Predicting reactivity to drug metabolism: beyond P450s-modelling FMOs and UGTs

In silico and in vitro studies: investigating the chemical composition, DFT, molecular docking, and dynamic simulation of Satureja candidissima (Munby) Briq essential oil as a potential antibacterial agent

This study aimed to investigate the chemical composition and antibacterial properties of the essential oil (EO) derived from the aerial parts of Satureja candidissima (Munby) Briq (SC), as well as the mechanisms of interaction between SCEO chemical components and target proteins related to antibacterial activity mechanisms using a molecular docking approach, and for more accuracy molecular dynamic simulation and DFT calculations were carried out. The GC-MS technique was used to analyze the chemical composition of SCEO. The results showed that SCEO contained various chemical compounds, with pulegone being identified as the major component (53.26%). The results also indicated the presence of (+)-menthone (11.02%), borneol (4.43%), 2-cyclohexen-1-one, 3-methyl-6-(1-methylethylidene) (2.50%), and 3-octanol (2.09%). The study revealed that the SCEO displayed antibacterial activity against all tested gram-positive bacteria. To further understand the mechanism behind its antibacterial activity, in silico molecular docking studies were performed. The results indicated that the antibacterial effect of SCEO compounds could be due to the combination with enoyl-[acyl-carrier-protein] reductase [NADPH] FabI (PDB ID: 4ALL) in a variety of ways. The molecular dynamics simulation analysis yielded favorable outcomes for the docked complex involving 1H-cycloprop[e]azulen-7-ol, decahydro-1,1,7-trimethyl-4-methylene, and 1,4,7-tetramethyldecahydro-1H-cyclopropa[e]azulen-4-ol with enoyl-[acyl-carrier-protein] reductase [NADPH]. Geometry optimization, coupled with Density Functional Theory (DFT), can be employed to assess the importance of quantum chemical descriptors in elucidating potential antibacterial activity. Quantum descriptors were computed based on EHOMO and ELUMO. The results of this study provide important insights into the potential use of Satureja candidissima (Munby) Briq EO as antibacterial agent.

Epidemiology and Risk Determinants of Drug-Induced Liver Injury: Current Knowledge and Future Research Needs

AIMS

Drug-induced liver injury (DILI) is a major global health concern resulting from adverse reactions to medications, supplements or herbal medicines. The relevance of DILI has grown with an aging population, the rising prevalence of chronic diseases and the increased use of biologics, including checkpoint inhibitors. This article aims to summarise current knowledge on DILI epidemiology and risk factors.

METHODS

This review critically appraises available evidence on DILI frequency, outcomes and risk determinants, focusing on drug properties and non-genetic host factors that may influence susceptibility.

RESULTS

DILI incidence varies across populations, with hospitalised patients experiencing notably higher rates than outpatients or the general population. Increased medication use, particularly among older adults and women, may partly explain age- and sex-based disparities in DILI incidence and reporting. Physiological changes associated with aging likely increase susceptibility to DILI in older adults, though further exposure-based studies are needed for definitive conclusions. Current evidence does not strongly support that women are inherently more susceptible to DILI than men; rather, susceptibility appears to depend on specific drugs. However, once DILI occurs, older age and female sex are associated with greater severity and poorer outcomes. Other less-studied host-related risk factors are also discussed based on available evidence.

CONCLUSIONS

This article summarises existing data on DILI frequency, outcomes, drug properties affecting hepatotoxicity and non-genetic host risk factors while identifying critical knowledge gaps. Addressing these gaps through future research could enhance understanding and support preventive measures.

Sulfotransferase-mediated phase II drug metabolism prediction of substrates and sites using accessibility and reactivity-based algorithms

Sulphotransferases (SULTs) are a major phase II metabolic enzyme class contributing ~20 % to the Phase II metabolism of FDA-approved drugs. Ignoring the potential for SULT-mediated metabolism leaves a strong potential for drug-drug interactions, often causing late-stage drug discovery failures or black-boxed warnings on FDA labels. The existing models use only accessibility descriptors and machine learning (ML) methods for class and site of sulfonation (SOS) predictions for SULT. In this study, a variety of accessibility, reactivity, and hybrid models and algorithms have been developed to make accurate substrate and SOS predictions. Unlike the literature models, reactivity parameters for the aliphatic or aromatic hydroxyl groups (R/Ar-O-H), the Bond Dissociation Energy (BDE) gave accurate models with a True Positive Rate (TPR)=0.84 for SOS predictions. We offer mechanistic insights to explain these novel findings that are not recognized in the literature. The accessibility parameters like the ratio of Chemgauss4 Score (CGS) and Molecular Weight (MW) CGS/MW and distance from cofactor (Dis) were essential for class predictions and showed TPR=0.72. Substrates consistently had lower BDE, Dis, and CGS/MW than non-substrates. Hybrid models also performed acceptablely for SOS predictions. Using the best models, Algorithms gave an acceptable performance in class prediction: TPR=0.62, False Positive Rate (FPR)=0.24, Balanced accuracy (BA)=0.69, and SOS prediction: TPR=0.98, FPR=0.60, and BA=0.69. A rule-based method was added to improve the predictive performance, which improved the algorithm TPR, FPR, and BA. Validation using an external dataset of drug-like compounds gave class prediction: TPR=0.67, FPR=0.00, and SOS prediction: TPR=0.80 and FPR=0.44 for the best Algorithm. Comparisons with standard ML models also show that our algorithm shows higher predictive performance for classification on external datasets. Overall, these models and algorithms (SOS predictor) give accurate substrate class and site (SOS) predictions for SULT-mediated Phase II metabolism and will be valuable to the drug discovery community in academia and industry. The SOS predictor is freely available for academic/non-profit research via the GitHub link.

Predicting routes of phase I and II metabolism based on quantum mechanics and machine learning

Unexpected metabolism could lead to the failure of many late-stage drug candidates or even the withdrawal of approved drugs. Thus, it is critical to predict and study the dominant routes of metabolism in the early stages of research.We describe the development and validation of a 'WhichEnzyme' model that accurately predicts the enzyme families most likely to be responsible for a drug-like molecule's metabolism. Furthermore, we combine this model with our previously published regioselectivity models for Cytochromes P450, Aldehyde Oxidases, Flavin-containing Monooxygenases, UDP-glucuronosyltransferases and Sulfotransferases - the most important Phase I and Phase II drug metabolising enzymes - and a 'WhichP450' model that predicts the Cytochrome P450 isoform(s) responsible for a compound's metabolism.The regioselectivity models are based on a mechanistic understanding of these enzymes' actions and use quantum mechanical simulations with machine learning methods to accurately predict sites of metabolism and the resulting metabolites. We train heuristics based on the outputs of the 'WhichEnzyme', 'WhichP450', and regioselectivity models to determine the most likely routes of metabolism and metabolites to be observed experimentally.Finally, we demonstrate that this combination delivers high sensitivity in identifying experimentally reported metabolites and higher precision than other methods for predicting in vivo metabolite profiles.

Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening, and drug design. This paper also discusses the historical development of AI in medicine, emphasizing its profound impact on healthcare. Furthermore, it addresses AI's role in the repositioning of existing drugs and the identification of drug combinations, underscoring its potential in revolutionizing drug delivery systems. The manuscript provides a comprehensive overview of the AI programs and platforms currently used in drug discovery, illustrating the technological advancements and future directions of this field. This study not only presents the current state of AI in drug discovery but also anticipates its future trajectory, highlighting the challenges and opportunities that lie ahead.

Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules

We present a transferable MACE interatomic potential that is applicable to open- and closed-shell drug-like molecules containing hydrogen, carbon, and oxygen atoms. Including an accurate description of radical species extends the scope of possible applications to bond dissociation energy (BDE) prediction, for example, in the context of cytochrome P450 (CYP) metabolism. The transferability of the MACE potential was validated on the COMP6 data set, containing only closed-shell molecules, where it reaches better accuracy than the readily available general ANI-2x potential. MACE achieves similar accuracy on two CYP metabolism-specific data sets, which include open- and closed-shell structures. This model enables us to calculate the aliphatic C-H BDE, which allows us to compare reaction energies of hydrogen abstraction, which is the rate-limiting step of the aliphatic hydroxylation reaction catalyzed by CYPs. On the "CYP 3A4" data set, MACE achieves a BDE RMSE of 1.37 kcal/mol and better prediction of BDE ranks than alternatives: the semiempirical AM1 and GFN2-xTB methods and the ALFABET model that directly predicts bond dissociation enthalpies. Finally, we highlight the smoothness of the MACE potential over paths of sp3C-H bond elongation and show that a minimal extension is enough for the MACE model to start finding reasonable minimum energy paths of methoxy radical-mediated hydrogen abstraction. Altogether, this work lays the ground for further extensions of scope in terms of chemical elements, (CYP-mediated) reaction classes and modeling the full reaction paths, not only BDEs.

Predicting Regioselectivity of Cytosolic Sulfotransferase Metabolism for Drugs

Cytosolic sulfotransferases (SULTs) are a family of enzymes responsible for the sulfation of small endogenous and exogenous compounds. SULTs contribute to the conjugation phase of metabolism and share substrates with the uridine 5'-diphospho-glucuronosyltransferase (UGT) family of enzymes. UGTs are considered to be the most important enzymes in the conjugation phase, and SULTs are an auxiliary enzyme system to them. Understanding how the regioselectivity of SULTs differs from that of UGTs is essential from the perspective of developing novel drug candidates. We present a general ligand-based SULT model trained and tested using high-quality experimental regioselectivity data. The current study suggests that, unlike other metabolic enzymes in the modification and conjugation phases, the SULT regioselectivity is not strongly influenced by the activation energy of the rate-limiting step of the catalysis. Instead, the prominent role is played by the substrate binding site of SULT. Thus, the model is trained only on steric and orientation descriptors, which mimic the binding pocket of SULT. The resulting classification model, which predicts whether a site is metabolized, achieved a Cohen's kappa of 0.71.

Predicting Regioselectivity of AO, CYP, FMO, and UGT Metabolism Using Quantum Mechanical Simulations and Machine Learning

Unexpected metabolism in modification and conjugation phases can lead to the failure of many late-stage drug candidates or even withdrawal of approved drugs. Thus, it is critical to predict the sites of metabolism (SoM) for enzymes, which interact with drug-like molecules, in the early stages of the research. This study presents methods for predicting the isoform-specific metabolism for human AOs, FMOs, and UGTs and general CYP metabolism for preclinical species. The models use semi-empirical quantum mechanical simulations, validated using experimentally obtained data and DFT calculations, to estimate the reactivity of each SoM in the context of the whole molecule. Ligand-based models, trained and tested using high-quality regioselectivity data, combine the reactivity of the potential SoM with the orientation and steric effects of the binding pockets of the different enzyme isoforms. The resulting models achieve κ values of up to 0.94 and AUC of up to 0.92.

Phytochemicals as potential inhibitors for COVID-19 revealed by molecular docking, molecular dynamic simulation and DFT studies

The COVID-19 pandemic outbreak demands the designing of potential drugs as there is no specific treatment available. Thanks to their safety and effectiveness, phytochemicals have been used to treat various diseases, including antiviral therapeutics. Molecular docking is a simple, quick, and effective way to screen a variety of molecules for structure-based drug design. Here, we investigate molecular docking experiments on compounds present in plant species, Cocculus hirsutus and Rhodiola rosea and show their potential for the treatment of COVID-19. Almost all the components showed higher binding affinity than the built-in ligand, and those with significantly higher binding affinity were explored further. Molecular mechanics-based generalized born surface area calculations were used to re-rank the top candidates, rhodionidin and cocsoline, and their stability in association with viral protease was confirmed. Density functional theory was used for detailed investigations of the geometries, and electrical properties of rhodionidin and cocsoline. Using the frontier molecular orbitals method, the charge transfer within the molecule was calculated. Chemical reactivity and intermolecular interactions were studied using molecular electrostatic potential maps. These in silico discoveries will simulate the identification of powerful COVID-19 inhibitors, and similar research is likely to make a significant contribution to antiviral drug discovery.

Supplementary information

The online version contains supplementary material available at 10.1007/s11224-022-01982-4.

Inhibition of Human UDP-Glucuronosyltransferases1A1-Mediated Bilirubin Glucuronidation by the Popular Flavonoids Baicalein, Baicalin, and Hyperoside Is Responsible for Herb (Shuang-Huang-Lian)-Induced Jaundice

Bilirubin-related adverse drug reactions (ADRs) or malady (e.g., jaundice) induced by some herbs rich in certain flavonoids have been widely reported. However, the causes and mechanisms of the ADRs are not well understood. The aim of this paper was to explore the mechanism of Shuang-huang-lian (SHL) injections and its major constituents-induced jaundice via inhibiting human UDP-glucuronosyltransferases1A1 (hUGT1A1)-mediated bilirubin glucuronidation. The inhibitory effects of SHL and its major constituents in the herbal medicine, including baicalein (BAI), baicalin (BA), and hyperoside (HYP), on bilirubin glucuroBBREVInidation were investigated. This study indicated that the average formation rates of bilirubin glucuronides [i.e., mono-glucuronide 1 (BMG1), BMG2, and bilirubin diglucuronide] displayed significant differences (P < 0.05). Specifically, the formation of BMGs was favored regardless of whether an inhibitor was absent or present. SHL, BAI, BA, and HYP dose-dependently inhibit bilirubin glucuronidation, showing the IC50 values against total bilirubin glucuronidation were in the range of (7.69 ± 0.94)-(37.09 ± 2.03) μg/ml, (4.51 ± 0.27)-(20.84 ± 1.99) μM, (22.36 ± 5.74)-(41.35 ± 2.40) μM, and (15.16 ± 1.12)-(42.80 ± 2.63) μM for SHL, BAI, BA, and HYP, respectively. Both inhibition kinetics assays and molecular docking simulations suggested that SHL, BAI, BA, and HYP significantly inhibited hUGT1A1-mediated bilirubin glucuronidation via a mixed-type inhibition. Collectively, some naturally occurring flavonoids (BAI, BA, and HYP) in SHL have been identified as the inhibitors against hUGT1A1-mediated bilirubin glucuronidation, which well explains the bilirubin-related ADRs or malady triggered by SHL in clinical settings. SIGNIFICANCE STATEMENT: Herbal products and their components (e.g., flavonoids), which been widely used across the entire world, may cause liver injury. As a commonly used herbal products rich in flavonoids, SHL injections easily lead to symptoms of liver injury (e.g., jaundice) owing to significant inhibition of hUGT1A1-mediated bilirubin glucuronidation by its flavonoid components (i.e., baicalein, baicalin, and hyperoside). Herb-induced bilirubin-related ADRs and the associated clinical significance should be seriously considered.

In Silico Tools and Software to Predict ADMET of New Drug Candidates

Implication of computational techniques and in silico tools promote not only reduction of animal experimentations but also save time and money followed by rational designing of drugs as well as controlled synthesis of those "Hits" which show drug-likeness and possess suitable absorption, distribution, metabolism, excretion, and toxicity (ADMET) profile. With globalization of diseases, resistance of drugs over the time and modification of viruses and microorganisms, computational tools, and artificial intelligence are the future of drug design and one of the important areas where the principles of sustainability and green chemistry (GC) perfectly fit. Most of the new drug entities fail in the clinical trials over the issue of drug-associated human toxicity. Although ecotoxicity related to new drugs is rarely considered, but this is the high time when ecotoxicity prediction should get equal importance along with human-associated drug toxicity. Thus, the present book chapter discusses the available in silico tools and software for the fast and preliminary prediction of a series of human-associated toxicity and ecotoxicity of new drug entities to screen possibly safer drugs before going into preclinical and clinical trials.

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.

Recent developments in predicting CYP-independent metabolism

As lead optimization efforts have successfully reduced metabolic liabilities due to cytochrome P450 (CYP)-mediated metabolism, there has been an increase in the frequency of involvement of non-CYP enzymes in the metabolism of investigational compounds. Although there have been numerous notable advancements in the characterization of non-CYP enzymes with respect to their localization, reaction mechanisms, species differences and identification of typical substrates, accurate prediction of non-CYP-mediated clearance, with a particular emphasis with the difficulties in accounting for any extrahepatic contributions, remains a challenge. The current manuscript comprehensively summarizes the recent advancements in the prediction of drug metabolism and the in vitro to in vitro extrapolation of clearance for substrates of non-CYP drug metabolizing enzymes.