Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

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.

A validated LC-MS/MS analytical method for the quantification of pemigatinib: metabolic stability evaluation in human liver microsomes

Pemigatinib (PMB) is a small molecule inhibitor of fibroblast growth factor receptor 1 (FGFR1), FGFR2 and FGFR3. On April 17, 2020, the US Food and Drug Administration granted accelerated approval for PMB for the treatment of adults with previously treated, unresectable metastatic or locally advanced cholangiocarcinoma with a fibroblast growth factor receptor 2 (FGFR2) fusion or other rearrangement. PMB is considered the first targeted treatment for cholangiocarcinoma approved in the US. In this study, in silico prediction of PMB metabolic stability was done using the WhichP450 module of the StarDrop software package. Further, an LC-MS/MS analytical method was developed for PMB quantification in human liver microsomes (HLM) to experimentally assess metabolic stability. PMB and flavopiridol (FVL), used as an internal standard IS, were resolved using an isocratic mobile phase and a C18 stationary phase. The LC-MS/MS method showed linearity in the range of 5 to 500 ng mL⁻¹ in an HLM matrix (R² = 0.9995). The lower limit of quantification (LLOQ) was 5 ng mL⁻¹, indicating sensitivity. The inter- and intra-day accuracy and precision were within a variability of 10, confirming the reproducibility of the method. The measured in vitro half-life and intrinsic clearance of PMB were 27.29 min and 25.40 μL min⁻¹ mg⁻¹, respectively. PMB showed a moderate extraction ratio suggesting good bioavailability. The developed analytical method is the first LC-MS/MS method specific for PMB quantification with application to metabolic stability assessment.

PMB showed a moderate extraction ratio suggesting good bioavailability. The developed analytical method is the first LC-MS/MS method specific for PMB quantification with application to metabolic stability assessment.

Profiling of in vivo, in vitro and reactive zorifertinib metabolites using liquid chromatography ion trap mass spectrometry

Zorifertinib (AZD-3759; ZFB) is a potent, novel, oral, small molecule used for the treatment of non-small cell lung cancer (NSCLC). ZFB is Epidermal Growth Factor Receptor (EGFR) inhibitor that is characterized by good permeability of the blood–brain barrier for (NSCLC) patients with EGFR mutations. The present research reports the profiling of in vitro, in vivo and reactive metabolites of ZFB. Prediction of vulnerable metabolic sites and reactivity pathways (cyanide and GSH) of ZFB were performed by WhichP450™ module (StarDrop software package) and XenoSite reactivity model (XenoSite Web Predictor-Home), respectively. ZFB in vitro metabolites were done by incubation with isolated perfused rat liver hepatocytes and rat liver microsomes (RLMs). Extraction of ZFB and its related metabolites from the incubation matrix was done by protein precipitation. In vivo metabolism was performed by giving ZFB (10 mg kg⁻¹) through oral gavage to Sprague Dawley rats that were housed in metabolic cages. Urine was collected at specific time intervals (0, 6, 12, 18, 24, 48, 72, 96 and 120 h) from ZFB dosing. The collected urine samples were filtered then stored at −70 °C. N-Methyl piperazine ring of ZFB undergoes phase I metabolism forming iminium intermediates that were stabilized using potassium cyanide as a trapping agent. Incubation of ZFB with RLMs were performed in the presence of 1.0 mM KCN and 1.0 mM glutathione to check reactive intermediates as it is may be responsible for toxicities associated with ZFB usage. For in vitro metabolites there were six in vitro phase I metabolites, three in vitro phase II metabolites, seven reactive intermediates (four GSH conjugates and three cyano adducts) of ZFB were detected by LC-IT-MS. For in vivo metabolites there were six in vivo phase I and three in vivo phase II metabolites of ZFB were detected by LC-IT-MS. In vitro and in vivo phase I metabolic pathways were N-demethylation, O-demethylation, hydroxylation, reduction, defluorination and dechlorination. In vivo phase II metabolic reaction was direct sulphate and glucuronic acid conjugation with ZFB.

Six in vitro phase I metabolites, three in vitro phase II metabolites, seven reactive intermediates (four GSH conjugates and three cyano adducts), six in vivo phase I and three in vivo phase II metabolites of ZFB were detected by LC-IT-MS.

Packaging and Delivery of Asthma Therapeutics

Asthma is a life-altering, chronic disease of heterogenous origin that features a complex interplay of immune and environmental signaling. Although very little progress has been made in prevention, diverse types of medications and delivery systems, including nanoscale systems, have been or are currently being developed to control airway inflammation and prevent exacerbations and fibrosis. These medications are delivered through mechanical methods, with various inhalers (with benefits and drawbacks) existing, and new types offering some variety in delivery. Of particular interest is the progress being made in nanosized materials for efficient penetration into the epithelial mucus layer and delivery into the deepest parts of the lungs. Liposomes, nanoparticles, and extracellular vesicles, both natural and synthetic, have been explored in animal models of asthma and have produced promising results. This review will summarize and synthesize the latest developments in both macro-(inhaler) and micro-sized delivery systems for the purpose of treating asthma patients.

Chemical reactivity prediction: current methods and different application areas

The ability to predict chemical reactivity of a molecule is highly desirable in drug discovery, both ex vivo (synthetic route planning, formulation, stability) and in vivo: metabolic reactions determine pharmacodynamics, pharmacokinetics and potential toxic effects, and early assessment of liabilities is vital to reduce attrition rates in later stages of development. Quantum mechanics offer a precise description of the interactions between electrons and orbitals in the breaking and forming of new bonds. Modern algorithms and faster computers have allowed the study of more complex systems in a punctual and accurate fashion, and answers for chemical questions around stability and reactivity can now be provided. Through machine learning, predictive models can be built out of descriptors derived from quantum mechanics and cheminformatics, even in the absence of experimental data to train on. In this article, current progress on computational reactivity prediction is reviewed: applications to problems in drug design, such as modelling of metabolism and covalent inhibition, are highlighted and unmet challenges are posed.

A Conserved Allosteric Site on Drug-Metabolizing CYPs: A Systematic Computational Assessment

Cytochrome P450 enzymes (CYPs) are the largest group of enzymes involved in human drug metabolism. Ligand tunnels connect their active site buried at the core of the membrane-anchored protein to the surrounding solvent environment. Recently, evidence of a superficial allosteric site, here denoted as hotspot 1 (H1), involved in the regulation of ligand access in a soluble prokaryotic CYP emerged. Here, we applied multi-scale computational modeling techniques to study the conservation and functionality of this allosteric site in the nine most relevant mammalian CYPs responsible for approximately 70% of drug metabolism. In total, we systematically analyzed over 44 μs of trajectories from conventional MD, cosolvent MD, and metadynamics simulations. Our bioinformatic analysis and simulations with organic probe molecules revealed the site to be well conserved in the CYP2 family with the exception of CYP2E1. In the presence of a ligand bound to the H1 site, we could observe an enlargement of a ligand tunnel in several members of the CYP2 family. Further, we could detect the facilitation of ligand translocation by H1 interactions with statistical significance in CYP2C8 and CYP2D6, even though all other enzymes except for CYP2C19, CYP2E1, and CYP3A4 presented a similar trend. As the detailed comprehension of ligand access and egress phenomena remains one of the most relevant challenges in the field, this work contributes to its elucidation and ultimately helps in estimating the selectivity of metabolic transformations using computational techniques.

In Silico Approaches In Carcinogenicity Hazard Assessment: Current Status and Future Needs

Historically, identifying carcinogens has relied primarily on tumor studies in rodents, which require enormous resources in both money and time. In silico models have been developed for predicting rodent carcinogens but have not yet found general regulatory acceptance, in part due to the lack of a generally accepted protocol for performing such an assessment as well as limitations in predictive performance and scope. There remains a need for additional, improved in silico carcinogenicity models, especially ones that are more human-relevant, for use in research and regulatory decision-making. As part of an international effort to develop in silico toxicological protocols, a consortium of toxicologists, computational scientists, and regulatory scientists across several industries and governmental agencies evaluated the extent to which in silico models exist for each of the recently defined 10 key characteristics (KCs) of carcinogens. This position paper summarizes the current status of in silico tools for the assessment of each KC and identifies the data gaps that need to be addressed before a comprehensive in silico carcinogenicity protocol can be developed for regulatory use.

Constructing xenobiotic maps of metabolism to predict enzymes catalyzing metabolites capable of binding to DNA

Background

The liver plays a major role in the metabolic activation of xenobiotics (drugs, chemicals such as pollutants, pesticides, food additives...). Among environmental contaminants of concern, heterocyclic aromatic amines (HAA) are xenobiotics classified by IARC as possible or probable carcinogens (2A or 2B). There exist little information about the effect of these HAA in humans. While HAA is a family of more than thirty identified chemicals, the metabolic activation and possible DNA adduct formation have been fully characterized in human liver for only a few of them (MeIQx, PhIP, A\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}αC).

Results

We have developed a modeling approach in order to predict all the possible metabolites of a xenobiotic and enzymatic profiles that are linked to the production of metabolites able to bind DNA. Our prediction of metabolites approach relies on the construction of an enriched and annotated map of metabolites from an input metabolite.The pipeline assembles reaction prediction tools (SyGMa), sites of metabolism prediction tools (Way2Drug, SOMP and Fame 3), a tool to estimate the ability of a xenobotics to form DNA adducts (XenoSite Reactivity V1), and a filtering procedure based on Bayesian framework. This prediction pipeline was evaluated using caffeine and then applied to HAA. The method was applied to determine enzymes profiles associated with the maximization of metabolites derived from each HAA which are able to bind to DNA. The classification of HAA according to enzymatic profiles was consistent with their chemical structures.

Conclusions

Overall, a predictive toxicological model based on an in silico systems biology approach opens perspectives to estimate the genotoxicity of various chemical classes of environmental contaminants. Moreover, our approach based on enzymes profile determination opens the possibility of predicting various xenobiotics metabolites susceptible to bind to DNA in both normal and physiopathological situations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04363-6.

CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes

The vast majority of approved drugs are metabolized by the five major cytochrome P450 (CYP) isozymes, 1A2, 2C9, 2C19, 2D6 and 3A4. Inhibition of CYP isozymes can cause drug-drug interactions with severe pharmacological and toxicological consequences. Computational methods for the fast and reliable prediction of the inhibition of CYP isozymes by small molecules are therefore of high interest and relevance to pharmaceutical companies and a host of other industries, including the cosmetics and agrochemical industries. Today, a large number of machine learning models for predicting the inhibition of the major CYP isozymes by small molecules are available. With this work we aim to go beyond the coverage of existing models, by combining data from several major public and proprietary sources. More specifically, we used up to 18815 compounds with measured bioactivities to train random forest classification models for the individual CYP isozymes. A major advantage of the new data collection over existing ones is the better representation of the minority class, the CYP inhibitors. With the new data collection we achieved inhibitor-to-non-inhibitor ratios in the order of 1:1 (CYP1A2) to 1:3 (CYP2D6). We show that our models reach competitive performance on external data, with Matthews correlation coefficients (MCCs) ranging from 0.62 (CYP2C19) to 0.70 (CYP2D6), and areas under the receiver operating characteristic curve (AUCs) between 0.89 (CYP2C19) and 0.92 (CYPs 2D6 and 3A4). Importantly, the models show a high level of robustness, reflected in a good predictivity also for compounds that are structurally dissimilar to the compounds represented in the training data. The best models presented in this work are freely accessible for academic research via a web service.

LC-MS/MS Estimation of Rociletinib Levels in Human Liver Microsomes: Application to Metabolic Stability Estimation

BACKGROUND

Rociletinib (CO-1686; RLC) is a new, small molecule that is orally administered to inhibit mutant-selective covalent inhibitor of most epidermal growth factor receptor (EGFR)-mutated forms, including T790M, L858R, and exon 19 deletions, but not exon 20 insertions. Non-small-cell lung cancer (NSCLC) with a gene mutation that encodes EGFR is sensitive to approved EGFR inhibitors, but usually resistance develops, which is frequently mediated by T790M EGFR mutation. RLC is an EGFR inhibitor found to be active in preclinical models of EGFR-mutated NSCLC with or without T790M.

METHODS

In silico drug metabolism prediction of RLC was executed with the aid of the WhichP450 module (StarDrop software package) to verify its metabolic liability. Second, a fast, accurate, and competent LC-MS/MS assay was developed for RLC quantification to determine its metabolic stability. RLC and bosutinib (BOS) (internal standard; IS) were separated using an isocratic elution system with a C18 column (reversed stationary phase).

RESULTS

The developed LC-MS/MS analytical method showed linearity of 5-500 ng/mL with r2 ≥ 0.9998 in the human liver microsomes (HLMs) matrix. A limit of quantification of 4.6 ng/mL revealed the sensitivity of the analytical method, while the acquired inter- and intra-day accuracy and precision values below 4.63% inferred the method reproducibility. RLC metabolic stability estimation was calculated using intrinsic clearance (20.15 µL/min/mg) and in vitro half-life (34.39 min) values.

CONCLUSION

RLC exhibited a moderate extraction ratio indicative of good bioavailability. The developed analytical method herein is the first LC-MS/MS assay for RLC metabolic stability.

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

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

CYPstrate: A Set of Machine Learning Models for the Accurate Classification of Cytochrome P450 Enzyme Substrates and Non-Substrates

The interaction of small organic molecules such as drugs, agrochemicals, and cosmetics with cytochrome P450 enzymes (CYPs) can lead to substantial changes in the bioavailability of active substances and hence consequences with respect to pharmacological efficacy and toxicity. Therefore, efficient means of predicting the interactions of small organic molecules with CYPs are of high importance to a host of different industries. In this work, we present a new set of machine learning models for the classification of xenobiotics into substrates and non-substrates of nine human CYP isozymes: CYPs 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, and 3A4. The models are trained on an extended, high-quality collection of known substrates and non-substrates and have been subjected to thorough validation. Our results show that the models yield competitive performance and are favorable for the detection of CYP substrates. In particular, a new consensus model reached high performance, with Matthews correlation coefficients (MCCs) between 0.45 (CYP2C8) and 0.85 (CYP3A4), although at the cost of coverage. The best models presented in this work are accessible free of charge via the "CYPstrate" module of the New E-Resource for Drug Discovery (NERDD).

In silico investigations on curcuminoids from Curcuma longa as positive regulators of the Wnt/β-catenin signaling pathway in wound healing

Background

Curcuma longa (Turmeric) is a traditionally used herb in wound healing. The efficacy of fresh turmeric paste to heal wounds has already been investigated in multiple ethnobotanical studies. Wnt/β-catenin signaling pathway plays a significant role in wound healing and injury repair processes which has been evident in different in vitro studies. This study aims to analyze the potentiality of curcuminoids (curcumin I, curcumin II and curcumin III) from Curcuma longa to bind and enhance the activity of two intracellular signaling proteins- casein kinase-1 (CK1) and glycogen synthase kinase-3β (GSK3B) involved in Wnt/β-catenin signaling pathway. This study is largely based on a computer-based molecular docking program which mimics the in vivo condition and works on a specific algorithm to interpret the binding affinity and poses of a ligand molecule to a receptor. Subsequently, drug likeness property, ADME/Toxicity profile, pharmacological activity, and site of metabolism of the curcuminoids were also analyzed.

Results

Curcumin I showed better affinity of binding with CK1 (− 10.31 Kcal/mol binding energy) and curcumin II showed better binding affinity (− 7.55 Kcal/mol binding energy) for GSK3B. All of the ligand molecules showed quite similar pharmacological properties.

Conclusion

Curcumin has anti-oxidant, anti-carcinogenic, anti-mutagenic, anti-coagulant, and anti-infective properties. Curcumin has also anti-inflammatory and wound healing properties. It hastens wound healing by acting on different stages of the natural wound healing process. In this study, three curcumins from Curcuma longa were utilized in this experiment in a search for a drug to be used in wound healing and injury repair processes. Hopefully, this study will raise research interest among researchers.

LC-MS/MS method for the quantification of the anti-cancer agent infigratinib: Application for estimation of metabolic stability in human liver microsomes

Infigratinib (INF) is a novel small molecule, administered orally, which acts as a human fibroblast growth factor receptors (FGFRs) inhibitor. FGFRs are a family of receptor tyrosine kinases (RTK) reported to be upregulated in various tumor cell types. In 1 December 2020, BridgeBio Pharma Inc. announced FDA approval of INF as a New Drug Application, granting it Priority Review for the treatment of cholangiocarcinoma (CCA). Thus, the current study aimed to establish a validated LC-MS/MS method to estimate the INF concentration in the HLM matrix. In silico prediction of INF metabolism was done using the StarDrop® WhichP450™ module to verify its metabolic stability. An accurate and efficient LC-MS/MS analytical method was developed for INF metabolic stability evaluation. INF and duvelisib (DVB) (internal standard; IS) were eluted using an isocratic mobile phase with a C₁₈ column as a stationary reversed phase. The established LC-MS/MS method showed a linear range over 5-500 ng/mL (r² ≥ 0.9998) in human liver microsomes (HLMs). The sensitivity of the method was confirmed at its limit of quantification (4.71 ng/mL), and reproducibility was indicated by inter- and intra-day accuracy and precision (within 7.3%). The evaluation of INF metabolic stability was assessed, which reflected an intrinsic clearance of 23.6 µL/min/mg and in vitro half-life of 29.4 min. The developed approach in the current study is the first LC-MS/MS method for INF metabolic stability assessment. Application of the developed method in HLM in vitro studies suggests that INF has a moderate extraction ratio, indicating relatively good predicted oral bioavailability.

CyProduct: A Software Tool for Accurately Predicting the Byproducts of Human Cytochrome P450 Metabolism

In silico metabolism prediction is a cheminformatic task of autonomously predicting the set of metabolic byproducts produced from a specified molecule and a set of enzymes or reactions. Here, we describe a novel machine learned in silico cytochrome P450 (CYP450) metabolism prediction suite, called CyProduct, that accurately predicts metabolic byproducts for a specified molecule and a human CYP450 isoform. It includes three modules: (1) CypReact, a tool that predicts if the query compound reacts with a given CYP450 enzyme, (2) CypBoM, a tool that accurately predicts the "bond site" of the reaction (i.e., which specific bonds within the query molecule react with the CYP isoform), and (3) MetaboGen, a tool that generates the metabolic byproducts based on CypBoM's bond-site prediction. CyProduct predicts metabolic biotransformation products for each of the nine most important human CYP450 enzymes. CypBoM uses an important new concept called "bond of metabolism" (BoM), which extends the traditional "site of metabolism" (SoM) by specifying the information about the set of chemical bonds that is modified or formed in a metabolic reaction (rather than the specific atom). We created a BoM database for 1845 CYP450-mediated Phase I reactions, then used this to train the CypBoM Predictor to predict the reactive bond locations on substrate molecules. CypBoM Predictor's cross-validated Jaccard score for reactive bond prediction ranged from 0.380 to 0.452 over the nine CYP450 enzymes. Over variants of a test set of 68 known CYP450 substrates and 30 nonreactants, CyProduct outperformed the other packages, including ADMET Predictor, BioTransformer, and GLORY, by an average of 200% (with respect to Jaccard score) in terms of predicting metabolites. The CyProduct suite and the data sets are freely available at https://bitbucket.org/wishartlab/cyproduct/src/master/.

Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs

Roughly 2.8% of annual hospitalizations are a result of adverse drug interactions in the United States, representing more than 245,000 hospitalizations. Drug-drug interactions commonly arise from major cytochrome P450 (CYP) inhibition. Various approaches are routinely employed in order to reduce the incidence of adverse interactions, such as altering drug dosing schemes and/or minimizing the number of drugs prescribed; however, often, a reduction in the number of medications cannot be achieved without impacting therapeutic outcomes. Nearly 80% of drugs fail in development due to pharmacokinetic issues, outlining the importance of examining cytochrome interactions during preclinical drug design. In this review, we examined the physiochemical and structural properties of small molecule inhibitors of CYPs 3A4, 2D6, 2C19, 2C9, and 1A2. Although CYP inhibitors tend to have distinct physiochemical properties and structural features, these descriptors alone are insufficient to predict major cytochrome inhibition probability and affinity. Machine learning based in silico approaches may be employed as a more robust and accurate way of predicting CYP inhibition. These various approaches are highlighted in the review.

MetaTREE, a Novel Database Focused on Metabolic Trees, Predicts an Important Detoxification Mechanism: The Glutathione Conjugation

(1) Background: Data accuracy plays a key role in determining the model performances and the field of metabolism prediction suffers from the lack of truly reliable data. To enhance the accuracy of metabolic data, we recently proposed a manually curated database collected by a meta-analysis of the specialized literature (MetaQSAR). Here we aim to further increase data accuracy by focusing on publications reporting exhaustive metabolic trees. This selection should indeed reduce the number of false negative data. (2) Methods: A new metabolic database (MetaTREE) was thus collected and utilized to extract a dataset for metabolic data concerning glutathione conjugation (MT-dataset). After proper pre-processing, this dataset, along with the corresponding dataset extracted from MetaQSAR (MQ-dataset), was utilized to develop binary classification models using a random forest algorithm. (3) Results: The comparison of the models generated by the two collected datasets reveals the better performances reached by the MT-dataset (MCC raised from 0.63 to 0.67, sensitivity from 0.56 to 0.58). The analysis of the applicability domain also confirms that the model based on the MT-dataset shows a more robust predictive power with a larger applicability domain. (4) Conclusions: These results confirm that focusing on metabolic trees represents a convenient approach to increase data accuracy by reducing the false negative cases. The encouraging performances shown by the models developed by the MT-dataset invites to use of MetaTREE for predictive studies in the field of xenobiotic metabolism.

Fiber-Reinforced-Phospholipid Vehicle-Based Delivery of l-Ascorbic Acid: Development, Characterization, ADMET Profiling, and Efficacy by a Randomized, Single-Dose, Crossover Oral Bioavailability Study

l-ascorbic acid (AA) or vitamin C is a crucial nutrient needed for optimal health. However, being unable to be synthesized by the body, it is thus necessary to be included in health care products. Moreover, AA is one of the antioxidants that occur naturally, which is used in pharmaceutical and food products as an antioxidant additive. However, AA is vulnerable to environmental settings and undergoes oxidative degradation to dehydroascorbic acid and further to inactive products. Therefore, new research strategies and approaches are required to augment its stability. The objective of this study is to develop and characterize a fiber-reinforced-phospholipid (FRP) matrix-based vehicle, Zeal-AA, for the delivery of AA and optimize the oral bioavailability of the obtained AA powder using an efficacy study by open-label, randomized, single-dose, two-treatment, two-sequence, two-period, two-way crossover. The structural and surface morphologies were analyzed by Fourier transform infrared spectroscopy, transmission electron microscopy, scanning electron microscopy, and differential scanning calorimetry studies. Encapsulation efficiency, mean particle size, size distribution, ζ-potential measurements, and ADMET profiling revealed the potential delivery system for AA. AUC0-t was found to be 55.23 (mg/dL) for Zeal-AA, whereas it was 9.38 (mg/dL) for AA, and C max was found to be 6.69 (mg/dL) for Zeal-AA, whereas it was 1.23 (mg/dL) for AA, with a fold difference of bioavailability in terms of AUC found to be 5.9 fold. The results show that a single oral dose of Zeal-AA is capable of rising the AA levels in the body relative to the control up to 24 h.

Carbonyl reduction pathway in hepatic in vitro metabolism of anthracyclines: Impact of structure on biotransformation rate

Carbonyl reduction biotransformation pathway of anthracyclines (doxorubicin, daunorubicin) is a significant process, associated with drug metabolism and elimination. However, it also plays a pivotal role in anthracyclines-induced cardiotoxicity and cancer resistance. Herein, carbonyl reduction of eight anthracyclines, at in vivo relevant concentrations (20 μM), was studied in human liver cytosol, to describe the relationship between their structure and metabolism. Significant differences of intrinsic clearance between anthracyclines, ranging from 0,62-74,9 μL/min/mg were found and associated with data from in silico analyses, considering their binding in active sites of the main anthracyclines-reducing enzymes: carbonyl reductase 1 (CBR1) and aldo-keto reductase 1C3 (AKR1C3). Partial atomic charges of carbonyl oxygen atom were also determined and considered as a factor associated with reaction rate. Structural features, including presence or absence of side-chain hydroxy group, a configuration of sugar chain hydroxy group, and tetracyclic rings substitution, affecting anthracyclines susceptibility for carbonyl reduction were identified.

Artificial Intelligence (AI) to the Rescue: Deploying Machine Learning to Bridge the Biorelevance Gap in Antioxidant Assays

Oxidative stress induced by excessive levels of reactive oxygen species (ROS) underlies several diseases. Therapeutic strategies to combat oxidative damage are, therefore, a subject of intense scientific investigation to prevent and treat such diseases, with the use of phytochemical antioxidants, especially polyphenols, being a major part. Polyphenols, however, exhibit structural diversity that determines different mechanisms of antioxidant action, such as hydrogen atom transfer (HAT) and single-electron transfer (SET). They also suffer from inadequate in vivo bioavailability, with their antioxidant bioactivity governed by permeability, gut-wall and first-pass metabolism, and HAT-based ROS trapping. Unfortunately, no current antioxidant assay captures these multiple dimensions to be sufficiently "biorelevant," because the assays tend to be unidimensional, whereas biorelevance requires integration of several inputs. Finding a method to reliably evaluate the antioxidant capacity of these phytochemicals, therefore, remains an unmet need. To address this deficiency, we propose using artificial intelligence (AI)-based machine learning (ML) to relate a polyphenol's antioxidant action as the output variable to molecular descriptors (factors governing in vivo antioxidant activity) as input variables, in the context of a biomarker selectively produced by lipid peroxidation (a consequence of oxidative stress), for example F2-isoprostanes. Support vector machines, artificial neural networks, and Bayesian probabilistic learning are some key algorithms that could be deployed. Such a model will represent a robust predictive tool in assessing biorelevant antioxidant capacity of polyphenols, and thus facilitate the identification or design of antioxidant molecules. The approach will also help to fulfill the principles of the 3Rs (replacement, reduction, and refinement) in using animals in biomedical research.

Identification and characterization of in vitro, in vivo, and reactive metabolites of tandutinib using liquid chromatography ion trap mass spectrometry

Tandutinib (TND) is a novel, oral small molecule designed for treating acute myeloid leukemia (AML) by inhibiting type III receptor tyrosine kinases. This study reports the use of in silico, in vivo, and in vitro methods to investigate the metabolism and possible metabolic bioactivation of TND. First, in silico metabolism of TND was assessed using the WhichP450™ module of the StarDrop® software to determine labile sites of metabolism in the TND chemical structure. Second, the XenoSite reactivity model, a web-based metabolism prediction software, was used to determine probable bioactive centers. Based on the in silico outcomes, a list of predicted metabolites and reactive intermediates were prepared. Third, in vitro and in vivo experiments were performed. In vitro TND metabolites were generated through incubation of TND with rat liver microsomes (RLMs). Another incubation of TND with RLMs was separately performed in the presence of GSH and KCN to check for the generation of reactive intermediates (soft and hard electrophiles). In vitro phase II metabolism was assessed by incubation of TND with isolated perfused rat hepatocytes. In vivo metabolism was investigated by oral gavage of TND (37 mg kg-1) in Sprague Dawley rats. Five in vitro phase I metabolites, one in vitro phase II and five reactive iminium intermediates (cyano adducts), six in vivo phase I, and one in vivo phase II metabolites of TND were characterized. The in vitro and in vivo metabolic pathways involved were O-dealkylation, α-hydroxylation, α-carbonyl formation, reduction, glucuronide, and sulfate conjugation. No GSH conjugate or its catabolic products were detected either in vitro or in vivo. Two cyclic tertiary rings of TND (piperazine and piperidine) were metabolically bioactivated to generate reactive iminium intermediates forming cyano adducts with KCN. The formed reactive intermediates may be the reason behind TND toxicity. In silico toxicological studies were performed for TND and its related (in vitro and in vivo) metabolites were evaluated using the DEREK software tool.

Use of artificial intelligence to enhance phenotypic drug discovery

Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The respective merits of the two main drug discovery approaches, phenotypic and target based, have divided opinion across the research community, because each hold different advantages for identifying novel molecular entities with a successful path to the market. Nevertheless, both have low translatability in the clinic. Artificial intelligence (AI) and adoption of machine learning (ML) tools offer the promise of revolutionising drug development, and overcoming obstacles in the drug discovery pipeline. Here, we assess the potential of target-driven and phenotypic-based approaches and offer a holistic description of the current state of the field, from both a scientific and industry perspective. With the emerging partnerships between AI/ML and pharma still in their relative infancy, we investigate the potential and current limitations with a particular focus on phenotypic drug discovery. Finally, we emphasise the value of public-private partnerships (PPPs) and cross-disciplinary collaborations to foster innovation and facilitate efficient drug discovery programmes.

Deciphering Reaction Determinants of Altered-Activity CYP2D6 Variants by Well-Tempered Metadynamics Simulation and QM/MM Calculations

The xenobiotic metabolizing enzyme CYP2D6 is the P450 cytochrome family member with the highest rate of polymorphism. This causes changes in the enzyme activity and specificity, which can ultimately lead to adverse reactions during drug treatment. To avoid or lower CYP-related toxicity risks, prediction of the most likely positions within a molecule where a metabolic reaction might occur is paramount. In order to obtain accurate predictions, it is crucial to understand all phenomena within the active site of the enzyme that contribute to an efficient substrate recognition and the subsequent catalytic reaction together with their relative weight within the overall thermodynamic context. This study aims to define the weight of the driving forces upon the C-H bond activation within CYP2D6 wild-type and a clinically relevant allelic variant with increased activity (CYP2D6*53) featuring two amino acid mutations in close vicinity of the heme. First, we investigated the steric and electrostatic complementarity of the substrate bufuralol using well-tempered metadynamics simulations with the aim to obtain the free energy profiles for each site of metabolism (SoM) within the different active sites. Second, the stereoelectronic complementarity was determined for each SoM within the two different active-site environments. Relying on the well-tempered metadynamics simulation energy profiles of each SoM, we identified the binding mode that was closest to the preferred transition-state geometry for efficient C-H bond activation. The binding modes were then used as starting structures for the quantum mechanics/molecular mechanics calculations performed to quantify the corresponding activation barriers. Our results show the relevance of the steric component in orienting the SoM in an energetically accessible position toward the heme. However, the corresponding intrinsic reactivity and electronic complementarity within the active site must be accurately evaluated in order to obtain a meaningful reaction prediction, from which the predominant SoM can be determined. The F120I mutation lowered the activation barrier for the major site and one of the minor SoMs. However, it had an impact neither on the CYP2D6 enantioselectivity preference of the oxidation reaction nor on the stereoselectivity from the substrate point of view.

Application of Advanced Technologies in Natural Product Research: A Review with Special Emphasis on ADMET Profiling

The successful conversion of natural products (NPs) into lead compounds and novel pharmacophores has emboldened the researchers to harness the drug discovery process with a lot more enthusiasm. However, forfeit of bioactive NPs resulting from an overabundance of metabolites and their wide dynamic range have created the bottleneck in NP researches. Similarly, the existence of multidimensional challenges, including the evaluation of pharmacokinetics, pharmacodynamics, and safety parameters, has been a concerning issue. Advancement of technology has brought the evolution of traditional natural product researches into the computer-based assessment exhibiting pretentious remarks about their efficiency in drug discovery. The early attention to the quality of the NPs may reduce the attrition rate of drug candidates by parallel assessment of ADMET profiling. This article reviews the status, challenges, opportunities, and integration of advanced technologies in natural product research. Indeed, emphasis will be laid on the current and futuristic direction towards the application of newer technologies in early-stage ADMET profiling of bioactive moieties from the natural sources. It can be expected that combinatorial approaches in ADMET profiling will fortify the natural product-based drug discovery in the near future.

In silico approach: biological prediction of nordentatin derivatives as anticancer agent inhibitors in the cAMP pathway

A combination of computational techniques has been carried out to predict the binding of nordentatin derivatives based on pyranocoumarin semi-synthesis with the target protein from the expression of the PDE4B gene. The inhibition of the cAMP pathway is the main target of anti-cancer drugs, which is responsible for uncontrolled cell division in cancer. Modeling was done using a combination of semi-empirical methods and the density functional theory (PM3-DFT/6-31G*/B3LYP) to obtain the optimal structure of a small ligand that could be modeled. Studies on the interaction of the ligands and amino acid residues on protein targets were carried out using a combination of molecular docking and molecular dynamic simulation. Molecular docking based on functional grid scores showed a very good native ligand pose with an RMSD of 0.93 Å in determining the initial coordinates of the ligand-receptor interactions. Furthermore, the amino acid residues responsible for interaction through H-bonds were Tyr103, His104, His177, Met217, and Gln313. The binding free energy (kcal mol-1) results of the candidates were PS-1 (-36.84 ± 0.31), PS-2 (-35.34 ± 0.28), PS-3 (-26.65 ± 0.30), PS-5 (-42.66 ± 0.26), PS-7 (-35.33 ± 0.23), and PS-9 (-32.57 ± 0.20), which are smaller than that of the native ligand Z72 (-24.20 ± 0.19), and thus these have good potential as drugs that can inhibit the cAMP pathway. These results provide theoretical information for the efficient inhibition of the cAMP pathway in the future.

LC-MS/MS Estimation of the Anti-Cancer Agent Tandutinib Levels in Human Liver Microsomes: Metabolic Stability Evaluation Assay

PURPOSE

Tandutinib (MLN518 or CT 53518) (TND) is a novel, oral, small-molecule inhibitor of type III receptor tyrosine kinases utilized for the treatment of acute myeloid leukemia (AML).

MATERIALS AND METHODS

In silico prediction of hepatic drug metabolism for TND was determined using the StarDrop® WhichP450™ module to confirm its metabolic liability. Second, an efficient and accurate LC-MS/MS method was established for TND quantification to evaluate metabolic stability. TND and entrectinib (ENC) (internal standard; IS) were resolved using an isocratic elution system with a reversed stationary phase (C8 column).

RESULTS

The established LC-MS/MS method exhibited linearity (5-500 ng/mL) with r2 ≥0.9999 in the human liver microsomes matrix. The method sensitivity was indicated by the limit of quantification (3.8 ng/mL), and reproducibility was revealed by inter- and intraday precision and accuracy (below 10.5%). TND metabolic stability estimation was calculated using intrinsic clearance (22.03 µL/min/mg) and in vitro half-life (29.0 min) values.

CONCLUSION

TND exhibited a moderate extraction ratio indicative of good bioavailability. According to the literature, the approach developed in the present study is the first established LC-MS/MS method for assessing TND metabolic stability.

Computer-Aided Estimation of Biological Activity Profiles of Drug-Like Compounds Taking into Account Their Metabolism in Human Body

Most pharmaceutical substances interact with several or even many molecular targets in the organism, determining the complex profiles of their biological activity. Moreover, due to biotransformation in the human body, they form one or several metabolites with different biological activity profiles. Therefore, the development and rational use of novel drugs requires the analysis of their biological activity profiles, taking into account metabolism in the human body. In silico methods are currently widely used for estimating new drug-like compounds' interactions with pharmacological targets and predicting their metabolic transformations. In this study, we consider the estimation of the biological activity profiles of organic compounds, taking into account the action of both the parent molecule and its metabolites in the human body. We used an external dataset that consists of 864 parent compounds with known metabolites. It is shown that the complex assessment of active pharmaceutical ingredients' interactions with the human organism increases the quality of computer-aided estimates. The toxic and adverse effects showed the most significant difference: reaching 0.16 for recall and 0.14 for precision.

Prediction of drug metabolites using neural machine translation

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

Dissecting the Structural Plasticity and Dynamics of Cytochrome P450 2B4 by Molecular Dynamics Simulations

The plasticity of cytochromes P450 (P450s) is known to contribute significantly to their catalytic capacity of metabolizing various substrates. Although numerous studies have been performed, factors governing the plasticity and dynamics of P450s are still not fully understood. In this study, taking CYP2B4 as an example, we dissect the protein plasticity and dynamics in different environments. CYP2B4 is featured by a high degree of plasticity, which exhibits open, closed, and intermediate states. By analyzing the CYP2B4 crystal structures, we identified the structural features for the closed, open, and intermediate states. Interestingly, formation of the dimer structure was found in the open and intermediate states. The subsequent molecular dynamics (MD) simulations of the open structure in water confirmed the importance of the dimer form in stabilizing the open conformations. MD simulations of the closed and open structures in the membrane environment and the free energies for opening the F-G cassette obtained from the umbrella sampling calculations indicate that the membrane environment is important for stabilizing the F-G cassette. The dynamical network analysis indicates that Asp105 on the B-C loop plays an important role in transiting the structure from the open to the intermediate state. Our results thus unveil the mechanisms of dimer formation and open-to-intermediate transition for CYP2B4 in the water and membrane environments.

The integrated use of in silico methods for the hepatotoxicity potential of Piper methysticum

Herbal products as supplements and therapeutic intervention have been used for centuries. However, their toxicities are not completely evaluated and the mechanisms are not clearly understood. Dried rhizome of the plant kava (Piper methysticum) is used for its anxiolytic, and sedative effects. The drug is also known for its hepatotoxicity potential. Major constituents of the plant were identified as kavalactones, alkaloids and chalcones in previous studies. Kava hepatotoxicity mechanism and the constituent that causes the toxicity have been debated for decades. In this paper, we illustrated the use of computational tools for the hepatotoxicity of kava constituents. The proposed mechanisms and major constituents that are most probably responsible for the toxicity have been scrutinized. According to the experimental and prediction results, the kava constituents play a substantial role in hepatotoxicity by some means or other via glutathione depletion, CYP inhibition, reactive metabolite formation, mitochondrial toxicity and cyclooxygenase activity. Some of the constituents, which have not been tested yet, were predicted to involve mitochondrial membrane potential, caspase-3 stimulation, and AhR activity. Since Nrf2 activation could be favorable for prevention of hepatotoxicity, we also suggest that these compounds should undergo testing given that they were predicted not to be activating Nrf2. Among the major constituents, alkaloids appear to be the least studied and the least toxic group in general. The outcomes of the study could help to appreciate the mechanisms and to prioritize the kava constituents for further testing.

Nitrogen-Walk Approach to Explore Bioisosteric Replacements in a Series of Potent A2B Adenosine Receptor Antagonists

A systematic exploration of bioisosteric replacements for furan and thiophene cores in a series of potent A_2BAR antagonists has been carried out using the nitrogen-walk approach. A collection of 42 novel alkyl 4-substituted-2-methyl-1,4-dihydrobenzo[4,5]imidazo[1,2-a]pyrimidine-3-carboxylates, which contain 18 different pentagonal heterocyclic frameworks at position 4, was synthesized and evaluated. This study enabled the identification of new ligands that combine remarkable affinity (K_i < 30 nM) and exquisite selectivity. The structure-activity relationship (SAR) trends identified were substantiated by a molecular modeling study, based on a receptor-driven docking model and including a systematic free energy perturbation (FEP) study. Preliminary evaluation of the CYP3A4 and CYP2D6 inhibitory activity in optimized ligands evidenced weak and negligible activity, respectively. The stereospecific interaction between hA_2BAR and the eutomer of the most attractive novel antagonist (S)-18g (K_i = 3.66 nM) was validated.

In-Silico Modeling in Drug Metabolism and Interaction: Current Strategies of Lead Discovery

BACKGROUND

Drug metabolism is a complex mechanism of human body systems to detoxify foreign particles, chemicals, and drugs through bio alterations. It involves many biochemical reactions carried out by invivo enzyme systems present in the liver, kidney, intestine, lungs, and plasma. After drug administration, it crosses several biological membranes to reach into the target site for binding and produces the therapeutic response. After that, it may undergo detoxification and excretion to get rid of the biological systems. Most of the drugs and its metabolites are excreted through kidney via urination. Some drugs and their metabolites enter into intestinal mucosa and excrete through feces. Few of the drugs enter into hepatic circulation where they go into the intestinal tract. The drug leaves the liver via the bile duct and is excreted through feces. Therefore, the study of total methodology of drug biotransformation and interactions with various targets is costly.

METHODS

To minimize time and cost, in-silico algorithms have been utilized for lead-like drug discovery. Insilico modeling is the process where a computer model with a suitable algorithm is developed to perform a controlled experiment. It involves the combination of both in-vivo and in-vitro experimentation with virtual trials, eliminating the non-significant variables from a large number of variable parameters. Whereas, the major challenge for the experimenter is the selection and validation of the preferred model, as well as precise simulation in real physiological status.

RESULTS

The present review discussed the application of in-silico models to predict absorption, distribution, metabolism, and excretion (ADME) properties of drug molecules and also access the net rate of metabolism of a compound.

CONCLUSION

It helps with the identification of enzyme isoforms; which are likely to metabolize a compound, as well as the concentration dependence of metabolism and the identification of expected metabolites. In terms of drug-drug interactions (DDIs), models have been described for the inhibition of metabolism of one compound by another, and for the compound-dependent induction of drug-metabolizing enzymes.

In Silico Pharmacogenetics CYP2D6 Study Focused on the Pharmacovigilance of Herbal Antidepressants

The annual increase in depression worldwide together with an upward trend in the use of alternative medicine as treatment asks for developing reliable safety profiles of herbal based medicine. A considerable risk on adverse reactions exists when herbal remedies are combined with prescription medication. Around 25% of the drugs, including many antidepressants, depend on the activity of CYP2D6 for their metabolism and corresponding efficacy. Therefore, probing CYP2D6 inhibition by the active substances in herbal based medicine within the wild-type enzyme and clinically relevant allelic variants is crucial to avoid toxicity issues. In this in silico study several compounds with herbal origin suggested to have antidepressant activity were analyzed on their CYP2D6 wild-type and CYP2D6*53 inhibition potential using molecular docking. In addition, several pharmacokinetic properties were evaluated to assess their probability to cross the blood brain barrier and subsequently reach sufficient brain bioavailability for the modulation of central nervous system targets as well as characteristics which may hint toward potential safety issues.

Natural Compounds as Pharmaceuticals: The Key Role of Cytochromes P450 Reactivity

The design of drugs from natural products is a re-emerging area due to the need for bioactive compounds. The exploitation of natural products and their derivatives obtained by biocatalysis is in line with the higher attention given today to new sustainable technologies that better preserve the environment (green chemistry). The research field of cytochromes P450 (CYPs) is continuously providing new enzymes and mutants that produce metabolites suitable for late-stage functionalization for new potential drugs. This review provides an overview of the exploitation of CYPs as biocatalysts in drug synthesis. Additionally, recent progress in protein and metabolic engineering is provided to show how these enzymes offer a toolbox that can be combined with other biocatalytic or chemical processes to build new platforms for the green production of new drugs.

Electrochemical and in silico approaches for liver metabolic oxidation of antitumor-active triazoloacridinone C-1305

5-Dimethylaminopropylamino-8-hydroxytriazoloacridinone (C-1305) is a promising antitumor compound developed in our laboratory. A better understanding of its metabolic transformations is still needed to explain the multidirectional mechanism of pharmacological action of triazoloacridinone derivatives at all. Thus, the aim of the current work was to predict oxidative pathways of C-1305 that would reflect its phase I metabolism. The multi-tool analysis of C-1305 metabolism included electrochemical conversion and in silico sites of metabolism predictions in relation to liver microsomal model. In the framework of the first approach, an electrochemical cell was coupled on-line to an electrospray ionization mass spectrometer. The effluent of the electrochemical cell was also injected onto a liquid chromatography column for the separation of different products formed prior to mass spectrometry analysis. In silico studies were performed using MetaSite software. Standard microsomal incubation was employed as a reference procedure. We found that C-1305 underwent electrochemical oxidation primarily on the dialkylaminoalkylamino moiety. An unknown N-dealkylated and hydroxylated C-1305 products have been identified. The electrochemical system was also able to simulate oxygenation reactions. Similar pattern of C-1305 metabolism has been predicted using in silico approach. Both proposed strategies showed high agreement in relation to the generated metabolic products of C-1305. Thus, we conclude that they can be considered as simple alternatives to enzymatic assays, affording time and cost efficiency.

•

Three different strategies for the investigation of C-1305 oxidative metabolism were presented.

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Phase I products of the antitumor agent were easily generated within a matrix-free environment.

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Products of C-1305 electrochemical oxidation were typical for P450-catalyzed reactions.

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We observed a good accordance between electrochemical, in silico, and enzymatic results.

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Electrochemical and in silico methods are fast alternatives for enzymatic assays.

Synthetic Derivatives of (+)-epi-α-Bisabolol Are Formed by Mammalian Cytochromes P450 Expressed in a Yeast Reconstituted Pathway

Identification of the enzyme(s) involved in complex biosynthetic pathways can be challenging. An alternative approach might be to deliberately diverge from the original natural enzyme source and use promiscuous enzymes from other organisms. In this paper, we have tested the ability of a series of human and animal cytochromes P450 involved in xenobiotic detoxification to generate derivatives of (+)-epi-α-bisabolol and attempt to produce the direct precursor of hernandulcin, a sweetener from Lippia dulcis for which the last enzymatic steps are unknown. Screening steps were implemented in vivo in S. cerevisiae optimized for the biosynthesis of oxidized derivatives of (+)-epi-α-bisabolol by coexpressing two key enzymes: the (+)-epi-α-bisabolol synthase and the NADPH cytochrome P450 reductase. Five out of 25 cytochromes P450 were capable of producing new hydroxylated regioisomers of (+)-epi-α-bisabolol. Of the new oxidized bisabolol products, the structure of one compound, 14-hydroxy-(+)-epi-α-bisabolol, was fully elucidated by NMR while the probable structure of the second product was determined. In parallel, the production of (+)-epi-α-bisabolol derivatives was enhanced through the addition of a supplementary genomic copy of (+)-epi-α-bisabolol synthase that augmented the final titer of hydroxylated product to 64 mg/L. We thus demonstrate that promiscuous drug metabolism cytochromes P450 can be used to produce novel compounds from a terpene scaffold.

Natural products against acute respiratory infections: Strategies and lessons learned

ETHNOPHARMACOLOGICAL RELEVANCE

A wide variety of traditional herbal remedies have been used throughout history for the treatment of symptoms related to acute respiratory infections (ARIs).

AIM OF THE REVIEW

The present work provides a timely overview of natural products affecting the most common pathogens involved in ARIs, in particular influenza viruses and rhinoviruses as well as bacteria involved in co-infections, their molecular targets, their role in drug discovery, and the current portfolio of available naturally derived anti-ARI drugs.

MATERIALS AND METHODS

Literature of the last ten years was evaluated for natural products active against influenza viruses and rhinoviruses. The collected bioactive agents were further investigated for reported activities against ARI-relevant bacteria, and analysed for the chemical space they cover in relation to currently known natural products and approved drugs.

RESULTS

An overview of (i) natural compounds active in target-based and/or phenotypic assays relevant to ARIs, (ii) extracts, and (iii) in vivo data are provided, offering not only a starting point for further in-depth phytochemical and antimicrobial studies, but also revealing insights into the most relevant anti-ARI scaffolds and compound classes. Investigations of the chemical space of bioactive natural products based on principal component analysis show that many of these compounds are drug-like. However, some bioactive natural products are substantially larger and have more polar groups than most approved drugs. A workflow with various strategies for the discovery of novel antiviral agents is suggested, thereby evaluating the merit of in silico techniques, the use of complementary assays, and the relevance of ethnopharmacological knowledge on the exploration of the therapeutic potential of natural products.

CONCLUSIONS

The longstanding ethnopharmacological tradition of natural remedies against ARIs highlights their therapeutic impact and remains a highly valuable selection criterion for natural materials to be investigated in the search for novel anti-ARI acting concepts. We observe a tendency towards assaying for broad-spectrum antivirals and antibacterials mainly discovered in interdisciplinary academic settings, and ascertain a clear demand for more translational studies to strengthen efforts for the development of effective and safe therapeutic agents for patients suffering from ARIs.

User-Friendly Quantum Mechanics: Applications for Drug Discovery

Quantum mechanics (QM) methods provide a fine description of receptor-ligand interactions and of chemical reactions. Their use in drug design and drug discovery is increasing, especially for complex systems including metal ions in the binding sites, for the design of highly selective inhibitors, for the optimization of bi-specific compounds, to understand enzymatic reactions, and for the study of covalent ligands and prodrugs. They are also used for generating molecular descriptors for predictive QSAR/QSPR models and for the parameterization of force fields. Thanks to the continuous increase of computational power offered by GPUs and to the development of sophisticated algorithms, QM methods are becoming part of the standard tools used in computer-aided drug design (CADD). We present the most used QM methods and software packages, and we discuss recent representative applications in drug design and drug discovery.

Scaffold-hopping as a strategy to address metabolic liabilities of aromatic compounds

Understanding and minimizing oxidative metabolism of aromatic compounds is a key hurdle in lead optimization. Metabolic processes not only clear compounds from the body, but they can also transform parent compounds into reactive metabolites. One particularly useful strategy when addressing metabolically labile or oxidation-prone structures is scaffold-hopping. Replacement of an aromatic system with a more electron-deficient ring system can often increase robustness towards cytochrome P450-mediated oxidation while conserving the structural requirements of the pharmacophore. The most common example of this substitution strategy, replacement of a phenyl ring with a pyridyl substituent, is prevalent throughout the literature; however scaffold-hopping encompasses a much wider scope of heterocycle replacement. This review will showcase recent examples where different scaffold-hopping approaches were used to reduce metabolic clearance or block the formation of reactive metabolites. Additionally, we will highlight considerations that should be made to garner the most benefit from a scaffold-hopping strategy for lead optimization.

FAME 3: Predicting the Sites of Metabolism in Synthetic Compounds and Natural Products for Phase 1 and Phase 2 Metabolic Enzymes

In this work we present the third generation of FAst MEtabolizer (FAME 3), a collection of extra trees classifiers for the prediction of sites of metabolism (SoMs) in small molecules such as drugs, druglike compounds, natural products, agrochemicals, and cosmetics. FAME 3 was derived from the MetaQSAR database ( Pedretti et al. J. Med. Chem. 2018 , 61 , 1019 ), a recently published data resource on xenobiotic metabolism that contains more than 2100 substrates annotated with more than 6300 experimentally confirmed SoMs related to redox reactions, hydrolysis and other nonredox reactions, and conjugation reactions. In tests with holdout data, FAME 3 models reached competitive performance, with Matthews correlation coefficients (MCCs) ranging from 0.50 for a global model covering phase 1 and phase 2 metabolism, to 0.75 for a focused model for phase 2 metabolism. A model focused on cytochrome P450 metabolism yielded an MCC of 0.57. Results from case studies with several synthetic compounds, natural products, and natural product derivatives demonstrate the agreement between model predictions and literature data even for molecules with structural patterns clearly distinct from those present in the training data. The applicability domains of the individual models were estimated by a new, atom-based distance measure (FAMEscore) that is based on a nearest-neighbor search in the space of atom environments. FAME 3 is available via a public web service at https://nerdd.zbh.uni-hamburg.de/ and as a self-contained Java software package, free for academic and noncommercial research.

Herb-Induced Liver Injury: Phylogenetic Relationship, Structure-Toxicity Relationship, and Herb-Ingredient Network Analysis

Currently, hundreds of herbal products with potential hepatotoxicity were available in the literature. A comprehensive summary and analysis focused on these potential hepatotoxic herbal products may assist in understanding herb-induced liver injury (HILI). In this work, we collected 335 hepatotoxic medicinal plants, 296 hepatotoxic ingredients, and 584 hepatoprotective ingredients through a systematic literature retrieval. Then we analyzed these data from the perspectives of phylogenetic relationship and structure-toxicity relationship. Phylogenetic analysis indicated that hepatotoxic medicinal plants tended to have a closer taxonomic relationship. By investigating the structures of the hepatotoxic ingredients, we found that alkaloids and terpenoids were the two major groups of hepatotoxicity. We also identified eight major skeletons of hepatotoxicity and reviewed their hepatotoxic mechanisms. Additionally, 15 structural alerts (SAs) for hepatotoxicity were identified based on SARpy software. These SAs will help to estimate the hepatotoxic risk of ingredients from herbs. Finally, a herb-ingredient network was constructed by integrating multiple datasets, which will assist to identify the hepatotoxic ingredients of herb/herb-formula quickly. In summary, a systemic analysis focused on HILI was conducted which will not only assist to identify the toxic molecular basis of hepatotoxic herbs but also contribute to decipher the mechanisms of HILI.

GLORY: Generator of the Structures of Likely Cytochrome P450 Metabolites Based on Predicted Sites of Metabolism

Computational prediction of xenobiotic metabolism can provide valuable information to guide the development of drugs, cosmetics, agrochemicals, and other chemical entities. We have previously developed FAME 2, an effective tool for predicting sites of metabolism (SoMs). In this work, we focus on the prediction of the chemical structures of metabolites, in particular metabolites of xenobiotics. To this end, we have developed a new tool, GLORY, which combines SoM prediction with FAME 2 and a new collection of rules for metabolic reactions mediated by the cytochrome P450 enzyme family. GLORY has two modes: MaxEfficiency and MaxCoverage. For MaxEfficiency mode, the use of predicted SoMs to restrict the locations in the molecule at which the reaction rules could be applied was explored. For MaxCoverage mode, the predicted SoM probabilities were instead used to develop a new scoring approach for the predicted metabolites. With this scoring approach, GLORY achieves a recall of 0.83 and can predict at least one known metabolite within the top three ranked positions for 76% of the molecules of a new, manually curated test set. GLORY is freely available as a web server at https://acm.zbh.uni-hamburg.de/glory/, and the datasets and reaction rules are provided in the Supplementary Material.

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

In this review, we present important, recent developments in the computational prediction of cytochrome P450 (CYP) metabolism in the context of drug discovery. We discuss in silico models for the various aspects of CYP metabolism prediction, including CYP substrate and inhibitor predictors, site of metabolism predictors (i.e., metabolically labile sites within potential substrates) and metabolite structure predictors. We summarize the different approaches taken by these models, such as rule‐based methods, machine learning, data mining, quantum chemical methods, molecular interaction fields, and docking. We highlight the scope and limitations of each method and discuss future implications for the field of metabolism prediction in drug discovery.