Current trends in drug metabolism and pharmacokinetics

Review: Mechanism of herbivores synergistically metabolizing toxic plants through liver and intestinal microbiota

Interspecific interactions are central to ecological research. Plants produce toxic plant secondary metabolites (PSMs) as a defense mechanism against herbivore overgrazing, prompting their gradual adaptation to toxic substances for tolerance or detoxification. P450 enzymes in herbivore livers bind to PSMs, whereas UDP-glucuronosyltransferase and glutathione S-transferase increase the hydrophobicity of the bound PSMs for detoxification. Intestinal microorganisms such as Bacteroidetes metabolize cellulase and other macromolecules to break down toxic components. However, detoxification is an overall response of the animal body, necessitating coordination among various organs to detoxify ingested PSMs. PSMs undergo detoxification metabolism through the liver and gut microbiota, evidenced by increased signaling processes of bile acids, inflammatory signaling molecules, and aromatic hydrocarbon receptors. In this context, we offer a succinct overview of how metabolites from the liver and gut microbiota of herbivores contribute to enhancing metabolic PSMs. We focused mainly on elucidating the molecular communication between the liver and gut microbiota involving endocrine, immune, and metabolic processes in detoxification. We have also discussed the potential for future alterations in the gut of herbivores to enhance the metabolic effects of the liver and boost the detoxification and metabolic abilities of PSMs.

Evaluation of gender differences in the pharmacokinetics of oral zileuton nanocrystalline formulation using a rat model

Zileuton is a leukotriene inhibitor used to treat asthma. As a BCS class II drug it exhibits challenges with solubility which likely impact its absorption. As patient gender significantly impacts the pharmacokinetics of many drugs, this study aimed to investigate potential gender-based pharmacokinetic differences after oral zileuton administration in rats. Male and female Sprague Dawley rats received single oral gavage doses of pure zileuton as an active pharmaceutical ingredient (30 mg/kg body weight (bw)), physical mixture (PM; at 30 mg/kg bw of the formulation contains zileuton, kollidon VA64 fine, dowfax2A1 and trehalose), and nanocrystalline formulation of zileuton (NfZ; at 30 mg/kg bw of the formulation). Plasma, tissue, and urine concentrations were quantified using high performance liquid chromatography (HPLC). Noncompartmental pharmacokinetic analysis showed higher zileuton levels in the plasma of female versus male rats across all evaluated forms of zileuton (API, PM, and NfZ). Female rats demonstrated higher peak plasma concentrations (Cmax) and increased area under the plasma concentration-time curve (AUC) relative to males, regardless of formulation. These findings reveal substantial gender disparities in the pharmacokinetics of zileuton in the rat model. This study emphasizes the critical need to evaluate gender differences during preclinical drug development to enable gender-based precision dosing strategies for equivalent efficacy/safety outcomes in male and female patients. Additional studies are warranted to investigate underlying mechanisms of such pharmacokinetic gender divergences.

Novel applications of Yinhua Miyanling tablets in ulcerative colitis treatment based on metabolomics and network pharmacology

BACKGROUND

Yinhua Miyanling tablets (YMT), comprising 10 Chinese medicinal compounds, is a proprietary Chinese medicine used in the clinical treatment of urinary tract infections. Medicinal compounds, extracts, or certain monomeric components in YMT all show good effect on ulcerative colitis (UC). However, no evidence supporting YMT as a whole prescription for UC treatment is available.

PURPOSE

To evaluate the anti-UC activity of YMT and elucidate the underlying mechanisms. The objective of the study was to provide evidence for the add-on development of YMT to treat UC.

METHODS

First, YMT's protective effect on the intestinal barrier was evaluated using a lipopolysaccharide (LPS)-induced Caco-2 intestinal injury model. Second, the UC mouse model was established using dextran sodium sulfate (DSS) to determine YMT's influence on symptoms, inflammatory factors, intestinal barrier, and histopathological changes in the colon. Third, an integrated method combining metabolomics and network pharmacology was employed to screen core targets and key metabolic pathways with crucial roles in YMT's therapeutic effect on UC. Molecular docking was employed to identify the key targets with high affinity. Finally, western blotting was performed to validate the mechanism of YMT action against UC.

RESULTS

YMT enhanced the transepithelial electrical resistance value and improved the expression of proteins of the tight junctions dose-dependently in LPS-induced Caco-2 cells. UC mice treated with YMT exhibited alleviated pathological lesions of the colon tissue in the in vivo pharmacodynamic experiments. The colonic lengths tended to be normal, and the levels of inflammatory factors (TNF-α, IL-6, and iNOS) along with those of the core enzymes (MPO, MDA, and SOD) improved. YMT effectively ameliorated DSS-induced colonic mucosal injury; pathological changes along with ultrastructure damage were significantly alleviated (evidenced by a relatively intact colon tissue, recovery of epithelial damage, repaired gland, reduced infiltration of inflammatory cells and epithelial cells arranged closely with dense microvilli). Seven key targets (IL-6, TNF-α, MPO, COX-2, HK2, TPH, and CYP1A2) and four key metabolic pathways (arachidonic acid metabolism, linoleate metabolism, glycolysis, and gluconeogenesis and tyrosine biosynthesis) were identified to play vital roles in the treatment on UC using YMT.

CONCLUSIONS

YMT exerts beneficial therapeutic effects on UC by regulating multiple endogenous metabolites, targets, and metabolic pathways, suggestive of its potential novel application in UC treatment.

Uridine 5'-Diphospho-glucuronosyltransferase 1A3 (UGT1A3) Prediction of Hepatic Clearance of Organic Anion Transporting Polypeptide 1B3 (OATP1B3) Substrate Telmisartan by Glucuronidation Using In Vitro-In Vivo Extrapolation (IVIVE)

BACKGROUND AND OBJECTIVE

The prediction of pharmacokinetic parameters for drugs metabolised by cytochrome P450 enzymes has been the subject of active research for many years, while the application of in vitro-in vivo extrapolation (IVIVE) techniques for non-cytochrome P450 enzymes has not been thoroughly evaluated. There is still no established quantitative method for predicting hepatic clearance of drugs metabolised by uridine 5'-diphospho-glucuronosyltransferases (UGTs), not to mention those which undergo hepatic uptake. The objective of the study was to predict the human hepatic clearance for telmisartan based on in vitro metabolic stability and hepatic uptake results.

METHODS

Telmisartan was examined in liver systems, allowing to estimate intrinsic clearance (CLint, in vitro) based on the substrate disappearance rate with the use of liquid chromatography tandem mass spectrometry (LC-MS/MS) technique. Obtained CLint, in vitro values were corrected for corresponding unbound fractions. Prediction of human hepatic clearance was made from scaled unbound CLint, in vitro data with the use of the well-stirred model, and finally referenced to the literature value of observed clearance in humans, allowing determination of the essential scaling factors.

RESULTS

The in vitro scaled CLint, in vitro by UGT1A3 was assessed using three systems, human hepatocytes, liver microsomes, and recombinant enzymes. Obtained values were scaled and hepatic metabolism clearance was predicted, resulting in significant clearance underprediction. Utilization of the extended clearance concept (ECC) and hepatic uptake improved prediction of hepatic metabolism clearance. The scaling factors for hepatocytes, assessing the in vitro-in vivo difference, changed from sixfold difference to only twofold difference with the application of the ECC.

CONCLUSIONS

The study showed that taking into consideration hepatic uptake of a drug allows us to obtain satisfactory scaling factors, hence enabling the prediction of in vivo hepatic glucuronidation from in vitro data.

Esculin targets TLR4 to protect against LPS-induced septic cardiomyopathy

BACKGROUND

Esculin, a main active ingredient from Cortex fraxini, possesses biological activities such as anti-thrombosis, anti-inflammatory, and anti-oxidation effects. However, the effects of Esculin on septic cardiomyopathy remains unclear. This study aimed to explore the protective properties and mechanisms of Esculin in countering sepsis-induced cardiac trauma and dysfunction.

METHODS AND RESULTS

In lipopolysaccharide (LPS)-induced mice model, Esculin could obviously improve heart injury and function. Esculin treatment also significantly reduced the production of inflammatory and apoptotic cells, the release of inflammatory cytokines, and the expression of oxidative stress-associated and apoptosis-associated markers in hearts compared to LPS injection alone. These results were consistent with those of in vitro experiments based on neonatal rat cardiomyocytes. Database analysis and molecular docking suggested that TLR4 was targeted by Esculin, as shown by stable hydrogen bonds formed between Esculin with VAL-308, ASN-307, CYS-280, CYS-304 and ASP-281 of TLR4. Esculin reversed LPS-induced upregulation of TLR4 and phosphorylation of NF-κB p65 in cardiomyocytes. The plasmid overexpressing TLR4 abolished the protective properties of Esculin in vitro.

CONCLUSION

We concluded that Esculin could alleviate LPS-induced septic cardiomyopathy via binding to TLR4 to attenuate cardiomyocyte inflammation, oxidative stress and apoptosis.

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.

Correlation between the gut microbiome and neurodegenerative diseases: a review of metagenomics evidence

A growing body of evidence suggests that the gut microbiota contributes to the development of neurodegenerative diseases via the microbiota-gut-brain axis. As a contributing factor, microbiota dysbiosis always occurs in pathological changes of neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. High-throughput sequencing technology has helped to reveal that the bidirectional communication between the central nervous system and the enteric nervous system is facilitated by the microbiota's diverse microorganisms, and for both neuroimmune and neuroendocrine systems. Here, we summarize the bioinformatics analysis and wet-biology validation for the gut metagenomics in neurodegenerative diseases, with an emphasis on multi-omics studies and the gut virome. The pathogen-associated signaling biomarkers for identifying brain disorders and potential therapeutic targets are also elucidated. Finally, we discuss the role of diet, prebiotics, probiotics, postbiotics and exercise interventions in remodeling the microbiome and reducing the symptoms of neurodegenerative diseases.

MS-BACL: enhancing metabolic stability prediction through bond graph augmentation and contrastive learning

MOTIVATION

Accurately predicting molecular metabolic stability is of great significance to drug research and development, ensuring drug safety and effectiveness. Existing deep learning methods, especially graph neural networks, can reveal the molecular structure of drugs and thus efficiently predict the metabolic stability of molecules. However, most of these methods focus on the message passing between adjacent atoms in the molecular graph, ignoring the relationship between bonds. This makes it difficult for these methods to estimate accurate molecular representations, thereby being limited in molecular metabolic stability prediction tasks.

RESULTS

We propose the MS-BACL model based on bond graph augmentation technology and contrastive learning strategy, which can efficiently and reliably predict the metabolic stability of molecules. To our knowledge, this is the first time that bond-to-bond relationships in molecular graph structures have been considered in the task of metabolic stability prediction. We build a bond graph based on 'atom-bond-atom', and the model can simultaneously capture the information of atoms and bonds during the message propagation process. This enhances the model's ability to reveal the internal structure of the molecule, thereby improving the structural representation of the molecule. Furthermore, we perform contrastive learning training based on the molecular graph and its bond graph to learn the final molecular representation. Multiple sets of experimental results on public datasets show that the proposed MS-BACL model outperforms the state-of-the-art model.

AVAILABILITY AND IMPLEMENTATION

The code and data are publicly available at https://github.com/taowang11/MS.

Computer-aided drug discovery of c-Abl kinase inhibitors from plant compounds against chronic myeloid leukemia

Chronic myeloid leukemia (CML) is a hematological malignancy characterized by the neoplastic transformation of hematopoietic stem cells, driven by the Philadelphia (Ph) chromosome resulting from a translocation between chromosomes 9 and 22. This Ph chromosome harbors the breakpoint cluster region (BCR) and the Abelson (ABL) oncogene (BCR-ABL1) which have a constitutive tyrosine kinase activity. However, the tyrosine kinase activity of BCR-ABL1 have been identified as a key player in CML initiation and maintenance through c-Abl kinase. Despite advancements in tyrosine kinase inhibitors, challenges such as efficacy, safety concerns, and recurring drug resistance persist. This study aims to discover potential c-Abl kinase inhibitors from plant compounds with anti-leukemic properties, employing drug-likeness assessment, molecular docking, in silico pharmacokinetics (ADMET) screening, density function theory (DFT), and molecular dynamics simulations (MDS). Out of 58 screened compounds for drug-likeness, 44 were docked against c-Abl kinase. The top hit compound (isovitexin) and nilotinib (control drug) were subjected to rigorous analyses, including ADMET profiling, DFT evaluation, and MDS for 100 ns. Isovitexin demonstrated a notable binding affinity (-15.492 kcal/mol), closely comparable to nilotinib (-16.826 kcal/mol), showcasing a similar binding pose and superior structural stability and reactivity. While these findings suggest isovitexin as a potential c-Abl kinase inhibitor, further validation through urgent in vitro and in vivo experiments is imperative. This research holds promise for providing an alternative avenue to address existing CML treatment and management challenges.Communicated by Ramaswamy H. Sarma.

Kinetic Characterization of Estradiol Glucuronidation by Liver Microsomes and Expressed UGT Enzymes: The Effects of Organic Solvents

BACKGROUND AND OBJECTIVE

In vitro glucuronidation of 17β-estradiol (estradiol) is often performed to assess the role of uridine 5'-diphospho-glucuronosyltransferase 1A1 (UGT1A1) in xenobiotic/drug metabolism. The objective of this study was to determine the effects of four commonly used organic solvents [i.e., dimethyl sulfoxide (DMSO), methanol, ethanol, and acetonitrile] on the glucuronidation kinetics of estradiol, which can be glucuronidated at C3 and C17 positions.

METHODS

The impacts of organic solvents on estradiol glucuronidation were determined by using expressed UGT enzymes and liver microsomes from both human and animals.

RESULTS

In human liver microsomes (HLM), methanol, ethanol, and acetonitrile significantly altered estradiol glucuronidation kinetics with increased Vmax (up to 2.6-fold) and CLmax (up to 2.8-fold) values. Altered estradiol glucuronidation in HLM was deduced to be attributed to the enhanced metabolic activities of UGT1A1 and UGT2B7, whose activities differ at the two glucuronidation positions. The effects of organic solvents on estradiol glucuronidation were glucuronidation position-, isozyme-, and solvent-specific. Furthermore, both ethanol and acetonitrile have a greater tendency to modify the glucuronidation activity of estradiol in animal liver microsomes.

CONCLUSION

Organic solvents such as methanol, ethanol, and acetonitrile showed great potential in adjusting the glucuronidation of estradiol. DMSO is the most suitable solvent due to its minimal influence on estradiol glucuronidation. Researchers should be cautious in selecting appropriate solvents to get accurate results when assessing the metabolism of a new chemical entity.

Assessing the efficacy of cinnamon compounds against H. pylori through molecular docking, MD Simulations and ADMET analyses

Antibiotics are the drugs that are used for the management of microbial diseases. However, these conventional synthetic drugs can harmfully affect the human health. Since phytochemicals are extracted from natural sources and, are hence relatively safer for human health, they are the enticing alternatives in this regard. Cinnamon is also one of those plants which is being employed as herbal medication for centuries against certain microbial infections due its significant therapeutic effectiveness. A well-known pathogenic bacterium called H. pylori causes a wide range of illnesses in human body. This pathogen's pathogenicity is determined by certain virulent proteins. In this study, some of such proteins, which included virB4, virB8, and virB9 were selected to evaluate the therapeutic efficiency of cinnamon compounds. These proteins were identified in different isolates of H. pylori. The structural modelling of all these proteins were performed initially in order to proceed them for molecular docking analysis. While, the docking studies illustrated that one of the cinnamon compounds, cinnamyl acetate, showed significant binding interactions with virB4 and virB9. However, benzyl benzoate which is another cinnamon compound, docked well with virB8. Afterwards, the MD simulations were incorporated to explore the interaction motions and structural stability of all the docked complexes. In this regard, the resultant maps of Bfactor, eigenvalues and elastic network model, among other factors ensured the structural stabilities of all the respective complexes. After these crucial estimations, benzyl benzoate and cinnamyl acetate underwent the ADMET investigation to assess their pharmacokinetic characteristics. SwissADME and ADMETLab 2.0 server were employed for this investigation. The compiled findings these servers revealed that both, benzyl benzoate and cinnamyl acetate, exhibited a significant level of pharmacokinetic and drug-likeness conformity.

Identifying the Transients and Transformation Products in Hydroxyl Radical-Methimazole Reactions Using DFT and UPLC-Q-TOF MS/MS Approaches

Oxidative reactions of the hydroxyl radical (·OH) with methimazole (MMI), an antithyroid drug, are crucial for understanding its fate in oxidizing environments. By synergistically integrating density functional theory and ultraperformance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF MS/MS) techniques, we elucidated the transients and transformation products (TPs) arising from the ·OH-MMI reactions. We probed two hydrogen-atom abstraction (HA) reactions, three radical adduct formation reactions, and single electron transfer (SET) at the M06-2X/6-311++G(d,p)/SMD(water) level. All proposed reaction channels, except for HA from the methyl group and SET, were found to be barrier-free. SET is the dominant oxidation pathway, accounting for 44% of oxidations, as determined by branching ratio analysis. The selenium analogue, MSeI, exhibited minor reactivity differences compared to MMI, yet its overall patterns resembled those of ·OH-MMI reactions. TPs were generated experimentally by reacting MMI with ·OH produced by UV-photolysis of H2O2. Eight TPs were identified from an approximately 24% degradation of MMI using UPLC-Q-TOF MS/MS analysis, and an additional two TPs were identified from the approximately 52% degraded MMI sample. The exact identities of all of the TPs were established through their corresponding fragmentation patterns. This study elucidates the drug's susceptibility to free radical species under physiologically relevant conditions.

Exploratory focused pharmacogenetic testing reveals novel markers associated with risperidone pharmacokinetics in Saudi children with autism

Background: Autism spectrum disorders (ASDs) encompass a broad range of phenotypes characterized by diverse neurological alterations. Genomic studies have revealed considerable overlap between the molecular mechanisms implicated in the etiology of ASD and genes involved in the pharmacokinetic (PK) and pharmacodynamic (PD) pathways of antipsychotic drugs employed in ASD management. Given the conflicting data originating from candidate PK or PD gene association studies in diverse ethnogeographic ASD populations, dosage individualization based on "actionable" pharmacogenetic (PGx) markers has limited application in clinical practice. Additionally, off-label use of different antipsychotics is an ongoing practice, which is justified given the shortage of approved cures, despite the lack of satisfactory evidence for its safety according to precision medicine. This exploratory study aimed to identify PGx markers predictive of risperidone (RIS) exposure in autistic Saudi children. Methods: This prospective cohort study enrolled 89 Saudi children with ASD treated with RIS-based antipsychotic therapy. Plasma levels of RIS and 9-OH-RIS were measured using a liquid chromatography-tandem mass spectrometry system. To enable focused exploratory testing, genotyping was performed with the Axiom PharmacoFocus Array, which included a collection of probe sets targeting PK/PD genes. A total of 720 PGx markers were included in the association analysis. Results: A total of 27 PGx variants were found to have a prominent impact on various RIS PK parameters; most were not located within the genes involved in the classical RIS PK pathway. Specifically, 8 markers in 7 genes were identified as the PGx markers with the strongest impact on RIS levels (p < 0.01). Four PGx variants in 3 genes were strongly associated with 9-OH-RIS levels, while 5 markers in 5 different genes explained the interindividual variability in the total active moiety. Notably, 6 CYP2D6 variants exhibited strong linkage disequilibrium; however, they significantly influenced only the metabolic ratio and had no considerable effects on the individual estimates of RIS, 9-OH-RIS, or the total active moiety. After correction for multiple testing, rs78998153 in UGT2B17 (which is highly expressed in the brain) remained the most significant PGx marker positively adjusting the metabolic ratio. For the first time, certain human leukocyte antigen (HLA) markers were found to enhance various RIS exposure parameters, which reinforces the gut-brain axis theory of ASD etiology and its suggested inflammatory impacts on drug bioavailability through modulation of the brain, gastrointestinal tract and/or hepatic expression of metabolizing enzymes and transporters. Conclusion: Our hypothesis-generating approach identified a broad spectrum of PGx markers that interactively influence RIS exposure in ASD children, which indicated the need for further validation in population PK modeling studies to define polygenic scores for antipsychotic efficacy and safety, which could facilitate personalized therapeutic decision-making in this complex neurodevelopmental condition.

Relevance of pharmacogenetics and pharmacogenomics in veterinary clinical practice: A review

The recent advances in high-throughput next-generation sequencing technologies have heralded the arrival of the Big Data era. As a result, the use of pharmacogenetics in drug discovery and individualized drug therapy has transformed the field of precision medicine. This paradigm shift in drug development programs has effectively reshaped the old drug development practices, which were primarily concerned with the physiological status of patients for drug development. Pharmacogenomics bridges the gap between pharmacodynamics and pharmacokinetics, advancing current diagnostic and treatment strategies and enabling personalized and targeted drug therapy. The primary goals of pharmacogenetic studies are to improve drug efficacy and minimize toxicities, to identify novel drug targets, to estimate drug dosage for personalized medicine, and to incorporate it as a routine diagnostic for disease susceptibility. Although pharmacogenetics has numerous applications in individualized drug therapy and drug development, it is in its infancy in veterinary medicine. The objective of this review is to present an overview of historical landmarks, current developments in various animal species, challenges and future perspectives of genomics in drug development and dosage optimization for individualized medicine in veterinary subjects.

Overcoming Chemotherapy Resistance in Metastatic Cancer: A Comprehensive Review

The management of metastatic cancer is complicated by chemotherapy resistance. This manuscript provides a comprehensive academic review of strategies to overcome chemotherapy resistance in metastatic cancer. The manuscript presents background information on chemotherapy resistance in metastatic cancer cells, highlighting its clinical significance and the current challenges associated with using chemotherapy to treat metastatic cancer. The manuscript delves into the molecular mechanisms underlying chemotherapy resistance in subsequent sections. It discusses the genetic alterations, mutations, and epigenetic modifications that contribute to the development of resistance. Additionally, the role of altered drug metabolism and efflux mechanisms, as well as the activation of survival pathways and evasion of cell death, are explored in detail. The strategies to overcome chemotherapy resistance are thoroughly examined, covering various approaches that have shown promise. These include combination therapy approaches, targeted therapies, immunotherapeutic strategies, and the repurposing of existing drugs. Each strategy is discussed in terms of its rationale and potential effectiveness. Strategies for early detection and monitoring of chemotherapy drug resistance, rational drug design vis-a-vis personalized medicine approaches, the role of predictive biomarkers in guiding treatment decisions, and the importance of lifestyle modifications and supportive therapies in improving treatment outcomes are discussed. Lastly, the manuscript outlines the clinical implications of the discussed strategies. It provides insights into ongoing clinical trials and emerging therapies that address chemotherapy resistance in metastatic cancer cells. The manuscript also explores the challenges and opportunities in translating laboratory findings into clinical practice and identifies potential future directions and novel therapeutic avenues. This comprehensive review provides a detailed analysis of strategies to overcome chemotherapy resistance in metastatic cancer. It emphasizes the importance of understanding the molecular mechanisms underlying resistance and presents a range of approaches for addressing this critical issue in treating metastatic cancer.

INTEDE 2.0: the metabolic roadmap of drugs

The metabolic roadmap of drugs (MRD) is a comprehensive atlas for understanding the stepwise and sequential metabolism of certain drug in living organisms. It plays a vital role in lead optimization, personalized medication, and ADMET research. The MRD consists of three main components: (i) the sequential catalyses of drug and its metabolites by different drug-metabolizing enzymes (DMEs), (ii) a comprehensive collection of metabolic reactions along the entire MRD and (iii) a systematic description on efficacy & toxicity for all metabolites of a studied drug. However, there is no database available for describing the comprehensive metabolic roadmaps of drugs. Therefore, in this study, a major update of INTEDE was conducted, which provided the stepwise & sequential metabolic roadmaps for a total of 4701 drugs, and a total of 22 165 metabolic reactions containing 1088 DMEs and 18 882 drug metabolites. Additionally, the INTEDE 2.0 labeled the pharmacological properties (pharmacological activity or toxicity) of metabolites and provided their structural information. Furthermore, 3717 drug metabolism relationships were supplemented (from 7338 to 11 055). All in all, INTEDE 2.0 is highly expected to attract broad interests from related research community and serve as an essential supplement to existing pharmaceutical/biological/chemical databases. INTEDE 2.0 can now be accessible freely without any login requirement at: http://idrblab.org/intede/.

Prediction of drug-drug interactions between roflumilast and CYP3A4/1A2 perpetrators using a physiologically-based pharmacokinetic (PBPK) approach

This study aimed to develop a physiologically-based pharmacokinetic (PBPK) model to predict changes in the pharmacokinetics (PK) and pharmacodynamics (PD, PDE4 inhibition) of roflumilast (ROF) and ROF N-oxide when co-administered with eight CYP3A4/1A2 perpetrators. The population PBPK model of ROF and ROF N-oxide has been successfully developed and validated based on the four clinical PK studies and five clinical drug-drug interactions (DDIs) studies. In PK simulations, every ratio of prediction to observation for PK parameters fell within the range 0.7 to 1.5. In DDI simulations, except for tow peak concentration ratios (Cmax) of ROF with rifampicin (prediction: 0.63 vs. observation: 0.19) and with cimetidine (prediction: 1.07 vs. observation: 1.85), the remaining predicted ratios closely matched the observed ratios. Additionally, the PBPK model suggested that co-administration with the three perpetrators (cimetidine, enoxacin, and fluconazole) may use with caution, with CYP3A4 strong inhibitor (ketoconazole and itraconazole) or with dual CYP3A41A2 inhibitor (fluvoxamine) may reduce to half-dosage or use with caution, while co-administration with CYP3A4 strong or moderate inducer (rifampicin, efavirenz) should avoid. Overall, the present PBPK model can provide recommendations for adjusting dosing regimens in the presence of DDIs.

Developing mathematical models to compare and analyse the pharmacokinetics of morphine and fentanyl

Background and Aims

The two-compartment model is generally used in pharmacokinetics to illustrate the distribution and excretion of drugs. In this study, we evaluated the distribution patterns of morphine and fentanyl by using a two-compartment model.

Methods

Using numeric analysis techniques, non-linear ordinary differential equations were used to mathematically analyse drug distribution, transition, and concentration in the body compartments. Math Works, Inc., MATLAB, version 2023a, a programming tool, was used to characterise the impact of initial concentration and rate constants on the kinetics of the drug. For a definite therapeutic concentration of morphine and fentanyl in blood, pharmacokinetic characteristics were plotted.

Results

The study results showed the time taken by morphine and fentanyl to reach a target concentration in the blood that is sufficient to generate the preferred therapeutic effects. The mathematical models comparing morphine and fentanyl pharmacokinetics showed that fentanyl reached the target therapeutic concentration 125 minutes earlier than morphine and was metabolised and removed from the body more rapidly (44 minutes earlier than morphine).

Conclusion

These comparative mathematical models on morphine and fentanyl enable the determination of drug dosages and understanding of drug efficacy that facilitates optimising dosing regimens. The right choice between them can be made based on the time to reach the target therapeutic concentration in the blood, elimination time, severity of pain, and patient characteristics.

Tissue-specific antioxidative response and metabolism of paralytic shellfish toxins in scallop (Chlamys farreri) mantle with Alexandrium dinoflagellate exposure

Bivalves show remarkable capacity to acclimate paralytic shellfish toxins (PSTs) produced by dinoflagellates, severely affecting fishery industry and public health. Here, transcriptomic response to PSTs-producing dinoflagellate (Alexandrium minutum) was investigated in Zhikong scallop (Chlamys farreri) mantle. The PSTs accumulated in C. farreri mantle continually increased during the 15 days exposure, with "oxidation-reduction" genes induced compared to the control group at the 1st and 15th day. Through gene co-expression network analysis, 16 PSTs-responsive modules were enriched with up- or down-regulated genes. The concentration of GTXs, major PSTs in A. minutum and accumulated in scallops, was correlated with the up-regulated magenta module, enriching peroxisome genes as the potential mantle-specific PSTs biomarker. Moreover, Hsp70B2s were inhibited throughout the exposure, which together with the expanded neurotransmitter transporter SLC6As, may play essential roles on neurotransmitter homeostasis in scallop mantle. These results paved the way for a comprehensive understanding of defensive mechanism and homeostatic response in scallop mantle against PSTs.

Unveiling the therapeutic promise of natural products in alleviating drug-induced liver injury: Present advancements and future prospects

Drug-induced liver injury (DILI) refers to adverse reactions to small chemical compounds, biological agents, and medical products. These reactions can manifest as acute or chronic damage to the liver. From 1997 to 2016, eight drugs, including troglitazone, nefazodone, and lumiracoxib, were removed from the market due to their liver-damaging effects, which can cause diseases. We aimed to review the recent research on natural products and their bioactive components as hepatoprotective agents in mitigating DILI. Recent articles were fetched via searching the PubMed, PMC, Google Scholar, and Web of Science electronic databases from 2010 to January 2023 using relevant keywords such as "natural products," "acetaminophen," "antibiotics," "paracetamol," "DILI," "hepatoprotective," "drug-induced liver injury," "liver failure," and "mitigation." The studies reveal that the antituberculosis drug (acetaminophen) is the most frequent cause of DILI, and natural products have been largely explored in alleviating acetaminophen-induced liver injury. They exert significant hepatoprotective effects by preventing mitochondrial dysfunction and inflammation, inhibiting oxidative/nitrative stress, and macromolecular damage. Due to the bioavailability and dietary nature, using natural products alone or as an adjuvant with existing drugs is promising. To advance DILI management, it is crucial to conduct well-designed randomized clinical trials to evaluate natural products' efficacy and develop new molecules clinically. However, natural products are a promising solution for remedying drug-induced hepatotoxicity and lowering the risk of DILI.

The clinical trials puzzle: How network effects limit drug discovery

The depth of knowledge offered by post-genomic medicine has carried the promise of new drugs, and cures for multiple diseases. To explore the degree to which this capability has materialized, we extract meta-data from 356,403 clinical trials spanning four decades, aiming to offer mechanistic insights into the innovation practices in drug discovery. We find that convention dominates over innovation, as over 96% of the recorded trials focus on previously tested drug targets, and the tested drugs target only 12% of the human interactome. If current patterns persist, it would take 170 years to target all druggable proteins. We uncover two network-based fundamental mechanisms that currently limit target discovery: preferential attachment, leading to the repeated exploration of previously targeted proteins; and local network effects, limiting exploration to proteins interacting with highly explored proteins. We build on these insights to develop a quantitative network-based model to enhance drug discovery in clinical trials.

The Bioavailability of Drugs-The Current State of Knowledge

Drug bioavailability is a crucial aspect of pharmacology, affecting the effectiveness of drug therapy. Understanding how drugs are absorbed, distributed, metabolized, and eliminated in patients' bodies is essential to ensure proper and safe treatment. This publication aims to highlight the relevance of drug bioavailability research and its importance in therapy. In addition to biochemical activity, bioavailability also plays a critical role in achieving the desired therapeutic effects. This may seem obvious, but it is worth noting that a drug can only produce the expected effect if the proper level of concentration can be achieved at the desired point in a patient's body. Given the differences between patients, drug dosages, and administration forms, understanding and controlling bioavailability has become a priority in pharmacology. This publication discusses the basic concepts of bioavailability and the factors affecting it. We also looked at various methods of assessing bioavailability, both in the laboratory and in the clinic. Notably, the introduction of new technologies and tools in this field is vital to achieve advances in drug bioavailability research. This publication also discusses cases of drugs with poorly described bioavailability, providing a deeper understanding of the complex challenges they pose to medical researchers and practitioners. Simultaneously, the article focuses on the perspectives and trends that may shape the future of research regarding bioavailability, which is crucial to the development of modern pharmacology and drug therapy. In this context, the publication offers an essential, meaningful contribution toward understanding and highlighting bioavailability's role in reliable patient treatment. The text also identifies areas that require further research and exploration.

Preclinical pharmacokinetic exploration of a novel osteoporotic quinazolinone-benzopyran-indole hybrid (S019-0385) using LC-MS/MS

1. The current investigation was to develop and validate the LC-MS/MS method in order to analyse the various pharmacokinetic parameters of S019-0385. A sensitive, selective, and robust LC-MS/MS approach was established and validated for measuring S019-0385 in female mice plasma and tissue, using optimal multiple reaction monitoring (MRM) transition m/z 488.25/329.12 on positive mode. On a Waters Symmetry Shield C18 column, the analyte was separated using acetonitrile and deionised water with formic acid within 6 min at 0.7 mL/min. Linearity (R2 ≥ 0.99) was observed across 0.195-100 ng/mL concentration range using linear least-squares regression.2. Blood-to-plasma ratio and plasma protein drug binding (%) in mice and human was assessed and found to be less than 1 and >83%, respectively. Absolute bioavailability (%F) of S019-0385 in female Swiss mice was exhibited to be 6.90%. Percent dose excreted S019-0385 in unchanged form through urine and faecal was found to be less than 2% and 0.5%, respectively.3. Following oral administration at 5 mg/kg, the concentration of S019-0385 in tissue distribution was found to be in the order of C small intestine > C bone > C lung > C spleen > C kidney > C liver > C heart > C brain.

Molecular modelling studies of substituted indole derivatives as novel influenza a virus inhibitors

The emergence of drug-resistant strains motivate researchers to find new innovative anti-IAV candidates with a different mode of action. In this work, molecular modelling strategies, such as 2D-QSAR, 3D-QSAR, molecular docking, molecular dynamics, FMOs, and ADMET were applied to some substituted indoles as IAV inhibitors. The best-developed 2D-QSAR models, MLR (Q2 = 0.7634, R2train = 0.8666) and ANN[4-3-1] (Q2 = 0.8699, R2train = 0.8705) revealed good statistical validation for the inhibitory response predictions. The 3D-QSAR models, CoMFA (Q2 = 0.504, R2train = 0.805) and CoMSIA/SEDHA (Q2 = 0.619, R2train = 0.813) are selected as the best 3D models following the global thresholds. In addition, the contour maps generated from the CoMFA and CoMSIA models illustrate the relationship between the molecular fields and the inhibitory effects of the studied molecules. The results of the studies led to the design of five new molecules (24a-e) with enhanced anti-IAV activities and binding potentials using the most active molecule (24) as the template scaffold. The conformational stability of the best-designed molecules with the NA protein showed hydrophobic and H-bonds with the key residues from the molecular dynamics simulations of 100 ns. Furthermore, the global reactivity indices from the DFT calculations portrayed the relevance of 24c in view of its smaller band gap as also justified by our QSAR and molecular simulation studies.Communicated by Ramaswamy H. Sarma.

In Silico Studies of Four Compounds of Cecropia obtusifolia against Malaria Parasite

Malaria is a disease that affects many people in the world. In Mexico, malaria remains an active disease in certain regions, particularly in the states of Chiapas and Chihuahua. While antimalarial effects have been attributed to some species of Cecropia in various countries, no such studies have been conducted in Mexico. Therefore, the objective of this study was to evaluate the in silico antimalarial activity of some active compounds identified according to the literature in the species of Cecropia obtusifolia, belonging to the Cecropiaceae family, such as ursolic acid, α-amyrin, chrysin, and isoorientin. These compounds were evaluated with specific molecular docking and molecular dynamics (MD) studies using three different malarial targets with the PDB codes 1CET, 2BL9, and 4ZL4 as well as the prediction of their pharmacokinetic (Pk) properties. Docking analysis revealed the following best binding energies (kcal/mol): isoorientin-1CET (-9.1), isoorientin-2BL9 (-8.8), and chrysin-4ZL4 (-9.6). MD simulation validated the stability of the complexes. Pharmacokinetics analysis suggested that the compounds would generally perform well if administered. Therefore, these results suggest that these compounds may be used as potential drugs for the treatment of malaria.

In silico approaches and in vitro assays identify a coumarin derivative as antiviral potential against SARS-CoV-2

COVID-19, a disease caused by SARS-CoV-2, was declared a pandemic in 2020 and created a global crisis in health systems, with more than 545 million confirmed cases and 6.33 million deaths. In this sense, this work aims to identify possible inhibitors of the SARS-CoV-2 RdRp enzyme using in silico approaches. RdRp is a crucial enzyme in the replication and assembly cycle of new viral particles and a critical pharmacological target in the treatment of COVID-19. We performed a virtual screening based on molecular docking from our in-house chemical library, which contains a diversity of 313 structures from different chemical classes. Nine compounds were selected since they showed important interactions with the active site from RdRp. Next, the ADME-Tox in silico predictions served as a filter and selected the three most promising compounds: a coumarin LMed-052, a hydantoin LMed-087, and a guanidine LMed-250. Molecular dynamics simulations revealed details such as changes in the positions of ligands and catalytic residues during the simulations compared to the complex from molecular docking studies. Binding free energy analysis was performed using the MMGBSA method, demonstrating that LMed-052 and LMed-087 have better affinities for the RdRp by energetic contributions to the stability of the complexes when compared to LMed-250. Furthermore, LMed-052 showed significant in vitro inhibition against MHV-3, decreasing 99% of viral titers. Finally, these findings are useful to guide structural modifications aiming to improve the potential of these compounds to act as inhibitors of SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

BDN-DDI: A bilinear dual-view representation learning framework for drug-drug interaction prediction

Drug-drug interactions (DDIs) refer to the potential effects of two or more drugs interacting with each other when used simultaneously, which may lead to adverse reactions or reduced drug efficacy. Accurate prediction of DDIs is a significant concern in recent years. Currently, the drug chemical substructure-based learning method has substantially improved DDIs prediction. However, we notice that most related works ignore the detailed interaction among atoms when extracting the substructure information of drugs. This problem results in incomplete information extraction and may limit the model's predictive ability. In this work, we proposed a novel framework named BDN-DDI (a bilinear dual-view representation learning framework for drug-drug interaction prediction) to infer potential DDIs. In the proposed framework, the encoder consists of six stacked BDN blocks, each of which extracts the feature representation of drug molecules through a bilinear representation extraction layer. The extracted feature is then used to learn embeddings of drug substructures from the single drug learning layer (intra-layer) and the drug-pair learning layer (inter-layer). Finally, the learned embeddings are fed into a decoder to predict DDI events. Based on our experiments, BDN-DDI has an AUROC value of over 99% for the warm-start task. Additionally, it outperformed the state-of-the-art methods by an average of 3.4% for the cold-start tasks. Finally, our method's effectiveness is further validated by visualizing several case studies.

Research in the Field of Drug Design and Development

The processes used by academic and industrial scientists to discover new drugs have recently experienced a true renaissance, with many new and exciting techniques being developed over the past 5-10 years alone. Drug design and discovery, and the search for new safe and well-tolerated compounds, as well as the ineffectiveness of existing therapies, and society's insufficient knowledge concerning the prophylactics and pharmacotherapy of the most common diseases today, comprise a serious challenge. This can influence not only the quality of human life, but also the health of whole societies, which became evident during the COVID-19 pandemic. In general, the process of drug development consists of three main stages: drug discovery, preclinical development using cell-based and animal models/tests, clinical trials on humans and, finally, forward moving toward the step of obtaining regulatory approval, in order to market the potential drug. In this review, we will attempt to outline the first three most important consecutive phases in drug design and development, based on the experience of three cooperating and complementary academic centers of the Visegrád group; i.e., Medical University of Lublin, Poland, Masaryk University of Brno, Czech Republic, and Comenius University Bratislava, Slovak Republic.

Cracking the intestinal lymphatic system window utilizing oral delivery vehicles for precise therapy

Oral administration is preferred over other drug delivery methods due to its safety, high patient compliance, ease of ingestion without discomfort, and tolerance of a wide range of medications. However, oral drug delivery is limited by the poor oral bioavailability of many drugs, caused by extreme conditions and absorption challenges in the gastrointestinal tract. This review thoroughly discusses the targeted drug vehicles to the intestinal lymphatic system (ILS). It explores the structure and physiological barriers of the ILS, highlighting its significance in dietary lipid and medication absorption and transport. The review presents various approaches to targeting the ILS using spatially precise vehicles, aiming to enhance bioavailability, achieve targeted delivery, and reduce first-pass metabolism with serve in clinic. Furthermore, the review outlines several methods for leveraging these vehicles to open the ILS window, paving the way for potential clinical applications in cancer treatment and oral vaccine delivery. By focusing on targeted drug vehicles to the ILS, this article emphasizes the critical role of these strategies in improving therapeutic efficacy and patient outcomes. Overall, this article emphasizes the critical role of targeted drug vehicles to the ILS and the potential impact of these strategies on improving therapeutic efficacy and patient outcomes.

Melatonin enhances cell death and suppresses the metastatic capacity of ovarian cancer cells by attenuating the signaling of multiple kinases

BACKGROUND

Ovarian cancer is a highly aggressive disease that is frequently diagnosed in advanced stages. Melatonin, with its numerous antitumor properties, holds great promise in cancer treatment. Herein, we investigated the effects of melatonin on apoptosis, cell migration, and kinase levels in human ovarian carcinoma SKOV-3 cells and determined whether these effects are mediated by the activation of the MT1 receptor.

METHODS

SKOV-3 cells were exposed to different concentrations of melatonin based on the presence of MT1 receptor, and we also performed specific silencing of the melatonin receptor gene MTNR1A.

RESULTS

Our findings revealed that melatonin reduced cell viability as shown by the MTT assay, and flow cytometry analysis showed increased rates of apoptosis and necrosis in all melatonin-treated cells. Melatonin significantly decreased the migratory and invasive capacities of the cells. Propidium iodide labeling indicated that melatonin induced cell cycle arrest by reducing DNA content in the S and G2/M phases in SKOV-3 cells. Additionally, the levels of AKT, ERK1/2, JNK, CREB, p70S6K, STAT3/5, and p38 MAP kinase involved in cell survival, proliferation, motility, and stress responses were depressed by melatonin and further reduced after MT1 knockdown. These molecules were found to be associated with lower overall survival in ovarian cancer patients.

CONCLUSIONS

Melatonin had obvious oncostatic actions on ovarian cancer cells, and MT1 receptor knockdown intensified its antitumor effect. The inhibition of the MT1 receptor resulted in a substantial reduction in the migratory and invasive capacities of the cells, suggesting its potential as a therapeutic target for the treatment of ovarian cancer.

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

Potential herb‒drug interactions between anti-COVID-19 drugs and traditional Chinese medicine

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread worldwide. Effective treatments against COVID-19 remain urgently in need although vaccination significantly reduces the incidence, hospitalization, and mortality. At present, antiviral drugs including Nirmatrelvir/Ritonavir (Paxlovid^TM), Remdesivir, and Molnupiravir have been authorized to treat COVID-19 and become more globally available. On the other hand, traditional Chinese medicine (TCM) has been used for the treatment of epidemic diseases for a long history. Currently, various TCM formulae against COVID-19 such as Qingfei Paidu decoction, Xuanfei Baidu granule, Huashi Baidu granule, Jinhua Qinggan granule, Lianhua Qingwen capsule, and Xuebijing injection have been widely used in clinical practice in China, which may cause potential herb-drug interactions (HDIs) in patients under treatment with antiviral drugs and affect the efficacy and safety of medicines. However, information on potential HDIs between the above anti-COVID-19 drugs and TCM formulae is lacking, and thus this work seeks to summarize and highlight potential HDIs between antiviral drugs and TCM formulae against COVID-19, and especially pharmacokinetic HDIs mediated by metabolizing enzymes and/or transporters. These well-characterized HDIs could provide useful information on clinical concomitant medicine use to maximize clinical outcomes and minimize adverse and toxic effects.

Radix Paeoniae Alba attenuates Radix Bupleuri-induced hepatotoxicity by modulating gut microbiota to alleviate the inhibition of saikosaponins on glutathione synthetase

Radix Bupleuri (RB) is commonly used to treat depression, but it can also lead to hepatotoxicity after long-term use. In many anti-depression prescriptions, RB is often used in combination with Radix Paeoniae Alba (RPA) as an herb pair. However, whether RPA can alleviate RB-induced hepatotoxicity remain unclear. In this work, the results confirmed that RB had a dose-dependent antidepressant effect, but the optimal antidepressant dose caused hepatotoxicity. Notably, RPA effectively reversed RB-induced hepatotoxicity. Afterward, the mechanism of RB-induced hepatotoxicity was confirmed. The results showed that saikosaponin A and saikosaponin D could inhibit GSH synthase (GSS) activity in the liver, and further cause liver injury through oxidative stress and nuclear factor kappa B (NF-κB)/NOD-like receptor thermal protein domain associated protein 3 (NLRP3) pathway. Furthermore, the mechanisms by which RPA attenuates RB-induced hepatotoxicity were investigated. The results demonstrated that RPA increased the abundance of intestinal bacteria with glycosidase activity, thereby promoting the conversion of saikosaponins to saikogenins in vivo. Different from saikosaponin A and saikosaponin D, which are directly combined with GSS as an inhibitor, their deglycosylation conversion products saikogenin F and saikogenin G exhibited no GSS binding activity. Based on this, RPA can alleviate the inhibitory effect of saikosaponins on GSS activity to reshape the liver redox balance and further reverse the RB-induced liver inflammatory response by the NF-κB/NLRP3 pathway. In conclusion, the present study suggests that promoting the conversion of saikosaponins by modulating gut microbiota to attenuate the inhibition of GSS is the potential mechanism by which RPA prevents RB-induced hepatotoxicity.

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.

Cost-Effective Pipeline for a Rational Design and Selection of Capsaicin Analogues Targeting TRPV1 Channels

Transient receptor potential (TRP) channels constitute a large group of membrane receptors associated with sensory pathways in vertebrates. One of the most studied is TRPV1, a polymodal receptor tuned for detecting heat and pungent compounds. Specific inhibition of the nociceptive transduction at the peripheral nerve represents a convenient approach to pain relief. While acting as a chemoreceptor, TRPV1 shows high sensitivity and selectivity for capsaicin. In contrast to the drugs available on the market that target the inflammatory system, TRPV1 antagonists act as negative modulators of nociceptive transduction. Therefore, the development of compounds modulating TRPV1 activity has expanded dramatically over time. Experimental data suggest that most agonist and antagonist drugs interact at or near capsaicin's binding site. In particular, the properties of capsaicin's head play an essential role in modulating potency and affinity. Here, we explored a cost-efficient pipeline to predict the effects of introducing chemical modifications into capsaicin's head region. An extensive set of molecules was selected by first considering the geometrical properties of capsaicin's binding site and then molecular docking. Finally, the novel ligands were ranked by combining molecular and pharmacokinetic predictions.

Deuterodehalogenation Under Net Reductive or Redox-Neutral Conditions Enabled by Paired Electrolysis

Interest in deuterated active pharmaceutical ingredients (APIs) is increasing as deuteration holds promise for kinetic isotope effect (KIE) regulated fine-tuning of API performance. Moreover, deuterium isotope labeling is frequently carried out to study organic and bioorganic reaction mechanisms and to facilitate complex target synthesis. As such, methods for highly selective deuteration of organic molecules are highly desirable. Herein, we present an electrochemical method for the selective deuterodehalogenation of benzylic halides via a radical-polar crossover mechanism, using inexpensive deuterium oxide (D2 O) as the deuterium source. We demonstrate broad functional group compatibility across a range of aryl and heteroaryl benzylic halides. Furthermore, we uncover a sequential paired electrolysis regime, which permits switching between net reductive and overall redox-neutral reactions of sulfur-containing substrates simply by changing the identity of the sacrificial reductant employed.

MecDDI: Clarified Drug-Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug-Drug Interaction Prediction

Drug-drug interactions (DDIs) are a major concern in clinical practice and have been recognized as one of the key threats to public health. To address such a critical threat, many studies have been conducted to clarify the mechanism underlying each DDI, based on which alternative therapeutic strategies are successfully proposed. Moreover, artificial intelligence-based models for predicting DDIs, especially multilabel classification models, are highly dependent on a reliable DDI data set with clear mechanistic information. These successes highlight the imminent necessity to have a platform providing mechanistic clarifications for a large number of existing DDIs. However, no such platform is available yet. In this study, a platform entitled "MecDDI" was therefore introduced to systematically clarify the mechanisms underlying the existing DDIs. This platform is unique in (a) clarifying the mechanisms underlying over 1,78,000 DDIs by explicit descriptions and graphic illustrations and (b) providing a systematic classification for all collected DDIs based on the clarified mechanisms. Due to the long-lasting threats of DDIs to public health, MecDDI could offer medical scientists a clear clarification of DDI mechanisms, support healthcare professionals to identify alternative therapeutics, and prepare data for algorithm scientists to predict new DDIs. MecDDI is now expected as an indispensable complement to the available pharmaceutical platforms and is freely accessible at: https://idrblab.org/mecddi/.

Extracellularly Detectable Electrochemical Signals of Living Cells Originate from Metabolic Reactions

Direct detection of cellular redox signals has shown immense potential as a novel living cell analysis tool. However, the origin of such signals remains unknown, which hinders the widespread use of electrochemical methods for cellular research. In this study, the authors found that intracellular metabolic pathways that generate adenosine triphosphate (ATP) are the main contributors to extracellularly detectable electrochemical signals. This is achieved through the detection of living cells (4,706 cells/chip, linearity: 0.985) at a linear range of 7,466-48,866. Based on this discovery, the authors demonstrated that the cellular signals detected by differential pulse voltammetry (DPV) can be rapidly amplified with a developed medium containing metabolic activator cocktails (MACs). The DPV approach combined with MAC treatment shows a remarkable performance to detect the effects of the anticancer drug CPI-613 on cervical cancer both at a low drug concentration (2 µm) and an extremely short treatment time (1 hour). Furthermore, the senescence of mesenchymal stem cells could also be sensitively quantified using the DPV+MAC method even at a low passage number (P6). Collectively, their findings unveiled the origin of redox signals in living cells, which has important implications for the characterization of various cellular functions and behaviors using electrochemical approaches.

OAT3 Participates in Drug-Drug Interaction between Bentysrepinine and Entecavir through Interactions with M8-A Metabolite of Bentysrepinine-In Rats and Humans In Vitro

Bentysrepinine (Y101) is a novel phenylalanine dipeptide for the treatment of hepatitis B virus. Renal excretion played an important role in the elimination of Y101 and its metabolites, M8 and M9, in healthy Chinese subjects, although the molecular mechanisms of renal excretion and potential drug-drug interactions (DDIs) remain unclear. The present study aimed to determine the organic anion transporters (OATs) involved in the renal disposition of Y101 and to predict the potential DDI between Y101 and entecavir, the first-line agent against HBV and a substrate of OAT1/3. Pharmacokinetic studies and uptake assays using rat kidney slices, as well as hOAT1/3-HEK293 cells, were performed to evaluate potential DDI. The co-administration of probenecid (an inhibitor of OATs) significantly increased the plasma concentrations and area under the plasma concentration-time curves of M8 and M9 but not Y101, while reduced renal clearance and the cumulative urinary excretion of M8 were observed in rats. The time course of Y101 and M8 uptake via rat kidney slices was temperature-dependent. Moreover, the uptake of M8 was inhibited significantly by probenecid and benzylpenicillin, but not by p-aminohippurate or tetraethyl ammonium. M8 was found to be a substrate of hOAT3, but Y101 is not a substrate of either hOAT1 or hOAT3. Additionally, the entecavir inhibited the uptake of M8 in the hOAT3-transfected cells and rat kidney slices in vitro. Interestingly, no significant changes were observed in the pharmacokinetic parameters of Y101, M8 or entecavir, regardless of intravenous or oral co-administration of Y101 and entecavir in rats. In conclusion, M8 is a substrate of OAT3 in rats and humans. Furthermore, M8 also mediates the DDI between Y101 and entecavir in vitro, mediated by OAT3. We speculate that it would be safe to use Y101 with entecavir in clinical practice. Our results provide useful information with which to predict the DDIs between Y101 and other drugs that act as substrates of OAT3.

Evaluation of Intestinal Drug Absorption and Interaction Using Quadruple Single-Pass Intestinal Perfusion Coupled with Mass Spectrometry Imaging

Visualization and characterization of the intestinal membrane transporter-mediated drug absorption and interaction are challenging due to the complex physical and chemical environment. In this work, an integrated strategy was developed for in situ visualization and assessment of the drug absorption and interaction in rat intestines using quadruple single-pass intestinal perfusion (Q-SPIP) technique coupled with matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI MSI). Compared with the traditional SPIP only available for perfusion of one single intestinal segment, the Q-SPIP model can simultaneously perfuse four individual segments of each rat intestine (duodenum, jejunum, ileum, and colon), enabling to obtain rich data from one rat. Subsequently, the drug distribution and absorption in rat intestinal tissue were accurately visualized by using an optimized MALDI MSI approach. The utility and versatility of this strategy were demonstrated via the examination of P-glycoprotein (P-gp)-mediated intestinal absorption of berberine (BBR) and its combination with natural products possessing inhibitory potency against P-gp. The change in the spatial distribution of BBR was resolved, and MALDI results showed that the signal intensity of BBR in defined regions was enhanced following coperfusion with P-gp inhibitors. However, enhanced absorption of BBR after coperfusion with the P-gp inhibitor was not observed in the ulcerative colitis rat model, which may be due to the damage to the intestinal barrier. This study exemplifies the availability and utility of Q-SPIP coupled with MALDI MSI in the examination of transporter-mediated intestinal drug absorption and interaction for fundamental inquiries into the preclinical prediction of oral absorption and drug interaction potential.

Recent Advances in Novel Recombinant RNAs for Studying Post-transcriptional Gene Regulation in Drug Metabolism and Disposition

Drug-metabolizing enzymes and transporters are major determinants of the absorption, disposition, metabolism, and excretion (ADME) of drugs, and changes in ADME gene expression or function may alter the pharmacokinetics/ pharmacodynamics (PK/PD) and further influence drug safety and therapeutic outcomes. ADME gene functions are controlled by diverse factors, such as genetic polymorphism, transcriptional regulation, and coadministered medications. MicroRNAs (miRNAs) are a superfamily of regulatory small noncoding RNAs that are transcribed from the genome to regulate target gene expression at the post-transcriptional level. The roles of miRNAs in controlling ADME gene expression have been demonstrated, and such miRNAs may consequently influence cellular drug metabolism and disposition capacity. Several types of miRNA mimics and small interfering RNA (siRNA) reagents have been developed and widely used for ADME research. In this review article, we first provide a brief introduction to the mechanistic actions of miRNAs in post-transcriptional gene regulation of drug-metabolizing enzymes, transporters, and transcription factors. After summarizing conventional small RNA production methods, we highlight the latest advances in novel recombinant RNA technologies and applications of the resultant bioengineered RNA (BioRNA) agents to ADME studies. BioRNAs produced in living cells are not only powerful tools for general biological and biomedical research but also potential therapeutic agents amenable to clinical investigations.

YAP regulates the liver size during the fasting-refeeding transition in mice

Liver is the central hub regulating energy metabolism during feeding-fasting transition. Evidence suggests that fasting and refeeding induce dynamic changes in liver size, but the underlying mechanisms remain unclear. Yes-associated protein (YAP) is a key regulator of organ size. This study aims to explore the role of YAP in fasting- and refeeding-induced changes in liver size. Here, fasting significantly reduced liver size, which was recovered to the normal level after refeeding. Moreover, hepatocyte size was decreased and hepatocyte proliferation was inhibited after fasting. Conversely, refeeding promoted hepatocyte enlargement and proliferation compared to fasted state. Mechanistically, fasting or refeeding regulated the expression of YAP and its downstream targets, as well as the proliferation-related protein cyclin D1 (CCND1). Furthermore, fasting significantly reduced the liver size in AAV-control mice, which was mitigated in AAV Yap (5SA) mice. Yap overexpression also prevented the effect of fasting on hepatocyte size and proliferation. Besides, the recovery of liver size after refeeding was delayed in AAV Yap shRNA mice. Yap knockdown attenuated refeeding-induced hepatocyte enlargement and proliferation. In summary, this study demonstrated that YAP plays an important role in dynamic changes of liver size during fasting-refeeding transition, which provides new evidence for YAP in regulating liver size under energy stress.

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.

Investigation of cytochrome P450 inhibitory properties of deoxyshikonin, a bioactive compound from Lithospermum erythrorhizon Sieb. et Zucc

Deoxyshikonin, a natural naphthoquinone compound extracted from Lithospermum erythrorhizon Sieb. et Zucc (Boraginaceae), has a wide range of pharmacological activities, including anti-tumor, anti-bacterial and wound healing effects. However, the inhibitory effect of deoxyshikonin on cytochrome P450 (CYP) remains unclear. This study investigated the potential inhibitory effects of deoxyshikonin on CYP1A2, 2B1/6, 2C9/11, 2D1/6, 2E1 and 3A2/4 enzymes in human and rat liver microsomes (HLMs and RLMs) by the cocktail approach in vitro. The single-point inactivation experiment showed that deoxyshikonin presented no time-dependent inhibition on CYP activities in HLMs and RLMs. Enzyme inhibition kinetics indicated that in HLMs, deoxyshikonin was not only a competitive inhibitor of CYP1A2 and 2E1, but also a mixed inhibitor of CYP2B6, 2C9, 2D6 and 3A4, with Ki of 2.21, 1.78, 1.68, 0.20, 4.08 and 0.44 μM, respectively. In RLMs, deoxyshikonin not only competitively inhibited CYP2B1 and 2E1, but also exhibited mixed inhibition on CYP1A2, 2C11, 2D1 and 3A2, with Ki values of no more than 18.66 μM. In conclusion, due to the low Ki values of deoxythiokonin on CYP enzymes in HLMs, this may lead to drug-drug interactions (DDI) and potential toxicity.