Pharmacogenetics of the anti-HCV drug sofosbuvir: a preliminary study

A transcriptional regulatory network of HNF4α and HNF1α involved in human diseases and drug metabolism

HNF4α and HNF1α are core transcription factors involved in the development and progression of a variety of human diseases and drug metabolism. They play critical roles in maintaining the normal growth and function of multiple organs, mainly the liver, and in the metabolism of endogenous and exogenous substances. The twelve isoforms of HNF4α may exhibit different physiological functions, and HNF4α and HNF1α show varying or even opposing effects in different types of diseases, particularly cancer. Additionally, the regulation of CYP450, phase II drug-metabolizing enzymes, and drug transporters is affected by several factors. This article aims to review the role of HNF4α and HNF1α in human diseases and drug metabolism, including their structures and physiological functions, affected diseases, regulated drug metabolism genes, influencing factors, and related mechanisms. We also propose a transcriptional regulatory network of HNF4α and HNF1α that regulates the expression of target genes related to disease and drug metabolism.

Pharmacogenes that demonstrate high association evidence according to CPIC, DPWG, and PharmGKB

BACKGROUND

Different levels of evidence related to the variable responses of individuals to drug treatment have been reported in various pharmacogenomic (PGx) databases. Identification of gene-drug pairs with strong association evidence can be helpful in prioritizing the implementation of PGx guidelines and focusing on a gene panel. This study aimed to determine the pharmacogenes with the highest evidence-based association and to indicate their involvement in drug-gene interactions.

METHODOLOGY

The publicly available datasets CPIC, DPWG, and PharmGKB were selected to determine the pharmacogenes with the highest drug outcome associations. The upper two levels of evidence rated by the three scoring methods were specified (levels A-B in CPIC, 3-4 in DPWG, or 1-2 levels in PharmGKB). The identified pharmacogenes were further ranked in this study based on the number of medications they interacted with.

RESULTS

Fifty pharmacogenes, with high to moderately high evidence of associations with drug response alterations, with potential influence on the therapeutic and/or toxicity outcomes of 152 drugs were identified. CYP2D6, CYP2C9, CYP2C19, G6PD, HLA-B, SLCO1B1, CACNA1S, RYR1, MT-RNR1, and IFNL4 are the top 10 pharmacogenes, where each is predicted to impact patients' responses to ≥5 drugs.

CONCLUSION

This study identified the most important pharmacogenes based on the highest-ranked association evidence and their frequency of involvement in affecting multiple drugs. The obtained data is useful for customizing a gene panel for PGx testing. Identifying the strength of scientific evidence supporting drug-gene interactions aids drug prescribers in making the best clinical decision.

Pharmacogenomic Biomarkers

Why does the usual dose of medication work for a person while another individual cannot give the expected response to the same drug? On the other hand, how come half of the usual dose of an analgesic relieves an individual’s pain immediately, as another man continue to suffer even after taking double dose? Although a treatment method has been successfully used in majority of the population for many years, why does the same therapy cause serious side effects in another region of the world? Most presently approved therapies are not effective in all patients. For example, 20-40% of patients with depression respond poorly or not at all to antidepressant drug therapy. Many patients are resistant to the effects of antiasthmatics and antiulcer drugs or drug treatment of hyperlipidemia and many other diseases. The reason for all those is basically interindividual differences in genomic structures of people, which are explained in this chapter in terms of the systems and the most frequently used drugs in clinical treatment.

Factors Influencing the Intracellular Concentrations of the Sofosbuvir Metabolite GS-331007 (in PBMCs) at 30 Days of Therapy

Sofosbuvir (SOF) is an HCV NS5B polymerase inhibitor, and GS-331007 is its major metabolite. The aim of this study was to investigate whether clinical and pharmacological factors could influence GS-331007 intracellular (IC) concentrations in peripheral blood mononuclear cells (PBMCs) associated with a sustained virological response in patients treated with SOF and ribavirin (RBV). Drug levels were analyzed using liquid chromatography at different days of therapy, whereas variants in genes encoding transporters and nuclear factors were investigated using real-time PCR. This study enrolled 245 patients treated with SOF; 245 samples were analyzed for pharmacogenetics and 50 were analyzed for IC pharmacokinetics. The GS-331007 IC concentration at 30 days was associated with its plasma concentration determinate at 30, 60 and 90 days of SOF-therapy and with daclatasvir concentrations at 7 days of therapy. No genetic polymorphism affected IC exposure. In linear multivariate analysis, ledipasvir treatment, baseline albumin and estimated glomerular filtration rate were significant predictors of IC exposure. This study presents data on an IC evaluation in a cohort of patients treated with SOF, also considering pharmacogenetics. These results could be useful for regions where SOF–RBV treatment is considered the standard of care; moreover, they could further deepen the knowledge of IC exposure for similar drugs in the future.

Progress and Challenges in the Use of a Liver-on-a-Chip for Hepatotropic Infectious Diseases

The liver is a target organ of life-threatening pathogens and prominently contributes to the variation in drug responses and drug-induced liver injury among patients. Currently available drugs significantly decrease the morbidity and mortality of liver-dwelling pathogens worldwide; however, emerging clinical evidence reveals the importance of host factors in the design of safe and effective therapies for individuals, known as personalized medicine. Given the primary adherence of cells in conventional two-dimensional culture, the use of these one-size-fit-to-all models in preclinical drug development can lead to substantial failures in assessing therapeutic safety and efficacy. Advances in stem cell biology, bioengineering and material sciences allow us to develop a more physiologically relevant model that is capable of recapitulating the human liver. This report reviews the current use of liver-on-a-chip models of hepatotropic infectious diseases in the context of precision medicine including hepatitis virus and malaria parasites, assesses patient-specific responses to antiviral drugs, and designs personalized therapeutic treatments to address the need for a personalized liver-like model. Second, most organs-on-chips lack a monitoring system for cell functions in real time; thus, the review discusses recent advances and challenges in combining liver-on-a-chip technology with biosensors for assessing hepatocyte viability and functions. Prospectively, the biosensor-integrated liver-on-a-chip device would provide novel biological insights that could accelerate the development of novel therapeutic compounds.

Real-life study on the pharmacokinetic of remdesivir in ICU patients admitted for severe COVID-19 pneumonia

Remdesivir is one of the most encouraging treatments against SARS‐CoV‐2 infection. After intravenous infusion, RDV is rapidly metabolized (t_1/2 = 1 h) within the cells to its active adenosine triphosphate analogue form (GS‐443902) and then it can be found in plasma in its nucleoside analogue form (GS‐441524). In this real‐life study, we describe the remdesivir and GS‐441524 concentrations at three time points in nine ICU patients, through a validated ultra‐high‐performance liquid chromatography tandem mass spectrometry (UHPLC–MS/MS) method. The observed data confirmed the very rapid conversion of RDV to its metabolite and the quite long half‐life of GS‐441524. The mean C_min, C_max and AUC_0–24, were < 0.24 ng/mL and 122.3 ng/mL, 2637.3 ng/mL and 157.8 ng/mL, and 5171.2 ng*h/mL and 3676.5 ng*h/ml, respectively, for RDV and GS‐441524. Three out of nine patients achieved a C_max > 2610 ng/mL and 140 ng/mL and AUC_0–24 > 1560 ng*h/mL and 2230 ng*h/mL for RDV and GS‐441524, respectively. The mean t_1/2 value for GS‐441524 was 26.3 h. Despite the low number of patients, these data can represent an interesting preliminary report on the variability of RDV and GS‐441524 concentrations in a real‐life ICU setting.

Pharmacogenetics and the treatment of HIV-/HCV-coinfected patients

This review will summarize the role of pharmacogenetics in the natural history of hepatitis C, particularly in patients with HIV/HCV and will take the perspective of pharmacogenetics and its influence on the response to antiviral therapy and the susceptibility to develop adverse effects. This review will also devote a section to host genetics in other clinical situations, such as disease progression and acute HCV infection, which may determine whether treatment of HIV-/HCV-coinfected patients is implemented or deferred.