Modern Computational Strategies for Designing Drugs to Curb Human Diseases: A Prospect

Dar, Khalid Bashir; Bhat, Aashiq Hussain; Amin, Shajrul; Hamid, Rizwan; Anees, Suhail; Anjum, Syed; Reshi, Bilal Ahmad; Zargar, Mohammad Afzal; Akbar, Muhammad; Ganie, Showkat Ahmad

doi:10.2174/1568026619666190119150741

Cited by 30 publications

(12 citation statements)

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“…Structural similarity is one of key strategies for drug discovery ( Dar et al, 2018 ; Trosset and Cavé, 2019 ). Diverse structures and biological activities beign known for naturally occurring triterpenoids, we hypothesized that some compounds with structures similar to celastrol could present similar anti-inflammatory and anti-obesity activities, while providing key drugging profiles in safety and availability.…”

Section: Discussionmentioning

confidence: 99%

Creation of an Anti-Inflammatory, Leptin-Dependent Anti-Obesity Celastrol Mimic with Better Druggability

et al. 2021

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Obesity is characterized by an excessive body mass, but is also closely associated with metabolic syndrome. And, so far, only limited pharmacological treatments are available for obesity management. Celastrol, a pentacyclic triterpenoid from a traditional Chinese medicine (Tripterygium wilfordii Hook.f.), has shown remarkable potency against obesity, inflammation and cancer, but its high toxicity, low natural abundance and tedious chemical synthesis hindered its translation into clinics. In the present work, a triterpenoid library was screened for compounds with both high natural abundance and structural similarity to celastrol; from this library, glycyrrhetinic acid (GA), a compound present in extremely high yields in Glycyrrhiza uralensis Fisch. ex DC., was selected as a possible scaffold for a celastrol mimic active against obesity. A simple chemical modification of GA resulted in GA-02, a derivative that suppressed 68% of food intake in diet-induced obesity mice and led to 26.4% weight loss in 2 weeks. GA-02 plays a role in obesity treatment by re-activating leptin signaling and reducing systemic and, more importantly, hypothalamic inflammation. GA-02 was readily bioavailable with unnoticeable in vitro and in vivo toxicities. The strategy of scaffold search and modification on the basis of bio-content and structural similarity has proved to be a green, economic, efficient and practical way of widening the medicinal applications of “imperfect” bioactive natural compounds.

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Section: Discussionmentioning

confidence: 99%

Creation of an Anti-Inflammatory, Leptin-Dependent Anti-Obesity Celastrol Mimic with Better Druggability

et al. 2021

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“…However, given the high number of compounds present in these libraries, their screening cannot be done with the expensive and time-consuming biochemical or cell-based assays mentioned above. Instead, this enormous task becomes feasible by means of structural bioinformatics virtual screening, as already done for many other human diseases [101,102]. Indeed, only high performance computing (HPC) allows the handling of the huge amounts of data deriving from exhaustive conformational space sampling or from molecular dynamics simulations while investigating CFTR flexibility.…”

Section: Discussionmentioning

confidence: 99%

Recent Strategic Advances in CFTR Drug Discovery: An Overview

Rusnati

D’Ursi

Pedemonte

et al. 2020

IJMS

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Cystic fibrosis transmembrane conductance regulator (CFTR)-rescuing drugs have already transformed cystic fibrosis (CF) from a fatal disease to a treatable chronic condition. However, new-generation drugs able to bind CFTR with higher specificity/affinity and to exert stronger therapeutic benefits and fewer side effects are still awaited. Computational methods and biosensors have become indispensable tools in the process of drug discovery for many important human pathologies. Instead, they have been used only piecemeal in CF so far, calling for their appropriate integration with well-tried CF biochemical and cell-based models to speed up the discovery of new CFTR-rescuing drugs. This review will give an overview of the available structures and computational models of CFTR and of the biosensors, biochemical and cell-based assays already used in CF-oriented studies. It will also give the reader some insights about how to integrate these tools as to improve the efficiency of the drug discovery process targeted to CFTR.

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“…Toward that end, an algorithm for fast (exact) chemical similarity search and database retrieval is introduced that optimally exploits the sparse representation of chemical moieties as binary fingerprints. Docking and docking-based in silico screening, on the other hand, rely on shape complementarity between putative inhibitors and target proteins, requiring structural information about the proposed target and its relevant conformational states which may be unavailable 39,40 . The results of docking simulations may also be very sensitive to the choice of a target’s conformation, choice of the empirical force fields, docking programs and sampling depth 4,12,15 .…”

Section: Discussionmentioning

confidence: 99%

Accelerating Drug Discovery and Repurposing by Combining Transcriptional Signature Connectivity with Docking

Reigle

Shamsaei

et al. 2020

Preprint

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The development of targeted treatment options for precision medicine is hampered by a slow and costly process of drug screening. While small molecule docking simulations are often applied in conjunction with cheminformatic methods to reduce the number of candidate molecules to be tested experimentally, the current approaches suffer from high false positive rates and are computationally expensive. Here, we present a novel in silico approach for drug discovery and repurposing, dubbed connectivity enhanced Structure Activity Relationship (ceSAR) that improves on current methods by combining docking and virtual screening approaches with pharmacogenomics and transcriptional signature connectivity analysis. ceSAR builds on the landmark LINCS library of transcriptional signatures of over 20,000 drug-like molecules and ~5,000 gene knock-downs (KDs) to connect small molecules and their potential targets. For a set of candidate molecules and specific target gene, candidate molecules are first ranked by chemical similarity to their ‘concordant’ LINCS analogs that share signature similarity with a knock-down of the target gene. An efficient method for chemical similarity search, optimized for sparse binary fingerprints of chemical moieties, is used to enable fast searches for large libraries of small molecules. A small subset of candidate compounds identified in the first step is then re-scored by combining signature connectivity with docking simulations. On a set of 20 DUD-E benchmark targets with LINCS KDs, the consensus approach reduces significantly false positive rates, improving the median precision 3-fold over docking methods at the extreme library reduction. We conclude that signature connectivity and docking provide complementary signals, offering an avenue to improve the accuracy of virtual screening while reducing run times by multiple orders of magnitude.

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Modern Computational Strategies for Designing Drugs to Curb Human Diseases: A Prospect

Cited by 30 publications

References 0 publications

Creation of an Anti-Inflammatory, Leptin-Dependent Anti-Obesity Celastrol Mimic with Better Druggability

Creation of an Anti-Inflammatory, Leptin-Dependent Anti-Obesity Celastrol Mimic with Better Druggability

Recent Strategic Advances in CFTR Drug Discovery: An Overview

Accelerating Drug Discovery and Repurposing by Combining Transcriptional Signature Connectivity with Docking

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