Application of pharmacogenetics in oncology

Pharmacogenetic Profiling of Genes Associated with Outcomes of Chemotherapy in Omani Healthy Controls

BACKGROUND/OBJECTIVES

Pharmacogenomic screening plays a crucial role in optimizing chemotherapy outcomes and minimizing toxicity. Characterizing the baseline distribution of genetic variants in specific populations is essential to inform the prioritization of drug-gene combinations for clinical implementation. The objective of this study was to investigate the distribution of pharmacogenetic variants in 36 genes related to the fluoropyrimidine (FP) pathway among healthy Omani individuals, forming a foundation for future studies in cancer patients receiving FP-based chemotherapy.

METHODS

Ninety-eight healthy Omani participants aged ≥18 years were recruited at the Sultan Qaboos Comprehensive Cancer Care and Research Center. Whole-blood samples were collected, and genomic DNA was extracted. Targeted next-generation sequencing was performed using a custom Ion AmpliSeq panel covering coding exons and splice-site regions of 36 genes involved in FP metabolism and response.

RESULTS

A total of 999 variants were detected across the 36 genes, with 63.3% being heterozygous. The ABCC4 gene had the highest mutation frequency (76 mutations), while DHFR and SMUG1 had the lowest (<10 mutations). In DPYD, four functionally significant variants were found at frequencies ranging from 1 to 8.2% of the population. Missense mutations were also observed in MTHFR and UGT1A1. Three actionable variants in DPYD and MTHFR, associated with 5-fluorouracil and/or capecitabine response, were identified. Additionally, 27 novel single-nucleotide polymorphisms of unknown clinical significance were detected.

CONCLUSIONS

This study reveals key pharmacogenetic variants in the Omani population, underscoring the importance of integrating pharmacogenomic testing into routine care to support safer, more personalized chemotherapy in the region.

Unveiling Pharmacogenomics Insights into Circular RNAs: Toward Precision Medicine in Cancer Therapy

Pharmacogenomics is revolutionizing precision medicine by enabling tailored therapeutic strategies based on an individual genetic and molecular profile. Circular RNAs (circRNAs), a distinct subclass of endogenous non-coding RNAs, have recently emerged as key regulators of drug resistance, tumor progression, and therapeutic responses. Their covalently closed circular structure provides exceptional stability and resistance to exonuclease degradation, positioning them as reliable biomarkers and novel therapeutic targets in cancer management. This review provides a comprehensive analysis of the interplay between circRNAs and pharmacogenomics, focusing on their role in modulating drug metabolism, therapeutic efficacy, and toxicity profiles. We examine how circRNA-mediated regulatory networks influence chemotherapy resistance, alter targeted therapy responses, and impact immunotherapy outcomes. Additionally, we discuss emerging experimental tools and bioinformatics techniques for studying circRNAs, including multi-omics integration, machine learning-driven biomarker discovery, and high-throughput sequencing technologies. Beyond their diagnostic potential, circRNAs are being actively explored as therapeutic agents and drug delivery vehicles. Recent advancements in circRNA-based vaccines, engineered CAR-T cells, and synthetic circRNA therapeutics highlight their transformative potential in oncology. Furthermore, we address the challenges of standardization, reproducibility, and clinical translation, emphasizing the need for rigorous biomarker validation and regulatory frameworks to facilitate their integration into clinical practice. By incorporating circRNA profiling into pharmacogenomic strategies, this review underscores a paradigm shift toward highly personalized cancer therapies. circRNAs hold immense potential to overcome drug resistance, enhance treatment efficacy, and optimize patient outcomes, marking a significant advancement in precision oncology.

Enhancing pharmacogenomic data accessibility and drug safety with large language models: a case study with Llama3.1

Pharmacogenomics (PGx) holds the promise of personalizing medical treatments based on individual genetic profiles, thereby enhancing drug efficacy and safety. However, the current landscape of PGx research is hindered by fragmented data sources, time-consuming manual data extraction processes, and the need for comprehensive and up-to-date information. This study aims to address these challenges by evaluating the ability of Large Language Models (LLMs), specifically Llama3.1-70B, to automate and improve the accuracy of PGx information extraction from the FDA Table of Pharmacogenomic Biomarkers in Drug Labeling (FDA PGx Biomarker table), which is well-structured with drug names, biomarkers, therapeutic area, and related labeling texts. Our primary goal was to test the feasibility of LLMs in streamlining PGx data extraction, as an alternative to traditional, labor-intensive approaches. Llama3.1-70B achieved 91.4% accuracy in identifying drug-biomarker pairs from single labeling texts and 82% from mixed texts, with over 85% consistency in aligning extracted PGx categories from FDA PGx Biomarker table and relevant scientific abstracts, demonstrating its effectiveness for PGx data extraction. By integrating data from diverse sources, including scientific abstracts, this approach can support pharmacologists, regulatory bodies, and healthcare researchers in updating PGx resources more efficiently, making critical information more accessible for applications in personalized medicine. In addition, this approach shows potential of discovering novel PGx information, particularly of underrepresented minority ethnic groups. This study highlights the ability of LLMs to enhance the efficiency and completeness of PGx research, thus laying a foundation for advancements in personalized medicine by ensuring that drug therapies are tailored to the genetic profiles of diverse populations.

Machine learning in oncological pharmacogenomics: advancing personalized chemotherapy

This review analyzes the application of machine learning (ML) in oncological pharmacogenomics, focusing on customizing chemotherapy treatments. It explores how ML can analyze extensive genomic, proteomic, and other omics datasets to identify genetic patterns associated with drug responses. This, in turn, facilitates personalized therapies that are more effective and have fewer side effects. Recent studies have emphasized ML's revolutionary role of ML in personalized oncology treatment by identifying genetic variability and understanding cancer pharmacodynamics. Integrating ML with electronic health records and clinical data shows promise in refining chemotherapy recommendations by considering the complex influencing factors. Although standard chemotherapy depends on population-based doses and treatment regimens, customized techniques use genetic information to tailor treatments for specific patients, potentially enhancing efficacy and reducing adverse effects.However, challenges, such as model interpretability, data quality, transparency, ethical issues related to data privacy, and health disparities, remain. Machine learning has been used to transform oncological pharmacogenomics by enabling personalized chemotherapy treatments. This review highlights ML's potential of ML to enhance treatment effectiveness and minimize side effects through detailed genetic analysis. It also addresses ongoing challenges including improved model interpretability, data quality, and ethical considerations. The review concludes by emphasizing the importance of rigorous clinical trials and interdisciplinary collaboration in the ethical implementation of ML-driven personalized medicine, paving the way for improved outcomes in cancer patients and marking a new frontier in cancer treatment.

Impact of ALDH1A1 and NQO1 gene polymorphisms on the response and toxicity of chemotherapy in Bangladeshi breast cancer patients

PURPOSE

Cyclophosphamide, Epirubicin/Doxorubicin, 5-fluorouracil (CEF or CAF) chemotherapy has long been a standard first-line treatment for breast cancer. The genetic variations of enzymes that are responsible for the metabolism of these drugs have been linked to altered treatment response and toxicity. Two drug-metabolizing enzymes ALDH1A1 and NQO1 are critically involved in the pathways of CEF/CAF metabolism. This study aimed to evaluate the effect of ALDH1A1 (rs13959) and NQO1 (rs1800566) polymorphisms on treatment response and toxicities caused by adjuvant (ACT) and neoadjuvant chemotherapy (NACT) where CEF/CAF combination was used to treat Bangladeshi breast cancer patients.

METHODS

A total of 330 patients were recruited from various hospitals, with 150 receiving neoadjuvant chemotherapy and 180 receiving adjuvant chemotherapy. To extract genomic DNA, a non-enzymatic simple salting out approach was adopted. The polymerase chain reaction-restriction fragment length polymorphism method was used to detect genetic polymorphisms. Unconditional logistic regression was used to derive odds ratios (ORs) with 95% confidence intervals (CIs) to study the association between genetic polymorphisms and clinical outcome and toxicity.

RESULTS

A statistically significant association was observed between ALDH1A1 (rs13959) polymorphism and treatment response (TT vs. CC: aOR = 6.40, p = 0.007; recessive model: aOR = 6.38, p = 0.002; allele model: p = 0.032). Patients with the genotypes TT and CT + TT of the NQO1 (rs1800566) polymorphism had a significantly higher risk of toxicities such as anemia (aOR = 0.34, p = 0.006 and aOR = 0.58, p = 0.021), neutropenia (aOR = 0.42, p = 0.044 and aOR = 0.57, p = 0.027), leukopenia (aOR = 0.33, p = 0.010 and aOR = 0.46, p = 0.005), and gastrointestinal toxicity (aOR = 0.30, p = 0.02 and aOR = 0.38, p = 0.006) when compared to the wild CC genotype, while patients with the genotype CT had a significant association with gastrointestinal toxicity (aOR = 0.42, p = 0.02) and leukopenia (aOR = 0.52, p = 0.010). The TT and CT + TT genotypes of rs13959 had a significantly higher risk of anemia (aOR = 2.00, p = 0.037 and aOR = 1.68, p = 0.029). There was no significant association between rs1800566 polymorphism and treatment response.

CONCLUSION

Polymorphisms in ALDH1A1 (rs13959) and NQO1 (rs1800566) may be useful in predicting the probability of treatment response and adverse effects from CEF or CAF-based chemotherapy in breast cancer patients.

Unlocking the potential of Molecular Tumor Boards: from cutting-edge data interpretation to innovative clinical pathways

The emerging era of precision medicine is characterized by an increasing availability of targeted anticancer therapies and by the parallel development of techniques to obtain more refined molecular data, whose interpretation may not always be straightforward. Molecular tumor boards gather various professional figures, in order to leverage the analysis of molecular data and provide prognostic and predictive insights for clinicians. In addition to healthcare development, they could also become a tool to promote knowledge and research spreading. A growing body of evidence on the application of molecular tumor boards to clinical practice is forming and positive signals are emerging, although a certain degree of heterogeneity exists. This work analyzes molecular tumor boards' potential workflows, figures involved, data sources, sample matrices and eligible patients, as well as available evidence and learning examples. The emerging concept of multi-institutional, disease-specific molecular tumor boards is also considered by presenting two ongoing nationwide experiences.

Hallmarks of cancer resistance

This review explores the hallmarks of cancer resistance, including drug efflux mediated by ATP-binding cassette (ABC) transporters, metabolic reprogramming characterized by the Warburg effect, and the dynamic interplay between cancer cells and mitochondria. The role of cancer stem cells (CSCs) in treatment resistance and the regulatory influence of non-coding RNAs, such as long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs), are studied. The chapter emphasizes future directions, encompassing advancements in immunotherapy, strategies to counter adaptive resistance, integration of artificial intelligence for predictive modeling, and the identification of biomarkers for personalized treatment. The comprehensive exploration of these hallmarks provides a foundation for innovative therapeutic approaches, aiming to navigate the complex landscape of cancer resistance and enhance patient outcomes.

Influence of CYP2C19 and CYP2D6 on side effects of aripiprazole and risperidone: A systematic review

Variability in hepatic cytochrome P450 (CYP) enzymes such as 2C19 and 2D6 may influence side-effect and efficacy outcomes for antipsychotics. Aripiprazole and risperidone are two commonly prescribed antipsychotics, metabolized primarily through CYP2D6. Here, we aimed to provide an overview of the effect of CYP2C19 and CYP2D6 on side-effects of aripiprazole and risperidone, and expand on existing literature by critically examining methodological issues associated with pharmacogenetic studies. A PRISMA compliant search of six electronic databases (Pubmed, PsychInfo, Embase, Central, Web of Science, and Google Scholar) identified pharmacogenetic studies on aripiprazole and risperidone. 2007 publications were first identified, of which 34 were included. Quality of literature was estimated using Newcastle-Ottowa Quality Assessment Scale (NOS) and revised Cochrane Risk of Bias tool. The average NOS score was 5.8 (range: 3-8) for risperidone literature and 5 for aripiprazole (range: 4-6). All RCTs on aripiprazole were rated as high risk of bias, and four out of six for risperidone literature. Study populations ranged from healthy volunteers to inpatient individuals in psychiatric units and included adult and pediatric samples. All n = 34 studies examined CYP2D6. Only one study genotyped for CYP2C19 and found a positive association with neurological side-effects of risperidone. Most studies did not report any relationship between CYP2D6 and any side-effect outcome. Heterogeneity between and within studies limited the ability to synthesize data and draw definitive conclusions. Studies lacked statistical power due to small sample size, selective genotyping methods, and study design. Large-scale randomized trials with multiple measurements, providing robust evidence on this topic, are suggested.

Individualized Pharmacotherapy Utilizing Genetic Biomarkers and Novel In Vitro Systems As Predictive Tools for Optimal Drug Development and Treatment

In the area of drug development and clinical pharmacotherapy, a profound understanding of the pharmacokinetics and potential adverse reactions associated with the drug under investigation is paramount. Essential to this endeavor is a comprehensive understanding about interindividual variations in absorption, distribution, metabolism, and excretion (ADME) genetics and the predictive capabilities of in vitro systems, shedding light on metabolite formation and the risk of adverse drug reactions (ADRs). Both the domains of pharmacogenomics and the advancement of in vitro systems are experiencing rapid expansion. Here we present an update on these burgeoning fields, providing an overview of their current status and illuminating potential future directions. SIGNIFICANCE STATEMENT: There is very rapid development in the area of pharmacogenomics and in vitro systems for predicting drug pharmacokinetics and risk for adverse drug reactions. We provide an update of the current status of pharmacogenomics and developed in vitro systems on these aspects aimed to achieve a better personalized pharmacotherapy.

Genetic diversity of variants involved in drug response among Tunisian and Italian populations toward personalized medicine

Adverse drug reactions (ADR) represent a significant contributor to morbidity and mortality, imposing a substantial financial burden. Genetic ancestry plays a crucial role in drug response. The aim of this study is to characterize the genetic variability of selected pharmacogenes involved with ADR in Tunisians and Italians, with a comparative analysis against global populations. A cohort of 135 healthy Tunisians and 737 Italians were genotyped using a SNP array. Variants located in 25 Very Important Pharmacogenes implicated in ADR were extracted from the genotyping data. Distribution analysis of common variants in Tunisian and Italian populations in comparison to 24 publicly available worldwide populations was performed using PLINK and R software. Results from Principle Component and ADMIXTURE analyses showed a high genetic similarity among Mediterranean populations, distinguishing them from Sub-Saharan African and Asian populations. The Fst comparative analysis identified 27 variants exhibiting significant differentiation between the studied populations. Among these variants, four SNPs rs622342, rs3846662, rs7294, rs5215 located in SLC22A1, HMGCR, VKORC1 and KCNJ11 genes respectively, are reported to be associated with ethnic variability in drug responses. In conclusion, correlating the frequencies of genotype risk variants with their associated ADRs would enhance drug outcomes and the implementation of personalized medicine in the studied populations.

Moving toward precision medicine to predict drug sensitivity in patients with metastatic breast cancer

Tumor heterogeneity represents a major challenge in breast cancer, being associated with disease progression and treatment resistance. Precision medicine has been extensively applied to dissect tumor heterogeneity and, through a deeper molecular understanding of the disease, to personalize therapeutic strategies. In the last years, technological advances have widely improved the understanding of breast cancer biology and several trials have been developed to translate these new insights into clinical practice, with the ultimate aim of improving patients' outcomes. In the era of molecular oncology, genomics analyses and other methodologies are shaping a new treatment algorithm in breast cancer care. In this manuscript, we review the main steps of precision medicine to predict drug sensitivity in breast cancer from a translational point of view. Genomic developments and their clinical implications are discussed, along with technological advancements that could broaden precision medicine applications. Current achievements are put into perspective to provide an overview of the state-of-art of breast cancer precision oncology as well as to identify future research directions.

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in a revolutionary era in healthcare by individualizing diagnostics and treatments according to each patient's uniquely evolving health status. This groundbreaking method of tailoring disease prevention and treatment considers individual variations in genes, environments, and lifestyles. The goal of precision medicine is to target the "five rights": the right patient, the right drug, the right time, the right dose, and the right route. In this pursuit, in silico techniques have emerged as an anchor, driving precision medicine forward and making this a realistic and promising avenue for personalized therapies. With the advancements in high-throughput DNA sequencing technologies, genomic data, including genetic variants and their interactions with each other and the environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) and pharmacodynamic (PD) data, and mathematical models further contribute to drug optimization, drug behavior prediction, and drug-drug interaction identification. Digital health, wearables, and computational tools offer continuous monitoring and real-time data collection, enabling treatment adjustments. Furthermore, the incorporation of extensive datasets in computational tools, such as electronic health records (EHRs) and omics data, is also another pathway to acquire meaningful information in this field. Although they are fairly new, machine learning (ML) algorithms and artificial intelligence (AI) techniques are also resources researchers use to analyze big data and develop predictive models. This review explores the interplay of these multiple in silico approaches in advancing precision medicine and fostering individual healthcare. Despite intrinsic challenges, such as ethical considerations, data protection, and the need for more comprehensive research, this marks a new era of patient-centered healthcare. Innovative in silico techniques hold the potential to reshape the future of medicine for generations to come.

The consequences of data dispersion in genomics: a comparative analysis of data sources for precision medicine

BACKGROUND

Genomics-based clinical diagnosis has emerged as a novel medical approach to improve diagnosis and treatment. However, advances in sequencing techniques have increased the generation of genomics data dramatically. This has led to several data management problems, one of which is data dispersion (i.e., genomics data is scattered across hundreds of data repositories). In this context, geneticists try to remediate the above-mentioned problem by limiting the scope of their work to a single data source they know and trust. This work has studied the consequences of focusing on a single data source rather than considering the many different existing genomics data sources.

METHODS

The analysis is based on the data associated with two groups of disorders (i.e., oncology and cardiology) accessible from six well-known genomic data sources (i.e., ClinVar, Ensembl, GWAS Catalog, LOVD, CIViC, and CardioDB). Two dimensions have been considered in this analysis, namely, completeness and concordance. Completeness has been evaluated at two levels. First, by analyzing the information provided by each data source with regard to a conceptual schema data model (i.e., the schema level). Second, by analyzing the DNA variations provided by each data source as related to any of the disorders selected (i.e., the data level). Concordance has been evaluated by comparing the consensus among the data sources regarding the clinical relevance of each variation and disorder.

RESULTS

The data sources with the highest completeness at the schema level are ClinVar, Ensembl, and CIViC. ClinVar has the highest completeness at the data level data source for the oncology and cardiology disorders. However, there are clinically relevant variations that are exclusive to other data sources, and they must be considered in order to provide the best clinical diagnosis. Although the information available in the data sources is predominantly concordant, discordance among the analyzed data exist. This can lead to inaccurate diagnoses.

CONCLUSION

Precision medicine analyses using a single genomics data source leads to incomplete results. Also, there are concordance problems that threaten the correctness of the genomics-based diagnosis results.

At the right dose: personalised (N-of-1) dosing for precision oncology

The objective of oncology therapeutics, especially in the age of precision medicine, is to give the right drug(s) to the right patient at the right time. Yet, a major challenge is finding the right dose for each patient. Determining safe and efficacious doses of oncology treatments, especially for novel combination therapies, can be challenging. Moreover, traditionally, dosing cancer drugs is based on giving each patient the same dose (a flat dose) or a dose based on surface area/weight. But patients' ability to tolerate drugs is influenced by additional factors including, but not limited to age, gender, race, comorbidities, organ function, and metabolism. Herein, we present evidence that, in the era of targeted drugs, individualised drug dosing determined by starting at reduced doses and using intrapatient dose escalation can yield safe and effective personalised dosing of novel combinations of approved drugs that have not previously undergone formal phase I trials and can also optimise dosing of tested drug regimens.

The crosstalk between non-coding RNA polymorphisms and resistance to lung cancer therapies

Lung cancer (LC) is one of the most common cancer-related mortality in the world. Even with intensive multimodality therapies, lung cancer has a poor prognosis and a high morbidity rate. This review focused on the role of non-coding RNA polymorphisms such as lncRNAs and miRNAs in the resistance to LC therapies, which could open promising avenue for better therapeutic response. Of note, there is currently no valid biomarker to predict lung cancer sensitivity in patients during treatment. Since genetic variations cause many challenges in treating patients, genotyping of known polymorphisms must be thoroughly explored to find desirable treatment platforms. With this knowledge, individualized treatments could become more possible in management of LC.

N-of-1 trials: The epitome of personalized medicine?

Observational studies are notoriously susceptible to bias, and parallel-group randomized trials are important to identify the best overall treatment for eligible patients. Yet, such trials can be expected to be a misleading indicator of the best treatment for some subgroups or individual patients. In selected circumstances, patients can be treated in n-of-1 trials to address the inherent heterogeneity of treatment response in clinical populations. Such trials help to accomplish the ultimate goal of all biomedical research, to optimize the care of individual patients.

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.

Design, Synthesis, and Antiproliferative Activity of Benzopyran-4-One-Isoxazole Hybrid Compounds

The biological significance of benzopyran-4-ones as cytotoxic agents against multi-drug resistant cancer cell lines and isoxazoles as anti-inflammatory agents in cellular assays prompted us to design and synthesize their hybrid compounds and explore their antiproliferative activity against a panel of six cancer cell lines and two normal cell lines. Compounds 5a-d displayed significant antiproliferative activities against all the cancer cell lines tested, and IC50 values were in the range of 5.2-22.2 μM against MDA-MB-231 cancer cells, while they were minimally cytotoxic to the HEK-293 and LLC-PK1 normal cell lines. The IC50 values of 5a-d against normal HEK-293 cells were in the range of 102.4-293.2 μM. Compound 5a was screened for kinase inhibitory activity, proteolytic human serum stability, and apoptotic activity. The compound was found inactive towards different kinases, while it completely degraded after 2 h of incubation with human serum. At 5 μM concentration, it induced apoptosis in MDA-MB-231 by 50.8%. Overall, these findings suggest that new benzopyran-4-one-isoxazole hybrid compounds, particularly 5a-d, are selective anticancer agents, potentially safe for human cells, and could be synthesized at low cost. Additionally, Compound 5a exhibits potential anticancer activity mediated via inhibition of cancer cell proliferation and induction of apoptosis.

Pharmaco-Omics in Psoriasis: Paving the Way towards Personalized Medicine

The emergence of high-throughput approaches has had a profound impact on personalized medicine, evolving the identification of inheritable variation to trajectory analyses of transient states and paving the way for the unveiling of response biomarkers. The utilization of the multi-layered pharmaco-omics data, including genomics, transcriptomics, proteomics, and relevant biological information, has facilitated the identification of key molecular biomarkers that can predict the response to therapy, thereby optimizing treatment regiments and providing the framework for a tailored treatment plan. Despite the availability of multiple therapeutic options for chronic diseases, the highly heterogeneous clinical response hinders the alleviation of disease signals and exacerbates the annual burden and cost of hospitalization and drug regimens. This review aimed to examine the current state of the pharmaco-omic approaches performed in psoriasis, a common inflammatory disease of the skin. We sought to identify central studies that investigate the inter-individual variability and explore the underlying molecular mechanisms of drug response progression via biological profiling in psoriatic patients administered with the extended therapeutic armamentarium of psoriasis, incorporating conventional therapies, small molecules, as well as biological drugs that inhibit central pathogenic cytokines involved in the disease pathogenesis.

Analysis of the TSER and G>C variants in the TYMS gene: a high frequency of low expression genotypes predicted in the Mexican population

BACKGROUND

In the TYMS gene promoter, there is a repeat polymorphism (TSER) that affects the expression level of the thymidylate synthetase (TS) enzyme involved in the response to some anticancer drugs. The G>C transversion located in the TSER*3R allele decreases the expression level of the TS enzyme avoiding the upstream stimulatory factor (USF-1) binding site. Despite the biomedical impact of the SNP G>C, only TSER has been reported in most worldwide populations. Thus, we studied both TSER and SNP G>C variants in the Mexican population.

SUBJECTS AND METHODS

A population sample (n = 156) was genotyped for the TSER and G>C variants by PCR and PCR-RFLPs, respectively, followed by PAGE and silver staining.

RESULTS

For TSER, the most frequent allele was 2 R (52.56%), as well as the genotype 2 R/3R (42.3%). Comparison with Latin American, European, and American (USA) populations suggest a heterogeneous worldwide distribution (F_ST-value = 0.01564; p-value = 0.0000). When the G>C variant was included (2RG, 3RG, and 3RC), a high frequency of low expression genotypes was observed: 2RG/2RG, 2RG/3RC, and 3RC/3RC (84.6%).

CONCLUSION

The high frequency of genotypes associated with low TS enzyme expression justifies obtaining the TYMS gene variant profile in Mexican patient's candidates to pharmaceutical treatments like 5'-Fluoracil, methotrexate, and pemetrex.

BI-847325, a selective dual MEK and Aurora kinases inhibitor, reduces aggressive behavior of anaplastic thyroid carcinoma on an in vitro three-dimensional culture

BACKGROUND

Anaplastic thyroid carcinoma (ATC) is the most aggressive subtype of thyroid cancer. In this study, we used a three-dimensional in vitro system to evaluate the effect of a dual MEK/Aurora kinase inhibitor, BI-847325 anticancer drug, on several cellular and molecular processes involved in cancer progression.

METHODS

Human ATC cell lines, C643 and SW1736, were grown in alginate hydrogel and treated with IC₅₀ values of BI-847325. The effect of BI-847325 on inhibition of kinases function of MEK1/2 and Aurora kinase B (AURKB) was evaluated via Western blot analysis of phospho-ERK1/2 and phospho-Histone H3 levels. Sodium/iodide symporter (NIS) and thyroglobulin (Tg), as two thyroid-specific differentiation markers, were measured by qRT-PCR as well as flow cytometry and immunoradiometric assay. Apoptosis was assessed by Annexin V/PI flow cytometry and BIM, NFκB1, and NFκB2 expressions. Cell cycle distribution and proliferation were determined via P16, AURKA, and AURKB expressions as well as PI and CFSE flow cytometry assays. Multidrug resistance was evaluated by examining the expression of MDR1 and MRP1. Angiogenesis and invasion were investigated by VEGF expression and F-actin labeling with Alexa Fluor 549 Phalloidin.

RESULTS

Western blot results showed that BI-847325 inhibits MEK1/2 and AURKB functions by decreasing phospho-ERK1/2 and phospho-Histone H3 levels. BI-847325 induced thyroid differentiation markers and apoptosis in ATC cell lines. Inversely, BI-847325 intervention decreased multidrug resistance, cell cycle progression, proliferation, angiogenesis, and invasion at the molecular and/or cellular levels.

CONCLUSION

The results of the present study suggest that BI-857,325 might be an effective multi-targeted anticancer drug for ATC treatment.

Implementation of Whole-Genome and Transcriptome Sequencing Into Clinical Cancer Care

PURPOSE

The combination of whole-genome and transcriptome sequencing (WGTS) is expected to transform diagnosis and treatment for patients with cancer. WGTS is a comprehensive precision diagnostic test that is starting to replace the standard of care for oncology molecular testing in health care systems around the world; however, the implementation and widescale adoption of this best-in-class testing is lacking.

METHODS

Here, we address the barriers in integrating WGTS for cancer diagnostics and treatment selection and answer questions regarding utility in different cancer types, cost-effectiveness and affordability, and other practical considerations for WGTS implementation.

RESULTS

We review the current studies implementing WGTS in health care systems and provide a synopsis of the clinical evidence and insights into practical considerations for WGTS implementation. We reflect on regulatory, costs, reimbursement, and incidental findings aspects of this test.

CONCLUSION

WGTS is an appropriate comprehensive clinical test for many tumor types and can replace multiple, cascade testing approaches currently performed. Decreasing sequencing cost, increasing number of clinically relevant aberrations and discovery of more complex biomarkers of treatment response, should pave the way for health care systems and laboratories in implementing WGTS into clinical practice, to transform diagnosis and treatment for patients with cancer.

Molecular Biomarkers in Cancer

Molecular cancer biomarkers are any measurable molecular indicator of risk of cancer, occurrence of cancer, or patient outcome. They may include germline or somatic genetic variants, epigenetic signatures, transcriptional changes, and proteomic signatures. These indicators are based on biomolecules, such as nucleic acids and proteins, that can be detected in samples obtained from tissues through tumor biopsy or, more easily and non-invasively, from blood (or serum or plasma), saliva, buccal swabs, stool, urine, etc. Detection technologies have advanced tremendously over the last decades, including techniques such as next-generation sequencing, nanotechnology, or methods to study circulating tumor DNA/RNA or exosomes. Clinical applications of biomarkers are extensive. They can be used as tools for cancer risk assessment, screening and early detection of cancer, accurate diagnosis, patient prognosis, prediction of response to therapy, and cancer surveillance and monitoring response. Therefore, they can help to optimize making decisions in clinical practice. Moreover, precision oncology is needed for newly developed targeted therapies, as they are functional only in patients with specific cancer genetic mutations, and biomarkers are the tools used for the identification of these subsets of patients. Improvement in the field of cancer biomarkers is, however, needed to overcome the scientific challenge of developing new biomarkers with greater sensitivity, specificity, and positive predictive value.

Nephrotoxicity in cancer treatment: An update

It has been estimated that nearly 80% of anticancer drug-treated patients receive potentially nephrotoxic drugs, while the kidneys play a central role in the excretion of anticancer drugs. Nephrotoxicity has long been a serious complication that hampers the effectiveness of cancer treatment and continues to influence both mortality and length of hospitalization among cancer patients exposed to either conventional cytotoxic agents or targeted therapies. Kidney injury arising from anticancer drugs tends to be associated with preexisting comorbidities, advanced cancer stage, and the use of concomitant non-chemotherapeutic nephrotoxic drugs. Despite the prevalence and impact of kidney injury on therapeutic outcomes, the field is sorely lacking in an understanding of the mechanisms driving cancer drug-induced renal pathophysiology, resulting in quite limited and largely ineffective management of anticancer drug-induced nephrotoxicity. Consequently, there is a clear imperative for understanding the basis for nephrotoxic manifestations of anticancer agents for the successful management of kidney injury by these drugs. This article provides an overview of current preclinical research on the nephrotoxicity of cancer treatments and highlights prospective approaches to mitigate cancer therapy-related renal toxicity.

Development of an optimized and generic cost-utility model for analyzing genome-guided treatment data

Economic evaluation is an integral component of informed public health decision-making in personalized medicine. However, performing economic evaluation assessments often requires specialized knowledge, expertise, and significant resources. To this end, developing generic models can significantly assist towards providing the necessary evidence for the cost-effectiveness of genome-guided therapeutic interventions, compared to the traditional drug treatment modalities. Here, we report a generic cost-utility analysis model, developed in R, which encompasses essential economic evaluation steps. Specifically, critical steps towards a comprehensive deterministic and probabilistic sensitivity analysis were incorporated in our model, while also providing an easy-to-use graphical user interface, which allows even non-experts in the field to produce a fully comprehensive cost-utility analysis report. To further demonstrate the model's reproducibility, two sets of data were assessed, one stemming from in-house clinical data and one based on previously published data. By implementing the generic model presented herein, we show that the model produces results in complete concordance with the traditionally performed cost-utility analysis for both datasets. Overall, this work demonstrates the potential of generic models to provide useful economic evidence for personalized medicine interventions.

Real-World Impact of a Pharmacogenomics-Enriched Comprehensive Medication Management Program

The availability of clinical decision support systems (CDSS) and other methods for personalizing medicine now allows evaluation of their real-world impact on healthcare delivery. For example, addressing issues associated with polypharmacy in older patients using pharmacogenomics (PGx) and comprehensive medication management (CMM) is thought to hold great promise for meaningful improvements across the goals of the Quadruple Aim. However, few studies testing these tools at scale, using relevant system-wide metrics, and under real-world conditions, have been published to date. Here, we document a reduction of ~$7000 per patient in direct medical charges (a total of $37 million over 5288 enrollees compared to 22,357 non-enrolled) in Medicare Advantage patients (≥65 years) receiving benefits through a state retirement system over the first 32 months of a voluntary PGx-enriched CMM program. We also observe a positive shift in healthcare resource utilization (HRU) away from acute care services and toward more sustainable and cost-effective primary care options. Together with improvements in medication risk assessment, patient/provider communication via pharmacist-mediated medication action plans (MAP), and the sustained positive trends in HRU, we suggest these results validate the use of a CDSS to unify PGx and CMM to optimize care for this and similar patient populations.

Genetic Polymorphisms in Pharmaceuticals and Chemotherapy

The study of genetic polymorphisms has significantly advanced the field of personalized medicine. Polymorphism of genes influence the efficacy of drugs used for treating medical conditions such as depression, cardiac diseases, thromboembolic disorders, oncological diseases, etc. The study of genetic polymorphism is beneficial for drug safety as well as for assessing therapeutic outcomes. Understanding and detecting genetic polymorphisms early on in patients can be useful in selecting the correct chemotherapeutic agent and appropriate dosage for a patient. Knowing the genetic profile of a patient and the interindividual response to various drugs significantly influences the proper selection of medication - a key step towards personalized medicine. Polymorphisms also make patients susceptible to certain cancers and identification of these polymorphisms early can be useful for a personalized treatment plan. The Genome-Wide Association Studies (GWAS) project where millions of genetic variants in the genomes of many individuals are studied to identify connections between what is present on the gene and the phenotype of the patient has enhanced the prospect of personalized medicine. GWAS has been used to identify hundreds of diseases associated to genetic polymorphisms. Individual pharmacokinetic profiles of patients to drugs enable the development of early surveillance protocols to prophylactically prevent patients from having adverse reactions. Furthermore, patient-derived cellular organoids are another advancement that allows researchers to screen for polymorphisms of the patient for adverse reactions from chemotherapy and will allow for the development of new medications that are specific to the profile of the patient’s tumor. These advances have led to significant progress towards personalized medicine. The functional consequences of genetic polymorphism on cancer drugs and treatment are studied here.

Pharmacogenomics: An Update on Biologics and Small-Molecule Drugs in the Treatment of Psoriasis

Pharmacogenomic studies allowed the reasons behind the different responses to treatments to be understood. Its clinical utility, in fact, is demonstrated by the reduction in adverse drug reaction incidence and the improvement of drug efficacy. Pharmacogenomics is an important tool that is able to improve the drug therapy of different disorders. In particular, this review will highlight the current pharmacogenomics knowledge about biologics and small-molecule treatments for psoriasis. To date, studies performed on genes involved in the metabolism of biological drugs (tumor necrosis factor inhibitors and cytokines inhibitors) and small molecules (apremilast, dimethyl fumarate, and tofacitinib) have provided conflicting results, and further investigations are necessary in order to establish a set of biomarkers to be introduced into clinical practice.

Individualized Drugs' Selection by Evaluation of Drug Properties, Pharmacogenomics and Clinical Parameters: Performance of a Bioinformatic Tool Compared to a Clinically Established Counselling Process

Purpose

Inefficacy and safety concerns are main medications’ problems, especially in the case of poly-therapies, when drug–drug interactions may alter the expected drug disposition. Ongoing efforts are aimed to establish drug selection processes aimed to preemptive evaluation of a plethora of factors affecting patient’s specific drug response, including pharmacogenomic markers, in order to minimize prescription of improper medications. In previous years, we established at the University Hospital Sant’Andrea of Rome, Italy, a Precision Medicine Service based on a multi-disciplinary experts’ team. The team is in charge to produce a drug therapy counselling report, including pharmacogenomic, pharmacokinetic and pharmacodynamic considerations. In this study, we aimed to evaluate the performance of this established “manual” process of therapy selection with a novel bioinformatic tool, the Drug-PIN system.

Patients and Methods

A total of 200 patients diagnosed with Major Depressive Disorders or a depressive episode in Bipolar Disorder, with at least three previous failed treatments, who underwent pharmacogenomic profiling and therapy counselling in the Sant’Andrea Hospital from 2017 to 2020. The baseline poly-therapy of these patients was re-evaluated and optimized by Drug-PIN. Results of the Drug-PIN poly-therapy evaluation/optimization were compared with the results of the original poly-therapy evaluation/optimization by therapy counselling. To compare the results between the two processes, the risk associated with each poly-therapy was classified as low, moderate, or high.

Results

The number of baseline poly-therapies classified in low-, moderate- or high-risk did not change significantly between manual system or Drug-PIN system. As the counselling process, also the Drug-PIN system produces a significant decrease in the predicted treatment-associated risk.

Conclusion

Drug-PIN substantially replicates the output of the counselling process, allowing a substantial reduction in the time needed for therapy evaluation. Availability of an effective bioinformatic tool for proper drug selection is expected to exponentially increase the actuation of targeted therapy strategies.

Force Field Parameters for Fe2+4S2-4 Clusters of Dihydropyrimidine Dehydrogenase, the 5-Fluorouracil Cancer Drug Deactivation Protein: A Step towards In Silico Pharmacogenomics Studies

The dimeric dihydropyrimidine dehydrogenase (DPD), metalloenzyme, an adjunct anti-cancer drug target, contains highly specialized 4 × Fe²⁺₄S²⁻₄ clusters per chain. These clusters facilitate the catalysis of the rate-limiting step in the pyrimidine degradation pathway through a harmonized electron transfer cascade that triggers a redox catabolic reaction. In the process, the bulk of the administered 5-fluorouracil (5-FU) cancer drug is inactivated, while a small proportion is activated to nucleic acid antimetabolites. The occurrence of missense mutations in DPD protein within the general population, including those of African descent, has adverse toxicity effects due to altered 5-FU metabolism. Thus, deciphering mutation effects on protein structure and function is vital, especially for precision medicine purposes. We previously proposed combining molecular dynamics (MD) and dynamic residue network (DRN) analysis to decipher the molecular mechanisms of missense mutations in other proteins. However, the presence of Fe²⁺₄S²⁻₄ clusters in DPD poses a challenge for such in silico studies. The existing AMBER force field parameters cannot accurately describe the Fe²⁺ center coordination exhibited by this enzyme. Therefore, this study aimed to derive AMBER force field parameters for DPD enzyme Fe²⁺ centers, using the original Seminario method and the collation features Visual Force Field Derivation Toolkit as a supportive approach. All-atom MD simulations were performed to validate the results. Both approaches generated similar force field parameters, which accurately described the human DPD protein Fe²⁺₄S²⁻₄ cluster architecture. This information is crucial and opens new avenues for in silico cancer pharmacogenomics and drug discovery related research on 5-FU drug efficacy and toxicity issues.