Drug Metabolism in Preclinical Drug Development: A Survey of the Discovery Process, Toxicology, and Computational Tools

In Vitro Hepatic Clearance Evaluations of Per- and Polyfluoroalkyl Substances (PFAS) across Multiple Structural Categories

Toxicokinetic (TK) assays and in vitro-in vivo extrapolation (IVIVE) models are New Approach Methods (NAMs) used to translate in vitro points of departure to exposure estimates required to reach equivalent blood concentrations. Per- and polyfluoroalkyl substances (PFAS) are a large chemical class with wide-ranging industrial applications for which only limited toxicity data are available for human health evaluation. To address the lack of TK data, a pooled primary human hepatocyte suspension model was used with targeted liquid chromatography-mass spectrometry to investigate substrate depletion for 54 PFAS. A median value of 4.52 μL/(min x million cells) was observed across those that showed significant clearance, with 35 displaying no substrate depletion. Bayesian modeling propagated uncertainty around clearance values for use in IVIVE models. Structural evaluations showed the fluorotelomer carboxylic acids were the only PFAS carboxylates showing appreciable clearance, and per- and polyfluorosulfonamides were more readily metabolized than other PFAS sulfonates. Biotransformation product prediction, using the chemical transformation simulator, suggested hydrolysis of PFAS sulfonamides to more stable sulfonic acids, which is an important consideration for exposure modeling. This effort greatly expands the PFAS in vitro toxicokinetic dataset, enabling refined TK modeling, in silico tool development, and NAM-based human health evaluations across this important set of emerging contaminants.

A Radioactivity-mass Spectrometry Calibration Method Coupled with Biosynthesis to Generate a Metabolite Standard for Enzyme Kinetics Studies

In early stages of drug development, the absence of authentic metabolite standards often results in semi-quantitative measurements of metabolite formation in reaction phenotyping studies using mass spectrometry (MS), leading to inaccuracies in the determination of enzyme kinetic parameters, such as the Michaelis constant (Km). Moreover, it is impossible to ascertain the maximum rate of enzyme-catalyzed reactions (kcat or Vmax). The use of radiolabeled parent compounds can circumvent this problem. However, radiometric detection exhibits significantly lower sensitivity compared to MS. To address these challenges, we have developed a stepwise approach that leverages biosynthesized radiolabeled and non-radiolabeled metabolites as standards, enabling accurate determination of Km, kcat or Vmax without the need for authentic metabolite standards. This approach, using the carbon-14 [14C] labeled metabolite to calibrate the unlabeled metabolite (14C calibration method), combines radiometric with LC-MS/MS detection to generate both [14C]-labeled and unlabeled metabolite standard curves to ensure that the sample concentrations measured are accurately quantitated. Two case studies were presented to demonstrate the utility of this method. We first compared the accuracy of the 14C calibration method to the use of authentic standards for quantitating imipramine metabolites. Next, we biosynthesized and quantitated the metabolites of BI 894416 using 14C calibration method and evaluated the enzyme kinetics of metabolite formation. The Km values of the metabolite formation demonstrated substantially improved accuracy compared to MS semi-quantitation. Moreover, the 14C calibration method offers a streamlined approach to prepare multiple metabolite standards from a single biosynthesis, reducing the time required for structure elucidation and metabolite synthesis.

Assessment of sedative activity of fraxin: In vivo approach along with receptor binding affinity and molecular interaction with GABAergic system

Insomnia is a sleep disorder in which you have trouble falling and/or staying asleep. This research aims to evaluate the sedative effects of fraxin (FX) on sleeping mice induced by thiopental sodium (TS). In addition, a molecular docking study was conducted to investigate the molecular processes underlying these effects. The study used adult male Swiss albino mice and administered FX (10 and 20 mg/kg, i.p.) and diazepam (DZP) (2 mg/kg) either separately or in combination within the different groups to examine their modulatory effects. After a period of 30 min, the mice that had been treated were administered (TS: 20 mg/kg, i.p.) to induce sleep. The onset of sleep for the mice and the length of their sleep were manually recorded. Additionally, a computational analysis was conducted to predict the role of gamma-aminobutyric acid (GABA) receptors in the sleep process and evaluate their pharmacokinetics and toxicity. The outcomes indicated that FX extended the length of sleep and reduced the time it took to fall asleep. When the combined treatment of FX and DZP showed synergistic sedative action. Also, FX had a binding affinity of -7.2 kcal/mol, while DZP showed -8.4 kcal/mol. The pharmacokinetic investigation of FX demonstrated favorable drug-likeness and strong pharmacokinetic characteristics. Ultimately, FX demonstrated a strong sedative impact in the mouse model, likely via interacting with the GABAA receptor pathways.

Drug disposition in cholestasis: An important concern

Cholestasis, a chronic liver condition, disrupts bile acid homeostasis and complicates drug disposition, posing significant challenges in medicating cholestatic patients. Drug metabolism enzymes and transporters (DMETs) are pivotal in drug clearance. Research indicates that cholestasis leads to alterations in both hepatic and extrahepatic DMETs, with changes in expression and function documented in rodents and humans. This review synthesizes the modifications in key drug disposition components within cholestasis, focusing on cytochrome P450 (CYP450), drug transporters, and their substrates. Additionally, we briefly discuss certain drugs that have demonstrated efficacy in restoring DMET expression in cholestatic conditions. Ultimately, these insights necessitate a reevaluation of drug selection and dosing guidelines for patients with cholestasis.

Effects of hesperidin on the histological structure, oxidative stress, and apoptosis in the liver and kidney induced by NiCl2

The aim of this study was to investigate the effect of hesperidin on the liver and kidney dysfunctions induced by nickel. The mice were divided into six groups: nickel treatment with 80 mg/kg, 160 mg/kg, 320 mg/kg hesperidin groups, 0.5% CMC-Na group, nickel group, and blank control group. Histopathological techniques, biochemistry, immunohistochemistry, and the TUNEL method were used to study the changes in structure, functions, oxidative injuries, and apoptosis of the liver and kidney. The results showed that hesperidin could alleviate the weight loss and histological injuries of the liver and kidney induced by nickel, and increase the levels of lactate dehydrogenase (LDH), alanine aminotransferase (GPT), glutamic oxaloacetic transaminase (GOT) in liver and blood urea nitrogen (BUN), creatinine (Cr) and N-acetylglucosidase (NAG) in kidney. In addition, hesperidin could increase the activities of superoxide dismutase (SOD), catalase (CAT), glutathione (GSH), and glutathione peroxidase (GSH-Px) in the liver and kidney, decrease the content of malondialdehyde (MDA) and inhibit cell apoptosis. It is suggested that hesperidin could help inhibit the toxic effect of nickel on the liver and kidney.

Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles

Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.

The potential impact of CYP and UGT drug-metabolizing enzymes on brain target site drug exposure

Drug metabolism is one of the critical determinants of drug disposition throughout the body. While traditionally associated with the liver, recent research has unveiled the presence and functional significance of drug-metabolizing enzymes (DMEs) within the brain. Specifically, cytochrome P-450 enzymes (CYPs) and UDP-glucuronosyltransferases (UGTs) enzymes have emerged as key players in drug biotransformation within the central nervous system (CNS). This comprehensive review explores the cellular and subcellular distribution of CYPs and UGTs within the CNS, emphasizing regional expression and contrasting profiles between the liver and brain, humans and rats. Moreover, we discuss the impact of species and sex differences on CYPs and UGTs within the CNS. This review also provides an overview of methodologies for identifying and quantifying enzyme activities in the brain. Additionally, we present factors influencing CYPs and UGTs activities in the brain, including genetic polymorphisms, physiological variables, pathophysiological conditions, and environmental factors. Examples of CYP- and UGT-mediated drug metabolism within the brain are presented at the end, illustrating the pivotal role of these enzymes in drug therapy and potential toxicity. In conclusion, this review enhances our understanding of drug metabolism's significance in the brain, with a specific focus on CYPs and UGTs. Insights into the expression, activity, and influential factors of these enzymes within the CNS have crucial implications for drug development, the design of safe drug treatment strategies, and the comprehension of drug actions within the CNS. To that end, CNS pharmacokinetic (PK) models can be improved to further advance drug development and personalized therapy.

Unraveling the Interaction Mechanism of the Compounds From Cladophora sp to Recognize Prospective Larvicidal and Bactericidal Activities: In vitro and In Silico Approaches

The present investigation aims to validate the larvicidal and antibacterial potential of Cladophora sp through in vitro and in silico approaches. The presence of phytoconstituents, functional groups and the compounds responsible for antibacterial and larvicidal activity were assessed through FT-IR and GC-MS analyses which unveiled the existence of active secondary metabolites, hydroxyl, alkane and carbonyl groups. The larvicidal and antibacterial activity of algal extract were examined and revealed complete mortality and substantial zone of inhibition was observed against Culex quinquefasciatus and E. coli. To support the in vitro investigation in silico studies were performed. Molecular docking investigations of the selected compounds from GC-MS which exhibited favorable agreement with drug likeness and ADMET properties indicated robust interactions with the larvicidal and bacterial proteins showcasing considerable binding affinities. Notably, 1,2,4-Oxadiazole, 3-(1,3-benzodioxol-5-yl)-5-[(4-iodo-1H-pyrazol-1-yl) methyl]- exhibited strong interactions with the target proteins. Density Functional Theory revealed that the energy gap of the lead compound was reduced and substantiates the occurrence of intermolecular charge transfer. Molecular Dynamic simulations confirms the stability and flexibility of the lead compound. Hence, this investigation offers computational perspectives on the molecular interactions of Cladophora sp, suggesting its suitability as a promising biocontrol agent.

Design of β-Keto Esters with Antibacterial Activity: Synthesis, In Vitro Evaluation, and Theoretical Assessment of Their Reactivity and Quorum-Sensing Inhibition Capacity

This work proposes the design of β-keto esters as antibacterial compounds. The design was based on the structure of the autoinducer of bacterial quorum sensing, N-(3-oxo-hexanoyl)-l-homoserine lactone (3-oxo-C6-HSL). Eight β-keto ester analogues were synthesised with good yields and were spectroscopically characterised, showing that the compounds were only present in their β-keto ester tautomer form. We carried out a computational analysis of the reactivity and ADME (absorption, distribution, metabolism, and excretion) properties of the compounds as well as molecular docking and molecular dynamics calculations with the LasR and LuxS quorum-sensing (QS) proteins, which are involved in bacterial resistance to antibiotics. The results show that all the compounds exhibit reliable ADME properties and that only compound 7 can present electrophile toxicity. The theoretical reactivity study shows that compounds 6 and 8 present a differential local reactivity regarding the rest of the series. Compound 8 presents the most promising potential in terms of its ability to interact with the LasR and LuxS QS proteins efficiently according to its molecular docking and molecular dynamics calculations. An initial in vitro antimicrobial screening was performed against the human pathogenic bacteria Pseudomonas aeruginosa and Staphylococcus aureus as well as the phytopathogenic bacteria Pseudomonas syringae and Agrobacterium tumefaciens. Compounds 6 and 8 exhibit the most promising results in the in vitro antimicrobial screening against the panel of bacteria studied.

Discovery of novel potent drugs for influenza by inhibiting the vital function of neuraminidase via fragment-based drug design (FBDD) and molecular dynamics simulation strategies

The current work describes a fragment linking methodology to generate new neuraminidase inhibitors. A total number of 28,977 fragments from Zinc 20 have been obtained and screened for neuraminidase receptor affinity. Using Schrödinger software, the highest-scoring 270 fragment hits (with scores greater than -7.6) were subjected to fragment combining to create 100 new molecules. These 100 novel compounds were studied using XP docking to evaluate the molecular interaction modes and their binding affinity to neuraminidase receptor. The top ten molecules were selected, for ADMET, drug-likeness features. Based on these characteristics, the best four developed molecules and Zanamivir were submitted to a molecular dynamics simulation investigation to estimate their dynamics within the neuraminidase receptor using Gromacs software. All MD simulation findings show that the generated complexes are very stable when compared to the clinical inhibitor (Zanamivir). In addition, the four designed neuraminidase inhibitors formed very stable complexes with neuraminidase receptor (with total binding energies ranging from -83.50 to -107.85 Kj/mol) according to the total binding energy calculated by MM-PBSA. For the objective of developing new influenza medications, these novel molecules have the potential to be further evaluated in vitro and in vivo for influenza drug discovery.Communicated by Ramaswamy H. Sarma.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

Metabolism-Guided Optimization of Tryptophanol-Derived Isoindolinone p53 Activators

For the first time, the pharmacokinetic (PK) profile of tryptophanol-derived isoindolinones, previously reported as p53 activators, was investigated. From the metabolites' identification, performed by liquid chromatography coupled to high resolution tandem mass spectrometry (LC-HRMS/MS), followed by their preparation and structural elucidation, it was possible to identify that the indole C2 and C3 are the main target of the cytochrome P450 (CYP)-promoted oxidative metabolism in the tryptophanol-derived isoindolinone scaffold. Based on these findings, to search for novel p53 activators a series of 16 enantiopure tryptophanol-derived isoindolinones substituted with a bromine in indole C2 was prepared, in yields of 62-89%, and their antiproliferative activity evaluated in human colon adenocarcinoma HCT116 cell lines with and without p53. Structural optimization led to the identification of two (S)-tryptophanol-derived isoindolinones 3.9-fold and 1.9-fold more active than hit SLMP53-1, respectively. Compounds' metabolic stability evaluation revealed that this substitution led to a metabolic switch, with the impact of Phase I oxidative metabolism being minimized. Through differential scanning fluorimetry (DSF) experiments, the most active compound of the series in cell assays led to an increase in the protein melting temperature (Tm) of 10.39 °C, suggesting an effective binding to wild-type p53 core domain.

Evaluation of Free Online ADMET Tools for Academic or Small Biotech Environments

For a new molecular entity (NME) to become a drug, it is not only essential to have the right biological activity also be safe and efficient, but it is also required to have a favorable pharmacokinetic profile including toxicity (ADMET). Consequently, there is a need to predict, during the early stages of development, the ADMET properties to increase the success rate of compounds reaching the lead optimization process. Since Lipinski's rule of five, the prediction of pharmacokinetic parameters has evolved towards the current in silico tools based on empirical approaches or molecular modeling. The commercial specialized software for performing such predictions, which is usually costly, is, in many cases, not among the possibilities for research laboratories in academia or at small biotech companies. Nevertheless, in recent years, many free online tools have become available, allowing, more or less accurately, for the prediction of the most relevant pharmacokinetic parameters. This paper studies 18 free web servers capable of predicting ADMET properties and analyzed their advantages and disadvantages, their model-based calculations, and their degree of accuracy by considering the experimental data reported for a set of 24 FDA-approved tyrosine kinase inhibitors (TKIs) as a model of a research project.

Drug Metabolism of Hepatocyte-like Organoids and Their Applicability in In Vitro Toxicity Testing

Emerging advances in the field of in vitro toxicity testing attempt to meet the need for reliable human-based safety assessment in drug development. Intrahepatic cholangiocyte organoids (ICOs) are described as a donor-derived in vitro model for disease modelling and regenerative medicine. Here, we explored the potential of hepatocyte-like ICOs (HL-ICOs) in in vitro toxicity testing by exploring the expression and activity of genes involved in drug metabolism, a key determinant in drug-induced toxicity, and the exposure of HL-ICOs to well-known hepatotoxicants. The current state of drug metabolism in HL-ICOs showed levels comparable to those of PHHs and HepaRGs for CYP3A4; however, other enzymes, such as CYP2B6 and CYP2D6, were expressed at lower levels. Additionally, EC50 values were determined in HL-ICOs for acetaminophen (24.0−26.8 mM), diclofenac (475.5−>500 µM), perhexiline (9.7−>31.5 µM), troglitazone (23.1−90.8 µM), and valproic acid (>10 mM). Exposure to the hepatotoxicants showed EC50s in HL-ICOs comparable to those in PHHs and HepaRGs; however, for acetaminophen exposure, HL-ICOs were less sensitive. Further elucidation of enzyme and transporter activity in drug metabolism in HL-ICOs and exposure to a more extensive compound set are needed to accurately define the potential of HL-ICOs in in vitro toxicity testing.

Genomics in Treatment Development

The Human Genome Project mapped the 3 billion base pairs in the human genome, which ushered in a new generation of genomically focused treatment development. While this has been very successful in other areas, neuroscience has been largely devoid of such developments. This is in large part because there are very few neurological or mental health conditions that are related to single-gene variants. While developments in pharmacogenomics have been somewhat successful, the use of genetic information in practice has to do with drug metabolism and adverse reactions. Studies of drug metabolism related to genetic variations are an important part of drug development. However, outside of cancer biology, the actual translation of genomic information into novel therapies has been limited. Epigenetics, which relates in part to the effects of the environment on DNA, is a promising newer area of relevance to CNS disorders. The environment can induce chemical modifications of DNA (e.g., cytosine methylation), which can be induced by the environment and may represent either shorter- or longer-term changes. Given the importance of environmental influences on CNS disorders, epigenetics may identify important treatment targets in the future.

Design, Synthesis, and Pharmacological Evaluation of Second-Generation Soluble Adenylyl Cyclase (sAC, ADCY10) Inhibitors with Slow Dissociation Rates

Soluble adenylyl cyclase (sAC: ADCY10) is an enzyme involved in intracellular signaling. Inhibition of sAC has potential therapeutic utility in a number of areas. For example, sAC is integral to successful male fertility: sAC activation is required for sperm motility and ability to undergo the acrosome reaction, two processes central to oocyte fertilization. Pharmacologic evaluation of existing sAC inhibitors for utility as on-demand, nonhormonal male contraceptives suggested that both high intrinsic potency, fast on and slow dissociation rates are essential design elements for successful male contraceptive applications. During the course of the medicinal chemistry campaign described here, we identified sAC inhibitors that fulfill these criteria and are suitable for in vivo evaluation of diverse sAC pharmacology.

Deep learning identifies explainable reasoning paths of mechanism of action for drug repurposing from multilayer biological network

The discovery and repurposing of drugs require a deep understanding of the mechanism of drug action (MODA). Existing computational methods mainly model MODA with the protein-protein interaction (PPI) network. However, the molecular interactions of drugs in the human body are far beyond PPIs. Additionally, the lack of interpretability of these models hinders their practicability. We propose an interpretable deep learning-based path-reasoning framework (iDPath) for drug discovery and repurposing by capturing MODA on by far the most comprehensive multilayer biological network consisting of the complex high-dimensional molecular interactions between genes, proteins and chemicals. Experiments show that iDPath outperforms state-of-the-art machine learning methods on a general drug repurposing task. Further investigations demonstrate that iDPath can identify explicit critical paths that are consistent with clinical evidence. To demonstrate the practical value of iDPath, we apply it to the identification of potential drugs for treating prostate cancer and hypertension. Results show that iDPath can discover new FDA-approved drugs. This research provides a novel interpretable artificial intelligence perspective on drug discovery.

Drug metabolism and drug transport of the 100 most prescribed oral drugs

Safe and effective use of drugs requires an understanding of metabolism and transport. We identified the 100 most prescribed drugs in six countries and conducted a literature search on in vitro data to assess contribution of Phase I and II enzymes and drug transporters to metabolism and transport. Eighty-nine of the 100 drugs undergo drug metabolism or are known substrates for drug transporters. Phase I enzymes are involved in metabolism of 67 drugs, while Phase II enzymes mediate metabolism of 18 drugs. CYP3A4/5 is the most important Phase I enzyme involved in metabolism of 43 drugs followed by CYP2D6 (23 drugs), CYP2C9 (23 drugs), CYP2C19 (22 drugs), CYP1A2 (14 drugs) and CYP2C8 (11 drugs). More than half of the drugs (54 drugs) are known substrates for drug transporters. P-glycoprotein (P-gp) is known to be involved in transport of 30 drugs, while breast cancer resistance protein (BCRP) facilitates transport of 11 drugs. A considerable proportion of drugs are subject to a combination of Phase I metabolism, Phase II metabolism and/or drug transport. We conclude that the majority of the most frequently prescribed drugs depend on drug metabolism or drug transport. Thus, understanding variability of drug metabolism and transport remains a priority.

Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method

Determining environmental chemical carcinogenicity is urgently needed as humans are increasingly exposed to these chemicals. In this study, we developed a hybrid neural network (HNN) method called HNN-Cancer to predict potential carcinogens of real-life chemicals. The HNN-Cancer included a new SMILES feature representation method by modifying our previous 3D array representation of 1D SMILES simulated by the convolutional neural network (CNN). We developed binary classification, multiclass classification, and regression models based on diverse non-congeneric chemicals. Along with the HNN-Cancer model, we developed models based on the random forest (RF), bootstrap aggregating (Bagging), and adaptive boosting (AdaBoost) methods for binary and multiclass classification. We developed regression models using HNN-Cancer, RF, support vector regressor (SVR), gradient boosting (GB), kernel ridge (KR), decision tree with AdaBoost (DT), KNeighbors (KN), and a consensus method. The performance of the models for all classifications was assessed using various statistical metrics. The accuracy of the HNN-Cancer, RF, and Bagging models were 74%, and their AUC was ~0.81 for binary classification models developed with 7994 chemicals. The sensitivity was 79.5% and the specificity was 67.3% for the HNN-Cancer, which outperforms the other methods. In the case of multiclass classification models with 1618 chemicals, we obtained the optimal accuracy of 70% with an AUC 0.7 for HNN-Cancer, RF, Bagging, and AdaBoost, respectively. In the case of regression models, the correlation coefficient (R) was around 0.62 for HNN-Cancer and RF higher than the SVM, GB, KR, DTBoost, and NN machine learning methods. Overall, the HNN-Cancer performed better for the majority of the known carcinogen experimental datasets. Further, the predictive performance of HNN-Cancer on diverse chemicals is comparable to the literature-reported models that included similar and less diverse molecules. Our HNN-Cancer could be used in identifying potentially carcinogenic chemicals for a wide variety of chemical classes.

Influence of Circadian Rhythm on Drug Metabolism in 3D Hepatic Spheroids

Circadian rhythms are characterized as oscillations that fluctuate based on a 24h cycle and are responsible for regulation of physiological functions. While the internal clock synchronizes gene expression using external cues like light, a similar synchronization can be induced in vitro by incubating the cells with an increased percentage of serum followed by its rapid removal. Previous studies have suggested that synchronization of HepG2 cell line induced the rhythmic expression of drug metabolizing enzymes (DME) most specifically the cytochrome P450 enzymes. However, there is a lack of evidence demonstrating the influence of 3D microenvironment on the rhythmicity of these genes. To understand this interplay, gene expression of the circadian machinery and CYP450s were compared using the model human hepatocarcinoma cell line, HepG2. Upon serum shock synchronization, gene and protein expression of core clock regulators was assessed and rhythmic expression of these genes was demonstrated. Further insight into the interrelations between various gene pairs was obtained using statistical analysis. Using RNA sequencing, an in-depth understanding of the widespread effects of circadian regulation on genes involved in metabolic processes in the liver was obtained. This study aids in the better understanding of chronopharmacokinetic events in humans using physiologically relevant 3D culture systems. This article is protected by copyright. All rights reserved.

Exogenous metabolite feeding on altering antibiotic susceptibility in Gram-negative bacteria through metabolic modulation: a review

BACKGROUND

The rise of antimicrobial resistance at an alarming rate is outpacing the development of new antibiotics. The worrisome trends of multidrug-resistant Gram-negative bacteria have enormously diminished existing antibiotic activity. Antibiotic treatments may inhibit bacterial growth or lead to induce bacterial cell death through disruption of bacterial metabolism directly or indirectly. In light of this, it is imperative to have a thorough understanding of the relationship of bacterial metabolism with antimicrobial activity and leverage the underlying principle towards development of novel and effective antimicrobial therapies.

OBJECTIVE

Herein, we explore studies on metabolic analyses of Gram-negative pathogens upon antibiotic treatment. Metabolomic studies revealed that antibiotic therapy caused changes of metabolites abundance and perturbed the bacterial metabolism. Following this line of thought, addition of exogenous metabolite has been employed in in vitro, in vivo and in silico studies to activate the bacterial metabolism and thus potentiate the antibiotic activity.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Exogenous metabolites were discovered to cause metabolic modulation through activation of central carbon metabolism and cellular respiration, stimulation of proton motive force, increase of membrane potential, improvement of host immune protection, alteration of gut microbiome, and eventually facilitating antibiotic killing. The use of metabolites as antimicrobial adjuvants may be a promising approach in the fight against multidrug-resistant pathogens.

Study of the metabolic stability profiles of perampanel, rufinamide and stiripentol and prediction of drug interactions using HepaRG cells as an in vitro human model

New-generation antiepileptic drugs as perampanel, rufinamide and stiripentol emerged as alternatives in chronic epilepsy polytherapy. Hence, their metabolic stability and potential involvement in relevant drug-drug interactions (DDI) are of great clinical interest, being HepaRG cells herein used as an in vitro human model. To characterize their metabolic stability profiles, HepaRG cells were incubated with perampanel (1 μM), rufinamide (100 μM) or stiripentol (5 μM) for 12-h. HepaRG cells, pretreated with known CYP450 isoenzymes inducers (rifampicin, phenytoin, phenobarbital, omeprazole and carbamazepine), were also incubated with perampanel, rufinamide or stiripentol to assess possible DDI mediated by CYP induction. Results suggest a considerable decrease in perampanel and stiripentol concentrations over 12-h; contrary, rufinamide concentrations did not variated. Cells pretreatment with all inducers significantly decreased stiripentol concentrations (between 20.3% and 31.9%), suggesting a considerable potential for DDI. Rufinamide concentrations only decreased when preincubated with rifampicin and with the highest tested concentrations of the remaining inducers. Perampanel levels decreased with rifampicin, carbamazepine and phenobarbital, supporting the involvement of CYP3A4-mediated metabolism. Besides relevant information concerning the metabolic stability profile and potential DDIs of the new antiepileptics here studied, it was also reinforced the HepaRG cells suitability as a reliable in vitro model to foresee in vivo metabolism in humans.

Design and Analysis of Pharmacokinetics, Pharmacodynamics and Toxicological analysis of Cannabidiol analogs using in silico Tools

Background:

Background: Cannabidiol (CBD), a non‐psychoactive phytocannabinoid from Cannabis Sativa, has become an interesting option for medicinal chemists in the development of new drug candidates

Objective:

Objective: This study aims to propose analogs with therapeutic potential from the CBD scaffold. Methods: The 16 analogs proposed were designed using the PubChem Sketcher V. 2.4® software. Already, CBD analogs were subjected to different in silico tools, such as Molinspiration®; SwissADME®; SwissTargetPrediction® and OSIRIS Property Explorer

Results and Discussio:

Results and Discussion: The screening of CBD analogs carried out in this study showed compounds 9 and 16 with good affinity for interactions with CB1 and CB2 receptors. Pharmacokinetic data showed that these two compounds have good oral absorption. Finally, in silico toxicity data showed that these compounds pose no risk of a toxic event in humans

Conclusion:

Conclusion: CBD analogs 9 and 16 would have a better profile of drug candidates to be further tested in vitro and in vivo models.: CBD analogs 9 and 16 would have a better profile of drug candidates to be further tested in vitro and in vivo models.

Novel insights into conjugation of antitumor-active unsymmetrical bisacridine C-2028 with glutathione: Characteristics of non-enzymatic and glutathione S-transferase-mediated reactions

Unsymmetrical bisacridines (UAs) are a novel potent class of antitumor-active therapeutics. A significant route of phase II drug metabolism is conjugation with glutathione (GSH), which can be non-enzymatic and/or catalyzed by GSH-dependent enzymes. The aim of this work was to investigate the GSH-mediated metabolic pathway of a representative UA, C-2028. GSH-supplemented incubations of C-2028 with rat, but not with human, liver cytosol led to the formation of a single GSH-related metabolite. Interestingly, it was also revealed with rat liver microsomes. Its formation was NADPH-independent and was not inhibited by co-incubation with the cytochrome P450 (CYP450) inhibitor 1-aminobenzotriazole. Therefore, the direct conjugation pathway occurred without the prior CYP450-catalyzed bioactivation of the substrate. In turn, incubations of C-2028 and GSH with human recombinant glutathione S-transferase (GST) P1-1 or with heat-/ethacrynic acid-inactivated liver cytosolic enzymes resulted in the presence or lack of GSH conjugated form, respectively. These findings proved the necessary participation of GST in the initial activation of the GSH thiol group to enable a nucleophilic attack on the substrate molecule. Another C-2028-GSH S-conjugate was also formed during non-enzymatic reaction. Both GSH S-conjugates were characterized by combined liquid chromatography/tandem mass spectrometry. Mechanisms for their formation were proposed. The ability of C-2028 to GST-mediated and/or direct GSH conjugation is suspected to be clinically important. This may affect the patient's drug clearance due to GST activity, loss of GSH, or the interactions with GSH-conjugated drugs. Moreover, GST-mediated depletion of cellular GSH may increase tumor cell exposure to reactive products of UA metabolic transformations.

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We investigated the GSH-mediated metabolic pathway of antitumor bisacridine C-2028.

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Non-enzymatic and GST-catalyzed GSH conjugation of C-2028 was observed.

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The action of human recombinant GSTP1-1 in C-2028 metabolism was proved.

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GSH conjugation occurred without the prior CYP450-mediated activation of C-2028.

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GSH conjugation of C-2028 molecule took place on the system containing nitro group.

Microbes, Clinical trials, Drug Discovery, and Vaccine Development: The Current Perspectives

Because of the frequent emergence of novel microbial species and the re-emergence of genetic variants of hitherto known microbes, the global healthcare system, and human health has been thrown into jeopardy. Also, certain microbes that possess the ability to develop multi-drug resistance (MDR) have limited the treatment options in cases of serious infections, and increased hospital and treatment costs, and associated morbidity and mortality. The recent discovery of the novel Coronavirus (n-CoV), the Severe Acute Respiratory Syndrome CoV-2 (SARS-CoV-2) that is causing the CoV Disease-19 (COVID-19) has resulted in severe morbidity and mortality throughout the world affecting normal human lives. The major concern with the current pandemic is the non-availability of specific drugs and an incomplete understanding of the pathobiology of the virus. It is therefore important for pharmaceutical establishments to envisage the discovery of therapeutic interventions and potential vaccines against the novel and MDR microbes. Therefore, this review is attempted to update and explore the current perspectives in microbes, clinical research, drug discovery, and vaccine development to effectively combat the emerging novel and re-emerging genetic variants of microbes.

Anti-Inflammatory Potential of Daturaolone from Datura innoxia Mill.: In Silico, In Vitro and In Vivo Studies

Exploration of leads with therapeutic potential in inflammatory disorders is worth pursuing. In line with this, the isolated natural compound daturaolone from Datura innoxia Mill. was evaluated for its anti-inflammatory potential using in silico, in vitro and in vivo models. Daturaolone follows Lipinski's drug-likeliness rule with a score of 0.33. Absorption, distribution, metabolism, excretion and toxicity prediction show strong plasma protein binding; gastrointestinal absorption (Caco-2 cells permeability = 34.6 nm/s); no blood-brain barrier penetration; CYP1A2, CYP2C19 and CYP3A4 metabolism; a major metabolic reaction, being aliphatic hydroxylation; no hERG inhibition; and non-carcinogenicity. Predicted molecular targets were mainly inflammatory mediators. Molecular docking depicted H-bonding interaction with nuclear factor kappa beta subunit (NF-κB), cyclooxygenase-2, 5-lipoxygenase, phospholipase A2, serotonin transporter, dopamine receptor D1 and 5-hydroxy tryptamine. Its cytotoxicity (IC₅₀) value in normal lymphocytes was >20 µg/mL as compared to cancer cells (Huh7.5; 17.32 ± 1.43 µg/mL). Daturaolone significantly inhibited NF-κB and nitric oxide production with IC₅₀ values of 1.2 ± 0.8 and 4.51 ± 0.92 µg/mL, respectively. It significantly reduced inflammatory paw edema (81.73 ± 3.16%), heat-induced pain (89.47 ± 9.01% antinociception) and stress-induced depression (68 ± 9.22 s immobility time in tail suspension test). This work suggests a possible anti-inflammatory role of daturaolone; however, detailed mechanistic studies are still necessary to corroborate and extrapolate the findings.

GC-MS Analysis, Molecular Docking and Pharmacokinetic Properties of Phytocompounds from Solanum torvum Unripe Fruits and Its Effect on Breast Cancer Target Protein

This study was designed to identify phytocompounds from the aqueous extract of Solanum torvum unripe fruits using GC–MS analysis against breast cancer. For this, the identified phytocompounds were subjected to perform molecular docking studies to find the effects on breast cancer target protein. Pharmacokinetic properties were also tested for the identified phytocompounds to evaluate the ADMET properties. Molecular docking studies were done using docking software PyRx, and pharmacokinetic properties of phytocompounds were evaluated using SwissADME. From the results, ten best compounds were identified from GC–MS analysis against breast cancer target protein. Of which, three compounds showed very good binding affinity with breast cancer target protein. They are ergost-25-ene-3,6-dione,5,12-dihydroxy-,(5.alpha.,12.beta.) (− 7.3 kcal/mol), aspidospermidin-17-ol,1-acetyl-16-methoxy (− 6.7 kcal/mol) and 2-(3,4-dichlorophenyl)-4-[[2-[1-methyl-2-pyrrolidinyl]ethyl amino]-6-[trichloromethyl]-s-triazine (− 6.7 kcal/mol). Further, docking study was performed for the synthetic drug doxorubicin to compare the efficiency of phytocompounds. The binding affinity of ergost-25-ene-3,6-dione,5,12-dihydroxy-,(5.alpha.,12.beta.) is higher than the synthetic drug doxorubicin (− 7.2 kcal/mol), and the binding affinity of other compounds is also very near to the drug. Hence, the present study concludes that the phytocompounds from the aqueous extract of Solanum torvum unripe fruits have the potential ability to treat breast cancer.

Cytochrome P450 enzymes: a review on drug metabolizing enzyme inhibition studies in drug discovery and development

Assessment of drug candidate's potential to inhibit cytochrome P450 (CYP) enzymes remains crucial in pharmaceutical drug discovery and development. Both direct and time-dependent inhibition of drug metabolizing CYP enzymes by the concomitant administered drug is the leading cause of drug-drug interactions (DDIs), resulting in the increased toxicity of the victim drug. In this context, pharmaceutical companies have grown increasingly diligent in limiting CYP inhibition liabilities of drug candidates in the early stages and examining risk assessments throughout the drug development process. This review discusses different strategies and decision-making processes for assessing the drug-drug interaction risks by enzyme inhibition and lays particular emphasis on in vitro study designs and interpretation of CYP inhibition data in a stage-appropriate context.

Can preclinical drug development help to predict adverse events in clinical trials?

The development of novel therapeutics is associated with high rates of attrition, with unexpected adverse events being a major cause of failure. Serious adverse events have led to organ failure, cancer development and deaths that were not expected outcomes in clinical trials. These life-threatening events were not identified during therapeutic development due to the lack of preclinical safety tests that faithfully represented human physiology. We highlight the successful application of several novel technologies, including high-throughput screening, organs-on-chips, microbiome-containing drug-testing platforms and humanised mouse models, for mechanistic studies and prediction of toxicity. We propose the incorporation of similar preclinical tests into future drug development to reduce the likelihood of hazardous therapeutics entering later-stage clinical trials.

Accurate acid dissociation constant (pKa) calculation for the sulfachloropyridazine and similar molecules

Accurate calculation of the acid dissociation constant (pK_a) has fundamental importance for the description of molecular systems with pharmacological activities. The search for a more appropriate procedure for its determination is always welcome and has aroused increasing interest from the scientific community. In this sense, this work presents a computational study involving the combination of ten DFT functionals (M062X, M06L, B3LYP, BLYP, PBEPBE, BP86, LC-BLYP, SPBE, CAM-B3LYP, LC-PBEPBE) and HF method, eight basis set functions (6-311G, 6-311 + G, 6-311G(d,p), 6-311 + G(d,p), 6-311+ +G(d,p), 6-311(2d,2p), 6-311+ +G(2d,2p), and aug-cc-pVDZ), and three solvation models (SMD, PCM, and CPCM) for an accurate sulfachloropyridazine (SCR) pK_a determination. It was found that the smallest deviation (0.02 unit of pK_a) between the current study and experimental result was achieved with the BLYP/6-311 + G(d,p)/PCM combination. Therefore, this combination was extended to calculate the pK_a of six SCR similar molecules selected through the eletroshape similarity method. For all these molecules, the difference between the obtained results and experimental data ranged between 0.14 and 0.69 units of pK_a. This feature suggests that the obtained combination can determine pK_a with experimental precision for complexes that are formed by sulfonamide functional group (SO₂NHR). Graphical Abstract A computational study involving the combination of different levels of theory, basis sets and solvation models for an accurate sulfanamide pK_a determination.

Agarose Micro-Well Platform for Rapid Generation of Homogenous 3D Tumor Spheroids

In recent years, 3D culture of tumor spheroids has managed to revolutionize cancer research and drug discovery. 2D monolayer cells grown in cell culture flasks undergo radical changes in cell behavior, structure, and function owing to varying environmental cues and are unable to provide predictive data for preclinical evaluation. 3D tumor spheroids can better recapitulate tumor architecture, cell-cell and cell-matrix connectivity, and the tissue complexity of tumors grown in animal models. However, many of the existing techniques to culture 3D spheroids are time-consuming and ineffective and produce irregular-shaped spheroids that cannot be easily incorporated in biological assays. The set of protocols described herein makes use of a commercial hair brush as a template to create concave micro-well impressions in agarose. This technique is easy, inexpensive, and adaptable and also has the ability to produce uniform, homogenous cancer spheroids, with large diameter (∼1000 μm) and thickness (∼250 μm), within 24 to 48 hr after cell seeding. The 3D spheroids produced using the agarose micro-well platform function as an excellent 3D in vitro model for understanding the extent of penetration, uptake, and distribution of targeted cargos such as a diagnostic or therapeutic agents for identification and treatment of cancer. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Fabrication of agarose micro-well scaffold for growing tumor spheroids using a commercial hair brush Basic Protocol 2: Formation of homogenous tumor spheroids in agarose micro-well platform Basic Protocol 3: Assessing viability of 3D tumor spheroids grown in agarose micro-wells using confocal microscopy Basic Protocol 4: Analyzing uptake and penetration of targeted fluorescent bioconjugate in 3D tumor spheroids using two-photon imaging.

Selection of safe artemisinin derivatives using a machine learning-based cardiotoxicity platform and in vitro and in vivo validation

The majority of drug candidates fails the approval phase due to unwanted toxicities and side effects. Establishment of an effective toxicity prediction platform is of utmost importance, to increase the efficiency of the drug discovery process. For this purpose, we developed a toxicity prediction platform with machine-learning strategies. Cardiotoxicity prediction was performed by establishing a model with five parameters (arrhythmia, cardiac failure, heart block, hypertension, myocardial infarction) and additional toxicity predictions such as hepatotoxicity, reproductive toxicity, mutagenicity, and tumorigenicity are performed by using Data Warrior and Pro-Tox-II software. As a case study, we selected artemisinin derivatives to evaluate the platform and to provide a list of safe artemisinin derivatives. Artemisinin from Artemisia annua was described first as an anti-malarial compound and later its anticancer properties were discovered. Here, random forest feature selection algorithm was used for the establishment of cardiotoxicity models. High AUC scores above 0.830 were achieved for all five cardiotoxicity indications. Using a chemical library of 374 artemisinin derivatives as a case study, 7 compounds (deoxydihydro-artemisinin, 3-hydroxy-deoxy-dihydroartemisinin, 3-desoxy-dihydroartemisinin, dihydroartemisinin-furano acetate-d3, deoxyartemisinin, artemisinin G, artemisinin B) passed the toxicity filtering process for hepatotoxicity, mutagenicity, tumorigenicity, and reproductive toxicity in addition to cardiotoxicity. Experimental validation with the cardiomyocyte cell line AC16 supported the findings from the in silico cardiotoxicity model predictions. Transcriptomic profiling of AC16 cells upon artemisinin B treatment revealed a similar gene expression profile as that of the control compound, dexrazoxane. In vivo experiments with a Zebrafish model further substantiated the in silico and in vitro data, as only slight cardiotoxicity in picomolar range was observed. In conclusion, our machine-learning approach combined with in vitro and in vivo experimentation represents a suitable method to predict cardiotoxicity of drug candidates.

Biomimetic microsystems for cardiovascular studies

Traditional tissue culture platforms have been around for several decades and have enabled key findings in the cardiovascular field. However, these platforms failed to recreate the mechanical and dynamic features found within the body. Organs-on-chips (OOCs) are cellularized microfluidic-based devices that can mimic the basic structure, function, and responses of organs. These systems have been successfully utilized in disease, development, and drug studies. OOCs are designed to recapitulate the mechanical, electrical, chemical, and structural features of the in vivo microenvironment. Here, we review cardiovascular-themed OOC studies, design considerations, and techniques used to generate these cellularized devices. Furthermore, we will highlight the advantages of OOC models over traditional cell culture vessels, discuss implementation challenges, and provide perspectives on the state of the field.

Fungicidal Activity of a Safe 1,3,4-Oxadiazole Derivative Against Candida albicans

Candida albicans is the most common species isolated from nosocomial bloodstream infections. Due to limited therapeutic arsenal and increase of drug resistance, there is an urgent need for new antifungals. Therefore, the antifungal activity against C. albicans and in vivo toxicity of a 1,3,4-oxadiazole compound (LMM6) was evaluated. This compound was selected by in silico approach based on chemical similarity. LMM6 was highly effective against several clinical C. albicans isolates, with minimum inhibitory concentration values ranging from 8 to 32 µg/mL. This compound also showed synergic effect with amphotericin B and caspofungin. In addition, quantitative assay showed that LMM6 exhibited a fungicidal profile and a promising anti-biofilm activity, pointing to its therapeutic potential. The evaluation of acute toxicity indicated that LMM6 is safe for preclinical trials. No mortality and no alterations in the investigated parameters were observed. In addition, no substantial alteration was found in Hippocratic screening, biochemical or hematological analyzes. LMM6 (5 mg/kg twice a day) was able to reduce both spleen and kidneys fungal burden and further, promoted the suppresses of inflammatory cytokines, resulting in infection control. These preclinical findings support future application of LMM6 as potential antifungal in the treatment of invasive candidiasis.

Structure-based molecular modeling in SAR analysis and lead optimization

In silico methods like molecular docking and pharmacophore modeling are established strategies in lead identification. Their successful application for finding new active molecules for a target is reported by a plethora of studies. However, once a potential lead is identified, lead optimization, with the focus on improving potency, selectivity, or pharmacokinetic parameters of a parent compound, is a much more complex task. Even though in silico molecular modeling methods could contribute a lot of time and cost-saving by rationally filtering synthetic optimization options, they are employed less widely in this stage of research. In this review, we highlight studies that have successfully used computer-aided SAR analysis in lead optimization and want to showcase sound methodology and easily accessible in silico tools for this purpose.

Application and assessment of deep learning for the generation of potential NMDA receptor antagonists

Uncompetitive antagonists of the N-methyl d-aspartate receptor (NMDAR) have demonstrated therapeutic benefit in the treatment of neurological diseases such as Parkinson's and Alzheimer's, but some also cause dissociative effects that have led to the synthesis of illicit drugs. The ability to generate NMDAR antagonists in silico is therefore desirable for both new medication development and preempting and identifying new designer drugs. Recently, generative deep learning models have been applied to de novo drug design as a means to expand the amount of chemical space that can be explored for potential drug-like compounds. In this study, we assess the application of a generative model to the NMDAR to achieve two primary objectives: (i) the creation and release of a comprehensive library of experimentally validated NMDAR phencyclidine (PCP) site antagonists to assist the drug discovery community and (ii) an analysis of both the advantages conferred by applying such generative artificial intelligence models to drug design and the current limitations of the approach. We apply, and provide source code for, a variety of ligand- and structure-based assessment techniques used in standard drug discovery analyses to the deep learning-generated compounds. We present twelve candidate antagonists that are not available in existing chemical databases to provide an example of what this type of workflow can achieve, though synthesis and experimental validation of these compounds are still required.

'In silico' toxicology methods in drug safety assessment

While experimental animal investigation has historically been the most conventional approach conducted to assess drug safety and is currently considered the main method for determining drug toxicity, these studies are constricted by cost, time, and ethical approvals. Over the last 20 years, there have been significant advances in computational sciences and computer data processing, while knowledge of alternative techniques and their application has developed into a valuable skill in toxicology. Thus, the application of in silico methods in drug safety assessment is constantly increasing. They are very complex and are grounded on accumulated knowledge from toxicology, bioinformatics, biochemistry, statistics, mathematics, as well as molecular biology. This review will summarize current state-of-the-art scientific data on the use of in silico methods in toxicity testing, taking into account their shortcomings, and highlighting the strategies that should deliver consistent results, while covering the applications of in silico methods in preclinical trials and drug impurities toxicity testing.

Designer, Programmable 3D DNA Nanodevices to Probe Biological Systems

DNA nanotechnology is a unique field that provides simple yet robust design techniques for self-assembling nanoarchitectures with extremely high potential for biomedical applications. Though the field began to exploit DNA to build various nanoscale structures, it has now taken a different path, diverging from the creation of complex structures to functional DNA nanodevices that explore various biological systems and mechanisms. Here, we present a brief overview of DNA nanotechnology, summarizing the key strategies for construction of various DNA nanodevices, with special focus on three-dimensional (3D) nanocages or polyhedras. We then discuss biological applications of 3D DNA nanocages, particularly tetrahedral DNA cages, in their ability to program and modulate cellular systems, in biosensing, and as tools for targeted therapeutics. We conclude with a final discussion on challenges and perspectives of 3D DNA nanodevices in biomedical applications.

Voltammetric Behaviour of Drug Molecules as a Predictor of Metabolic Liabilities

Electron transfer plays a vital role in drug metabolism and underlying toxicity mechanisms. Currently, pharmaceutical research relies on pharmacokinetics (PK) and absorption, distribution, metabolism, elimination and toxicity (ADMET) measurements to understand and predict drug reactions in the body. Metabolic stability (and toxicity) prediction in the early phases of the drug discovery and development process is key in identifying a suitable lead compound for optimisation. Voltammetric methods have the potential to overcome the significant barrier of new drug failure rates, by giving insight into phase I metabolism events which can have a direct bearing on the stability and toxicity of the parent drug being dosed. Herein, we report for the first time a data-mining investigation into the voltammetric behaviour of reported drug molecules and their correlation with metabolic stability (indirectly measured via t½), as a potential predictor of drug stability/toxicity in vivo. We observed an inverse relationship between oxidation potential and drug stability. Furthermore, we selected and prepared short- (<10 min) and longer-circulation (>2 h) drug molecules to prospectively survey the relationship between oxidation potential and stability.

Withametelin: a biologically active withanolide in cancer, inflammation, pain and depression

Withanolides are natural medicinal agents whose safety and therapeutic profiles make them valuable to mankind. Among multiple withanolides, withametelin is underexplored. The present study was aimed to create a general biological profile of isolated withametelin from Datura innoxia Mill. targeting different biological models. In-silico studies include drug-likeliness, pharmacokinetics, toxicity, molecular targets and cytotoxicity to cancer cell lines predictions. In silico directed preliminary in-vitro evaluation comprised of cancer/normal cell cytotoxicity, DPPH and protein kinase inhibition assays while in-vivo bioactivities include antiinflammatory, analgesic, antidepressant and anticoagulant assays. Pharmacological findings were strengthened by molecular docking studies to check interactions with various proteins and to propose the future path of studies. Results indicated compliance with Lipinski drug-likeliness rule (score −0.55). ADMET prediction showed strong plasma protein binding, GI absorption (Caco-2 cells permeability = 46.74 nm/s), blood brain barrier penetration (Cbrain/Cblood = 0.31), efflux by P-glycoprotein, metabolism by CYP1A2, CYP2C19 and CYP3A4, medium hERG inhibition and non-carcinogenicity in rodents. Predicted molecular targets included mainly receptors (glucocorticoid, kappa opioid, delta opioid, adrenergic and dopamine), oxidoreductase (arachidonate 5-lipoxygenase and cyclooxygenase-2), enzymes (HMG-CoA reductase) and kinase (NFκb). Withametelin was more cytotoxic to cancer cells (DU145 IC₅₀ 7.67 ± 0.54 µM) than normal lymphocytes (IC₅₀ 33.55 ± 1.31 µM). It also showed good antioxidant and protein kinase inhibition potentials. Furthermore, withametelin (20 mg/kg) significantly reduced inflammatory paw edema (68.94 ± 5.55%), heat-induced pain (78.94 ± 6.87%) and immobility time (50%) in animals. Molecular docking showed hydrogen bonding interactions (binding energies: −11.3 to −7.8 kcal/mol) with arachidonate 5 lipoxygenase, NFκb and glucocorticoid receptor. Withametelin has potential for advance investigations for its cytotoxic, anti-inflammatory, analgesic and antidepressant activities.

Prediction of drug metabolites using neural machine translation

Metabolic processes in the human body can alter the structure of a drug affecting its efficacy and safety. As a result, the investigation of the metabolic fate of a candidate drug is an essential part of drug design studies. Computational approaches have been developed for the prediction of possible drug metabolites in an effort to assist the traditional and resource-demanding experimental route. Current methodologies are based upon metabolic transformation rules, which are tied to specific enzyme families and therefore lack generalization, and additionally may involve manual work from experts limiting scalability. We present a rule-free, end-to-end learning-based method for predicting possible human metabolites of small molecules including drugs. The metabolite prediction task is approached as a sequence translation problem with chemical compounds represented using the SMILES notation. We perform transfer learning on a deep learning transformer model for sequence translation, originally trained on chemical reaction data, to predict the outcome of human metabolic reactions. We further build an ensemble model to account for multiple and diverse metabolites. Extensive evaluation reveals that the proposed method generalizes well to different enzyme families, as it can correctly predict metabolites through phase I and phase II drug metabolism as well as other enzymes. Compared to existing rule-based approaches, our method has equivalent performance on the major enzyme families while it additionally finds metabolites through less common enzymes. Our results indicate that the proposed approach can provide a comprehensive study of drug metabolism that does not restrict to the major enzyme families and does not require the extraction of transformation rules.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

In Silico ADME/Tox Profiling of Natural Products: A Focus on BIOFACQUIM

Natural products continue to be major sources of bioactive compounds and drug candidates not only because of their unique chemical structures but also because of their overall favorable metabolism and pharmacokinetic properties. The number of publicly accessible natural product databases has increased significantly in the past few years. However, the systematic ADME/Tox profile has been reported on a limited basis. For instance, BIOFACQUIM was recently published as a public database of natural products from Mexico, a country with a rich source of biomolecules. However, its ADME/Tox profile has not been reported. Herein, we discuss the results of an in-depth in silico ADME/Tox profile of natural products in BIOFACQUIM and other large public collections of natural products. It was concluded that the absorption and distribution profiles of compounds in BIOFACQUIM are similar to those of approved drugs, while the metabolism profile is comparable to that in the other natural product databases. The excretion profile of compounds in BIOFACQUIM is different from that of the approved drugs, but their predicted toxicity profile is comparable. This work further contributes to the deeper characterization of natural product collections as major sources of bioactive compounds with therapeutic potential.

Cytochrome P450s regulates aloperine-induced pathological changes in mouse liver and kidney

Aloperine is a major active component in Sophora alopecuroides L that plays diverse pharmacological properties. Recent studies have indicated the potential effect of aloperine against hypertension and cancers. However, possible toxicity of aloperine has not been carefully studied in vivo. The aim of this study was to assess the effect of intraperitoneal aloperine injection on mouse liver and kidney tissues and to investigate the role of CYP450 genes in aloperine-induced toxicity. 72 BALB/c mice were randomly divided into four groups: vehicle control group (normal saline), low-dose group (4 mg/kg), medium-dose group (8 mg/kg), and high-dose group (16 mg/kg). 18 mice in each group were intraperitoneally injected with aloperine daily for 4 weeks, and were then kept for another 1 or 4 weeks without aloperine treatment. Serum was colleted for analysis of serum biochemical indexes including ALT, AST, BUN and CRE. The liver and kidney were collected for analysis of histopathologic changes and CYP450 expression and activity. Vacuolization of cytoplasm in liver cells, swelling in kidney tubular cells, increased levels of ALT, AST, BUN, and CRE, and alteration in the expression and activity of CYP450 were observed in the high-dose group after 4 weeks of treatment. However, all aloperine-induced damages were recovered to a certain degree after maintained without aloperine for 1 week, and fully recovered after maintained without aloperine for 4 weeks. These findings suggested that aloperine regulated the expression of CYP450, which was possibly involved in aloperine-induced reversible toxicity in mouse liver and kidney tissues.

Caffeic Acid Phenethyl Ester Assisted by Reversible Electroporation-In Vitro Study on Human Melanoma Cells

Melanoma is one of the most serious skin cancers. The incidence of this malignant skin lesion is continuing to increase worldwide. Melanoma is resistant to chemotherapeutic drugs and highly metastatic. Surgical resection can only be used to treat melanoma in the early stages, while chemotherapy is limited due to melanoma multi-drug resistance. The overexpression of glutathione S-transferase (GST) may have a critical role in this resistance. Caffeic acid phenethyl ester (CAPE) is a natural phenolic compound, which occurs in many plants. Previous studies demonstrated that CAPE suppresses the growth of melanoma cells and induces reactive oxygen species generation. It is also known that bioactivation of CAPE to its corresponding quinone metabolite by tyrosinase would lead to GST inhibition and selective melanoma cell death. We investigated the biochemical toxicity of CAPE in combination with microsecond electropermeabilization in two human melanoma cell lines. Our results indicate that electroporation of melanoma cells in the presence of CAPE induced high oxidative stress, which correlates with high cytotoxicity. Moreover, it can disrupt the metabolism of cancer cells by inducing apoptotic cell death. Electroporation of melanoma cells may be an efficient CAPE delivery system, enabling the application of this compound, while reducing its dose and exposure time.

Relative Expression of Mouse Udp-glucuronosyl Transferase 2b1 Gene in the Livers, Kidneys, and Hearts: The Influence of Nonsteroidal Anti-inflammatory Drug Treatment

BACKGROUND

Mouse Udp-glucuronosyl Transferase (UGT) 2b1 is equivalent to the human UGT2B7 enzyme, which is a phase II drug-metabolising enzyme and plays a major role in the metabolism of xenobiotic and endogenous compounds. This study aimed to find the relative expression of the mouse ugt2b1 gene in the liver, kidney, and heart organs and the influence of Nonsteroidal Anti-inflammatory Drug (NSAID) administration.

METHODS

Thirty-five Blab/c mice were divided into 5 groups and treated with different commonly-used NSAIDs; diclofenac, ibuprofen, meloxicam, and mefenamic acid for 14 days. The livers, kidneys, and hearts were isolated, while the expression of ugt2b1 gene was analysed with a quantitative real-time polymerase chain reaction technique.

RESULTS

It was found that the ugt2b1 gene is highly expressed in the liver, and then in the heart and the kidneys. NSAIDs significantly upregulated (ANOVA, p < 0.05) the expression of ugt2b1 in the heart, while they downregulated its expression (ANOVA, p < 0.05) in the liver and kidneys. The level of NSAIDs' effect on ugt2b1 gene expression was strongly correlated (Spearman's Rho correlation, p < 0.05) with NSAID's lipophilicity in the liver and its elimination half-life in the heart.

CONCLUSION

This study concluded that the mouse ugt2b1 gene was mainly expressed in the liver, as 14-day administration of different NSAIDs caused alterations in the expression of this gene, which may influence the metabolism of xenobiotic and endogenous compounds.