Present and future in vitro approaches for drug metabolism

In silico ADME/tox comes of age: twenty years later

In the early 2000s pharmaceutical drug discovery was beginning to use computational approaches for absorption, distribution, metabolism, excretion and toxicity (ADME/Tox, also known as ADMET) prediction. This emphasis on prediction was an effort to reduce the risk of later stage failures from ADME/Tox.Much has been written in the intervening twenty plus years and significant expenditure has occurred in companies developing these in silico capabilities which can be gleaned from publications. It is therefore an appropriate time to briefly reflect on what was proposed then and what the reality is today.20 years ago, we tended to optimise bioactivity and perhaps one ADME/Tox property at a time. Previously pharmaceutical companies needed a whole infrastructure for models - in silico and in vitro experts, IT, champions on a project team, educators and management support. Now we are in the age of generative de novo design where bioactivity and many ADME/Tox properties can be optimised and large language model technologies are available.There are also some challenges such as the focus on very large molecules which may be outside of current ADME/Tox models.We provide an opportunity to look forward with the increasing public data for ADME/Tox as well as expanded types of algorithms available.

Antifungal activity of eumelanin-inspired indoylenepheyleneethynylene against Cryptococcus neoformans

Cryptococcus neoformans is an opportunistic fungal pathogen that causes meningitis in >152,000 immunocompromised individuals annually, leading to 112,000 yearly deaths. The four classes of existing antifungal agents target plasma membrane sterols (ergosterol), nucleic acid synthesis, and cell wall synthesis. Existing drugs are not highly effective against Cryptococcus, and antifungal drug resistance is an increasing problem. A novel antimicrobial compound, a eumelanin-inspired indoylenepheyleneethynylene, EIPE-1, was synthesized and has antimicrobial activity against Gram-positive bacteria, including methicillin-resistant Staphylococcus aureus (MSRA), but not towards Gram-negative organisms. Based on EIPE-1's antibacterial activity, we hypothesized that EIPE-1 could have antifungal activity. For these studies, we tested EIPE-1 against C. neoformans strain H99 and 6 additional cryptococcal clinical isolates. We examined antifungal activity, cytotoxicity, effects on fungal gene expression, and mechanism of action of EIPE-1. Results showed that EIPE-1 has fungicidal effects on seven cryptococcal strains with MICs ranging from 1.56 to 3.125 μg/mL depending on the strain, and it is non-toxic to mammalian cells. We conducted scanning and transmission electron microscopy on the exposed cells to examine structural changes to the organism following EIPE-1 treatment. Cells exposed displayed structural changes to their cell wall and membranes, with internal contents leaking out of the cells. To understand the effect of EIPE-1 on fungal gene expression, RNA sequencing was conducted. Results showed that EIPE-1 affects several processes involved stress response, ergosterol biosynthesis, capsule biosynthesis, and cell wall attachment and remodeling. Therefore, our studies demonstrate that EIPE-1 has antifungal activity against C. neoformans, which affects both cellular structure and gene expression of multiple fungal pathways involved in cell membrane stability and viability.

Safety Aspects of Herb Interactions: Current Understanding and Future Prospects

BACKGROUND

The use of herbal medicines is on the rise throughout the world due to their perceived safety profile. However, incidences of herb-drug, herb-herb and herb-food interactions considering safety aspects have opened new arenas for discussion.

OBJECTIVE

The current study aims to provide comprehensive insights into the various types of herb interactions, the mechanisms involved, their assessment, and historical developments, keeping herbal safety at the central point of discussion.

METHODS

The authors undertook a focused/targeted literature review and collected data from various databases, including Science Direct, Wiley Online Library, Springer, PubMed, and Google Scholar. Conventional literature on herbal remedies, such as those by the WHO and other international or national organizations.

RESULTS

The article considered reviewing the regulations, interaction mechanisms, and detection of herb-herb, herb-drug and herb-food interactions in commonly used yet vital plants, including Glycyrrhiza glabra, Mentha piperita, Aloe barbadensis, Zingiber officinale, Gingko biloba, Withania somnifera, etc. The study found that healthcare professionals worry about patients not informing them about their herbal prescriptions (primarily used with conventional treatment), which can cause herb-drug/herb-food/herb-herb interactions. These interactions were caused by altered pharmacodynamic and pharmacokinetic processes, which might be explained using in-vivo, in-vitro, in-silico, pharmacogenomics, and pharmacogenetics. Nutrivigilance may be the greatest method to monitor herb-food interactions, but its adoption is limited worldwide.

CONCLUSION

This article can serve as a lead for clinicians, guiding them regarding herb-drug, herb-food, and herb-herb interactions induced by commonly consumed plant species. Patients may also be counseled to avoid conventional drugs, botanicals, and foods with a restricted therapeutic window.

Effects of bergapten on the pharmacokinetics of macitentan in rats both in vitro and in vivo

Macitentan was approved by the United States Food and Drug Administration (FDA) in 2013 for the treatment of pulmonary arterial hypertension (PAH). Bergapten is a furanocoumarin that is abundant in Umbelliferae and Rutaceae plants and is widely used in many Chinese medicine prescriptions. Considering the possible combination of these two compounds, this study is aimed to investigate the effects of bergapten on the pharmacokinetics of macitentan both in vitro and in vivo. Rat liver microsomes (RLMs), human liver microsomes (HLMs), and recombinant human CYP3A4 (rCYP3A4) were used to investigate the inhibitory effects and mechanisms of bergapten on macitentan in vitro. In addition, pharmacokinetic parameters were also studied in vivo. Rats were randomly divided into two groups (six rats per group), with or without bergapten (10 mg/kg), and pretreated for 7 days. An oral dose of 20 mg/kg macitentan was administered to each group 30 min after bergapten or 0.5% CMC-Na administration on day 7. Blood was collected from the tail veins, and the plasma concentrations of macitentan and its metabolites were assessed by ultra-performance liquid chromatography - tandem mass spectrometer (UPLC-MS/MS). Finally, we analyzed the binding force of the enzyme and two small ligands by in silico molecular docking to verify the inhibitory effects of bergapten on macitentan. The in vitro results revealed that the IC₅₀ values for RLMs, HLMs, and rCYP3A4 were 3.84, 17.82 and 12.81 μM, respectively. In vivo pharmacokinetic experiments showed that the AUC_(0-t), AUC_(0-∞), and C_max of macitentan in the experimental group (20,263.67 μg/L*h, 20,378.31 μg/L*h and 2,999.69 μg/L, respectively) increased significantly compared with the control group (7,873.97 μg/L*h, 7,897.83 μg/L*h and 1,339.44 μg/L, respectively), while the CL_z/F (1.07 L/h/kg) of macitentan and the metabolite-parent ratio (MR) displayed a significant decrease. Bergapten competitively inhibited macitentan metabolism in vitro and altered its pharmacokinetic characteristics in vivo. Further molecular docking analysis was also consistent with the experimental results. This study provides a reference for the combined use of bergapten and macitentan in clinical practice.

Machine Learning Models Identify New Inhibitors for Human OATP1B1

The uptake transporter OATP1B1 (SLC01B1) is largely localized to the sinusoidal membrane of hepatocytes and is a known victim of unwanted drug-drug interactions. Computational models are useful for identifying potential substrates and/or inhibitors of clinically relevant transporters. Our goal was to generate OATP1B1 in vitro inhibition data for [3H] estrone-3-sulfate (E3S) transport in CHO cells and use it to build machine learning models to facilitate a comparison of seven different classification models (Deep learning, Adaboosted decision trees, Bernoulli naïve bayes, k-nearest neighbors (knn), random forest, support vector classifier (SVC), logistic regression (lreg), and XGBoost (xgb)] using ECFP6 fingerprints to perform 5-fold, nested cross validation. In addition, we compared models using 3D pharmacophores, simple chemical descriptors alone or plus ECFP6, as well as ECFP4 and ECFP8 fingerprints. Several machine learning algorithms (SVC, lreg, xgb, and knn) had excellent nested cross validation statistics, particularly for accuracy, AUC, and specificity. An external test set containing 207 unique compounds not in the training set demonstrated that at every threshold SVC outperformed the other algorithms based on a rank normalized score. A prospective validation test set was chosen using prediction scores from the SVC models with ECFP fingerprints and were tested in vitro with 15 of 19 compounds (84% accuracy) predicted as active (≥20% inhibition) showed inhibition. Of these compounds, six (abamectin, asiaticoside, berbamine, doramectin, mobocertinib, and umbralisib) appear to be novel inhibitors of OATP1B1 not previously reported. These validated machine learning models can now be used to make predictions for drug-drug interactions for human OATP1B1 alongside other machine learning models for important drug transporters in our MegaTrans software.

Advances in the study of liver microsomes in the in vitro metabolism and toxicity evaluation of foodborne contaminants

Foodborne contaminants are closely related to anthropologic activities and represent an important food safety hazard. The study of metabolic transformation and toxic side effects of foodborne contaminants in the body is important for their safety assessment. Liver microsomes contain a variety of enzymes related to substance metabolism and biotransformation. An in vitro model simulating liver metabolic transformation is associated with a significant advantage in the study of the metabolic transformation mechanisms of contaminants. This review summarizes the recent progress in the application of liver microsomes in metabolic transformation and toxicity evaluation of various foodborne pollutants based on metabolic kinetics, molecular docking and enzyme inhibition studies. The purpose of this review is to distinguish the existing studies involving liver microsomes and provide strategies for their application in the future. Finally, the prospects and challenges of the liver microsomal model are discussed.

Development of a biomarker to monitor target engagement after treatment with dihydroorotate dehydrogenase inhibitors

Dihydroorotate dehydrogenase (DHODH) catalyzes a key step in pyrimidine biosynthesis and has recently been validated as a therapeutic target for malaria through clinical studies on the triazolopyrimidine-based Plasmodium DHODH inhibitor DSM265. Selective toxicity towards Plasmodium species could be achieved because malaria parasites lack pyrimidine salvage pathways, and DSM265 selectively inhibits Plasmodium DHODH over the human enzyme. However, while DSM265 does not inhibit human DHODH, it inhibits DHODH from several preclinical species, including mice, suggesting that toxicity could result from on-target DHODH inhibition in those species. We describe here the use of dihydroorotate (DHO) as a biomarker of DHODH inhibition. Treatment of mammalian cells with DSM265 or the mammalian DHODH inhibitor teriflunomide led to increases in DHO where the extent of biomarker buildup correlated with both dose and inhibitor potency on DHODH. Treatment of mice with leflunomide (teriflunomide prodrug) caused a large dose-dependent buildup of DHO in blood (up to 16-fold) and urine (up to 5,400-fold) that was not observed for mice treated with DSM265. Unbound plasma teriflunomide levels reached 20-85-fold above the mouse DHODH IC50, while free DSM265 levels were only 1.6-4.2-fold above, barely achieving ∼ IC90 concentrations, suggesting that unbound DSM265 plasma levels are not sufficient to block the pathway in vivo. Thus, any toxicity associated with DSM265 treatment in mice is likely caused by off-target mechanisms. The identification of a robust biomarker for mammalian DHODH inhibition represents an important advance to generally monitor for on-target effects in preclinical and clinical applications of DHODH inhibitors used to treat human disease.

Human Sulfotransferase Assays With PAPS Production in situ

For in vitro investigations on human sulfotransferase (SULT) catalyzed phase II metabolism, the costly cofactor 3′-phosphoadenosine-5′-phosphosulfate (PAPS) is generally needed. In the present study, we developed and optimized a new approach that combines SULT-dependent biotransformation using recombinant and permeabilized fission yeast cells (enzyme bags) with PAPS production in situ applying quality by design principles. In the initial application of the procedure, yeast cells expressing human SULT1A3 were used for the production of 4′-hydroxypropranolol-4-O-sulfate from 4-hydroxypropranolol. The optimized protocol was then successfully transferred to other sulfonation reactions catalyzed by SULT2A1, SULT1E1, or SULT1B1. The concomitant degradation of some sulfoconjugates was investigated, and further optimization of the reaction conditions was performed in order to reduce product loss. Also, the production of stable isotope labelled sulfoconjugates was demonstrated utilizing isotopically labelled substrates or ³⁴S-sulfate. Overall, this new approach results in higher space-time yields while at the same time reducing experimental cost.

Antiparasitic potential of ethanolic extracts of Carpesii Fructus against Miamiensis avidus in hirame natural embryo cell line and their effects on immune response- and biotransformation-related genes

Scuticociliatosis, caused by Miamiensis avidus, is a severe parasitic disease affecting marine organisms, particularly Paralichthys olivaceus. The aim of this study was to assess the antiparasitic potential of ethanolic extracts of Carpesii Fructus (EECF), the dried fruit of Carpesium abrotanoides L., which is used in traditional Chinese medicine, in vitro. We found that 50%, 70%, and 100% EECF induced morphological changes in M. avidus, including reduced motility, cell shrinkage, and lysis. Nearly 100% cell lysis was observed in M. avidus after 2 h of treating with 100% EECF. After 24 h, the survival rates of M. avidus treated with 100%, 70%, and 50% EECF were 10%, 20%, and 30%, respectively. Additionally, the mRNA levels of immune response-related (IL-1β, IL-8, TNF-α, and CD8-α) and biotransformation-related (CYP1A, CYP1B, CYP3A4, and UGT2B19) genes increased with 70% and 100% EECF treatment and decreased with 50% EECF treatment following pretreatment with concanavalin A. The viability of hirame natural embryo (HINAE) cells was reduced by 50%, 70%, and 100% EECF (100 mg/L) and was between 67 and 80%. The IC₅₀ values of 50%, 70%, 90%, and 100% EECF in HINAE cells were 102.3, 42.93, 39.15, and 38.39 mg/L, respectively. These results indicated that 50% EECF was less toxic to HINAE cells than 70% or 100% EECF, while still exhibiting antiparasitic activity against M. avidus. Therefore, we demonstrated the role of EECF as a natural antiparasitic agent against M. avidus. Our findings suggest that Carpesii Fructus has potential use as an antiparasitic agent in the aquaculture industry.

Controlled Drug Delivery Systems: Current Status and Future Directions

The drug delivery system enables the release of the active pharmaceutical ingredient to achieve a desired therapeutic response. Conventional drug delivery systems (tablets, capsules, syrups, ointments, etc.) suffer from poor bioavailability and fluctuations in plasma drug level and are unable to achieve sustained release. Without an efficient delivery mechanism, the whole therapeutic process can be rendered useless. Moreover, the drug has to be delivered at a specified controlled rate and at the target site as precisely as possible to achieve maximum efficacy and safety. Controlled drug delivery systems are developed to combat the problems associated with conventional drug delivery. There has been a tremendous evolution in controlled drug delivery systems from the past two decades ranging from macro scale and nano scale to intelligent targeted delivery. The initial part of this review provides a basic understanding of drug delivery systems with an emphasis on the pharmacokinetics of the drug. It also discusses the conventional drug delivery systems and their limitations. Further, controlled drug delivery systems are discussed in detail with the design considerations, classifications and drawings. In addition, nano-drug delivery, targeted and smart drug delivery using stimuli-responsive and intelligent biomaterials is discussed with recent key findings. The paper concludes with the challenges faced and future directions in controlled drug delivery.

Determination of the Absolute Configuration of Bioactive Indole-Containing Pyrazino[2,1-b]quinazoline-3,6-diones and Study of Their In Vitro Metabolic Profile

In recent decades, fungi-derived naturally occurring quinazolines have emerged as potential drug candidates. Nevertheless, most studies are conducted for bioactivity assays, and little is known about their absorption, distribution, metabolism, and elimination (ADME) properties. To perform metabolic studies, the synthesis of the naturally occurring quinazolinone, fiscalin B (1), and its chloro derivative, 4-((1H-indol-3-yl)methyl)-8,10-dichloro-1-isobutyl-1,2-dihydro-6H-pyrazino[2,1-b]quinazoline-3,6(4H)-dione (2), disclosed as an antibacterial agent, was performed in a gram scale using a microwave-assisted polycondensation reaction with 22% and 17% yields, respectively. The structure of the non-natural (+)-fiscalin B was established, for the first time, by X-ray crystallography as (1R,4S)-1, and the absolute configuration of the naturally occurring fiscalin B (-)-1 was confirmed by comparison of its calculated and experimental electronic circular dichroism (ECD) spectra as (1S,4R)-1. in vitro metabolic studies were monitored for this class of natural products for the first time by ultra-high-performance liquid chromatography (UHPLC) coupled with high-resolution mass spectrometry (HRMS). The metabolic characteristics of 1 and 2 in human liver microsomes indicated hydration and hydroxylation mass changes introduced to the parent drugs.

Biomimetic Chromatographic Studies Combined with the Computational Approach to Investigate the Ability of Triterpenoid Saponins of Plant Origin to Cross the Blood-Brain Barrier

Biomimetic (non-cell based in vitro) and computational (in silico) studies are commonly used as screening tests in laboratory practice in the first stages of an experiment on biologically active compounds (potential drugs) and constitute an important step in the research on the drug design process. The main aim of this study was to evaluate the ability of triterpenoid saponins of plant origin to cross the blood-brain barrier (BBB) using both computational methods, including QSAR methodology, and biomimetic chromatographic methods, i.e., High Performance Liquid Chromatography (HPLC) with Immobilized Artificial Membrane (IAM) and cholesterol (CHOL) stationary phases, as well as Bio-partitioning Micellar Chromatography (BMC). The tested compounds were as follows: arjunic acid (Terminalia arjuna), akebia saponin D (Akebia quinata), bacoside A (Bacopa monnieri) and platycodin D (Platycodon grandiflorum). The pharmacokinetic BBB parameters calculated in silico show that three of the four substances, i.e., arjunic acid, akebia saponin D, and bacoside A exhibit similar values of brain/plasma equilibration rate expressed as logPSFubrain (the average logPSFubrain: -5.03), whereas the logPSFubrain value for platycodin D is -9.0. Platycodin D also shows the highest value of the unbound fraction in the brain obtained using the examined compounds (0.98). In these studies, it was found out for the first time that the logarithm of the analyte-micelle association constant (logKMA) calculated based on Foley's equation can describe the passage of substances through the BBB. The most similar logBB values were obtained for hydrophilic platycodin D, applying both biomimetic and computational methods. All of the obtained logBB values and physicochemical parameters of the molecule indicate that platycodin D does not cross the BBB (the average logBB: -1.681), even though the in silico estimated value of the fraction unbound in plasma is relatively high (0.52). As far as it is known, this is the first paper that shows the applicability of biomimetic chromatographic methods in predicting the penetration of triterpenoid saponins through the BBB.

Identifying metabolites of diphenidol by liquid chromatography-quadrupole/orbitrap mass spectrometry using rat liver microsomes, human blood, and urine samples

In recent years, diphenidol [1,1-diphenyl-4-piperidino-1-butanol] has been one of the drugs that appears in suicide cases, but there are few research data on its metabolic pathways and main metabolites. Metabolite identification plays a key role in drug safety assessment and clinical application. In this study, in vivo and in vitro samples were analyzed with ultra-high-performance liquid chromatography-quadrupole/electrostatic field orbitrap high-resolution mass spectrometry. Structural elucidation of the metabolites was performed by comparing their molecular weights and product ions with those of the parent drug. As a result, 10 Phase I metabolites and 5 glucuronated Phase II metabolites were found in a blood sample and a urine sample from authentic cases. Three other Phase I metabolites were identified in the rat liver microsomes incubation solution. The results showed that the main metabolic pathways of diphenidol in the human body include hydroxylation, oxidation, dehydration, N-dealkylation, methylation, and conjugation with glucuronic acid. This study preliminarily clarified the metabolic pathways and main metabolites of diphenidol. For the development of new methods for the identification of diphenidol consumption, we recommend using M2-2 as a marker of diphenidol entering the body. The results of this study provide a theoretical basis for the pharmacokinetics and forensic scientific research of diphenidol.

Digital Microfluidics-Enabled Analysis of Individual Variation in Liver Cytochrome P450 Activity

The superfamily of hepatic cytochrome P450 (CYP) enzymes is responsible for the intrinsic clearance of the majority of therapeutic drugs in humans. However, the kinetics of drug clearance via CYPs varies significantly among individuals due to both genetic and external factors, and the enzyme amount and function are also largely impacted by many liver diseases. In this study, we developed a new methodology, based on digital microfluidics (DMF), for rapid determination of individual alterations in CYP activity from human-derived liver samples in biopsy-scale. The assay employs human liver microsomes (HLMs), immobilized on magnetic beads to facilitate determination of the activity of microsomal CYP enzymes in a parallelized system at physiological temperature. The thermal control is achieved with the help of a custom-designed, inkjet-printed microheater array modularly integrated with the DMF platform. The CYP activities are determined with the help of prefluorescent, enzyme-selective model compounds by quantifying the respective fluorescent metabolites based on optical readout in situ. The selectivity and sensitivity of the assay was established for four different CYP model reactions, and the diagnostic concept was validated by determining the interindividual variation in one of the four model reaction activities, that is, ethoxyresorufin O-deethylation (CYP1A1/2), between five donors. Overall, the developed protocol consumes only about 15 μg microsomal protein per assay. It is thus technically adaptable to screening of individual differences in CYP enzyme function from biopsy-scale liver samples in an automated fashion, so as to support tailoring of medical therapies, for example, in the context of liver disease diagnosis.

Electrochemical microreactor combined with mass spectrometry for online oxidation and real-time detection of alkaloids

The main purpose of the present study was to investigate the prototypes and oxidation products of alkaloids with the use of an online electrochemistry/quadrupole time-of-flight mass spectrometry system. The metabolism of oxidative phase I and II was simulated in an electrochemical reaction cell. The metabolic processes for coptisine and jatrorrhizine were simulated in a thin-layer cell fitted with a glassy carbon working electrode, while the metabolic processes for berberine and palmatine were simulated by using a boron-doped diamond working electrode. By using the new experimental system, dehydrogenation, demethylation, methylation, hydroxylation, and the formation of two hydroxylation adducts were detected by applying different potentials to the electrochemical cell. The online reaction with glutathione yielded different covalent glutathione adducts. The results obtained from the electrochemical simulation were found to be in good accordance with those reported previously in vivo, showing that electrochemistry/mass spectrometry is an effective tool for studying metabolic reactions for various complex components. Moreover, analysis of alkaloids in liver microsomes by liquid chromatography coupled with mass spectrometry confirmed the possibility of using an electrochemistry technique to simulate the metabolism of target compounds.

A comparative study on the in vitro biotransformation of medicagenic acid using human liver microsomes and S9 fractions

Many natural products are prodrugs which are biotransformed and activated after oral administration. The investigation of gastrointestinal and hepatic biotransformation can be facilitated by in vitro screening methods. This study compares two widely used in vitro models for hepatic biotransformation: 1) human S9 fractions and 2) human liver microsomes and cytosolic fractions in a two-step sequence, with the purpose of identifying differences in the biotransformation of medicagenic acid, the putative precursor of active metabolites, responsible for the medicinal effects of the herb Herniaria hirsuta. The combination of liquid chromatography coupled to high-resolution mass spectrometry with subsequent suspect and non-target data analysis allowed the identification of thirteen biotransformation products, four of which are reported here for the first time. Eight biotransformation products resulting from oxidative Phase I reactions were identified. Phase II conjugation reactions resulted in the formation of three glucuronidated and two sulfated biotransformation products. No major differences could be observed between incubations with human liver S9 or when utilizing human microsomal and cytosolic fractions. Apart from two metabolites, both methods rendered the same qualitative metabolic profile, with minor quantitative differences. As a result, both protocols applied in this study can be used to study in vitro human liver biotransformation reactions.

Investigation of the Inhibitory Effect of Simvastatin on the Metabolism of Lidocaine Both in vitro and in vivo

Background

Lidocaine has cardiovascular and neurologic toxicity, which is dose-dependent. Due to CYP3A4-involved metabolism, lidocaine may be prone to drug-drug interactions.

Materials and Methods

Given statins have the possibility of combination with lidocaine in the clinic, we established in vitro models to assess the effect of statins on the metabolism of lidocaine. Further pharmacokinetic alterations of lidocaine and its main metabolite, monoethylglycinexylidide in rats influenced by simvastatin, were investigated.

Results

In vitro study revealed that simvastatin, among the statins, had the most significant inhibitory effect on lidocaine metabolism with IC₅₀ of 39.31 µM, 50 µM and 15.77 µM for RLM, HLM and CYP3A4.1, respectively. Consistent with in vitro results, lidocaine concomitantly used with simvastatin in rats was associated with 1.2-fold AUC_(0-t), 1.2-fold AUC_(0-∞), and 20%-decreased clearance for lidocaine, and 1.4-fold C_max for MEGX compared with lidocaine alone.

Conclusion

Collectively, these results implied that simvastatin could evidently inhibit the metabolism of lidocaine both in vivo and in vitro. Accordingly, more attention and necessary therapeutic drug monitoring should be paid to patients with the concomitant coadministration of lidocaine and simvastatin so as to avoid unexpected toxicity.

Guidelines for the experimental design of pharmacokinetic studies with nanomaterials in preclinical animal models

A shared feature in the value proposition of every nanomaterial-based drug delivery systems is the desirable improvement in the disposition (or ADME) and pharmacokinetic profiles of the encapsulated drug being delivered. Remarkable progress has been made towards understanding the complex and multifactorial relationships between pharmacokinetic profiles and nanomaterial physicochemical properties, biological interactions, species physiology, etc. These advances have fuelled the rational design of numerous nanomaterials with long-circulation times and improved tissue accumulation (e.g., in tumours). Unfortunately, a central weakness in many of these research efforts has been the inconsistent and insufficient characterisation of the pharmacokinetic profiles of nanomaterials in scientific reporting-a problem affecting the majoirty of of contemporary nanomaterials literature and innovative nanomaterials in early stages of preclinical development especially. Given the significant role of pharmacokinetic assessments to serve as guideposts for deciding whether to continue with the preclinical development and clinical translation of drug delivery systems, the prevalence of poor pharmacokinetic characterisations in nanomaterials research is particularly alarming. A conspicuous problem in many reports is the inappropriate selection of experimental designs and methodologies for studying nanomaterial pharmacokinetics, the consequences of which are increased uncertainty over the accurate interpretation of reported pharmacokinetic data and diminished experimental reproducibility throughout the field. Thus, there is renewed interest in the establishment of consistent and comprehensive strategies for designing preclinical experiments to assess the pharmacokinetics of nanomaterials with diverse physicochemical properties. Towards this end, herein are proposed simple guidelines for the experimental design of pharmacokinetic studies with nanomaterials drawn from the best research practices, principle strategies, and important considerations used in industry for collecting pharmacokinetic data in preclinical animal models. Specifically, key experimental design factors in these studies are identified and examined in the context of nanomaterials for optimality, including blood sampling strategy and technique, sample allocation and sampling time window, test species selection, experimental sources of pharmacokinetic variability, etc. Methods for noninvasive imaging-derived pharmacokinetic assessments of theranostic nanomaterials are also explored with particular focus on emission tomography imaging modalities. Taken together, this review will provide nanomaterial researchers with practical knowledge and pragmatic recommendations for selecting the best designs and methodologies for assessing the pharmacokinetic profiles of their nanomaterials, and hopefully maximise the chances of translational success of these innovative products into humans.

Comparative Proteomics Analysis of Human Liver Microsomes and S9 Fractions

Human liver microsomes (HLM) and human liver S9 fractions (HLS9) are commonly used to study drug metabolism in vitro. However, a quantitative comparison of HLM and HLS9 proteomes is lacking, resulting in the arbitrary selection of one hepatic preparation over another and in difficulties with data interpretation. In this study, we applied a label-free global absolute quantitative proteomics method to the analysis of HLS9 and the corresponding HLM prepared from 102 individual human livers. A total of 3137 proteins were absolutely quantified, and 3087 of those were determined in both HLM and HLS9. Protein concentrations were highly correlated between the two hepatic preparations (R = 0.87, P < 0.0001). We reported the concentrations of 98 drug-metabolizing enzymes (DMEs) and 51 transporters, and demonstrated significant differences between their abundances in HLM and HLS9. We also revealed the protein-protein correlations among these DMEs and transporters and the sex effect on the HLM and HLS9 proteomes. Additionally, HLM and HLS9 displayed distinct expression patterns for protein markers of cytosol and various cellular organelles. Moreover, we evaluated the interindividual variability of three housekeeping proteins, and identified five proteins with low variation across individuals that have the potential to serve as new internal controls for western blot experiments. In summary, these results will lead to better understanding of data obtained from HLM and HLS9 and assist in in vitro-in vivo extrapolations. Knowing the differences between HLM and HLS9 also allows us to make better-informed decisions when choosing between these two hepatic preparations for in vitro drug metabolism studies. SIGNIFICANCE STATEMENT: This investigation revealed significant differences in protein concentrations of drug-metabolizing enzymes and transporters between human liver microsomes and S9 fractions. We also determined the protein-protein correlations among the drug-metabolizing enzymes and transporters and the sex effect on the proteomes of these two hepatic preparations. The results will help interpret data obtained from these two preparations and allow us to make more informed decisions when choosing between human liver microsomes and S9 fractions for in vitro drug metabolism studies.

Molecular Dynamics Simulation Framework to Probe the Binding Hypothesis of CYP3A4 Inhibitors

The Cytochrome P450 family of heme-containing proteins plays a major role in catalyzing phase I metabolic reactions, and the CYP3A4 subtype is responsible for the metabolism of many currently marketed drugs. Additionally, CYP3A4 has an inherent affinity for a broad spectrum of structurally diverse chemical entities, often leading to drug-drug interactions mediated by the inhibition or induction of the metabolic enzyme. The current study explores the binding of selected highly efficient CYP3A4 inhibitors by docking and molecular dynamics (MD) simulation protocols and their binding free energy calculated using the WaterSwap method. The results indicate the importance of binding pocket residues including Phe57, Arg105, Arg106, Ser119, Arg212, Phe213, Thr309, Ser312, Ala370, Arg372, Glu374, Gly481 and Leu483 for interaction with CYP3A4 inhibitors. The residue-wise decomposition of the binding free energy from the WaterSwap method revealed the importance of binding site residues Arg106 and Arg372 in the stabilization of all the selected CYP3A4-inhibitor complexes. The WaterSwap binding energies were further complemented with the MM(GB/PB)SA results and it was observed that the binding energies calculated by both methods do not differ significantly. Overall, our results could guide towards the use of multiple computational approaches to achieve a better understanding of CYP3A4 inhibition, subsequently leading to the design of highly specific and efficient new chemical entities with suitable ADMETox properties and reduced side effects.

Study of the in vitro metabolic profile of a new α2-adrenergic agonist in rat and human liver microsomes by using liquid chromatography-multiple-stage mass spectrometry and nuclear magnetic resonance

A potent synthetic α₂-adrenergic agonist called PT-31, (3-(2-chloro-6-fluorobenzyl)-imidazolidine-2,4-dione), was recently detected as a potential drug to be used as an adjuvant drug to treat chronic pain. The excellent pharmacological property of PT-31 highlights the importance in elucidating its metabolism, which could provide valuable information about its metabolite profile for further pharmacokinetics studies and additionally to estimate the impact of its metabolites on the efficacy, safety and elimination of PT-31. In this work, the study of the in vitro metabolism of PT-31 was initially carried out by using a liquid chromatography coupled to ion trap multiple-stage mass spectrometer (LC-IT-MSⁿ) and a hybrid triple quadrupole/linear ion trap mass spectrometer (LC-QTrap). The production of at least three unknown oxidative metabolites was observed. Structural identification of the unknown metabolites was carried out by combination of LC-MS experiments, including selected reaction monitoring (SRM) and multi-stage full scan experiments. Further analysis of ¹H-NMR led to the structural confirmation of the major metabolite. The results indicated that PT-31 was metabolized by a hydroxylation reaction in the imidazolidine-2,4-dione ring in rat and human liver microsomes, producing the metabolite 3-(2-chloro-6-fluorobenzyl)-5-hydroxyimidazolidine-2,4-dione in rat liver microsomes. A carbon hydroxylation onto the benzyl ring, produced two other minor metabolites of the PT-31 in rat liver microsomes.

Understanding the relevance of herb-drug interaction studies with special focus on interplays: a prerequisite for integrative medicine

Integrative medicine refers to the blending of conventional and evidence-based complementary medicines and therapies with the aim of using the most appropriate of either or both modalities for ultimate patient benefits. One of the major hurdles for the same is the chances of potential herb–drug interactions (HDIs). These HDIs could be beneficial or harmful, or even fatal; therefore, a thorough understanding of the eventualities of HDIs is essential so that a successful integration of the modern and complementary alternative systems of medicine could be achieved. Here, we summarize all the important points related to HDIs, including types, tools/methods for study, and prediction of the HDIs, along with a special focus on interplays between drug metabolizing enzymes and transporters. In addition, this article covers future perspective, with a focus on background endogenous players of interplays and approaches to predict the drug–disease–herb interactions so as to fetch the desired effects of these interactions.

Use of high-throughput enzyme-based assay with xenobiotic metabolic capability to evaluate the inhibition of acetylcholinesterase activity by organophosphorous pesticides

The inhibition of acetylcholinesterase (AChE) has pharmaceutical applications as well as potential neurotoxic effects. The in vivo metabolites of some chemicals including organophosphorus pesticides can become more potent AChE inhibitors compared to their parental compounds. To account for the effects of biotransformation, we have developed and characterized a high-throughput screening method for identifying AChE inhibitors that become active or more potent following xenobiotic metabolism. In this study, an enzyme-based assay was developed in 1536-well plates using recombinant human AChE combined with human or rat liver microsomes. The AChE activity was measured by two methods with different readouts: colorimetric and fluorescent. The assay exhibited exceptional performance characteristics including large assay signal window, low well-to-well variability and high reproducibility. The performance of the assays with microsomes was characterized by testing a group of known AChE inhibitors including parent compounds and their metabolites. Large potency differences between the parent compounds and the metabolites were observed in the assay with microsome addition. Both assay readouts were required for maximal sensitivity. These results demonstrate that this platform is a promising method to profile large numbers of chemicals that require metabolic activation for inhibiting AChE activity.

Data Mining and Computational Modeling of High-Throughput Screening Datasets

We are now seeing the benefit of investments made over the last decade in high-throughput screening (HTS) that is resulting in large structure activity datasets entering public and open databases such as ChEMBL and PubChem. The growth of academic HTS screening centers and the increasing move to academia for early stage drug discovery suggests a great need for the informatics tools and methods to mine such data and learn from it. Collaborative Drug Discovery, Inc. (CDD) has developed a number of tools for storing, mining, securely and selectively sharing, as well as learning from such HTS data. We present a new web based data mining and visualization module directly within the CDD Vault platform for high-throughput drug discovery data that makes use of a novel technology stack following modern reactive design principles. We also describe CDD Models within the CDD Vault platform that enables researchers to share models, share predictions from models, and create models from distributed, heterogeneous data. Our system is built on top of the Collaborative Drug Discovery Vault Activity and Registration data repository ecosystem which allows users to manipulate and visualize thousands of molecules in real time. This can be performed in any browser on any platform. In this chapter we present examples of its use with public datasets in CDD Vault. Such approaches can complement other cheminformatics tools, whether open source or commercial, in providing approaches for data mining and modeling of HTS data.

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets

Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further using multiple metrics with much larger scale comparisons, prospective testing as well as assessment of different fingerprints and DNN architectures beyond those used.

Role of traditional Islamic and Arabic plants in cancer therapy

ETHNO PHARMACOLOGICAL RELEVANCE

This review article underlines individual Traditional Islamic and Arabic plant (TAI) and their role in treating cancer. The aim of the study is to specifically evaluate the progress of herbs, Arabic and Islamic traditional herbs in particular, applied in cancer treatment, so far.

MATERIALS AND METHODS

Islamic and Arabic plants were selected and identified through different literature survey using "Google scholar", "Web of science", "Scopus" and "PubMed". Each plant, from identified Arabic and Islamic plants list, was search individually for the most cited articles in the aforementioned databases using the keywords, "Anticancer", "Uses in cancer treatment", "Ethno pharmacological importance in cancer" etc.

RESULTS

The current review about Islamic and Arabic plants illuminates the importance of Islamic and Arabic plants and their impact in treating cancer. There is a long list of Islamic and Arabic plants used in cancer as mentioned in review with enormous amount of literature. Each plant has been investigated for its anticancer potential. The literature survey as mentioned in table shows; these plants are widely utilized in cancer as a whole, a part thereof or in the form of isolated chemical constituent.

CONCLUSIONS

This review strongly supports the fact; Arabic and Islamic traditional plants have emerged as a good source of complementary and alternative medicine in treating cancer. Traditional Arab-Islamic herbal-based medicines might be promising for new cancer therapeutics with low toxicity and minimal side effects. The plants used are mostly in crude form and still needs advance research for the isolation of phytochemicals and establishing its cellular and molecular role in treating cancer.

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

BACKGROUND

While establishing efficacy in translational models and humans through clinically-relevant endpoints for disease is of great interest, assessing the potential toxicity of a putative therapeutic drug is critical. Toxicological assessments in the pre-clinical discovery phase help to avoid future failure in the clinical phases of drug development. Many in vitro assays exist to aid in modular toxicological assessment, such as hepatotoxicity and genotoxicity. While these methods have provided tremendous insight into human toxicity by investigational new drugs, they are expensive, require substantial resources, and do not account for pharmacogenomics as well as critical ADME properties. Computational tools can fill this niche in toxicology if in silico models are accurate in relating drug molecular properties to toxicological endpoints as well as reliable in predicting important drug-target interactions that mediate known adverse events or adverse outcome pathways (AOPs).

METHODS

We undertook an unstructured search of multiple bibliographic databases for peer-reviewed literature regarding computational methods in predictive toxicology for in silico drug discovery. As this review paper is meant to serve as a survey of available methods for the interested reader, no focused criteria were applied. Literature chosen was based on the writers' expertise and intent in communicating important aspects of in silico toxicology to the interested reader.

CONCLUSION

This review provides a purview of computational methods of pre-clinical toxicologic assessments for novel small molecule drugs that may be of use for novice and experienced investigators as well as academic and commercial drug discovery entities.

Comparative evaluation of 11 in silico models for the prediction of small molecule mutagenicity: role of steric hindrance and electron-withdrawing groups

The goal of this investigation was to perform a comparative analysis on how accurately 11 routinely-used in silico programs correctly predicted the mutagenicity of test compounds that contained either bulky or electron-withdrawing substituents. To our knowledge this is the first study of its kind in the literature. Such substituents are common in many pharmaceutical agents so there is a significant need for reliable in silico programs to predict precisely whether they truly pose a risk for mutagenicity. The predictions from each program were compared to experimental data derived from the Ames II test, a rapid reverse mutagenicity assay with a high degree of agreement with the traditional Ames assay. Eleven in silico programs were evaluated and compared: Derek for Windows, Derek Nexus, Leadscope Model Applier (LSMA), LSMA featuring the in vitro microbial Escherichia coli-Salmonella typhimurium TA102 A-T Suite (LSMA+), TOPKAT, CAESAR, TEST, ChemSilico (±S9 suites), MC4PC and a novel DNA docking model. The presence of bulky or electron-withdrawing functional groups in the vicinity of a mutagenic toxicophore in the test compounds clearly affected the ability of each in silico model to predict non-mutagenicity correctly. This was because of an over reliance on the part of the programs to provide mutagenicity alerts when a particular toxicophore is present irrespective of the structural environment surrounding the toxicophore. From this investigation it can be concluded that these models provide a high degree of specificity (ranging from 71% to 100%) and are generally conservative in their predictions in terms of sensitivity (ranging from 5% t o 78%). These values are in general agreement with most other comparative studies in the literature. Interestingly, the DNA docking model was the most sensitive model evaluated, suggesting a potentially useful new mode of screening for mutagens. Another important finding was that the combination of a quantitative structure-activity relationship and an expert rules system appeared to offer little advantage in terms of sensitivity, despite of the requirement for such a screening paradigm under the ICH M7 regulatory guideline.

XMetDB: an open access database for xenobiotic metabolism

Xenobiotic metabolism is an active research topic but the limited amount of openly available high-quality biotransformation data constrains predictive modeling. Current database often default to commonly available information: which enzyme metabolizes a compound, but neither experimental conditions nor the atoms that undergo metabolization are captured. We present XMetDB, an open access database for drugs and other xenobiotics and their respective metabolites. The database contains chemical structures of xenobiotic biotransformations with substrate atoms annotated as reaction centra, the resulting product formed, and the catalyzing enzyme, type of experiment, and literature references. Associated with the database is a web interface for the submission and retrieval of experimental metabolite data for drugs and other xenobiotics in various formats, and a web API for programmatic access is also available. The database is open for data deposition, and a curation scheme is in place for quality control. An extensive guide on how to enter experimental data into is available from the XMetDB wiki. XMetDB formalizes how biotransformation data should be reported, and the openly available systematically labeled data is a big step forward towards better models for predictive metabolism.

Current LC-MS methods and procedures applied to the identification of new steroid metabolites

The study of the metabolism of steroids has a long history; from the first characterizations of the major metabolites of steroidal hormones in the pre-chromatographic era, to the latest discoveries of new forms of excretions. The introduction of mass spectrometers coupled to gas chromatography at the end of the 1960's represented a major breakthrough for the elucidation of new metabolites. In the last two decades, this technique is being complemented by the use of liquid chromatography-mass spectrometry (LC-MS). In addition of becoming fundamental in clinical steroid determinations due to its excellent specificity, throughput and sensitivity, LC-MS has emerged as an exceptional tool for the discovery of new steroid metabolites. The aim of the present review is to provide an overview of the current LC-MS procedures used in the quest of novel metabolic products of steroidal hormones and exogenous steroids. Several aspects regarding LC separations are first outlined, followed by a description of the key processes that take place in the mass spectrometric analysis, i.e. the ionization of the steroids in the source and the fragmentation of the selected precursor ions in the collision cell. The different analyzers and approaches employed together with representative examples of each of them are described. Special emphasis is placed on triple quadrupole analyzers (LC-MS/MS), since they are the most commonly employed. Examples on the use of precursor ion scan, neutral loss scan and theoretical selected reaction monitoring strategies are also explained.

Electrochemistry-mass spectrometry for in-vitro determination of selected chemotherapeutics and their electrochemical products in comparison to in-vivo approach

Chemotherapeutics are among the most frequently prescribed medications in modern medicine. They are widely prescribed; however, problems with organisms developing resistance to these drugs means that their efficacy may be lost, so care should be taken to avoid unnecessary prescription. It is therefore of great interest to study the detailed metabolism of these biologically active compounds. This study aimed at developing an efficient analytical protocol for the determination of in-vitro electrochemical products of selected antibiotic drugs (amoxicillin, cefotaxime, fluconazole, linezolid, metronidazole and moxifloxacin). Combination of electrochemistry (EC) and mass spectrometry (MS) was applied for the in-vitro determination of the studied antibiotics and their electrochemical products. To identify the structure of the detected electrochemical products, MS/MS experiments were performed. This was one of the first applications of the EC system for generation of electrochemical products produced from antibiotic drugs. Adjustment of appropriate conditions and such parameters as the potential value, mobile phase (pH), working electrode and temperature had significant influence on electrochemical simulations and the creation of selected derivatives. Consequently, several working electrodes were evaluated for this purpose. In most of the studied cases, mainly two types of products were observed. One corresponded to an increase in mass by 14Da, which can be explained by a process consisting of oxidation (+16 m/z) and dehydrogenation (-2 m/z); The second in turn showed mass reduction by 14Da, which can be attributed to the loss of -CH2 as a result of N-demethylation. The performed experiments consisted of two stages: electrochemical oxidation of the analyzed samples (phase I of metabolic transformation), and addition of glutathione (GSH) for follow-up reactions (phase II conjunction). The electrochemical results were compared to in-vivo experiments by analyzing urine samples from patients after antibiotic drugs have been administered.. Overall, the comparison of electrochemistry to in-vivo experiments shows the high potential of EC-MS as a fast analytical tool in the prediction of electrochemical conversion that could be applied to therapeutic drug monitoring and pharmacokinetic studies as well.

Content and activity of human liver microsomal protein and prediction of individual hepatic clearance in vivo

The lack of information concerning individual variation in content and activity of human liver microsomal protein is one of the most important obstacles for designing personalized medicines. We demonstrated that the mean value of microsomal protein per gram of liver (MPPGL) was 39.46 mg/g in 128 human livers and up to 19-fold individual variations existed. Meanwhile, the metabolic activities of 10 cytochrome P450 (CYPs) were detected in microsomes and liver tissues, respectively, which showed huge individual variations (200-fold). Compared with microsomes, the activities of liver tissues were much suitable to express the individual variations of CYP activities. Furthermore, individual variations in the in vivo clearance of tolbutamide were successfully predicted with the individual parameter values. In conclusion, we offer the values for MPPGL contents in normal liver tissues and build a new method to assess the in vitro CYP activities. In addition, large individual variations exist in predicted hepatic clearance of tolbutamide. These findings provide important physiological parameters for physiologically-based pharmacokinetics models and thus, establish a solid foundation for future development of personalized medicines.

Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery

Integrated computational approaches for Mycobacterium tuberculosis (Mtb) are useful to identify new molecules that could lead to future tuberculosis (TB) drugs. Our approach uses information derived from the TBCyc pathway and genome database, the Collaborative Drug Discovery TB database combined with 3D pharmacophores and dual event Bayesian models of whole-cell activity and lack of cytotoxicity. We have prioritized a large number of molecules that may act as mimics of substrates and metabolites in the TB metabolome. We computationally searched over 200,000 commercial molecules using 66 pharmacophores based on substrates and metabolites from Mtb and further filtering with Bayesian models. We ultimately tested 110 compounds in vitro that resulted in two compounds of interest, BAS 04912643 and BAS 00623753 (MIC of 2.5 and 5 μg/mL, respectively). These molecules were used as a starting point for hit-to-lead optimization. The most promising class proved to be the quinoxaline di-N-oxides, evidenced by transcriptional profiling to induce mRNA level perturbations most closely resembling known protonophores. One of these, SRI58 exhibited an MIC = 1.25 μg/mL versus Mtb and a CC₅₀ in Vero cells of >40 μg/mL, while featuring fair Caco-2 A-B permeability (2.3 x 10⁻⁶ cm/s), kinetic solubility (125 μM at pH 7.4 in PBS) and mouse metabolic stability (63.6% remaining after 1 h incubation with mouse liver microsomes). Despite demonstration of how a combined bioinformatics/cheminformatics approach afforded a small molecule with promising in vitro profiles, we found that SRI58 did not exhibit quantifiable blood levels in mice.

Negligible Pharmacokinetic Interaction of Red Ginseng and Losartan, an Antihypertensive Agent, in Sprague-Dawley Rats

Red ginseng (RG) is one of the top selling herbal medicines in Korea, but is not recommended in hypertensive patients. In this study, the pharmacokinetic (PK) interaction between RG and losartan, an antihypertensive drug, was examined. RG was orally administered for 2 wk to male Sprague-Dawley (S-D) rats at either control (0), 0.5, 1, or 2 g/kg/d for 2 wk. After the last administration of RG and 30 min later, all animals were treated with 10 mg/kg losartan by oral route. In addition, some S-D rats were administered RG orally for 21 d at 2 g/kg followed by losartan intravenously (iv) at 10 mg/kg/d. Post losartan administration, plasma samples were collected at 5, 15, and 30 min and 1, 1.5, 2, 3, 6, 12, and 24 h. Plasma concentrations of losartan and E-3174, the active metabolite of losartan, were analyzed by a high-pressure liquid chromatography-tandem mass spectrometer system (LC-MS/MS). Oral losartan administration showed dose-dependent pharmacokinetics (PK) increase with time to maximum plasma, but this was not significant between different groups. There was no significant change in tmax with E-3174 PK. With iv losartan, pharmacokinetics showed elevation of area under the plasma concentration-time curve from time zero extrapolated to infinitity. There was not a significant change in AUCinf with E-3174 PK. Therefore, RG appeared to interfere with biotransformation of losartan, as RG exerted no marked effect on E-3174 PK in S-D rats. Data demonstrated that oral or iv treatment with losartan in rats pretreated with RG for 2 wk showed that losartan PK was affected but E-3174 PK remained unchanged among different dose groups. These results suggested that RG induces negligible influence on losartan and E-3174 PK in rats.

Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools

In-silico methods have been explored as potential tools for assessing ADME and ADME regulatory properties particularly in early drug discovery stages. Machine learning methods, with their ability in classifying diverse structures and complex mechanisms, are well suited for predicting ADME and ADME regulatory properties. Recent efforts have been directed at the broadening of application scopes and the improvement of predictive performance with particular focuses on the coverage of ADME properties, and exploration of more diversified training data, appropriate molecular features, and consensus modeling. Moreover, several online machine learning ADME prediction servers have emerged. Here we review these progresses and discuss the performances, application prospects and challenges of exploring machine learning methods as useful tools in predicting ADME and ADME regulatory properties.

Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets

On the order of hundreds of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) models have been described in the literature in the past decade which are more often than not inaccessible to anyone but their authors. Public accessibility is also an issue with computational models for bioactivity, and the ability to share such models still remains a major challenge limiting drug discovery. We describe the creation of a reference implementation of a Bayesian model-building software module, which we have released as an open source component that is now included in the Chemistry Development Kit (CDK) project, as well as implemented in the CDD Vault and in several mobile apps. We use this implementation to build an array of Bayesian models for ADME/Tox, in vitro and in vivo bioactivity, and other physicochemical properties. We show that these models possess cross-validation receiver operator curve values comparable to those generated previously in prior publications using alternative tools. We have now described how the implementation of Bayesian models with FCFP6 descriptors generated in the CDD Vault enables the rapid production of robust machine learning models from public data or the user’s own datasets. The current study sets the stage for generating models in proprietary software (such as CDD) and exporting these models in a format that could be run in open source software using CDK components. This work also demonstrates that we can enable biocomputation across distributed private or public datasets to enhance drug discovery.

Antitumor activity of DMAKO-05, a novel shikonin derivative, and its metabolism in rat liver microsome

The antitumor activity of shikonin derivatives is largely dependent on the generation of superoxide radicals and the alkylation activity of their naphthoquinone moiety. However, our recent study showed that 1,4-dioxime-5,8-dimethoxynaphthalene (DMAKO-05), a novel shikonin derivative, displayed more potential antitumor activity and less toxicity compared to fluorouracil (5-FU) both in vitro and in vivo, even though the hydroxyl and carbonyl groups of its naphthoquinone structure were shielded. To understand the underlying mechanisms, we investigated the metabolism of DMAKO-05 in rat liver microsomes. The kinetic analysis indicated that DMAKO-05 underwent a biphasic metabolism in rat liver microsomes. The inhibition experiments showed that CYP1A and CYP3A were the major enzymes in the metabolism of DMAKO-05, along with partial contribution from CYP2A. In addition, the structures of eight DMAKO-05 metabolites, which were characterized by accurate mass and MS/MS fragmentograms, implied that DMAKO-05 was mainly metabolized through the oxygenation of its naphthoquinone nucleus and the hydrolysis of its side chain, instead of the oxidation of hydroxyimine to ketone. Therefore, DMAKO-05 should not be considered as a traditional naphthoquinone prodrug.

In vitro metabolic stability of exendin-4: pharmacokinetics and identification of cleavage products

The aim of this study was to investigate the metabolic stability and cleavage sites of exendin-4 in rat tissue homogenates, as well as to identify the types of proteases involved in exendin-4 degradation. The stability of exendin-4 in kidney and liver homogenates from rats was evaluated using liquid chromatography-electrospray ionization mass spectrometry (LC-ESI-MS) with gradient elution. Furthermore, we used a combination of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and LC-ESI-MS/MS to identify the structures of the major degradation products of exendin-4, and peptidase inhibitors were used to characterize exendin-4 degradation in rat liver and kidney homogenates and to identify the proteases involved in exendin-4 metabolism. Exendin-4 had a half-life of 7.8 and 100.9 min in the kidney and liver homogenate, respectively. The enzymes most likely to be involved in the degradation of exendin-4 were aminopeptidases, serineproteases, and metalloproteases. Exendin-4(15-39) and exendin-4(16-39) were the predominant direct exendin-4 metabolites in the kidney, and the main product of exendin-4 metabolism in the liver was exendin-4(12-39). Our results indicated that the metabolism of exendin-4 involved an initial endoproteolytic cleavage and subsequent exoproteolytic digestion. The degradation of exendin-4 in the kidney and liver homogenates followed distinct patterns, and the primary cleavage sites of exendin-4 degradation in rat kidney homogenates were located after AA-14, and -15, whereas those in rat liver homogenates were located after AA-11.