3D-Pharmacophore mapping of thymidine-based inhibitors of TMPK as potential antituberculosis agents

Andrade, Carolina Horta; Pasqualoto, Kerly Fernanda Mesquita; Ferreira, Elizabeth Igne; Hopfinger, A. J.

doi:10.1007/s10822-010-9323-y

Cited by 21 publications

(11 citation statements)

References 23 publications

(48 reference statements)

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“…Several of these studies have either suggested compounds for testing that have been validated or in several cases this validation is yet to be achieved. These hybrid methods can confirm the pharmacophore or QSAR model, with recent examples including thymidine analogs as inhibitors of thymidine monophosphate kinase (TMPK) [41]. Furthermore, in a search for InhA inhibitors, a 3D-QSAR-derived pharmacophore model was used to narrow down a set of 230,000 compounds to 299 top-scoring hits and ultimately 30 whose lowest energy docked conformation showed significant interactions with key active site residues, i.e.…”

Section: Computational Cheminformatic Tools and Their Usesmentioning

confidence: 99%

Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery

Ekins¹,

Freundlich²,

Choi³

et al. 2011

Trends in Microbiology

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We are witnessing the growing menace of both increasing cases of drug-sensitive and drug-resistant Mycobacterium tuberculosis strains and the challenge to produce the first new tuberculosis (TB) drug in well over 40 years. The TB community, having invested in extensive high-throughput screening efforts, is faced with the question of how to optimally leverage this data in order to move from a hit to a lead to a clinical candidate and potentially a new drug. Complementing this approach, yet conducted on a much smaller scale, cheminformatic techniques have been leveraged and are herein reviewed. We suggest these computational approaches should be more optimally integrated in a workflow with experimental approaches to accelerate TB drug discovery.

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Section: Computational Cheminformatic Tools and Their Usesmentioning

confidence: 99%

Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery

Ekins¹,

Freundlich²,

Choi³

et al. 2011

Trends in Microbiology

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“…Due to all the aforementioned factors, we expect a shift in the QSAR role. QSAR would move from being applied to lead optimization processes toward a role as a tool integrating early stages of computeraided drug design and experimental drug-discovery pipelines [19,20]. However, there are still challenges to be overcome, such as the implementation of robust validation processes and automation of different parts involved in the generation of predictive QSAR models.…”

Section: Future Perspective In New Technologies and Large-scale Qsarmentioning

confidence: 99%

Current Trends in Quantitative Structure–Activity Relationship Validation and Applications On Drug Discovery

2017

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Quantitative structure-activity relationship history, current status & the importance of validationMethods to correlate biological activity and chemical structure of compounds have been employed since 1868 [1]. Early on, quantitative structure-activity relationship (QSAR) analyses were performed using experimentally determined physicochemical properties, such as logarithm of water/n-octanol partition coefficient (log P), hydrophobic constant (π) and Hammet electronic constant (σ), which were then correlated with the biological activity of the tested compounds [2].Nowadays, there are several prerequisites to construct and apply QSAR models. From the applicability point of view, QSAR requires a compound set that has been tested against an identified molecular target, cell, tissue, or even microorganism, under the same experimental conditions, and possesses the minimum variance in the observed responses [3]. Once an appropriate dataset has been selected, the main steps of a QSAR modeling require molecular/physicochemical properties, followed by variable selection, model generation from different algorithms and, most importantly, a validation process using internal and external datasets.After the generation and validation of a QSAR model, the model can be employed in predictions of biological activity of new samples and a physicochemical interpretation of the observed phenomena could be also conducted, providing insights for the design of new bioactive chemicals and/or their molecular mechanism of action. Subsequently, QSAR models have been widely employed in several steps of the drug design process, for instance, in order to understand and predict the compound binding affinity for a specific molecular target. QSAR modeling is also applied to better comprehend general phenomena such as pharmacokinetics and toxicity-related end points, which generally are standard measurements and for which there are available large datasets [4].Complementarily, although animal testing is still considered crucial to the evaluation of chemical safety, toxicity testing is moving toward a greater understanding of the disease at multiple biological levels, so as to develop alternative methods [5,6]. From the biological point of view, various models representing several layers of human physiology and metabolism, both healthy and disease based, have been developed. In order to address general problems, such as skin sensitization, hepatotoxicity and DNA harming agents, some research groups have built local QSAR models using mechanistic information [6,7]. However, in the future, we can easily expect that improvements in the validation techniques and data quality will follow the growth in the data generation, leading to broader QSAR models.There are several relevant articles in the QSAR field describing methods and validation techniques [8], besides extensive literature on troubleshooting, which makes this field still highly attractive for research and

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“…The 3D‐pharmacophore maps were then used to design structural changes on the TMPKmt inhibitors and to predict anti‐TMPKmt activity for novel and more potent compounds. Finally, the receptor‐independent 4D‐QSAR models provided insights about the possible mechanism of action of the TMPKmt inhibitors …”

Section: Experimental Strategies Commonly Used To Find New Anti‐tb Drugsmentioning

confidence: 99%

In silico approaches and chemical space of anti‐P‐type ATPase compounds for discovering new antituberculous drugs

2017

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Tuberculosis (TB) is one of the most important public health problems around the world. The emergence of multi-drug-resistant (MDR) and extensively drug-resistant (XDR) Mycobacterium tuberculosis strains has driven the finding of alternative anti-TB targets. In this context, P-type ATPases are interesting therapeutic targets due to their key role in ion homeostasis across the plasma membrane and the mycobacterial survival inside macrophages. In this review, in silico and experimental strategies used for the rational design of new anti-TB drugs are presented; in addition, the chemical space distribution based on the structure and molecular properties of compounds with anti-TB and anti-P-type ATPase activity is discussed. The chemical space distribution compared to public compound libraries demonstrates that natural product libraries are a source of novel chemical scaffolds with potential anti-P-type ATPase activity. Furthermore, compounds that experimentally display anti-P-type ATPase activity belong to a chemical space of molecular properties comparable to that occupied by those approved for oral use, suggesting that these kinds of molecules have a good pharmacokinetic profile (drug-like) for evaluation as potential anti-TB drugs.

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3D-Pharmacophore mapping of thymidine-based inhibitors of TMPK as potential antituberculosis agents

Cited by 21 publications

References 23 publications

Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery

Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery

Current Trends in Quantitative Structure–Activity Relationship Validation and Applications On Drug Discovery

In silico approaches and chemical space of anti‐P‐type ATPase compounds for discovering new antituberculous drugs

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