Structure-based design and synthesis of HIV-1 protease inhibitors employing β-d-mannopyranoside scaffolds

Murphy, Paul V.; O'Brien, Julie L.; Gorey-Feret, Lorraine J.; Smith, Amos B.

doi:10.1016/s0960-894x(02)00220-2

Cited by 22 publications

(6 citation statements)

References 50 publications

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“…In addition, 56 CPIs were obtained as shown in S4 Table. Among them, 25 out of 56 predicted CPIs are consistent with experimental results; the remaining 2 CPIs were predicted to be active but were experimentally validated as “Inconclusive” and “Inactive”, respectively[27,28,37–43,29–36]. The success rate of 44.6% (25/56) and the failure rate of 1.8% (1/56) illustrate the reliability of mt-QSAR method.…”

Section: Resultssupporting

confidence: 64%

Targeting against HIV/HCV Co-infection using Machine Learning-based multitarget-quantitative structure-activity relationships (mt-QSAR) Methods

Wei

et al. 2019

Preprint

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24Co-infection between HIV-1 and HCV is common today in certain populations. However, treatment of 25 co-infection is full of challenges with special consideration for potential hepatic safety and drug-drug 26 interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for 27 HIV/HCV co-infection. However, identification of one molecule acting on multiple targets simultaneously by 28 experimental evaluation is costly and time-consuming. In silico target prediction tools provide more 29 opportunities for the development of multitarget inhibitors. In this study, by combining naive Bayesian (NB) 30 and support vector machine (SVM) algorithms with two types of molecular fingerprints (MACCS and ECFP6), 31 60 classification models were constructed to predict the active compounds toward 11 HIV-1 targets and 4 32 HCV targets based on the multitarget-quantitative structure-activity relationships (mt-QSAR). 5-fold cross-33 validation and test set validation was performed to confirm the performance of 60 classification models. Our 34 results show that 60 mt-QSAR models appeared to have high classification accuracy in terms of ROC-AUC 35 values ranging from 0.83 to 1 with a mean value of 0.97 for HIV-1 models, and ROC-AUC values ranging 36 from 0.84 to 1 with a mean value of 0.96 for HCV. Furthermore, the 60 models were applied to 37 comprehensively predict the potential targets for additional 46 compounds including 27 approved HIV-1 drugs, 38 10 approved HCV drugs and 9 selected compounds known to be active on one or more targets of HIV-1 or 39 those of HCV. Finally, 18 hits including 7 HIV-1 approved drugs, 4 HCV approved drugs and 7 compounds 40 were predicted to be HIV/HCV co-infection multitarget inhibitors. The reported bioactivity data confirmed 41 that 7 compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding 42 affinities. Of those remaining predicted hits and chemical-protein interaction pairs involving the potential 43 ability to suppress HIV/HCV co-infection deserve further investigation by experiments. This investigation 44 shows that the mt-QSAR method is available to predict chemical-protein interaction for discovering 45 multitarget inhibitors and provide a unique perspective on HIV/HCV co-infection treatment. 3 46 48 syndrome (AIDS), a pandemic disease[1]. In addition, hepatitis C virus (HCV) infection causes acute and 49 chronic liver disease, including cirrhosis and hepatocellular carcinoma[2]. Unfortunately, since HIV-1 and 50 HCV have similar routes of transmission, the risk of HIV/HCV co-infection is very high[3]. According to the 51 World Health organization (WHO), about 2.3 million persons of the estimated 36.7 million living with HIV 52 had been infected with HCV globally in 2015. It is necessary for those people to be diagnosed and provided 53 with effective and reasonable treatment for both HIV and hepatitis as a priority[4]. However, in the treatment 54 of HIV/HCV co-infection, special considerations must be ...

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Section: Resultssupporting

confidence: 64%

Targeting against HIV/HCV Co-infection using Machine Learning-based multitarget-quantitative structure-activity relationships (mt-QSAR) Methods

Wei

et al. 2019

Preprint

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“…6). 6) is reported the inhibitor 16 in the bound conformation and the β-D-mannopiranosidebased HIV-protease inhibitor 17, provided of an IC50 value in the micromolar range [44]. Molecular modeling experiments showed that the β-D-mannopyranoside ring has good overlapping with the peptide-like backbone and the hydroxyethylene isostere of the inhibitor bound to HIV protease and could be a replacement for part of the peptide that binds to enzyme.…”

Section: Privileged Structures Able To Interact With More Than a Givementioning

confidence: 98%

Bacterial Symbionts: Prospects for the Sustainable Production of Invertebrate-Derived Pharmaceuticals

Piel

2006

CMC

167

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Among the strategies that can lead to the discovery of new drugs, the identification and use of privileged structures, molecular fragments that are able to interact with more than one target, gained particular attention, in an attempt to find new drugs in a shorter time with respect to other strategies. These structures, that have been identified mainly by empirical observations, can target only a given protein family, or can be able to interact with more, unrelated targets. This review deals with structures not covered in recent papers on this topic, and emphasizes the importance of understanding the structure-target relationships, that confer the privileged status.

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“…In (Fig. 6) is reported the inhibitor 16 in the bound conformation and the β-D-mannopiranosidebased HIV-protease inhibitor 17, provided of an IC50 value in the micromolar range [44].…”

Section: Privileged Structures Able To Interact With More Than a Givementioning

confidence: 99%

Privileged Structures as Leads in Medicinal Chemistry

Costantino¹,

Barlocco

2006

CMC

314

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The sol-gel process is an inorganic polymerization process taking place in mild conditions, allowing the association of mineral phases with organic or biological systems. The possibility to immobilize drugs, enzymes, antibodies and even whole cells without loss of their biological activity led to the development of diagnostic tools, drug delivery carriers as well as new hosts for artificial organ design. These systems take profit from the wide variety of chemical compositions, dimensions and forms that can be achieved via sol-gel chemistry. Recent advances involve multi-functional "smart" devices combining biocompatibility, biological activity and stimuli-responsive materials. The design of such novel devices with significant added value when compared to current products is probably a key factor when foreseeing industrial developments of sol-gel materials in medicinal science.

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Structure-based design and synthesis of HIV-1 protease inhibitors employing β-d-mannopyranoside scaffolds

Cited by 22 publications

References 50 publications

Targeting against HIV/HCV Co-infection using Machine Learning-based multitarget-quantitative structure-activity relationships (mt-QSAR) Methods

Targeting against HIV/HCV Co-infection using Machine Learning-based multitarget-quantitative structure-activity relationships (mt-QSAR) Methods

Bacterial Symbionts: Prospects for the Sustainable Production of Invertebrate-Derived Pharmaceuticals

Privileged Structures as Leads in Medicinal Chemistry

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