XenoSite: Accurately Predicting CYP-Mediated Sites of Metabolism with Neural Networks

Identification and characterization of GSK-9089 metabolites through high resolution-mass spectrometry based in vitro and in vivo rat biological sample analysis

Estrogen related receptors (ERRs) agonist GSK-9089 (DY-131) reported to pose a potential in increasing exercise endurance. High resolution mass spectrometry (HRMS) based analysis has utmost importance in the detection, identification, or characterization of a molecule including its metabolites in human body. In this study, in vitro metabolism profile of GSK-9089 was investigated after incubation with liver microsomes and S9 fractions. Additionally, in vivo metabolites of the molecule were identified in plasma, urine, and faeces samples of rats. Structures of all the potential metabolites were revealed by employing an in silico tool and HRMS based analysis through data-dependent and data-independent mining strategies. Nine unknown metabolites of GSK-9089 have been identified which were found to be present in a trace amount in in vivo matrices. Most of the in vitro and in vivo phase I metabolites of the molecule were formed after imine bond hydrolysis followed by deamidation, oxidation, and N-oxidation. The molecule underwent phase II metabolism to generate more polar metabolites mainly through glucuronide, sulfate conjugation biotransformation reactions. The in vitro and in vivo metabolites of GSK-9089 could be useful to identify the abuse of this ERRs agonist in the future.

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

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

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.

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules

INTRODUCTION

Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary.

AREAS COVERED

This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review.

EXPERT OPINION

ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.

Anticancer activity, DFT study, ADMET prediction, and molecular docking of novel α-sulfamidophosphonates

A series of novel α-sulfamidophosphonate derivatives (3a-3 g) were synthesized and evaluated for anticancer activity against different human cancer cell lines (PRI, K562, and JURKAT). The antitumor activity of all compounds using the MTT test remains moderate compared to the standard drug chlorambucil. Compounds 3c and 3 g were found to be more active anticancer agent against PRI and K562 cells with IC50 value 0.056-0.097 and 0.182-0.133 mM, respectively. Molecular docking study related to binding affinity and binding mode analysis showed that synthesized compounds had potential to inhibit glutamate carboxypeptidase II (GCPII). Furthermore, computational analysis was performed through Density Functional Theory (DFT) utilizing the B3LYP 6-31 G (d, p) basis set and the theoretical results were correlated with experimental data. The ADME/toxicity analyses carried out by Swiss ADME and OSIRIS software show that all synthesized molecules exhibited good pharmacokinetics, bioavailability, and had no toxicity profile.

MetSim: Integrated Programmatic Access and Pathway Management for Xenobiotic Metabolism Simulators

Xenobiotic metabolism is a key consideration in evaluating the hazards and risks posed by environmental chemicals. A number of software tools exist that are capable of simulating metabolites, but each reports its predictions in a different format and with varying levels of detail. This makes comparing the performance and coverage of the tools a practical challenge. To address this shortcoming, we developed a metabolic simulation framework called MetSim, which comprises three main components. A graph-based schema was developed to allow metabolism information to be harmonized. The schema was implemented in MongoDB to store and retrieve metabolic graphs for subsequent analysis. MetSim currently includes an application programming interface for four metabolic simulators: BioTransformer, the OECD Toolbox, EPA's chemical transformation simulator (CTS), and tissue metabolism simulator (TIMES). Lastly, MetSim provides functions to help evaluate simulator performance for specific data sets. In this study, a set of 112 drugs with 432 reported metabolites were compiled, and predictions were made using the 4 simulators. Fifty-nine of the 112 drugs were taken from the Small Molecule Pathway Database, with the remainder sourced from the literature. The human models within BioTransformer and CTS (Phase I only) and the rat models within TIMES and the OECD Toolbox (Phase I only) were used to make predictions for the chemicals in the data set. The recall and precision (recall, precision) ranked in order of highest recall for each individual tool were CTS (0.54, 0.017), BioTransformer (0.50, 0.008), Toolbox in vitro (0.40, 0.144), TIMES in vivo (0.40, 0.133), Toolbox in vivo (0.40, 0.118), and TIMES in vitro (0.39, 0.128). Combining all of the model predictions together increased the overall recall (0.73, 0.008). MetSim enabled insights into the performance and coverage of in silico metabolic simulators to be more efficiently derived, which in turn should aid future efforts to evaluate other data sets.

D-CyPre: a machine learning-based tool for accurate prediction of human CYP450 enzyme metabolic sites

The advancement of graph neural networks (GNNs) has made it possible to accurately predict metabolic sites. Despite the combination of GNNs with XGBOOST showing impressive performance, this technology has not yet been applied in the realm of metabolic site prediction. Previous metabolic site prediction tools focused on bonds and atoms, regardless of the overall molecular skeleton. This study introduces a novel tool, named D-CyPre, that amalgamates atom, bond, and molecular skeleton information via two directed message-passing neural networks (D-MPNN) to predict the metabolic sites of the nine cytochrome P450 enzymes using XGBOOST. In D-CyPre Precision Mode, the model produces fewer, but more accurate results (Jaccard score: 0.497, F1: 0.660, and precision: 0.737 in the test set). In D-CyPre Recall Mode, the model produces less accurate, but more comprehensive results (Jaccard score: 0.506, F1: 0.669, and recall: 0.720 in the test set). In the test set of 68 reactants, D-CyPre outperformed BioTransformer on all isoenzymes and CyProduct on most isoenzymes (5/9). For the subtypes where D-CyPre outperformed CyProducts, the Jaccard score and F1 scores increased by 24% and 16% in Precision Mode (4/9) and 19% and 12% in Recall Mode (5/9), respectively, relative to the second-best CyProduct. Overall, D-CyPre provides more accurate prediction results for human CYP450 enzyme metabolic sites.

The landscape of small-molecule prodrugs

Prodrugs are derivatives with superior properties compared with the parent active pharmaceutical ingredient (API), which undergo biotransformation after administration to generate the API in situ. Although sharing this general characteristic, prodrugs encompass a wide range of different chemical structures, therapeutic indications and properties. Here we provide the first holistic analysis of the current landscape of approved prodrugs using cheminformatics and data science approaches to reveal trends in prodrug development. We highlight rationales that underlie prodrug design, their indications, mechanisms of API release, the chemistry of promoieties added to APIs to form prodrugs and the market impact of prodrugs. On the basis of this analysis, we discuss strengths and limitations of current prodrug approaches and suggest areas for future development.

Trichoplusia ni Transcriptomic Responses to the Phytosaponin Aglycone Hederagenin: Sex-Related Differences

Many plant species, particularly legumes, protect themselves with saponins. Previously, a correlation was observed between levels of oleanolic acid-derived saponins, such as hederagenin-derived compounds, in the legume Medicago truncatula and caterpillar deterrence. Using concentrations that reflect the foliar levels of hederagenin-type saponins, the sapogenin hederagenin was not toxic to 4th instar caterpillars of the cabbage looper Trichoplusia ni nor did it act as a feeding deterrent. Female caterpillars consumed more diet than males, presumably to obtain the additional nutrients required for oogenesis, and are, thus, exposed to higher hederagenin levels. When fed the hederagenin diet, male caterpillars expressed genes encoding trypsin-like proteins (LOC113500509, LOC113501951, LOC113501953, LOC113501966, LOC113501965, LOC113499659, LOC113501950, LOC113501948, LOC113501957, LOC113501962, LOC113497819, LOC113501946, LOC113503910) as well as stress-responsive (LOC113503484, LOC113505107) proteins and cytochrome P450 6B2-like (LOC113493761) at higher levels than females. In comparison, female caterpillars expressed higher levels of cytochrome P450 6B7-like (LOC113492289). Bioinformatic tools predict that cytochrome P450s could catalyze the oxygenation of hederagenin which would increase the hydrophilicity of the compound. Expression of a Major Facilitator Subfamily (MFS) transporter (LOC113492899) showed a hederagenin dose-dependent increase in gene expression suggesting that this transporter may be involved in sapogenin efflux. These sex-related differences in feeding and detoxification should be taken into consideration in insecticide evaluations to minimize pesticide resistance.

In vitro metabolism of the emerging contaminant 6PPD-quinone in human and rat liver microsomes: Kinetics, pathways, and mechanism

N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine-quinone (6PPD-Q) is an ozonation product of the rubber antioxidant N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD). 6PPD-Q has recently been detected in various environmental media, which may enter the human body via inhalation and skin contact pathways. However, the human metabolism of 6PPD-Q has remained unknown. This study investigated the in vitro Cytochrome P450-mediated metabolism of 6PPD-Q in human and rat liver microsomes (HLMs and RLMs). 6PPD-Q was significantly metabolized at lower concentrations but slowed at high concentrations. The intrinsic clearance (CLint) of 6PPD-Q was 21.10 and 18.58 μL min-1 mg-1 protein of HLMs and RLMs, respectively, suggesting low metabolic ability compared with other reported pollutants. Seven metabolites and one intermediate were identified, and metabolites were predicted immunotoxic or mutagenic toxicity. Mono- and di-oxygenation reactions were the main phase I in vitro metabolic pathways. Enzyme inhibition experiments and molecular docking techniques were further used to reveal the metabolic mechanism. CYP1A2, 3A4, and 2C19, especially CYP1A2, play critical roles in 6PPD-Q metabolism in HLMs, whereas 6PPD-Q is extensively metabolized in RLMs. Our study is the first to demonstrate the in vitro metabolic profile of 6PPD-Q in HLMs and RLMs. The results will significantly contribute to future human health management targeting the emerging pollutant 6PPD-Q.

Removal of enrofloxacin using Eichhornia crassipes in microcosm wetlands

The global consumption of antibiotics leads to their possible occurrence in the environment. In this context, nature-based solutions (NBS) can be used to sustainably manage and restore natural and modified ecosystems. In this work, we studied the efficiency of the NBS free-water surface wetlands (FWSWs) using Eichhornia crassipes in microcosm for enrofloxacin removal. We also explored the behavior of enrofloxacin in the system, its accumulation and distribution in plant tissues, the detoxification mechanisms, and the possible effects on plant growth. Enrofloxacin was initially taken up by E. crassipes (first 100 h). Notably, it accumulated in the sediment at the end of the experimental time. Removal rates above 94% were obtained in systems with sediment and sediment + E. crassipes. In addition, enrofloxacin was found in leaves, petioles, and roots (8.8-23.6 µg, 11-78.3 µg, and 10.2-70.7 µg, respectively). Furthermore, enrofloxacin, the main degradation product (ciprofloxacin), and other degradation products were quantified in the tissues and chlorosis was observed on days 5 and 9. Finally, the degradation products of enrofloxacin were analyzed, and four possible metabolic pathways of enrofloxacin in E. crassipes were described.

Active Learning Approach for Guiding Site-of-Metabolism Measurement and Annotation

The ability to determine and predict metabolically labile atom positions in a molecule (also called "sites of metabolism" or "SoMs") is of high interest to the design and optimization of bioactive compounds, such as drugs, agrochemicals, and cosmetics. In recent years, several in silico models for SoM prediction have become available, many of which include a machine-learning component. The bottleneck in advancing these approaches is the coverage of distinct atom environments and rare and complex biotransformation events with high-quality experimental data. Pharmaceutical companies typically have measured metabolism data available for several hundred to several thousand compounds. However, even for metabolism experts, interpreting these data and assigning SoMs are challenging and time-consuming. Therefore, a significant proportion of the potential of the existing metabolism data, particularly in machine learning, remains dormant. Here, we report on the development and validation of an active learning approach that identifies the most informative atoms across molecular data sets for SoM annotation. The active learning approach, built on a highly efficient reimplementation of SoM predictor FAME 3, enables experts to prioritize their SoM experimental measurements and annotation efforts on the most rewarding atom environments. We show that this active learning approach yields competitive SoM predictors while requiring the annotation of only 20% of the atom positions required by FAME 3. The source code of the approach presented in this work is publicly available.

Computer-aided analysis for identification of novel analogues of ketoprofen based on molecular docking, ADMET, drug-likeness and DFT studies for the treatment of inflammation

Computer-based drug design is increasingly used in strategies for discovering new molecules for therapeutic purposes. The targeted drug is ketoprofen (KTP), which belongs to the family of non-steroidal anti-inflammatory drugs, which are widely used for the treatment of pain, fever, inflammation and certain types of cancers. In an attempt to rationalize the search for 72 new potential anti-inflammatory compounds on the COX-2 enzyme, we carried out an in silico protocol that successfully combines molecular docking towards COX-2 receptor (5F1A), ADMET pharmacokinetic parameters, drug-likeness rules and molecular electrostatic potential (MEP). It was found that six of the compounds analyzed satisfy with the associated values to physico-chemical properties as key evaluation parameters for the drug-likeness and demonstrate a hydrophobic character which makes their solubility in aqueous media difficult and easy in lipids. All the compounds presented good ADMET profile and they showed an interaction with the amino acids responsible for anti-inflammatory activity of the COX-2 isoenzyme. The calculation of the MEP of the six analogues reveals new preferential sites involving the formation of new bonds. Consequently, this result allowed us to understand the origin of the potential increase in the anti-inflammatory activity of the candidates. Finally, it was obtained that six compounds have a binding mode, binding energy, and stability in the active site of COX-2 like the reference drug ketoprofen, suggesting that these compounds could become a powerful candidate in the inhibition of the COX-2 enzyme.Communicated by Ramaswamy H. Sarma.

Comparison and summary of in silico prediction tools for CYP450-mediated drug metabolism

The cytochrome P450 (CYP450) enzyme system is responsible for the metabolism of more than two-thirds of xenobiotics. This review summarizes reports of a series of in silico tools for CYP450 enzyme-drug interaction predictions, including the prediction of sites of metabolism (SOM) of a drug and the identification of inhibitor/substrates for CYP subtypes. We also evaluated four prediction tools to identify CYP inhibitors utilizing 52 of the most frequently prescribed drugs. ADMET Predictor and CYPlebrity demonstrated the best performance. We hope that this review provides guidance for choosing appropriate enzyme prediction tools from a variety of in silico platforms to meet individual needs. Such predictions are useful for medicinal chemists to prioritize their designed compounds for further drug discovery.

Virtual Screening of Different Subclasses of Lignans with Anticancer Potential and Based on Genetic Profile

Cancer is a multifactorial disease that continues to increase. Lignans are known to be important anticancer agents. However, due to the structural diversity of lignans, it is difficult to associate anticancer activity with a particular subclass. Therefore, the present study sought to evaluate the association of lignan subclasses with antitumor activity, considering the genetic profile of the variants of the selected targets. To do so, predictive models were built against the targets tyrosine-protein kinase ABL (ABL), epidermal growth factor receptor erbB1 (EGFR), histone deacetylase (HDAC), serine/threonine-protein kinase mTOR (mTOR) and poly [ADP-ribose] polymerase-1 (PARP1). Then, single nucleotide polymorphisms were mapped, target mutations were designed, and molecular docking was performed with the lignans with the best predicted biological activity. The results showed more anticancer activity in the dibenzocyclooctadiene, furofuran and aryltetralin subclasses. The lignans with the best predictive values of biological activity showed varying binding energy results in the presence of certain genetic variants.

Prediction of Sites of Metabolism of CYP3A4 Substrates Utilizing Docking-Derived Geometric Features

Cytochrome P450 3A4 (CYP3A4) is one of the major drug-metabolizing enzymes in the human body and is responsible for the metabolism of ∼50% of clinically used drugs. Therefore, the identification of the compound's sites of metabolism (SOMs) mediated by CYP3A4 is of utmost importance in the early stage of drug discovery and development. Herein, docking-based approaches incorporating geometric features were used for SOMs prediction of CYP3A4 substrates. The cross-docking poses of a relatively large data set containing 474 substrates were analyzed in depth, and a widely observed geometric pattern called the close proximity of SOMs was derived from the poses. On the basis of the close proximity, several structure-based models have been constructed, which demonstrated better performance than those structure-based models using the criterion of Fe-SOM distance. For further improving the prediction performance, the structure-based models were also combined with the well-known ligand-based model SMARTCyp. One combined model exhibited good performance on the SOMs prediction of an external substrate set containing kinase inhibitors, PROTACs, approved drugs, and some lead compounds.

Novel 2-alkythio-4-chloro-N-[imino(heteroaryl)methyl]benzenesulfonamide Derivatives: Synthesis, Molecular Structure, Anticancer Activity and Metabolic Stability

A series of novel 2-alkythio-4-chloro-N-[imino-(heteroaryl)methyl]benzenesulfonamide derivatives, 8-24, were synthesized in the reaction of the N-(benzenesulfonyl)cyanamide potassium salts 1-7 with the appropriate mercaptoheterocycles. All the synthesized compounds were evaluated for their anticancer activity in HeLa, HCT-116 and MCF-7 cell lines. The most promising compounds, 11-13, molecular hybrids containing benzenesulfonamide and imidazole moieties, selectively showed a high cytotoxic effect in HeLa cancer cells (IC₅₀: 6-7 μM) and exhibited about three times less cytotoxicity against the non-tumor cell line HaCaT cells (IC₅₀: 18-20 μM). It was found that the anti-proliferative effects of 11, 12 and 13 were associated with their ability to induce apoptosis in HeLa cells. The compounds increased the early apoptotic population of cells, elevated the percentage of cells in the sub-G1 phase of the cell cycle and induced apoptosis through caspase activation in HeLa cells. For the most active compounds, susceptibility to undergo first-phase oxidation reactions in human liver microsomes was assessed. The results of the in vitro metabolic stability experiments indicated values of the factor t_½ for 11-13 in the range of 9.1-20.3 min and suggested the hypothetical oxidation of these compounds to sulfenic and subsequently sulfinic acids as metabolites.

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.

Green alga Chlamydomonas reinhardtii can effectively remove diclofenac from the water environment - A new perspective on biotransformation

The use of unicellular algae to remove xenobiotics (including drugs) from wastewaters is one of the rapidly developing areas of environmental protection. Numerous data indicate that for efficient phycoremediation three processes are important, i.e. biosorption, bioaccumulation, and biotransformation. Although biosorption and bioaccumulation do not raise any serious doubts, biotransformation is more problematic since its products can be potentially more toxic than the parent compounds posing a threat to organisms living in a given environment, including organisms that made this transformation. Thus, two questions need to be answered before the proper algae strain is chosen for phycoremediation, namely what metabolites are produced during biotransformation, and how resistant is the analyzed strain to a mixture of parent compound and metabolites that appear over the course of culture? In this work, we evaluated the remediation potential of the model green alga Chlamydomonas reinhardtii in relation to non-steroidal anti-inflammatory drugs (NSAIDs), as exemplified by diclofenac. To achieve this, we analysed the susceptibility of C. reinhardtii to diclofenac as well as its capability to biosorption, bioaccumulation, and biotransformation of the drug. We have found that even at a relatively high concentration of diclofenac the algae maintained their vitality and were able to remove (37.7%) DCF from the environment. A wide range of phase I and II metabolites of diclofenac (38 transformation products) was discovered, with many of them characteristic rather for animal and bacterial biochemical pathways than for plant metabolism. Due to such a large number of detected products, 18 of which were not previously reported, the proposed scheme of diclofenac transformation by C. reinhardtii not only significantly contributes to broadening the knowledge in this field, but also allows to suggest possible pathways of degradation of xenobiotics with a similar structure. It is worth pointing out that a decrease in the level of diclofenac in the media observed in this study cannot be fully explained by biotransformation (8.4%). The mass balance analysis indicates that other processes (total 22%), such as biosorption, a non-extractable residue formation, or complete decomposition in metabolic cycles can be involved in the diclofenac disappearance, and those findings open the prospects of further research.

Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives

Drug metabolism and excretion play crucial roles in determining the efficacy and safety of drug candidates, and predicting these processes is an essential part of drug discovery and development. In recent years, artificial intelligence (AI) has emerged as a powerful tool for predicting drug metabolism and excretion, offering the potential to speed up drug development and improve clinical success rates. This review highlights recent advances in AI-based drug metabolism and excretion prediction, including deep learning and machine learning algorithms. We provide a list of public data sources and free prediction tools for the research community. We also discuss the challenges associated with the development of AI models for drug metabolism and excretion prediction and explore future perspectives in the field. We hope this will be a helpful resource for anyone who is researching in silico drug metabolism, excretion, and pharmacokinetic properties.

Discovery of fluorinated 2‑Styryl 4(3H)-quinazolinone as potential therapeutic hit for oral cancer

Oral squamous cell carcinoma (OSCC) is the most common malignant epithelial neoplasm, affects the mouth and throat, and accounts for 90 % of oral cancers. Considering the associated morbidity with neck dissections and the limitation of existing therapeutic agents, the discovery and development of new anticancer drugs/drug candidates for oral cancer treatment are of the utmost need. In this context, reported here is the identification of fluorinated 2‑styryl 4(3H)-quinazolinone as a promising hit for oral cancer. Preliminary studies indicate that the compound blocks the transition of G1 to S phase, thereby leading to arrest in the G1/S phase. Subsequent RNA-seq analysis revealed that the compound induces the activation of molecular pathways involved in apoptosis (such as TNF signalling through NF-κB, p53 pathways) and cell differentiation and suppresses the pathways of cellular growth and development (such as KRAS signaling) in CAL-27 cancer cells. It is noted that identified hit complies with a favorable range of ADME properties as per the computational analysis.

Kolocouris A. Synthesis and Testing of Analogs of the Tuberculosis Drug Candidate SQ109 against Bacteria and Protozoa: Identification of Lead Compounds against Mycobacterium abscessus and Malaria Parasites

SQ109 is a tuberculosis drug candidate that has high potency against Mycobacterium tuberculosis and is thought to function at least in part by blocking cell wall biosynthesis by inhibiting the MmpL3 transporter. It also has activity against bacteria and protozoan parasites that lack MmpL3, where it can act as an uncoupler, targeting lipid membranes and Ca2+ homeostasis. Here, we synthesized 18 analogs of SQ109 and tested them against M. smegmatis, M. tuberculosis, M. abscessus, Bacillus subtilis, and Escherichia coli, as well as against the protozoan parasites Trypanosoma brucei, T. cruzi, Leishmania donovani, L. mexicana, and Plasmodium falciparum. Activity against the mycobacteria was generally less than with SQ109 and was reduced by increasing the size of the alkyl adduct, but two analogs were ∼4-8-fold more active than SQ109 against M. abscessus, including a highly drug-resistant strain harboring an A309P mutation in MmpL3. There was also better activity than found with SQ109 with other bacteria and protozoa. Of particular interest, we found that the adamantyl C-2 ethyl, butyl, phenyl, and benzyl analogs had 4-10× increased activity against P. falciparum asexual blood stages, together with low toxicity to a human HepG2 cell line, making them of interest as new antimalarial drug leads. We also used surface plasmon resonance to investigate the binding of inhibitors to MmpL3 and differential scanning calorimetry to investigate binding to lipid membranes. There was no correlation between MmpL3 binding and M. tuberculosis or M. smegmatis cell activity, suggesting that MmpL3 is not a major target in mycobacteria. However, some of the more active species decreased lipid phase transition temperatures, indicating increased accumulation in membranes, which is expected to lead to enhanced uncoupler activity.

An Insight into the Metabolism of 2,5-Disubstituted Monotetrazole Bearing Bisphenol Structures: Emerging Bisphenol A Structural Congeners

The non-estrogenic 2,5-disubstituted tetrazole core-bearing bisphenol structures (TbB) are being researched as emerging structural congeners of Bisphenol A, an established industrial endocrine disruptor. However, there is no understanding of TbB's adverse effects elicited via metabolic activation. Therefore, the current study aimed to investigate the metabolism of TbB ligands, with in silico results serving as a guide for in vitro studies. The Cytochrome P450 enzymes (CYP) inhibitory assay of TbB ligands on the seven human liver CYP isoforms (i.e., 1A2, 2A6, 2D6, 2C9, 2C8, 2C19, and 3A4) using human liver microsomes (HLM) revealed TbB ligand 223-3 to have a 50% inhibitory effect on all the CYP isoforms at a 10 μM concentration, except 1A2. The TbB ligand 223-10 inhibited 2B6 and 2C8, whereas the TbB ligand 223-2 inhibited only 2C9. The first-order inactivity rate constant (Kobs) studies indicated TbB ligands 223-3, 223-10 to be time-dependent (TD) inhibitors, whereas the TbB 223-2 ligand did not show such a significant effect. The 223-3 exhibited a TD inhibition for 2C9, 2C19, and 1A2 with Kobs values of 0.0748, 0.0306, and 0.0333 min-1, respectively. On the other hand, the TbB ligand 223-10 inhibited 2C9 in a TD inhibition manner with Kobs value 0.0748 min-1. However, the TbB ligand 223-2 showed no significant TD inhibition effect on the CYPs. The 223-2 ligand biotransformation pathway by in vitro studies in cryopreserved human hepatocytes suggested the clearance via glucuronidation with the predominant detection of only 223-2 derived mono glucuronide as a potential inactive metabolite. The present study demonstrated that the 223-2 ligand did not elicit any significant adverse effect via metabolic activation, thus paving the way for its in vivo drug-drug interactions (DDI) studies.

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as "message-passing paradigms", "spatial-symmetry-preserving networks", "hybrid de novo design", and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.

Evaluation of Free Online ADMET Tools for Academic or Small Biotech Environments

For a new molecular entity (NME) to become a drug, it is not only essential to have the right biological activity also be safe and efficient, but it is also required to have a favorable pharmacokinetic profile including toxicity (ADMET). Consequently, there is a need to predict, during the early stages of development, the ADMET properties to increase the success rate of compounds reaching the lead optimization process. Since Lipinski's rule of five, the prediction of pharmacokinetic parameters has evolved towards the current in silico tools based on empirical approaches or molecular modeling. The commercial specialized software for performing such predictions, which is usually costly, is, in many cases, not among the possibilities for research laboratories in academia or at small biotech companies. Nevertheless, in recent years, many free online tools have become available, allowing, more or less accurately, for the prediction of the most relevant pharmacokinetic parameters. This paper studies 18 free web servers capable of predicting ADMET properties and analyzed their advantages and disadvantages, their model-based calculations, and their degree of accuracy by considering the experimental data reported for a set of 24 FDA-approved tyrosine kinase inhibitors (TKIs) as a model of a research project.

Structure-based pharmacophore modeling, virtual screening and simulation studies for the identification of potent anticancerous phytochemical lead targeting cyclin-dependent kinase 2

Cyclin-dependent kinases are of critical importance in directing various cell cycle phases making them as potential tumor targets. Cyclin-dependent kinase 2 (CDK2) in particular plays a significant part during cell cycle events and its imbalance roots out tumorogenic environment. Herein, we built a structure-based pharmacophore model complementing the ATP pocket site of CDK2 with four pharmacophoric features, using a series of structures obtained from cluster analysis during MD simulation assessment. This was followed by its validation and further database screening against Taiwan indigenous plants database (5284 compounds). The screened compounds were subjected toward Lipinski's rule (RO5) and ADMET filter followed by docking analysis and simulation study. In filtering hits (10 compounds) via molecular docking against CDK2, Schinilenol with -8.1 kcal/mol fetched out as a best lead phytoinhibitor in the presence of standard drug (Dinaciclib). Additionally, pharmacophore mapping analysis also indicated relative fit values of dinaciclib and schinilenol as 2.37 and 2.31, respectively. Optimization, flexibility prediction and the stability of CDK2 in complex with the ligands were also ascertained by means of molecular dynamics for 50 ns, which further proposed schinilenol having better binding stability than dinaciclib with RMSD values ranging from 0.31 to 0.34 nm. Reactivity site, biological activity detection and cardiotoxicity assessment also proposed schinilenol as a better phytolead inhibitor than the existing dinaciclib. Abbreviations: CDK2: Cyclin dependent kinase2; ATP: Adenosine triphosphate; MD: Molecular dynamics, RO5: Rule of five; ADMET: Absorption, distribution, metabolism, and excretion; RMSD: Root mean square deviation; DS: Discovery Studio; SOM: Site of metabolism; RBPM: receptor based pharmacophore model; TIP: Schinilenol; hERG: human Ether-à-go-go - Related GeneCommunicated by Ramaswamy H. Sarma.

Pharmacokinetic Study and Metabolite Identification of 1-(3'-bromophenyl)-heliamine in Rats

Tetrahydroisoquinolines have been widely investigated for the treatment of arrhythmias. 1-(3'-bromophenyl)-heliamine (BH), an anti-arrhythmias agent, is a synthetic tetrahydroisoquinoline. This study focuses on the pharmacokinetic characterization of BH, as well as the identification of its metabolites, both in vitro and in vivo. A UHPLC-MS/MS method was developed and validated to quantify BH in rat plasma with a linear range of 1-1000 ng/mL. The validated method was applied to a pharmacokinetic study in rats. The maximum concentration C_max (568.65 ± 122.14 ng/mL) reached 1.00 ± 0.45 h after oral administration. The main metabolic pathways appeared to be phase-I of demethylation, dehydrogenation, and epoxidation, and phase II of glucuronide and sulfate metabolites. Finally, a total of 18 metabolites were characterized, including 10 phase I metabolites and 8 phase II metabolites. Through the above studies, we have gained a better understanding of the absorption and metabolism of BH in vitro and in vivo, which will provide us with guidance for future in-depth studies on this compound.

Pivotal role of nitrogen heterocycles in Alzheimer's disease drug discovery

Alzheimer's disease (AD) is a detrimental neurodegenerative disease that progressively worsens with time. Clinical options are limited and only provide symptomatic relief to AD patients. The search for effective anti-AD compounds is ongoing with a few already in Phase III clinical trials, yet to be approved. Heterocycles containing nitrogen are important to biological processes owing to their abundance in nature, their function as subunits of biological molecules and/or macromolecular structures, and their biological activities. The present review discusses previously used strategies, SAR, relevant in vitro and in vivo studies, and success stories of nitrogen-containing heterocyclic compounds in AD drug discovery. Also, we propose strategies for designing and developing novel potent anti-AD small molecules that can be used as treatments for AD.

Machine learning methods for pKa prediction of small molecules: Advances and challenges

The acid-base dissociation constant (pK_a) is a fundamental property influencing many ADMET properties of small molecules. However, rapid and accurate pK_a prediction remains a great challenge. In this review, we outline the current advances in machine-learning-based QSAR models for pK_a prediction, including descriptor-based and graph-based approaches, and summarize their pros and cons. Moreover, we highlight the current challenges and future directions regarding experimental data, crucial factors influencing pK_a and in silico prediction tools. We hope that this review can provide a practical guidance for the follow-up studies.

Comprehensive metabolite identification study of arterolane using hydrophilic interaction liquid chromatography with quadrupole-time-of-flight mass spectrometry

RATIONALE

In 2012, arterolane (ART) in combination with piperaquine received approval in India for the treatment of plasmodium-induced malaria; however, to date, a detailed metabolite identification study of ART has not been reported. Being polar in nature, ART shows early elution on reversed-phase columns which might be the rate-limiting factor of its systematic analytical studies. We have utilized hydrophilic interaction liquid chromatography (HILIC) to separate in vitro and in vivo metabolites of ART.

METHODS

The possible sites of metabolism were predicted by XenoSite software to obtain an initial assessment. In vitro studies were conducted by incubating the drug with liver microsomes such as human, rat and human S9 fractions. Later, in vivo studies were performed to check the metabolites in urine, faeces and plasma. The samples were pooled and subjected to the protein precipitation method before analysis by liquid chromatography/quadrupole-time-of-flight mass spectrometry (LC/QTOFMS).

RESULTS

We have observed 15 metabolites in this study which were phase I metabolites formed due to hydroxylation, dihydroxylation, peroxide bond scission and oxidation. Here, we report 11 metabolites of ART for the first time. The metabolic pathways and plausible structures were proposed according to accurate mass measurements and its MS/MS data.

CONCLUSIONS

The present study comprehensively reports the in vitro and in vivo metabolism of ART mentioning 11 novel metabolites. Here, extensive use of HILIC has helped to efficiently separate various metabolites. These findings would help prospects of ART disposition and its congeners.

Active Compounds in Zingiber officinale as Possible Redox Inhibitors of 5-Lipoxygenase Using an In Silico Approach

5-Lipoxygenase (5-LOX) converts arachidonic acid to lipidic inflammatory mediators such as leukotrienes (LTs). In diseases such as asthma, LTs contribute to a physiopathology that could be reverted by blocking 5-LOX. Natural products with anti-inflammatory potential such as ginger have been used as nutraceuticals since ancient times. 6-Gingerol and 6-shogaol are the most abundant compounds in the ginger rhizome; they possess anti-inflammatory, antioxidant, and chemopreventive properties. In the present study, 6-gingerol and 6-shogaol structures were analyzed and compared with two commercial 5-LOX inhibitors (zileuton and atreleuton) and with other inhibitor candidates (3f, NDGA, CP 209, caffeic acid, and caffeic acid phenethyl ester (CAPE)). The pharmacokinetics and toxicological properties of 6-gingerol, 6-shogaol, and the other compounds were evaluated. Targeted molecular coupling was performed to identify the optimal catalytic pocket for 5-LOX inhibition. The results showed that 6-gingerol and 6-shogaol follow all of the recommended pharmacokinetic parameters. These compounds could be inhibitors of 5-LOX because they present specific interactions with the residues involved in molecular inhibition. The current study demonstrated the potential of 6-gingerol and 6-shogaol as anti-inflammatory agents that inhibit 5-LOX, as they present a high level of performance in the toxicological analysis and could be catabolized by the cytochrome p450 enzymatic complex; however, 6-gingerol was superior in safety compared to 6-shogaol.

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.

Consensus Prediction of Human Gut Microbiota-Mediated Metabolism Susceptibility for Small Molecules by Machine Learning, Structural Alerts, and Dietary Compounds-Based Average Similarity Methods

The human gut microbiota (HGM) colonizing human gastrointestinal tract (HGT) confers a repertoire of dynamic and unique metabolic capacities that are not possessed by the host and therefore is tentatively perceived as an alternative metabolic ″organ″ besides the liver in the host. Nevertheless, the significant contribution of HGM to the overall human metabolism is often overlooked in the modern drug discovery pipeline. Hence, a systematic evaluation of HGM-mediated drug metabolism is gradually important, and its computational prediction becomes increasingly necessary. In this work, a new data set containing both the HGM-mediated metabolism susceptible (HGMMS) and insusceptible (HGMMI) compounds (329 vs 320) was manually curated. Based on this data set, the first machine learning (ML) model, a new structural alerts (SA) model, and the K-nearest neighboring dietary compounds-based average similarity (AS) model were proposed to directly predict the HGM-mediated metabolism susceptibility for small molecules, and exhibit promising performance on three independent test sets. Finally, consensus prediction (ML/SA/AS) for DrugBank molecules revealed an intriguing phenomenon that a typical Michael acceptor ″α,β-unsaturated carbonyl group″ is a very common warhead for the design of covalent inhibitors and inclined to be metabolized by HGM in anaerobic HGT to generate the reduced metabolite without the reactive warhead, which could be a new concern to medicinal chemists. To the best of our knowledge, we gleaned the first HGMMS/HGMMI data set, developed the first HGMMS/HGMMI classification model, implemented a relatively comprehensive program based on ML/SA/AS approaches, and found a new phenomenon on the HGM-mediated deactivation of an extensively used warhead for covalent inhibitors.

Inhibiting Hv1 channel in peripheral sensory neurons attenuates chronic inflammatory pain and opioid side effects

Both opioids and nonsteroidal anti-inflammatory drugs (NSAIDS) produce deleterious side effects and fail to provide sustained relief in patients with chronic inflammatory pain. Peripheral neuroinflammation (PN) is critical for initiation and development of inflammatory pain. A better understanding of molecular mechanisms underlying PN would facilitate the discovery of new analgesic targets and the development of new therapeutics. Emerging evidence suggests that peripheral sensory neurons are not only responders to painful stimuli, but are also actively engaged in inflammation and immunity, whereas the intrinsic regulatory mechanism is poorly understood. Here we report the expression of proton-selective ion channel Hv1 in peripheral sensory neurons in rodents and humans, which was previously shown as selectively expressed in microglia in mammalian central nervous system. Neuronal Hv1 was up-regulated by PN or depolarizing stimulation, which in turn aggravates inflammation and nociception. Inhibiting neuronal Hv1 genetically or by a newly discovered selective inhibitor YHV98-4 reduced intracellular alkalization and ROS production in inflammatory pain, mitigated the imbalance in downstream SHP-1-pAKT signaling, and also diminished pro-inflammatory chemokine release to alleviate nociception and morphine-induced hyperalgesia and tolerance. Thus, our data reveal neuronal Hv1 as a novel target in analgesia strategy and managing opioids-related side effects.

UHPLC-Q-TOF-MS/MS based metabolite profiling of duvelisib and establishment of its metabolism mechanisms

Duvelisib is a dual inhibitor of phosphoinositide 3 kinase that received global approval by USFDA in 2018 to treat follicular lymphoma after at least two prior systemic therapies. An extensive literature search revealed that till date, metabolites of duvelisib are not characterized and information on the same is not available in any literature. Moreover, its metabolism pathway is yet to be established. This study aimed to investigate and characterize the metabolites of duvelisib generated in microsomes and S9 fractions. In this study, five duvelisib metabolites have been identified using UHPLC-Q-TOF-MS/MS technique of analysis. The structural characterisation of the metabolites was performed by comparing the fragmentation pattern of duvelisib and its metabolites through an accurate mass measurement technique. Three metabolites were found to be generated through phase I hydroxylation and dechlorination reaction. The other two metabolites were generated through a phase II glucuronidation reaction. The metabolism mechanism established through this study can be useful to improve the safety profile of the drug of its similar category in the future after establishment their toxicity profile of the identified metabolites.

Pharmacokinetic Tools and Applications

Drug toxicity, as well as therapeutic activity, is contingent upon the parent drug, or a derivative thereof, reaching the relevant site of action in the body, at sufficient concentration, over a given period of time. Thus, the potential to truly elicit an effect is governed by both the intrinsic activity/toxicity of the drug (or its transformation products) and its pharmacokinetic profile. As the pharmaceutical industry has become increasingly aware of the role of pharmacokinetics in determining drug activity and toxicity, the range of software, both freely available and commercial, to predict relevant properties has proliferated. Such tools can be considered on three different levels, applicable at different stages within the drug development process and providing increasing detail and relevance of information. Level (i) is the prediction of fundamental physicochemical properties that can be used to screen vast virtual libraries of potential candidates. Level (ii), predicting the individual absorption, distribution, metabolism, and excretion (ADME) characteristics of potential drugs, can also be applied to many compounds simultaneously. Level (iii), predicting the concentration-time profile of a drug in blood or specific tissues/organs for individuals or a population, is the most sophisticated level of prediction, applied to fewer candidates. In this chapter, in silico tools for predicting ADME-relevant properties, across these three levels, and the applications of this information, are described using exemplar, freely available resources. Further resources are signposted but not all are considered in detail as the purpose here is more to provide an introduction to the capabilities and practicalities of the tools, rather than to provide an exhaustive review of all the tools available.

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.

Metabolic study of cafestol using in silico approach, zebrafish water tank experiments and liquid chromatography high-resolution mass spectrometry analyses

Coffee is one of the most consumed beverages worldwide. Cafestol is an endogenous coffee diterpene present in raw coffee beans and also found in hot beverages, with several biological activities. However, there is still little information on this molecule after ingestion of coffee infusion. Zebrafish (Danio rerio) is a promising in vivo model for metabolic studies due to the annotation of mammalian orthologs to encode enzymes related to drug metabolism. Experiments using Zebrafish Water Tank (ZWT) model produce more significant number of metabolites for molecular investigation in a cleaner matrix than other classical models, such as purified hepatocytes. This work aimed to investigate the biotransformation of cafestol by the ZWT model using ultra-performance liquid chromatography coupled to hybrid quadrupole-orbitrap high-resolution mass spectrometry equipped with electrospray ionization (UPLC-HRMS) supported by in silico approach using SMARTCyp, Way2Drug and XenoSite Softwares. Twenty-five metabolites of cafestol were proposed by in silico analysis, in which 5 phase I metabolites were confirmed in the ZWT by UPLC and MS/HRMS investigation: 6-hydroxy-cafestol, 6,12-dihydroxy-cafestol, 2-oxo-cafestol, 6-oxo-cafestol and one isomer whose position in the carboxyl group was not determined. These metabolites were observed during 9 h of the experiment, whose contents were associated with the behavioral responses of the fish.

Structure-activity relationship and molecular modelling studies of quinazolinedione derivatives MMV665916 as potential antimalarial agent

A series of new quinazolinedione derivatives have been readily synthesized and evaluated for their in vitro antiplasmodial growth inhibition activity. Most of the compounds inhibited P. falciparum FcB1 strain in the low to medium micromolar concentration. The 2-ethoxy 8ag', 2-trifluoromethoxy 8ai' and 4-fluoro-2-methoxy 8ak' showed the best inhibitory activity with EC₅₀ values around 5 µM and were non-toxic to the primary human fibroblast cell line AB943. However, these compounds were less potent than the original hit MMV665916, which showed remarkable growth inhibition with EC₅₀ value of 0.4 µM and presented the highest selectivity index (SI > 250). In addition, a novel approach for determining the docking poses of these quinazolinedione derivatives with their potential protein target, the P. falciparum farnesyltransferase PfFT, was investigated.

A systematic UHPLC-Q-TOF-MS/MS based analytical approach for characterization of flibanserin metabolites and establishment of biotransformation pathway

A systematic metabolite profiling approach has paramount importance in detecting, identifying, and characterizing drug metabolites. Till date, there is no report published on the comprehensive metabolic fate of flibanserin (FLB). In this study, the structure of entire potential metabolites of FLB has been elucidated by execution of in silico tool and high resolution mass spectrometry based metabolite profiling strategy employing data-dependent and data-independent approaches. In vitro metabolism profile was investigated after incubating FLB with liver microsomes (rat and human) and S9 fractions in presence of their respective co-factors. In vivo metabolites were identified from rat plasma, urine, feces, and brain tissue samples. An efficient extraction technique was developed that made it possible to identify the metabolites generated even in extremely low concentrations. Extraction was carried out by precipitating protein and thereafter solid-phase extraction to enrich their concentration in the sample before analysis. Fourteen new metabolites have been identified and characterized. Most of the metabolites of FLB were generated due to hydrolysis and oxidation followed by glucuronide, sulfate, and methyl conjugation. Additionally, a spiking study was employed to confirm the presence of N-oxide metabolite in human liver S9 fraction and rat urine samples. Moreover, we have established the probable biotransformation pathway of FLB and successfully analyzed the toxicity potential of the metabolites using Pro Tox-II software.

Deep Learning Coordinate-Free Quantum Chemistry

Computing quantum chemical properties of small molecules and polymers can provide insights valuable into physicists, chemists, and biologists when designing new materials, catalysts, biological probes, and drugs. Deep learning can compute quantum chemical properties accurately in a fraction of time required by commonly used methods such as density functional theory. Most current approaches to deep learning in quantum chemistry begin with geometric information from experimentally derived molecular structures or pre-calculated atom coordinates. These approaches have many useful applications, but they can be costly in time and computational resources. In this study, we demonstrate that accurate quantum chemical computations can be performed without geometric information by operating in the coordinate-free domain using deep learning on graph encodings. Coordinate-free methods rely only on molecular graphs, the connectivity of atoms and bonds, without atom coordinates or bond distances. We also find that the choice of graph-encoding architecture substantially affects the performance of these methods. The structures of these graph-encoding architectures provide an opportunity to probe an important, outstanding question in quantum mechanics: what types of quantum chemical properties can be represented by local variable models? We find that Wave, a local variable model, accurately calculates the quantum chemical properties, while graph convolutional architectures require global variables. Furthermore, local variable Wave models outperform global variable graph convolution models on complex molecules with large, correlated systems.

Machine Learning in Drug Discovery: A Review

This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.

HIFs: New arginine mimic inhibitors of the Hv1 channel with improved VSD-ligand interactions

The human voltage-gated proton channel Hv1 is a drug target for cancer, ischemic stroke, and neuroinflammation. It resides on the plasma membrane and endocytic compartments of a variety of cell types, where it mediates outward proton movement and regulates the activity of NOX enzymes. Its voltage-sensing domain (VSD) contains a gated and proton-selective conduction pathway, which can be blocked by aromatic guanidine derivatives such as 2-guanidinobenzimidazole (2GBI). Mutation of Hv1 residue F150 to alanine (F150A) was previously found to increase 2GBI apparent binding affinity more than two orders of magnitude. Here, we explore the contribution of aromatic interactions between the inhibitor and the channel in the presence and absence of the F150A mutation, using a combination of electrophysiological recordings, classic mutagenesis, and site-specific incorporation of fluorinated phenylalanines via nonsense suppression methodology. Our data suggest that the increase in apparent binding affinity is due to a rearrangement of the binding site allowed by the smaller residue at position 150. We used this information to design new arginine mimics with improved affinity for the nonrearranged binding site of the wild-type channel. The new compounds, named “Hv1 Inhibitor Flexibles” (HIFs), consist of two “prongs,” an aminoimidazole ring, and an aromatic group connected by extended flexible linkers. Some HIF compounds display inhibitory properties that are superior to those of 2GBI, thus providing a promising scaffold for further development of high-affinity Hv1 inhibitors.