Reactive oxygen species (ROS) are a class of highly reactive molecules derived from the metabolism of oxygen. ROS, including superoxide radicals, hydroxyl radical and hydrogen peroxide molecules are often generated as by products of biological reactions or from exogenous factors. There is extensive evidence to involve ROS in the development of degenerative diseases. Evidence suggests that compounds especially from natural sources are capable of providing protection against free radicals. This has attracted a great deal of research interest in natural antioxidants. It is necessary to Screen out medicinal plants for their antioxidant potential. Therefore an attempt has been made to review different in vitro models for estimating antioxidant properties of natural products from medicinal plants. All the models are described along with the different standards that can be used for estimation. In the end, a large number of plants showing in vitro antioxidant activity are listed but in vivostudies are lacking. 

	 

	Key words: Antioxidant assay, in vitro models, antioxidant medicinal plants.

In vitro models for antioxidant activity evaluation and some medicinal plants possessing antioxidant properties: An overview

Dengue fever, which is a disease caused by the dengue virus (DENV), is a major unsolved issue in many tropical and sub-tropical regions of the world. The absence of treatment that effectively prevent further viral propagation inside the human’s body resulted in a high number of deaths globally each year. Thus, novel anti-dengue therapies are required for effective treatment. Hu-man hexokinase II (HKII), which is the first enzyme in the glycolytic pathway, is an important drug target due to its significant impact on viral replication and survival in host cells. In this study, 23.1 million compounds were computationally-screened against HKII using the Ultrafast Shape Recognition with a CREDO Atom Types (USRCAT) algorithm. In total, 300 compounds with the highest similarity scores relative to three reference molecules, known as Al-pha-D-glucose (GLC), Beta-D-glucose-6-phosphate (BG6), and 2-deoxyglucose (2DG), were aligned. Of these 300 compounds, 165 were chosen for further structure-based screening, based on their similarity scores, ADME analysis, the Lipinski’s Rule of Five, and virtual toxicity test results. The selected analogues were subsequently docked against each domain of the HKII structure (PDB ID: 2NZT) using AutoDock Vina programme. The three top-ranked compounds for each query were then selected from the docking results based on their binding energy, the number of hydrogen bonds formed, and the specific catalytic residues. The best docking results for each analogue were observed for the C-terminus of Chain B. The top-ranked analogues of GLC, compound 10, compound 26, and compound 58, showed predicted binding energies of −7.2, −7.0, and −6.10 kcal/mol and 7, 5, and 2 hydrogen bonds, respectively. The analogues of BG6, compound 30, compound 36, and compound 38, showed predicted binding energies of −7.8, −7.4, and −7.0 kcal/mol and 11, 9, and 5 hydrogen bonds, while the top three analogues of 2DG, known as compound 1, compound 4, and compound 31, showed predicted binding energies of −6.8, −6.3, and −6.3 kcal/mol and 4, 3, and 1 hydrogen bonds, sequentially. The highest-ranked compounds in the docking analysis were then selected for molecular dynamics simulation, where compound 10, compound 30, and compound 1, which are the analogues of GLC, BG6, and 2DG, have shown strong protein-ligand stability with an RMSD value of ±5.0 A° with a 5 H bond, ±4.0 A° with an 8 H bond, and ±0.5 A° with a 2 H bond, respectively, compared to the reference molecules throughout the 20 ns simulation time. Therefore, by using the computational studies, we pro-posed novel compounds, which may act as potential drugs against DENV by inhibiting HKII’s activity.

https://www.mdpi.com/2673-6284/10/1/1/pdf

Virtual screening for potential inhibitors of human hexokinase II for the development of anti-dengue therapeutics

Hepatoprotective and antioxidant activity of ethanolic extracts of edible lotus (Nelumbo nucifera Gaertn.) leaves

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

/pdf/artificial-intelligence-to-deep-learning-machine-3ql51vg1ul.pdf

Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

The past decade has witnessed a paradigm shift in preclinical drug discovery with structure-based drug design (SBDD) making a comeback while high-throughput screening (HTS) methods have continued to generate disappointing results. There is a deficit of information between identified hits and the many criteria that must be fulfilled in parallel to convert them into preclinical candidates that have a real chance to become a drug. This gap can be bridged by investigating the interactions between the ligands and their receptors. Accurate calculations of the free energy of binding are still elusive; however progresses were made with respect to how one may deal with the versatile role of water. A corpus of knowledge combining X-ray structures, bioinformatics and molecular modeling techniques now allows drug designers to routinely produce receptor homology models of increasing quality. These models serve as a basis to establish and validate efficient rationales used to tailor and/or screen virtual libraries with enhanced chances of obtaining hits. Many case reports of successful SBDD show how synergy can be gained from the combined use of several techniques. The role of SBDD with respect to two different classes of widely investigated pharmaceutical targets: (a) protein kinases (PK) and (b) G-protein coupled receptors (GPCR) is discussed. Throughout these examples prototypical situations covering the current possibilities and limitations of SBDD are presented.

Current progress in Structure-Based Rational Drug Design marks a new mindset in drug discovery.

Antioxidant and hepatoprotective activity of ethanolic and aqueous extracts of Momordica dioica Roxb. leaves.

Computational screening of databases has become increasingly popular in the pharmaceutical research. Virtual screening uses computer based methods to discover new ligands on the basis of biological structures. Virtual screening is divided into structural based screening (docking) and screening using active compounds as templates (ligand based virtual screening). Ligand based screening techniques mainly focus on comparing molecular similarity analyses of compounds with known and unknown moiety, regardless of the methods of the used algorithm. Docking is a computational tool of structure based drug design to predict protein ligand interaction geometries and binding affinities. In this review we provide an overview of the already used ligand based virtual screening and the docking with various databases, filters, scores and applications in the recent research in the pharmaceutical field.

https://www.mdpi.com/2218-0532/76/3/333/pdf

Virtual Screening: A Fast Tool for Drug Design

Ibuprofen–polyethylene glycol (PEG) conjugates (PEG-Ibu) were prepared and their potential as a prolonged release system was investigated. Two PEG-Ibu conjugates were synthesized from Ibuprofen and PEG with two different molecular weights by esterification in the presence of DCC and DMAP. The PEG-Ibu conjugates were characterized by FT-IR, 1H NMR, Mass spectroscopy and DSC analysis. The solubility study in aqueous system showed an increase in solubility of conjugates. The dissolution / hydrolysis studies showed a specific acid–base catalysis pattern dependent on the pH of the medium. This indicated a good chemical stability in aqueous buffer solution of acidic medium and the extended release behavior was found in both prodrugs after 9 hour. The results demonstrate that, in the same condition, the rate of hydrolysis for PEG4000-Ibu is slower than other. The Writhing induced by acetic acid experiment and paw edema test after oral administration showed that both conjugates had extended analgesic and anti-inflammatory effects compared with Ibuprofen. These results suggest that PEG-Ibu could be a promising NSAID prodrug with an extended pharmacological effect owing to delayed-release of parent drug.

/pdf/in-vitro-and-in-vivo-study-of-poly-ethylene-glycol-28b0wzxwlv.pdf

In Vitro and In Vivo Study of Poly(ethylene glycol) Conjugated Ibuprofen to Extend the Duration of Action

The present study was designed to investigate of possible potential fracture healing activity of 400 mg/kg ethanolic extract of Lepidium sativum L. seeds in internally fixed rats’ femoral osteotomy model. The experimental protocol designed as the animals were divided into two groups (n=6) like control and test groups and was administrated vehicle and extract, respectively. X–Ray method was use for of assessment of bone healing activity. It was taken for each animal of control and test group at 2nd, 4th and 8th weeks. After fourth week, X‐ray photographs shows that test group had significantly larger callus formation and more disposing of osseous material as compare to control group. After eight weeks, X‐Ray photographs indicated that the fractures bone of test group animals had seen as complete joined but not in control group. The present study data revealed that ethanolic extract of Lepidium sativum seeds may be significant fracture healing activity.

Fracture healing activity of ethanolic extract of lepidium sativum l. seeds in internally fixed rats' femoral osteotomy model

Aim: This study was aimed to justify the scientific basis in folklore use of Solanum xanthocarpum (solanaceae) as an Immunomodulatory agent in India. Materials and Methods: The ethanol and aqueous extracts of S. xanthocarpum fruit (SXE-1 and SXA-1), root (SXE-2 and SXA-2) and whole plant (SXE-3 and SXA-3) were evaluated for immunomodulatory activity by using cold water swim endurance stress test, delayed type hyper sensitivity reaction, carbon clearance test and CCl4 induced oxidative stress model on experimental animals. The solasodaine content in S. xanthocarpum bioactive test samples were quantified by HPLC to establish phyto-pharmacological relationship. Results: The 200 mg/kg/day p.o. dose of test samples were showed significant changes in swimming survival time and at same doses test samples were significantly increased hypersensitivity, decrease in carbon clearance and reduced oxidative stress. Where, SXE-1 and SXA-1 exhibited maximum degree of immunomodulatory effect compared to SXE-2, SXA-2, SXE-3, SXA-3 and respective standard drugs used in this study. The hematological, biochemical and histopathological studies were supported respective results of the test samples. The solasodaine content in SXA-1 (1.146%), SXE-1 (0.894%), SXE-2 (0.643%) and SXA-2 (0.620%) were estimated by HPLC. Conclusion: These observations established the traditional claim and thus S. xanthocarpum could be a potent immunomodulatory agent for use in near future. The immunostimolatory effect may be due to presence of solasodaine in S. xanthocarpum extracts.

Anurekha Jain

Papers

Antioxidant and hepatoprotective activity of ethanolic and aqueous extracts of Momordica dioica Roxb. leaves.

Virtual Screening: A Fast Tool for Drug Design

In Vitro and In Vivo Study of Poly(ethylene glycol) Conjugated Ibuprofen to Extend the Duration of Action

Fracture healing activity of ethanolic extract of lepidium sativum l. seeds in internally fixed rats' femoral osteotomy model

Evaluation of Comparative Immunomodulatory Potential of Solanum xanthocarpum Root and Fruits on Experimental Animal