Computational Insights into Pharmacokinetic Profiling of Amygdalin: An In-Silico Study

Amygdalin is a naturally occurring cyanogenic glycoside which has been used as an alternative anti-cancer agent despite controversies surrounding its efficacy and safety. This study utilized computational approaches to investigate the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of amygdalin based on its molecular structure. Amygdalin was modeled in ChemBio3D and submitted to SwissADME and admetSAR servers for ADMET parameter prediction. The in-silico simulations indicated suboptimal pharmacological properties for amygdalin, including low lipophilicity, poor bioavailability, minimal blood-brain barrier permeability and non-compliance with drug-likeness criteria. Additional pharmacokinetic modeling through Simcyp suggested rapid clearance and short half-life after intravenous administration.While toxicity was predicted to be low at regular dosages, the overall pharmacological limitations may pose challenges for amygdalin’s efficacy as an anti-cancer therapy. The computational findings provide comprehensive insights into amygdalin’s drug-like behavior and can inform future in vitro/in vivo investigations on this naturally derived compound

ADMET Investigations On A Synthetic Derivative Of Genistein, And Molecular Docking Experiments Targeting Estrogen Receptor-α (ER-α) In The Pancreas

The main goal of the current research was to perform ADMET and molecular docking studies for a synthetic genistein derivative that can imitate Estrogen and function as an endocrine disruptor, activating the ER receptor on beta-cells in the pancreas to release insulin. The created molecule was molecularly docked using the online molecular docking research tool Dockthor. NGL viewer, an online program for viewing Dockthor data, displayed the docking experiment results. The 2D legend-protein interactions were estimated with BIOVIA Discovery Studio Visualizer. Estrogen-Receptor Alpha was the targeted target, while Compound-A was employed as the legend. In this study, we created a synthetic derivative of genistein, an analogue of Estrogen in terms of ER-α receptor binding. We used molecular docking to evaluate the affinity of compound-A binding to the ER-α and its 2D interactions and Ramachandran plots. We then ran ADMET experiments on the molecule, which revealed a substantial relationship with the molecule's Estrogen Receptor binding capabilities, as well as scores for absorption, distribution, metabolism, excretion, and toxicity

Analisis Sifat Mirip Obat, Prediksi ADMET, dan Penambatan Molekular Isatinil-2-Aminobenzoilhidrazon dan kompleks logam transisi Co(II), Ni(II), Cu(II), Zn(II) Terhadap BCL2-XL

This article reports an drug-likeness analysis, ADMET profile, and molecular docking of isatinyl-2-aminobenzoylhydrazone (ISABH) and its transition metals Co(II), Ni(II), Cu(II), and Zn(II) complexes. SwissADME analysis for drug-likeness indicated that ISABH and Ni-ISABH met all parameters of the Lipinski rule. These compounds also showed good pharmacological criteria by admetSAR for their ADMET prediction. The molecular docking of all compounds against the main regulatory protein for apoptosis BCL-2 (PDB code: 2W3L) revealed that they well-interacted with the protein expressed by binding affinity of -6.1, -8.3; -8.3; -7.5; and -8.5 kcal/mol for ISABH, Cu-ISABH, Co-ISABH, Ni-ISABH, and Zn-ISABH, respectively

IDENTIFICATION OF PHYTOCOMPOUNDS FROM ARGEMONE MEXICANA AS INHIBITORS OF EPSTEIN-BARR NUCLEAR ANTIGEN TO COMBAT INFECTIOUS MONONUCLEOSIS

Objectives: Mono or infectious mononucleosis (IM) is often referred to as the kissing illness. Epstein-Barr virus (EBV), which causes mono, is spread by saliva. Kissing, sharing a drink, or eating utensils with a person who has mononucleosis can transmit the disease to healthy individuals. This study investigates several bioactive compounds derived from plants to forecast how effective plant-based ligands will be at preventing IM. Methods: The purpose of the current study was to use computational techniques to assess the effectiveness of several phytochemicals against the EBV. The virtual screening tool PyRx was used to systematically perform molecular docking. The top 6 phytocompounds from Argemone mexicana were chosen among them to test their compatibility with the EBV nuclear antigen. Using ADMET filters, the ligands’ pharmacological evaluation was performed. Results: The phytocompounds Coptisine, Sanguinarine, and Dihydrosanguinarine from the plant A. mexicana were discovered to be the most potent antagonistic for the proteins EBV Nuclear Antigen 1 and EBV nuclear antigen 2. Conclusion: All of these bioactive chemicals could be considered of as deserving candidates for the suppression of IM due to their strong affinity for the protein. Among the top ligand, the phytoconstituent Coptisine demonstrated better binding with both targets

SuperCYPsPred - a web server for the prediction of cytochrome activity

Cytochrome P450 enzymes (CYPs)-mediated drug metabolism influences drug pharmacokinetics and results in adverse outcomes in patients through drug-drug interactions (DDIs). Absorption, distribution, metabolism, excretion and toxicity (ADMET) issues are the leading causes for the failure of a drug in the clinical trials. As details on their metabolism are known for just half of the approved drugs, a tool for reliable prediction of CYPs specificity is needed. The SuperCYPsPred web server is currently focused on five major CYPs isoenzymes, which includes CYP1A2, CYP2C19, CYP2D6, CYP2C9 and CYP3A4 that are responsible for more than 80% of the metabolism of clinical drugs. The prediction models for classification of the CYPs inhibition are based on well-established machine learning methods. The models were validated both on cross-validation and external validation sets and achieved good performance. The web server takes a 2D chemical structure as input and reports the CYP inhibition profile of the chemical for 10 models using different molecular fingerprints, along with confidence scores, similar compounds, known CYPs information of drugs-published in literature, detailed interaction profile of individual cytochromes including a DDIs table and an overall CYPs prediction radar chart (http://insilico-cyp.charite.de/SuperCYPsPred/).The web server does not require log in or registration and is free to use

Potential Indonesian Natural Compound as antiviral for COVID-19 targeting the RdRp: In silico Study

Research related to SARS-CoV-2 drugs is still ongoing. In this initial research, we perform a computational approach on SARS-CoV-2 inhibitors. RNA-dependent RNA polymerase (RdRp) is one of the functional proteins in SARS-CoV-2 that can be a target for drug development, which has an essential function in the viral replication process synthesizing the RNA genome of the virus. This study used the RdRp-Remdesivir complex structure from RCSB with ID PDB 7BV2, with a resolution of 2.5 Å. Currently, Remdesivir is under the clinical trial phase as a Covid-19 drug. In this study, we tested a thousand natural Indonesian compounds used as SARS-CoV-2 RdRp inhibitors obtained from the Indonesian natural compounds database (HerbalDB). The first stage of this computational analysis was pharmacophore modeling structure-based drug design. The natural compounds were analyzed based on their steric and electronic similarities to Remdesivir. A molecular docking simulation was then performed to obtain binding energy and bond stability to produce natural compounds that can inhibit RdRp SARS-CoV-2. The final stage was the molecular dynamics simulation that explored the conformational space of natural compounds and proteins. The ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) test was carried out on the five best compounds to obtain these natural compounds' computational pharmacology and pharmacokinetics. The simulation identified Sotetsuflavone (CID: 5494868) from Cycas revoluta, Grossamide (CID: 5322012) from Cannabis sativa, and 6-Hydroxyluteolin-6,7-disulfate (CID: 13845917) from Lippia nodiflora are the best compounds that can inhibit RdRp SARS-CoV-2. These potential compounds can then be tested in-vitro and in-vivo in the future. &nbsp

Solubility and ADMET profiles of short oligomers of lactic acid

Polylactic acid (PLA) is a polymer with an increased potential to be used in different medical applications, including tissue engineering and drug-carries. The use of PLA in medical applications implies the evaluation of the human organism\u27s response to the polymer inserting and to its degradation products. Consequently, within this study, we have investigated the solubility and ADMET profiles of the short oligomers (having the molecular weight lower than 3000 Da) resulting in degradation products of PLA. There is a linear decrease of the molar solubility of investigated oligomers with molecular weight. The results that are obtained also reveal that short oligomers of PLA have promising pharmacological profiles and limited toxicological effects on humans. These oligomers are predicted as potential inhibitors of the organic anion transporting peptides OATP1B1 and OATP1B3, they present minor probability to affect the androgen and glucocorticoid receptors, have a weak potential of hepatotoxicity, and may produce eye injuries. These outcomes may be used to guide or to supplement in vitro and/or in vivo toxicity tests such as to enhance the biodegradation properties of the biopolymer.</p

LightGBM: An Effective and Scalable Algorithm for Prediction of Chemical Toxicity – Application to the Tox21 and Mutagenicity Datasets

Machine learning algorithms have attained widespread use in assessing the potential toxicities of pharmaceuticals and industrial chemicals because of their faster-speed and lower-cost compared to experimental bioassays. Gradient boosting is an effective algorithm that often achieves high predictivity, but historically the relative long computational time limited its applications in predicting large compound libraries or developing in silico predictive models that require frequent retraining. LightGBM, a recent improvement of the gradient boosting algorithm inherited its high predictivity but resolved its scalability and long computational time by adopting leaf-wise tree growth strategy and introducing novel techniques. In this study, we compared the predictive performance and the computational time of LightGBM to deep neural networks, random forests, support vector machines, and XGBoost. All algorithms were rigorously evaluated on publicly available Tox21 and mutagenicity datasets using a Bayesian optimization integrated nested 10-fold cross-validation scheme that performs hyperparameter optimization while examining model generalizability and transferability to new data. The evaluation results demonstrated that LightGBM is an effective and highly scalable algorithm offering the best predictive performance while consuming significantly shorter computational time than the other investigated algorithms across all Tox21 and mutagenicity datasets. We recommend LightGBM for applications in in silico safety assessment and also in other areas of cheminformatics to fulfill the ever-growing demand for accurate and rapid prediction of various toxicity or activity related endpoints of large compound libraries present in the pharmaceutical and chemical industry

A novel optimized deep learning method for protein-protein prediction in bioinformatics

Proteins have been shown to perform critical activities in cellular processes and are required for the organism's existence and proliferation. On complicated protein-protein interaction (PPI) networks, conventional centrality approaches perform poorly. Machine learning algorithms based on enormous amounts of data do not make use of biological information's temporal and spatial dimensions. As a result, we developed a sequence-dependent PPI prediction model using an Aquila and shark noses-based hybrid prediction technique. This model operates in two stages: feature extraction and prediction. The features are acquired using the semantic similarity technique for good results. The acquired features are utilized to predict the PPI using hybrid deep networks long short-term memory (LSTM) networks and restricted Boltzmann machines (RBMs). The weighting parameters of these neural networks (NNs) were changed using a novel optimization approach hybrid of aquila and shark noses (ASN), and the results revealed that our proposed ASN-based PPI prediction is more accurate and efficient than other existing techniques