scholarly journals Identification of an Orally Bioavailable, Brain-Penetrant Compound with Selectivity for the Cannabinoid Type 2 Receptor

Molecules ◽  
2022 ◽  
Vol 27 (2) ◽  
pp. 509
Author(s):  
Meirambek Ospanov ◽  
Suresh P. Sulochana ◽  
Jason J. Paris ◽  
John M. Rimoldi ◽  
Nicole Ashpole ◽  
...  
Keyword(s):  
Endocannabinoid System ◽  
Rational Drug Design ◽  
Response Analysis ◽  
Intravenous Route ◽  
Cb2 Receptor ◽  
Potential Efficacy ◽  
Elimination Half ◽  
Rational Drug ◽  
The Central Nervous System ◽  
Orally Bioavailable

Modulation of the endocannabinoid system (ECS) is of great interest for its therapeutic relevance in several pathophysiological processes. The CB2 subtype is largely localized to immune effectors, including microglia within the central nervous system, where it promotes anti-inflammation. Recently, a rational drug design toward precise modulation of the CB2 active site revealed the novelty of Pyrrolo[2,1-c][1,4]benzodiazepines tricyclic chemotype with a high conformational similarity in comparison to the existing leads. These compounds are structurally unique, confirming their chemotype novelty. In our continuing search for new chemotypes as selective CB2 regulatory molecules, following SAR approaches, a total of 17 selected (S,E)-11-[2-(arylmethylene)hydrazono]-PBD analogs were synthesized and tested for their ability to bind to the CB1 and CB2 receptor orthosteric sites. A competitive [3H]CP-55,940 binding screen revealed five compounds that exhibited >60% displacement at 10 μM concentration. Further concentration-response analysis revealed two compounds, 4k and 4q, as potent and selective CB2 ligands with sub-micromolar activities (Ki = 146 nM and 137 nM, respectively). In order to support the potential efficacy and safety of the analogs, the oral and intravenous pharmacokinetic properties of compound 4k were sought. Compound 4k was orally bioavailable, reaching maximum brain concentrations of 602 ± 162 ng/g (p.o.) with an elimination half-life of 22.9 ± 3.73 h. Whether administered via the oral or intravenous route, the elimination half-lives ranged between 9.3 and 16.7 h in the liver and kidneys. These compounds represent novel chemotypes, which can be further optimized for improved affinity and selectivity toward the CB2 receptor.

Improving the Accuracy of Protein-Ligand Binding Affinity Prediction by Deep Learning Models: Benchmark and Model

2019 ◽  
Author(s):  
Mohammad Rezaei ◽  
Yanjun Li ◽  
Xiaolin Li ◽  
Chenglong Li
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
Binding Affinity ◽  
Benchmark Dataset ◽  
Rational Drug Design ◽  
Learning Models ◽  
Structure Based Drug Design ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Rational Drug

<b>Introduction:</b> The ability to discriminate among ligands binding to the same protein target in terms of their relative binding affinity lies at the heart of structure-based drug design. Any improvement in the accuracy and reliability of binding affinity prediction methods decreases the discrepancy between experimental and computational results.<br><b>Objectives:</b> The primary objectives were to find the most relevant features affecting binding affinity prediction, least use of manual feature engineering, and improving the reliability of binding affinity prediction using efficient deep learning models by tuning the model hyperparameters.<br><b>Methods:</b> The binding site of target proteins was represented as a grid box around their bound ligand. Both binary and distance-dependent occupancies were examined for how an atom affects its neighbor voxels in this grid. A combination of different features including ANOLEA, ligand elements, and Arpeggio atom types were used to represent the input. An efficient convolutional neural network (CNN) architecture, DeepAtom, was developed, trained and tested on the PDBbind v2016 dataset. Additionally an extended benchmark dataset was compiled to train and evaluate the models.<br><b>Results: </b>The best DeepAtom model showed an improved accuracy in the binding affinity prediction on PDBbind core subset (Pearson’s R=0.83) and is better than the recent state-of-the-art models in this field. In addition when the DeepAtom model was trained on our proposed benchmark dataset, it yields higher correlation compared to the baseline which confirms the value of our model.<br><b>Conclusions:</b> The promising results for the predicted binding affinities is expected to pave the way for embedding deep learning models in virtual screening and rational drug design fields.

Rational Drug Design Approach of Receptor Tyrosine Kinase Type III Inhibitors

2020 ◽  
Vol 26 (42) ◽  
pp. 7623-7640 ◽  
Author(s):  
Cheolhee Kim ◽  
Eunae Kim
Keyword(s):  
Drug Design ◽  
Tyrosine Kinase ◽  
Receptor Tyrosine Kinase ◽  
Synergistic Effects ◽  
Rational Drug Design ◽  
Computational Techniques ◽  
Biological Targets ◽  
Type Iii ◽  
Theoretical Approaches ◽  
Rational Drug

: Rational drug design is accomplished through the complementary use of structural biology and computational biology of biological macromolecules involved in disease pathology. Most of the known theoretical approaches for drug design are based on knowledge of the biological targets to which the drug binds. This approach can be used to design drug molecules that restore the balance of the signaling pathway by inhibiting or stimulating biological targets by molecular modeling procedures as well as by molecular dynamics simulations. Type III receptor tyrosine kinase affects most of the fundamental cellular processes including cell cycle, cell migration, cell metabolism, and survival, as well as cell proliferation and differentiation. Many inhibitors of successful rational drug design show that some computational techniques can be combined to achieve synergistic effects.

Recent Advances in the Rational Drug Design Based on Multi-target Ligands

2020 ◽  
Vol 27 (28) ◽  
pp. 4720-4740 ◽  
Author(s):  
Ting Yang ◽  
Xin Sui ◽  
Bing Yu ◽  
Youqing Shen ◽  
Hailin Cong
Keyword(s):  
Drug Resistance ◽  
Clinical Practice ◽  
Side Effects ◽  
Drug Design ◽  
Rational Drug Design ◽  
Health Conditions ◽  
Pharmacokinetic Parameters ◽  
Single Target ◽  
Rational Drug ◽  
Huge Challenge

Multi-target drugs have gained considerable attention in the last decade owing to their advantages in the treatment of complex diseases and health conditions linked to drug resistance. Single-target drugs, although highly selective, may not necessarily have better efficacy or fewer side effects. Therefore, more attention is being paid to developing drugs that work on multiple targets at the same time, but developing such drugs is a huge challenge for medicinal chemists. Each target must have sufficient activity and have sufficiently characterized pharmacokinetic parameters. Multi-target drugs, which have long been known and effectively used in clinical practice, are briefly discussed in the present article. In addition, in this review, we will discuss the possible applications of multi-target ligands to guide the repositioning of prospective drugs.

Non-Covalent Interactions with Aromatic Rings: Current Understanding and Implications for Rational Drug Design

2013 ◽  
Vol 19 (36) ◽  
pp. 6522-6533 ◽  
Author(s):  
Shanshan Li ◽  
Yuan Xu ◽  
Qiancheng Shen ◽  
Xian Liu ◽  
Jing Lu ◽  
...  
Keyword(s):  
Drug Design ◽  
Rational Drug Design ◽  
Current Understanding ◽  
Aromatic Rings ◽  
Rational Drug ◽  
Non Covalent Interactions ◽  
Covalent Interactions

Rational Drug Design Paradigms: The Odyssey for Designing Better Drugs

2015 ◽  
Vol 18 (3) ◽  
pp. 238-256 ◽  
Author(s):  
Tahsin Kellici ◽  
Dimitrios Ntountaniotis ◽  
Eleni Vrontaki ◽  
George Liapakis ◽  
Panagiota Moutevelis-Minakakis ◽  
...  
Keyword(s):  
Drug Design ◽  
Rational Drug Design ◽  
Rational Drug

scholarly journals Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge

2021 ◽  
Author(s):  
Teresa Danielle Bergazin ◽  
Nicolas Tielker ◽  
Yingying Zhang ◽  
Junjun Mao ◽  
M. R. Gunner ◽  
...  
Keyword(s):  
Free Energy ◽  
Partition Coefficients ◽  
Physical Property ◽  
Rational Drug Design ◽  
Log P ◽  
Prediction Methods ◽  
Research Groups ◽  
Reasonable Accuracy ◽  
Statistical Assessment ◽  
Rational Drug

AbstractThe Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pKa for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pKa challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pKa challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pKa values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pKa prediction methods.

Endocannabinoid system in the neurodevelopment of GABAergic interneurons: implications for neurological and psychiatric disorders

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Chang-geng Song ◽  
Xin Kang ◽  
Fang Yang ◽  
Wan-qing Du ◽  
Jia-jia Zhang ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Endocannabinoid System ◽  
Autism Spectrum ◽  
Network Activity ◽  
Inhibitory Neurons ◽  
Gabaergic Interneurons ◽  
Neural Stem Progenitor Cells ◽  
The Central Nervous System ◽  
Interneuron Development

Abstract In mature mammalian brains, the endocannabinoid system (ECS) plays an important role in the regulation of synaptic plasticity and the functioning of neural networks. Besides, the ECS also contributes to the neurodevelopment of the central nervous system. Due to the increase in the medical and recreational use of cannabis, it is inevitable and essential to elaborate the roles of the ECS on neurodevelopment. GABAergic interneurons represent a group of inhibitory neurons that are vital in controlling neural network activity. However, the role of the ECS in the neurodevelopment of GABAergic interneurons remains to be fully elucidated. In this review, we provide a brief introduction of the ECS and interneuron diversity. We focus on the process of interneuron development and the role of ECS in the modulation of interneuron development, from the expansion of the neural stem/progenitor cells to the migration, specification and maturation of interneurons. We further discuss the potential implications of the ECS and interneurons in the pathogenesis of neurological and psychiatric disorders, including epilepsy, schizophrenia, major depressive disorder and autism spectrum disorder.

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