In silico ADME/Pharmacokinetic and Target Prediction Studies of Ethambutol as Drug Molecule

Background: The conventional approach for the development of any pharmaceutically active molecule is a time-consuming and costly process because the synthesis is followed by laboratory tests which are then followed by long clinical trials. Hence a faster approach is desired. This article discusses Ethambutol, a frontline anti-tubercular drug that has its properties predicted by the SwissADME tool and the results would be compared with the findings published in the literature. Objective: The main objective is to study the predicted and experimental ADME properties, compare them. As well as study the predicted targets and understand the use of SwissADME for designing other drug molecules. Method: SwissADME, an online tool for ADME prediction was used along with Swiss Target Prediction to understand the targets of the drug. Further, experimental data was obtained from the available scientific literature. Results: We found certain similarities between the predicted and experimental data. However, there were some variations, depending on the testing conditions. The results are interpreted ahead in the article. Conclusion: Ethambutol’s predicted ADME properties are discussed and as per findings from results, it can be concluded that other drug molecules can be similarly predicted using these tools. Also, based on predicted data we can reformulate and prepare some different preparations of the drug.

Investigations on spectroscopic characterizations, molecular docking, NBO, drug-Likeness, and ADME properties of 4H-1,2,4-triazol-4-amine by combined computational approach

In this study, the spectroscopic characterization, frontier molecular orbital analysis, and natural bond orbital analysis (NBO) analysis were executed to determine the movement of electrons within the molecule and the stability, and charge delocalization of the 4H-1,2,4-triazol-4-amine (4-AHT) through density functional theory (DFT) approach and B3LYP/6-311++G(d,p) level of theory. Surface plots of the hybrids’ Molecular Electrostatic Potential (MEP) revealed probable electrophilic and nucleophilic attacking sites. The discussed ligand were observed to be characterized by various spectral studies (FT-IR, UV-Vis). The calculated IR was found to be correlated with experimental values. The UV-Vis data of the molecule was used to analyze the visible absorption maximum (λmax) using the time-dependent DFT method. Since the principle of drug-likeness is usually used in combinatorial chemistry to minimize depletion in pharmacological investigations and growth, drug-likeness and ADME properties were calculated in this research to establish 4-AHT molecule bioavailability. Furthermore, molecular docking studies were carried out. Molecular docking analysis was performed for the title ligand inside the active site of the Epidermal Growth Factor Receptor (EGFR). The title compound’s anti-tumor activity against the cancer cell, in which EGFR is strongly expressed, prompted us to conduct molecular docking into the ATP binding site of EGFR to predict whether this molecule has an analogous binding mode to the EGFR inhibitors (PDB: ID: 1M17).

Gaultheria leucocarpa var. yunnanensis for Treating Rheumatoid Arthritis—An Assessment Combining Machine Learning–Guided ADME Properties Prediction, Network Pharmacology, and Pharmacological Assessment

Background: Dianbaizhu (Gaultheria leucocarpa var. yunnanensis), a traditional Chinese/ethnic medicine (TC/EM), has been used to treat rheumatoid arthritis (RA) for a long time. The anti–rheumatic arthritis fraction (ARF) of G. yunnanensis has significant anti-inflammatory and analgesic activities and is mainly composed of methyl salicylate glycosides, flavonoids, organic acids, and others. The effective ingredients and rudimentary mechanism of ARF remedying RA have not been elucidated to date.Purpose: The aim of the present study is to give an insight into the effective components and mechanisms of Dianbaizhu in ameliorating RA, based on the estimation of the absorption, distribution, metabolism, and excretion (ADME) properties, analysis of network pharmacology, and in vivo and in vitro validations.Study design and methods: The IL-1β–induced human fibroblast-like synoviocytes of RA (HFLS-RA) model and adjuvant-induced arthritis in the rat model were adopted to assess the anti-RA effect of ARF. The components in ARF were identified by using UHPLC-LTQ-Orbitrap-MSn. The quantitative structure–activity relationship (QSAR) models were developed by using five machine learning algorithms, alone or in combination with genetic algorithms for predicting the ADME properties of ARF. The molecular networks and pathways presumably referring to the therapy of ARF on RA were yielded by using common databases and visible software, and the experimental validations of the key targets conducted in vitro.Results: ARF effectively relieved RA in vivo and in vitro. The five optimized QSAR models that were developed showed robustness and predictive ability. The characterized 48 components in ARF had good biological potency. Four key signaling pathways were obtained, which were related to both cytokine signaling and cell immune response. ARF suppressed IL-1β–induced expression of EGFR, MMP 9, IL2, MAPK14, and KDR in the HFLS-RA .Conclusions: ARF has good druggability and high exploitation potential. Methyl salicylate glycosides and flavonoids play essential roles in attuning RA. ARF may partially attenuate RA by regulating the expression of multi-targets in the inflammation–immune system. These provide valuable information to rationalize ARF and other TC/EMs in the treatment of RA.

Development and implementation of an enterprise-wide predictive model for early absorption, distribution, metabolism and excretion properties

Background: Accurate prediction of absorption, distribution, metabolism and excretion (ADME) properties can facilitate the identification of promising drug candidates. Methodology & Results: The authors present the Janssen generic Target Product Profile (gTPP) model, which predicts 18 early ADME properties, employs a graph convolutional neural network algorithm and was trained on between 1000–10,000 internal data points per predicted parameter. gTPP demonstrated stronger predictive power than pretrained commercial ADME models and automatic model builders. Through a novel logging method, the authors report gTPP usage for more than 200 Janssen drug discovery scientists. Conclusion: The investigators successfully enabled the rapid and systematic implementation of predictive ML tools across a drug discovery pipeline in all therapeutic areas. This experience provides useful guidance for other large-scale AI/ML deployment efforts.

The application of prodrug-based drug delivery strategy in anticancer drugs

: Cancer is the second leading cause of human death after cardiovascular disease, and the most used drugs in clinics are cytotoxic agents. However, these drugs have some inherent disadvantages, such as the risk of toxicity, low selectivity, poor solubility, and so on. To overcome these shortcomings, a variety of drug delivery strategies based on prodrugs have been developed. The application of drug delivery systems can optimize ADME properties of cytotoxic agents and improve their selectivity at the target, thereby greatly enhancing the anticancer effect in clinics. At present, it has become mainstream in drug design. This review systematically summarized the studies of prodrug-based drug delivery systems over the past five to ten years, according to four aspects, solubility, controlled release, in situ concentration, and targeting.

Comparison of Pinoresinol and its Diglucoside on their ADME Properties and Vasorelaxant Effects on Phenylephrine-Induced Model

Pinoresinol (PINL) and pinoresinol diglucoside (PDG), two natural lignans found in Eucommia ulmoides Oliv. (Duzhong), have several pharmacological activities. However, there is no report available on their absorption, distribution, metabolism, and elimination (ADME) properties. Given the possible wide spectrum of their application in therapeutic areas, this area should be investigated. This work studied the in vitro ADME properties of PDG and PINL, including their kinetic solubility, permeability across monolayer cells (PAMPA), protein binding, and metabolic stabilities in liver microsomes. The in vivo pharmacokinetic study and in vitro vasorelaxant effects on isolated phenylephrine-induced aortic rings of PINL and PDG were also investigated. It was found that both of their kinetic solubility in PBS (pH 7.4) was greater than 100 μM, indicating that they are both soluble compounds. The permeability investigations (Peff) by PAMPA indicated that PINL had higher permeability than PDG (p < 0.05). Both components represented moderate plasma protein binding activities (average binding rate in human plasma: PINL 89.03%, PDG 45.21%) and low metabolic rate (t1/2 in human liver microsome: PINL 1509.5 min, PDG 1004.8 min). Furthermore, the results of pharmacokinetic studies indicated that PINL might be eliminated less quickly than PDG from the rat plasma, and its cumulative urinary excretion was much lower than that of PDG. The phenylephrine-induced aortic rings demonstrated concentration-dependent vasorelaxation in PDG, PINL, or their combination group. The vasorelaxant effects of PINL were more obvious than those of PDG, whereas the vasorelaxant effect of the combinations was significantly better than that of the single component (p < 0.05). The similarity or difference between PINL and its diglucoside in these pharmaceutical aspects may offer valuable insights into the further exploration of lignans and might contribute to relevant studies involving natural products with similar molecular structure and their glucosides.

A Promising Anticancer Agent Dimethoxycurcumin: Aspects of Pharmacokinetics, Efficacy, Mechanism, and Nanoformulation for Drug Delivery

Curcumin is a well-known anticancer natural product with various significant bioactivities that has been well documented, but its widespread use is mainly hindered by insufficient ADME properties such as poor solubility and low metabolic stability. Dimethoxycurcumin (DiMC) is a kind of lipophilic compound derived from curcumin that maintains its anticancer potency and has greatly improved systematic bioavailability. Therefore, DiMC is regarded as a promising plant-derived anticancer agent that deserves to be well developed. Herein, we concentrate on the published work by those from original research groups concerned with the pharmacokinetics, efficacy, and mechanism of DiMC involved in the treatment of various tumors, as well as the nanoformulations for effective drug delivery.