scholarly journals Synthesis, Structural Confirmation, and Biosynthesis of 22-OH-PD1n-3 DPA

Molecules ◽  
2019 ◽  
Vol 24 (18) ◽  
pp. 3228 ◽  
Author(s):  
Jannicke Irina Nesman ◽  
Karoline Gangestad Primdahl ◽  
Jørn Eivind Tungen ◽  
Fransesco Palmas ◽  
Jesmond Dalli ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Human Serum ◽  
Secondary Metabolism ◽  
Stereoselective Synthesis ◽  
Docosapentaenoic Acid ◽  
Lipid Mediators ◽  
Human Neutrophils ◽  
Human Monocytes ◽  
Structure Activity ◽  
Formed Product

PD1n-3 DPA belongs to the protectin family of specialized pro-resolving lipid mediators. The protectins are endogenously formed mediators that display potent anti-inflammatory properties and pro-resolving bioactivities and have attracted interest in drug discovery. However, few studies have been reported of the secondary metabolism of the protectins. To investigate the metabolic formation of the putative C22 mono-hydroxylated product, coined 22-OH-PD1n-3 DPA, a stereoselective synthesis was performed. LC/MS-MS data of synthetic 22-OH-PD1n-3 DPA matched the data for the biosynthetic formed product. Cellular studies revealed that 22-OH-PD1n-3 DPA is formed from n-3 docosapentaenoic acid in human serum, and we confirmed that 22-OH-PD1n-3 DPA is a secondary metabolite produced by ω-oxidation of PD1n-3 DPA in human neutrophils and in human monocytes. The results reported are of interest for enabling future structure–activity relationship studies and provide useful molecular insight of the metabolism of the protectin class of specialized pro-resolving mediators.

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Model Building ◽  
Chemical Space ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Efficient Manner ◽  
Qsar Analysis ◽  
Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

Triazol: a privileged scaffold for proteolysis targeting chimeras

2019 ◽  
Vol 11 (22) ◽  
pp. 2919-2973 ◽  
Author(s):  
Li-Wen Xia ◽  
Meng-Yu Ba ◽  
Wei Liu ◽  
Weyland Cheng ◽  
Chao-Ping Hu ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Anticancer Activity ◽  
Mode Of Action ◽  
Critical Analysis ◽  
Enzyme Inhibitors ◽  
Structure Activity Relationship ◽  
Target Protein ◽  
Activity Relationship ◽  
Structure Activity ◽  
Privileged Scaffold

Current traditional drugs such as enzyme inhibitors and receptor agonists/antagonists present inherent limitations due to occupancy-driven pharmacology as the mode of action. Proteolysis targeting chimeras (PROTACs) are composed of an E3 ligand, a connecting linker and a target protein ligand, and are an attractive approach to specifically knockdown-targeted proteins utilizing an event-driven mode of action. The length, hydrophilicity and rigidity of connecting linkers play important role in creating a successful PROTAC. Some PROTACs with a triazole linker have displayed promising anticancer activity. This review provides an overview of PROTACs with a triazole scaffold and discusses its structure–activity relationship. Important milestones in the development of PROTACs are addressed and a critical analysis of this drug discovery strategy is also presented.

scholarly journals SAR by kinetics for drug discovery in protein misfolding diseases

2018 ◽  
Vol 115 (41) ◽  
pp. 10245-10250 ◽  
Author(s):  
Sean Chia ◽  
Johnny Habchi ◽  
Thomas C. T. Michaels ◽  
Samuel I. A. Cohen ◽  
Sara Linse ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Protein Misfolding ◽  
Amyloid Beta Peptide ◽  
Oligomer Formation ◽  
Chemical Derivatives ◽  
Structure Activity ◽  
Beta Peptide ◽  
Misfolding Diseases ◽  
Aβ Oligomer

To develop effective therapeutic strategies for protein misfolding diseases, a promising route is to identify compounds that inhibit the formation of protein oligomers. To achieve this goal, we report a structure−activity relationship (SAR) approach based on chemical kinetics to estimate quantitatively how small molecules modify the reactive flux toward oligomers. We use this estimate to derive chemical rules in the case of the amyloid beta peptide (Aβ), which we then exploit to optimize starting compounds to curtail Aβ oligomer formation. We demonstrate this approach by converting an inactive rhodanine compound into an effective inhibitor of Aβ oligomer formation by generating chemical derivatives in a systematic manner. These results provide an initial demonstration of the potential of drug discovery strategies based on targeting directly the production of protein oligomers.

scholarly journals Multitarget, Selective Compound Design Yields Picomolar Inhibitors of a Kinetoplastid Pteridine Reductase 1

2020 ◽  
Author(s):  
Ina Poehner ◽  
Antonio Quotadamo ◽  
Joanna Panecka-Hofman ◽  
Rosaria Luciani ◽  
Matteo Santucci ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Dihydrofolate Reductase ◽  
Multidisciplinary Approach ◽  
Microwave Assisted Synthesis ◽  
Crystallographic Structure ◽  
Computational Docking ◽  
Design And Optimization ◽  
Microwave Assisted ◽  
Structure Activity ◽  
Further Development

The optimization of compounds with multiple targets in the drug discovery cycle is a difficult multidimensional problem. Here, we present a systematic, multidisciplinary approach to the development of selective anti-parasitic compounds. Efficient microwave-assisted synthesis of pteridines along with iterations of crystallographic structure determination were used to validate computational docking predictions and support derivation of a structure-activity relationship for multitarget inhibition. This approach yielded compounds showing picomolar inhibition of <i>T. brucei</i> pteridine reductase 1 (PTR1), nanomolar inhibition of <i>L. major</i> PTR1, along with selective submicromolar inhibition of parasitic dihydrofolate reductase (DHFR). Moreover, by combining design for polypharmacology with a property-based on-parasite optimization, we found three compounds that exhibited micromolar EC<sub>50</sub> values against <i>T. brucei brucei</i>, whilst retaining their target inhibition. Our results provide a basis for the further development of pteridine-based compounds and we expect our multitarget approach to be generally applicable to the design and optimization of anti-infective agents.<br>

scholarly journals Multitarget, Selective Compound Design Yields Picomolar Inhibitors of a Kinetoplastid Pteridine Reductase 1

2020 ◽  
Author(s):  
Ina Poehner ◽  
Antonio Quotadamo ◽  
Joanna Panecka-Hofman ◽  
Rosaria Luciani ◽  
Matteo Santucci ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Dihydrofolate Reductase ◽  
Multidisciplinary Approach ◽  
Microwave Assisted Synthesis ◽  
Crystallographic Structure ◽  
Computational Docking ◽  
Design And Optimization ◽  
Microwave Assisted ◽  
Structure Activity ◽  
Further Development

The optimization of compounds with multiple targets in the drug discovery cycle is a difficult multidimensional problem. Here, we present a systematic, multidisciplinary approach to the development of selective anti-parasitic compounds. Efficient microwave-assisted synthesis of pteridines along with iterations of crystallographic structure determination were used to validate computational docking predictions and support derivation of a structure-activity relationship for multitarget inhibition. This approach yielded compounds showing picomolar inhibition of <i>T. brucei</i> pteridine reductase 1 (PTR1), nanomolar inhibition of <i>L. major</i> PTR1, along with selective submicromolar inhibition of parasitic dihydrofolate reductase (DHFR). Moreover, by combining design for polypharmacology with a property-based on-parasite optimization, we found three compounds that exhibited micromolar EC<sub>50</sub> values against <i>T. brucei brucei</i>, whilst retaining their target inhibition. Our results provide a basis for the further development of pteridine-based compounds and we expect our multitarget approach to be generally applicable to the design and optimization of anti-infective agents.<br>

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