Establishment and application of a predictive model for gefitinib-induced severe rash based on pharmacometabolomic profiling and polymorphisms of transporters in non-small cell lung cancer

Genetic Variants in the ABCB1 and ABCG2 Gene Drug Transporters Involved in Gefitinib-Associated Adverse Reaction: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis aimed to verify the association between the genetic variants of adenosine triphosphate (ATP)-binding cassette subfamily B member 1 (ABCB1) and ATP-binding cassette subfamily G member 2 (ABCG2) genes and the presence and severity of gefitinib-associated adverse reactions. We systematically searched PubMed, Virtual Health Library/Bireme, Scopus, Embase, and Web of Science databases for relevant studies published up to February 2024. In total, five studies were included in the review. Additionally, eight genetic variants related to ABCB1 (rs1045642, rs1128503, rs2032582, and rs1025836) and ABCG2 (rs2231142, rs2231137, rs2622604, and 15622C>T) genes were analyzed. Meta-analysis showed a significant association between the ABCB1 gene rs1045642 TT genotype and presence of diarrhea (OR = 5.41, 95% CI: 1.38-21.14, I2 = 0%), the ABCB1 gene rs1128503 TT genotype and CT + TT group and the presence of skin rash (OR = 4.37, 95% CI: 1.51-12.61, I2 = 0% and OR = 6.99, 95%CI: 1.61-30.30, I2= 0%, respectively), and the ABCG2 gene rs2231142 CC genotype and presence of diarrhea (OR = 3.87, 95% CI: 1.53-9.84, I2 = 39%). No ABCB1 or ABCG2 genes were positively associated with the severity of adverse reactions associated with gefitinib. In conclusion, this study showed that ABCB1 and ABCG2 variants are likely to exhibit clinical implications in predicting the presence of adverse reactions to gefitinib.

Pharmacokinetics in Pharmacometabolomics: Towards Personalized Medication

Indiscriminate drug administration may lead to drug therapy results with varying effects on patients, and the proposal of personalized medication can help patients to receive effective drug therapy. Conventional ways of personalized medication, such as pharmacogenomics and therapeutic drug monitoring (TDM), can only be implemented from a single perspective. The development of pharmacometabolomics provides a research method for the realization of precise drug administration, which integrates the environmental and genetic factors, and applies metabolomics technology to study how to predict different drug therapeutic responses of organisms based on baseline metabolic levels. The published research on pharmacometabolomics has achieved satisfactory results in predicting the pharmacokinetics, pharmacodynamics, and the discovery of biomarkers of drugs. Among them, the pharmacokinetics related to pharmacometabolomics are used to explore individual variability in drug metabolism from the level of metabolism of the drugs in vivo and the level of endogenous metabolite changes. By searching for relevant literature with the keyword "pharmacometabolomics" on the two major literature retrieval websites, PubMed and Web of Science, from 2006 to 2023, we reviewed articles in the field of pharmacometabolomics that incorporated pharmacokinetics into their research. This review explains the therapeutic effects of drugs on the body from the perspective of endogenous metabolites and pharmacokinetic principles, and reports the latest advances in pharmacometabolomics related to pharmacokinetics to provide research ideas and methods for advancing the implementation of personalized medication.

A metabolomics study: Could plasma metabolites be a guide for the prevention of tamsulosin side effects?

BACKGROUND

The understanding of precision medicine, which aims for high efficacy and low toxicity in treatments, has gained more importance with omics technologies. In this study, it was aimed to reach new suggestions for low-toxicity treatment by clarifying the relationship between tamsulosin side effects and metabolome profiles.

MATERIALS AND METHODS

Plasma samples of control and tamsulosin-treated rats were analyzed by LC-Q-TOF/MS/MS. MS/MS data was processed in XCMS software for the identification of metabolite and metabolic pathway analysis. Data were classified with MATLAB 2019b for multivariate data analysis. 34m/z values were found to be significantly different between the drug and control groups (P≤0.01 and fold analysis≥1.5) and identified by comparing METLIN and HMDB databases.

RESULTS

According to multivariate data analysis, α-Linolenic Acid, Thiamine, Retinoic acid, 1.25-Dihydroxyvitamin D3-26.23-Lactone, L-Glutamine, L-Serine, Retinaldehyde, Sphingosine 1-phosphate, L-Lysine, 23S.25-Dihydroxyvitamin D3, Sphinganine, L-Cysteine, Uridine 5'-diphosphate, Calcidiol, L-Tryptophan, L-Alanine levels changed significantly compared to the control group. Differences in the metabolisms of Retinol, Sphingolipid, Alanine-Aspartate-Glutamate, Glutathione, Fatty Acid, Tryptophan, and biosynthesis of Aminoacyl-tRNA, and Unsaturated Fatty Acid have been successfully demonstrated by metabolic pathway analysis. According to our study, vitamin A and D supplements can be recommended to prevent side effects such as asthenia, rhinitis, nasal congestion, dizziness and IFIS in the treatment of tamsulosin. Alteration of aminoacyl-tRNA biosynthesis and sphingolipid metabolism pathways during tamsulosin treatment is effective in the occurrence of nasal congestion.

CONCLUSIONS

Our study provides important information for tamsulosin therapy with high efficacy and low side effects in precision medicine.

Understanding the Risk of Drug Interactions Between Ritonavir-Containing COVID-19 Therapies and Small-Molecule Kinase Inhibitors in Patients With Cancer

PURPOSE

The introduction of COVID-19 therapies containing ritonavir has markedly expanded the scope of use for this medicine. As a strong cytochrome P450 3A4 inhibitor, the use of ritonavir is associated with a high drug interaction risk. There are currently no data to inform clinician regarding the likely magnitude and duration of interaction between ritonavir-containing COVID-19 therapies and small-molecule kinase inhibitors (KIs) in patients with cancer.

METHODS

Physiologically based pharmacokinetic modeling was used to conduct virtual clinical trials with a parallel group study design in the presence and absence of ritonavir (100 mg twice daily for 5 days). The magnitude and time course of changes in KI exposure when coadministered with ritonavir was evaluated as the primary outcome.

RESULTS

Dosing of ritonavir resulted in a > 2-fold increase in steady-state area under the plasma concentration-time curve and maximal concentration for six of the 10 KIs. When the KI was coadministered with ritonavir, dose reductions to between 10% and 75% of the original dose were required to achieve an area under the plasma concentration-time curve within 1.25-fold of the value in the absence of ritonavir.

CONCLUSION

To our knowledge, this study provides the first data to assist clinicians' understanding of the drug interaction risk associated with administering ritonavir-containing COVID-19 therapies to patients with cancer who are currently being treated with KIs. These data may support clinicians to make more informed dosing decisions for patients with cancer undergoing treatment with KIs who require treatment with ritonavir-containing COVID-19 antiviral therapies.

SNPs in 3'UTR miRNA Target Sequences Associated with Individual Drug Susceptibility

The complementary interaction of microRNAs (miRNAs) with their binding sites in the 3'untranslated regions (3'UTRs) of target gene mRNAs represses translation, playing a leading role in gene expression control. MiRNA recognition elements (MREs) in the 3'UTRs of genes often contain single nucleotide polymorphisms (SNPs), which can change the binding affinity for target miRNAs leading to dysregulated gene expression. Accumulated data suggest that these SNPs can be associated with various human pathologies (cancer, diabetes, neuropsychiatric disorders, and cardiovascular diseases) by disturbing the interaction of miRNAs with their MREs located in mRNA 3'UTRs. Numerous data show the role of SNPs in 3'UTR MREs in individual drug susceptibility and drug resistance mechanisms. In this review, we brief the data on such SNPs focusing on the most rigorously proven cases. Some SNPs belong to conventional genes from the drug-metabolizing system (in particular, the genes coding for cytochromes P450 (CYP 450), phase II enzymes (SULT1A1 and UGT1A), and ABCB3 transporter and their expression regulators (PXR and GATA4)). Other examples of SNPs are related to the genes involved in DNA repair, RNA editing, and specific drug metabolisms. We discuss the gene-by-gene studies and genome-wide approaches utilized or potentially utilizable to detect the MRE SNPs associated with individual response to drugs.

Pharmacometabolomics-Based Translational Biomarkers: How to Navigate the Data Ocean

Metabolome is the end point of the genome-environment interplay, and enables an important holistic overview of individual adaptability and host responses to environmental, ecological, as well as endogenous changes such as disease. Pharmacometabolomics is the application of metabolome knowledge to decipher the mechanisms of interindividual and intraindividual variations in drug efficacy and safety. Pharmacometabolomics also contributes to prediction of drug treatment outcomes on the basis of baseline (predose) and postdose metabotypes through mathematical modeling. Thus, pharmacometabolomics is a strong asset for a diverse community of stakeholders interested in theory and practice of evidence-based and precision/personalized medicine: academic researchers, public health scholars, health professionals, pharmaceutical, diagnostics, and biotechnology industries, among others. In this expert review, we discuss pharmacometabolomics in four contexts: (1) an interdisciplinary omics tool and field to map the mechanisms and scale of interindividual variability in drug effects, (2) discovery and development of translational biomarkers, (3) advance digital biomarkers, and (4) empower drug repurposing, a field that is increasingly proving useful in the current era of Covid-19. As the applications of pharmacometabolomics are growing rapidly in the current postgenome era, next-generation proteomics and metabolomics follow the example of next-generation sequencing analyses. Pharmacometabolomics can also empower data reliability and reproducibility through multiomics integration strategies, which use each data layer to correct, connect with, and inform each other. Finally, we underscore here that contextual data remain crucial for precision medicine and drug development that stand the test of time and clinical relevance.

FOXM1 Variant Contributes to Gefitinib Resistance via Activating Wnt/β-Catenin Signal Pathway in Patients with Non-Small Cell Lung Cancer

PURPOSE

Although gefitinib prolonged the progression-free survival (PFS) of patients with non-small cell lung cancer (NSCLC), unpredictable resistance limited its clinical efficacy. Novel predictive biomarkers with explicit mechanisms are urgently needed.

EXPERIMENTAL DESIGN

A total of 282 patients with NSCLC with gefitinib treatment were randomly assigned in a 7:3 ratio to exploratory (n = 192) and validation (n = 90) cohorts. The candidate polymorphisms were selected with Haploview4.2 in Hapmap and genotyped by a MassARRAY system, and the feature variables were identified through Randomforest Survival analysis. Tanswell and clonogenic assays, base editing and cell-derived tumor xenograft model were performed to uncover the underlying mechanism.

RESULTS

We found that the germline missense polymorphism rs3742076 (A>G, S628P), located in transactivation domain of FOXM1, was associated with PFS in exploratory (median PFS: GG vs. GA&AA, 9.20 vs. 13.37 months, P = 0.00039, HR = 2.399) and validation (median PFS: GG vs. GA&AA, 8.13 vs. 13.80 months, P = 0.048, HR = 2.628) cohorts. We elucidated that rs3742076_G conferred resistance to gefitinib by increasing protein stability of FOXM1 and facilitating an aggressive phenotype in vitro and in vivo through activating wnt/β-catenin signaling pathway. Meanwhile, FOXM1 level was highly associated with prognosis in patients with EGFR-mutant NSCLC. Mechanistically, FOXM1 rs3742076_G upregulated wnt/β-catenin activity by directly binding to β-catenin in cytoplasm and promoting transcription of β-catenin in nucleus. Remarkably, inhibition of β-catenin markedly reversed rs3742076_G-induced gefitinib resistance and aggressive phenotypes.

CONCLUSIONS

These findings characterized rs3742076_G as a gain-of-function polymorphism in mediating gefitinib resistance and tumor aggressiveness, and highlighted the variant as a predictive biomarker in guiding gefitinib treatment.

Evaluating the utility of therapeutic drug monitoring in the clinical use of small molecule kinase inhibitors: a review of the literature

Introduction: Orally administered small molecule kinase inhibitors (KI) are a key class of targeted anti-cancer medicines that have contributed substantially to improved survival outcomes in patients with advanced disease. Since the introduction of KIs in 2001, there has been a building body of evidence that the benefit derived from these drugs may be further enhanced by individualizing dosing on the basis of concentration.Areas covered: This review considers the rationale for individualized KI dosing and the requirements for robust therapeutic drug monitoring (TDM). Current evidence supporting TDM-guided KI dosing is presented and critically evaluated, and finally potential approaches to address translational challenges for TDM-guided KI dosing and alternate approaches to support individualization of KI dosing are discussed.Expert opinion: Intuitively, the individualization of KI dosing through an approach such as TDM-guided dosing has great potential to enhance the effectiveness and tolerability of these drugs. However, based on current literature evidence it is unrealistic to propose that TDM-guided KI dosing should be routinely implemented into clinical practice.