Metabolic bioactivation and drug-related adverse effects: current status and future directions from a pharmaceutical research perspective

MetaPredictor: in silico prediction of drug metabolites based on deep language models with prompt engineering

Metabolic processes can transform a drug into metabolites with different properties that may affect its efficacy and safety. Therefore, investigation of the metabolic fate of a drug candidate is of great significance for drug discovery. Computational methods have been developed to predict drug metabolites, but most of them suffer from two main obstacles: the lack of model generalization due to restrictions on metabolic transformation rules or specific enzyme families, and high rate of false-positive predictions. Here, we presented MetaPredictor, a rule-free, end-to-end and prompt-based method to predict possible human metabolites of small molecules including drugs as a sequence translation problem. We innovatively introduced prompt engineering into deep language models to enrich domain knowledge and guide decision-making. The results showed that using prompts that specify the sites of metabolism (SoMs) can steer the model to propose more accurate metabolite predictions, achieving a 30.4% increase in recall and a 16.8% reduction in false positives over the baseline model. The transfer learning strategy was also utilized to tackle the limited availability of metabolic data. For the adaptation to automatic or non-expert prediction, MetaPredictor was designed as a two-stage schema consisting of automatic identification of SoMs followed by metabolite prediction. Compared to four available drug metabolite prediction tools, our method showed comparable performance on the major enzyme families and better generalization that could additionally identify metabolites catalyzed by less common enzymes. The results indicated that MetaPredictor could provide a more comprehensive and accurate prediction of drug metabolism through the effective combination of transfer learning and prompt-based learning strategies.

Evidence for cytochrome P450 3A4-mediated metabolic activation of SCO-267

SCO-267 is a potent G-protein-coupled receptor 40 agonist that is undergoing clinical development for the treatment of type 2 diabetes mellitus. The current work was undertaken to investigate the bioactivation potential of SCO-267 in vitro and in vivo. Three SCO-267-derived glutathione (GSH) conjugates (M1-M3) were found both in rat and human liver microsomal incubations supplemented with GSH and nicotinamide adenine dinucleotide phosphate. Two GSH conjugates (M1-M2) together with two N-acetyl-cysteine conjugates (M4-M5) were detected in the bile of rats receiving SCO-267 at 10 mg/kg. The identified conjugates suggested the generation of quinone-imine and ortho-quinone intermediates. CYP3A4 was demonstrated to primarily catalyze the bioactivation of SCO-267. In addition, SCO-267 concentration-, time-, and NADPH-dependently inactivated CYP3A in human liver microsomes using testosterone as a probe substrate, along with KI and kinact values of 4.91 μM and 0.036 min-1 , respectively. Ketoconazole (a competitive inhibitor of CYP3A) displayed no significant protective effect on SCO-267-induced CYP3A inactivation. However, inclusion of GSH showed significant protection. These findings revealed that SCO-267 undergoes a facile CYP3A4-catalyzed bioactivation with the generation of quinone-imine and ortho-quinone intermediates, which were assumed to be involved in SCO-267 induced CYP3A inactivation. These findings provide further insight into the bioactivation pathways involved in the generation of reactive, potentially toxic metabolites of SCO-267. Further studies are needed to evaluate the influence of SCO-267 metabolism on the safety of this drug in vivo.

Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis

Understanding the metabolic fate of a xenobiotic substance can help inform its potential health risks and allow for the identification of signature metabolites associated with exposure. The need to characterize metabolites of poorly studied or novel substances has shifted exposure studies towards non-targeted analysis (NTA), which often aims to profile many compounds within a sample using high-resolution liquid-chromatography mass-spectrometry (LCMS). Here we evaluate the suitability of suspect screening analysis (SSA) liquid-chromatography mass-spectrometry to inform xenobiotic chemical metabolism. Given a lack of knowledge of true metabolites for most chemicals, predictive tools were used to generate potential metabolites as suspect screening lists to guide the identification of selected xenobiotic substances and their associated metabolites. Thirty-three substances were selected to represent a diverse array of pharmaceutical, agrochemical, and industrial chemicals from Environmental Protection Agency's ToxCast chemical library. The compounds were incubated in a metabolically-active in vitro assay using primary hepatocytes and the resulting supernatant and lysate fractions were analyzed with high-resolution LCMS. Metabolites were simulated for each compound structure using software and then combined to serve as the suspect screening list. The exact masses of the predicted metabolites were then used to select LCMS features for fragmentation via tandem mass spectrometry (MS/MS). Of the starting chemicals, 12 were measured in at least one sample in either positive or negative ion mode and a subset of these were used to develop the analysis workflow. We implemented a screening level workflow for background subtraction and the incorporation of time-varying kinetics into the identification of likely metabolites. We used haloperidol as a case study to perform an in-depth analysis, which resulted in identifying five known metabolites and five molecular features that represent potential novel metabolites, two of which were assigned discrete structures based on in silico predictions. This workflow was applied to five additional test chemicals, and 15 molecular features were selected as either reported metabolites, predicted metabolites, or potential metabolites without a structural assignment. This study demonstrates that in some-but not all-cases, suspect screening analysis methods provide a means to rapidly identify and characterize metabolites of xenobiotic chemicals.

Computational Investigation of the Bisphenolic Drug Metabolism by Cytochrome P450: What Factors Favor Intramolecular Phenol Coupling

Intramolecular phenol coupling reactions of alkaloids can lead to active metabolites catalyzed by the mammalian cytochrome P450 enzyme (P450); however, the mechanistic knowledge of such an "unusual" process is lacking. This work performs density functional theory computations to reveal the P450-mediated metabolic pathway leading from R-reticuline to the morphine precursor salutaridine by exploring possible intramolecular phenol coupling mechanisms involving diradical coupling, radical addition, and electron transfer. The computed results show that the outer-sphere electron transfer with a high barrier (>20.0 kcal/mol) is unlikely to happen. However, for inter-sphere intramolecular phenol coupling, it reveals that intramolecular phenol coupling of R-reticuline proceeds via the diradical mechanism consecutively by compound I and protonated compound II of P450 rather than the radical addition mechanism. The existence of a much higher radical rebound barrier than that of H-abstraction in the quartet high-spin state can endow the R-reticuline phenoxy radical with a sufficient lifetime to enable intramolecular phenol coupling, while the H-abstraction/radical rebound mode with a negligible rebound barrier leading to phenol hydroxylation can only happen in the doublet low-spin state. Therefore, the ratio [coupling]/[hydroxylation] can be approximately reflected by the relative yield of the high-spin and low-spin H-abstraction by P450, which thus can provide a theoretical ratio of 16:1 for R-reticuline, which is in accordance with previous experimental results. Especially, the high rebound barrier of the phenoxy radical derived from the weak electron-donating ability of the phenoxy radical is revealed as an intrinsic nature. Therefore, the revealed intramolecular phenol coupling mechanism can be potentially extended to several other bisphenolic drugs to infer groups of unexpected metabolites in organisms.

Machine Learning in Drug Metabolism Study

Metabolic reactions in the body transform the administered drug into metabolites. These metabolites exhibit diverse biological activities. Drug metabolism is the major underlying cause of drug overdose-related toxicity, adversative drug effects and the drug's reduced efficacy. Though metabolic reactions deactivate a drug, drug metabolites are often considered pivotal agents for off-target effects or toxicity. On the other side, in combination drug therapy, one drug may influence another drug's metabolism and clearance and is thus considered one of the primary causes of drug-drug interactions. Today with the advancement of machine learning, the metabolic fate of a drug candidate can be comprehensively studied throughout the drug development procedure. Naïve Bayes, Logistic Regression, k-Nearest Neighbours, Decision Trees, different Boosting and Ensemble methods, Support Vector Machines and Artificial Neural Network boosted Deep Learning are some machine learning algorithms which are being extensively used in such studies. Such tools are covering several attributes of drug metabolism, with an emphasis on the prediction of drug-drug interactions, drug-target-interactions, clinical drug responses, metabolite predictions, sites of metabolism, etc. These reports are crucial for evaluating metabolic stability and predicting prospective drug-drug interactions, and can help pharmaceutical companies accelerate the drug development process in a less resourcedemanding manner than what in vitro studies offer. It could also help medical practitioners to use combinatorial drug therapy in a more resourceful manner. Also, with the help of the enormous growth of deep learning, traditional fields of computational drug development like molecular interaction fields, molecular docking, quantitative structure-toactivity relationship (QSAR) studies and quantum mechanical simulations are producing results which were unimaginable couple of years back. This review provides a glimpse of a few contextually relevant machine learning algorithms and then focuses on their outcomes in different studies.

Deep-tissue real-time imaging of drug-induced liver injury with peroxynitrite-responsive 19F MRI nanoprobes

Peroxynitrite is an important biomarker for assessing drug-induced liver injury (DILI), which is critical for the development and use of drugs. Herein, we report the development of peroxynitrite-responsive self-assembled ¹⁹F MRI nanoprobes, which enable the sensitive imaging of peroxynitrite in L02 cells subjected to oxidative stress and living mice with DILI.

Metabolic profiling of norepinephrine reuptake inhibitor atomoxetine

Atomoxetine (ATX), a selective and potent inhibitor of the presynaptic norepinephrine transporter, is used mainly to treat attention-deficit hyperactivity disorder. Although multiple adverse effects associated with ATX have been reported including severe liver injuries, the mechanisms of ATX-related toxicity remain largely unknown. Metabolism frequently contributes to adverse effects of a drug through reactive metabolites, and the bioactivation status of ATX is still not investigated yet. Here, we systematically investigated ATX metabolism, bioactivation, species difference in human, mouse, and rat liver microsomes (HLM, MLM, and RLM) and in mice using metabolomic approaches as mice and rats are commonly used animal models for the studies of drug toxicity. We identified thirty one ATX metabolites and adducts in LMs and mice, 16 of which are novel. In LMs, we uncovered two methoxyamine-trapped aldehydes, two cyclization metabolites, detoluene-ATX, and ATX-N-hydroxylation for the first time. Detoluene-ATX and one cyclization metabolite were also observed in mice. Using chemical inhibitors and recombinant CYP enzymes, we demonstrated that CYP2C8 and CYP2B6 mainly contribute to the formation of aldehyde; CYP2D6 is the dominant enzyme for the formation of ATX cyclization and detoluene-ATX; CYP3A4 is major enzyme responsible for the hydroxylamine formation. The findings concerning aldehydes should be very useful to further elucidate the mechanistic aspects of adverse effects associated with ATX from metabolic angles. Additionally, the species differences for each metabolite should be helpful to investigate the contribution of specific metabolites to ATX toxicity and possible drug-drug interactions in suitable models.

Evaluation of hepatic CYP2D1 activity and hepatic clearance in type I and type II diabetic rat models, before and after treatment with insulin and metformin

INTRODUCTION

Conversion in the metabolism of drugs occurs in diabetes mellitus. Considering the importance of metabolic enzymes' activities on the efficacy and safety of medicines, the changes in liver enzymatic activity of CYP2D1 and its related hepatic clearance, by using Dextromethorphan as probe in the animal model of type I and type II diabetes, before and after treatment, was assessed in this study.

METHODS

Male Wistar rats were randomly divided into 6 groups. Seven days after induction of diabetes type I and type II, treatment groups were received insulin and metformin daily for 14 days, respectively. In day 21, rats were subjected to liver perfusion by Krebs-Henseleit buffer containing Dextromethorphan as CYP2D1 probe. Perfusate samples were analyzed by HPLC fluorescence method in order to evaluate any changes in CYP2D1 activity.

RESULTS

The average metabolic ratio of dextromethorphan and hepatic clearance were changed from 0.012 ± 0.004 and 6.3 ± 0.1 in the control group to 0.006 ± 0.0008 and 5.2 ± 0.2 in the untreated type I diabetic group, and 0.008 ± 0.003 and 5.0 ± 0.6 in the untreated type II diabetic rats. Finally, the mean metabolic ratio and hepatic clearance were changed to 0.008 ± 0.001 and 5.4 ± 0.1, and 0.013 ± 0.003 and 6.1 ± 0.4 in the treated groups with insulin and metformin, respectively.

CONCLUSION

In type I diabetic rats, corresponding treatment could slightly improve enzyme activity, whereas the hepatic clearance and enzyme activity reached to the normal level in type II group. Graphical abstract .

Structure-Based Virtual Screening, Molecular Dynamics and Binding Free Energy Calculations of Hit Candidates as ALK-5 Inhibitors

Activin-like kinase 5 (ALK-5) is involved in the physiopathology of several conditions, such as pancreatic carcinoma, cervical cancer and liver hepatoma. Cellular events that are landmarks of tumorigenesis, such as loss of cell polarity and acquisition of motile properties and mesenchymal phenotype, are associated to deregulated ALK-5 signaling. ALK-5 inhibitors, such as SB505154, GW6604, SD208, and LY2157299, have recently been reported to inhibit ALK-5 autophosphorylation and induce the transcription of matrix genes. Due to their ability to impair cell migration, invasion and metastasis, ALK-5 inhibitors have been explored as worthwhile hits as anticancer agents. This work reports the development of a structure-based virtual screening (SBVS) protocol aimed to prospect promising hits for further studies as novel ALK-5 inhibitors. From a lead-like subset of purchasable compounds, five molecules were identified as putative ALK-5 inhibitors. In addition, molecular dynamics and binding free energy calculations combined with pharmacokinetics and toxicity profiling demonstrated the suitability of these compounds to be further investigated as novel ALK-5 inhibitors.

Chinese Herbal Medicine Hepatotoxicity: The Evaluation and Recognization Based on Large-scale Evidence Database

Background:

Due to the special nature of Chinese Herbal medicine and the complexity of its clinical use, it is difficult to identify and evaluate its toxicity and resulting herb induced liver injury (HILI).

Methods:

First, the database would provide full profile of HILI from the basic ingredients to clinical out-comes by the most advanced algorithms of artificial intelligence, and it is also possible that we can predict possibilities of HILI after patients taking Chinese herbs by individual patient evaluation and prediction. Second, the database would solve the chaos and lack of the relevant data faced by the current basic re-search and clinical practice of Chinese Herbal Medicine. Third, we can also screen the susceptible patients from the database and thus prevent the accidents of HILI from the very beginning.

Results:

The Roussel Uclaf Causality Assessment Method (RUCAM) is the most accepted method to evalu-ate DILI, but at present before using the RUCAM evaluation method, data resource collection and analysis are yet to be perfected. Based on existing research on drug-metabolizing enzymes mediating reactive me-tabolites (RMs), the aim of this study is to explore the possibilities and methods of building multidimen-sional hierarchical database composing of RMs evidence library, Chinese herbal evidence library, and indi-vidualized reports evidence library of herb induced liver injury HILI.

Conclusion:

The potential benefits lie in its ability to organize, use vast amounts of evidence and use big data mining techniques at the center for Chinese herbal medicine liver toxicity research, which is the most difficult key point of scientific research to be investigated in the next few years.

A high-throughput glutathione trapping assay with combined high sensitivity and specificity in high-resolution mass spectrometry by applying product ion extraction and data-dependent neutral loss

Reactive metabolites are thought to play a pivotal role in the pathogenesis of some drug-induced liver injury (DILI) and idiosyncratic adverse drug reactions (IADRs), which is of concern to patient safety and has been a cause of drugs being withdrawn from the market place. To identify drugs with a lower propensity for causing DILI and/or IADRs, high-throughput assays to capture reactive metabolites are required in pharmaceutical industry for early drug discovery risk assessment. We describe the development of an assay to detect glutathione adducts with combined high sensitivity, enhanced specificity, and rapid data analysis. In this assay, compounds were incubated with human liver microsomes and a mixture of 1:1 of GSH (γ-GluCysGly): GSX(γ-GluCysGly-¹³ C₂ ¹⁵ N) in a 96-well plate format. UPLC-UV and LTQ Orbitrap XL were employed to detect GSH-adducts using the following mass spectrometry setups: (a) selected ion monitoring (SIM) at m/z of 274 ± 3 Da in negative mode with in-source fragmentation (SCID), which enables simultaneously monitoring two characteristic product ions of m/z 272.0888 (γ-glutamyl-dehydroalanyl-glycine) and 275.0926 (γ-glutamyl-dehydroalanyl-glycine-¹³ C₂ ¹⁵ N); (b) full scan mode for acquisition of exact mass of glutathione adducts; (c) data-dependent MS² scan through isotopic matching (M:M + 3.00375 = 1:1) for monitoring neutral loss fragments (144 Da from dehydroalanyl-glycine) and for structural information of glutathione adducts. This approach was qualified using eight compounds known to form GSH conjugates as reported in the literature. The high sensitivity and specificity were demonstrated in identifying unique CysGly adducts in the case of clozapine, diclofenac, and raloxifene and in identifying GSH-adducts of fragmented parent molecules in the case of amodiaquine and troglitazone. In addition, LC-UV chromatograms in the presence or absence of GSH/GSX allowed for identification of the rearranged glutathione adducts without aforementioned characteristic fragment ions. Implement of this assay in drug discovery small molecule programs has successfully guided drug design.

Mechanism for Covalent Binding of MLN3126, an Oral Chemokine C-C Motif Receptor 9 Antagonist, to Serum Albumins

N-{4-Chloro-2-[(1-oxidopyridin-4-yl)carbonyl]phenyl}-4-(propan-2-yloxy)benzenesulfonamide (MLN3126) is an orally available chemokine C-C motif receptor 9 selective antagonist. In nonclinical pharmacokinetic studies of MLN3126, nonextractable radioactivity was observed in plasma after oral administration of 14C-labeled MLN3126 ([14C]MLN3126) to Sprague-Dawley (SD) rats. In this study, the nonextractable radioactive component was digested with trypsin or a nonspecific protease, pronase, after chemical reduction to obtain drug-peptide adducts or drug-amino acid adducts. The chemical structure of these adducts was characterized by liquid chromatography/mass spectrometry. The results demonstrated that the major part of the nonextractable radioactivity was accounted for by covalent binding via the Schiff base formed specifically between the ε-amino group of lysine residue 199 in rat serum albumin and the carbonyl group of MLN3126. The half-life (t1/2) of the total radioactivity in plasma during and after 21 daily multiple oral administrations of [14C]MLN3126 to SD rats was approximately 5-fold shorter than the reported t1/2 of albumin in rats. The data indicated that the covalent binding was reversible under physiologic conditions. The formation of the covalent binding was also confirmed in in vitro incubations with serum albumins from rats, humans, and dogs in the same manner, indicating that there are no qualitative interspecies differences in the formation of the Schiff base.

Why Prodrugs and Propesticides Succeed

What are the advantages of bioactivation in optimizing drugs and pesticides? Why are there so many prodrugs and propesticides? These questions are examined here by considering compounds selected on the basis of economic value or market success in 2015. The 100 major drugs and 90 major pesticides are divided into ones acting directly and those definitely or possibly requiring bioactivation. Established or candidate prodrugs accounted for 19% of the total drug sales, with corresponding values of 20, 37, and 17% for proinsecticides, proherbicides, and profungicides. The 19 prodrugs acting in humans generally had better pharmacodynamic/pharmacokinetic properties for target enzyme, receptor, tissue, or organ specificity due to their physical properties (lipophilicity and stabilization). Bioactivation usually involved hydrolases or cytochrome P450 oxidation or reduction. Prodrugs considered are neuroactive aripiprazole, eletriptan, desvenlafaxin, lisdexamfetamine, quetiapine, and fesoterodine; cholesterol-lowering atorvastatin, ezetimibe, and fenofibrate; various prodrugs activated by esterases or sulfatases, ciclesonide, oseltamivir, dabigatran; omega-3 fatty acid ethyl esters and esterone sulfate; and five others with various targets (sofosbuvir, fingolimod, clopidogrel, dapsone, and sildenafil). The proinsecticides are the neuroactive chlorpyrifos, thiamethoxam, and indoxacarb, two spiro enol ester inhibitors of acetyl CoA carboxylase (ACCase), and the bacterial protein delta-endotoxin. The proherbicides considered are five ACCase inhibitors including pinoxaden and clethodim, three protox inhibitors (saflufenacil, flumioxazin, and canfentrazone-ethyl), and three with various targets (fluroxypyr, isoxaflutole, and clomazone). The profungicides are prothioconazole, mancozeb, thiophanate-methyl, dazomet, and fosetyl-aluminum. The prodrug and propesticide concept is broadly applicable and has created some of the most selective pharmaceutical and pest control agents, illustrated here by major compounds that partially overcome pharmacokinetic limitations of potency and selectivity in the corresponding direct-acting compounds. The challenges of molecular design extend beyond the target site fit to the bioactivatable precursor and the fascinating chemistry and biology matched against the complexity of life processes.

Computational Approach to Structural Alerts: Furans, Phenols, Nitroaromatics, and Thiophenes

Structural alerts are commonly used in drug discovery to identify molecules likely to form reactive metabolites and thereby become toxic. Unfortunately, as useful as structural alerts are, they do not effectively model if, when, and why metabolism renders safe molecules toxic. Toxicity due to a specific structural alert is highly conditional, depending on the metabolism of the alert, the reactivity of its metabolites, dosage, and competing detoxification pathways. A systems approach, which explicitly models these pathways, could more effectively assess the toxicity risk of drug candidates. In this study, we demonstrated that mathematical models of P450 metabolism can predict the context-specific probability that a structural alert will be bioactivated in a given molecule. This study focuses on the furan, phenol, nitroaromatic, and thiophene alerts. Each of these structural alerts can produce reactive metabolites through certain metabolic pathways but not always. We tested whether our metabolism modeling approach, XenoSite, can predict when a given molecule's alerts will be bioactivated. Specifically, we used models of epoxidation, quinone formation, reduction, and sulfur-oxidation to predict the bioactivation of furan-, phenol-, nitroaromatic-, and thiophene-containing drugs. Our models separated bioactivated and not-bioactivated furan-, phenol-, nitroaromatic-, and thiophene-containing drugs with AUC performances of 100%, 73%, 93%, and 88%, respectively. Metabolism models accurately predict whether alerts are bioactivated and thus serve as a practical approach to improve the interpretability and usefulness of structural alerts. We expect that this same computational approach can be extended to most other structural alerts and later integrated into toxicity risk models. This advance is one necessary step toward our long-term goal of building comprehensive metabolic models of bioactivation and detoxification to guide assessment and design of new therapeutic molecules.

Characterizing novel metabolic pathways of melatonin receptor agonist agomelatine using metabolomic approaches

Agomelatine (AGM), an analog of melatonin, is a potential agonist at melatonin receptors 1/2 and a selective antagonist at 5-hydroxytryptamine 2C receptors. AGM is widely used for the treatment of major depressive episodes in adults. However, multiple adverse effects associated with AGM have been reported in clinical practice. It is little known about AGM metabolism in vitro and in vivo, although metabolism plays a pivotal role in its efficacy and safety. To elucidate metabolic pathways of AGM, we systemically investigated AGM metabolism and its bioactivation in human liver microsomes (HLM) and mice using metabolomic approaches. We identified thirty-eight AGM metabolites and adducts, among which thirty-two are novel. In HLM, we uncovered five GSH-trapped adducts and two semicarbazide-trapped aldehydes. Moreover, we characterized three N-acetyl cysteine conjugated-AGM adducts in mouse urine and feces, which were formed from the degradation of AGM_GSH adducts. Using recombinant CYP450 isoenzymes and chemical inhibitors, we demonstrated that CYP1A2 and CYP3A4 are primary enzymes contributing to the formation of AGM_GSH adducts and AGM_hydrazones. This study provided a global view of AGM metabolism and identified the novel pathways of AGM bioactivation, which could be utilized for further understanding the mechanism of adverse effects related to AGM and possible drug-drug interactions.

Meconium Atazanavir Concentrations and Early Language Outcomes in HIV-Exposed Uninfected Infants With Prenatal Atazanavir Exposure

OBJECTIVE

To investigate whether prenatal atazanavir (ATV) exposure, assessed by meconium antiretroviral (ARV) quantification, predicts early child language outcomes. Prenatal ATV exposure previously was associated with poorer language development in 1-year olds.

METHODS

Pregnant women with HIV and their uninfected infants enrolled in the Surveillance Monitoring of Antiretroviral Therapy Toxicities study. Meconium ARV concentrations were quantified by liquid chromatography-tandem mass spectrometry. Language development at 1 year was assessed with MacArthur-Bates Communicative Development Inventory (CDI) and Bayley Scales of Infant and Toddler Development-Third Edition (Bayley-III). Late language emergence was defined as ≥ 1 of 4 CDI scores ≤ 10th percentile for age. Associations between fetal ATV exposure timing and duration, meconium ATV concentration, and language outcomes were evaluated, adjusting for potential confounders.

RESULTS

Through 2013, meconium samples were available from 175 of 432 infants with prenatal ATV exposure. Valid Bayley-III (n = 93) and CDI (n = 106) assessments also were available. After adjustment for potential confounders, higher ATV meconium concentrations were associated with lower late language emergence risk (P = 0.04) and cumulative ATV exposure duration also was associated with higher Bayley-III Language scores (P = 0.03). Maternal ATV duration and initiation week correlated with ATV meconium concentrations (positively and negatively, respectively).

CONCLUSIONS

Higher meconium ATV concentrations were protective against developmental language delays at 1 year, suggesting the importance of fetal ATV detoxification into meconium. This information supports ATV exposure safety for infant language development. ATV is a preferred ARV for pregnant women with HIV, suggesting the importance of ATV safety investigations. Additionally, further pursuit of the influences on language development in HIV-exposed uninfected infants is required.

Predicting drug metabolism: experiment and/or computation?

Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy. To reduce the risk of costly clinical-stage attrition due to the metabolic characteristics of drug candidates, there is a need for efficient and reliable ways to predict drug metabolism in vitro, in silico and in vivo. In this Perspective, we provide an overview of the state of the art of experimental and computational approaches for investigating drug metabolism. We highlight the scope and limitations of these methods, and indicate strategies to harvest the synergies that result from combining measurement and prediction of drug metabolism.

Irreversible binding of an anticancer compound (BI-94) to plasma proteins

1. We investigated the mechanisms responsible for the in vivo instability of a benzofurazan compound BI-94 (NSC228148) with potent anti-cancer activity. 2. BI-94 was stable in MeOH, water, and in various buffers at pHs 2.5-5, regardless of the buffer composition. In contrast, BI-94 was unstable in NaOH and at pHs 7-9, regardless of the buffer composition. BI-94 disappeared immediately after spiking into mice, rat, monkey, and human plasma. BI-94 stability in plasma can be only partially restored by acidifying it, which indicated other mechanisms in addition to pH for BI-94 instability in plasma. 3. BI-94 formed adducts with the trapping agents, glutathione (GSH) and N-acetylcysteine (NAC), in vivo and in vitro via nucleophilic aromatic substitution reaction. The kinetics of adduct formation showed that neutral or physiological pHs enhanced and accelerated GSH and NAC adduct formation with BI-94, whereas acidic pHs prevented it. Therefore, physiological pHs not only altered BI-94 chemical stability but also enhanced adduct formation with endogenous nucleophiles. In addition, adduct formation with human serum albumin-peptide 3 (HSA-T3) at the Cys34 position was demonstrated. 4. In conclusion, BI-94 was unstable at physiological conditions due to chemical instability and irreversible binding to plasma proteins.

Structural and energetic analysis to provide insight residues of CYP2C9, 2C11 and 2E1 involved in valproic acid dehydrogenation selectivity

Docking and molecular dynamics (MD) simulation have been two computational techniques used to gain insight about the substrate orientation within protein active sites, allowing to identify potential residues involved in the binding and catalytic mechanisms. In this study, both methods were combined to predict the regioselectivity in the binding mode of valproic acid (VPA) on three cytochrome P-450 (CYP) isoforms CYP2C9, CYP2C11, and CYP2E1, which are involved in the biotransformation of VPA yielding reactive hepatotoxic intermediate 2-n-propyl-4-pentenoic acid (4nVPA). There are experimental data about hydrogen atom abstraction of the C4-position of VPA to yield 4nVPA, however, there are not structural evidence about the binding mode of VPA and 4nVPA on CYPs. Therefore, the complexes between these CYP isoforms and VPA or 4nVPA were studied to explore their differences in binding and energetic stabilization. Docking results showed that VPA and 4nVPA are coupled into CYPs binding site in a similar conformation, but it does not explain the VPA hydrogen atom abstraction. On the other hand, MD simulations showed a set of energetic states that reorient VPA at the first ns, then making it susceptible to a dehydrogenation reaction. For 4nVPA, multiple binding modes were observed in which the different states could favor either undergo other reaction mechanism or ligand expulsion from the binding site. Otherwise, the energetic and entropic contribution point out a similar behavior for the three CYP complexes, showing as expected a more energetically favorable binding free energy for the complexes between CYPs and VPA than with 4nVPA.

Real-time imaging of oxidative and nitrosative stress in the liver of live animals for drug-toxicity testing

Current drug-safety assays for hepatotoxicity rely on biomarkers with low predictive power. The production of radical species, specifically reactive oxygen species (ROS) and reactive nitrogen species (RNS), has been proposed as an early unifying event linking the bioactivation of drugs to hepatotoxicity and as a more direct and mechanistic indicator of hepatotoxic potential. Here we present a nanosensor for rapid, real-time in vivo imaging of drug-induced ROS and RNS for direct evaluation of acute hepatotoxicity. By combining fluorescence resonance energy transfer (FRET) and chemiluminescence resonance energy transfer (CRET), our semiconducting polymer–based nanosensor simultaneously and differentially detects RNS and ROS using two optically independent channels. Drug-induced hepatotoxicity and its remediation are imaged longitudinally in mice following systemic challenge with acetaminophen or isoniazid. Dose-dependent ROS and RNS activity is detected in the liver within minutes of drug challenge, preceding histological changes, protein nitration and DNA double strand break induction.

CPY3A4-mediated α-hydroxyaldehyde formation in saquinavir metabolism

Saquinavir (SQV) is a protease inhibitor widely used for the treatment of human immunodeficiency virus (HIV) infection. We profiled SQV metabolism in mice using a metabolomic approach. Thirty SQV metabolites were identified in mouse feces and urine, of which 20 are novel. Most metabolites observed in mice were recapitulated in human liver microsomes. Among these novel metabolites, one α-hydroxyaldehyde produced from SQV N-dealkylation was noted and verified for the first time. Meanwhile, the corresponding product (3S)-N-tert-butyldecahydro-isoquinoline-3-carboxamide and its further metabolites were identified in mouse urine. The α-hydroxyaldehyde pathway was confirmed by using semicarbazide as a trapping reagent as well. Using recombinant cytochrome P450 (CYP450) isoenzymes and Cyp3a-null mice, CYP3A was identified as the dominant enzyme contributing to the formation of α-hydroxyaldehyde. This study enhances our knowledge of SQV metabolism, which can be used for predicting drug-drug interactions and further understanding the mechanism of adverse effects associated with SQV.

In vitro bioactivation of a selective estrogen receptor modulator (2S,3R)-(+)-3-(3-hydroxyphenyl)-2-[4-(2-pyrrolidin-1-ylethoxy)phenyl]-2,3-dihydro-1,4-benzoxathiin-6-ol (I) in liver microsomes: formation of adenine adducts

As part of our efforts to develop safer selective estrogen receptor modulators (SERMs), compound I {(2S,3R)-(+)-3-(3-hydroxyphenyl)-2-[4-(2-pyrrolidin-1-ylethoxy)-phenyl]-2,3-dihydro-1,4-benzoxathiin-6-ol} was previously identified as a lead for further development. Subsequent studies showed that compound I is genotoxic in both in vitro Chinese hamster ovary (CHO) cells and in vivo mouse studies. To better understand the possible mechanisms for the observed genetoxicity effects, in vitro incubations of I with liver microsomes of human, monkey, and mouse in the presence of adenine were performed, which led to the detection of five adenine adducts. The formation of these adducts was NADPH-dependent, suggesting the involvement of oxidative bioactivation catalyzed by cytochrome P450 enzymes. The mechanism for the formation of the major adenine adduct (A1) involves the formation of a reactive ring-opened para-quinone intermediate. The formation of four other adenine adducts may involve the formation of a reactive epoxide or ortho-quinone intermediate. Furthermore, incubations of compound I with human hepatocytes showed dose-dependent DNA damages in Comet assays. All of the above suggest that some reactive metabolites of compound I, formed through bioactivation mechanisms, have a potential to interact with DNA molecules in vitro and in vivo. This may be one of the causes of the genotoxicity observed preclinically both in vitro and in vivo. This case study demonstrated an approach using in vitro DNA trapping assays for assessing the genotoxicity potential of drug candidates.

Characterization of model peptide adducts with reactive metabolites of naphthalene by mass spectrometry

Naphthalene is a volatile polycyclic aromatic hydrocarbon generated during combustion and is a ubiquitous chemical in the environment. Short term exposures of rodents to air concentrations less than the current OSHA standard yielded necrotic lesions in the airways and nasal epithelium of the mouse, and in the nasal epithelium of the rat. The cytotoxic effects of naphthalene have been correlated with the formation of covalent protein adducts after the generation of reactive metabolites, but there is little information about the specific sites of adduction or on the amino acid targets of these metabolites. To better understand the chemical species produced when naphthalene metabolites react with proteins and peptides, we studied the formation and structure of the resulting adducts from the incubation of model peptides with naphthalene epoxide, naphthalene diol epoxide, 1,2-naphthoquinone, and 1,4-naphthoquinone using high resolution mass spectrometry. Identification of the binding sites, relative rates of depletion of the unadducted peptide, and selectivity of binding to amino acid residues were determined. Adduction occurred on the cysteine, lysine, and histidine residues, and on the N-terminus. Monoadduct formation occurred in 39 of the 48 reactions. In reactions with the naphthoquinones, diadducts were observed, and in one case, a triadduct was detected. The results from this model peptide study will assist in data interpretation from ongoing work to detect peptide adducts in vivo as markers of biologic effect.

Development of a profiling strategy for metabolic mixtures by combining chromatography and mass spectrometry with cell-based GPCR signaling

In this study, we developed an in-line methodology that combines analytical with pharmacological techniques to characterize metabolites of human histamine H(4) receptor (hH(4)R) ligands. Liquid chromatographic separation of metabolic mixtures is coupled to high-resolution fractionation into 96- or 384-well plates and directly followed by a cell-based reporter gene assay to measure receptor signaling. The complete methodology was designed, optimized, validated, and ultimately miniaturized into a high-density well plate format. Finally, the methodology was demonstrated in a metabolic profiling setting for three hH(4)R lead compounds and the drug clozapine. This new methodology comprises integrated analytical separations, mass spectrometry, and a cell-based signal transduction-driven reporter gene assay that enables the implementation of comprehensive metabolic profiling earlier in the drug discovery process.

Alternative (non-animal) methods for cosmetics testing: current status and future prospects-2010

The 7th amendment to the EU Cosmetics Directive prohibits to put animal-tested cosmetics on the market in Europe after 2013. In that context, the European Commission invited stakeholder bodies (industry, non-governmental organisations, EU Member States, and the Commission's Scientific Committee on Consumer Safety) to identify scientific experts in five toxicological areas, i.e. toxicokinetics, repeated dose toxicity, carcinogenicity, skin sensitisation, and reproductive toxicity for which the Directive foresees that the 2013 deadline could be further extended in case alternative and validated methods would not be available in time. The selected experts were asked to analyse the status and prospects of alternative methods and to provide a scientifically sound estimate of the time necessary to achieve full replacement of animal testing. In summary, the experts confirmed that it will take at least another 7-9 years for the replacement of the current in vivo animal tests used for the safety assessment of cosmetic ingredients for skin sensitisation. However, the experts were also of the opinion that alternative methods may be able to give hazard information, i.e. to differentiate between sensitisers and non-sensitisers, ahead of 2017. This would, however, not provide the complete picture of what is a safe exposure because the relative potency of a sensitiser would not be known. For toxicokinetics, the timeframe was 5-7 years to develop the models still lacking to predict lung absorption and renal/biliary excretion, and even longer to integrate the methods to fully replace the animal toxicokinetic models. For the systemic toxicological endpoints of repeated dose toxicity, carcinogenicity and reproductive toxicity, the time horizon for full replacement could not be estimated.

Profiling the reactive metabolites of xenobiotics using metabolomic technologies

A predominant pathway of xenobiotic-induced toxicity is initiated by bioactivation. Characterizing reactive intermediates will provide information on the structure of reactive species, thereby defining a potential bioactivation mechanism. Because most reactive metabolites are not stable, it is difficult to detect them directly. Reactive metabolites can form adducts with trapping reagents, such as glutathione, which makes the reactive metabolites detectable. However, it is challenging to "fish" these adducts out from a complex biological matrix, especially for adducts generated via uncommon metabolic pathways. In this regard, we developed a novel approach based upon metabolomic technologies to screen trapped reactive metabolites. The bioactivation of pulegone, acetaminophen, and clozapine were reexamined by using this metabolomic approach. In all these cases, a large number of trapped reactive metabolites were readily identified. These data indicate that this metabolomic approach is an efficient tool to profile xenobiotic bioactivation.

Metabolism of gambogic acid in rats: a rare intestinal metabolic pathway responsible for its final disposition

Gambogic acid (GA) is a promising natural anticancer candidate. Although the anticancer activity of GA has been well demonstrated, information regarding the metabolic fate of GA is limited. Previous studies suggested that GA is mainly excreted into intestinal tract in rats through bile after intravenous administration, whereas only traces appeared in the feces, suggesting that GA is metabolized extensively in the intestine. However, there has been no report about the intestinal metabolism of GA either in animals or humans. In this study, large amounts of two sulfonic acid metabolites of GA were found in the feces samples of rats after intravenous administration, and their structures were identified as 10-α sulfonic acid GA and 10-β sulfonic acid GA by comparison of the retention times and spectral data with those of synthesized reference substances using liquid chromatography-diode array detector-tandem mass spectrometry. This rare intestinal metabolic pathway mainly involves Michael addition of the sulfite ion to the 9,10 carbon-carbon double bond of α,β-unsaturated ketone. In addition, a more detailed metabolic profile in rats is proposed, according to the results of in vitro and in vivo studies. It was found that GA can be metabolized by a variety of routes, including monooxidation, hydration, glutathionylation, glucuronidation, and glucosidation in the liver of rats. These findings provide information on the major metabolic soft spot of GA in the intestine and liver of rats, which is not only useful in the future human metabolic study of this compound but also of value in the metabolic studies of GA analogs.

CYP3A-mediated generation of aldehyde and hydrazine in atazanavir metabolism

Atazanavir (ATV) is an antiretroviral drug of the protease inhibitor class. Multiple adverse effects of ATV have been reported in clinical practice, such as jaundice, nausea, abdominal pain, and headache. The exact mechanisms of ATV-related adverse effects are unknown. It is generally accepted that a predominant pathway of drug-induced toxicity is through the generation of reactive metabolites. Our current study was designed to explore reactive metabolites of ATV. We used a metabolomic approach to profile ATV metabolism in mice and human liver microsomes. We identified 5 known and 13 novel ATV metabolites. Three potential reactive metabolites were detected and characterized for the first time: one aromatic aldehyde, one α-hydroxyaldehyde, and one hydrazine. These potential reactive metabolites were primarily generated by CYP3A. Our results provide a clue for studies on ATV-related adverse effects from the aspect of metabolic activation. Further studies are suggested to illustrate the impact of these potential reactive metabolites on ATV-related adverse effects.

Risk assessment and mitigation strategies for reactive metabolites in drug discovery and development

Drug toxicity is a leading cause of attrition of candidate drugs during drug development as well as of withdrawal of drugs post-licensing due to adverse drug reactions in man. These adverse drug reactions cause a broad range of clinically severe conditions including both highly reproducible and dose dependent toxicities as well as relatively infrequent and idiosyncratic adverse events. The underlying risk factors can be split into two groups: (1) drug-related and (2) patient-related. The drug-related risk factors include metabolic factors that determine the propensity of a molecule to form toxic reactive metabolites (RMs), and the RM and non-RM mediated mechanisms which cause cell and tissue injury. Patient related risk factors may vary markedly between individuals, and encompass genetic and non-genetic processes, e.g. environmental, that influence the disposition of drugs and their metabolites, the nature of the adverse responses elicited and the resulting biological consequences. We describe a new strategy, which builds upon the strategies used currently within numerous pharmaceutical companies to avoid and minimize RM formation during drug discovery, and that is intended to reduce the likelihood that candidate drugs will cause toxicity in the human population. The new strategy addresses drug-related safety hazards, but not patient-related risk factors. A common target organ of toxicity is the liver and to decrease the likelihood that candidate drugs will cause liver toxicity (both non-idiosyncratic and idiosyncratic), we propose use of an in vitro Hepatic Liability Panel alongside in vitro methods for the detection of RMs. This will enable design and selection of compounds in discovery that have reduced propensity to cause liver toxicity. In vitro Hepatic Liability is assessed using toxicity assays that quantify: CYP 450 dependent and CYP 450 independent cell toxicity; mitochondrial impairment; and inhibition of the Bile Salt Export Pump. Prior to progression into development, a Hepatotoxicity Hazard Matrix combines data from the Hepatic Liability Panel with the Estimated RM Body Burden. The latter is defined as the level of covalent binding of radiolabelled drug to human hepatocyte proteins in vitro adjusted for the predicted human dose. We exemplify the potential value of this approach by consideration of the thiazolidinedione class of drugs.