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

Veratrum parviflorum: An Underexplored Source for Bioactive Steroidal Alkaloids

Plants of the Veratrum genus have been used throughout history for their emetic properties, rheumatism, and for the treatment of high blood pressure. However, inadvertent consumption of these plants, which resemble wild ramps, induces life-threatening side effects attributable to an abundance of steroidal alkaloids. Several of the steroidal alkaloids from Veratrum spp. have been investigated for their ability to antagonize the Hedgehog (Hh) signaling pathway, a key pathway for embryonic development and cell proliferation. Uncontrolled activation of this pathway is linked to the development of various cancers; most notably, basal cell carcinoma and acute myeloid leukemia. Additional investigation of Veratrum spp. may lead to the identification of novel alkaloids with the potential to serve as chemotherapeutics. V. parviflorum is a relatively uncommon species of Veratrum that resides in the southeastern regions of North America. The phytochemical profile of this plant remains largely unexplored; however, bioactive steroidal alkaloids, including cyclopamine, veratramine, veratridine, and verazine were identified in its extract. The structural elucidation and bioactivity assessment of steroidal alkaloids in lesser abundance within the extract of V. parviflorum may yield potent Hh pathway inhibitors. This review seeks to consolidate the botanical and phytochemical information regarding V. parviflorum.

Anticancer effects of veratramine via the phosphatidylinositol-3-kinase/serine-threonine kinase/mechanistic target of rapamycin and its downstream signaling pathways in human glioblastoma cell lines

AIMS

Antitumor effects of veratramine in prostate and liver cancers has been investigated, but it is still unclear whether veratramine can be used as an effective therapeutic agent for glioma. The aim of this study was to evaluate the potential pharmacological mechanism of veratramine in glioma.

MAIN METHODS

Using four types of human glioblastoma cell lines, including A172, HS-683, T98G, and U-373-MG the dose-dependent antitumor effect of veratramine was evaluated. The cytotoxicity and cell proliferation were examined by CCK-8, and cell proliferation was further confirmed by anchorage-independent colony formation assay. The cell cycle distribution and apoptotic rate was assessed by flow cytometry, and apoptosis was further evaluated by apoptosis assay. The migration and invasiveness capacity were analyzed by using transwell. Protein and mRNA levels of related factors were determined by western blotting and RT-qPCR, respectively.

KEY FINDINGS

Veratramine markedly induced apoptosis, suppressed the cell proliferation via the cell cycle G₀/G₁ phase arrest, and reduced the capacity for the migration and invasion in human glioblastoma multiforme cell lines. Moreover, veratramine was sufficient to affect the phosphatidylinositol-3-kinase/serine-threonine kinase/mechanistic target of rapamycin signaling pathway and its downstream Mdm2/p53/p21 pathway in human glioblastoma cell lines.

SIGNIFICANCE

Antitumor effects of veratramine in suppression of glioma progression was mediated by the regulation of PI3K/Akt/mTOR and Mdm2/p53/p21 signaling pathway.

Review: Veratrum californicum Alkaloids

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.

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

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 screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

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

Veratrum californicum is a rich source of steroidal alkaloids, many of which have proven to be antagonists of the Hedgehog (Hh) signaling pathway that becomes aberrant in over twenty types of cancer. These alkaloids first became known in the 1950's due to their teratogenic properties, which resulted in newborn and fetal lambs developing cyclopia as a result of pregnant ewes consuming Veratrum californicum. It was discovered that the alkaloids in V. californicum were concentrated in the root and rhizome of the plant with much lower amounts of the most active alkaloid, cyclopamine, present in the aerial plant, especially in the late growth season. Inspired by the limitations in analytical instrumentation and methods available to researchers at the time of the original investigation, we have used state-of-the-art instrumentation and modern analytical methods to quantitate four steroidal alkaloids based on study parameters including plant part, harvest location, and growth stage. The results of the current inquiry detail differences in alkaloid composition based on the study parameters, provide a detailed assessment for alkaloids that have been characterized previously (cyclopamine, veratramine, muldamine and isorubijervine), and identify at least six alkaloids that have not been previously characterized. This study provides insight into optimal harvest time, plant growth stage, harvest location, and plant part required to isolate, yet to be characterized, alkaloids of interest for exploration as Hh pathway antagonists with desirable medicinal properties.