Role of Genetic Polymorphisms in Drug-Metabolizing Enzyme-Mediated Toxicity and Pharmacokinetic Resistance to Anti-Cancer Agents: A Review on the Pharmacogenomics Aspect

An evaluation of the performance of the PAP-PCR method in detecting UGT1A1 gene polymorphisms

BACKGROUND

Gilbert's syndrome (GS) is a hereditary disorder caused by mutations in the UGT1A1 gene, leading to unconjugated hyperbilirubinemia. Accurate detection of UGT1A1 gene polymorphisms is crucial for diagnosis and management of GS. This study evaluated the clinical application of pyrophosphorolysis-activated polymerization PCR (PAP-PCR) method in dection of UGT1A1 gene polymorphisms.

METHODS AND RESULTS

Whole-blood samples from 53 patients with GS were genotyped using PAP-PCR for UGT1A1 variations, including UGT1A1*60 (c.-3279T > G), UGT1A1*28 ([TA]6 > [TA]7), UGT1A1*6 (c.211G > A, G71R), UGT1A1*27 (c.686C > A, P229Q), UGT1A1*63 (c.1091C > T, P364L), and UGT1A1*7 (c.1456T > G, Y486D). For each locus, all cases were verified using the direct sequencing to assess PAP-PCR precision. One mutation type per locus was selected to investigate reproducibility and detection limits. The UGT1A1 gene polymorphism detected using PAP-PCR showed that, among the 53 patients, 83.02% presented missense mutations at UGT1A1*60 (c.-3279T > G), 54.72% had heterozygous or homozygous insertions in the TATA box in the promoter, 52.38% had the UGT1A1*6 variant (c.211G > A, G71R). The results obtained using the PAP-PCR and direct sequencing methods were almost consistent among all patients with UGT1A1 gene variants. However, there were different results in the promoter region variants among 2 patients in which PAP-PCR showed [TA]7 homozygosity, whereas direct sequencing revealed [TA]7/[TA]6 heterozygosity. This inconsistent result had been confirmed to be caused by differences in DNA polymerase activity.

CONCLUSIONS

PAP-PCR could effectively detect UGT1A1 gene polymorphisms associated with Gilbert's syndrome. And it had exhibited superior accuracy in the analysis of TA repeat sequences compared to direct sequencing method. Additionally, due to its reduced program complexity, easy execution, and simple standardization, PAP-PCR may be a highly favorable option for clinical diagnosis.

Targeting Wnt signaling in cancer drug resistance: Insights from pre-clinical and clinical research

Cancer drug resistance, encompassing both acquired and intrinsic chemoresistance, remains a significant challenge in the clinical management of tumors. While advancements in drug discovery and the development of various small molecules and anti-cancer compounds have improved patient responses to chemotherapy, the frequent and prolonged use of these drugs continues to pose a high risk of developing chemoresistance. Therefore, understanding the primary mechanisms underlying drug resistance is crucial. Wnt proteins, as secreted signaling molecules, play a pivotal role in transmitting signals from the cell surface to the nucleus. Aberrant expression of Wnt proteins has been observed in a variety of solid and hematological tumors, where they contribute to key processes such as proliferation, metastasis, stemness, and immune evasion, often acting in an oncogenic manner. Notably, the role of the Wnt signaling pathway in modulating chemotherapy response in human cancers has garnered significant attention. This review focuses on the involvement of Wnt signaling and its related molecular pathways in drug resistance, highlighting their associations with cancer hallmarks, stemness, and tumorigenesis linked to chemoresistance. Additionally, the overexpression of Wnt proteins has been shown to accelerate cancer drug resistance, with regulation mediated by non-coding RNAs. Elevated Wnt activity reduces cell death in cancers, particularly by affecting mechanisms like apoptosis, autophagy, and ferroptosis. Furthermore, pharmacological compounds and small molecules have demonstrated the potential to modulate Wnt signaling in cancer therapy. Given its impact, Wnt expression can also serve as a prognostic marker and a factor influencing survival outcomes in human cancers.

Enhancing breast cancer treatment through pharmacogenomics: A narrative review

Pharmacogenomics has become integral to personalised medicine in breast cancer, utilising genetic insights to customize treatment strategies and enhance patient outcomes. Understanding how genetic variations influence drug metabolism, response, and toxicity is crucial for guiding treatment selection and dosing regimens. Genetic polymorphisms in drug-metabolizing enzymes and transporters significantly impact pharmacokinetic variability, influencing the efficacy and safety of chemotherapy agents and targeted therapies. Biomarkers associated with the hormone receptor status of breast cancer and mutations serve as key determinants of treatment response, aiding in the selection of therapies. Despite substantial progress in understanding the pharmacogenomic landscape of breast cancer, efforts to identify novel genetic markers and refine treatment optimisation strategies are required. Genome-wide association studies and advanced sequencing technologies hold promise for uncovering genetic determinants of drug response variability and elucidating complex pharmacogenomic interactions. The future of pharmacogenomics in breast cancer lies in real-time treatment monitoring, the discovery of additional predictive markers, and the seamless integration of pharmacogenomic data into clinical decision-making processes. However, translating pharmacogenomic discoveries into routine clinical practice requires collaborative efforts among stakeholders to address implementation challenges and ensure equitable access to genetic testing. By embracing pharmacogenomics, clinicians can tailor treatment approaches to individual patients, maximizing therapeutic benefits while minimizing adverse effects. This review discusses the integration of pharmacogenomics in breast cancer treatment, highlighting the significance of understanding genetic influences on treatment response and toxicity, and the potential of advanced technologies in refining treatment strategies.

Long-Term Pharmacokinetic Follow-Up of Abiraterone Acetate in Patients with Metastatic Castration-Resistant Prostate Cancer

This ABIGENE pharmacokinetic (PK) study sought mainly to characterize the unchanged drug PK during long-term abiraterone acetate (AA) administration in advanced prostate cancer patients (81 patients). It was observed that individual AA concentrations remained constant over treatment time, with no noticeable changes during repeated long-term drug administration for up to 120 days. There was no correlation between AA concentrations and survival outcomes. However, a significant association between higher AA concentrations and better clinical benefit was observed (p = 0.041). The safety data did not correlate with the AA PK data. A significant positive correlation (r = 0.40, p < 0.001) was observed between mean AA concentration and patient age: the older the patient, the higher the AA concentration. Patient age was found to impact steady-state AA concentration: the older the patient, the higher the mean AA concentration. Altogether, these data may help to guide future research and clinical trials in order to maximize the benefits of AA metastatic castration-resistant prostate cancer patients.

Validation of single nucleotide polymorphisms potentially related to R-CHOP resistance in diffuse large B-cell lymphoma patients

Aim: Diffuse large B-cell lymphoma (DLBCL) is the most common B-cell non-Hodgkin lymphoma (NHL). Despite the availability of clinical and molecular algorithms applied for the prediction of prognosis, in up to 30%-40% of patients, intrinsic or acquired drug resistance occurs. Constitutional genetics may help to predict R-CHOP resistance. This study aimed to validate previously identified single nucleotide polymorphisms (SNPs) in the literature as potential predictors of R-CHOP resistance in DLBCL patients, SNPs. Methods: Twenty SNPs, involved in R-CHOP pharmacokinetics/pharmacodynamics or other pathobiological processes, were investigated in 185 stage I-IV DLBCL patients included in a multi-institution pharmacogenetic study to validate their previously identified correlations with resistance to R-CHOP. Results: Correlations between rs2010963 (VEGFA gene) and sex (P = 0.046), and rs1625895 (TP53 gene) and stage (P = 0.003) were shown. After multivariate analyses, a concordant effect (i.e., increased risk of disease progression and death) was observed for rs1883112 (NCF4 gene) and rs1800871 (IL10 gene). When patients were grouped according to the revised International Prognostic Index (R-IPI), both these SNPs further discriminated progression-free survival (PFS) and overall survival (OS) of the R-IPI-1-2 subgroup. Overall, patients harboring the rare allele showed shorter PFS and OS compared with wild-type patients. Conclusions: Two out of the 20 study SNPs were validated. Thus, these results support the role of previously identified rs1883112 and rs1800871 in predicting DLBCL resistance to R-CHOP and highlight their ability to further discriminate the prognosis of R-IPI-1-2 patients. These data point to the need to also focus on host genetics for a more comprehensive assessment of DLBCL patient outcomes in future prospective trials.

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine

INTRODUCTION

Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties.

AREAS COVERED

The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged.

EXPERT OPINION

AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.

Scaffold hopping for designing of potent and selective CYP1B1 inhibitors to overcome docetaxel resistance: synthesis and evaluation

Cytochrome P450 1B1, a tumor-specific overexpressed enzyme, significantly impairs the pharmacokinetics of several commonly used anticancer drugs including docetaxel, paclitaxel and cisplatin, leading to the problem of resistance to these drugs. Currently, there is no CYP1B1 inhibition-based adjuvant therapy available to treat this resistance problem. Hence, in the current study, exhaustive in-silico studies including scaffold hopping followed by molecular docking, three-dimensional quantitative structure-activity relationships (3D-QSAR), molecular dynamics and free energy perturbation studies were carried out to identify potent and selective CYP1B1 inhibitors. Initially, scaffold hopping analysis was performed against a well-reported potent and selective CYP1B1 inhibitor (i.e. compound 3n). A total of 200 scaffolds were identified along with their shape and field similarity scores. The top three scaffolds were further selected on the basis of these scores and their synthesis feasibility to design some potent and selective CYP1B1 inhibitors using the aforementioned in-silico techniques. Designed molecules were further synthesized to evaluate their CYP1B1 inhibitory activity and docetaxel resistance reversal potential against CYP1B1 overexpressed drug resistance MCF-7 cell line. In-vitro results indicated that compounds 2a, 2c and 2d manifested IC50 values for CYP1B1 ranging from 0.075, 0.092 to 0.088 μM with at least 10-fold selectivity. At low micromolar concentrations, compounds 1e, 1f, 2a and 2d exhibited promising cytotoxic effects in the docetaxel-resistant CYP1B1 overexpressed MCF-7 cell line. In particular, compound 2a is most effective in reversing the resistance with IC50 of 29.0 ± 3.6 μM. All of these discoveries could pave the way for the development of adjuvant therapy capable of overcoming CYP1B1-mediated resistance.

Scaffold hopping based designing of selective ALDH1A1 inhibitors to overcome cyclophosphamide resistance: synthesis and biological evaluation

Aldehyde dehydrogenase 1A1 (ALDH1A1) is an isoenzyme that catalyzes the conversion of aldehydes to acids. However, the overexpression of ALDH1A1 in a variety of malignancies is the major cause of resistance to an anti-cancer drug, cyclophosphamide (CP). CP is a prodrug that is initially converted into 4-hydroxycyclophosphamide and its tautomer aldophosphamide, in the liver. These compounds permeate into the cell and are converted as active metabolites, i.e., phosphoramide mustard (PM), through spontaneous beta-elimination. On the other hand, the conversion of CP to PM is diverted at the level of aldophosphamide by converting it into inactive carboxyphosphamide using ALDH1A1, which ultimately leads to high drug inactivation and CP resistance. Hence, in combination with our earlier work on the target of resistance, i.e., ALDH1A1, we hereby report selective ALDH1A1 inhibitors. Herein, we selected a lead molecule from our previous virtual screening and implemented scaffold hopping analysis to identify a novel scaffold that can act as an ALDH1A1 inhibitor. This results in the identification of various novel scaffolds. Among these, on the basis of synthetic feasibility, the benzimidazole scaffold was selected for the design of novel ALDH1A1 inhibitors, followed by machine learning-assisted structure-based virtual screening. Finally, the five best compounds were selected and synthesized. All synthesized compounds were evaluated using in vitro enzymatic assay against ALDH1A1, ALDH2, and ALDH3A1. The results disclosed that three molecules A1, A2, and A3 showed significant selective ALDH1A1 inhibitory potential with an IC50 value of 0.32 μM, 0.55 μM, and 1.63 μM, respectively, and none of the compounds exhibits potency towards the other two ALDH isoforms i.e. ALDH2 and ALDH3A1. Besides, the potent compounds (A1, A2, and A3) have been tested for in vitro cell line assay in combination with mafosfamide (analogue of CP) on two cell lines i.e. A549 and MIA-PaCa-2. All three compounds show significant potency to reverse mafosfamide resistance by inhibiting ALDH1A1 against these cell lines.

Design and Synthesis of Novel Near-Infrared Fluorescence Probes Based on an Open Conformation of a Cytochrome P450 1B1 Complex for Molecular Imaging of Colorectal Tumors

Cytochrome P450 1B1 (CYP1B1) is induced during the early stage of cancer and is universally overexpressed in tumors. Thus, it was considered as a potential biomarker for the monitoring of cancer. In this study, we designed and synthesized CYP1B1-targeted near-infrared (NIR) fluorescence molecular probes based on the latest reported open conformation of the CYP1B1-α-naphthoflavone (ANF) complex. According to the architecture of the open channel, we introduced linkers and a Cy5.5 fragment at the 5' position of ANF derivatives with strong CYP1B1 inhibitory activity to obtain probes 19-21. Then, in vitro cell-based studies showed that the probes could be enriched in tumor cells by binding to CYP1B1. During in vivo and ex vivo imaging in a xenograft mouse model, probe 19 with the best binding affinity was proven to be able to identify tumor sites in both fluorescence imaging and photoacoustic imaging modes.

3D-QSAR assisted identification of selective CYP1B1 inhibitors: an effective bioisosteric replacement/molecular docking/electrostatic complementarity analysis

Cytochrome P450-1B1 is a majorly overexpressed drug-metabolizing enzyme in tumors and is responsible for inactivation and subsequent resistance to a variety of anti-cancer drugs, i.e., docetaxel, tamoxifen, and cisplatin. In the present study, a 3D quantitative structure-activity relationship (3D-QSAR) model has been constructed for the identification, design, and optimization of novel CYP1B1 inhibitors. The model has been built using a set of 148 selective CYP1B1 inhibitors. The developed model was evaluated based on certain statistical parameters including q2 and r2 which showed the acceptable predictive and descriptive capability of the generated model. The developed 3D-QSAR model assisted in understanding the key molecular fields which were firmly related to the selective CYP1B1 inhibition. A theoretic approach for the generation of new lead compounds with optimized CYP1B1 receptor affinity has been performed utilizing bioisosteric replacement analysis. These generated molecules were subjected to a developed 3D-QSAR model to predict the inhibitory activity potentials. Furthermore, these compounds were scrutinized through the activity atlas model, molecular docking, electrostatic complementarity, molecular dynamics, and waterswap analysis. The final hits might act as selective CYP1B1 inhibitors which could address the issue of resistance. This 3D-QSAR includes several chemically diverse selective CYP1B1 receptor ligands and well accounts for the individual ligand's inhibition affinities. These features of the developed 3D-QSAR model will ensure future prospective applications of the model to speed up the identification of new potent and selective CYP1B1 receptor ligands.

Computational analysis of the functional and structural impact of the most deleterious missense mutations in the human Protein C

Protein C (PC) is a vitamin K-dependent factor that plays a crucial role in controlling anticoagulant processes and acts as a cytoprotective agent to promote cell survival. Several mutations in human PC are associated with decreased protein production or altered protein structure, resulting in PC deficiency. In this study, we conducted a comprehensive analysis of nonsynonymous single nucleotide polymorphisms in human PC to prioritize and confirm the most high-risk mutations predicted to cause disease. Of the 340 missense mutations obtained from the NCBI database, only 26 were classified as high-risk mutations using various bioinformatic tools. Among these, we identified that 12 mutations reduced the stability of protein, and thereby had the greatest potential to disturb protein structure and function. Molecular dynamics simulations revealed moderate alterations in the structural stability, flexibility, and secondary structural organization of the serine protease domain of human PC for five missense mutations (L305R, W342C, G403R, V420E, and W444C) when compared to the native structure that could maybe influence its interaction with other molecules. Protein-protein interaction analyses demonstrated that the occurrence of these five mutations can affect the regular interaction between PC and activated factor V. Therefore, our findings assume that these mutants can be used in the identification and development of therapeutics for diseases associated with PC dysfunction, although assessment the effect of these mutations need to be proofed in in-vitro.

Crystal Structure of DNA Replication Protein SsbA Complexed with the Anticancer Drug 5-Fluorouracil

Single-stranded DNA-binding proteins (SSBs) play a crucial role in DNA metabolism by binding and stabilizing single-stranded DNA (ssDNA) intermediates. Through their multifaceted roles in DNA replication, recombination, repair, replication restart, and other cellular processes, SSB emerges as a central player in maintaining genomic integrity. These attributes collectively position SSBs as essential guardians of genomic integrity, establishing interactions with an array of distinct proteins. Unlike Escherichia coli, which contains only one type of SSB, some bacteria have two paralogous SSBs, referred to as SsbA and SsbB. In this study, we identified Staphylococcus aureus SsbA (SaSsbA) as a fresh addition to the roster of the anticancer drug 5-fluorouracil (5-FU) binding proteins, thereby expanding the ambit of the 5-FU interactome to encompass this DNA replication protein. To investigate the binding mode, we solved the complexed crystal structure with 5-FU at 2.3 Å (PDB ID 7YM1). The structure of glycerol-bound SaSsbA was also determined at 1.8 Å (PDB ID 8GW5). The interaction between 5-FU and SaSsbA was found to involve R18, P21, V52, F54, Q78, R80, E94, and V96. Based on the collective results from mutational and structural analyses, it became evident that SaSsbA's mode of binding with 5-FU diverges from that of SaSsbB. This complexed structure also holds the potential to furnish valuable comprehension regarding how 5-FU might bind to and impede analogous proteins in humans, particularly within cancer-related signaling pathways. Leveraging the information furnished by the glycerol and 5-FU binding sites, the complexed structures of SaSsbA bring to the forefront the potential viability of several interactive residues as potential targets for therapeutic interventions aimed at curtailing SaSsbA activity. Acknowledging the capacity of microbiota to influence the host's response to 5-FU, there emerges a pressing need for further research to revisit the roles that bacterial and human SSBs play in the realm of anticancer therapy.

Abigene, a Prospective, Multicentric Study of Abiraterone Acetate Pharmacogenetics in Metastatic Castration-Resistant Prostate Cancer

Abiraterone acetate (AA) is the first-in-class of drugs belonging to the second-generation of agents inhibiting androgen neosynthesis in advanced prostate cancer. A cumulative experience attests that germinal gene polymorphisms may play a role in the prediction of anticancer agent pharmacodynamics variability. In the present prospective, multicentric study, gene polymorphisms of CYP17A1 (AA direct target) and the androgen transporter genes SLCO2B1 and SLCO1B3 (potential modulators of AA activity) were confronted with AA pharmacodynamics (treatment response and toxicity) in a group of 137 advanced prostate cancer patients treated in the first line by AA. The median follow-up was 56.3 months (95% CI [52.5-61]). From multivariate analysis, rs2486758 C/C (CYP17A1) and PSA (≥10 ng/mL) were associated with a shorter 3-year biological PFS (HR = 4.05, IC95% [1.46-11.22]; p = 0.007 and HR = 2.08, IC95% [1.31-3.30]; p = 0.002, respectively). From a multivariate analysis, the rs743572 (CYP17A1) and performance status were independently associated with significant toxicity (OR = 3.78 (IC95% [1.42-9.75]; p = 0.006 and OR = 4.54; IC95% [1.46-13.61]; p = 0.007, respectively). Host genome characteristics may help to predict AA treatment efficacy and identify patients at risk for toxicity.