactions (ADRs) have significant genetic associations. Nala PGx Core^® is a multi-gene qPCR-based panel of 20 allele variants, comprising 18 SNPs and two CYP2D6 copy number markers across four pharmacogenes - CYP2C9, CYP2C19, CYP2D6 and SLCO1B1. Methods: In this study, we validated the performance of Nala PGx Core^® against benchmark methods, on the Singaporean and Indonesian populations. Results & conclusion: Nala PGx Core^® demonstrated robust and accurate genotyping when compared with other established benchmarks. Furthermore, the panel successfully characterized alleles of clinical relevance, such as CYP2D6*10 and CYP2D6*36, across major ethnic groups present of Singapore and Indonesia, suggesting its potential for adoption in clinical workflows regionally.

Cystic Fibrosis Lung Disease Modifiers and Their Relevance in the New Era of Precision Medicine

Our understanding of cystic fibrosis (CF) has grown exponentially since the discovery of the cystic fibrosis transmembrane conductance regulator (CFTR) gene in 1989. With evolving genetic and genomic tools, we have come to better understand the role of CFTR genotypes in the pathophysiology of the disease. This, in turn, has paved the way for the development of modulator therapies targeted at mutations in the CFTR, which are arguably one of the greatest advances in the treatment of CF. These modulator therapies, however, do not target all the mutations in CFTR that are seen in patients with CF and, furthermore, a variation in response is seen in patients with the same genotype who are taking modulator therapies. There is growing evidence to support the role of non-CFTR modifiers, both genetic and environmental, in determining the variation seen in CF morbidity and mortality and also in the response to existing therapies. This review focusses on key findings from studies using candidate gene and genome-wide approaches to identify CF modifier genes of lung disease in cystic fibrosis and considers the interaction between modifiers and the response to modulator therapies. As the use of modulator therapies expands and we gain data around outcomes, it will be of great interest to investigate this interaction further. Going forward, it will also be crucial to better understand the relative influence of genomic versus environmental factors. With this understanding, we can truly begin to deliver personalised care by better profiling the likely disease phenotype for each patient and their response to treatment.

Precision Medicine: Applied Concepts of Pharmacogenomics in Patients with Various Diseases and Polypharmacy

Over the last century, the process of choosing medications to treat certain diseases has evolved significantly [...].