Indexing Natural Products for Their Potential Anti-Diabetic Activity: Filtering and Mapping Discriminative Physicochemical Properties

Artifical intelligence: a virtual chemist for natural product drug discovery

Nature is full of a bundle of medicinal substances and its product perceived as a prerogative structure to collaborate with protein drug targets. The natural product's (NPs) structure heterogeneity and eccentric characteristics inspired scientists to work on natural product-inspired medicine. To gear NP drug-finding artificial intelligence (AI) to confront and excavate unexplored opportunities. Natural product-inspired drug discoveries based on AI to act as an innovative tool for molecular design and lead discovery. Various models of machine learning produce quickly synthesizable mimetics of the natural products templates. The invention of novel natural products mimetics by computer-assisted technology provides a feasible strategy to get the natural product with defined bio-activities. AI's hit rate makes its high importance by improving trail patterns such as dose selection, trail life span, efficacy parameters, and biomarkers. Along these lines, AI methods can be a successful tool in a targeted way to formulate advanced medicinal applications for natural products. 'Prediction of future of natural product based drug discovery is not magic, actually its artificial intelligence'Communicated by Ramaswamy H. Sarma.

A Brief Review of Machine Learning-Based Bioactive Compound Research

Bioactive compounds are often used as initial substances for many therapeutic agents. In recent years, both theoretical and practical innovations in hardware-assisted and fast-evolving machine learning (ML) have made it possible to identify desired bioactive compounds in chemical spaces, such as those in natural products (NPs). This review introduces how machine learning approaches can be used for the identification and evaluation of bioactive compounds. It also provides an overview of recent research trends in machine learning-based prediction and the evaluation of bioactive compounds by listing real-world examples along with various input data. In addition, several ML-based approaches to identify specific bioactive compounds for cardiovascular and metabolic diseases are described. Overall, these approaches are important for the discovery of novel bioactive compounds and provide new insights into the machine learning basis for various traditional applications of bioactive compound-related research.

Natural product drug discovery in the artificial intelligence era

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.

A Novel Docetaxel-Biotin Chemical Conjugate for Prostate Cancer Treatment

A novel conjugate of docetaxel and biotin (designated as IDD-1010) was designed and chemically synthesized via an ester linkage at position 2’ carbon in docetaxel. The synthesized pure IDD-1010 exhibits a potent anti-cancer activity in in vitro and in vivo studies. At 10 nM, IDD-1010 has induced increased apoptosis and mitotic arrest of PC3-Luc prostate cancer cells, causing aneuploidy and cell death at higher concentrations. Toxicology studies indicate that the maximal tolerated dose (MTD) of IDD-1010 is 150 mg/kg in mice; equivalent to about 12.2 mg/kg of body weight, or to about an 850 mg dose for a patient weighing 70 kg. The MTD-treated mice exhibited weight gain similar to that of the control group, with no gross pathological signs at 14 days post-dosing. At a lower dose, IDD-1010 treatment did not lead to any significant weight loss in mice, although decreased the tumor volume stemming from injecting cancer cells into the dorsal loop of mouse prostate, and it was found to be more potent than Paclitaxel (reference drug). Similarly, IDD-1010 treatment significantly reduced tumor weight and thereby increased the percentage of mice survival as compared to reference drug-treated and control groups. To summarize, the described experiments using IDD-1010, as compared to the reference drug, strongly suggest a potential treatment utility with a wider therapeutic window for prostate cancer. Henceforth, clinical research on such a novel drug candidate would be greatly worthwhile.

Forty-One Plant Extracts Screened for Dual Antidiabetic and Antioxidant Functions: Evaluating the Types of Correlation between -Amylase Inhibition and Free Radical Scavenging

Dysregulation of glucose homeostasis followed by chronic hyperglycemia is a hallmark of diabetes mellitus (DM), a disease spreading as a worldwide pandemic for which there is no satisfactory dietary treatment or cure. The development of glucose-controlling drugs that can prevent complications of DM, such as hyperglycemia and oxidative stress, which contribute to the impairment of the key physiological processes in the body, is of grave importance. In pursuit of this goal, this study screened 41 plant extracts for their antidiabetic and antioxidant activities by employing assays to test for α-amylase inhibition and free radical scavenging activity (FRSA) and by measuring glucose uptake in L6-GLUT4myc cells. While extracts of Rhus coriaria, Punica granatum, Olea europaea, Pelargonium spp., Stevia rebaudiana, and Petroselinum crispum demonstrated significant α-amylase inhibition, the extracts of Rhus coriaria and Pelargonium spp. also demonstrated increased FRSA, and the extract of Rhus coriaria stimulated glucose uptake. These natural extracts, which are believed to have fewer side effects because they are prepared from edible plants, interfere with the process in the small intestine that breaks down dietary carbohydrates into monosaccharide and disaccharide derivatives, and thereby suppress increases in diet-induced blood glucose; hence, they may have clinical value for type 2 diabetes management. The Pelargonium spp. and Rhus coriaria extracts demonstrated the highest antidiabetic and antioxidant activities. Both plants may offer valuable medical benefits, especially because they can be taken as dietary supplements by patients with diabetes and can serve as sources of new, natural-based antidiabetic drug candidates. The enhancement of cellular glucose uptake stimulated by Rhus coriaria extract could lead to the development of clinical applications that regulate blood glucose levels from within the circulatory system. Isolating bioactive substances from these plant extracts and testing them in diabetic mice will significantly advance the development of natural drugs that have both antidiabetic and free radical-scavenging properties, likely with lesser side effects.

Machine learning approaches for elucidating the biological effects of natural products

Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of bioactivity and the study of structure-activity relationships. Over the past decade, an emerging trend for combining these approaches with the study of natural products (NPs) has developed in order to manage the challenge of the discovery of bioactive NPs. In the present review, we will introduce the basic principles and protocols for using the ML approach to investigate the bioactivity of NPs, citing a series of practical examples regarding the study of anti-microbial, anti-cancer, and anti-inflammatory NPs, etc. ML algorithms manage a variety of classification and regression problems associated with bioactive NPs, from those that are linear to non-linear and from pure compounds to plant extracts. Inspired by cases reported in the literature and our own experience, a number of key points have been emphasized for reducing modeling errors, including dataset preparation and applicability domain analysis.

Correlation between Antibacterial Activity and Free-Radical Scavenging: In-Vitro Evaluation of Polar/Non-Polar Extracts from 25 Plants

Objectives: The current study aimed to measure the antioxidant and antibacterial activities of 25 wild Palestinian edible plants, which were subjected to extraction by polar and non-polar solvents. Correlations between free radical scavenging activity and antibacterial activity of the extracts were assessed for both polar and non-polar fractions. Materials: Twenty-five wild edible plant species that are frequently consumed by people in Palestine (mainly in a rural area) were examined. Among them, 10 plant species were among those with the highest mean cultural importance values, according to an ethnobotanical survey that was conducted in the West Bank, Palestine, a few years ago. Method: The protocol of the DPPH assay for testing free-radical scavenging was utilized for determining EC50 values, while microdilution tests were conducted to determine the 50% inhibitory concentration (IC50) of the extracts for the microorganism Staphylococcus mutans. Results and Discussion: Eight extracts (non-polar fractions) were found to possess an antibacterial IC50 of less than 20 ppm, such as Foeniculum vulgare, Salvia palaestinafruticose, Micromeria fruticose, Trigonella foenum-graecum, Cichorium pumilum jacq, Salvia hierosolymitana boiss, Ruta chalepensis, and Chrysanthemum coronarium. The polar fractions possess higher antioxidant activity, while non-polar fraction possess higher antibacterial activity. Looking at all the results together can deceive and lead to the conclusion that there is no correlation between antibacterial activity against S. mutans and free radical scavenging (R2 equals 0.0538). However, in-depth analysis revealed that non-polar plant extracts with an EC50 of free radical scavenging ≤100 ppm have a four-fold order of enrichment toward more activity against S. mutans. These findings are of high importance for screening projects. A four-fold order of enrichment could save plenty of time and many in screening projects. The antibacterial active extracts marked by low-medium free radical scavenging might act through a mechanism of action other than that of highly active, free radical scavenging extracts. Conclusion: The screening of antioxidant and antimicrobial activity performed on 25 selected wild plant extracts revealed a satisfactory free radical scavenging and antimicrobial potential that could be of value in the management of oxidative stress. Further studies are recommended to explore novel and highly active natural antibacterial products.

Correlation between cytotoxicity in cancer cells and free radical-scavenging activity: In vitro evaluation of 57 medicinal and edible plant extracts

Cancer is a complex interaction among multiple signaling pathways involving a variety of target molecules. Cancer causes morbidity and mortality in millions of people worldwide, and due to its prevalence, the discovery of novel anticancer drugs is urgently required. Nature is considered an important source of the discovery of anticancer treatments, and many of the cytotoxic medicines in clinics today are derived from plants and other natural sources. Reactive oxygen species (ROS) induce a variety of human cancers, and antioxidants or scavengers are used to counteract them. The current study reports on the screening of extracts from 57 plants that are used in the galilee district as a food and/or for traditional medicine. Investigating the free radical scavenging capacity and these plants, and their cytotoxicity, may prove helpful to high-throughput screening projects that use antioxidants and cytotoxic natural products. The current study assessed the correlation between free radical scavenging and cytotoxicity. Correlational analysis is important for increasing the efficiency of the screening process. In the present study, free radical scavenging was assessed using a DPPH assay, while cytotoxicity was measured using a XTT assay. A total of 9 extracts were indicated to exhibit EC₅₀ values <250 µg/ml, and 4 others exhibited a high antioxidant content, with EC₅₀ values, for free radical scavenging, of <0.5 µg/ml. An in-depth analysis of the results revealed that the extracts of plants that exhibit an EC₅₀ of free radical scavenging ≤10 µg/ml show a degree of enrichment toward increased cytotoxicity. It is recommended that future studies test the validity of the conclusions of the current study on other cancer cell-lines, and isolate and identify the bioactive agents that are found in the most cytotoxic extracts of plants.

Phytochemical Composition and Biological Activities of Wild Scolymus maculatus L

Background: The wild population of spotted golden thistle, Scolymus maculatus, which belongs to the Compositae family, is believed to be one of the multi-curative wild plants mentioned in Flora Palaestina. This study aims to disclose the phytochemical composition, antioxidant potential, and antimicrobial activity of wild S. maculatus collected from the farms of Kabul, a village in northwest Galilee, for the first time. Methods: The phytochemical components of crude S. maculatus extracts from methanol, ethyl acetate, and n-hexane solvents were separated and identified using gas chromatography-mass spectrometry (GC-MS) in the electron impact (EI) mode. The free radical scavenging of the plant extracts was measured by DPPH assay. The microdilution test was used to determine the minimum inhibitory concentrations (MICs) of different S. maculatus extracts and to evaluate their antimicrobial activities. Results: Thirty-two phytochemicals were found in S. maculatus extracts including stigmasterol, γ-sitosterol, lupeol, lupeol acetate, and β-amyrin. Phytochemicals, such as 2-linoleoylglycerol, γ-sitosterol, β-amyrin, lupeol, (3α)-12-oleanen-3-yl acetate, and lupenyl acetate, were found to dominate the methanol extract. Most of these compounds were also observed in ethyl acetate and n-hexane extracts, but at different levels, in addition to some other minor compounds. The various extracts were investigated for their antioxidant and antimicrobial activity. The ethanolic and the methanolic extracts were shown to exhibit the highest free radical scavenging by DPPH assay with a half-maximally effective concentration (EC₅₀) of 0.37 and 0.65 mg/mL respectively, while the other three extracts (aqueous, ethyl acetate and n-hexane) were less active and their EC₅₀ (effective concentration at which DPPH radical was scavenged by 50%) were above 1.0 mg/mL. Moreover, MICs were determined to be effective against Staphylococcus aureus, Salmonella typhimurium, and Candida albicans microorganisms. Ethyl acetate and the ethanolic extracts are active against the three types of microorganisms at a minimum inhibitory concentration (MIC) of 0.5 mg/mL, while aqueous and the n-hexane extracts are inactive against Salmonella typhimurium. Conclusions: The results show that S. maculatus extracts are a rich source of compounds that can play an important role in human health, and in a broader context, in the treatment of various diseases, such antimicrobial and antioxidant-related ailments.

Indexing Natural Products for their Antifungal Activity by Filters-based Approach: Disclosure of Discriminative Properties

Methods:

A set of 240 FDA-approved antifungal drugs, which represent the active domain, and a set of 2,892 natural products, which represent the inactive domain, were used to construct predictive models and to index natural products for their antifungal bioactivity. The area under the curve for the produced predictive model was 0.89. When applying it to a database that is composed of active/inactive chemicals, we succeeded to detect 42% of the actives (antifungal drugs) in the top one percent of the screened chemicals, compared with one-percent when using a random model.

Results and Conclusion:

Eight natural products, which were highly scored as likely antifungal drugs, are disclosed. Searching PubMed showed only one molecule (Flindersine) out of the eight that have been tested was reported as an antifungal. The other seven phytochemicals await evaluation for their antifungal bioactivity in a wet laboratory.</P>

A New Approach for Indexing Honey for Its Heath/Medicinal Benefits: Visualization of the Concept by Indexing Based on Antioxidant and Antibacterial Activities

Background: The goals of the current study were to address a new concept termed a health benefits' index (HBI) and to verify the type of correlation between the pricing of honey and its HBI/medicinal properties. Diverse types of honey from different origins and places were investigated for their antioxidant and antimicrobial activity. Methods: We have utilized a modified protocol of the DPPH assay for measuring free radical scavenging and the microdilution test for the determination of antibacterial/antifungal minimum inhibitory concentrations (MICs). MICs were determined against Staphylococcus aureus, Escherichia coli, Salmonella typhimurium, and Candida albicans microorganisms. Employing a "combined benefits approach" enabled us to attach to each honey type a unique number of HBI that correlate with honey health and medicinal values. Results: The various types of honey demonstrated significant but variable antioxidant, antibacterial, and antifungal activities. Types of wildflower-labeled honey were found to have a wide range of HBI values and medicinal properties, probably due to their containing different nectar contents/phytochemicals. Moreover, an inconsiderable correlation was detected between the market prices of different types of honey and their HBIs. Conclusions: The proposed index of health benefits could be recalculated/updated following measurement of more and more medicinal properties, such as anti-inflammatory, antidiabetic, and anticancer activities. This index could be used as an effective tool for consumers of honey to evaluate the real value of the purchased product.

ADNCD: a compendious database on anti-diabetic natural compounds focusing on mechanism of action

Diabetes is a deteriorating metabolic ailment which negatively affects different organs; however, its prime target is insulin secreting pancreatic β-cells. Although, different medications have been affirmed for diabetes management and numerous drugs are undergoing clinical trials, no significant breakthrough has yet been achieved. Available drugs either show some side effects or provide only short-term alleviation. The rationales behind the failure of current anti-diabetic treatment strategy are association of complex patho-physiologies and participation of various organs. Consequently, there is a critical need to search for multi-effect drugs that might impede various patho-physiological mechanisms related to diabetes. Fortunately, one natural compound could act on several diabetes linked targets. Thus, natural compounds might be regarded as a viable alternative choice to improve the progression as well as side effects of diabetes. Despite the fact that immense literatures are available on natural compounds indicating promising outcomes against diabetes, more systematic studies are still needed to establish them as effective anti-diabetic agents. Till date, we are unable to access all the information regarding modes of action, toxicity risks and physicochemical properties of anti-diabetic natural compounds on one platform. Hence, anti-diabetic natural compounds database (ADNCD) has been created to categorize each anti-diabetic natural compound on the basis of their mode of action and to provide compendious information of their physicochemical properties and toxicity risks. In short, ADNCD has imperative information for the researchers working in the field of diabetes drug development.

Capturing antibacterial natural products with in silico techniques

The aim of the present study was to index natural products in order to facilitate the discovery of less expensive antibacterial therapeutic drugs. Thus, for modeling purposes, the present study utilized a set of 628 antibacterial drugs, representing the active domain, and 2,892 natural products, representing the inactive domain. In addition, using the iterative stochastic elimination algorithm, 36 unique filters were identified, which were then used to construct a highly discriminative and robust model tailored to index natural products for their antibacterial bioactivity. The area attained under the curve was 0.957, indicating a highly discriminative and robust prediction model. Utilizing the proposed model to virtually screen a mixed set of active and inactive substances enabled the present study to capture 72% of the antibacterial drugs in the top 1% of the sample, yielding an enrichment factor of 72. In total, 10 natural products that scored highly as antibacterial drug candidates with the proposed indexing model were reported. PubMed searches revealed that 2 molecules out of the 10 (caffeine and ricinine) have been tested and identified as showing antibacterial activity. The other 8 phytochemicals await experimental evaluation. Due to the efficiency and rapidity of the proposed prediction model, it could be applied to the virtual screening of large chemical databases to facilitate the drug discovery and development processes for antibacterial drug candidates.

Sesamin from Cuscuta palaestina natural plant extracts: Directions for new prospective applications

The aim of this study is to disclose the potential bioactive components of Cuscuta palaestina, a native parasitic natural plant of flora palaestina and to open direction towards new prospective application. GC-MS analysis identified 18 components in the methanolic extract of C. palaestina for the first time. The most appealing among them are Sesamin and two other phytosterols (Campesterol and Stigmasterol), all of which are documented in the scientific literature for their anticancer activity. Quantitation of Sesamin extracted from C. palaestina by HPLC-PDA with the use of three organic solvents showed that the Sesamin content in the methanolic extract was the highest. Following the disclosure of Sesamin presence in C. palaestina, we raised the question of whether it is produced naturally in C. palaestina or acquired from the host plant. The quantitation of Sesamin in C. palaestina was performed while being with five different host plants, and was compared with the amount of Sesamin in C. palaestina grown alone. The findings reveal that Sesamin is an endogenous secondary metabolite in C. palaestina. Thus, further studies are required to prove if C. palaestina can be used as an alternative source of anticancer phytochemicals, mainly Sesamin, and if proteins in the Sesamin production pathway could be valid biological targets for the development of novel and selective pesticides for control/ eradication of C. palaestina and maybe some other Cuscuta species. As well, the findings from this study raise a big question of whether inferring Sesamin production in C. palaestina could reduce its attack ability to host plants.

Antidiabetic Potential of Monoterpenes: A Case of Small Molecules Punching above Their Weight

Monoterpenes belong to the terpenoids class of natural products and are bio-synthesized through the mevalonic acid pathway. Their small molecular weight coupled with high non-polar nature make them the most abundant components of essential oils which are often considered to have some general antioxidant and antimicrobial effects at fairly high concentrations. These compounds are however reported to have antidiabetic effects in recent years. Thanks to the ingenious biosynthetic machinery of nature, they also display a fair degree of structural complexity/diversity for further consideration in structure-activity studies. In the present communication, the merit of monoterpenes as antidiabetic agents is scrutinized by assessing recent in vitro and in vivo studies reported in the scientific literature. Both the aglycones and glycosides of these compounds of rather small structural size appear to display antidiabetic along with antiobesity and lipid lowering effects. The diversity of these effects vis-à-vis their structures and mechanisms of actions are discussed. Some key pharmacological targets include the insulin signaling pathways and/or the associated PI3K-AKT (protein kinase B), peroxisome proliferator activated receptor-γ (PPARγ), glucose transporter-4 (GLUT4) and adenosine monophosphate-activated protein kinase (AMPK) pathways; proinflammatory cytokines and the NF-κB pathway; glycogenolysis and gluconeogenesis in the liver; glucagon-like-1 receptor (GLP-1R); among others.

Nature is the best source of anticancer drugs: Indexing natural products for their anticancer bioactivity

Cancer is considered one of the primary diseases that cause morbidity and mortality in millions of people worldwide and due to its prevalence, there is undoubtedly an unmet need to discover novel anticancer drugs. However, the traditional process of drug discovery and development is lengthy and expensive, so the application of in silico techniques and optimization algorithms in drug discovery projects can provide a solution, saving time and costs. A set of 617 approved anticancer drugs, constituting the active domain, and a set of 2,892 natural products, constituting the inactive domain, were employed to build predictive models and to index natural products for their anticancer bioactivity. Using the iterative stochastic elimination optimization technique, we obtained a highly discriminative and robust model, with an area under the curve of 0.95. Twelve natural products that scored highly as potential anticancer drug candidates are disclosed. Searching the scientific literature revealed that few of those molecules (Neoechinulin, Colchicine, and Piperolactam) have already been experimentally screened for their anticancer activity and found active. The other phytochemicals await evaluation for their anticancerous activity in wet lab.

Nature is the best source of anticancer drugs: Indexing natural products for their anticancer bioactivity

Cancer is considered one of the primary diseases that cause morbidity and mortality in millions of people worldwide and due to its prevalence, there is undoubtedly an unmet need to discover novel anticancer drugs. However, the traditional process of drug discovery and development is lengthy and expensive, so the application of in silico techniques and optimization algorithms in drug discovery projects can provide a solution, saving time and costs. A set of 617 approved anticancer drugs, constituting the active domain, and a set of 2,892 natural products, constituting the inactive domain, were employed to build predictive models and to index natural products for their anticancer bioactivity. Using the iterative stochastic elimination optimization technique, we obtained a highly discriminative and robust model, with an area under the curve of 0.95. Twelve natural products that scored highly as potential anticancer drug candidates are disclosed. Searching the scientific literature revealed that few of those molecules (Neoechinulin, Colchicine, and Piperolactam) have already been experimentally screened for their anticancer activity and found active. The other phytochemicals await evaluation for their anticancerous activity in wet lab.