Toward in silico structure-based ADMET prediction in drug discovery

Bactericidal activity of esculetin is associated with impaired cell wall synthesis by targeting glutamate racemase of Neisseria gonorrhoeae

Neisseria gonorrhoeae (NG), the causative organism of gonorrhea, has been classified by the World Health Organization as 'Priority' two organism owing to its increased resistance to antibiotics and even failure of recommended dual therapy with ceftriaxone and azithromycin. As a result, the general and reproductive health of infected individuals is severely compromised. The imminent public health catastrophe of antimicrobial-resistant gonococci cannot be understated, as t he of severe complications and sequelae of infection are not only increasing but their treatment has also become more expensive. Tenacious attempts are underway to discover novel drug targets as well as new drugs to fight against NG. Therefore, a considerable number of phytochemicals have been tested for their remedial intercession via targeting bacterial proteins. The MurI gene encodes for an enzyme called glutamate racemase (MurI) that is primarily involved in peptidoglycan (PG) biosynthesis and is specific to the bacterial kingdom and hence can be exploited as a potential drug target for the treatment of bacterial diseases. Accordingly, diverse families of phytochemicals were screened in silico for their binding affinity with N. Gonorrhoeae MurI (NG-MurI) protein. Esculetin, one of the shortlisted compounds, was evaluated for its functional, structural, and anti-bacterial activity. Treatment with esculetin resulted in growth inhibition, cell wall damage, and altered permeability as revealed by fluorescence and electron microscopy. Furthermore, esculetin inhibited the racemization activity of recombinant, purified NG-MurI protein, one of the enzymes required for peptidoglycan biosynthesis. Our results suggest that esculetin could be further explored as a lead compound for developing new drug molecules against multidrug-resistant strains.

Identification of potential inhibitors of Mtb InhA: a pharmacoinformatics approach

The emergence of superbugs of multi-drug resistant (MDR/RR) and extensively drug-resistant (XDR) Mycobacterium tuberculosis (Mtb) strains at a faster rate is posing a serious threat to Tuberculosis (TB) control worldwide. Mtb enoyl-acyl carrier protein reductase (InhA) is a well-established target of the front-line anti-TB prodrug Isoniazid (INH), which requires activation by Catalase-peroxidase enzyme (KatG) in order to inhibit InhA enzyme, that is crucial for the biosynthesis of the mycobacterial cell wall. Currently, due to widespread resistance to this drug, it is necessary to identify new clinical candidates that directly inhibit InhA enzyme and do not require activation by KatG, thereby circumventing most of the resistance mechanisms. In the present study, high-throughput virtual screening of ASINEX database was carried out to identify potential direct inhibitors of Mtb InhA. Best twenty compounds with good binding energies ranging between -12.36 and -9.27 kcal/mol were selected as promising virtual screening hits. These molecules were subjected to ADME study followed by toxicity prediction. Finally, four top-ranked molecules which are structurally diverse and possess best binding affinity than the co-crystalized ligand have been chosen for MD simulation studies followed by MM-GBSA analysis to validate and ensure the stability of hits in the active site of the enzyme. Based on the 100 ns MD simulation studies and binding free energy estimates, three hit molecules B244, B369, and B310 could be considered as potential inhibitors for Mtb InhA, which are likely to be potent against INH-resistant Mtb strains after successful experimental validation.Communicated by Ramaswamy H. Sarma.

The CryoEM structure of human serum albumin in complex with ligands

Human serum albumin (HSA) is the most prevalent plasma protein in the human body, accounting for 60 % of the total plasma protein. HSA plays a major pharmacokinetic function, serving as a facilitator in the distribution of endobiotics and xenobiotics within the organism. In this paper we report the cryoEM structures of HSA in the apo form and in complex with two ligands (salicylic acid and teniposide) at a resolution of 3.5, 3.7 and 3.4 Å, respectively. We expand upon previously published work and further demonstrate that sub-4 Å maps of ∼60 kDa proteins can be routinely obtained using a 200 kV microscope, employing standard workflows. Most importantly, these maps allowed for the identification of small molecule ligands, emphasizing the practical applicability of this methodology and providing a starting point for subsequent computational modeling and in silico optimization.

Synthesis, crystal structure, Hirshfeld surface, computational and biological studies of spiro-oxindole derivatives as MDM2-p53 inhibitors

The spiro-oxindole derivatives were synthesized via a 1,3-dipolar cycloaddition approach and characterized by FT-IR, 1H, 13C NMR and mass spectral techniques. The single crystal XRD of 6d further validates the formation of compounds. DFT calculations indicated the reactive nature of compound 6d. Docking studies with 5LAW disclosed the minimum binding energy of - 10.83 kcal/mol for 6d. Furthermore, safe oral bioavailability was ensured by the physicochemical, pharmacokinetic, and toxicity predictions. The anticancer analysis of synthesized compounds showed substantial activity against A549 cells, notably with an IC50 value of 8.13 ± 0.66 µM for 6d compared to standard doxorubicin. 6d was also evaluated for cytotoxicity against L929 healthy cells and A549, showing selectivity towards A549 than healthy cells. AO/EB staining method showed early apoptotic cellular death in the A549 cell line in a dose-dependent manner.

Denoising Drug Discovery Data for Improved Absorption, Distribution, Metabolism, Excretion, and Toxicity Property Prediction

Predicting absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of small molecules is a key task in drug discovery. A major challenge in building better ADMET models is the experimental error inherent in the data. Furthermore, ADMET predictors are typically regression tasks due to the continuous nature of the data, which makes it difficult to apply existing denoising methods from other domains as they largely focus on classification tasks. Here, we develop denoising schemes based on deep learning to address this. We find that the training error (TE) can be used to identify the noise in regression tasks while ensemble-based and forgotten event-based metrics fail to detect the noise. The most significant performance increase occurs when the original model is finetuned with the denoised data using TE as the noise detection metric. Our method has the ability to improve models with medium noise and does not degrade the performance of models with noise outside this range (low noise and high noise regimes). To our knowledge, our denoising scheme is the first to improve model performance for ADMET data and has implications for improving models for experimental assay data in general.

Artificial intelligence for small molecule anticancer drug discovery

INTRODUCTION

The transition from conventional cytotoxic chemotherapy to targeted cancer therapy with small-molecule anticancer drugs has enhanced treatment outcomes. This approach, which now dominates cancer treatment, has its advantages. Despite the regulatory approval of several targeted molecules for clinical use, challenges such as low response rates and drug resistance still persist. Conventional drug discovery methods are costly and time-consuming, necessitating more efficient approaches. The rise of artificial intelligence (AI) and access to large-scale datasets have revolutionized the field of small-molecule cancer drug discovery. Machine learning (ML), particularly deep learning (DL) techniques, enables the rapid identification and development of novel anticancer agents by analyzing vast amounts of genomic, proteomic, and imaging data to uncover hidden patterns and relationships.

AREA COVERED

In this review, the authors explore the important landmarks in the history of AI-driven drug discovery. They also highlight various applications in small-molecule cancer drug discovery, outline the challenges faced, and provide insights for future research.

EXPERT OPINION

The advent of big data has allowed AI to penetrate and enable innovations in almost every stage of medicine discovery, transforming the landscape of oncology research through the development of state-of-the-art algorithms and models. Despite challenges in data quality, model interpretability, and technical limitations, advancements promise breakthroughs in personalized and precision oncology, revolutionizing future cancer management.

New thiazolidine-2,4-diones as potential anticancer agents and apoptotic inducers targeting VEGFR-2 kinase: Design, synthesis, in silico and in vitro studies

BACKGROUND

VEGFR-2 has emerged as a prominent positive regulator of cancer progression.

AIM

Discovery of new anticancer agents and apoptotic inducers targeting VEGFR-2.

METHODS

Design and synthesis of new thiazolidine-2,4-diones followed by extensive in vitro studies, including VEGFR-2 inhibition assay, MTT assay, apoptosis analysis, and cell migration assay. In silico investigations including docking, MD simulations, ADMET, toxicity, and DFT studies were performed.

RESULTS

Compound 15 showed the strongest VEGFR-2 inhibitory activity with an IC50 value of 0.066 μM. Additionally, most of the synthesized compounds showed anti-proliferative activity against HepG2 and MCF-7 cancer cell lines at the micromolar range with IC50 values ranging from 0.04 to 4.71 μM, relative to sorafenib (IC50 = 2.24 ± 0.06 and 3.17 ± 0.01 μM against HepG2 and MCF-7, respectively). Also, compound 15 showed selectivity indices of 1.36 and 2.08 against HepG2 and MCF-7, respectively. Furthermore, compound 15 showed a significant apoptotic effect and arrested the cell cycle of MCF-7 cells at the S phase. Moreover, compound 15 had a significant inhibitory effect on the ability of MCF-7 cells to heal from. Docking studies revealed that the synthesized thiazolidine-2,4-diones have a binding pattern approaching sorafenib. MD simulations indicated the stability of compound 15 in the active pocket of VEGFR-2 for 200 ns. ADMET and toxicity studies indicated an acceptable pharmacokinetic profile. DFT studies confirmed the ability of compound 15 to interact with VEGFR-2.

CONCLUSION

Compound 15 has promising anticancer activity targeting VEGFR-2 with significant activity as an apoptosis inducer.

Toxicity, Pharmacokinetic Profile, and Compound-Protein Interaction Study of Polygonum minus Huds Extract

Several phytochemicals with potential for bioactivity can be found in Polygonum minus (PM). The goal of this investigation was to establish the minimally toxic dose of PM for pharmaceutical use. To explain the stability and reactivity of the compounds under study, the lowest unoccupied molecular orbital (LUMO), the highest occupied molecular orbital (HOMO), and the natural bond orbital were all combined. Additionally, the cytotoxicity of the aqueous and ethanolic extract of PM on the (Hs888Lu) cell line was determined using the MTS Assay Kit (cell proliferation) (colorimetric). The hematological, hepatic, and renal functions were examined during the acute toxicity test on Sprague Dawley rats. SwissADME and ADMET were used to investigate the absorption, distribution, metabolism, excretion, and toxicity (ADMET) of the chemicals isolated from PM, including gallic acid, quercetin, rutin, and coumaric acid (PMCs). Molecular docking was used to examine the inhibitory effect against human H+/K+ ATPase, cyclooxygenase-2, and acetylcholinesterase. The outcomes indicated that neither the aqueous nor the ethanolic extract of PM is harmful. The development of plant-based medicine was made possible by the phenolic chemicals, primarily quercetin and rutin, which exhibit a considerable binding affinity to human H+/K+ ATPase, cyclooxygenase-2, and acetylcholinesterase.

New Theobromine Apoptotic Analogue with Anticancer Potential Targeting the EGFR Protein: Computational and In Vitro Studies

AIM

The aim of this study was to design and examine a novel epidermal growth factor receptor (EGFR) inhibitor with apoptotic properties by utilizing the essential structural characteristics of existing EGFR inhibitors as a foundation.

METHOD

The study began with the natural alkaloid theobromine and developed a new semisynthetic derivative (T-1-PMPA). Computational ADMET assessments were conducted first to evaluate its anticipated safety and general drug-likeness. Deep density functional theory (DFT) computations were initially performed to validate the three-dimensional (3D) structure and reactivity of T-1-PMPA. Molecular docking against the EGFR proteins was conducted to investigate T-1-PMPA's binding affinity and inhibitory potential. Additional molecular dynamics (MD) simulations over 200 ns along with MM-GPSA, PLIP, and principal component analysis of trajectories (PCAT) experiments were employed to verify the binding and inhibitory properties of T-1-PMPA. Afterward, T-1-PMPA was semisynthesized to validate the proposed design and in silico findings through several in vitro examinations.

RESULTS

DFT studies indicated T-1-PMPA's reactivity using electrostatic potential, global reactive indices, and total density of states. Molecular docking, MD simulations, MM-GPSA, PLIP, and ED suggested the binding and inhibitory properties of T-1-PMPA against the EGFR protein. The in silico ADMET predicted T-1-PMPA's safety and general drug-likeness. In vitro experiments demonstrated that T-1-PMPA effectively inhibited EGFRWT and EGFR790m, with IC50 values of 86 and 561 nM, respectively, compared to Erlotinib (31 and 456 nM). T-1-PMPA also showed significant suppression of the proliferation of HepG2 and MCF7 malignant cell lines, with IC50 values of 3.51 and 4.13 μM, respectively. The selectivity indices against the two cancer cell lines indicated the overall safety of T-1-PMPA. Flow cytometry confirmed the apoptotic effects of T-1-PMPA by increasing the total percentage of apoptosis to 42% compared to 31, and 3% in Erlotinib-treated and control cells, respectively. The qRT-PCR analysis further supported the apoptotic effects by revealing significant increases in the levels of Casp3 and Casp9. Additionally, T-1-PMPA controlled the levels of TNFα and IL2 by 74 and 50%, comparing Erlotinib's values (84 and 74%), respectively.

CONCLUSION

In conclusion, our study's findings suggest the potential of T-1-PMPA as a promising apoptotic anticancer lead compound targeting the EGFR.

Analysis of pharmacokinetic profile and ecotoxicological character of cefepime and its photodegradation products

An important problem is the impact of photodegradation on product toxicity in biological tests, which may be complex and context-dependent. Previous studies have described the pharmacology of cefepime, but the toxicological effects of its photodegradation products remain largely unknown. Therefore, photodegradation studies were undertaken in conditions similar to those occurring in biological systems insilico, in vitro, in vivo and ecotoxicological experiments. The structures of four cefepime photodegradation products were determined by UPLC-MS/MS method. The calculated in silico ADMET profile indicates that carcinogenic potential is expected for compounds CP-1, cefepime, CP-2 and CP-3. The Cell Line Cytomotovity Predictor 2.0 tool was used to predict the cytotoxic effects of cefepime and related compounds in non-transformed and cancer cell lines. The results indicate that possible actions include: non-small cell lung cancer, breast adenocarcinoma, prostate cancer and papillary renal cell carcinoma. OPERA models were used to predict absorption, distribution, metabolism and excretion (ADME) endpoints, and potential bioactivity of CP-2, cefepime and CP-4. The results obtained in silico show that after 96h of exposure, cefepime, CP-1, CP-2, and CP-3 are moderately toxic in the zebrafish model, while CP-4 is highly toxic. On the contrary, cefepime is more toxic to T. platyurus (highly toxic) compared to the zebrafish model, similar to products CP-4, CP-3 and CP-2. In vitro cytotoxicity studies were performed by MTT assay and in vivo acute embryo toxicity studies using Danio rerio embryos and larvae. In vitro showed an increase in the cytotoxicity of products with the longest exposure period i.e. for 8 h. Additionally, at a concentration of 200 μg/mL, statistically significant changes in metabolic activity were observed depending on the irradiation time. In vivo studies conducted with Zebrafish showed that both cefepime and its photodegradation products have only low toxicity. Assessment of potential ecotoxicity included Microbiotests on invertebrates (Thamnotoxkit F and Daphtoxkit F), and luminescence inhibition tests (LumiMara). The observed toxicity of the tested solutions towards both Thamnocephalus platyurus and Daphnia magna indicates that the parent substance (unexposed) has lower toxicity, which increases during irradiation. The acute toxicity (Lumi Mara) of nonirradiated cefepime solution is low for all tested strains (<10%), but mixtures of cefepime and its photoproducts showed growth inhibition against all tested strains (except #6, Photobacterium phoreum). Generally, it can be concluded that after UV-Vis irradiation, the mixture of cefepime phototransformation products shows a significant increase in toxicity.

ADMET-PrInt: Evaluation of ADMET Properties: Prediction and Interpretation

Great progress in the development of computational strategies for drug design applications has revolutionized the process of searching for new drugs. Although the focus of in silico strategies is still put on the provision of the desired activity of a compound to the considered target, characterization of a compound in terms of its physicochemical and ADMET properties becomes an indispensable element of computer-aided drug design protocols. In the study, an online application ADMET-PrInt for in silico assessment of selected compound features: cardiotoxicity, solubility, genotoxicity, membrane permeability, and plasma protein binding was prepared. In addition to the prediction of particular property, ADMET-PrInt enables also the identification of compound features influencing this property thanks to the application of two explainability approaches: local interpretabile model-agnostic explanations and counterfactual analysis. It is an important factor for medicinal chemists, as it greatly facilitates the process of optimization of the compound structure in terms of the evaluated properties. The intuitive webpage, available at admet.if-pan.krakow.pl, allows making use of all predictive and interpretability models also by nonexperts and nonprogrammers.

Computer-Aided Strategy on 5-(Substituted benzylidene) Thiazolidine-2,4-Diones to Develop New and Potent PTP1B Inhibitors: QSAR Modeling, Molecular Docking, Molecular Dynamics, PASS Predictions, and DFT Investigations

A set of 5-(substituted benzylidene) thiazolidine-2,4-dione derivatives was explored to study the main structural requirement for the design of protein tyrosine phosphatase 1B (PTP1B) inhibitors. Utilizing multiple linear regression (MLR) analysis, we constructed a robust quantitative structure-activity relationship (QSAR) model to predict inhibitory activity, resulting in a noteworthy correlation coefficient (R2) of 0.942. Rigorous cross-validation using the leave-one-out (LOO) technique and statistical parameter calculations affirmed the model's reliability, with the QSAR analysis revealing 10 distinct structural patterns influencing PTP1B inhibitory activity. Compound 7e(ref) emerged as the optimal scaffold for drug design. Seven new PTP1B inhibitors were designed based on the QSAR model, followed by molecular docking studies to predict interactions and identify structural features. Pharmacokinetics properties were assessed through drug-likeness and ADMET studies. After that density functional theory (DFT) was conducted to assess the stability and reactivity of potential diabetes mellitus drug candidates. The subsequent dynamic simulation phase provided additional insights into stability and interactions dynamics of the top-ranked compound 11c. This comprehensive approach enhances our understanding of potential drug candidates for treating diabetes mellitus.

Computational exploration of microsomal cytochrome P450 3A1 enzyme modulation by phytochemicals of Cichorium intybus L.: Insights into drug metabolism

Drug metabolism plays a crucial role in drug fate, including therapeutic inactivation or activation, as well as the formation of toxic compounds. This underscores the importance of understanding drug metabolism in drug discovery and development. Considering the substantial costs associated with traditional drug development methods, computational approaches have emerged as valuable tools for predicting the metabolic fate of drug candidates. With this in mind, the present study aimed to investigate the potential mechanisms underlying the modulation of microsomal cytochrome P450 3A1 (CYP3A1) enzyme activity by various phytochemicals found in Cichorium intybus L., commonly known as chicory. To achieve this goal, several in silico methods, including molecular docking and molecular dynamics (MD) simulation, were employed to explore computationally the microsomal CYP3A1 enzyme. Schrodinger software was utilized for the molecular docking study, which involved the interaction analysis between CYP3A1 and 28 phytoconstituents of Cichorium intybus. Virtual screening of 28 compounds from chicory led to the identification of the top five ranked compounds. These compounds were evaluated for drug-likeness properties, pharmacokinetic profiles, and predicted binding affinities to CYP3A1. Caffeoylshikimic acid and cichoric acid emerged as promising candidates due to their favorable characteristics, including good oral bioavailability and high binding affinities to CYP3A1. Molecular dynamics simulations were conducted to assess the stability of caffeoylshikimic acid within the CYP3A1 binding pocket. The results demonstrated that caffeoylshikimic acid maintained stable interactions with the enzyme throughout the simulation, suggesting its potential as an effective modulator of CYP3A1 activity. The findings of this study have the potential to provide valuable insights into the complex molecular mechanisms by which Cichorium intybus L. acts on hepatocytes and modulates CYP3A1 enzyme expression or activity. By elucidating the impact of these phytochemicals on drug metabolism, this research contributes to our understanding of how chicory may interact with drugs and influence their efficacy and safety profiles.

Computer-Assisted Drug Discovery of a Novel Theobromine Derivative as an EGFR Protein-Targeted Apoptosis Inducer

The overexpression of the Epidermal Growth Factor Receptor (EGFR) marks it as a pivotal target in cancer treatment, with the aim of reducing its proliferation and inducing apoptosis. This study aimed at the CADD of a new apoptotic EGFR inhibitor. The natural alkaloid, theobromine, was used as a starting point to obtain a new semisynthetic (di-ortho-chloro acetamide) derivative (T-1-DOCA). Firstly, T-1-DOCA's total electron density, energy gap, reactivity indices, and electrostatic surface potential were determined by DFT calculations, Then, molecular docking studies were carried out to predict the potential of T-1-DOCA against wild and mutant EGFR proteins. T-1-DOCA's correct binding was further confirmed by molecular dynamics (MD) over 100 ns, MM-GPSA, and PLIP experiments. In vitro, T-1-DOCA showed noticeable efficacy compared to erlotinib by suppressing EGFRWT and EGFRT790M with IC50 values of 56.94 and 269.01 nM, respectively. T-1-DOCA inhibited also the proliferation of H1975 and HCT-116 malignant cell lines, exhibiting IC50 values of 14.12 and 23.39 µM, with selectivity indices of 6.8 and 4.1, respectively, indicating its anticancer potential and general safety. The apoptotic effects of T-1-DOCA were indicated by flow cytometric analysis and were further confirmed through its potential to increase the levels of BAX, Casp3, and Casp9, and decrease Bcl-2 levels. In conclusion, T-1-DOCA, a new apoptotic EGFR inhibitor, was designed and evaluated both computationally and experimentally. The results suggest that T-1-DOCA is a promising candidate for further development as an anti-cancer drug.

Role of Conformational Dynamics of Sulfotransferases SULT1A1 and SULT1A3 in Substrate Specificity

Sulfotransferases (SULTs) are phase II metabolizing enzymes catalyzing the sulfoconjugation from the co-factor 3'-Phosphoadenosine 5'-Phosphosulfate (PAPS) to a wide variety of endogenous compounds, drugs and natural products. Although SULT1A1 and SULT1A3 share 93% identity, SULT1A1, the most abundant SULT isoform in humans, exhibits a broad substrate range with specificity for small phenolic compounds, while SULT1A3 displays a high affinity toward monoamine neurotransmitters like dopamine. To elucidate the factors determining the substrate specificity of the SULT1 isoenzymes, we studied the dynamic behavior and structural specificities of SULT1A1 and SULT1A3 by using molecular dynamics (MD) simulations and ensemble docking of common and specific substrates of the two isoforms. Our results demonstrated that while SULT1A1 exhibits a relatively rigid structure by showing lower conformational flexibility except for the lip (loop L1), the loop L2 and the cap (L3) of SULT1A3 are extremely flexible. We identified protein residues strongly involved in the recognition of different substrates for the two isoforms. Our analyses indicated that being more specific and highly flexible, the structure of SULT1A3 has particularities in the binding site, which are crucial for its substrate selectivity.

Discovery of new VEGFR-2 inhibitors and apoptosis inducer-based thieno[2,3-d]pyrimidine

Background: VEGFR-2 is a key regulator of cancer cell proliferation, migration and angiogenesis. Aim: Development of thieno[2,3-d]pyrimidine derivatives as potential anti-cancer agents targeting VEGFR-2. Methods: Seven in vitro and nine in silico studies were conducted. Results: Compound 10d demonstrated strong anticancer potential, boosting apoptosis based on VEGFR-2 inhibition. It arrested the S phase of the cell cycle and upregulated the apoptotic factors. Docking and molecular dynamics simulation studies confirm the stability of the VEGFR-2-10d complex and suggest that these compounds have good binding affinities to VEGFR-2. In addition, the drug-likeness was confirmed. Conclusion: Thieno[2,3-d]pyrimidines, particularly compound 10d, has good anticancer effects and may contribute to the development of new anticancer therapies.

In vitro and in silico evaluation of new thieno[2,3-d]pyrimidines as anti-cancer agents and apoptosis inducers targeting VEGFR-2

In this study, new thieno[2,3-d]pyrimidine derivatives that could have potential anticancer activity by inhibiting the VEGFR-2 receptor have been designed, synthesized, and investigated. The thieno[2,3-d]pyrimidine derivatives showed strong in vitro abilities to inhibit VEGFR-2 and to prevent cancer cell growth in two different types of cancer cells, MCF-7 and HepG2. Particularly, compound 22 showed the most potent anti-VEGFR-2 activity with an IC50 value of 0.58 µM. Additionally, compound 22 exhibited good anti-proliferative activity against both MCF-7 and HepG2 cancer cell lines, with IC50 values of 11.32 ± 0.32 and 16.66 ± 1.22 µM, respectively. Further investigations revealed that compound 22 induced cell cycle arrest at the G2/M phase and promoted both early and late apoptosis in the MCF-7 cancer cells. Compound 22 also increased the level of BAX (2.8-fold), and reduced the level of Bcl-2 (2.2-fold), hence increasing the rate of apoptosis. Compound 22 also revealed 2.9-fold and 2.8-fold higher levels of caspase-8 and caspase-9, respectively, in the treated MCF-7 cancer cells compared to the control cell lines. The MD simulations showed that the VEGFR-2-22 complex was structurally and energytically stable over 100 ns, while the MM-GBSA study indicated its stable thermodynamic behavior. The bi-dimensional projection analysis confirmed the proper binding of the VEGFR-2-22 complex, while the DFT studies provided optimized geometry, charge distribution, FMO, ESP, the total density of state, and QTAIM maps of compound 22. Finally, computational ADMET studies were performed to assess the drug development potential of the thieno[2,3-d]pyrimidine derivatives. Overall, this study suggests that compound 22 has the potential as an anticancer lead compound by inhibiting VEGFR-2, which may be a guide for future drug design and development.

Identification of novel inhibitors of Neisseria gonorrhoeae MurI using homology modeling, structure-based pharmacophore, molecular docking, and molecular dynamics simulation-based approach

MurI is one of the most significant role players in the biosynthesis of the peptidoglycan layer in Neisseria gonorrhoeae (Ng). We attempted to highlight the structural and functional relationship between Ng-MurI and D-glutamate to design novel molecules targeting this interaction. The three-dimensional (3D) model of the protein was constructed by homology modeling and the quality and consistency of generated model were assessed. The binding site of the protein was identified by molecular docking studies and a pharmacophore was identified using the interactions of the control ligand. The structure-based pharmacophore model was validated and employed for high-throughput virtual screening and molecular docking to identify novel Ng-MurI inhibitors. Finally, the model was optimized by molecular dynamics (MD) simulations and the optimized model complex with the substrate glutamate and novel molecules facilitated us to confirm the stability of the protein-ligand docked complexes. The 100 ns MD simulations of the potential lead compounds with protein confirmed that the modeled complexes were stable. This study identifies novel potential compounds with good fitness and docking scores, which made the interactions of biological significance within the protein active site. Hence, the identified compounds may act as new leads to design and develop Ng-MurI inhibitors.Communicated by Ramaswamy H. Sarma.

Design, synthesis, anti-proliferative evaluation, docking, and MD simulation studies of new thieno[2,3-d]pyrimidines targeting VEGFR-2

In this work, new thieno[2,3-d]pyrimidine-derived compounds possessing potential anticancer activities were designed and synthesized to target VEGFR-2. The thieno[2,3-d]pyrimidine derivatives were tested in vitro for their abilities to inhibit VEGFR-2 and to prevent cancer cell growth in two types of cancer cells, MCF-7 and HepG2. Compound 18 exhibited the strongest anti-VEGFR-2 potential with an IC50 value of 0.084 μM. Additionally, it displayed excellent proliferative effects against MCF-7 and HepG2 cancer cell lines, with IC50 values of 10.17 μM and 24.47 μM, respectively. Further studies revealed that compound 18 induced cell cycle arrest in G2/M phase and promoted apoptosis in MCF-7 cancer cells. Apoptosis was stimulated by compound 18 by increasing BAX (3.6-fold) and decreasing Bcl-2 (3.1-fold). Additionally, compound 18 significantly raised the levels of caspase-8 (2.6-fold) and caspase-9 (5.4-fold). Computational techniques were also used to investigate the VEGFR-2-18 complex at a molecular level. Molecular docking and molecular dynamics simulations were performed to assess the structural and energetic features of the complex. The protein-ligand interaction profiler analysis identified the 3D interactions and binding conformation of the VEGFR-2-18 complex. Essential dynamics (ED) study utilizing principal component analysis (PCA) described the protein dynamics of the VEGFR-2-18 complex at various spatial scales. Bi-dimensional projection analysis confirmed the proper binding of the VEGFR-2-18 complex. In addition, the DFT studies provided insights into the structural and electronic properties of compound 18. Finally, computational ADMET and toxicity studies were conducted to evaluate the potential of the thieno[2,3-d]pyrimidine derivatives for drug development. The results of the study suggested that compound 18 could be a promising anticancer agent that may provide effective treatment options for cancer patients. Furthermore, the computational techniques used in this research provided valuable insights into the molecular interactions of the VEGFR-2-18 complex, which may guide future drug design efforts. Overall, this study highlights the potential of thieno[2,3-d]pyrimidine derivatives as a new class of anticancer agents and provides a foundation for further research in this area.

MetaSpot: A General Approach for Recognizing the Reactive Atoms Undergoing Metabolic Reactions Based on the MetaQSAR Database

The prediction of drug metabolism is attracting great interest for the possibility of discarding molecules with unfavorable ADME/Tox profile at the early stage of the drug discovery process. In this context, artificial intelligence methods can generate highly performing predictive models if they are trained by accurate metabolic data. MetaQSAR-based datasets were collected to predict the sites of metabolism for most metabolic reactions. The models were based on a set of structural, physicochemical, and stereo-electronic descriptors and were generated by the random forest algorithm. For each considered biotransformation, two types of models were developed: the first type involved all non-reactive atoms and included atom types among the descriptors, while the second type involved only non-reactive centers having the same atom type(s) of the reactive atoms. All the models of the first type revealed very high performances; the models of the second type show on average worst performances while being almost always able to recognize the reactive centers; only conjugations with glucuronic acid are unsatisfactorily predicted by the models of the second type. Feature evaluation confirms the major role of lipophilicity, self-polarizability, and H-bonding for almost all considered reactions. The obtained results emphasize the possibility of recognizing the sites of metabolism by classification models trained on MetaQSAR database. The two types of models can be synergistically combined since the first models identify which atoms can undergo a given metabolic reactions, while the second models detect the truly reactive centers. The generated models are available as scripts for the VEGA program.

Using In Silico Approach for Metabolomic and Toxicity Prediction of Alternariol

Alternariol is a metabolite produced by Alternaria fungus that can contaminate a variety of food and feed materials. The objective of the present paper was to provide a prediction of Phase I and II metabolites of alternariol and a detailed ADME/Tox profile for alternariol and its metabolites using an in silico working model based on the MetaTox, SwissADME, pKCMS, and PASS online computational programs. A number of 12 metabolites were identified as corresponding to the metabolomic profile of alternariol. ADME profile for AOH and predicted metabolites indicated a moderate or high intestinal absorption probability but a low probability to penetrate the blood-brain barrier. In addition to cytotoxic, mutagenic, carcinogenic, and endocrine disruptor effects, the computational model has predicted other toxicological endpoints for the analyzed compounds, such as vascular toxicity, haemato-toxicity, diarrhea, and nephrotoxicity. AOH and its metabolites have been predicted to act as a substrate for different isoforms of phase I and II drug-metabolizing enzymes and to interact with the response to oxidative stress. In conclusion, in silico methods can represent a viable alternative to in vitro and in vivo tests for the prediction of mycotoxins metabolism and toxicity.

Not all benzimidazole derivatives are microtubule destabilizing agents

In recent years, microtubule-targeting agents (MTAs) have gained considerable interest in developing novel small-molecule anticancer drugs. MTAs demonstrate anticancer activity either as microtubule-stabilizing agents (paclitaxel) or microtubule-destabilizing agents (nocodazole). FDA-approved drugs containing a benzimidazole ring (nocodazole, albendazole, mebendazole, etc.) are well-known microtubule-destabilizing agents. Thus, most recent research on benzimidazole scaffold-based MTAs focuses on developing microtubule-destabilizing agents. However, there is no report on the benzimidazole scaffold-based microtubule-stabilizing agent. Here, we present the benzimidazole derivatives NI-11 and NI-18 that showed a profound anticancer activity as microtubule-stabilization agents. About twenty benzimidazole analogues were synthesized with excellent yield (80.0% ∼ 98.0%) and tested for their anticancer activity using two cancer cell lines (A549, MCF-7) and one normal cell line (MRC-5). NI-11 showed IC₅₀ values of 2.90, 7.17, and 16.9 µM in A549, MCF-7, and MRC-5 cell lines. NI-18 showed IC₅₀ values of 2.33, 6.10, and 12.1 µM in A549, MCF-7, and MRC-5 cell lines. Thus, NI-11 and NI-18 demonstrated selectivity indexes of 5.81 and 5.20, respectively, which are much higher than the currently available anticancer agents. NI-11 and NI-18 inhibited the cancer cell motility and migration, induced the early phase apoptosis. Both of these comounds were found to show an upregulation of DeY-α-tubulin and downregulation of Ac-α-tubulin expressions in cancer cells. Eventhough the reported benzimidazole scaffold-based commercially available drugs are known to be microtubule-destabilizing agents, the analogues NI-11 and NI-18 were found to have microtubule-stabilizing activity. The in vitro tubulin polymerization assay and the immunofluorescence assay results indicate that the NI-11 and NI-18 exhibit anticancer activity by stabilizing the microtubule network.

Sodium alginate encapsulated iron oxide decorated with thymoquinone nanocomposite induces apoptosis in human breast cancer cells via PI3K-Akt-mTOR pathway

The present study investigated the cytotoxicity and proapoptotic properties of iron oxide-sodium-alginate-thymoquinone nanocomposites against breast cancer MDA-MB-231 cells in vitro and in silico. This study used chemical synthesis to formulate the nanocomposite. Electron microscopies such as scanning (SEM) and transmission (TEM), Fourier transform infrared (FT-IR), Ultraviolet-Visible, Photoluminescence spectroscopy, selected area (electron) diffraction (SAED), energy dispersive X-ray analysis (EDX), and X-ray diffraction studies (XRD) were used to characterize the synthesized ISAT-NCs and the average size of them was found to be 55 nm. To evaluate the cytotoxic, antiproliferative, and apoptotic potentials of ISAT-NCs on MDA-MB-231 cells, MTT assays, FACS-based cell cycle studies, annexin-V-PI staining, ELISA, and qRT-PCR were used. PI3K-Akt-mTOR receptors and thymoquinone were predicted using in-silico docking studies. Cell proliferation is reduced in MDA-MB-231 cells due to ISAT-NC cytotoxicity. As a result of FACS analysis, ISAT-NCs had nuclear damage, ROS production, and elevated annexin-V levels, which resulted in cell cycle arrest in the S phase. The ISAT-NCs in MDA-MB-231 cells were found to downregulate PI3K-Akt-mTOR regulatory pathways in the presence of inhibitors of PI3K-Akt-mTOR, showing that these regulatory pathways are involved in apoptotic cell death. We also predicted the molecular interaction between thymoquinone and PI3K-Akt-mTOR receptor proteins using in-silico docking studies which also support PI3K-Akt-mTOR signaling inhibition by ISAT-NCs in MDA-MB-231 cells. As a result of this study, we can conclude that ISAT-NCs inhibit the PI3K-Akt-mTOR pathway in breast cancer cell lines, causing apoptotic cell death.

A Computational Study on Selected Alkaloids as SARS-CoV-2 Inhibitors: PASS Prediction, Molecular Docking, ADMET Analysis, DFT, and Molecular Dynamics Simulations

Despite treatments and vaccinations, it remains difficult to develop naturally occurring COVID-19 inhibitors. Here, our main objective is to find potential lead compounds from the retrieved alkaloids with antiviral and other biological properties that selectively target the main SARS-CoV-2 protease (Mpro), which is required for viral replication. In this work, 252 alkaloids were aligned using Lipinski's rule of five and their antiviral activity was then assessed. The prediction of activity spectrum of substances (PASS) data was used to confirm the antiviral activities of 112 alkaloids. Finally, 50 alkaloids were docked with Mpro. Furthermore, assessments of molecular electrostatic potential surface (MEPS), density functional theory (DFT), and absorption, distribution, metabolism, excretion, and toxicity (ADMET) were performed, and a few of them appeared to have potential as candidates for oral administration. Molecular dynamics simulations (MDS) with a time step of up to 100 ns were used to confirm that the three docked complexes were more stable. It was found that the most prevalent and active binding sites that limit Mpro'sactivity are PHE294, ARG298, and GLN110. All retrieved data were compared to conventional antivirals, fumarostelline, strychnidin-10-one (L-1), 2,3-dimethoxy-brucin (L-7), and alkaloid ND-305B (L-16) and were proposed as enhanced SARS-CoV-2 inhibitors. Finally, with additional clinical or necessary study, it may be able to use these indicated natural alkaloids or their analogs as potential therapeutic candidates.

Environmentally-Friendly Pesticidal Activities of Callicarpa and Karomia Essential Oils from Vietnam and Their Microemulsions

There is an ongoing interest to identify alternative pesticidal agents to avoid the chronic problems associated with synthetic pesticides. Essential oils have shown promise as botanical pest control agents. In the present study, the essential oils of four members of the Lamiaceae (Callicarpa candicans, C. erioclona, C. macrophylla, and Karomia fragrans; Vietnamese names: Nàng nàng, Tu châu lông mem, Tu châu lá to and Cà diện, respectively), obtained from wild populations in Vietnam, have been obtained by hydrodistillation and analyzed by gas chromatography-mass spectrometry. The essential oils were formulated into microemulsions and the essential oils and their microemulsions were screened for mosquito larvicidal activity against Aedes aegypti, Aedes albopictus, Culex quinquefasciatus, and for molluscicidal activity against Pomacea canaliculata. Atractylone and (E)-caryophyllene dominated the volatiles of C. candicans (CCEO) and C. erioclona (CEEO), while the major component in C. macrophylla (CMEO) and K. fragrans (KFEO) was (E)-caryophyllene. The essential oils and microemulsions of both C. candicans and C. erioclona exhibited excellent larvicidal activity against all three mosquito species (Ae. aegypti, Ae. albopictus, and Cx. quinquefasciatus) with LC₅₀ values <10 μg/mL. Additionally, the larvicidal activity of the microemulsions were significantly improved compared with their free essential oils, especially for C. candicans and C. erioclona. All four essential oils and their microemulsions showed excellent molluscicidal activity with LC₅₀ <10 μg/mL. In most cases, the essential oils and microemulsions showed greater pesticidal activity against target organisms than the non-target freshwater fish, Oreochromis niloticus. The in silico studies on physicochemical and ADMET properties of the major components in the studied essential oils were also investigated and most of the compounds possessed a favorable ADMET profile. Computational modeling studies of the studied compounds demonstrated a favorable binding interaction with the mosquito odorant-binding protein target and support atractylone, β-selinene, and caryophyllene oxide as potential inhibitors. Based on the observed pesticidal activities of the essential oils and their microemulsions, the Callicarpa species and K. fragrans should be considered for potential cultivation and further exploration as botanical pesticidal agents.

Molecular modeling study of pyrrolidine derivatives as novel myeloid cell leukemia-1 inhibitors through combined 3D-QSAR, molecular docking, ADME/Tox and MD simulation techniques

A series of pyrrolidine derivatives have been used to study the main structural requirements for designing novel Mcl-1 inhibitors. For this purpose, three models CoMSIA, CoMFA and HQSAR were generated using QSAR molecular modeling techniques. The statistical results of the CoMFA (Q2 = 0.689; R = 0.999; R2pred = 0.986), CoMSIA (Q2 = 0.614; R2 = 0.923; R2pred = 0.815) and HQSAR (Q2= 0.603; R2 = 0.662; R2pred = 0.743) models showed good stability and predictability. The results of the models were presented as contours and colored fragments indicating the favorable and unfavorable contribution to the inhibitory activity of Mcl-1. Based on the obtained results, four new compounds were designed with more potent predicted pIC50 inhibitory activity. The ADME/Tox results and the pharmacokinetic properties revealed that these four compounds are orally bioavailable and show good permeability. In addition the four compounds showing non-inhibitors of CYP3A4 and CYP2D6 with the exception of Pred03. At the level of toxicity profile, the compounds Pred01, Pred02 and Pred03 showed interesting results and showed no AMES toxicity, no hERG inhibition and no skin sensitization. Molecular docking results were used to uncover the mode of interaction between the ligand and key residues of protein binding site. Molecular docking results were supported by molecular simulation and binding free energy estimation (MMPBSA). These results demonstrate the stability of the analyzed compounds in the target protein binding site during a 100 ns trajectory. Finally, all these results create a strong lead to develop promising new Pyrrolidine-based inhibitors against Mcl-1.Communicated by Ramaswamy H. Sarma.

Experimental and computational models to investigate intestinal drug permeability and metabolism

Oral administration is the preferred route for drug administration that leads to better therapy compliance. The intestine plays a key role in the absorption and metabolism of oral drugs, therefore, new intestinal models are being continuously proposed, which contribute to the study of intestinal physiology, drug screening, drug side effects, and drug-drug interactions.Advances in pharmaceutical processes have produced more drug formulations, causing challenges for intestinal models. To adapt to the rapid evolution of pharmaceuticals, more intestinal models have been created. However, because of the complexity of the intestine, few models can take all aspects of the intestine into account, and some functions must be sacrificed to investigate other areas. Therefore, investigators need to choose appropriate models according to the experimental stage and other requirements to obtain the desired results.To help researchers achieve this goal, this review summarised the advantages and disadvantages of current commonly used intestinal models and discusses possible future directions, providing a better understanding of intestinal models.

Anticancer potential of phytochemicals from Oroxylum indicum targeting Lactate Dehydrogenase A through bioinformatic approach

In recent years, small molecule inhibition of LDHA (Lactate Dehydrogenase A) has evolved as an appealing option for anticancer therapy. LDHA catalyzes the interconversion of pyruvate and lactate in the glycolysis pathway to play a crucial role in aerobic glycolysis. Therefore, in the current investigation LDHA was targeted with bioactive phytochemicals of an ethnomedicinally important plant species Oroxylum indicum (L.) Kurz. A total of 52 phytochemicals were screened against LDHA protein through molecular docking, ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) assay and molecular dynamics simulation to reveal three potential lead compounds such as Chrysin-7-O-glucuronide (-8.2 kcal/mol), Oroxindin (-8.1 kcal/mol) and Oroxin A (-8.0 kcal/mol). ADMET assay unveiled favorable pharmacokinetic, pharmacodynamic and toxicity properties for all the lead compounds. Molecular dynamics simulation exhibited significant conformational stability and compactness. MM/GBSA free binding energy calculations further corroborated the selection of top candidates where Oroxindin (-46.47 kcal/mol) was found to be better than Chrysin-7-O-glucuronide (-45.72 kcal/mol) and Oroxin A (-37.25 kcal/mol). Aldolase reductase and Xanthine dehydrogenase enzymes were found as potential drug targets and Esculin, the FDA approved drug was identified as structurally analogous to Oroxindin. These results could drive in establishing novel medications targeting LDHA to fight cancer.

Recent Advances of DprE1 Inhibitors against Mycobacterium tuberculosis: Computational Analysis of Physicochemical and ADMET Properties

Decaprenylphosphoryl-β-d-ribose 2'-epimerase (DprE1) is a critical flavoenzyme in Mycobacterium tuberculosis, catalyzing a vital step in the production of lipoarabinomannan and arabinogalactan, both of which are essential for cell wall biosynthesis. Due to its periplasmic localization, DprE1 is a susceptible target, and several compounds with diverse scaffolds have been discovered that inhibit this enzyme, covalently or noncovalently. We evaluated a total of ∼1519 DprE1 inhibitors disclosed in the literature from 2009 to April 2022 by performing an in-depth analysis of physicochemical descriptors and absorption, distribution, metabolism, excretion, and toxicity (ADMET), to gain new insights into these properties in DprE1 inhibitors. Several molecular properties that should facilitate the design and optimization of future DprE1 inhibitors are described, allowing for the development of improved analogues targeting M. tuberculosis.

Identification of hepatoprotective traditional Chinese medicines based on the structure–activity relationship, molecular network, and machine learning techniques

The efforts focused on discovering potential hepatoprotective drugs are critical for relieving the burdens caused by liver diseases. Traditional Chinese medicine (TCM) is an important resource for discovering hepatoprotective agents. Currently, there are hundreds of hepatoprotective products derived from TCM available in the literature, providing crucial clues to discover novel potential hepatoprotectants from TCMs based on predictive research. In the current study, a large-scale dataset focused on TCM-induced hepatoprotection was established, including 676 hepatoprotective ingredients and 205 hepatoprotective TCMs. Then, a comprehensive analysis based on the structure–activity relationship, molecular network, and machine learning techniques was performed at molecular and holistic TCM levels, respectively. As a result, we developed an in silico model for predicting the hepatoprotective activity of ingredients derived from TCMs, in which the accuracy exceeded 85%. In addition, we originally proposed a material basis and a drug property-based approach to identify potential hepatoprotective TCMs. Consequently, a total of 12 TCMs were predicted to hold potential hepatoprotective activity, nine of which have been proven to be beneficial to the liver in previous publications. The high rate of consistency between our predictive results and the literature reports demonstrated that our methods were technically sound and reliable. In summary, systematical predictive research focused on the hepatoprotection of TCM was conducted in this work, which would not only assist screening of potential hepatoprotectants from TCMs but also provide a novel research mode for discovering the potential activities of TCMs.

Predicting epitopes for vaccine development using bioinformatics tools

Epitope-based DNA vaccine development is one application of bioinformatics or in silico studies, that is, computational methods, including mathematical, chemical, and biological approaches, which are widely used in drug development. Many in silico studies have been conducted to analyze the efficacy, safety, toxicity effects, and interactions of drugs. In the vaccine design process, in silico studies are performed to predict epitopes that could trigger T-cell and B-cell reactions that would produce both cellular and humoral immune responses. Immunoinformatics is the branch of bioinformatics used to study the relationship between immune responses and predicted epitopes. Progress in immunoinformatics has been rapid and has led to the development of a variety of tools that are used for the prediction of epitopes recognized by B cells or T cells as well as the antigenic responses. However, the in silico approach to vaccine design is still relatively new; thus, this review is aimed at increasing understanding of the importance of in silico studies in the design of vaccines and thereby facilitating future research in this field.

A Rapid Assessment Model for Liver Toxicity of Macrolides and an Integrative Evaluation for Azithromycin Impurities

Impurities in pharmaceuticals of potentially hazardous materials may cause drug safety problems. Macrolide antibiotic preparations include active pharmaceutical ingredients (APIs) and different types of impurities with similar structures, and the amount of these impurities is usually very low and difficult to be separated for toxicity evaluation. Our previous study indicated that hepatotoxicity induced by macrolides was correlated with c-fos overexpression. Here, we report an assessment of macrolide-related liver toxicity by ADMET prediction, molecular docking, structure–toxicity relationship, and experimental verification via detection of the c-fos gene expression in liver cells. The results showed that a rapid assessment model for the prediction of hepatotoxicity of macrolide antibiotics could be established by calculation of the -CDOCKER interaction energy score with the FosB/JunD bZIP domain and then confirmed by the detection of the c-fos gene expression in L02 cells. Telithromycin, a positive compound of liver toxicity, was used to verify the correctness of the model through comparative analysis of liver toxicity in zebrafish and cytotoxicity in L02 cells exposed to telithromycin and azithromycin. The prediction interval (48.1∼53.1) for quantitative hepatotoxicity in the model was calculated from the docking scores of seven macrolide antibiotics commonly used in clinics. We performed the prediction interval to virtual screening of azithromycin impurities with high hepatotoxicity and then experimentally confirmed by liver toxicity in zebrafish and c-fos gene expression. Simultaneously, we found the hepatotoxicity of azithromycin impurities may be related to the charge of nitrogen (N) atoms on the side chain group at the C5 position via structure–toxicity relationship of azithromycin impurities with different structures. This study provides a theoretical basis for improvement of the quality of macrolide antibiotics.

In Silico Structure-Based Approach for Group Efficiency Estimation in Fragment-Based Drug Design Using Evaluation of Fragment Contributions

The notion of a contribution of a specific group in an organic molecule's property and/or activity is both common in our thinking and is still not strictly correct due to the inherent non-additivity of free energy with respect to molecular fragments composing a molecule. The fragment- based drug discovery (FBDD) approach has proven to be fruitful in addressing the above notions. The main difficulty of the FBDD, however, is in its reliance on the low throughput and expensive experimental means of determining the fragment-sized molecules binding. In this article we propose a way to enhance the throughput and availability of the FBDD methods by judiciously using an in silico means of assessing the contribution to ligand-receptor binding energy of fragments of a molecule under question using a previously developed in silico Reverse Fragment Based Drug Discovery (R-FBDD) approach. It has been shown that the proposed structure-based drug discovery (SBDD) type of approach fills in the vacant niche among the existing in silico approaches, which mainly stem from the ligand-based drug discovery (LBDD) counterparts. In order to illustrate the applicability of the approach, our work retrospectively repeats the findings of the use case of an FBDD hit-to-lead project devoted to the experimentally based determination of additive group efficiency (GE)-an analog of ligand efficiency (LE) for a group in the molecule-using the Free-Wilson (FW) decomposition. It is shown that in using our in silico approach to evaluate fragment contributions of a ligand and to estimate GE one can arrive at similar decisions as those made using the experimentally determined activity-based FW decomposition. It is also shown that the approach is rather robust to the choice of the scoring function, provided the latter demonstrates a decent scoring power. We argue that the proposed approach of in silico assessment of GE has a wider applicability domain and expect that it will be widely applicable to enhance the net throughput of drug discovery based on the FBDD paradigm.

Ameliorations in dyslipidemia and atherosclerotic plaque by the inhibition of HMG-CoA reductase and antioxidant potential of phytoconstituents of an aqueous seed extract of Acacia senegal (L.) Willd in rabbits

The assigned work was aimed to examine the capability of phytoconstituents of an aqueous seed extract of Acacia senegal (L.) Willd to inhibit HMG-CoA reductase and regression of the atherosclerotic plaque. The chemical fingerprinting of the test extract was assessed by LC-MS/MS. Consequently, the analyses of in-vitro, in-vivo, and in-silico were executed by using the standard protocols. The in-vitro assessment of the test extract revealed 74.1% inhibition of HMG-CoA reductase. In-vivo assessments of the test extract indicated that treated hypercholesterolemic rabbits exhibited a significant (P≤0.001) amelioration in the biomarker indices of the dyslipidaemia i.e., atherogenic index, Castelli risk index(I&II), atherogenic coefficient along with lipid profile. Subsequently, significant reductions were observed in the atherosclerotic plaque and antioxidant levels. The in-silico study of molecular docking shown interactions capabilities of the leading phytoconstituents of the test extract i.e., eicosanoic acid, linoleic acid, and flavan-3-ol with target protein of HMG-CoA reductase. The values of RSMF and potential energy of top docked complexes were show significant interactions. Accordingly, the free energy of solvation, interaction angle, radius of gyration and SASA were shown significant stabilities of top docked complex. The cumulative data of results indicate phytoconstituents of an aqueous seed extract of Acacia senegal have capabilities to inhibit the HMG-CoA reductase and improve the levels of antioxidants.

β-Caryophyllene promotes oxidative stress and apoptosis in KB cells through activation of mitochondrial-mediated pathway - An in-vitro and in-silico study

Beta-caryophyllene (BCP), are natural bicyclic sesquiterpenes which are present in numerous plants worldwide. BCP has antioxidant, antimicrobial, and antifungal properties. Here, we studied its anticancer, anti-inflammatory, and cytotoxic effects. Cells treated with BCP, in a dose-dependent manner, exhibited morphological changes, showed lower cell growth, underwent apoptosis and lost the ability to metastasis through the suppression of NF-ҡ B via PI3K/AKT signalling pathway. These results elucidate that the inhibition of NF-ҡ B and PI3K/AKT is one of the most important mechanism by which BCP suppresses cancer cell proliferation and enhances apoptosis.

Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9

Cytochrome P450 2C9 (CYP2C9) is a major drug-metabolizing enzyme that represents 20% of the hepatic CYPs and is responsible for the metabolism of 15% of drugs. A general concern in drug discovery is to avoid the inhibition of CYP leading to toxic drug accumulation and adverse drug-drug interactions. However, the prediction of CYP inhibition remains challenging due to its complexity. We developed an original machine learning approach for the prediction of drug-like molecules inhibiting CYP2C9. We created new predictive models by integrating CYP2C9 protein structure and dynamics knowledge, an original selection of physicochemical properties of CYP2C9 inhibitors, and machine learning modeling. We tested the machine learning models on publicly available data and demonstrated that our models successfully predicted CYP2C9 inhibitors with an accuracy, sensitivity and specificity of approximately 80%. We experimentally validated the developed approach and provided the first identification of the drugs vatalanib, piriqualone, ticagrelor and cloperidone as strong inhibitors of CYP2C9 with IC values <18 μM and sertindole, asapiprant, duvelisib and dasatinib as moderate inhibitors with IC50 values between 40 and 85 μM. Vatalanib was identified as the strongest inhibitor with an IC50 value of 0.067 μM. Metabolism assays allowed the characterization of specific metabolites of abemaciclib, cloperidone, vatalanib and tarafenacin produced by CYP2C9. The obtained results demonstrate that such a strategy could improve the prediction of drug-drug interactions in clinical practice and could be utilized to prioritize drug candidates in drug discovery pipelines.

Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints

The well-known concept of quantitative structure-activity relationships (QSAR) has been gaining significant interest in the recent years. Data, descriptors, and algorithms are the main pillars to build useful models that support more efficient drug discovery processes with in silico methods. Significant advances in all three areas are the reason for the regained interest in these models. In this book chapter we review various machine learning (ML) approaches that make use of measured in vitro/in vivo data of many compounds. We put these in context with other digital drug discovery methods and present some application examples.

Computational Analysis of Chemical Space of Natural Compounds Interacting with Sulfotransferases

The aim of this study was to investigate the chemical space and interactions of natural compounds with sulfotransferases (SULTs) using ligand- and structure-based in silico methods. An in-house library of natural ligands (hormones, neurotransmitters, plant-derived compounds and their metabolites) reported to interact with SULTs was created. Their chemical structures and properties were compared to those of compounds of non-natural (synthetic) origin, known to interact with SULTs. The natural ligands interacting with SULTs were further compared to other natural products for which interactions with SULTs were not known. Various descriptors of the molecular structures were calculated and analyzed. Statistical methods (ANOVA, PCA, and clustering) were used to explore the chemical space of the studied compounds. Similarity search between the compounds in the different groups was performed with the ROCS software. The interactions with SULTs were additionally analyzed by docking into different experimental and modeled conformations of SULT1A1. Natural products with potentially strong interactions with SULTs were outlined. Our results contribute to a better understanding of chemical space and interactions of natural compounds with SULT enzymes and help to outline new potential ligands of these enzymes.

Artificial Intelligence and the Future of Diagnostic and Therapeutic Radiopharmaceutical Development:: In Silico Smart Molecular Design

Novel diagnostic and therapeutic radiopharmaceuticals are increasingly becoming a central part of personalized medicine. Continued innovation in the development of new radiopharmaceuticals is key to sustained growth and advancement of precision medicine. Artificial intelligence has been used in multiple fields of medicine to develop and validate better tools for patient diagnosis and therapy, including in radiopharmaceutical design. In this review, we first discuss common in silico approaches and focus on their usefulness and challenges in radiopharmaceutical development. Next, we discuss the practical applications of in silico modeling in design of radiopharmaceuticals in various diseases.

Screening of anti-Acinetobacter baumannii phytochemicals, based on the potential inhibitory effect on OmpA and OmpW functions

Therapeutic options including last-line or combined antibiotic therapies for multi-drug-resistant strains of Acinetobacter baumannii are ineffective. The outer membrane protein A (OmpA) and outer membrane protein W (OmpW) are two porins known for their different cellular functions. Identification of natural compounds with the potentials to block these putative porins can attenuate the growth of the bacteria and control the relating diseases. The current work aimed to screen a library of 384 phytochemicals according to their potentials to be used as a drug, and potentials to inhibit the function of OmpA and OmpW in A. baumannii. The phytocompounds were initially screened based on their physico-chemical, absorption, distribution, metabolism, excretion and toxicity (ADMET) drug-like properties. Afterwards, the selected ligands were subjected to standard docking calculations against the predicted three-dimensional structure of OmpA and OmpW in A. baumannii. We identified three phytochemicals (isosakuranetin, aloe-emodin and pinocembrin) possessing appreciable binding affinity towards the selected binding pocket of OmpA and OmpW. Molecular dynamics simulation analysis confirmed the stability of the complexes. Among them, isosakuranetin was suggested as the best phytocompound for further in vitro and in vivo study.

Synthesis, molecular modelling, in vitro and in vivo evaluation of conophylline inspired novel benzyloxy substituted indole glyoxylamides as potent pancreatic lipase inhibitors

Pancreatic lipase is a digestive enzyme involved in the hydrolysis of dietary fats. Orlistat, a potent pancreatic lipase inhibitor, is widely prescribed for long-term obesity treatment. Nevertheless, orlistat is reported for severe adverse effects including hepatotoxicity and pancreatitis. In the present study, a novel series of 11 benzyloxy substituted indole glyoxylamides were designed, synthesized and evaluated for in vitro pancreatic lipase inhibitory activity. Three analogues, 10b, 11b and 11c, exhibited potent activity (IC₅₀ ≤ 2.5 µM), with 11b exhibiting a potent IC₅₀ of 1.68 µM comparable to orlistat (IC₅₀ = 0.99 µM). Further, 11b exhibited reversible competitive inhibition with an inhibitory constant value of 0.98 μM. Molecular docking of these analogues was in agreement with in vitro results, wherein the MolDock scores exhibited significant correlation with their inhibitory activity (Pearson's r = 0.7122). A 50 ns molecular dynamics simulation of 11b-pancreatic lipase complex confirmed the role of extended alkyl interactions along with π-π stacking and π-cation interactions, in stabilizing the ligand (Maximum RMSD ≈ 3 Å) in the active site. Gastro-intestinal absorption and toxicity prediction of the three potent analogues highlighted the suitability of 11b for in vivo experiments. 11b at a dose of 20 mg/kg exhibited anti-obesity efficacy comparable to orlistat (10 mg/kg), wherein the serum triglycerides were found to be 94.95 and 83.85 mg/dL, respectively. Further, faecal triglyceride quantification indicated 11b to act through pancreatic lipase inhibition similar to orlistat. The present study identified a novel pancreatic lipase inhibitory benzyloxy substituted bis(indolyl) glyoxylamide 11b, with promising anti-obesity activity.Communicated by Ramaswamy H. Sarma.