Structure–activity relationships of diamine inhibitors of cytochrome P450 (CYP) 3A as novel pharmacoenhancers, part I: Core region

Liu, Hongtao; Xu, Lianhong; Hui, Hon; Vivian, Randall W.; Callebaut, Christian; Murray, Bernard P.; Hong, Allen Y.; Lee, Melody S.; Tsai, Luke Y.; Chau, Jennifer; Stray, Kirsten M.; Cannizzaro, Carina; Choi, Y.; Rhodes, Gerry; Desai, Manoj C.

doi:10.1016/j.bmcl.2013.12.058

Cited by 11 publications

(5 citation statements)

References 20 publications

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“…As mentioned in the Introduction, both CYP3A4 inhibitors currently marketed as pharmacoenhancers, ritonavir and cobicistat (Figure ), were developed based on chemical structure–activity relationships studies rather than the CYP3A4 crystal structure. Ritonavir is a large peptidomimetic drug designed to inhibit an HIV-I protease, whereas cobicistat was developed through ritonavir derivatization. , Synthesis of both compounds is a complex process requiring production of specific stereoisomers. To evaluate our pharmacophore model, we decided to build CYP3A4 inhibitors from scratch using a general simple scaffold (Figure ) where the modules containing the side and terminal groups (R 1 , R 2 , and R 3 ) are linked to the heme-ligating pyridine via a flexible backbone.…”

Section: Resultsmentioning

confidence: 99%

“…Ritonavir is a large peptidomimetic drug designed to inhibit an HIV-I protease, 9 whereas cobicistat was developed through ritonavir derivatization. 13,34 Synthesis of both compounds is a complex process requiring production of specific stereoisomers. To evaluate our pharmacophore model, we decided to build CYP3A4 inhibitors from scratch using a general simple scaffold (Figure 3) where the modules containing the side and terminal groups (R 1 , R 2 , and R 3 ) are linked to the heme-ligating pyridine via a flexible backbone.…”

Section: Inhibitor Designmentioning

confidence: 99%

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Structure-Based Inhibitor Design for Evaluation of a CYP3A4 Pharmacophore Model

et al. 2015

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Human cytochrome P450 3A4 (CYP3A4) is a key xenobiotic-metabolizing enzyme that oxidizes and clears the majority of drugs. CYP3A4 inhibition may lead to drug–drug interactions, toxicity, and other adverse effects but, in some cases, could be beneficial and enhance therapeutic efficiency of coadministered pharmaceuticals that are metabolized by CYP3A4. On the basis of our investigations of analogs of ritonavir, a potent CYP3A4 inactivator and pharmacoenhancer, we have built a pharmacophore model for a CYP3A4-specific inhibitor. This study is the first attempt to test this model using a set of rationally designed compounds. The functional and structural data presented here agree well with the proposed pharmacophore. In particular, we confirmed the importance of a flexible backbone, the H-bond donor/acceptor moiety, and aromaticity of the side group analogous to Phe-2 of ritonavir and demonstrated the leading role of hydrophobic interactions at the sites adjacent to the heme and phenylalanine cluster in the ligand binding process. The X-ray structures of CYP3A4 bound to the rationally designed inhibitors provide deeper insights into the mechanism of the CYP3A4–ligand interaction. Most importantly, two of our compounds (15a and 15b) that are less complex than ritonavir have comparable submicromolar affinity and inhibitory potency for CYP3A4 and, thus, could serve as templates for synthesis of second generation inhibitors for further evaluation and optimization of the pharmacophore model.

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Section: Resultsmentioning

confidence: 99%

Section: Inhibitor Designmentioning

confidence: 99%

Structure-Based Inhibitor Design for Evaluation of a CYP3A4 Pharmacophore Model

et al. 2015

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“…In addition, 56 CPIs were determined, as shown in Table S4 in the Supporting Data. Among them, 25 out of 56 predicted CPIs have been verified in the literature [35,36,37,38,39,40,41,42,43,44,45,46,47,48,49]; two out of 56 CPIs were predicted to be active but were validated as “inconclusive” and “inactive” [50,51]. A success rate of 44.6% (25/56) and a failure rate of 1.8% (1/56) illustrate the reliability of the multiple QSAR method.…”

Section: Resultsmentioning

confidence: 99%

Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method

Wei

et al. 2019

IJMS

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Human immunodeficiency virus type-1 and hepatitis C virus (HIV/HCV) coinfection occurs when a patient is simultaneously infected with both human immunodeficiency virus type-1 (HIV-1) and hepatitis C virus (HCV), which is common today in certain populations. However, the treatment of coinfection is a challenge because of the special considerations needed to ensure hepatic safety and avoid drug–drug interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for HIV/HCV coinfection. However, the identification of one molecule that acts on multiple targets simultaneously by experimental evaluation is costly and time-consuming. In silico target prediction tools provide more opportunities for the development of multitarget inhibitors. In this study, by combining Naïve Bayes (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints, MACCS and extended connectivity fingerprints 6 (ECFP6), 60 classification models were constructed to predict compounds that were active against 11 HIV-1 targets and four HCV targets based on a multiple quantitative structure–activity relationships (multiple QSAR) method. Five-fold cross-validation and test set validation were performed to measure the performance of the 60 classification models. Our results show that the 60 multiple QSAR models appeared to have high classification accuracy in terms of the area under the ROC curve (AUC) values, which ranged from 0.83 to 1 with a mean value of 0.97 for the HIV-1 models and from 0.84 to 1 with a mean value of 0.96 for the HCV models. Furthermore, the 60 models were used to comprehensively predict the potential targets of an additional 46 compounds, including 27 approved HIV-1 drugs, 10 approved HCV drugs and nine selected compounds known to be active against one or more targets of HIV-1 or HCV. Finally, 20 hits, including seven approved HIV-1 drugs, four approved HCV drugs, and nine other compounds, were predicted to be HIV/HCV coinfection multitarget inhibitors. The reported bioactivity data confirmed that seven out of nine compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding affinities. The remaining predicted hits and chemical-protein interaction pairs with the potential ability to suppress HIV/HCV coinfection are worthy of further experimental investigation. This investigation shows that the multiple QSAR method is useful in predicting chemical-protein interactions for the discovery of multitarget inhibitors and provides a unique strategy for the treatment of HIV/HCV coinfection.

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“…Many thiazole-based compounds are now in clinical use, such as the anticancer drug dasatinib, 2 and the anti-HIV drug ritonavir. 3 Additionally, thiazoles have three carbon atoms at the 2-, 4-, and 5-positions, to which a wide variety of substituents can be principally introduced. Therefore, a wide range of thiazole derivatives have been developed.…”

Section: Introductionmentioning

confidence: 99%