Recent advances in the translation of drug metabolism and pharmacokinetics science for drug discovery and development

Molecular probes for super-resolution imaging of drug dynamics

Super-resolution molecular probes (SRMPs) are essential tools for visualizing drug dynamics within cells, transcending the resolution limits of conventional microscopy. In this review, we provide an overview of the principles and design strategies of SRMPs, emphasizing their role in accurately tracking drug molecules. By illuminating the intricate processes of drug distribution, diffusion, uptake, and metabolism at a subcellular and molecular level, SRMPs offer crucial insights into therapeutic interventions. Additionally, we explore the practical applications of super-resolution imaging in disease treatment, highlighting the significance of SRMPs in advancing our understanding of drug action. Finally, we discuss future perspectives, envisioning potential advancements and innovations in this field. Overall, this review serves to inform and practitioners about the utility of SRMPs in driving innovation and progress in pharmacology, providing valuable insights for drug development and optimization.

Pharmacokinetics-Pharmacodynamics Modeling for Evaluating Drug-Drug Interactions in Polypharmacy: Development and Challenges

Polypharmacy is commonly employed in clinical settings. The potential risks of drug-drug interactions (DDIs) can compromise efficacy and pose serious health hazards. Integrating pharmacokinetics (PK) and pharmacodynamics (PD) models into DDIs research provides a reliable method for evaluating and optimizing drug regimens. With advancements in our comprehension of both individual drug mechanisms and DDIs, conventional models have begun to evolve towards more detailed and precise directions, especially in terms of the simulation and analysis of physiological mechanisms. Selecting appropriate models is crucial for an accurate assessment of DDIs. This review details the theoretical frameworks and quantitative benchmarks of PK and PD modeling in DDI evaluation, highlighting the establishment of PK/PD modeling against a backdrop of complex DDIs and physiological conditions, and further showcases the potential of quantitative systems pharmacology (QSP) in this field. Furthermore, it explores the current advancements and challenges in DDI evaluation based on models, emphasizing the role of emerging in vitro detection systems, high-throughput screening technologies, and advanced computational resources in improving prediction accuracy.

Covalent targeted radioligands potentiate radionuclide therapy

Targeted radionuclide therapy, in which radiopharmaceuticals deliver potent radionuclides to tumours for localized irradiation, has addressed unmet clinical needs and improved outcomes for patients with cancer1-4. A therapeutic radiopharmaceutical must achieve both sustainable tumour targeting and fast clearance from healthy tissue, which remains a major challenge5,6. A targeted ligation strategy that selectively fixes the radiopharmaceutical to the target protein in the tumour would be an ideal solution. Here we installed a sulfur (VI) fluoride exchange (SuFEx) chemistry-based linker on radiopharmaceuticals to prevent excessively fast tumour clearance. When the engineered radiopharmaceutical binds to the tumour-specific protein, the system undergoes a binding-to-ligation transition and readily conjugates to the tyrosine residues through the 'click' SuFEx reaction. The application of this strategy to a fibroblast activation protein (FAP) inhibitor (FAPI) triggered more than 80% covalent binding to the protein and almost no dissociation for six days. In mice, SuFEx-engineered FAPI showed 257% greater tumour uptake than did the original FAPI, and increased tumour retention by 13-fold. The uptake in healthy tissues was rapidly cleared. In a pilot imaging study, this strategy identified more tumour lesions in patients with cancer than did other methods. SuFEx-engineered FAPI also successfully achieved targeted β- and α-radionuclide therapy, causing nearly complete tumour regression in mice. Another SuFEx-engineered radioligand that targets prostate-specific membrane antigen (PSMA) also showed enhanced therapeutic efficacy. Considering the broad scope of proteins that can potentially be ligated to SuFEx warheads, it might be possible to adapt this strategy to other cancer targets.

Dendronized Polymer-Derived Nanomedicines for Mitochondrial Dynamics Regulation and Immune Modulation

The effects of dendron side chains in polymeric conjugates on tumor penetration and antigen presentation are systematically examined. Three polymer-gemcitabine (Gem) conjugates (pG0-Gem, pG1-Gem, pG2-Gem) are designed and prepared. The pG2-Gem conjugate uniquely binds to the mitochondria of tumor cells, thus regulating mitochondrial dynamics. The interaction between the pG2-Gem conjugate and the mitochondria promotes great penetration and accumulation of the conjugate at the tumor site, resulting in pronounced antitumor effects in an animal model. Such encouraging therapeutic effects can be ascribed to immune modulation since MHC-1 antigen presentation is significantly enhanced due to mitochondrial fusion and mitochondrial metabolism alteration after pG2-Gem treatment. Crucially, the drug-free dendronized polymer, pG2, is identified to regulate mitochondrial dynamics, and the regulation is independent of the conjugated Gem. Furthermore, the combination of pG2-Gem with anti-PD-1 antibody results in a remarkable tumor clearance rate of 87.5% and a prolonged survival rate of over 150 days, demonstrating the potential of dendronized polymers as an innovative nanoplatform for metabolic modulation and synergistic tumor immunotherapy.

Discovery of GluN2A subtype-selective N-methyl-d-aspartate (NMDA) receptor ligands

The N-methyl-d-aspartate (NMDA) receptors, which belong to the ionotropic Glutamate receptors, constitute a family of ligand-gated ion channels. Within the various subtypes of NMDA receptors, the GluN1/2A subtype plays a significant role in central nervous system (CNS) disorders. The present article aims to provide a comprehensive review of ligands targeting GluN2A-containing NMDA receptors, encompassing negative allosteric modulators (NAMs), positive allosteric modulators (PAMs) and competitive antagonists. Moreover, the ligands' structure-activity relationships (SARs) and the binding models of representative ligands are also discussed, providing valuable insights for the clinical rational design of effective drugs targeting CNS diseases.

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.

Online electrochemistry coupling liquid chromatography-mass spectrometry for rapid investigation on the phase I and phase II simulated metabolic reactions of flavonoids

In this study, an online electrochemistry coupling high-performance liquid chromatography-mass spectrometry (EC-HPLC-MS) technology has been developed for simulating metabolic reactions and rapid analysis of metabolites of flavone, quercetin, and rutin, which are not only widely present compounds with pharmacological activity in nature, but also have structural similarity and variability. The simulated metabolic processes of the substrates (phase I and phase II metabolism) were implemented on the surface of glassy carbon electrode (GCE) by using different electrochemical methods. After online chromatographic separation, the products were transmitted to a mass spectrometer for detection, in order to speculate relevant reaction pathways and structural information of the reaction product. The main metabolites, including methylation, hydroxylation, hydrolysis, and conjugation reaction products, had been successfully identified through the designed in situ hyphenated technique. Furthermore, compared with metabolites produced by in vitro incubation of rat liver microsomes, it was found that the products of electrochemical simulated metabolism were more abundant with diverse metabolic pathways. The results indicated that the proposed method exhibited advantages in the sample pretreatment process and detection cycle without compromising the reliability and accuracy of the results.

Discovery of benzopyridone cyanoacetates as new type of potential broad-spectrum antibacterial candidates

Unique benzopyridone cyanoacetates (BCs) as new type of promising broad-spectrum antibacterial candidates were discovered with large potential to combat the lethal multidrug-resistant bacterial infections. Many prepared BCs showed broad antibacterial spectrum with low MIC values against the tested strains. Some highly active BCs exhibited rapid sterilization capacity, low resistant trend and good predictive pharmacokinetic properties. Furthermore, the highly active sodium BCs (NaBCs) displayed low hemolysis and cytotoxicity, and especially octyl NaBC 5g also showed in vivo potent anti-infective potential and appreciable pharmacokinetic profiles. A series of preliminary mechanistic explorations indicated that these active BCs could effectively eliminate bacterial biofilm and destroy membrane integrity, thus resulting in the leakage of bacterial cytoplasm. Moreover, their unique structures might further bind to intracellular DNA, DNA gyrase and topoisomerase IV through various direct noncovalent interactions to hinder bacterial reproduction. Meanwhile, the active BCs also induced bacterial oxidative stress and metabolic disturbance, thereby accelerating bacterial apoptosis. These results provided a bright hope for benzopyridone cyanoacetates as potential novel multitargeting broad-spectrum antibacterial candidates to conquer drug resistance.

Transfer learning enhanced graph neural network for aldehyde oxidase metabolism prediction and its experimental application

Aldehyde oxidase (AOX) is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics. AOX-mediated metabolism can result in unexpected outcomes, such as the production of toxic metabolites and high metabolic clearance, which can lead to the clinical failure of novel therapeutic agents. Computational models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism liability. In this study, we developed a novel graph neural network called AOMP for predicting AOX-mediated metabolism. AOMP integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction, while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both tasks. AOMP significantly outperformed the benchmark methods in both cross-validation and external testing. Using AOMP, we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability, which were validated through in vitro experiments. Furthermore, for the convenience of the community, we established the first online service for AOX metabolism prediction based on AOMP, which is freely available at https://aomp.alphama.com.cn.

Hepatoprotective Effects of Flavonoids against Benzo[a]Pyrene-Induced Oxidative Liver Damage along Its Metabolic Pathways

Benzo[a]pyrene (B[a]P), a highly carcinogenic polycyclic aromatic hydrocarbon primarily formed during incomplete organic matter combustion, undergoes a series of hepatic metabolic reactions once absorbed into the body. B[a]P contributes to liver damage, ranging from molecular DNA damage to the onset and progression of various diseases, including cancer. Specifically, B[a]P induces oxidative stress via reactive oxygen species generation within cells. Consequently, more research has focused on exploring the underlying mechanisms of B[a]P-induced oxidative stress and potential strategies to counter its hepatic toxicity. Flavonoids, natural compounds abundant in plants and renowned for their antioxidant properties, possess the ability to neutralize the adverse effects of free radicals effectively. Although extensive research has investigated the antioxidant effects of flavonoids, limited research has delved into their potential in regulating B[a]P metabolism to alleviate oxidative stress. This review aims to consolidate current knowledge on B[a]P-induced liver oxidative stress and examines the role of flavonoids in mitigating its toxicity.

In Vitro Metabolism and In Vivo Pharmacokinetics Profiles of Hydroxy-α-Sanshool

Hydroxy-α-sanshool (HAS) is the predominant active compound in Zanthoxylum bungeanum Maxim (ZBM). Our present work was aimed to explore the in vitro metabolism characteristics, and in vivo pharmacokinetic (PK) profile of HAS. Plasma (human), liver microsomes, and hepatocytes (human, monkey, dog, mouse, and rat) were collected for HAS metabolism studies in vitro and HAS elimination rates in liver microsomes and hepatocytes of different species were investigated. In addition, five recombinant human CYP enzymes were used to identify CYP isoforms of HAS. Finally, the PK properties of HAS in rats in vivo were studied by oral administration (p.o.). The results showed that HAS stably metabolized in human and rat liver microsomes and human hepatocytes, and the binding of HAS to human plasma proteins was nonspecific; HAS has strong inhibitory effects on CYP2C9 and CYP2D6 of human liver microsomes. In addition, in vivo PK study, HAS is rapidly absorbed in rats after oral administration. In conclusion, the in vivo and in vitro metabolic studies of HAS in this study provide data support for its further development and application, and the metabolic profiles of different species can be used as a reference for its safety evaluation.

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/.

Development and validation of an LC-MS/MS method for the determination of AZD7648 in rat plasma: Application to a pharmacokinetic study

AZD7648 is a potent DNA-PK inhibitor that is being developed for the treatment of ovarian cancer. The study aimed to develop a simple and sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to determine the concentration of AZD7648 in rat. AZD7648 was extracted from plasma by acetonitrile-mediated protein precipitation. The quantification was performed on a Thermo Vantage TSQ mass spectrometer with ibrutinib as an internal standard. A Waters Acquity UPLC BEH C18 column combined with 0.1% aqueous formic acid and acetonitrile was employed for chromatographic separation. The precursor-to-product ion transitions were m/z 421.2 > 337.2 and m/z 441.2 > 138.1 for AZD7648 and internal standard, respectively. This method was successfully validated according to the US Food and Drug Administration guidance. The calibration curve was linear over the concentration range of 0.5-1,000 ng/ml with correlation coefficient >0.999. The precision expressed as the coefficient of variation was <8.09%, while the accuracy expressed as relative error ranged from -10.00 to 9.08%. The mean recovery was >94.49%. AZD7648 was stable in rat plasma after storage under certain conditions. The validated method was demonstrated to be selective, sensitive and reliable, and has been successfully applied to the pharmacokinetic study of AZD7648 in rat plasma after oral and intravenous administration (1 mg/kg).

A Novel Milli-fluidic Liver Tissue Chip with Continuous Recirculation for Predictive Pharmacokinetics Applications

A crucial step in lead selection during drug development is accurate estimation and optimization of hepatic clearance using in vitro methods. However, current methods are limited by factors such as lack of physiological relevance, short culture/incubation times that are not consistent with drug exposure patterns in patients, use of drug absorbing materials, and evaporation during long-term incubation. To address these technological needs, we developed a novel milli-fluidic human liver tissue chip (LTC) that was designed with continuous media recirculation and optimized for hepatic cultures using human primary hepatocytes. Here, we characterized the LTC using a series of physiologically relevant metrics and test compounds to demonstrate that we could accurately predict the PK of both low- and high-clearance compounds. The non-biological characterization indicated that the cyclic olefin copolymer (COC)-based LTC exhibited negligible evaporation and minimal non-specific binding of drugs of varying ionic states and lipophilicity. Biologically, the LTC exhibited functional and polarized hepatic culture with sustained metabolic CYP activity for at least 15 days. This long-term culture was then used for drug clearance studies for low- and high-clearance compounds for at least 12 days, and clearance was estimated for a range of compounds with high in vitro-in vivo correlation (IVIVC). We also demonstrated that LTC can be induced by rifampicin, and the culture age had insignificant effect on depletion kinetic and predicted clearance value. Thus, we used advances in bioengineering to develop a novel purpose-built platform with high reproducibility and minimal variability to address unmet needs for PK applications.

Development and Validation of a UHPLC-MS/MS Method for the Quantification of a Novel PYGB Inhibitor in Plasma: Application to Pharmacokinetic Studies

In previous studies, we reported compound 1 (5-chloro-N-(4-oxo-2,2-dipropyl-3,4-dihydro-2H-benzo[e][1,3]oxazin-6-yl)-1H-indole-2-carboxamide) as a novel PYGB inhibitor, and found that it had better anti-ischemic brain injury activity. In this study, we established and validated a novel UHPLC-MS/MS method for the quantitative determination of compound 1 in plasma, then applied the method to study the pharmacokinetic parameters and brain tissue distribution of compound 1 in SD (Sprague-Dawley) rats after intravenous administration. The experimental results showed that the method met the validation requirements set by the US FDA in terms of linearity, accuracy, precision, and stability. The validated method was then used for pharmacokinetic studies in rat plasma, and it was found that compound 1 exhibited linear pharmacokinetic characteristics when administered in the dose range of 0.8-3.2 mg/kg. Finally, we also conducted a brief preliminary investigation of the brain tissue distribution of compound 1 in rats after injection and found that the brain tissue concentrations at 0.25 h and 2 h of administration were 440 ± 19.1 ng/kg and 111 ± 23.9 ng/kg, respectively. Additionally, the CBrain/CPlasma ratio was 0.112 ± 0.0185 and 0.112 ± 0.0292, respectively. These results indicated that compound 1 was able to cross the blood-brain barrier. This study provides important support for the application of compound 1 in ischemic brain injury diseases.

Non-clinical pharmacokinetics study in rat plasma, tissues and excreta of honokiol derivative HM475 by UPLC-Q-TOF-MS/MS

To provide the basis for further development and research of drugs, non-clinical pharmacokinetics studies were conducted on HM475, which is composed of natural active molecules honokiol and metformin through cyclization. In this paper, HM475 was studied from six aspects by gavage and intraperitoneal injection: 1) Acute toxicity of HM475 in rats, 2) Pharmacokinetic characteristics of HM475 in rats, 3) Distribution characteristics of HM475 in heart, liver, spleen, lung, and kidney, small intestine, fat and brain of rats, 4) Main metabolic pathways of HM475 in rats, 5) Excretion of HM475 in rats, 6) Determination of protein binding rate of HM475 in bovine plasma, rabbit plasma, and rat plasma. Acute toxicity of HM475 on SD rats was evaluated by maximum dose method. The metabolic analysis method of HM475 in rats was first established by UPLC-Q-TOF-MS/MS technology, and the pharmacokinetic characteristics of oral administration and intraperitoneal injection were studied. Experimental results showed that HM475 had no obvious acute toxicity. The absolute oral bioavailability of HM475 was 38.45 %, and the drug concentration in plasma was higher than that in tissues. Combined with the process characteristics of HM475 in vivo, it is inferred that HM475 has enterohepatic circulation. In this study, non-clinical pharmacokinetics were systematically studied to provide data support for the clinical pharmacokinetics and pharmacodynamics of HM475, to more accurately predict the pharmacokinetic behavior of HM475 in human body and provide scientific data for the compound to enter clinical research.

Recent advances in measurement of metabolic clearance, metabolite profile and reaction phenotyping of low clearance compounds

INTRODUCTION

Low metabolic clearance is usually a highly desirable property of drug candidates in order to reduce dose and dosing frequency. However, measurement of low clearance can be challenging in drug discovery. A number of new tools have recently been developed to address the gaps in the measurement of intrinsic clearance, identification of metabolites, and reaction phenotyping of low clearance compounds.

AREAS COVERED

The new methodologies of low clearance measurements are discussed, including the hepatocyte relay, HepatoPac®, HμREL®, and spheroid systems. In addition, metabolite formation rate determination and in vivo allometric scaling approaches are covered as alternative methods for low clearance measurements. With these new methods, measurement of ~ 20-fold lower limit of intrinsic clearance can be achieved. The advantages and limitations of each approach are highlighted.

EXPERT OPINION

Although several novel methods have been developed in recent years to address the challenges of low clearance, these assays tend to be time and labor intensive and costly. Future innovations focusing on developing systems with high enzymatic activities, ultra-sensitive universal quantifiable detectors, and artificial intelligence will further enhance our ability to explore the low clearance space.

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.

Metabolite Identification of Isopropoxy Benzene Guanidine in Rat Liver Microsomes by Using UHPLC-Q-TOF-MS/MS

Isopropoxy benzene guanidine (IBG) is a guanidine derivative with antibacterial activity against multidrug-resistant bacteria. A few studies have revealed the metabolism of IBG in animals. The aim of the current study was to identify potential metabolic pathways and metabolites of IBG. The detection and characterization of metabolites were performed with high-performance liquid chromatography tandem mass spectrometry (UHPLC-Q-TOF-MS/MS). Seven metabolites were identified from the microsomal incubated samples by using the UHPLC-Q-TOF-MS/MS system. The metabolic pathways of IBG in the rat liver microsomes involved O-dealkylation, oxygenation, cyclization, and hydrolysis. Hydroxylation was the main metabolic pathway of IBG in the liver microsomes. This research investigated the in vitro metabolism of IBG to provide a basis for the further pharmacology and toxicology of this compound.

Herb-drug interaction of Xingnaojing injection and Edaravone via pharmacokinetics, mixed inhibition of UGTs, and molecular docking

BACKGROUND

Xingnaojing injection (XNJ) is a famous emergency Traditional Chinese medicine (TCM) derived from the classical Chinese prescription named An-Gong-Niu-Huang Pill. XNJ is often used along with Edaravone injection (EDA) to treat acute ischemic stroke, they have a synergistic effect in improving patients' blood coagulation and neurological function. However, this combination also causes herb-drug interactions (HDIs), raising the risk of adverse reactions. At present, little is known about the pharmacokinetics and potential mechanism of XNJ combined with EDA.

PURPOSE

This study investigates the pharmacokinetics and potential mechanism of the HDIs between XNJ and EDA.

STUDY DESIGN AND METHODS

The pharmacokinetic interactions between XNJ and EDA were studied by GC-MS in rats, and the inhibition of XNJ and (-)-borneol on UDP-glucuronosyltransferase (UGTs) were assayed by LC-MS/MS in vitro. In vitro-in vivo extrapolation (IVIVE) and molecular docking were performed to reveal the potential for HDIs.

RESULTS

The AUC_0-∞ of (-)-borneol was increased by 1.25-fold in group EDA+XNJ 10 min later, and the C_max of edaravone was increased by 1.6-fold in group XNJ+EDA 10 min later (p < 0.05). XNJ and (-)-borneol inhibited UGTs-mediated edaravone metabolism in HLM and RLM with a similar inhibitory intensity, in which both of them have stronger inhibition in RLM. These findings demonstrated that (-)-borneol in XNJ mainly exerted UGTs inhibition, which was consistent with the pharmacokinetic assays. (-)-Borneol moderately inhibited UGT2B7 and UGT1A6 by a mixed inhibition mechanism, with K_i values of 101.393 and 136.217 μM, respectively. Due to the blood concentration of injection was dramatically increased, the HDIs caused by the inhibitory effect of XNJ on UGTs should be highly emphasized. The binding energies of (-)-borneol and edaravone toward UGT2B7 were -6.254 and -6.643 kcal/mol, and the scores towards UGT1A6 were -5.220 and -6.469 kcal/mol, respectively. Moreover, (-)-borneol has similar free energies to many drugs metabolized by UGT2B7 and UGT1A6.

CONCLUSIONS

(-)-Borneol modulates the pharmacokinetic behavior of edaravone via mixed inhibition of UGT2B7 and UGT1A6. It provides a theoretical basis for the synergistic effect of XNJ and EDA combinations in clinical practice. When XNJ is used along with UGT2B7 and UGT1A6 substrates, it should be used clinically with caution.

Recent advances in the in vitro and in vivo methods to assess impact of P-glycoprotein and breast cancer resistance protein transporters in central nervous system drug disposition

One challenge in central nervous system (CNS) drug discovery has been ensuring the blood-brain barrier (BBB) penetration of compounds at an efficacious concentration that provides suitable safety margins for clinical investigation. Research providing for the accurate prediction of brain penetration of compounds during preclinical discovery is important to a CNS program. In the BBB, P-glycoprotein (P-gp) (ABCB1) and breast cancer resistance protein (BCRP) (ABCG2) transporters have been demonstrated to play a major role in the active efflux of endogenous compounds and xenobiotics out of the brain microvessel cells and back to the systemic circulation. In the past 10 years, there has been significant technological improvement in the sensitivity of quantitative proteomics methods, in vivo imaging, in vitro methods of organoid and microphysiological systems, as well as in silico quantitative physiological based pharmacokinetic and systems pharmacology models. Scientists continually leverage these advancements to interrogate the distribution of compounds in the CNS which may also show signals of substrate specificity of P-gp and/or BCRP. These methods have shown promise toward predicting and quantifying the unbound concentration(s) within the brain relevant for efficacy or safety. In this review, the authors have summarized the in vivo, in vitro, and proteomics advancements toward understanding the contribution of P-gp and/or BCRP in restricting the entry of compounds to the CNS of either healthy or special populations. Special emphasis has been provided on recent investigations on the application of a proteomics-informed approach to predict steady-state drug concentrations in the brain. Moreover, future perspectives regarding the role of these transporters in newer modalities are discussed.

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.

Open access databases available for the pesticide lead discovery

Pesticide research is a multi-disciplinary collaborative study, and big data analysis based on integrating information from databases benefits decision-making in pesticide research. In the last 40 years, dozens of pesticide-related databases have been built up to describe their biological activities, toxicity, modes of action, and environmental risks, etc. However, these data are scattered and overlapping in different databases in multiple inconsistent formats, which is not convenient for information analysis and comparison. In this study, the content of 26 open access databases related to pesticide research was illustrated according to the information provided for the ligand-based drug design (LBDD) and receptor-based (or structure-based drug design, SBDD), and was summarized into three categories:1) the correspondence between the chemical structures and functional properties (biological activity, resistance, toxicity, environmental adaptation); 2) action mode study (target identification, target structures, and biological pathways); 3) computational servers for pesticide design. To our knowledge, this is the first review about the open access databases for pesticide research. The data classification could facilitate the information accessibility for pesticide research, and speed up the decision-making process in pesticide discovery.

DrugMAP: molecular atlas and pharma-information of all drugs

The efficacy and safety of drugs are widely known to be determined by their interactions with multiple molecules of pharmacological importance, and it is therefore essential to systematically depict the molecular atlas and pharma-information of studied drugs. However, our understanding of such information is neither comprehensive nor precise, which necessitates the construction of a new database providing a network containing a large number of drugs and their interacting molecules. Here, a new database describing the molecular atlas and pharma-information of drugs (DrugMAP) was therefore constructed. It provides a comprehensive list of interacting molecules for >30 000 drugs/drug candidates, gives the differential expression patterns for >5000 interacting molecules among different disease sites, ADME (absorption, distribution, metabolism and excretion)-relevant organs and physiological tissues, and weaves a comprehensive and precise network containing >200 000 interactions among drugs and molecules. With the great efforts made to clarify the complex mechanism underlying drug pharmacokinetics and pharmacodynamics and rapidly emerging interests in artificial intelligence (AI)-based network analyses, DrugMAP is expected to become an indispensable supplement to existing databases to facilitate drug discovery. It is now fully and freely accessible at: https://idrblab.org/drugmap/.

In silico models to predict tubular secretion or reabsorption clearance pathway using physicochemical properties and structural characteristics

1. Renal clearance is one of the main pathways for a drug to be cleared from plasma. The aim of this study is to develop in-silico models to find out the relationship between the type of renal clearance, and structural parameters.2. Literature data were used to categorize the drugs into those that undergo tubular secretion and those that undergo reabsorption. Different structural descriptors (VolSurf descriptors, Abraham solvation parameters, data warrior descriptors, logarithm of distribution coefficient at pH =7.4 (logD7.4)) were applied to develop a mechanistic model for estimating renal clearance class whether its secretion or reabsorption.3. The results of this study show that logD7.4, and the number of hydrogen bond donors as well as available uncharged species (AUS7.4) are the most effective descriptors to establish mechanistic models for predicting renal clearance class. The classification models were established with a level of accuracy of more than 75%.4. Developed models of this study can be helpful to predict renal clearance class for new drug candidates with an acceptable error. Hydrophilicity and hydrogen bond formation ability of drugs are among the main descriptors.