Native V. californicum Alkaloid Combinations Induce Differential Inhibition of Sonic Hedgehog Signaling

Turner, Matthew; Cruz, Roberto; Elwell, Jordan; French, John K.; Mattos, Jared; McDougal, Owen M.

doi:10.3390/molecules23092222

Cited by 6 publications

(18 citation statements)

References 34 publications

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“…2020 identified that saquinavir, lithospermic acid, and 11m_32045235 were promising therapeutic compound against SARS-Cov-2 main protease, whereas Selvaraj et al 2020 demonstrated that TCM 57,025, TCM 3495, TCM 5376, TCM 20,111, and TCM 31,007 were therapeutic compounds that interact with the substrate-binding site of N7-MTase [ 194 , 195 ]. On the same trend, Cruz et al 2018 concluded that ZINC91881108 was potent compound against RIPK2, whereas Simoben et al 2018 demonstrated eight novel N-(2,5-dioxopyrrolidin-3-yl)-n-alkylhydroxamate derivatives as smHDAC8 inhibitors with IC 50 values ranging from 4.4 to 20.3 µM against smHDAC8 [ 196 , 197 ] [Fig. 4 ].…”

Section: Applications Of Artificial Intelligence In Drug Development mentioning

confidence: 99%

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

et al. 2021

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Graphic abstract Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure–activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure–activity relationship to drug repositioning, protein misfolding to protein–protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screeni...

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Section: Applications Of Artificial Intelligence In Drug Development mentioning

confidence: 99%

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

et al. 2021

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“…Initially given the name alkaloid Q, 4 is produced by V. californicum and is known to be a Hh signaling pathway antagonist as well as a non-depolarizing action potential blocker in the giant axon of squid and crayfish [ 1 , 73 , 80 ]. Compound 4 is not teratogenic in sheep, but did demonstrate marginal activity as a teratogen in hamsters [ 81 ].…”

Section: Alkaloids Identified In Veratrum Californicummentioning

confidence: 99%

“…Compound 4 is not teratogenic in sheep, but did demonstrate marginal activity as a teratogen in hamsters [ 81 ]. The bioactivity of 4 was tested in combination with 2 and 3 by measuring Gli protein activity in Shh-Light II cells [ 73 ]. The Gli family members are transcription factors that can be monitored in order to determine whether the Hh pathway is activated or not [ 84 , 85 ].…”

Section: Alkaloids Identified In Veratrum Californicummentioning

confidence: 99%

“…The Gli family members are transcription factors that can be monitored in order to determine whether the Hh pathway is activated or not [ 84 , 85 ]. The results demonstrated that the combination of alkaloids did have an inhibitory effect on Hh signaling [ 73 ].…”

Section: Alkaloids Identified In Veratrum Californicummentioning

confidence: 99%

“…Compound 5 was identified by HPLC-MS by comparison to a commercially available standard [ 70 , 73 ].…”

Section: Alkaloids Identified In Veratrum Californicummentioning

confidence: 99%

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Review: Veratrum californicum Alkaloids

et al. 2021

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Veratrum spp. grow throughout the world and are especially prevalent in high mountain meadows of North America. All parts of Veratrum plants have been used for the treatment of ailments including injuries, hypertension, and rheumatic pain since as far back as the 1600s. Of the 17–45 Veratrum spp., Veratrum californicum alkaloids have been proven to possess favorable medicinal properties associated with inhibition of hedgehog (Hh) pathway signaling. Aberrant Hh signaling leads to proliferation of over 20 cancers, including basal cell carcinoma, prostate and colon among others. Six of the most well-studied V. californicum alkaloids are cyclopamine (1), veratramine (2), isorubijervine (3), muldamine (4), cycloposine (5), and veratrosine (6). Recent inspection of the ethanolic extract from V. californicum root and rhizome via liquid chromatography–mass spectrometry has detected up to five additional alkaloids that are proposed to be verazine (7), etioline (8), tetrahydrojervine (9), dihydrojervine (10), 22-keto-26-aminocholesterol (11). For each alkaloid identified or proposed in V. californicum, this review surveys literature precedents for extraction methods, isolation, identification, characterization and bioactivity to guide natural product drug discovery associated with this medicinal plant.

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Steroidal alkaloid variation in Veratrum californicum as determined by modern methods of analytical analysis

et al. 2019

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Native V. californicum Alkaloid Combinations Induce Differential Inhibition of Sonic Hedgehog Signaling

Cited by 6 publications

References 34 publications

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Review: Veratrum californicum Alkaloids

Steroidal alkaloid variation in Veratrum californicum as determined by modern methods of analytical analysis

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