Pharmacogenomics of Antihypertensive Drugs in Brazil: recent progress and clinical implications

Background: The available antihypertensive drugs are effective and well tolerated agents. However, only about half of patients with treated hypertension achieve appropriate blood pressure control. Genetic and non-genetic factors contribute to the interindividual variability of the therapeutic response Objective: This review constitutes a comprehensive update of the pharmacogenomics of antihypertensive drugs and their clinical implications in Brazil. Results: Twenty-five studies explored the influence of gene variants on drug response in patients with primary, resistant, or gestational hypertension. Variants in BDKRB2, NOS3, PRKCA, and VEGFA influenced the response to enalapril in patients with primary hypertension. AGT and MMP2 variants were associated with high risk of resistance to antihypertensive treatment, whereas NOS2 variants were related to low risk. Moreover, NAT2 slow acetylators showed an increased response to hydralazine in patients with resistant hypertension. HMOX1, NAMPT, MMP9, NOS3 and TIMP1 variants might be markers of drug responsiveness in hypertensive or preeclamptic pregnant women. Power and replication of studies, polygenic nature of response to therapy, and treatment with multiple drugs were important challenges to be overcome for identifying genetic predictors of antihypertensive response in Brazil. Conclusion: Pharmacogenomic studies in Brazilian cohorts provide some evidences of variants, mainly in pharmacodynamics genes, which influence the response to antihypertensive drugs. However, some findings are limited by cohort size or therapeutic scheme and may be influenced by interactions with other genetic and non-genetic factors. Therefore, further investigations are needed to elucidate the contribution of pharmacogenomics to the efficacy and safety of antihypertensive therapy.

An ATM/CHK2 Signaling Pathway Induces Nuclear Translocation of SRPK2 in Cisplatin-Treated HeLa Cells

Chemotherapeutic agents are frequently used to treat various cancers, but the mechanisms mediating the cellular response to the drugs are still not fully understood. We previously reported that the nuclear translocation of serine/arginine protein kinases (SRPKs), triggered by the exposure of cells to DNA damage-inducers, plays a pivotal role in drug responsiveness. Here, we investigated the mechanism linking the nuclear accumulation of SRPK2 to the cisplatin treatment of HeLa cells. We present experimental evidence that nuclear SRPK2 acts downstream of Chk2 in the ATM/Chk2 cascade. The inhibition of ATM or Chk2 kinase activity by specific low-molecular-weight inhibitors restricted SRPK2 to the cytoplasm and conferred tolerance to cisplatin treatment. A similar effect was achieved by treating cells with SRPIN340, a selective SRPK1/2 inhibitor, thus confirming previous findings that kinase activity is indispensable for the nuclear import of SRPKs. These data add to previous findings that support a decisive role of SRPKs in coordinating cellular response to DNA damage.

Morphological Response in Cancer Spheroids for Screening Photodynamic Therapy Parameters

Photodynamic therapy (PDT) is a treatment which uses light-activated compounds to produce reactive oxygen species, leading to membrane damage and cell death. Multicellular cancer spheroids are a preferable alternative for PDT evaluation in comparison to monolayer cell cultures due to their ability to better mimic in vivo avascular tumour characteristics such as hypoxia and cell-cell interactions, low cost, and ease of production. However, inconsistent growth kinetics and drug responsiveness causes poor experimental reproducibility and limits their usefulness. Herein, we used image analysis to establish a link between human melanoma C8161 spheroid morphology and drug responsiveness. Spheroids were pre-selected based on sphericity, area, and diameter, reducing variation in experimental groups before treatment. Spheroid morphology after PDT was analyzed using AnaSP and ReViSP, MATLAB-based open-source software, obtaining nine different parameters. Spheroids displayed a linear response between biological assays and morphology, with area (R2 = 0.7219) and volume (R2 = 0.6138) showing the best fit. Sphericity, convexity, and solidity were confirmed as poor standalone indicators of spheroid viability. Our results indicate spheroid morphometric parameters can be used to accurately screen inefficient treatment combinations of novel compounds.

EXTH-20. SINGLE-CELL DRUG ACTIVITY MAPPING IN GLIOBLASTOMA IDENTIFIES EXTENDED DRUG RESPONSE HETEROGENEITY AND THERAPY-INDUCED CELLULAR PLASTICITY

Glioblastoma (GBM) remains a highly malignant and incurable brain tumour. The inability to achieve clinical improvements in GBM treatment can be attributed to the excessive heterogeneity and plasticity of GBM cells, which is reflected by the presence of various cellular states within each tumour. How each of these tumour cell subtypes respond to therapy remains largely unknown. In this work, we developed a functional diagnostic analysis pipeline to measure therapeutic activity in GBM tumour cells at single-cell resolution using mass cytometry by time-of-flight (CyTOF). By applying an optimised GBM-specific and therapy-tailored antibody panel, we measured therapeutic activity upon exposure to ionising radiation (RT) or a small molecule MDM2 inhibitor (AMG232) in a cohort of patient-derived GBM cell lines (n=14). As such, extended heterogeneity in drug responsiveness was reflected by diverse degrees of alterations in cell cycle progression and apoptotic signalling, in addition to shifts in tumoral phenotypic states implying therapy-induced plasticity. A similar approach was used to measure drug activity in freshly resected tumour samples (n=18) harvested from different tumour regions (core or invasive front) within hours following surgery. Accordingly, we identified highly variable fractions of responsive tumour and microenvironmental cell populations in a patient-specific way. The ability to measure drug activity at single-cell resolution in a patient-tailored manner by applying a genotype-agnostic method, paves the way for advanced precision cancer medicine in GBM by offering a novel approach to more precisely select eligible patients for prospective clinical trials.

The Role of CD4+ T Cells and Microbiota in the Pathogenesis of Asthma

Asthma, a chronic respiratory disease involving variable airflow limitations, exhibits two phenotypes: eosinophilic and neutrophilic. The asthma phenotype must be considered because the prognosis and drug responsiveness of eosinophilic and neutrophilic asthma differ. CD4+ T cells are the main determinant of asthma phenotype. Th2, Th9 and Tfh cells mediate the development of eosinophilic asthma, whereas Th1 and Th17 cells mediate the development of neutrophilic asthma. Elucidating the biological roles of CD4+ T cells is thus essential for developing effective asthma treatments and predicting a patient’s prognosis. Commensal bacteria also play a key role in the pathogenesis of asthma. Beneficial bacteria within the host act to suppress asthma, whereas harmful bacteria exacerbate asthma. Recent literature indicates that imbalances between beneficial and harmful bacteria affect the differentiation of CD4+ T cells, leading to the development of asthma. Correcting bacterial imbalances using probiotics reportedly improves asthma symptoms. In this review, we investigate the effects of crosstalk between the microbiota and CD4+ T cells on the development of asthma.

Precision Medicine for Rheumatoid Arthritis: The Right Drug for the Right Patient-Companion Diagnostics

Despite the growing number of biologic and JAK inhibitor therapeutic agents available to treat various systemic autoimmune illnesses, the lack of a validated companion diagnostic (CDx) to accurately predict drug responsiveness for an individual results in many patients being treated for years with expensive, ineffective, or toxic drugs. This review will focus primarily on rheumatoid arthritis (RA) therapeutics where the need is greatest due to poor patient outcomes if the optimum drug is delayed. We will review current FDA-approved biologic and small molecule drugs and why RA patients switch these medications. We will discuss the sampling of various tissues for potential CDx and review early results from studies investigating drug responsiveness utilizing advanced technologies including; multiplex testing of cytokines and proteins, autoantibody profiling, genomic analysis, proteomics, miRNA analysis, and metabolomics. By using these new technologies for CDx the goal is to improve RA patient outcomes and achieve similar successes like those seen in oncology using precision medicine guided therapeutics.

Performance Comparisons of AlexNet and GoogLeNet in Cell Growth Inhibition IC50 Prediction

Drug responses in cancer are diverse due to heterogenous genomic profiles. Drug responsiveness prediction is important in clinical response to specific cancer treatments. Recently, multi-class drug responsiveness models based on deep learning (DL) models using molecular fingerprints and mutation statuses have emerged. However, for multi-class models for drug responsiveness prediction, comparisons between convolution neural network (CNN) models (e.g., AlexNet and GoogLeNet) have not been performed. Therefore, in this study, we compared the two CNN models, GoogLeNet and AlexNet, along with the least absolute shrinkage and selection operator (LASSO) model as a baseline model. We constructed the models by taking drug molecular fingerprints of drugs and cell line mutation statuses, as input, to predict high-, intermediate-, and low-class for half-maximal inhibitory concentration (IC50) values of the drugs in the cancer cell lines. Additionally, we compared the models in breast cancer patients as well as in an independent gastric cancer cell line drug responsiveness data. We measured the model performance based on the area under receiver operating characteristic (ROC) curves (AUROC) value. In this study, we compared CNN models for multi-class drug responsiveness prediction. The AlexNet and GoogLeNet showed better performances in comparison to LASSO. Thus, DL models will be useful tools for precision oncology in terms of drug responsiveness prediction.

Icotinib, an Effective Treatment Option for Patients With Lung Adenocarcinoma Harboring Compound EGFR L858R and A871G Mutation

Compound epidermal growth factor receptor (EGFR) mutations are defined as double or multiple independent mutations of the EGFR tyrosine kinase domain (TKD), in which an EGFR-tyrosine kinase inhibitor (TKI)-sensitizing mutation is identified together with a mutation of unclarified clinical significance. Lung adenocarcinoma with compound EGFR mutation shows poor clinical response to EGFR-TKIs. Kobayashi et al. reported a non-small-cell lung cancer (NSCLC) patient whose tumor had EGFR exon21 L858R/A871G mutation presented rapid disease progression to erlotinib. However, in this case, we present an EGFR exon21 L858R/A871G mutation patient exerted significant benefit to icotinib, another first-generation EGFR-TKI, indicating that different EGFR-TKIs have diversiform sensitive sites and therapeutic effects, consistent mutation sites might achieve heterogeneous benefits from different EGFR-TKIs. Our case report provides promising EGFR-TKI for clinical treatment with EGFR exon21 L858R/A871G mutation in NSCLC. More dedicated efforts are needed to clarify their biologic effects on disease course and drug responsiveness.