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Journal of Drug Delivery and Therapeutics
Open Access to Pharmaceutical and Medical Research
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Open Access Full Text Article Research Article
vNN model cross validation towards Accuracy, Sensitivity, Specificity and kappa performance measures of β-caryophyllene using a restricted-unrestricted applicability domain on Artificial Intelligence & Machine Learning approach based in-silico prediction of ADMET properties
K. Krishnaveni1, M. Sabitha2, M. Murugan2, C. Kandeepan2, S. Ramya3, T. Loganathan4, R. Jayakumararaj5*
1 Department of Zoology, GTN College, Dindigul, TamilNadu, India
2 PG Department of Zoology, Arulmigu Palaniandavar College of Arts & Culture, Palani-624601, India
3 Yadava College (Men), Thiruppalai - 625014, Madurai, TamilNadu, India
4 Department of Plant Biology & Plant Biotechnology, LN Government College (A), Ponneri-601204, India
5 Department of Botany, Government Arts College, Melur – 625106, Madurai, TN, India
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Article Info: ________________________________________ Article History: Received 31 December 2021 Reviewed 06 February 2022 Accepted 11 February 2022 Published 22 February 2022 ________________________________________ Cite this article as: Krishnaveni K, Sabitha M, Murugan M, Kandeepan C, Ramya S, Loganathan T, Jayakumararaj R, vNN model cross validation towards Accuracy, Sensitivity, Specificity and kappa performance measures of β-caryophyllene using a restricted-unrestricted applicability domain on Artificial Intelligence & Machine Learning approach based in-silico prediction of ADMET properties, Journal of Drug Delivery and Therapeutics. 2022; 12(1-s):123-131 DOI: http://dx.doi.org/10.22270/jddt.v12i1-s.5222 ________________________________________ *Address for Correspondence: R. Jayakumararaj, Department of Botany, Government Arts College, Melur – 625106, Madurai, TN, India |
Abstract ______________________________________________________________________________________________________________ Wide-reaching interest in use of plant derived secondary metabolites (PDSMs) has been growing world over. This implies that the beneficial effects of plant based natural products (PBNPs) being rediscovered and harnessed for the development of novel drugs; drug-leads and structural background for drug design. Survey of literature on the indigenous use of traditional medicinal knowledge bestows ethnopharmacological potentials of PBNPs that has inspired drug design and development through research in drug discovery; PBNPs provide baseline for the development of drug leads against various pharmacological targets. Studies indicate that Cymbopogon martini Essential Oil (CMEO) exhibits a wide range of biological activities (anticancer, antidiabetic, antibacterial, antifungal, antioxidant, hepatoprotective and insecticide) attributed to the presence of bio-active secondary metabolites (BASM). However, wide-spread application of CMEO is limited because of typical odor, color and taste. In the present study, In-silico Absorption, Distribution, Metabolism, Elimination, Toxicity (ADMET) studies of β-caryophyllene (BCP) from Cymbopogon martinii is envisaged. β-caryophyllene enhances wound healing through multiple routes therefore, Non AMES toxic, Non-carcinogens, and biodegradable nature of β-caryophyllene prospects this compound as considered as an apt lead for the development of drugs for the treatment of cuts and wounds besides other diseases viz., diabetics, cancer, metabolic inflammation and other long term complications associated with these diseases. In particular, BCP inhibits cytochrome P450 isoforms (CYP3A4) that metabolize xenobiotics and results in adverse effects. Pharmacokinetic properties and predicted score values of β-caryophyllene have highlighted that both pharmacological and toxicological aspects need to be further investigated before put to clinical trials. Keywords: ADMET; β-caryophyllene; Cymbopogon martinii; Cardiotoxicity; Cytotoxicity; Drugability; Pharmacokinetics; Bioactivity; Essential Oil; |
INTRODUCTION
Cymbopogon is widely distributed in tropical and subtropical regions of Africa, Asia and America. Genus Cymbopogon comprises of more than 144 species, and is well known for its essential oils1-4. Studies have led to the isolation of alkaloids, volatile and non-volatile terpenoids, flavonoids, carotenoids and tannins from Cymbopogon spp. Cymbopogon martinii native to India and China is cultivated for its aromatic essential oil. Popular as Palmarosa, this plant is known for its typical aroma and therapeutic application, commonly used as condiment and food preservative. CMEO BASMs are endowed with splendid pharmacological properties5. CMEO is of commercial importance and is being extensively used in perfumes, soaps, cosmetics and toiletry products6. CMEO is used as an effective insect repellent when applied on stored grain and beans7, anti-helminthic against nematodes8, antifungal9 and mosquito repellent10. In Ayurvedic medicine, it is used to treat skin problems and relieve nerve pain. Reports indicate that immunomodulatory action of CMEO had been evaluated towards production of pro- and anti-inflammatory cytokines (TNF-α and IL-10) by human monocytes in vitro11.
CMEO contains bioactive compounds of GRAS nature. Due to its overwhelming physiochemical and biological properties CMEO has been used in aromatherapy as volatile phyto-pharmaceuticals, flavor and fragrances agent in food and cosmetic industries, and more recently, natural preservatives/ additives in biopesticides12,13. CMEOs are concentrated form of liquid mixtures of volatile phyto-compounds with unique structural chemistry comprising of terpenoid and non-terpenoid hydrocarbons and their oxygenated derivatives, natural color, odor and flavor14. Furthermore, concern about the negative effect of synthetic chemicals as food additives warrants “GO” products with no or lesser side effects.
Interest in natural products as alternatives for synthetic additives is attributed to (a) their synergy with other preservation methods (b) generally regarded as safe, and (c) PBNPs are endowed with antioxidant, antidiabetic, antimutagenic, antitoxigenic and antibacterial properties15,16 CMEO has proven fungicidal effect against pathogenic fungi and yeasts, including Aspergillus spp., Candida albicans, Monilia sitophila and Trichophyton tonsurae16. In Ayurvedic medicine - Charak gave the decoction of CM to treat abdominal, spleen, liver disorders, jaundice, and fever. In Sushruta, decoction of CM is prescribed in inflammation of throat, chest pain, indigestion, bronchitis, cough and asthma17.
BCP possesses pharmacological activities ranging from pain killer to neurological/ metabolic disorders. Chemically, sesquiterpenoids, a group of PBSM consists of 15 carbon atom backbone with a wide array of chemical structures18. Among them, β-caryophyllene is the major component in Cannabis sativa, Chenopodium ambrosioides, Cinnamomum spp., Citrus myrtfolia, Cymbopogon spp., Mentha piperita, Ocimum spp., Origanum vulgare, Piper nigrum, Rosmarinus officinalis, Salvia officinalis, Syzygium aromaticum, Thymus vulgaris, Zingiber officinale19. BCP mainly occurs as trans-caryophyllene ((E)-BCP) mixed with small amount of isomers (Z)-β-caryophyllene ((Z)-BCP) and α-humulene (α-caryophyllene) and its oxidation derivative, β-caryophyllene oxide (BCPO).
BCP belongs to cannabinoid family and cannabinoid receptors CB1-R and CB2-R (metabotropic receptors) of G protein (protein binding GTP)-coupled receptors, are involved in the regulation of neurotransmitters responsible for energy balance, through metabolism and immune response20. Aforementioned receptors are bound and activated by endogenous anandamide. Both receptors are bound to proteins, act as mediators of cellular responses to biomolecules. Expression of CB2-Rs in CNS is increased in neurodegenerative pathologies, such as Parkinson’s disease, Alzheimer’s disease21, amyotrophic lateral sclerosis (ALS), glioma, spinal/ brain injuries, stroke, ischemia, anxiety, depression, colitis, fibrosis and liver ischemia, atherosclerosis, osteoporosis, osteoarthritis, diabetes, obesity and cancer18.
BCP is the subject of interest in recent past, quantum of research about the effects in vitro and in vivo on animals have been conducted, and thus experimental data about its biological properties have been verified. However, in-depth studies still remain a major prerequisite to translate the findings in animal models into promising pre-clinical and clinical trials on humans. Pre-clinical studies have revealed that BCP is a modulator of nervous system and exerts beneficial effects on neurodegenerative and inflammatory pathologies18. Further, it has been proved that BCP/ BCPO significantly influence microRNA that regulates gene expression, portraying the role of BCP/ BCPO as biomarkers of neuroinflammation22.
In addition, BCP is involved in cell-to-cell communication, regulates functioning of blood–brain barrier (BBB), and modulates filament network/ repair brain injuries. BCP prevents apoptosis, by inhibiting the expression of Bax and caspase-3 and increasing Bcl-2 one23. It reduces phosphorylation of JNK (c-Jun N-terminal Kinase), that increases HO-1 (heme oxygenase-1) during pathological conditions. All these effects are related to CB2/Nrf2 pathway. BCP is cytoprotective towards CNS due to its modulation of redox state and reduce inflammation as chemotherapeutic agent24. BCP inhibits production of nitric oxide, hydrogen peroxide, TNF-α, IFN-γ, IL-1725 thus reduce macrophage infiltration. BCP is useful in multiple sclerosis management, as demonstrated by murine model of multiple sclerosis (MS). BCP exhibits antidepressant properties by the activation of CB2-R26.
BCP/ BCPO have shown cytotoxic activity against various cancer cell lines. In particular, BCP reduce proliferation of two colon cancer cell lines, HT-29/ HCT-116, and a pancreas cancer cell line, PANC-1. In the intestinal cancer cell line CaCo-2, BCP has not been able to exert a significant effect on cell growth, unlike α-humulene. BCP enhances effectiveness of antitumor drugs. In particular, it increases the activity of paclitaxel in lots of cell lines: MCF-7 (breast cancer), L-929 (mouse fibroblasts), DLD-1 (colon cancer)27. BCP according to OECD guideline 423 is a 5th category substance. No damage to the gastric mucosa has been observed, nor have changes in other internal organs (brain, heart, liver, lungs, spleen, and kidney) or in haematological parameters been reported. Moreover, Ames test has shown no mutagenicity, body weight decreased by 5% in female Swiss mice.
In fact, there exist Cytochrome P450-mediated pharmacokinetic interactions between herbs, food, and dietary supplements in cancer treatments and other metabolic disorders28. A significant inhibition by BCP has been reported against enzymes involved in metabolism and xenobiotic detoxification18. The extent of the inhibition of cytochrome P450 isoform CYP1A2 is greater in rat microsomes than in human, further BCP inhibits CYP3A4, a key cytochrome P450 isoform involved in metabolism of xenobiotics. Finally, terpenes exert a weak inhibition against CYP2A6, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP2E1.
Owing to the growing importance of BCP, it is planned to evaluate the occurrence of this important endocannabinoid in the selected plant species. Therefore, CMEO was used as natural sources of BCP in order to provide the pharmaceutical, nutraceutical and aroma industries a summary of BCP from ADMET perspective with a view that ADMET profiling of BCP would pave way for the inclusion of BCP as food supplement and ensure compliance of similar botanicals in terms of quality and safety.
MATERIALS AND METHODS
Collection, Preparation and Analysis
Samples were collected from Alagar Hills, (Eastern Ghats), Madurai District, TamilNadu, INDIA during Nov 2021. The leaf samples were well preserved, taken to laboratory, identified by using flora29,30 shade dried and processed as per the protocol previously described30, however, with modifications in temperature and duration of processing of sample and GCMS was carried out.
ADMET Prediction
Drug-like features and Drug Score were evaluated using Molinspiration online tool (http://molinspiration.com/) while properties related to absorption, distribution, metabolism, excretion and toxicity (ADMET) were accessed using ADMET SAR2 web-server31-33.
RESULTS AND DISCUSSION
Ethnobotanical studies coupled with the use of AI-driven solutions to enable pre-clinical drug discovery is growing within the pharmaceutical industry34-51. It has the potential to deliver across the drug discovery and development value chain, starting from target identification and reaching through clinical development. 2D, 3D structures, molecular and biological properties of β-caryophyllene in CMEO is given in Fig 1. Molecular and biological properties of β-caryophyllene depicts that the molecule contains 318.1 hydrogen atoms well within the optimal range of 100~600. Van der Waals volume of the molecule was estimated as 316.597 with a density value of 1.005. Number of hydrogen bond acceptors in the compounds was 5 whereas the Number of hydrogen bond donors was 0. Number of rotatable bonds and Number of rings in the molecule were 7 and 2 respectively. Toxic (mutagenic, toxicology, irritant, reproductive properties) risk assessment towards drugability/ drug score indicated that the compounds were neither mutagenic nor toxic to biological system. Performance measures of vNN models in 10-fold cross validation using a restricted/ unrestricted applicability domain is given in Table 1a, b. All the parameters studied were drugable and had good score for Druggability Properties of β-caryophyllene - Lipinski's rule of 5 violations` - 0; Veber rule - Good; Egan rule - Good; Oral PhysChem score (Traffic Lights) - 1; GSK's 4/400 score - Good; Pfizer's 3/75 score - Bad; Weighted quantitative estimate of drug-likeness (QEDw) score - 0.514; Solubility - 4277.90; Solubility Forecast Index – Good (Table 2). Pharmacokinetic properties, predicted score values of β-caryophyllene is given in Table 3.
Chemical kingdom |
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Organic compounds |
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Super class |
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Lipids and lipid-like molecules |
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Class |
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Prenol lipids |
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Subclass |
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Sesquiterpenoids |
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PubChem Identifier |
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5281515 |
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ChEBI Identifier |
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10357 |
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CAS Identifier |
: |
87-44-5 |
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Canonical SMILES |
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C/C/1=CCCC(=C)[C@@H]2[C@@H](CC1)C(C2)(C)C |
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InChI Key |
: |
NPNUFJAVOOONJE-GFUGXAQUSA-N |
ADMET properties of β-Caryophyllene
The implemented Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) prediction models, including their performance measures were predicted online.33 The 15 models cover a diverse set of ADMET endpoints. Some of the models have already been published, including those for Maximum Recommended Therapeutic Dose (MRTD), chemical mutagenicity, human liver microsomal (HLM), Pgp inhibitor/ substrates.
Liver Toxicity - Drug-induced liver injury (DILI)
DILI has been one of the most commonly cited reasons for drug withdrawals from the market. This application predicts whether a compound could cause DILI. The dataset of 1,431 compounds was obtained from four sources used. This dataset contains both pharmaceuticals and non-pharmaceuticals; classified a compound as causing DILI if it was associated with a high risk of DILI and not if there was no such risk.
Cytotoxicity (HepG2)
Cytotoxicity is the degree to which a chemical causes damage to cells. Cytotoxicity prediction model was developed using in vitro data on toxicity against HepG2 cells for 6,000 structurally diverse compounds, collected from ChEMBL. In developing the model, compounds with an IC50 ≤ 10 μM were considered in the in vitro assay as cytotoxic.
Human Liver Microsomal (HLM)
Human liver microsomal (HLM) stability assay is commonly used to identify and exclude compounds that are too rapidly metabolized. For a drug to achieve effective therapeutic concentrations in the body, it cannot be metabolized too rapidly by the liver. Compounds with a half-life of 30 min or longer in an HLM assay are considered as stable; otherwise they are considered unstable. HLM data was retrieved from the ChEMBL database, manually curated, and classified compounds as stable or unstable based on the reported half-life (T1/2 > 30 min was considered stable, and T1/2 < 30 min unstable. The final dataset contained 3,654 compounds. Of these, 2,313 compounds were classified as stable and 1,341 as unstable.
Cytochrome P450 enzyme (CYP) inhibition
CYPs constitute a superfamily of proteins that play an important role in the metabolism and detoxification of xenobiotics. In vitro data derived from five main drug-metabolizing CYPs - 1A2, 3A4, 2D6, 2C9, and 2C19 was used to develop CYP inhibition models. CYP inhibitors were retrieved from PubChem and classified a compound with an IC50 ≤ 10 μM for an enzyme as an inhibitor of the enzyme. Predictions have been provided for the following enzymes: CYP1A2, CYP3A4, CYP2D6, CYP2C9, and CYP2C19 (Table 1).
Membrane Transporters - Blood-Brain Barrier (BBB)
Blood-brain barrier (BBB) is a highly selective barrier that separates the circulating blood from the central nervous system. vNN-based BBB model has been developed using 352 compounds whose BBB permeability values (log-BB) were obtained from the literature. Compounds were classified with log-BB values of less than –0.3 and greater than +0.3 as BBB non-permeable and permeable.
Pgp Substrates and Inhibitors
P-glycoprotein (Pgp) is an essential cell membrane protein that extracts many foreign substances from the cell. Cancer cells often overexpress Pgp, which increases the efflux of chemotherapeutic agents from the cell and prevents treatment by reducing the effective intracellular concentrations of such agents—a phenomenon known as multidrug resistance. For this reason, identifying compounds that can either be transported out of the cell by Pgp (substrates) or impair Pgp function (inhibitors) is of great interest. Models were developed to predict both Pgp substrates and Pgp inhibitors. Pgp substrate dataset was collected with a dataset consisting of measurements of 422 substrates and 400 non-substrates. To generate a large Pgp inhibitor dataset, datasets were combined and duplicates were removed to form a combined dataset consisting of a training set of 1,319 inhibitors and 937 non-inhibitors.
hERG (Cardiotoxicity)
Human ether-à-go-go-related gene (hERG) codes for a potassium ion channel involved in the normal cardiac repolarization activity of the heart. Drug-induced blockade of hERG function can cause long QT syndrome, which may result in arrhythmia and death. As much as 282 known hERG blockers were retrieved from the literature and classified compounds with an IC50 cut-off value of 10 μM or less as blockers. A set of 404 compounds were collected with IC50 values greater than 10 μM from ChEMBL and classified them as non-blockers.
MMP (Mitochondrial Toxicity)
Given the fundamental role of mitochondria in cellular energetics and oxidative stress, mitochondrial dysfunction has been implicated in cancer, diabetes, neurodegenerative disorders, and cardiovascular diseases. Dataset of chemical-induced changes in mitochondrial membrane potential (MMP) was used based on the assumption that a compound that causes mitochondrial dysfunction is also likely to reduce the MMP. vNN-based MMP prediction model was developed using 6,261 compounds collected from a previous study that screened a library of 10,000 compounds (~8,300 unique chemicals) at 15 concentrations, each in triplicate, to measure changes in the MMP in HepG2 cells. Data depict that 913 compounds decreased the MMP, whereas 5,395 compounds had no effect.
Mutagenicity (Ames test):
Mutagens are chemicals that cause abnormal genetic mutations leading to cancer. A common way to assess a chemical’s mutagenicity is the Ames test. The prediction model was developed, using a literature dataset of 6,512 compounds, of which 3,503 were Ames-positive. Details of the model and its performance rate indicate the molecule is Non-mutagenic (Table 3).
Maximum Recommended Therapeutic Dose (MRTD)
Maximum Recommended Therapeutic Dose (MRTD) is an estimated upper daily dose that is safe. A prediction model was built based on a dataset of MRTD values publically disclosed by FDA, mostly of single-day oral doses for an average adult with a body weight of 60 kg, for 1,220 compounds (most of which were small organic drugs). Organometallics were excluded, high-molecular weight polymers (>5,000 Da), nonorganic chemicals, mixtures of chemicals, and very small molecules (<100 Da). We used an external test set of 160 compounds that were collected by the FDA for validation. The total dataset for our model contained 1,185 compounds. The predicted MRTD value is 20 mg/day based upon an average adult weighing 60 kg.
β-Caryophyllene from CMEO is used in the treatment of colitis, osteoarthritis, diabetes, cerebral ischemia, anxiety and depression, liver fibrosis as the phytocompounds possess antitumor, antioxidant, anti-infectious, anti-inflammatory, and analgesic activities and effects on central nervous system, endocrine system, disorders such as cardiac remodeling after myocardial infarction, body weight changes, dyslipidemia, cerebral ischemia, hepato-nephrotoxicity, stress, and anxiety. Anti-inflammatory activity of CMEO has been attributed to the presence and synergistic activity of carnosol and carnosic, rosmarinic, ursolic, oleanolic, and micromeric acids (A)18. Based on the ADMET data this drug can be proposed for preclinical and clinical studies in different diseases and pathological conditions.
CONCLUSION
C. martini (Palmarosa) contains a large variety of bioactive molecules with great therapeutic potential. CMEO has been used in traditional medicine due to pharmacological potential of phytochemicals. However, development of new formulations containing other less common CMEO extracts is warranted through trials to establish the credentials of pharmacologically active phyto-compounds towards safety/ efficacy, in treating various pathological conditions including COVID-19 and other viral infections owing to modulation of immune-inflammatory responses and druggable nature of CMEO. In particular, BCP is a unique dietary molecule, with negligible toxicity, without of psychotropic effects, widely distributed in plants, with high degree of oral bioavailability, druggable properties and functionality. However, the use of BCP shall remain indecisive until in-silico observations could be experimentally validated.
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32. Cheng F, Li W, Zhou Y, Shen J, Wu Z, Liu G, Lee PW, Tang Y. admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. J. Chem. Inf. Model. 2012; 52(11):3099–3105. https://doi.org/10.1021/ci300367a
33. Schyman, P., R. Liu, V. Desai, and A. Wallqvist. vNN web server for ADMET predictions. Frontiers in Pharmacology. 2017; 8:889. https://doi.org/10.3389/fphar.2017.00889
34. Vijayan RS, Kihlberg J, Cross JB, Poongavanam V. Enhancing preclinical drug discovery with artificial intelligence. Drug discovery today. 2021 Nov 25. https://doi.org/10.1016/j.drudis.2021.11.023
35. Ramya S, Neethirajan K, Jayakumararaj R. Profile of bioactive compounds in Syzygium cumini-a review. Journal of Pharmacy research. 2012; 5(8):4548-53.
36. Loganathan T, Barathinivas A, Soorya C, Balamurugan S, Nagajothi TG, Ramya S, Jayakumararaj R. Physicochemical, Druggable, ADMET Pharmacoinformatics and Therapeutic Potentials of Azadirachtin-a Prenol Lipid (Triterpenoid) from Seed Oil Extracts of Azadirachta indica A. Juss. Journal of Drug Delivery and Therapeutics. 2021; 11(5):33-46. https://doi.org/10.22270/jddt.v11i5.4981
37. Kalaimathi RV, Jeevalatha A, Basha AN, Kandeepan C, Ramya S, Loganathan T, Jayakumararaj R. In-silico Absorption, Distribution, Metabolism, Elimination and Toxicity profile of Isopulegol from Rosmarinus officinalis. Journal of Drug Delivery and Therapeutics. 2022; 12(1):102-8. https://doi.org/10.22270/jddt.v12i1.5188
38. Jeevalatha A, Kalaimathi RV, Basha AN, Kandeepan C, Ramya S, Loganathan T, Jayakumararaj R. Profile of bioactive compounds in Rosmarinus officinalis. Journal of Drug Delivery and Therapeutics. 2022; 12(1):114-22. https://doi.org/10.22270/jddt.v12i1.5189
39. Ramya S, Murugan M, Krishnaveni K, Sabitha M, Kandeepan C, Jayakumararaj R. In-silico ADMET profile of Ellagic Acid from Syzygium cumini: A Natural Biaryl Polyphenol with Therapeutic Potential to Overcome Diabetic Associated Vascular Complications. Journal of Drug Delivery and Therapeutics. 2022; 12(1):91-101. https://doi.org/10.22270/jddt.v12i1.5179
40. Soorya C, Balamurugan S, Ramya S, Neethirajan K, Kandeepan C, Jayakumararaj R. Physicochemical, ADMET and Druggable properties of Myricetin: A Key Flavonoid in Syzygium cumini that regulates metabolic inflammations. Journal of Drug Delivery and Therapeutics. 2021; 11(4):66-73 https://doi.org/10.22270/jddt.v11i4.4890
41. Sabitha M, Krishnaveni K, Murugan M, Basha AN, Pallan GA, Kandeepan C, Ramya S, Jayakumararaj R. In-silico ADMET predicated Pharmacoinformatics of Quercetin-3-Galactoside, polyphenolic compound from Azadirachta indica, a sacred tree from Hill Temple in Alagarkovil Reserve Forest, Eastern Ghats, INDIA. Journal of Drug Delivery and Therapeutics. 2021; 11(5-S):77-84. https://doi.org/10.22270/jddt.v11i5-S.5026
42. Sundari A, Jayakumararaj R. Medicinal plants used to cure cuts and wounds in Athur region of Thoothukudi district in Tamil Nadu, India. Journal of Drug Delivery and Therapeutics. 2020; 10(6-s):26-30. https://doi.org/10.22270/jddt.v10i6-s.4429
43. Sundari A, Jayakumararaj R. Herbal remedies used to treat skin disorders in Arasankulam region of Thoothukudi District in Tamil Nadu, India. Journal of Drug Delivery and Therapeutics. 2020; 10(5):33-8. https://doi.org/10.22270/jddt.v10i5.4277
44. Rajasekaran C, Meignanam E, Vijayakumar V, Kalaivani T, Ramya S, Premkumar N, Siva R, Jayakumararaj R Investigations on antibacterial activity of leaf extracts of Azadirachta indica A. Juss (Meliaceae): a traditional medicinal plant of India. Ethnobotanical leaflets. 2008; 2008(1):161.
45. Ramya S. Jayakumararaj R In Vitro Evaluation of Antibacterial Activity Using Crude Extracts of Catharanthus roseus L.(G.) Don. Ethnobotanical Leaflets. 2008; 2008(1):140.
46. Shanmugam S, Sundari A, Muneeswaran S, Vasanth C, Jayakumararaj R, Rajendran K. Ethnobotanical Indices on medicinal plants used to treat poisonous bites in Thiruppuvanam region of Sivagangai district in Tamil Nadu, India. Journal of Drug Delivery and Therapeutics. 2020; 10(6-s):31-6. https://doi.org/10.22270/jddt.v10i6-s.4432
47. Ramya S, Alaguchamy N, Maruthappan VM, Sivaperumal R, Sivalingam M, Krishnan A, Govindaraji V, Kannan K, Jayakumararaj R. Wound healing ethnomedicinal plants popular among the Malayali tribes in Vattal Hills, Dharmapuri, TN, India. Ethnobotanical Leaflets. 2009; 2009(10):6.
48. Sivaperumal R, Ramya S, Ravi AV, Rajasekaran C, Jayakumararaj R. Herbal remedies practiced by Malayali's to treat skin diseases. Environ We Int J Sci Tech. 2009; 4(1):35-44.
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50. Koyama S, Purk A, Kaur M, Soini HA, Novotny MV, Davis K, Kao CC, Matsunami H, Mescher A. Beta-caryophyllene enhances wound healing through multiple routes. PloS one. 2019; 14(12):e0216104. https://doi.org/10.1371/journal.pone.0216104
51. Francomano F, Caruso A, Barbarossa A, Fazio A, La Torre C, Ceramella J, Mallamaci R, Saturnino C, Iacopetta D, Sinicropi MS. β-Caryophyllene: a sesquiterpene with countless biological properties. Applied Sciences. 2019; 9(24):5420. https://doi.org/10.3390/app9245420
|
|
Molecular Properties |
Calculated Values |
|
5.17 |
||
|
0.00 |
||
|
natoms |
15 |
|
|
MW |
204.36 |
|
|
nON |
0 |
|
|
nOHNH |
0 |
|
|
nviolations |
1 |
|
|
Total volume |
229.95 |
|
|
Number of Rotatable Bonds |
0 |
|
|
Number of Acceptors |
0 |
|
|
Number of Donors |
0 |
|
|
Total Surface Area |
94.458 |
|
|
Biological Properties |
Calculated Score |
|
|
GPCR ligand |
-0.34 |
|
|
Ion channel modulator |
0.28 |
|
|
Kinase inhibitor |
-0.78 |
|
|
Nuclear receptor ligand |
0.13 |
|
|
Protease inhibitor |
-0.60 |
|
|
Enzyme inhibitor |
0.19 |
Figure 1: 2D, 3D structures, molecular and biological properties of β-caryophyllene
Table 1a: Summary of performance measures of vNN models in 10-fold cross validation using a restricted/ unrestricted applicability domain
Table 1b: Performance measures of vNN models in 10-fold cross validation using a restricted/ unrestricted applicability domain
|
Model |
Dataa |
d0b |
hc |
Accuracy |
Sensitivity |
Specificity |
kappa |
Rd |
Coverage |
|
DILI |
1427 |
0.60 |
0.50 |
0.71 |
0.70 |
0.73 |
0.42 |
0.66 |
|
|
1.00 |
0.20 |
0.67 |
0.62 |
0.72 |
0.34 |
1.00 |
|||
|
Cytotox (hep2g) |
6097 |
0.40 |
0.20 |
0.84 |
0.88 |
0.76 |
0.64 |
0.89 |
|
|
1.00 |
0.20 |
0.84 |
0.73 |
0.89 |
0.62 |
1.00 |
|||
|
HLM |
3219 |
0.40 |
0.20 |
0.81 |
0.72 |
0.87 |
0.59 |
0.91 |
|
|
1.00 |
0.20 |
0.81 |
0.70 |
0.87 |
0.57 |
1.00 |
|||
|
CYP1A2 |
7558 |
0.50 |
0.20 |
0.90 |
0.70 |
0.95 |
0.66 |
0.75 |
|
|
1.00 |
0.20 |
0.89 |
0.61 |
0.95 |
0.60 |
1.00 |
|||
|
CYP2C9 |
8072 |
0.50 |
0.20 |
0.91 |
0.55 |
0.96 |
0.54 |
0.76 |
|
|
1.00 |
0.20 |
0.90 |
0.44 |
0.96 |
0.46 |
1.00 |
|||
|
CYP2C19 |
8155 |
0.55 |
0.20 |
0.87 |
0.64 |
0.93 |
0.58 |
0.76 |
|
|
1.00 |
0.20 |
0.86 |
0.52 |
0.94 |
0.50 |
1.00 |
|||
|
CYP2D6 |
7805 |
0.50 |
0.20 |
0.89 |
0.61 |
0.94 |
0.57 |
0.75 |
|
|
1.00 |
0.20 |
0.88 |
0.52 |
0.95 |
0.51 |
1.00 |
|||
|
CYP3A4 |
10373 |
0.50 |
0.20 |
0.88 |
0.76 |
0.92 |
0.68 |
0.78 |
|
|
1.00 |
0.20 |
0.88 |
0.69 |
0.93 |
0.64 |
1.00 |
|||
|
BBB |
353 |
0.60 |
0.20 |
0.90 |
0.94 |
0.86 |
0.80 |
0.61 |
|
|
1.00 |
0.10 |
0.82 |
0.88 |
0.75 |
0.64 |
1.00 |
|||
|
Pgp Substrate |
822 |
0.60 |
0.20 |
0.79 |
0.80 |
0.79 |
0.58 |
0.66 |
|
|
1.00 |
0.20 |
0.73 |
0.73 |
0.74 |
0.47 |
1.00 |
|||
|
Pgp Inhibitor |
2304 |
0.50 |
0.20 |
0.85 |
0.91 |
0.73 |
0.66 |
0.76 |
|
|
1.00 |
0.10 |
0.81 |
0.86 |
0.74 |
0.61 |
1.00 |
|||
|
hERG |
685 |
0.70 |
0.70 |
0.84 |
0.84 |
0.83 |
0.68 |
0.80 |
|
|
1.00 |
0.20 |
0.82 |
0.82 |
0.83 |
0.64 |
1.00 |
|||
|
MMP |
6261 |
0.50 |
0.40 |
0.89 |
0.64 |
0.94 |
0.61 |
0.69 |
|
|
1.00 |
0.20 |
0.87 |
0.52 |
0.94 |
0.50 |
1.00 |
|||
|
AMES |
6512 |
0.50 |
0.40 |
0.82 |
0.86 |
0.75 |
0.62 |
0.79 |
|
|
1.00 |
0.20 |
0.79 |
0.82 |
0.75 |
0.57 |
1.00 |
|||
|
MRTDe |
1184 |
0.60 |
0.20 |
0.79 |
0.69 |
||||
|
1.00 |
0.20 |
0.74 |
1.00 |
aNumber of compounds in the dataset; bTanimoto-distance threshold value; cSmoothing factor; dPearson’s correlation coefficient; eRegression model.
Table 2: Summary of Physicochemical, Druggability, ADMET of β-caryophyllene
|
PROPERTY |
VALUE |
|
|
Molecular weight |
204.36 g/mol |
|
|
LogP |
4.73 |
|
|
LogD |
4.52 |
|
|
LogSw |
-3.87 |
|
|
Number of stereocenters |
2 |
|
|
Stereochemical complexity |
0.133 |
|
|
Fsp3 |
0.733 |
|
|
Topological polar surface area |
0.00 Å2 |
|
|
Number of hydrogen bond donors |
0 |
|
|
Number of hydrogen bond acceptors |
0 |
|
|
Number of smallest set of smallest rings (SSSR) |
1 |
|
|
Size of the biggest system ring |
11 |
|
|
Number of rotatable bonds |
0 |
|
|
Number of rigid bonds |
13 |
|
|
Number of charged groups |
0 |
|
|
Total charge of the compound |
0 |
|
|
Number of carbon atoms |
15 |
|
|
Number of heteroatoms |
0 |
|
|
Number of heavy atoms |
15 |
|
|
Ratio between the number of non-carbon atoms and carbon atoms |
0 |
|
|
Lipinski's rule of 5 violations |
0 |
|
|
Veber rule |
Good |
|
|
Egan rule |
Good |
|
|
Oral PhysChem score (Traffic Lights) |
1 |
|
|
GSK's 4/400 score |
Good |
|
|
Pfizer's 3/75 score |
Bad |
|
|
Weighted quantitative estimate of drug-likeness (QEDw) score |
0.514 |
|
|
Solubility |
4277.90 |
|
|
Solubility Forecast Index |
Good |
|
|
Property |
Value |
Probability |
|
Human Intestinal Absorption |
HIA+ |
0.993 |
|
Blood Brain Barrier |
BBB+ |
0.954 |
|
Caco-2 permeable |
Caco2+ |
0.633 |
|
P-glycoprotein substrate |
Substrate |
0.578 |
|
P-glycoprotein inhibitor I |
Non-inhibitor |
0.599 |
|
P-glycoprotein inhibitor II |
Inhibitor |
0.669 |
|
CYP450 2C9 substrate |
Non-substrate |
0.900 |
|
CYP450 2D6 substrate |
Non-substrate |
0.839 |
|
CYP450 3A4 substrate |
Substrate |
0.578 |
|
CYP450 1A2 inhibitor |
Non-inhibitor |
0.670 |
|
CYP450 2C9 inhibitor |
Non-inhibitor |
0.625 |
|
CYP450 2D6 inhibitor |
Non-inhibitor |
0.928 |
|
CYP450 2C19 inhibitor |
Non-inhibitor |
0.596 |
|
CYP450 3A4 inhibitor |
Non-inhibitor |
0.867 |
|
CYP450 inhibitory promiscuity |
Low CYP Inhibitory Promiscuity |
0.843 |
|
Ames test |
Non AMES toxic |
0.917 |
|
Carcinogenicity |
Non-carcinogens |
0.686 |
|
Biodegradation |
Ready biodegradable |
0.573 |
|
Rat acute toxicity |
1.435 LD50, mol/kg |
NA |
|
hERG inhibition (predictor I) |
Weak inhibitor |
0.923 |
|
hERG inhibition (predictor II) |
Non-inhibitor |
0.854 |
Physicochemical properties were computed using FAF-Drugs4 (28961788); RDKit open-source cheminformatics platform. Druggabiity scoring schemes were computed using FAF-Drugs4 (28961788) and FAF-QED (28961788) open-source cheminformatics platform. ADMET were predicted using admetSAR (23092397) open-source tool.
Table 3: Pharmacokinetic properties, predicted score values of β-caryophyllene
|
PROPERTY |
NAME OF THE MODEL |
PREDICTED VALUE |
REFERENCE UNIT |
|
Absorption |
Water solubility |
-5.534 |
(log mol/L) |
|
Absorption |
Caco2 permeability |
1.434 |
(log P10-6 cm/s) |
|
Absorption |
Intestinal absorption (human) |
95.304 |
(% Absorbed) |
|
Absorption |
Skin Permeability |
-1.596 |
(log Kp) |
|
Absorption |
P-glycoprotein substrate |
No |
(Yes/No) |
|
Absorption |
P-glycoprotein I inhibitor |
No |
(Yes/No) |
|
Absorption |
P-glycoprotein II inhibitor |
No |
(Yes/No) |
|
Distribution |
VDss (human) |
0.653 |
(log L/kg) |
|
Distribution |
Fraction unbound (human) |
0.26 |
(Fu) |
|
Distribution |
BBB permeability |
0.74 |
(log BB) |
|
Distribution |
CNS permeability |
-2.182 |
(log PS) |
|
Metabolism |
CYP2D6 substrate |
No |
(Yes/No) |
|
Metabolism |
CYP3A4 substrate |
No |
(Yes/No) |
|
Metabolism |
CYP1A2 inhibitior |
No |
(Yes/No) |
|
Metabolism |
CYP2C19 inhibitior |
No |
(Yes/No) |
|
Metabolism |
CYP2C9 inhibitior |
No |
(Yes/No) |
|
Metabolism |
CYP2D6 inhibitior |
No |
(Yes/No) |
|
Metabolism |
CYP3A4 inhibitior |
No |
(Yes/No) |
|
Excretion |
Total Clearance |
1.088 |
(log ml/min/kg) |
|
Excretion |
Renal OCT2 substrate |
No |
(Yes/No) |
|
Toxicity |
AMES toxicity |
No |
(Yes/No) |
|
Toxicity |
Max. tolerated dose (human) |
0.489 |
(log mg/kg/day) |
|
Toxicity |
hERG I inhibitor |
No |
(Yes/No) |
|
Toxicity |
hERG II inhibitor |
No |
(Yes/No) |
|
Toxicity |
Oral Rat Acute Toxicity (LD50) |
1.678 |
(mol/kg) |
|
Toxicity |
Oral Rat Chronic Toxicity (LOAEL) |
1.44 |
(log mg/kg_bw/day) |
|
Toxicity |
Hepatotoxicity |
No |
(Yes/No) |
|
Toxicity |
Skin Sensitisation |
Yes |
(Yes/No) |
|
Toxicity |
T. pyriformis toxicity |
1.406 |
(log ug/L) |
|
Toxicity |
Minnow toxicity |
0.445 |
(log mM) |