Advances in drug metabolism and pharmacogenetics research in Australia

Computational exploration of microsomal cytochrome P450 3A1 enzyme modulation by phytochemicals of Cichorium intybus L.: Insights into drug metabolism

Drug metabolism plays a crucial role in drug fate, including therapeutic inactivation or activation, as well as the formation of toxic compounds. This underscores the importance of understanding drug metabolism in drug discovery and development. Considering the substantial costs associated with traditional drug development methods, computational approaches have emerged as valuable tools for predicting the metabolic fate of drug candidates. With this in mind, the present study aimed to investigate the potential mechanisms underlying the modulation of microsomal cytochrome P450 3A1 (CYP3A1) enzyme activity by various phytochemicals found in Cichorium intybus L., commonly known as chicory. To achieve this goal, several in silico methods, including molecular docking and molecular dynamics (MD) simulation, were employed to explore computationally the microsomal CYP3A1 enzyme. Schrodinger software was utilized for the molecular docking study, which involved the interaction analysis between CYP3A1 and 28 phytoconstituents of Cichorium intybus. Virtual screening of 28 compounds from chicory led to the identification of the top five ranked compounds. These compounds were evaluated for drug-likeness properties, pharmacokinetic profiles, and predicted binding affinities to CYP3A1. Caffeoylshikimic acid and cichoric acid emerged as promising candidates due to their favorable characteristics, including good oral bioavailability and high binding affinities to CYP3A1. Molecular dynamics simulations were conducted to assess the stability of caffeoylshikimic acid within the CYP3A1 binding pocket. The results demonstrated that caffeoylshikimic acid maintained stable interactions with the enzyme throughout the simulation, suggesting its potential as an effective modulator of CYP3A1 activity. The findings of this study have the potential to provide valuable insights into the complex molecular mechanisms by which Cichorium intybus L. acts on hepatocytes and modulates CYP3A1 enzyme expression or activity. By elucidating the impact of these phytochemicals on drug metabolism, this research contributes to our understanding of how chicory may interact with drugs and influence their efficacy and safety profiles.

Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives

Drug metabolism and excretion play crucial roles in determining the efficacy and safety of drug candidates, and predicting these processes is an essential part of drug discovery and development. In recent years, artificial intelligence (AI) has emerged as a powerful tool for predicting drug metabolism and excretion, offering the potential to speed up drug development and improve clinical success rates. This review highlights recent advances in AI-based drug metabolism and excretion prediction, including deep learning and machine learning algorithms. We provide a list of public data sources and free prediction tools for the research community. We also discuss the challenges associated with the development of AI models for drug metabolism and excretion prediction and explore future perspectives in the field. We hope this will be a helpful resource for anyone who is researching in silico drug metabolism, excretion, and pharmacokinetic properties.

Study on Genotyping Polymorphism and Sequencing of N-Acetyltransferase 2 (NAT2) among Al-Ahsa Population

One of the well-studied phase II drug metabolizing enzymes is N-acetyltransferase 2 (NAT2) which has an essential role in the detoxification and metabolism of several environmental toxicants and many therapeutic drugs like isoniazid (antituberculosis, TB) and antimicrobial sulfonamides. According to the variability in the acetylation rate among different ethnic groups, individuals could be classified into slow, intermediate, and fast acetylators; these variabilities in the acetylation rate are a result of single nucleotide polymorphisms (SNPs) in the coding sequence of NAT2. The variety of NAT2 acetylation status is associated with some diseases such as bladder cancer, colorectal cancer, rheumatoid arthritis, and diabetes mellitus. The main objectives of this research are to describe the genetic profile of NAT2 gene among the people of the Al-Ahsa region, to detect the significant SNPs of this gene, to determine the frequency of major NAT2 alleles and genotypes, and then categorize them into fast, intermediate, and slow acetylators. Blood samples were randomly collected from 96 unrelated people from Al-Ahsa population, followed by DNA extraction then amplifying the NAT2 gene by polymerase chain reaction (PCR); finally, functional NAT2 gene (exon 2) was sequenced using the Sanger sequencing method. The well-known seven genetic variants of NAT2 gene are 191G>A, 282C>T, 341T>C, 481C>T, 590G>A, 803A>G, and 857G>A were detected with allele frequencies 1%, 35.4%, 42.7%, 41.1%, 29.2%, 51%, and 5.7%, respectively. The most common NAT2 genetic variant among Al-Ahsa population was 803A>G with a high frequency 0.510 (95% confidence interval 0.44-0.581) followed by 341T>C 0.427 (95% confidence interval 0.357-0.497). The most frequent two haplotypes of NAT2 were NAT2∗6C (25.00%) and NAT2∗5A (22.92%) which were classified as a slow acetylators. According to trimodal distribution of acetylation activity, the predicted phenotype of Al-Ahsa population was found to be 5.21% rapid acetylators, 34.38% intermediate acetylators, and 60.42% were slow acetylators. In addition, this study found four novel haplotypes NAT2∗5TB, NAT2∗5AB, NAT2∗5ZA, and NAT2∗6W which were slow acetylators. This study revealed a high frequency of the NAT2 gene with slow acetylators (60.42%) in Al-Ahsa population, which might alter the drug's efficacy and vulnerability to some diseases.

Deep Learning Based Drug Metabolites Prediction

Drug metabolism research plays a key role in the discovery and development of drugs. Based on the discovery of drug metabolites, new chemical entities can be identified and potential safety hazards caused by reactive or toxic metabolites can be minimized. Nowadays, computational methods are usually complementary tools for experiments. However, current metabolites prediction methods tend to have high false positive rates with low accuracy and are usually only used for specific enzyme systems. In order to overcome this difficulty, a method was developed in this paper by first establishing a database with broad coverage of SMARTS-coded metabolic reaction rule, and then extracting the molecular fingerprints of compounds to construct a classification model based on deep learning algorithms. The metabolic reaction rule database we built can supplement chemically reasonable negative reaction examples. Based on deep learning algorithms, the model could determine which reaction types are more likely to occur than the others. In the test set, our method can achieve the accuracy of 70% (Top-10), which is significantly higher than that of random guess and the rule-based method SyGMa. The results demonstrated that our method has a certain predictive ability and application value.

Differentiation of Deprotonated Acyl-, N-, and O-Glucuronide Drug Metabolites by Using Tandem Mass Spectrometry Based on Gas-Phase Ion-Molecule Reactions Followed by Collision-Activated Dissociation

Glucuronidation, a common phase II biotransformation reaction, is one of the major in vitro and in vivo metabolism pathways of xenobiotics. In this process, glucuronic acid is conjugated to a drug or a drug metabolite via a carboxylic acid, a hydroxy, or an amino group to form acyl-, O-, and/or N-glucuronide metabolites, respectively. This process is traditionally thought to be a detoxification pathway. However, some acyl-glucuronides react with biomolecules in vivo, which may result in immune-mediated idiosyncratic drug toxicity (IDT). In order to avoid this, one may attempt in early drug discovery to modify the lead compounds in such a manner that they then have a lower probability of forming reactive acyl-glucuronide metabolites. Because most drugs or drug candidates bear multiple functionalities, e.g., hydroxy, amino, and carboxylic acid groups, glucuronidation can occur at any of those. However, differentiation of isomeric acyl-, N-, and O-glucuronide derivatives of drugs is challenging. In this study, gas-phase ion-molecule reactions between deprotonated glucuronide metabolites and BF₃ followed by collision-activated dissociation (CAD) in a linear quadrupole ion trap mass spectrometer were demonstrated to enable the differentiation of acyl-, N-, and O-glucuronides. Only deprotonated N-glucuronides and deprotonated, migrated acyl-glucuronides form the two diagnostic product ions: a BF₃ adduct that has lost two HF molecules, [M - H + BF₃ - 2HF]^-, and an adduct formed with two BF₃ molecules that has lost three HF molecules, [M - H + 2BF₃ - 3HF]^-. These product ions were not observed for deprotonated O-glucuronides and unmigrated, deprotonated acyl-glucuronides. Upon CAD of the [M - H + 2BF₃ - 3HF]^- product ion, a diagnostic fragment ion is formed via the loss of 2-fluoro-1,3,2-dioxaborale (MW of 88 Da) only in the case of deprotonated, migrated acyl-glucuronides. Therefore, this method can be used to unambiguously differentiate acyl-, N-, and O-glucuronides. Further, coupling this methodology with HPLC enables the differentiation of unmigrated 1-β-acyl-glucuronides from the isomeric acyl-glucuronides formed upon acyl migration. Quantum chemical calculations at the M06-2X/6-311++G(d,p) level of theory were employed to probe the mechanisms of the reactions of interest.

In silico approaches and tools for the prediction of drug metabolism and fate: A review

The fate of administered drugs is largely influenced by their metabolism. For example, endogenous enzyme-catalyzed conversion of drugs may result in therapeutic inactivation or activation or may transform the drugs into toxic chemical compounds. This highlights the importance of drug metabolism in drug discovery and development, and accounts for the wide variety of experimental technologies that provide insights into the fate of drugs. In view of the high cost of traditional drug development, a number of computational approaches have been developed for predicting the metabolic fate of drug candidates, allowing for screening of large numbers of chemical compounds and then identifying a small number of promising candidates. In this review, we introduce in silico approaches and tools that have been developed to predict drug metabolism and fate, and assess their potential to facilitate the virtual discovery of promising drug candidates. We also provide a brief description of various recent models for predicting different aspects of enzyme-drug reactions and provide a list of recent in silico tools used for drug metabolism prediction.

Identification of early biological changes in palmitate-treated isolated human islets

Background

Long-term exposure to elevated levels of free fatty acids (FFAs) is deleterious for beta-cell function and may contribute to development of type 2 diabetes mellitus (T2DM). Whereas mechanisms of impaired glucose-stimulated insulin secretion (GSIS) in FFA-treated beta-cells have been intensively studied, biological events preceding the secretory failure, when GSIS is accentuated, are poorly investigated. To identify these early events, we performed genome-wide analysis of gene expression in isolated human islets exposed to fatty acid palmitate for different time periods.

Results

Palmitate-treated human islets showed decline in beta-cell function starting from day two. Affymetrix Human Transcriptome Array 2.0 identified 903 differentially expressed genes (DEGs). Mapping of the genes onto pathways using KEGG pathway enrichment analysis predicted four islet biology-related pathways enriched prior but not after the decline of islet function and three pathways enriched both prior and after the decline of islet function. DEGs from these pathways were analyzed at the transcript level. The results propose that in palmitate-treated human islets, at early time points, protective events, including up-regulation of metallothioneins, tRNA synthetases and fatty acid-metabolising proteins, dominate over deleterious events, including inhibition of fatty acid detoxification enzymes, which contributes to the enhanced GSIS. After prolonged exposure of islets to palmitate, the protective events are outweighed by the deleterious events, which leads to impaired GSIS.

Conclusions

The study identifies temporal order between different cellular events, which either promote or protect from beta-cell failure. The sequence of these events should be considered when developing strategies for prevention and treatment of the disease.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5008-z) contains supplementary material, which is available to authorized users.

Construction of a Transcriptome-Driven Network at the Early Stage of Infection with Influenza A H1N1 in Human Lung Alveolar Epithelial Cells

We aimed to understand the molecular changes in host cells that accompany infection by the seasonal influenza A H1N1 virus because the initial response rapidly changes owing to the fact that the virus has a robust initial propagation phase. Human epithelial alveolar A549 cells were infected and total RNA was extracted at 30 min, 1 h, 2 h, 4 h, 8 h, 24 h, and 48 h post infection (h.p.i.). The differentially expressed host genes were clustered into two distinct sets of genes as the infection progressed over time. The patterns of expression were significantly different at the early stages of infection. One of the responses showed roles similar to those associated with the enrichment gene sets to known 'gp120 pathway in HIV.' This gene set contains genes known to play roles in preventing the progress of apoptosis, which infected cells undergo as a response to viral infection. The other gene set showed enrichment of 'Drug Metabolism Enzymes (DMEs).' The identification of two distinct gene sets indicates that the virus regulates the cell's mechanisms to create a favorable environment for its stable replication and protection of gene metabolites within 8 h.