In silico approaches for predicting ADME properties of drugs

Physiological liver microtissue 384-well microplate system for preclinical hepatotoxicity assessment of therapeutic small molecule drugs

Hepatotoxicity can lead to the discontinuation of approved or investigational drugs. The evaluation of the potential hepatoxicity of drugs in development is challenging because current models assessing this adverse effect are not always predictive of the outcome in human beings. Cell lines are routinely used for early hepatotoxicity screening, but to improve the detection of potential hepatotoxicity, in vitro models that better reflect liver morphology and function are needed. One such promising model is human liver microtissues. These are spheroids made of primary human parenchymal and nonparenchymal liver cells, which are amenable to high throughput screening. To test the predictivity of this model, the cytotoxicity of 152 FDA (US Food & Drug Administration)-approved small molecule drugs was measured as per changes in ATP content in human liver microtissues incubated in 384-well microplates. The results were analyzed with respect to drug label information, drug-induced liver injury (DILI) concern class, and drug class. The threshold IC50ATP-to-Cmax ratio of 176 was used to discriminate between safe and hepatotoxic drugs. "vMost-DILI-concern" drugs were detected with a sensitivity of 72% and a specificity of 89%, and "vMost-DILI-concern" drugs affecting the nervous system were detected with a sensitivity of 92% and a specificity of 91%. The robustness and relevance of this evaluation were assessed using a 5-fold cross-validation. The good predictivity, together with the in vivo-like morphology of the liver microtissues and scalability to a 384-well microplate, makes this method a promising and practical in vitro alternative to 2D cell line cultures for the early hepatotoxicity screening of drug candidates.

Evaluation of the in vivo acute toxicity and in vitro genotoxicity and mutagenicity of synthetic β-carboline alkaloids with selective cytotoxic activity against ovarian and breast cancer cell lines

The aim of this study was to evaluate the in vitro cytotoxic, genotoxic, and mutagenic potential and to determine the in silico ADME parameters of two synthetic β-carboline alkaloids developed as prototypes of antitumor agents (NQBio-06 and NQBio-21). Additionally, acute toxicity of the compounds was evaluated in mice. The results from the MTT assay showed that NQBio-06 presented higher cytotoxicity in the ovarian cancer cell line TOV-21 G (IC50 = 2.5 µM, selectivity index = 23.7). NQBio-21 presented an IC50 of 6.9 µM and a selectivity index of 14.5 against MDA-MB-231 breast cancer cells. Comet assay results showed that NQBio-06 did not induce chromosomal breaks in vitro, but NQBio-21 was genotoxic with and without metabolic activation (S9 fraction). Micronucleus assay showed that both compounds were mutagenic. In addition, metabolic activation enhanced this effect in vitro. The in silico predictions showed that the compounds met the criteria set by Lipinski's rules, had strong prediction for intestinal absorption, and were possible substrates for P-glycoprotein. The in vivo results demonstrated that both the compounds exhibited low acute toxicity. These results suggest that the mechanisms underlying the cytotoxicity of NQBio-06 and NQBio-21 are related to DNA damage induction and that the use of S9 enhanced these effects. In vivo analysis showed signs of toxicity after a single administration of the compounds in mice. These findings highlight the potential of β-carboline compounds as sources for the development of new anticancer chemotherapeutic agents.

Gastroprotective role of a flavonoid-rich subfraction from Fridericia chica (Bonpl.) L. G. Lohmann: a medicinal plant used in the Amazon region

Fridericia chica is an Amazonian plant used to treat stomach disorders. However, the pharmacological activity of flavonoids in the extract has yet to be investigated. Therefore, we considered that a flavonoid-rich F. chica subfraction (FRS) has gastroprotective functions. For this, before the induction of gastric ulcers with ethanol or piroxicam, the rats received vehicle (water), omeprazole (30 mg/kg), or FRS (30 mg/kg), and the ulcer area was measured macro and microscopically, and the antisecretory action was investigated in pylorus-ligated rats. In addition, the roles of nitric oxide (NO) and nonprotein sulfhydryl compounds (NP-SH) in the gastroprotective effects of FRS were studied. FRS reduced ethanol- and piroxicam-induced ulcerations by 81% and 77%, respectively, as confirmed histologically. Antioxidant effects were observed for FRS through the maintenance of GSH and LPO levels, and the SOD and CAT activity similar to those found in the nonulcerated group. Moreover, FRS avoided the increase in MPO activity and TNF, IL-6, IL-4 and IL-10 levels. Moreover, mucin staining increased in ulcerated rats receiving FRS, and the pharmacological mechanism gastroprotective seems to involve the NO and NP-SH in addition to antisecretory actions. The chemical study by mass spectrometry confirmed the presence of flavonoids in FRS, and molecular docking studies have shown that these compounds interact with cyclooxygenase-1 and NO synthase. Furthermore, there was no indication that FRS had cytotoxic effects. Our results support the popular use of F. chica, and we conclude that the gastroprotection effect promoted by FRS can be attributed to the combined effect of the flavonoids.

Explainable artificial intelligence-assisted virtual screening and bioinformatics approaches for effective bioactivity prediction of phenolic cyclooxygenase-2 (COX-2) inhibitors using PubChem molecular fingerprints

Cyclooxygenase-2 (COX-2) inhibitors are nonsteroidal anti-inflammatory drugs that treat inflammation, pain and fever. This study determined the interaction mechanisms of COX-2 inhibitors and the molecular properties needed to design new drug candidates. Using machine learning and explainable AI methods, the inhibition activity of 1488 molecules was modelled, and essential properties were identified. These properties included aromatic rings, nitrogen-containing functional groups and aliphatic hydrocarbons. They affected the water solubility, hydrophobicity and binding affinity of COX-2 inhibitors. The binding mode, stability and ADME properties of 16 ligands bound to the Cyclooxygenase active site of COX-2 were investigated by molecular docking, molecular dynamics simulation and MM-GBSA analysis. The results showed that ligand 339,222 was the most stable and effective COX-2 inhibitor. It inhibited prostaglandin synthesis by disrupting the protein conformation of COX-2. It had good ADME properties and high clinical potential. This study demonstrated the potential of machine learning and bioinformatics methods in discovering COX-2 inhibitors.

In Vitro Human Gastrointestinal Tract Simulation Systems: A Panoramic Review

Simulated human gastrointestinal (GI) tract systems are important for their applications in the fields of probiotics, nutrition and health. To date, various in vitro gut systems have been available to study GI tract dynamics and its association with health. In contrast to in vivo investigations, which are constrained by ethical considerations, in vitro models have several benefits despite the challenges involved in mimicking the GI environment. These in vitro models can be used for a range of research, from simple to dynamic, with one compartment to several compartments. In this review, we present a panoramic development of in vitro GI models for the first time through an evolutionary timeline. We tried to provide insight on designing an in vitro gut model, especially for novices. Latest developments and scope for improvement based on the limitations of the existing models were highlighted. In conclusion, designing an in vitro GI model suitable for a particular application is a multifaceted task. The bio-mimicking of the GI tract specific to geometrical, anatomical and mechanical features remains a challenge for the development of effective in vitro GI models. Advances in computer technology, artificial intelligence and nanotechnology are going to be revolutionary for further development. Besides this, in silico high-throughput technologies and miniaturisation are key players in the success of making in vitro modelling cost-effective and reducing the burden of in vivo studies.

Cheminformatics identification of modulators of key carbohydrate-metabolizing enzymes from C. cujete for type-2 diabetes mellitus intervention

Purpose

The therapeutic use of oral hypoglycaemic agents in the management of type-2 diabetes mellitus (T2DM) is without adverse effects; thus, calls for alternative and novel candidates from natural products in medicinal plants.

Method

The study explored molecular docking and molecular dynamics (MD) simulation approaches to identify key antidiabetic metabolites from Crescentia cujete.

Results

Molecular docking results identified four and/or five best compounds against each target enzyme (alpha-glucosidase, dipeptidyl peptidase-IV, aldose reductase, and protein tyrosine phosphatase-1B (PTP-1B)) implicated in diabetes. The resulting complexes (except against PTP-1B) had higher docking scores above respective standards (acarbose, Diprotin A, ranirestat). The MD simulation results revealed compounds such as benzoic acid (-48.414 kcal/mol) and phytol (-45.112 kcal/mol) as well as chlorogenic acid (-42.978 kcal/mol) and naringenin (-31.292 kcal/mol) had higher binding affinities than the standards [acarbose (-28.248 kcal/mol), ranirestat (-21.042 kcal/mol)] against alpha-glucosidase and aldose reductase, respectively while Diprotin A (-45.112 kcal/mol) and ursolic acid (-18.740 kcal/mol) presented superior binding affinities than the compounds [luteolin (-41.957 kcal/mol and naringenin (-16.518 kcal/mol)] against DPP-IV and PTP-1B respectively.

Conclusion

While isoflavone (alpha-glucosidase), xylocaine (DPP-IV), luteolin (aldose reductase,) and chlorogenic acid (PTP-1B) were affirmed as the best inhibitors of respective enzyme targets, luteolin, and chlorogenic acid may be suggested and proposed as probable candidates against T2DM and related retinopathy complication based on their structural stability, compactness and affinity for three (DPP-IV, aldose reductase, and PTP-1B) of the four targets investigated. Further studies are warranted in vitro and in vivo on the antihyperglycaemic effects of these drug candidates.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-023-01249-7.

MolFPG: Multi-level fingerprint-based Graph Transformer for accurate and robust drug toxicity prediction

Drug toxicity prediction is essential to drug development, which can help screen compounds with potential toxicity and reduce the cost and risk of animal experiments and clinical trials. However, traditional handcrafted feature-based and molecular-graph-based approaches are insufficient for molecular representation learning. To address the problem, we developed an innovative molecular fingerprint Graph Transformer framework (MolFPG) with a global-aware module for interpretable toxicity prediction. Our approach encodes compounds using multiple molecular fingerprinting techniques and integrates Graph Transformer-based molecular representation for feature learning and toxic prediction. Experimental results show that our proposed approach has high accuracy and reliability in predicting drug toxicity. In addition, we explored the relationship between drug features and toxicity through an interpretive analysis approach, which improved the interpretability of the approach. Our results highlight the potential of Graph Transformers and multi-level fingerprints for accelerating the drug discovery process by reliably, effectively alarming drug safety. We believe that our study will provide vital support and reference for further development in the field of drug development and toxicity assessment.

Halting aberrant DNA methylation via in silico Identification of potent inhibitors of DNMT3B enzyme: Atomistic insights

To date, Cancer remains a global threat due to its impact on growing life expectancy. With the many efforts and methods of combating the disease, complete success remains a challenge owing to several limitations including cancer cells developing resistance through mutations, off-target effect of some cancer drugs resulting in toxicities, among many others. Aberrant DNA methylation is understood to be the primary reason for improper gene silence, which can result in neoplastic transformation, carcinogenesis, and tumour progression. DNA methyltransferase B (DNMT3B) enzyme is considered a potential target for the treatment of several cancers due to its important role in DNA methylation. However, only a few DNMT3B inhibitors have been reported to date. Herein, in silico molecular recognition techniques such as Molecular docking, Pharmacophore-based virtual screen and MD simulation were employed to identify potential inhibitors of DNMT3B that can halt aberrancy in DNA methylation. Findings initially identified 878 hit compounds based on a designed pharmacophore model from the reference compound Hypericin. Molecular docking was used to rank the hits by testing their efficiency when bound to the target enzyme and the top three (3) selected. All three (3) of the top hits showed excellent pharmacokinetic properties but two (2) (Zinc33330198 and Zinc77235130) were identified to be non-toxic. Molecular dynamic simulation of the final two hits showed good stability, flexibility, and structural rigidity of the compounds on DNMT3B. Finally, thermodynamic energy estimations show both compounds had favourable free energies comprising - 26.04 kcal/mol for Zinc77235130 and - 15.73 kcal/mol for Zinc33330198. Amongst the final two hits, Zinc77235130 showed consistency in favourable results across all the tested parameters and was thus selected as the lead compound for further experimental validation. The identification of this lead compound will form important basis for the inhibition of aberrant DNA methylation in cancer therapy.

Design and synthesis of N-acyl and dimeric N-Arylpiperazine derivatives as potential antileishmanial agents

The current regime for leishmaniasis is associated with several adverse effects, expensive, parenteral treatment for longer periods and the emergence of drug resistance. To develop affordable and potent antileishmanial agents, a series of N-acyl and homodimeric aryl piperazines were synthesized with high purity, predicted druggable properties by in silico methods and investigated their antileishmanial activity. The in vitro biological activity of synthesized compounds against clinically validated intracellular amastigote and extracellular promastigote form of Leishmania donovani parasite showed eight compounds inhibited 50% amastigotes growth below 25 µM. The half maximal inhibitory concentration (IC₅₀) and cytotoxicity assessment of eight active compounds, 4a, 4d and 4e demonstrated activity with an IC₅₀ 2.0 - 9.1 µM and selectivity index 10 - 42. Compound 4d (IC₅₀ 2.0 µM, SI = 42) found to be the best among them with four-folds more potent and eight-folds less toxic than the control drug miltefosine. Overall, results demonstrated that compound 4d is a promising lead candidate for further development as antileishmanial drug.

Investigating the effect of dehydromiltirone on septic AKI using a network pharmacology method, molecular docking, and experimental validation

Acute kidney injury (AKI) is a severe and frequent complication of sepsis that occurs in intensive care units with inflammation and rapid decline in renal function as the main pathological features. Systemic inflammation, microvascular dysfunction, and tubule injury are the main causes of sepsis-induced AKI (SI-AKI). The high prevalence and death rate from SI-AKI is a great challenge for clinical treatment worldwide. However, in addition to hemodialysis, there is no effective drug to improve renal tissue damage and alleviate the decline in kidney function. We conducted a network pharmacological analysis of Salvia miltiorrhiza (SM), a traditional Chinese medicine, which is widely used for the treatment of kidney disease. Then, we combined molecular docking and a dynamics simulation to screen for the active monomer dehydromiltirone (DHT) that has therapeutic effects on SI-AKI and investigated its potential mechanism of action through experimental validation. The components and targets of SM were obtained by searching the database, and 32 overlapping genes were screened by intersection analysis with AKI targets. GO and KEGG data showed that the functions of a common gene were closely related to oxidative stress, mitochondrial function, and apoptosis. The molecular docking results combined with molecular dynamics simulations provide evidence for a binding model between DHT and cyclooxygenase-2 (COX2), both of which are mainly driven by van der Waals interactions and a hydrophobic effect. In vivo, we found that mice pretreated with an intraperitoneal injection of DHT (20 mg/kg/d) for 3 days ameliorated CLP surgery-induced renal function loss and renal tissue damage and inhibited inflammatory mediators IL-6, IL-1β, TNF-α, and MCP-1 production. In vitro, the DHT pretreatment decreased LPS-induced expression of COX2, inhibited cell death and oxidative stress, alleviated mitochondrial dysfunction, and restrained apoptosis in HK-2 cells. Our research indicates that the renal preventive effect of DHT is related to maintaining mitochondrial dynamic balance, restoring mitochondrial oxidative phosphorylation, and inhibiting cell apoptosis. The findings in this study provide a theoretical basis and a novel method for the clinical therapy of SI-AKI.

In silico and in vitro studies of GENT-EDTA encapsulated niosomes: A novel approach to enhance the antibacterial activity and biofilm inhibition in drug-resistant Klebsiella pneumoniae

Klebsiella pneumoniae (Kp) is a common pathogen inducing catheter-related biofilm infections. Developing effective therapy to overcome antimicrobial resistance (AMR) in Kp is a severe therapeutic challenge that must be solved. This study aimed to prepare niosome-encapsulated GENT (Gentamicin) and EDTA (Ethylenediaminetetraacetic acid) (GENT-EDTA/Nio) to evaluate its efficacy toward Kp strains. The thin-film hydration method was used to prepare various formulations of GENT-EDTA/Nio. Formulations were characterized for their physicochemical characteristics. GENT-EDTA/Nio properties were used for optimization with Design-Expert Software. Molecular docking was utilized to determine the antibacterial activity of GENT. The niosomes displayed a controlled drug release and storage stability of at least 60 days at 4 and 25 °C. GENT-EDTA/Nio performance as antimicrobial agents has been evaluated by employing agar well diffusion method, minimum bactericidal concentration (MBC), and minimum inhibitory concentration (MIC) against the Kp bacteria strains. Biofilm formation was investigated after GENT-EDTA/Nio administration through different detection methods, which showed that this formulation reduces biofilm formation. The effect of GENT-EDTA/Nio on the expression of biofilm-related genes (mrkA, ompA, and vzm) was estimated using QRT-PCR. MTT assay was used to evaluate the toxicity effect of niosomal formulations on HFF cells. The present study results indicate that GENT-EDTA/Nio decreases Kp's resistance to antibiotics and increases its antibiotic and anti-biofilm activity and could be helpful as a new approach for drug delivery.

Identification of natural products against enoyl-acyl-carrier-protein reductase in malaria via combined pharmacophore modeling, molecular docking and simulations studies

Plasmodium falciparum is counted as one of the deadly species causing malaria. In that respect, enoyl acyl carrier protein reductase is recognized as one of the attractive druggable targets for the identification of antimalarials. Thus, from the structural proteome of ENR, common feature pharmacophores were constructed. To identify the representative models, all the hypotheses were subjected to validation methods, like, test set, enrichment factor, and Güner-Henry method, and the selected representative hypotheses were used to screen out the drug-like natural products. Further, the screened candidates were advanced to molecular docking calculations. Based on the docking score criteria and presence of essential interaction with Tyr277, seven candidates were shortlisted to conduct the HYDE and QSAR assessment. Further, the stability of these complexes was evaluated by employing molecular dynamics simulations, molecular mechanics-generalized born surface area approach-based free binding energy calculations with the residue-wise contribution of PfENR to the total binding free energy of the complex. On comparing the root mean square deviation, and fluctuation plots of the docked candidates with the reference, all the candidates displayed stable behavior, and the same outcome was depicted from the secondary structure element. However, from the free energy calculations, and residue-wise contribution conducted after dynamics, it was observed that out of seven, only five candidates sustain the binding with Tyr277 and cofactor of PfENR. Therefore, in the current work, the hybrid study of screening and stability lead to the identification of five structurally diverse candidates that can be employed for the design of novel antimalarials.Communicated by Ramaswamy H. Sarma.

Molecular Modeling Identification of Key Secondary Metabolites from Xylopia aethiopica as Promising Therapeutics Targeting Essential Measles Viral Proteins

This study computationally screened three key compounds (vanillin (VAN), oxophoebine (OPB), and dihydrochalcone (DHC)) derived from Xylopia aethiopica (Guinea pepper), a medicinal plant with known antiviral activity, against key druggable measles virus (MV) proteins (fusion protein (FUP), haemagglutinin protein (HMG), and phosphoprotein (PSP)). Each molecular species was subjected to a 100 ns molecular dynamics (MD) simulation following docking, and a range of postdynamic parameters including free binding energy and pharmacokinetic properties were determined. The docking scores of the resulting OPB-FUP (-5.4 kcal/mol), OPB-HMG (-8.1 kcal/mol), and OPB-PSP (-8.0 kcal/mol) complexes were consistent with their respective binding energy values (-25.37, -28.74, and -40.68 kcal/mol), and higher than that of the reference standard, ribavirin (RBV) in each case. Furthermore, all the investigated compounds were thermodynamically compact and stable, especially HMG of MV, and this observation could be attributed to the resulting intermolecular interactions in each system. Overall, OPB may possess inhibitory properties against MV glycoproteins (FUP and HMG) and PSP that play important roles in the replication of MV and measles pathogenesis. While OPB could serve as a scaffold for the development of novel MV fusion and entry inhibitors, further in vitro and in vivo evaluation is highly recommended.

The translational roadmap of the gut models, focusing on gut-on-chip

It is difficult to model in vitro the intestine when seeking to include crosstalk with the gut microbiota, immune and neuroendocrine systems. Here we present a roadmap of the current models to facilitate the choice in preclinical and translational research with a focus on gut-on-chip. These micro physiological systems (MPS) are microfluidic devices that recapitulate in vitro the physiology of the intestine. We reviewed the gut-on-chips that had been developed in academia and industries as single chip and that have three main purpose: replicate the intestinal physiology, the intestinal pathological features, and for pharmacological tests.

The translational roadmap of the gut models, focusing on gut-on-chip

It is difficult to model in vitro the intestine when seeking to include crosstalk with the gut microbiota, immune and neuroendocrine systems. Here we present a roadmap of the current models to facilitate the choice in preclinical and translational research with a focus on gut-on-chip. These micro physiological systems (MPS) are microfluidic devices that recapitulate in vitro the physiology of the intestine. We reviewed the gut-on-chips that had been developed in academia and industries as single chip and that have three main purpose: replicate the intestinal physiology, the intestinal pathological features, and for pharmacological tests.

Studies on 1,4-Quinone Derivatives Exhibiting Anti-Leukemic Activity along with Anti-Colorectal and Anti-Breast Cancer Effects

Colorectal cancer (CRC), breast cancer, and chronic myeloid leukemia (CML) are life-threatening malignancies worldwide. Although potent therapeutic and screening strategies have been developed so far, these cancer types are still major public health problems. Therefore, the exploration of more potent and selective new agents is urgently required for the treatment of these cancers. Quinones represent one of the most important structures in anticancer drug discovery. We have previously identified a series of quinone-based compounds (ABQ-1-17) as anti-CML agents. In the current work, ABQ-3 was taken to the National Cancer Institute (NCI) for screening to determine its in vitro antiproliferative effects against a large panel of human tumor cell lines at five doses. ABQ-3 revealed significant growth inhibition against HCT-116 CRC and MCF-7 breast cancer cells with 2.00 µM and 2.35 µM GI₅₀ values, respectively. The MTT test also showed that ABQ-3 possessed anticancer effects towards HCT-116 and MCF-7 cells with IC₅₀ values of 5.22 ± 2.41 μM and 7.46 ± 2.76 μM, respectively. Further experiments indicated that ABQ-3 induced apoptosis in both cell lines, and molecular docking studies explicitly suggested that ABQ-3 exhibited DNA binding in a similar fashion to previously reported compounds. Based on in silico pharmacokinetic prediction, ABQ-3 might display drug-like features enabling this compound to become a lead molecule for future studies.

Application of machine learning techniques to the analysis and prediction of drug pharmacokinetics

In this review, we describe the current status and challenges in applying machine-learning techniques to the analysis and prediction of pharmacokinetic data. The theory of pharmacokinetics has been developed over decades on the basis of physiology and reaction kinetics. Mathematical models allow the reduction of pharmacokinetic data to parameter values, giving insight and understanding into ADME processes and predicting the outcome of different dosing scenarios. However, much information hidden in the data is lost through conceptual simplification with models. It is difficult to use mechanistic models alone to predict diverse pharmacokinetic time profiles, including inter-drug and inter-individual differences, in a cross-sectional manner. Machine learning is a prediction platform that can handle complex phenomena through data-driven analysis. As a resule, machine learning has been successfully adopted in various fields, including image recognition and language processing, and has been used for over two decades in pharmacokinetic research, primarily in the area of quantitative structure-activity relationships for pharmacokinetic parameters. Machine-learning models are generally known to provide better predictive performance than conventional linear models. Owing to the recent success in deep learning, models with new structures are being consistently proposed. These models include transfer learning and generative adversarial networks, which contribute to the effective use of a limited amount of data by diverting existing similar models or generating pseudo-data. How to make such newly emerging machine learning technologies applicable to meet challenges in the pharmacokinetics/pharmacodynamics field is now the key issue.

In Vitro Cytotoxicity Evaluation of Plastoquinone Analogues against Colorectal and Breast Cancers along with In Silico Insights

Colorectal cancer (CRC) and breast cancer are leading causes of death globally, due to significant challenges in detection and management. The late-stage diagnosis and treatment failures require the discovery of potential anticancer agents to achieve a satisfactory therapeutic effect. We have previously reported a series of plastoquinone analogues to understand their cytotoxic profile. Among these derivatives, three of them (AQ-11, AQ-12, and AQ-15) were selected by the National Cancer Institute (NCI) to evaluate their in vitro antiproliferative activity against a panel of 60 human tumor cell lines. AQ-12 exhibited significant antiproliferative activity against HCT-116 CRC and MCF-7 breast cancer cells at a single dose and further five doses. MTT assay was also performed for AQ-12 at different concentrations against these two cells, implying that AQ-12 exerted notable cytotoxicity toward HCT-116 (IC₅₀ = 5.11 ± 2.14 μM) and MCF-7 (IC₅₀ = 6.06 ± 3.09 μM) cells in comparison with cisplatin (IC₅₀ = 23.68 ± 6.81 μM and 19.67 ± 5.94 μM, respectively). This compound also augmented apoptosis in HCT-116 (62.30%) and MCF-7 (64.60%) cells comparable to cisplatin (67.30% and 78.80%, respectively). Molecular docking studies showed that AQ-12 bound to DNA, forming hydrogen bonding through the quinone scaffold. In silico pharmacokinetic determinants indicated that AQ-12 demonstrated drug-likeness with a remarkable pharmacokinetic profile for future mechanistic anti-CRC and anti-breast cancer activity studies.

Repurposing drugs and identification of inhibitors of integral proteins (spike protein and main protease) of SARS-CoV-2

The outbreak of Coronavirus infection (COVID-19) has prompted the World Health Organisation (WHO) to declare the outbreak, a Public Health Emergency of International concern. As part of the efforts to discover lead compounds for clinical use, 53 molecules were screened using molecular docking and dynamic simulations (MDS) techniques to identify potential inhibitors of SARS-CoV-2 spike protein (COVID-19 Sgp) and main protease (COVID-19 Mpro) or both. Lopinavir (LPV), nelfinavir (NEF), hydroxychloroquine (HCQ), remdesivir (RDV) and an irreversible inhibitor of SARS-CoV (N3) were used as standard drugs for COVID-19 Mpro, while zafirlukast (ZFK) and cefoperazone (CSP)) as standard drugs for COVID-19 Sgp. After 100 ns of MDS, with reference to standard drugs (N3, -52.463 Kcal/mol, NEF, -51.618 Kcal/mol, RDV, -48.780 Kcal/mol, LPV, -46.788 Kcal/mol, DRV, -33.655 Kcal/mol and HCQ, -21.065 Kcal/mol), five molecules, HCR, GRN, C3G, EGCG, and K7G were predicted to be promising inhibitors of COVID-19 Mpro with binding energies of -53.877 kcal/mol, -50.653 Kcal/mol, -48.600 kcal/mol, -47.798 kcal/mol and -46.902 kcal/mol, respectively. These lead molecules were then docked at receptor-binding domain (RBD) of COVID-19 Sgp to examine their inhibitory effects. C3G, GRN and K7G exhibited higher binding energies of -42.310 kcal/mol, -32.210 kcal/mol, -26.922 kcal/mol than the recorded values for the reference drugs (CSP, -35.509 kcal/mol, ZFK, -24.242 kcal/mol), respectively. The results of the binding energy and structural analyses from this study revealed that C3G, GRN and K7G could serve as potential dual inhibitors of COVID-19 Sgp and COVID-19 Mpro, while HCR and EGCG would be inhibitors of COVID-19 Mpro.Communicated by Ramaswamy H. Sarma.

Calculation of Exact Shapley Values for Support Vector Machines with Tanimoto Kernel Enables Model Interpretation

The support vector machine (SVM) algorithm is popular in chemistry and drug discovery. SVM models have black box character. Their predictions can be interpreted through feature weighting or the model-agnostic Shapley additive explanations (SHAP) formalism that locally approximates Shapley values (SVs) originating from game theory. We introduce an algorithm termed SV-expressed Tanimoto similarity (SVETA) for the exact calculation of SVs to explain SVM models employing the Tanimoto kernel, the gold standard for the assessment of molecular similarity. For a model system, the exact calculation of SVs is demonstrated. In an SVM-based compound classification task from drug discovery, only a limited correlation between exact SV and SHAP values is observed, prohibiting the use of approximate values for rationalizing predictions. For exemplary test compounds, atom-based mapping of prioritized features delineates coherent substructures that closely resemble those obtained by analyzing independently derived random forest models, thus providing consistent explanations.

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SVETA: new methodology for explaining support vector machine (SVM) predictions

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Tanimoto similarity-based SVM models are popular in chemistry

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SVETA enables the calculation of exact Shapley values for rationalizing SVM models

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SVETA-based feature mapping provides intuitive explanations of SVM decisions

Identification of Potential Insect Growth Inhibitor against Aedes aegypti: A Bioinformatics Approach

Aedes aegypti is the main vector that transmits viral diseases such as dengue, hemorrhagic dengue, urban yellow fever, zika, and chikungunya. Worldwide, many cases of dengue have been reported in recent years, showing significant growth. The best way to manage diseases transmitted by Aedes aegypti is to control the vector with insecticides, which have already been shown to be toxic to humans; moreover, insects have developed resistance. Thus, the development of new insecticides is considered an emergency. One way to achieve this goal is to apply computational methods based on ligands and target information. In this study, sixteen compounds with acceptable insecticidal activities, with 100% larvicidal activity at low concentrations (2.0 to 0.001 mg·L−1), were selected from the literature. These compounds were used to build up and validate pharmacophore models. Pharmacophore model 6 (AUC = 0.78; BEDROC = 0.6) was used to filter 4793 compounds from the subset of lead-like compounds from the ZINC database; 4142 compounds (dG < 0 kcal/mol) were then aligned to the active site of the juvenile hormone receptor Aedes aegypti (PDB: 5V13), 2240 compounds (LE < −0.40 kcal/mol) were prioritized for molecular docking from the construction of a chitin deacetylase model of Aedes aegypti by the homology modeling of the Bombyx mori species (PDB: 5ZNT), which aligned 1959 compounds (dG < 0 kcal/mol), and 20 compounds (LE < −0.4 kcal/mol) were predicted for pharmacokinetic and toxicological prediction in silico (Preadmet, SwissADMET, and eMolTox programs). Finally, the theoretical routes of compounds M01, M02, M03, M04, and M05 were proposed. Compounds M01−M05 were selected, showing significant differences in pharmacokinetic and toxicological parameters in relation to positive controls and interaction with catalytic residues among key protein sites reported in the literature. For this reason, the molecules investigated here are dual inhibitors of the enzymes chitin synthase and juvenile hormonal protein from insects and humans, characterizing them as potential insecticides against the Aedes aegypti mosquito.

Application of Physiologically Based Pharmacokinetic Modeling in Preclinical Studies: A Feasible Strategy to Practice the Principles of 3Rs

Pharmacokinetic characterization plays a vital role in drug discovery and development. Although involving numerous laboratory animals with error-prone, labor-intensive, and time-consuming procedures, pharmacokinetic profiling is still irreplaceable in preclinical studies. With physiologically based pharmacokinetic (PBPK) modeling, the in vivo profiles of drug absorption, distribution, metabolism, and excretion can be predicted. To evaluate the application of such an approach in preclinical investigations, the plasma pharmacokinetic profiles of seven commonly used probe substrates of microsomal enzymes, including phenacetin, tolbutamide, omeprazole, metoprolol, chlorzoxazone, nifedipine, and baicalein, were predicted in rats using bottom-up PBPK models built with in vitro data alone. The prediction's reliability was assessed by comparison with in vivo pharmacokinetic data reported in the literature. The overall predicted accuracy of PBPK models was good with most fold errors within 2, and the coefficient of determination (R2) between the predicted concentration data and the observed ones was more than 0.8. Moreover, most of the observation dots were within the prediction span of the sensitivity analysis. We conclude that PBPK modeling with acceptable accuracy may be incorporated into preclinical studies to refine in vivo investigations, and PBPK modeling is a feasible strategy to practice the principles of 3Rs.

Systems pharmacology-based drug discovery and active mechanism of natural products for coronavirus pneumonia (COVID-19): An example using flavonoids

BACKGROUND

Recently, the value of natural products has been extensively considered because these resources can potentially be applied to prevent and treat coronavirus pneumonia 2019 (COVID-19). However, the discovery of nature drugs is problematic because of their complex composition and active mechanisms.

METHODS

This comprehensive study was performed on flavonoids, which are compounds with anti-inflammatory and antiviral effects, to show drug discovery and active mechanism from natural products in the treatment of COVID-19 via a systems pharmacological model. First, a chemical library of 255 potential flavonoids was constructed. Second, the pharmacodynamic basis and mechanism of action between flavonoids and COVID-19 were explored by constructing a compound-target and target-disease network, targets protein-protein interaction (PPI), MCODE analysis, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment.

RESULTS

In total, 105 active flavonoid components were identified, of which 6 were major candidate compounds (quercetin, epigallocatechin-3-gallate (EGCG), luteolin, fisetin, wogonin, and licochalcone A). 152 associated targets were yielded based on network construction, and 7 family proteins (PTGS, GSK3β, ABC, NOS, EGFR, and IL) were included as central hub targets. Moreover, 528 GO items and 178 KEGG pathways were selected through enrichment of target functions. Lastly, molecular docking demonstrated good stability of the combination of selected flavonoids with 3CL Pro and ACEⅡ.

CONCLUSION

Natural flavonoids could enable resistance against COVID-19 by regulating inflammatory, antiviral, and immune responses, and repairing tissue injury. This study has scientific significance for the selective utilization of natural products, medicinal value enhancement of flavonoids, and drug screening for the treatment of COVID-19 induced by SARS-COV-2.

Identification of secondary metabolites from Crescentia cujete as promising antibacterial therapeutics targeting type 2A topoisomerases through molecular dynamics simulation

The potential of fluoroquinolones as remarkable antibacterial agents evolved from their ability to generate 'poison' complexes between type IIA topoisomerases [topo2As (DNA gyrases and topoisomerases IV)] and DNA. However, the overuse of fluoroquinolones coupled with chromosomal mutations in topo2As has increased incidence of resistance and consequently undermined the application of the currently available fluoroquinolones in clinical practice. In this study, the molecular mechanism of interaction between the secondary metabolites of Crescentia cujete (an underutilized plant with proven anti-bacterial activity) and topo2As was investigated using computational methods. Through molecular docking, the top five compounds with the best affinity for each topo2A were identified and subjected to molecular dynamics simulation over a period of 100 ns. The results revealed that the identified compounds had higher binding energy values than the reference standards against the topo2As except for topoisomerase IV ParC, and this was consistent with the results of the structural stability and compactness of the resulting complexes. Specifically, cistanoside D (-49.18 kcal/mol), chlorogenic acid (-55.55 kcal/mol), xylocaine (-33.08 kcal/mol), and naringenin (-35.48 kcal/mol) had the best affinity for DNA gyrase A, DNA gyrase B, topoisomerase IV ParC, and topoisomerase IV ParE, respectively. Of the constituents of C. cujete evaluated, only apigenin and luteolin had affinity for all the four targets. These observations are indicative of the identified compounds as potential inhibitors of topo2As as evidenced from the molecular interactions including hydrogen bonds established with the active site amino acids of the respective targets. This is the first in silico report on the antibacterial effect of C. cujete and the findings would guide structural modification of the identified compounds as novel inhibitors of topo2As for further in vitro and in vivo assessments.

Design, synthesis, and computational studies of benzimidazole derivatives as new antitubercular agents

The increase in the drug-resistant strains of Mycobacterium tuberculosis has led researchers to new drug targets. The development of new compounds that have effective inhibitory properties with the selective vital structure of Mycobacterium tuberculosis is required in new scientific approaches. The most important of these approaches is the development of inhibitor molecules for Mycobacterium cell wall targets. In this study, first of all, the antitubercular activity of 23 benzimidazole derivatives was experimentally determined. And then molecular docking studies were carried out with 4 different targets: Arabinosyltransferase C (EmbC), Filamentous Temperature Sensitive Mutant Z (FtsZ), Protein Tyrosine Phosphatase B (PtpB), and Decaprenylphosphoryl-β-D-ribose-2'-oxidase (DprE1). It has been determined that benzimidazole derivatives show activity through the DprE1 enzyme. It is known that DprE1, which has an important role in the synthesis of the cell envelope from Arabinogalactan, is also effective in the formation of drug resistance. Due to this feature, the DprE1 enzyme has become an important target for drug development studies. Also, it was chosen as a target for this study. This study aims to identify molecules that inhibit DprE1 for the development of more potent and selective antitubercular drugs. For this purpose, molecular docking studies by AutoDock Vina, and CDOCKER and molecular dynamics (MD) simulations and in silico ADME/Tox analysis were implemented for 23 molecules. The molecules exhibited binding affinity values of less than -8.0 kcal/mol. After determining the compound's anti-TB activities by a screening test, the best-docked results were detected using compounds 20, 21, and 30. It was found that 21, was the best molecule with its binding affinity value, which was supported by MD simulations and in silico ADME modeling results.Communicated by Ramaswamy H. Sarma.

Identification of the Active Compound of Liu Wei Di Huang Wan for Treatment of Gestational Diabetes Mellitus via Network Pharmacology and Molecular Docking

Liu Wei Di Huang Wan (LWDHW) is a well-known Chinese herbal compound, which has been prescribed for the treatment of gestational diabetes mellitus (GDM). We sought to clarify the potential therapeutic effects of LWDHW against GDM. Differentially expressed genes (DEGs) in GDM were firstly identified from the Gene Expression Omnibus (GEO) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to reveal the biological functions of the DEGs. Subsequently, the LWDHW-compound-target network was constructed based on public databases to identify the relationship between the active components in LWDHW and the corresponding targets. Furthermore, gene functional analysis and protein-protein interaction (PPI) network construction were applied to investigate the function of potential targets and to evaluate hub genes. Finally, molecular docking was used to verify the binding activities between active ingredients and hub targets. Thirteen active components and 39 corresponding therapeutic target genes were obtained via network pharmacology analysis. The enrichment analysis demonstrated that the anti-GDM effect of LWDHW included oxidoreductase activity, involvement in renal system process, and regulation of blood pressure, which may be achieved through regulation of serotonergic synapses, vascular smooth muscle contraction, and neuroactive ligand-receptor interaction pathways. Additionally, molecular docking revealed that the main active component, Mu Dan Pi, exhibited the best affinity for proteins encoded by hub genes. This study applied network pharmacology analysis and molecular docking to display the multicomponent and multitarget characteristics of LWDHW in the treatment of GDM. Our findings provide novel insights into the pathogenesis of GDM and the therapeutic mechanisms of LWDHW against GDM.

Machine learning & deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry

Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates and can be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review substantially discusses the recent updates in AI tools as cheminformatics application in medicinal chemistry for the data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry while improving small-molecule bioactivities and properties.

Discovery of thiazolidin-4-one analogue as selective GSK-3β inhibitor through structure based virtual screening

GSK-3β directly phosphorylate tubulin binding site of tau protein, indicating its importance in tau aggregation and, therefore, in Alzheimer's disease pathology. New GSK-3β inhibitors were identified using a structure-based screening, ADMET analysis. These studies revealed that ZINC09036109, ZINC72371723, ZINC72371725, and ZINC01373165 approached optimal ADMET properties along with good MM-GBSA dG binding. Protein kinase assays of these compounds against eight disease-relevant kinases were performed. During disease-relevant kinase profiling, ZINC09036109 ((E)-2-((3,4-dimethylphenyl)imino)-5-(3-methoxy-4-(naphthalen-2-ylmethoxy)benzyl)thiazolidin-4-one) emerged as a selective GSK-3β inhibitor with more than 10-fold selectivity over other disease-relevant kinases. Molecular dynamics study of ZINC09036109 molecule revealed interactions with Ile62, Phe67, Val135, Leu188, Asp200 amino acid residues of the binding site of GSK-3β, which were highly comparable to the co-crystallized molecule and hence validating comparative better activity of this compound compared to overall screened molecules.

Mollusc-Derived Brominated Indoles for the Selective Inhibition of Cyclooxygenase: A Computational Expedition

Inflammation plays an important role in different chronic diseases. Brominated indoles derived from the Australian marine mollusk Dicathais orbita (D. orbita) are of interest for their anti-inflammatory properties. This study evaluates the binding mechanism and potentiality of several brominated indoles (tyrindoxyl sulfate, tyrindoleninone, 6-bromoisatin, and 6,6′-dibromoindirubin) against inflammatory mediators cyclooxygenases-1/2 (COX-1/2) using molecular docking, followed by molecular dynamics simulation, along with physicochemical, drug-likeness, pharmacokinetic (pk), and toxicokinetic (tk) properties. Molecular docking identified that these indole compounds are anchored, with the main amino acid residues, positioned in the binding pocket of the COX-1/2, required for selective inhibition. Moreover, the molecular dynamics simulation based on root mean square deviation (RMSD), radius of gyration (Rg), solvent accessible surface area (SASA), and root mean square fluctuation (RMSF) analyses showed that these natural brominated molecules transit rapidly to a progressive constant configuration during binding with COX-1/2 and seem to accomplish a consistent dynamic behavior by maintaining conformational stability and compactness. The results were comparable to the Food and Drug Administration (FDA)-approved selective COX inhibitor, aspirin. Furthermore, the free energy of binding for the compounds assessed by molecular mechanics–Poisson–Boltzmann surface area (MM–PBSA) confirmed the binding capacity of indoles towards COX-1/2, with suitable binding energy values except for the polar precursor tyrindoxyl sulfate (with COX-1). The physicochemical and drug-likeness analysis showed zero violations of Lipinski’s rule, and the compounds are predicted to have excellent pharmacokinetic profiles. These indoles are projected to be non-mutagenic and free from hepatotoxicity, with no inhibition of human ether-a-go–go gene (hERG) I inhibitors, and the oral acute toxicity LD₅₀ in rats is predicted to be similar or lower than aspirin. Overall, this work has identified a plausible mechanism for selective COX inhibition by natural marine indoles as potential therapeutic candidates for the mitigation of inflammation.

A Novel Method for Predicting the Human Inherent Clearance and Its Application in the Study of the Pharmacokinetics and Drug-Drug Interaction between Azidothymidine and Fluconazole Mediated by UGT Enzyme

In order to improve the benefit–risk ratio of pharmacokinetic (PK) research in the early development of new drugs, in silico and in vitro methods were constructed and improved. Models of intrinsic clearance rate (CL_int) were constructed based on the quantitative structure–activity relationship (QSAR) of 7882 collected compounds. Moreover, a novel in vitro metabolic method, the Bio-PK dynamic metabolic system, was constructed and combined with a physiology-based pharmacokinetic model (PBPK) model to predict the metabolism and the drug–drug interaction (DDI) of azidothymidine (AZT) and fluconazole (FCZ) mediated by the phase II metabolic enzyme UDP-glycosyltransferase (UGT) in humans. Compared with the QSAR models reported previously, the goodness of fit of our CL_int model was slightly improved (determination coefficient (R²) = 0.58 vs. 0.25–0.45). Meanwhile, compared with the predicted clearance of 61.96 L/h (fold error: 2.95–3.13) using CL_int (8 µL/min/mg) from traditional microsomal experiment, the predicted clearance using CL_int (25 μL/min/mg) from Bio-PK system was increased to 143.26 L/h (fold error: 1.27–1.36). The predicted C_max and AUC (the area under the concentration–time curve) ratio were 1.32 and 1.84 (fold error: 1.36 and 1.05) in a DDI study with an inhibition coefficient (Ki) of 13.97 μM from the Bio-PK system. The results indicate that the Bio-PK system more truly reflects the dynamic metabolism and DDI of AZT and FCZ in the body. In summary, the novel in silico and in vitro method may provide new ideas for the optimization of drug metabolism and DDI research methods in early drug development.

Pharmacoinformatics-based phytochemical screening for anticancer impacts of yellow sweet clover, Melilotus officinalis (Linn.) Pall

To date, much attention has been paid to phytochemicals because of their diverse pharmacological effects on a variety of diseases such as cancer. In this regard, computer-aided drug design, as a cost- and time-effective approach, is primarily applied to investigate the drug candidates before their further costly in vitro and in vivo experimental evaluations. Accordingly, different signaling pathways and proteins can be targeted using such strategies. As a key protein for the initiation of eukaryotic DNA replication, mini-chromosome maintenance complex component 7 (MCM7) overexpression is related to the initiation and progression of aggressive malignancies. The current study was conducted to identify new potential natural compounds from the yellow sweet clover, Melilotus officinalis (Linn.) Pall, by examining the potential of 40 isolated phytochemicals against MCM7 protein. A structure-based pharmacophore model to the protein active site cavity was generated and followed by virtual screening and molecular docking. Overall, four compounds were selected for further evaluation based on their binding affinities. Our analyses revealed that two novel compounds, namely rosmarinic acid (PubChem CID:5281792) and melilotigenin (PubChem CID:14059499) might be druggable and offer safe usage in human. The stability of these two protein-ligand complex structures was confirmed through molecular dynamics simulation. The findings of this study reveal the potential of these two phytochemicals to serve as anticancer agents, while further pharmacological experiments are required to confirm their effectiveness against human cancers.

Anti-COVID-19 activity of some benzofused 1,2,3-triazolesulfonamide hybrids using in silico and in vitro analyses

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a pandemic fatal infection with no known treatment. The severity of the disease and the fast viral mutations forced the scientific community to search for potential solution. Here in the present manuscript, some benzofused1,2,3triazolesulfonamide hybrids were synthesized and evaluated for their anti- SARS-CoV-2 activity using in silico prediction then the most potent compounds were assessed using in-Vitro analysis. The in-Silico study was assessed against RNA dependent RNA polymerase, Spike protein S1, Main protease (3CLpro) and 2'-O-methyltransferase (nsp16). It was found that 4b and 4c showed high binding scores against RNA dependent RNA polymerase reached -8.40 and -8.75 ​kcal/mol, respectively compared to the approved antiviral (remdesivir -6.77 ​kcal/mol). Upon testing the binding score with SARS-CoV-2 Spike protein it was revealed that 4c exhibited the highest score (-7.22 ​kcal/mol) compared to the reference antibacterial drug Ceftazidime (-6.36 ​kcal/mol). Surprisingly, the two compounds 4b and 4c showed the highest binding scores against SARS-CoV-2 3CLpro (-8.75, -8.48 ​kcal/mol, respectively) and nsp16 (- 8.84 and - 8.89 ​kcal/mol, respectively) displaying many types of interaction with all the enzymes binding sites. The derivatives 4b and 4c were examined in vitro for their potential anti-SARS-CoV-2 and it was revealed that 4c was the most promising compound with IC50 reached 758.8108 ​mM and complete (100%) inhibition of the binding of SARS-CoV-2 virus to human ACE2 can be accomplished by using 0.01 ​mg.

Molecular docking analysis of modified gedunin from neem with snake venom enzymes

Snakebites are a problem due to the increasing number of deaths and permanent disabilities. There is currently a shortage of antidotes for snakebite. The existing antibody antidote, produced from horse/sheep plasma/sera is expensive, species-dependent, and causes fatal side effects. Therefore, it is of interest use of natural flavonoid named gedunin from the Azadirachta indica (Neem) plant species to combat snakebites. Thus, we show the molecular docking analysis of gedunin (C26H31N2O6F) with enzymes (common in snake species) such as 5-nucleotidase, acetyl cholinesterase, L-aao, metalloproteinase, serine, thrombin and phospholipase A2. The modified gedunin in the enzyme pocket showed improved pharmacological properties for further consideration in combating snakebites.

Optimizing a High-Throughput Solid-Phase Microextraction System to Determine the Plasma Protein Binding of Drugs in Human Plasma

Plasma protein binding refers to the binding of a drug to plasma proteins after entering the body. The measurement of plasma protein binding is essential during drug development and in clinical practice, as it provides a more detailed understanding of the available free concentration of a drug in the blood, which is in turn critical for pharmacokinetics and pharmacodynamics studies. In addition, the accurate determination of the free concentration of a drug in the blood is also highly important for therapeutic drug monitoring and in personalized medicine. The present study uses C₁₈-coated solid-phase microextraction 96-pin devices to determine the free concentrations of a set of drugs in plasma, as well as the plasma protein binding of drugs with a wide range of physicochemical properties. It should be noted that the extracted amounts used to calculate the binding constants and plasma protein bindings should be measured at respective equilibrium for plasma and phosphate buffer. Therefore, special attention is placed on properly determining the equilibration times required to correctly estimate the free concentrations of drugs in the investigated systems. The plasma protein binding values obtained with the 96-pin devices are consistent with those reported in the literature. The 96-pin device used in this research can be easily coupled with a Concept96 or other automated robotic systems to create an automated plasma protein binding determination protocol that is both more time and labor efficient compared to conventional equilibrium dialysis and ultrafiltration methods.

Molecular Docking Analysis of Flavonoid Compounds with Matrix Metalloproteinase- 8 for the Identification of Potential Effective Inhibitors

Background:

Matrix metalloproteinase-8 (MMP-8) participates in the degradation of different types of collagens in the extracellular matrix and basement membrane. Up-regulation of the MMP-8 has been demonstrated in many disorders including cancer development, tooth caries, periodontal/ peri-implant soft and hard tissue degeneration, and acute/chronic inflammation. Therefore, MMP-8 has become an encouraging target for therapeutic procedures for scientists. We carried out a molecular docking approach to study the binding affinity of 29 flavonoids, as drug candidates, with the MMP-8. Pharmacokinetic and toxicological properties of the compounds were also studied. Moreover, it was attempted to identify the most important amino acids participating in ligand binding based on the degree of each of the amino acids in the ligand-amino acid interaction network for MMP-8.

Methods:

Three-dimensional structure of the protein was gained from the RCSB database (PDB ID: 4QKZ). AutoDock version 4.0 and Cytoscape 3.7.2 were used for molecular docking and network analysis, respectively. Notably, the inhibitor of the protein in the crystalline structure of the 4QKZ was considered as a control test. Pharmacokinetic and toxicological features of compounds were predicted using bioinformatics web tools. Post-docking analyses were performed using BIOVIA Discovery Studio Visualizer version 19.1.0.18287.

Results and Discussions:

According to results, 24 of the studied compounds were considered to be top potential inhibitors for MMP-8 based on their salient estimated free energy of binding and inhibition constant as compared with the control test: Apigenin-7-glucoside, nicotiflorin, luteolin, glabridin, taxifolin, apigenin, licochalcone A, quercetin, isorhamnetin, myricetin, herbacetin, kaemferol, epicatechin, chrysin, amentoflavone, rutin, orientin, epiafzelechin, quercetin-3- rhamnoside, formononetin, isoliquiritigenin, vitexin, catechine, and isoquercitrin. Moreover, His- 197 was found to be the most important amino acid involved in the ligand binding for the enzyme.

Conclusion:

The results of the current study could be used in the prevention and therapeutic procedures of a number of disorders such as cancer progression and invasion, oral diseases, and acute/chronic inflammation. Although, in vitro and in vivo tests are inevitable in the future.

Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein

X-linked inhibitor of apoptosis protein (XIAP) is a member of inhibitor of apoptosis protein (IAP) family responsible for neutralizing the caspases-3, caspases-7, and caspases-9. Overexpression of the protein decreased the apoptosis process in the cell and resulting development of cancer. Different types of XIAP antagonists are generally used to repair the defective apoptosis process that can eliminate carcinoma from living bodies. The chemically synthesis compounds discovered till now as XIAP inhibitors exhibiting side effects, which is making difficulties during the treatment of chemotherapy. So, the study has design to identifying new natural compounds that are able to induce apoptosis by freeing up caspases and will be low toxic. To identify natural compound, a structure-based pharmacophore model to the protein active site cavity was generated following by virtual screening, molecular docking and molecular dynamics (MD) simulation. Initially, seven hit compounds were retrieved and based on molecular docking approach four compounds has chosen for further evaluation. To confirm stability of the selected drug candidate to the target protein the MD simulation approach were employed, which confirmed stability of the three compounds. Based on the finding, three newly obtained compounds namely Caucasicoside A (ZINC77257307), Polygalaxanthone III (ZINC247950187), and MCULE-9896837409 (ZINC107434573) may serve as lead compounds to fight against the treatment of XIAP related cancer, although further evaluation through wet lab is necessary to measure the efficacy of the compounds.

Pharmacological Network Reveals the Active Mechanism of Qi-Replenishing, Spleen-Strengthening, Phlegm-Dispelling, and Blood-Nourishing Fufang on Coronary Heart Disease

This study aimed to investigate the potential targets and pathways of qi-replenishing, spleen-strengthening, phlegm-dispelling, and blood-nourishing Fufang in the treatment of coronary heart disease (CHD). The composition of Fufang was identified, followed by screening of the active components using ADME. The targets of active components were predicted and screened based on the TCMSP and BATMAN databases and were cross-validated using the CTD database and DisGeNET. A functional enrichment analysis was performed using the ClueGO + CluePedia plugins and clusterProfiler in the R package. The protein-protein interaction (PPI) network was constructed using the STRING database and Cytoscape. Finally, a pharmacological network was constructed. A total of 27 overlapping targets were obtained after cross-validation. ALB, IL-6, and TNF were the hub genes in the PPI network. The pharmacological network included 59 nodes and 189 relation pairs. Among the 59 nodes, there were 2 herbal medicine nodes (Salvia miltiorrhiza and Astragalus mongholicus), 8 chemical component nodes (magnesium lithospermate B, neocryptotanshinone II, heteratisine, daphneolone, tanshinone IIA, tanshinone IIB, soyasapogenol B, and astragaloside II), 27 target protein nodes (such as ALB, TNF, IL-6, NFKB1, APOA1, APOA2, CYP1A1, and CYP1A2), and 22 pathway nodes (such as the toll-like receptor signaling pathway, IL-17 signaling pathway, and TNF signaling pathway). Therefore, we found that the genes TNF, IL-6, NFKB1, ALB, CYP1A1, CYP1A2, APOA1, and APOA2 might be important targets of the key active compounds neocryptotanshinone II and astragaloside II. These genes targeted by the key active compounds might regulate inflammation-related pathways and the level of albumin and cholesterol in CHD.

Evidence of TCM Theory in Treating the Same Disease with Different Methods: Treatment of Pneumonia with Ephedra sinica and Scutellariae Radix as an Example

Pneumonia is a serious global health problem and the leading cause of mortality in children. Antibiotics are the main treatment for bacterial pneumonia, but there are serious drug resistance problems. Traditional Chinese medicine (TCM) has been used to treat diseases for thousands of years and has a unique theory. This article takes the treatment of pneumonia with Ephedra sinica as a representative hot medicine and Scutellariae Radix as a representative cold medicine as an example. We explore and explain the theory of treating the same disease with different TCM treatments. Using transcriptomics and network pharmacology methods, GO, KEGG enrichment, and PPI network construction were carried out, demonstrating that Ephedra sinica plays a therapeutic role through the NF-κB and apoptosis signaling pathways targeting PLAU, CD40LG, BLC2L1, CASP7, and CXCL8. The targets of Scutellariae Radix through the IL-17 signaling pathway are MMP9, CXCL8, and MAPK14. Molecular docking technology was also used to verify the results. In short, our results provide evidence for the theory of treating the same disease with different treatments, and we also discuss future directions for traditional Chinese medicine.

Molecular docking study of flavonoid compounds for possible matrix metalloproteinase-13 inhibition

OBJECTIVES

Matrix metalloproteinase-13 (MMP-13) has been reported to be involved in different biological processes such as degradation of extracellular matrix proteins, activating or degrading some significant regulatory proteins, wound healing, tissue remodeling, cartilage degradation, bone development, bone mineralization, ossification, cell migration, and tumor cell invasion. Further, MMP-13 participates in many oral diseases such as tooth decay, gingivitis, and degradation of enamel and tissue around the implant. In addition, inhibition of MMP-13 has shown therapeutic properties for Alzheimer's disease (AD). We performed molecular docking to assess the binding affinity of 29 flavonoid compounds with the MMP-13. Additionally, pharmacokinetic and toxicity characteristics of the top-ranked flavonoids were studied. The current study also intended to identify the most important amino acids involved in the inhibition of MMP-13 based on topological feature (degree) in the ligand-amino acid network for MMP-13.

METHODS

Molecular docking and network analysis were studied using AutoDock and Cytoscape software, respectively. Pharmacokinetic and toxicity characteristics of compounds were predicted using bioinformatics web tools.

RESULTS

The results revealed that nine of the studied flavonoids had considerable estimated free energy of binding and inhibition constant: Rutin, nicotiflorin, orientin, vitexin, apigenin-7-glucoside, quercitrin, isoquercitrin, quercitrin-3-rhamnoside, and vicenin-2. Proline-242 was found to be the most important amino acid inhibiting the enzyme.

CONCLUSIONS

The results of the current study may be helpful in the prevention and therapeutic procedures of many disorders such as cancer, tooth caries, and AD. Nevertheless, validation tests are required in the future.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Identification of the natural compound inhibitors against Plasmodium falciparum plasmepsin-II via common feature based screening and molecular dynamics simulations

Malaria is counted amongst the deadly disease caused by Plasmodium falciparum. Recently, plasmepsin-II enzyme has gained much importance as an attractive drug target for the exploration of antimalarials. Therefore, the common feature pharmacophore models were generated from the crystallized complexes of the plasmepsin-II proteome. These models were subjected to a series of validation procedures, i.e. test set and Güner Henry studies to enlist the representative models. The selected representative hypotheses incorporating the most essential chemical features (common ZHHA) were screened against the natural product database to retrieve the potential candidates. To ensure the selection of the drug-like candidates, prior to screening, filtering steps (Drug-likeness and ADMET filters) were employed on the selected database. To study the interaction pattern of the candidates within the protein, these molecules were advanced to the molecular docking studies. Subsequently, based on the selected cut-off criteria obtained via redocking of the reference (4Z22), 15 compounds showed higher docking score (> -16.05 kcal/mol), and displayed the presence of hydrogen bonding with the crucial amino acids, i.e. Asp34 and Asp214. Further, the stability of the docked molecules was scrutinized via molecular dynamics simulations, and the results were compared with the reference compound 4Z22. All the docked compounds showed stable dynamics behaviour. Thus, in the present contribution, the combination of screening and stability procedures resulted in the identification of 15 hits that can serve as a new chemical space in the designing of the novel antimalarials.Communicated by Ramaswamy H. Sarma.

Synthesis and biological evaluation of 12 novel (-)-camphene-based 1,3,4-thiadiazoles against Mycobacterium tuberculosis

Aim: To evaluate the activity, cytotoxicity and efflux pumps inhibition of a series of 12 novels (-)-camphene-based 1,3,4-thiadiazoles (TDZs) against Mycobacterium tuberculosis ( Mtb). Materials & methods: The minimum inhibitory concentration (MIC), cytotoxicity for three cell lines, ethidium bromide accumulation and checkerboard methods were carried out. Results: Compounds (6a, 6b, 6c, 6g, 6h and 6j) showed significant anti- Mtb activity (MIC 3.9–7.8 μg/ml) and no antagonism with anti-TB drugs already used in the TB treatment. Selectivity index (SI) was also determined, with values reaching 42.9 for H37Rv strain and 97.1 for clinical isolate. Five compounds also showed bacterial efflux pumps inhibition and one showed modulator effect with three drugs. Conclusion: These six TDZs should be considered as new scaffolds to develop anti-TB drugs.

Network Pharmacology Identifies the Mechanisms of Action of TaohongSiwu Decoction Against Essential Hypertension

BACKGROUND TaohongSiwu decoction (THSWT), a traditional herbal formula, has been used to treat cardiovascular and cerebrovascular diseases such as essential hypertension (EH) in China. However, the pharmacological mechanism is not clear. To investigate the mechanisms of THSWT in the treatment of EH, we performed compounds, targets prediction and network analysis using a network pharmacology method. MATERIAL AND METHODS We selected chemical constituents and targets of THSWT according to TCMSP and UniProtKB databases and collected therapeutic targets on EH from Online Mendelian Inheritance in Man (OMIM), Drugbank and DisGeNET databases. The protein-protein interaction (PPI) was analyzed by using String database. Then network was constructed by using Cytoscape_v3.7.1, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment was performed by using Database for Annotation, Visualization and Integrated Discovery (DAVID) software. RESULTS The results of our network pharmacology research showed that the THSWT, composed of 6 Chinese herbs, contained 15 compounds, and 23 genes regulated the main signaling pathways related to EH. Moreover, the PPI network based on targets of THSWT on EH revealed the interaction relationship between targets. These core compounds were 6 of the 15 disease-related compounds in the network, kaempferol, quercetin, luteolin, Myricanone, beta-sitosterol, baicalein, and the core genes contained ADRB2, CALM1, HMOX1, JUN, PPARG, and VEGFA, which were regulated by more than 3 compounds and significantly associated with Calcium signaling pathway, cGMP-PKG signaling pathway, cAMP signaling pathway, PI3K-Akt signaling pathway, Rap1 signaling pathway, and Ras signaling pathway. CONCLUSIONS This network pharmacological study can reveal potential mechanisms of multi-target and multi-component THSWT in the treatment of EH, provide a scientific basis for studying the mechanism.

Exploring the ring potential of 2,4-diaminopyrimidine derivatives towards the identification of novel caspase-1 inhibitors in Alzheimer's disease therapy

Pro-inflammatory activation of caspase-1 in the neurodegenerative pathway has been associated with age-dependent cognitive impairment and Alzheimer's disease (AD) in humans. A recent report highlighted 2,4-diaminopyrimidine ring as an essential fragment in the inhibition of human caspase-1. However, the role of the ring and its enzyme inhibitory mechanism is not thoroughly investigated at the molecular level. The purpose of this study is therefore in twofold: (1) to understand the enzyme binding mechanism of the 2,4-diaminopyrimidine ring and (2) to search for more potent caspase-1 inhibitors that contain the ring, using integrative per-residue energy decomposition (PRED) pharmacophore modeling. Ligand interaction profile of a reference compound revealed a peculiar hydrogen formation of the amino group of 2,4-diaminopyrimidine with active site residue Arg341, possibly forming the bases for its inhibitory prowess against caspase-1. A generated pharmacophore model for structure-based virtual screening identified compounds, ZINC724667, ZINC09908119, and ZINC09933770, as potential caspase-1 inhibitors that possessed desirable pharmacokinetic and physiochemical properties. Further analyses revealed active site residues, Arg179, Ser236, Cys285, Gln283, Ser339, and Arg341, as crucial to inhibitor binding by stabilizing and forming hydrogen bonds, hydrophobic, and pi-pi interactions with the 2,4-diaminopyrimidine rings. Common interaction patterns of the hits could have accounted for their selective and high-affinity ligand binding, which was characterized by notable disruptions in caspase-1 structural architecture. These compounds could further be explored as potential leads in the development of novel caspase-1 inhibitors.

Role of pharmacokinetic consideration for the development of drug delivery systems: A historical overview

Drug delivery system is defined as a system or technology to achieve optimum therapeutic effects of drugs through precise control of their movements in the body. In order to optimize function of drug delivery systems aiming at targeting, their whole-body distribution profiles should be systematically evaluated and analyzed, where pharmacokinetic analysis based on the clearance concepts plays important role. Organ perfusion experiments combined with statistical moment analysis further supply detailed information on drug disposition at organ and cellular levels. Based on general relationship between physicochemical properties and distribution profile, macromolecular prodrugs or polymer conjugates of proteins are rationally designed and further introduction of ligand structure brings cell-specific delivery for them. These approaches are also applicable for particulate carriers such as liposomes and offer various opportunities for biological drugs such as nucleic acid drugs for their delivery. Mechanistic approach for dermal absorption analysis based on physiological skin model offers another opportunity in rational design of drug delivery. Potential of drug delivery technology in future medicines such as cell therapy and nanomaterial platform application is further discussed in relation to pharmacokinetic consideration.