INTRODUCTION
The ripple effect of COVID-19 outbreak has brought major changes and challenges to worldwide healthcare systems1. Since the outbreak of novel coronavirus disease – COVID – 19, celebrity of pharmaceutical drug development as intractable hot area of research and development (R&D) in the environs of pharma-industries is on the raise. Drug development in the post COVID era is a high-risk - high-return business that guarantees huge profit as returns upon success, paradoxically the success rate is extremely low2. Drug development requires extensive R&D program that includes clinical trials and investment-cost-returns approach for the successful development of a single drug from the industry into the market3. For these reasons, pharmaceutical companies sought for alternative strategies to increase the probability of success in their drug development project4. One of the possible solutions is to exploit AI&ML approach that has undergone tremendous ad-interim exponential growth5. As of now, multi-national pharmaceutical companies are adopting data mining and AI&ML technologies to reduce time and cost required for R&D program6.
Since antiquity, medicinal plants have been a valuable source of therapeutic agents, and even today most of the drugs available in the market are either obtained from plant based natural products or their derivative7 biomolecules with therapeutic potential. Medicinal plants remain a vital storehouse for the discovery of novel drug leads8-11. Plant Based Natural Products (PBNPs) offer unique features in comparison with their synthetic counterparts. PBNPs confer both advantages and challenges for the drug discovery process as they are characterized by structural scaffold diversity, functional specificity and molecular complexity8. In the past, pharmaceutical industry focused on libraries of synthetic compounds as a source for drug discovery9 however, this cumbersome process has been given up for their unwanted side effects. On the other hand, PBNPs leads with GRAS standards are easy to produce for resupply with good compatibility on high throughput screening (HTS) platforms8-11.
Catharanthus roseus (L) G. Don (Family – Apocynaceae), is native to Madagascar, but grown elsewhere as an ornamental plant in gardens, farms and landscape. In India, in Ayurvedic system of medicine, different parts of C. roseus have been reported for their use in the treatment of cancer, diabetes, stomach disorders, kidney, liver and cardiovascular diseases. Apart from India, this plant is used in traditional system of medicine in South Africa, China, Mexico and Malaysia, as remedy for diabetics12,13.
Significance of C. roseus in modern system of medicine has gained prominence after the characterization of anticancer indole alkaloids - vincristine and vinblastine. As isolation and purification process of vincristine and vinblastine from the leaves is a time-consuming and costly affair due to the low content of these compounds, Mekky et al.14 potentiated the biosynthesis of anticancer alkaloids vincristine and vinblastine in callus cultures of C. roseus. As of now, advanced and high-throughput separation/ analytical techniques have been used for isolation, purification, identification, characterization and quantitation of other alkaloids from the crude extract prepared from C. roseus13,15.
Alkaloids of C. roseus possess hypotensive, sedative, tranquilizing, and anticancer properties12. C. roseus is recommended for the treatment of nose bleeding, gum bleeding, mouth ulcers, and sore throats, hypertension, cystitis, gastritis, enteritis, and diarrhea and memory loss13. Alkaloids including vinblastine, vincristine, vinorelbine, and vinflunine isolated from this plant have proven antitumor activity15,16. Apart from biomedical application C. roseus is exploited for its antibacterial, biopesticidal activities17,18. Recent phytochemical investigation has revealed a total of 344 compounds including monoterpene indole alkaloids (MIAs) (110), bis-indole alkaloids (35), flavonoids (34), phenolic acids (9) and volatile constituents (156) have been reported in the various extracts and fractions of different plant parts of C. roseus13. Similarly aerial parts of C. roseus contain vindoline, vindolidine, vindolicine, roseadine, leurosine-N′b-oxide, leurocolombine, catharanthamine, pleuroside, dimethylvinblastin, 5′-oxoleurosine, leurosidineN′b-oxide, vinorelbine, vinzolidine, vineamine, raubasin, 16-epi-19S-vindolinine, and vindolinine16.
ALA (18:3n-3) is essential ω-3 fatty acid found in nuts19. It is necessary for normal growth and development thus an aspect of the human diet, probably because it is the main substrate for the synthesis of longer-chain fatty acids. ALA is the precursor of two long chain ω-3 fatty acids viz., EPA (eicosapentaenoic acid, 20:3n-5) and DHA (docosahexaenoic acid, 22:3n-6), both of them have vital roles in brain development, cardio-vascular health, inflammatory response, etc.20
The metabolic pathways of ALA have been reported by Fekete and Decsi, 201021. During the metabolic process, ALA is bio-transformed into EPA and DHA. ALA is readily converted to EPA has been reported to be 8%, while conversion rates of ALA to DHA has been reported to be 4%, no direct link between DHA concentration and increase in rate of intake has been reported yet. Burdge et al.22 reported that in humans, enzymes desaturase and elongase are involved in the bioconversion of ALA to EPA and DHA22,23 respectively. Pawlosky et al.24 reported that coefficient constant of EPA to DHA was about 4-fold higher in women than in men. Invariably, it has been reported that women have a higher concentration of DHA than men25. Further, high conversion rate of ALA to EPA/ DHA in women has been related to the level of estrogen26. In human system, ALA possesses hypo-cholesterolemic, nematicide, anti-arthritic, hepatoprotective anti-androgenic, hypo-cholesterolemic, 5-α reductase inhibitor, antihistaminic, anti-coronary, anti-eczemic, anti-acne properties25. Pharmacological studies show that ALA has the anti-metabolic syndrome, anticancer, anti-inflammatory, anti-oxidant, anti-obesity, neuro-protection properties25-28. Recently, it has been proved that ALA plays a major role in the functional regulation of gut microflora28.
MATERIALS AND METHODS
Botanical Description of the plant
Catharanthus roseus (L) G. Don (Family – Apocynaceae)
Habit: Suffrutex up to 1 m high, perennial, woody at the base, herbaceous above; Stem: glabrous or thinly pubescent; Leaves: opposite, obovate, oblong or oblanceolate, apex rounded or, rarely, sub-acute, apiculate, base cuneate, 4-8 cm long 1-3 cm broad, membranous to thinly coriaceous, glabrous or finely pubescent; Petiole 2-5 mm long; axillary glands forming a fringe, outer longer than inner; Stipules: absent; Flowers: axillary, solitary/ paired, subsessile, pink or white, or white with pink centre; Calyx: divided at base; sepals - 5, linear-subulate, 4-6 mm long, glabrous or pubescent; Corolla: salver-shaped; tube slender cylindrical, 2.3-2.6 cm long and 2-2.5 mm in dia, mouth constricted, thickened, pubescent; lobes broadly obovate, apiculate, 1.6-2.0 cm long; Stamens: 5, pentamerous, inserted near the mouth; anthers 2 mm long, subsessile. Disc replaced by 2 linear-subulate glands 2 mm long alternating with the carpels; Ovary: bicarpelary, 2 carpels, free; style filiform, stigma at the level of the anthers, capitate with a reflexed hyaline frill at the base; Ovules: numerous, 2-seriate. Fruit: two follicular mericarps erect, slightly spreading; follicles 2.5-3.5 cm long, cylindric, striate. Seeds: numerous, oblong, 2 mm long, black, rugose, grooved on one face; Cotyledons: flat, slightly shorter than the radicle; Endosperm: scanty; Fl & Fr: round the year.
Plant materials were collected from the College Campus, Identified and authenticated by Department of Botany, Government Arts College, Melur, Madurai, TamilNadu using Flora29.30. The plant material was shade dried, pulverized at room temperature, sieved and stored in vials until used. Dried powder was dissolved in double distilled water and the aqueous leaf extract of C. roseus was analyzed for phytochemicals using standard protocols. GCMS analysis was performed as described previously31-33.
ADMET predications
Phyto-components of AqLE of C. roseus were subjected to ADME prediction using QikProp (Schrödinger, LLC, NY) and toxicity prediction using TOPKAT (Accelrys, Inc., USA, 2015). QikProp develops and employs QSAR/QSPR models using partial least squares, principal component analysis and multiple linear regression to predict physicochemical significant descriptors and pharmaco-kinetically relevant properties that are essential for rational drug design. The computational toxicity was assessed using TOPKAT (TOxicity Prediction by Komputer Assisted Technology). TOPKAT calculates the toxicity on the basis of the quantitative structure-toxicity relationship (QSTR) model using linear regression on the structural descriptor and it considers 4 nearest neighbours with a similarity distance of <0.25 to assign probabilities of the toxicity such as carcinogenicity, mutagenicity, rat oral LD50, skin irritation, aerobic biodegradability34-38.
RESULTS
Isolation, purification and characterization of PBNPs (secondary metabolites) remains the key aspect of phytochemical screening39-49. In the present study ALA among the phyto-components of AqLE of Catharanthus roseus were ADMET predicted in-silico.
|
Chemical kingdom |
: |
Organic compounds |
|
Super class |
: |
Lipids and lipid-like molecules |
|
Class |
: |
Fatty Acyls |
|
Subclass |
: |
Lineolic acids derivatives |
|
PubChem Identifier |
: |
5280934 |
|
ChEBI Identifier |
: |
25048 |
|
CAS Identifier |
: |
28290-79-1 |
|
Synonyms |
: |
α-LINOLENIC ACID; |
|
Canonical SMILES |
: |
CC/C=CC/C=CC/C=CCCCCCCCC(=O)O |
|
InChI Key |
: |
DTOSIQBPPRVQHS-PDBXOOCHSA-N |
Physicochemical Properties
Molecular weight of ALA was calculated as 278.44 g/mol; LogP value was predicted as 5.66; LogD value was predicted as 3.68; LogSw value was predicted as -4.78. Number of stereo-centers was predicted as 0; Stereo-chemical complexity was predicted as 0.000; Fsp3 was predicted as 0.611; Topological polar surface area was calculated as 37.30Å2; Number of hydrogen bond donors was calculated as 1; Number of hydrogen bond acceptors was calculated as 1; Number of smallest set of smallest rings (SSSR) was calculated as 0; Size of the biggest system ring was calculated as 0; Number of rotatable bond was calculated as 13; Number of rigid bond was calculated as 4; Number of charged group was calculated as 1; Total charge of the compound was calculated as -1; Number of carbon atoms was ascertained as 18; Number of heteroatoms was ascertained as 2; Number of heavy atoms was ascertained as 20; Ratio between the number of non-carbon atoms and the number of carbon atoms was ascertained as 0.11 (Table 1). TPSA of ALA was calculated as 37.30; natoms in ALA was 20; MW of ALA was calculated as 278.44; nON was calculated as 2; nOHNH value was calculated as 1; nviolations value for ALA was calculated as 1; number of rotatable bonds in ALA was 13; and the theoretical volume of ALA was calculated as 306.47. The 3D structure of ALA is illustrated in Fig. 1.
Druggability Properties
In-silico studies are expected to reduce the risk of late-stage attrition of drug development and to optimize screening/ testing by looking at Druggability Properties, Lipinski's rule of 5 violations was predicted as 1; Veber rule was predicted as Good; Egan rule was predicted as Good; Oral PhysChem score (Traffic Lights) was predicted as 4; GSK's 4/400 score was predicted as Good; Pfizer's 3/75 score was predicted as BAD; Weighted quantitative estimate of drug-likeness (QEDw) score was predicted as 0.31; Solubility of EA was predicted as 2342.23; Solubility Forecast Index of EA was predicted as Good (Table 2).
ADMET Properties
Experimental evaluation of small-molecule is both time-consuming and expensive. On the other hand evolution of computational approaches to optimize pharmacokinetic and toxicity properties is said to drive the progression of drug discovery. Prediction of ADMET-associated properties of new chemicals, however, is a challenging task with only tenuous links between many physicochemical characteristics and pharmacokinetic and toxicity properties. This has led to a need for novel approaches to understand, explore, and predict ADMET properties of small molecules as a way to improve compound quality and success rate. ADMET prediction models, including performance measures for the selected candidate molecule ALA were performed online (Table 3).
Human Intestinal Absorption (HIA+) for ALA had a calculated probability value of 0.990; Blood Brain Barrier (BBB+) had a calculated probability value of 0.931; Caco-2 permeable (Caco2+) for ALA had a calculated probability value of 0.774; P-glycoprotein substrate that served as Non-substrate for ALA had a calculated probability value of 0.677; P-glycoprotein inhibitor I that served as Non-inhibitor for ALA had a calculated probability value of 0.950; P-glycoprotein inhibitor II for ALA served as Non-inhibitor and had a predicted probability value of 0.903 (Table 3).
CYP450 2C9 substrate for ALA served as Non-substrate with a predicted probability value of 0.774; CYP450 2D6 substrate for ALA served as Non-substrate with a predicted probability value of 0.908; CYP450 3A4 substrate for ALA served as Non-substrate with a predicted probability value of 0.688; CYP450 1A2 inhibitor for ALA worked as Inhibitor with a predicted probability value of 0.692; CYP450 2C9 inhibitor for ALA functioned as Non-inhibitor with a predicted probability value of 0.880; CYP450 2D6 inhibitor for ALA served as Non-inhibitor with a predicted probability value of 0.963; CYP450 2C19 inhibitor for ALA remained as Non-inhibitor with a predicted probability value of 0.964; CYP450 3A4 inhibitor for ALA was as Non-inhibitor with a predicted probability value of 0.947; CYP450 inhibitory promiscuity for ALA had Low CYP Inhibitory Promiscuity with a predicted probability value of 0.943 respectively (Table 3).
ADMET Ames test for ALA served as Non AMES toxic with a predicted probability value of 0.913; Carcinogenicity for ALA served as Non-carcinogens with a predicted probability value of 0.650; Biodegradation potential for ALA served as Ready biodegradable with a predicted probability value of 0.781; Rat acute toxicity 1.450 LD50, mol/kg for ALA was Not applicable; hERG inhibition (predictor I) for ALA served as Weak inhibitor with a predicted probability value of 0.882; hERG inhibition (predictor II) for ALA served as Non-inhibitor with a predicted probability value of 0.932 respectively (Table 3). In the present study 15 models covered a diverse set of ADMET endpoints including Maximum Recommended Therapeutic Dose (MRTD), chemical mutagenicity, human liver microsomal (HLM), Pgp inhibitor/ substrates. ADMET data for performance measures of vNN models in 10-fold cross validation using a restricted/ unrestricted applicability domain is given in Table 4a,b.
Liver Toxicity - DILI
Drug-induced liver injury (DILI) is one of the most commonly cited reasons for drug withdrawals from the market. This application predicts whether a compound could cause DILI. A dataset of 1,431 compounds was obtained from online sources. The dataset contained both pharmaceuticals and non-pharmaceuticals compounds; compounds were classified as causing DILI if it was associated with a high risk and non DILI if there was no such risk with the compound.
Cytotoxicity (HepG2)
Cytotoxicity is the degree to which a chemical causes damage to cells. A 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 model, compounds with IC50 ≤ 10 μM in in-vitro assay as cytotoxic was considered.
Metabolism - 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 were considered as stable; otherwise considered unstable. HLM data was retrieved from ChEMBL database, manually curated and classified 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 stable and 1,341 were unstable.
Metabolism - 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 was 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. Prediction values for CYP1A2, CYP3A4, CYP2D6, CYP2C9, and CYP2C19 is given in Table 4.
Membrane Transporters - BBB
The blood-brain barrier (BBB) is a highly selective barrier that separates the circulating blood from the central nervous system. A vNN-based BBB model was developed, using 352 compounds, their corresponding BBB permeability values (logBB) were obtained from online sources. The compounds were further classified with logBB values of less than –0.3 and greater than +0.3 as BBB non-permeable and permeable respectively.
Membrane Transporters - 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 through 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. In the present study, models were developed to predict both Pgp substrates and Pgp inhibitors. The dataset contained 422 substrates and 400 non-substrates. To generate a large Pgp inhibitor dataset, two 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. Results are given in Table 4.
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 term QT syndrome, which may result in arrhythmia which may ultimately lead to death. A data set of 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 with IC50 values greater than 10 μM were collected from ChEMBL and classified as non-blockers. Results are provided in Table 4.
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. In the present study a largest dataset of chemical-induced changes in mitochondrial membrane potential (MMP) were 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 screened library of 10,000 compounds at 15 concentrations, each in triplicate, to measure changes in the MMP in HepG2 cells. Data indicate that 913 compounds decreased the MMP, whereas 5,395 compounds had no or insignificant effect (Table 4).
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. In the present study, a prediction model was developed using a dataset of 6,512 compounds. Data indicate that 3,503 compounds were Ames-positive.
Maximum Recommended Therapeutic Dose (MRTD)
MRTD is an estimated upper daily dose that is safe. In the present study a prediction model was built based on a dataset of MRTD values publically disclosed by the FDA, mostly of single-day oral doses for an average adult with a body weight of 60 kg, for 1,220 compounds (small organic drugs) however organometallics, high-molecular weight polymers (>5,000 Da), nonorganic chemicals, mixtures of chemicals, and very small molecules (<100 Da) were excluded. An external test set of 160 compounds were used that were collected by the FDA for validation. The total dataset for the model contained 1,185 compounds. The predicted MRTD value is reported in mg/day unit based upon an average adult weighing 60 kg (Table 4).
Biological properties - G-PCRs (GPCRs)
GPCRs are the largest family of signalling proteins. Structurally, GPCRs are similar: extracellular N-terminus, seven membrane-spanning α-helices (TM), and intracellular C-terminus, with variable extracellular and intracellular elements. These cell surface receptors act like an inbox for messages in the form of light energy, peptides, lipids, sugars, and proteins. Calculated distribution of activity scores (version 2011.06) for GPCR ligands for the molecule was 0.33; kinase inhibitors, ion channel modulators, nuclear receptor ligands, protease inhibitors and other enzyme targets compared with scores for about 100'000 average drug-like molecules. The calculated value for Ion channel modulator (0.23); Kinase inhibitor (-0.19); Nuclear receptor ligand (0.35); Protease inhibitor (0.13); Enzyme inhibitor (0.42) respectively, the score allows efficient separation of active and inactive molecules. Further, cytoscape network of predicted human targets of ALA- human target proteins were predicted using STITCH (26590256), a database of chemical-protein interaction networks is provided in Fig 2. Further, predicted bioactivity target classes for ALA from Catharanthus roseus with probability score provided in Fig. 3.
pkCSM - pharmacokinetic properties of ALA using graph-based signatures
Drug development is a fine balance of optimizing drug like properties to maximize efficacy, safety, and pharmacokinetics. Many early stage drug discovery programs focus on identifying molecules that bind to a target of interest. While potency is a driving factor in these early stages, ultimately the pharmacokinetic and toxicity properties dictate whether it will ever advance its effectiveness and success therapeutically. Mathematical calculation and graph-theory based Graph modelling is an intuitive and well established mathematical representation of chemical entities, from where the descriptors encompassing both molecule structure and chemistry can be extracted for rational drug design. The pharmacokinetic properties of ALA predicted using graph-based signatures is given in Table 5.
DISCUSSION
Absorption: There is very limited information on the absorption of ALA in the human gut. However, absorption of ALA in humans is assumed to be efficient. Absorption can be determined through the difference between intake levels of ALA in foods and excretion in the feces. Absorption efficiency of ALA through the human gut and carrier-mediated transporters involved in the absorption is assumed to be quite high50.
Distribution: Available information on the distribution of ALA is limited. Lin and Salem51, reported whole body distribution of ALA in rats. Through inter conversion of EPA and DHA, DHA was deposited in brain, spinal cord, heart, testes, and eye over time. About 16–18% of ALA was deposited in adipose tissue, skin, and muscle. About 6% of ALA was elongated and desaturated, and stored, in muscle, adipose tissue, and carcass. Remaining 78% of ALA was eventually excreted.
Metabolism: Metabolic conversion of ALA to EPA and DHA is relatively poor because ALA absorbed in the system undergoes b-oxidation52. Although 67% of ALA undergoes b-oxidation in brain, only 30% of fatty acids, such as arachidonic acid, undergo b-oxidation in brain53. In the brain, small percentage of fatty acids undergoes b-oxidation. However, the rapidity of the process is difficult to measure owing to the pace of lipid metabolism occurs within seconds/ minutes, rather than hours.
Toxicity: It has been suggested that EDF containing enriched ALA is safe when orally administered to rats. Furthermore, EDF could reduce the increase of triglyceride levels in plasma. Prospective meta-analysis studies concluded that there exists no association between dietary intake of ALA and prostate cancer risk54. Therefore, overall evidence of prostate cancer risk with ALA remains inconclusive. Association of ALA with the risk of macular degeneration has been reported55, however, more research is required before any conclusion is drawn. Flaxseed/ oil is rich dietary sources of ALA is prospected to induce adverse gastrointestinal effects, such as flatulence, bloating and stomach aches/cramps56. Further it has been pointed out that ALA can induce lipid peroxidation when exposed to UV radiation, which may produce have adverse effects if not monitored57. ALA is well-known for its anti-inflammatory activity. Recently, it has been pointed out that ALA rich diet influences microbiota composition and villus morphology of the mouse small intestine.
CONCLUSION
ALA from AqLE of C. roseus was screened and ADMET predicted for the functional properties. It has been well established that in the human body, ALA is converted to EPA and DHA, which is protective against cardiovascular, neuronal, osteoporotic inflammatory diseases. In addition, EPA and DHA lower the blood cholesterol level that reduces the risk of heart disease. However, the conversion rates of ALA to EPA/ DHA is very low. With limited toxicological data, it is concluded that ALA is safe as a dietary ingredient because it doesn’t produce serious health problems, this essential fatty acid could be used as nutraceutical and pharmacological food ingredient. However, overall evidence on the association of ALA with risks remains inconclusive at this point of time. The data and mathematical calculation based in-silico predication models presented in the paper is hopefully is expected to facilitate the drug development process by enabling the rapid design, evaluation, and prioritization of ALA owing to its overwhelming biomedical applications.
REFERENCES
1. Ayati N, Saiyarsarai P, Nikfar S. Short and long term impacts of COVID-19 on the pharmaceutical sector. DARU Journal of Pharmaceutical Sciences 2020; 28(2):799-805. https://doi.org/10.1007/s40199-020-00358-5
2. Won JH, Lee H. Can the COVID-19 pandemic disrupt the current drug development practices? International Journal of Molecular Sciences 2021; 22(11):5457. https://doi.org/10.3390/ijms22115457
3. Tamimi NA, Ellis P. Drug development: from concept to marketing!. Nephron Clinical Practice 2009; 113(3):c125-31. https://doi.org/10.1159/000232592
4. Kiriiri GK, Njogu PM, Mwangi AN. Exploring different approaches to improve the success of drug discovery and development projects: a review. Future Journal of Pharmaceutical Sciences 2020; 6(1):1-2. https://doi.org/10.1186/s43094-020-00047-9
5. Kim H, Kim E, Lee I, Bae B, Park M, Nam H. Artificial intelligence in drug discovery: a comprehensive review of data-driven and machine learning approaches. Biotechnology and Bioprocess Engineering 2020; 25(6):895-930. https://doi.org/10.1007/s12257-020-0049-y
6. Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery Molecular diversity 2021; 25(3):1315-60. https://doi.org/10.1007/s11030-021-10217-3
7. Balunas MJ, Kinghorn AD. Drug discovery from medicinal plants Life sciences 2005; 78(5):431-41. https://doi.org/10.1016/j.lfs.2005.09.012
8. Atanasov AG, Zotchev SB, Dirsch VM, Supuran CT. Natural products in drug discovery: advances and opportunities. Nature Reviews Drug Discovery 2021; 20(3):200-16. https://doi.org/10.1038/s41573-020-00114-z
9. Atanasov AG, Waltenberger B, Pferschy-Wenzig EM, Linder T, Wawrosch C, Uhrin P, Temml V, Wang L, Schwaiger S, Heiss EH, Rollinger JM. Discovery and resupply of pharmacologically active plant-derived natural products: A review. Biotechnology advances 2015; 33(8):1582-614. https://doi.org/10.1016/j.biotechadv.2015.08.001
10. Newman DJ, Cragg GM. Natural products as sources of new drugs over the nearly four decades from 01/1981 to 09/2019. Journal of Natural Products. 2020; 83(3):770-803. https://doi.org/10.1021/acs.jnatprod.9b01285
11. Dzobo K. The Role of Natural Products as Sources of Therapeutic Agents for Innovative Drug Discovery. Reference Module in Biomedical Sciences. 2021; B978-0-12-820472-6.00041-4.
12. Das S, Sharangi AB. Madagascar periwinkle (Catharanthus roseus L.): Diverse medicinal and therapeutic benefits to humankind. Journal of Pharmacognosy and Phytochemistry. 2017; 6(5):1695-701.
13. Kumar S, Singh B, Singh R. Catharanthus roseus (L.) G. Don: A review of its ethnobotany, phytochemistry, ethnopharmacology and toxicities. Journal of Ethnopharmacology. 2022; 284:114647. https://doi.org/10.1016/j.jep.2021.114647
14. Mekky H, Al-Sabahi J, Abdel-Kreem MF. Potentiating biosynthesis of the anticancer alkaloids vincristine and vinblastine in callus cultures of Catharanthus roseus. South African Journal of Botany. 2018; 114:29-31. https://doi.org/10.1016/j.sajb.2017.10.008
15. Tiong SH, Looi CY, Hazni H, Arya A, Paydar M, Wong WF, Cheah SC, Mustafa MR, Awang K. Antidiabetic and antioxidant properties of alkaloids from Catharanthus roseus (L.) G. Don. Molecules. 2013; 18(8):9770-84. https://doi.org/10.3390/molecules18089770
16. Singh B, Sharma RA. Secondary Metabolites of Medicinal Plants, 4 Vol Set: Ethnopharmacological Properties, Biological Activity and Production Strategies. John Wiley & Sons; 2020. https://doi.org/10.1002/9783527825578
17. Ramya S. In Vitro Evaluation of Antibacterial Activity Using Crude Extracts of Catharanthus roseus L. (G.) Don. Ethnobotanical Leaflets. 2008; 2008(1):140.
18. Ramya S. Biopesticidal effect of leaf extracts of Catharanthus roseus L (G) Don. on the larvae of gram pod borer-Helicoverpa armígera (Hübner). Ethnobotanical Leaflets. 2008; 2008(1):145.
19. Yuan Q, Xie F, Huang W, Hu M, Yan Q, Chen Z, Zheng Y, Liu L. The review of alpha‐linolenic acid: Sources, metabolism, and pharmacology. Phytotherapy Research. 2022; 36(1):164-88. https://doi.org/10.1002/ptr.7295
20. Allaire J, Couture P, Leclerc M, Charest A, Marin J, Lépine MC, Talbot D, Tchernof A, Lamarche B. A randomized, crossover, head-to-head comparison of eicosapentaenoic acid and docosahexaenoic acid supplementation to reduce inflammation markers in men and women: the Comparing EPA to DHA (ComparED) Study. The American journal of clinical nutrition. 2016; 104(2):280-7. https://doi.org/10.3945/ajcn.116.131896
21. Fekete K, Decsi T. Long-chain polyunsaturated fatty acids in inborn errors of metabolism. Nutrients. 2010; 2(9):965-74. https://doi.org/10.3390/nu2090965
22. Burdge GC, Calder PC. Conversion of α-linolenic acid to longer-chain polyunsaturated fatty acids in human adults. Reproduction Nutrition Development. 2005; 45(5):581-97. https://doi.org/10.1051/rnd:2005047
23. Zhang JY, Kothapalli KS, Brenna JT. Desaturase and elongase limiting endogenous long chain polyunsaturated fatty acid biosynthesis. Current opinion in clinical nutrition and metabolic care. 2016; 19(2):103. https://doi.org/10.1097/MCO.0000000000000254
24. Pawlosky R, Hibbeln J, Lin Y, Salem N. n-3 fatty acid metabolism in women. British Journal of Nutrition. 2003; 90(5):993-4. https://doi.org/10.1079/BJN2003985
25. Domenichiello AF, Kitson AP, Bazinet RP. Is docosahexaenoic acid synthesis from α-linolenic acid sufficient to supply the adult brain? Progress in lipid research. 2015; 59:54-66. https://doi.org/10.1016/j.plipres.2015.04.002
26. Kitson AP, Stroud CK, Stark KD. Elevated production of docosahexaenoic acid in females: potential molecular mechanisms. Lipids. 2010; 45(3):209-24. https://doi.org/10.1007/s11745-010-3391-6
27. Samanta S. Potential bioactive components and health promotional benefits of tea (Camellia sinensis) Journal of American College of Nutrition. 2020:1-29. https://doi.org/10.1080/07315724.2020.1827082
28. Todorov H, Kollar B, Bayer F, Brandão I, Mann A, Mohr J, Pontarollo G, Formes H, Stauber R, Kittner JM, Endres K. α-Linolenic acid-rich diet influences microbiota composition and villus morphology of the mouse small intestine. Nutrients. 2020; 12(3):732. https://doi.org/10.3390/nu12030732
29. Matthew KM. Flora of the Tamilnadu Carnatic. The Rapinat Herbarium, St. Joseph's College, Tiruchirapalli, India; 198117.
30. Gamble JS, Fischer CE. Flora of the Presidency of Madras. London, UK: West, Newman and Adlard; 1915. https://doi.org/10.5962/bhl.title.21628
31. Kandeepan C, Sabitha M, Parvathi K, Senthilkumar N, Ramya S, Boopathi NM, Jayakumararaj R. Phytochemical Screening, GCMS Profile, and In-silico properties of Bioactive Compounds in Methanolic Leaf Extracts of Moringa oleifera. Journal of Drug Delivery and Therapeutics. 2022; 12(2):87-99. https://doi.org/10.22270/jddt.v12i2.5250
32. Soorya C, Balamurugan S, Basha AN, Kandeepan C, Ramya S, Jayakumararaj R. Profile of Bioactive Phyto-compounds in Essential Oil of Cymbopogon martinii from Palani Hills, Western Ghats, India. JDDT 2021; 11(4):60-65 https://doi.org/10.22270/jddt.v11i4.4887
33. Loganathan T, Barathinivas A, Soorya C, Balamurugan S, Nagajothi TG, Jayakumararaj R. GCMS Profile of Bioactive Secondary Metabolites with Therapeutic Potential in the Ethanolic Leaf Extracts of Azadirachta indica: A Sacred Traditional Medicinal Plant of India. JDDT 2021; 11(4-S):119-126. https://doi.org/10.22270/jddt.v11i4-S.4967
34. 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
35. 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
36. Kandeepan C, Kalaimathi RV, Jeevalatha A, Basha AN, Ramya S, Jayakumararaj R. In-silico ADMET Pharmacoinformatics of Geraniol (3, 7-dimethylocta-trans-2, 6-dien-1-ol)-acyclic monoterpene alcohol drug from Leaf Essential Oil of Cymbopogon martinii from Sirumalai Hills (Eastern Ghats), India. Journal of Drug Delivery and Therapeutics. 2021; 11(4):109-118. https://doi.org/10.22270/jddt.v11i4-S.4965
37. 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. Journal of Drug Delivery and Therapeutics. 2022; 12(1-S):123-131. https://doi.org/10.22270/jddt.v12i1-S.5222
38. 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
39. 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):114-122. https://doi.org/10.22270/jddt.v12i1.5189
40. 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
41. 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.
42. Ramya S, Jepachanderamohan PJ, Kalayanasundaram M, Jayakumararaj R. In vitro antibacterial prospective of crude leaf extracts of Melia azedarach Linn. against selected bacterial strains. Ethnobotanical Leaflets. 2009; 2009(1):32
43. Ramya S, Krishnasamy G, Jayakumararaj R, Periathambi N, Devaraj A. Bioprospecting Solanum nigrum Linn.(Solanaceae) as a potential source of Anti-Microbial agents against selected Bacterial strains. Asian Journal of Biomedical and Pharmaceutical Sciences. 2012; 2(12):65
44. 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
45. Ramya S, Neethirajan K, Jayakumararaj R. Profile of bioactive compounds in Syzygium cumini-a review. Journal of Pharmacy research. 2012; 5(8):4548-53
46. 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
47. 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
48. 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
49. 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
50. Kim KB, Nam YA, Kim HS, Hayes AW, Lee BM. α-Linolenic acid: Nutraceutical, pharmacological and toxicological evaluation. Food and chemical toxicology. 2014; 70:163-78. https://doi.org/10.1016/j.fct.2014.05.009
51. Lin YH, Salem N. Whole body distribution of deuterated linoleic and α-linolenic acids and their metabolites in the rat. Journal of Lipid Research. 2007; 48(12):2709-24. https://doi.org/10.1194/jlr.M700369-JLR200
52. Poumès‐Ballihaut C, Langelier B, Houlier F, Alessandri JM, Durand G, Latge C, Guesnet P. Comparative bioavailability of dietary α‐linolenic and docosahexaenoic acids in the growing rat. Lipids. 2001; 36(8):793-800. https://doi.org/10.1007/s11745-001-0786-5
53. Barceló-Coblijn G, Murphy EJ. Alpha-linolenic acid and its conversion to longer chain n− 3 fatty acids: Benefits for human health and a role in maintaining tissue n− 3 fatty acid levels. Progress in lipid research. 2009; 48(6):355-74. https://doi.org/10.1016/j.plipres.2009.07.002
54. Carleton AJ, Sievenpiper JL, de Souza R, McKeown-Eyssen G, Jenkins DJ. Case-control and prospective studies of dietary α-linolenic acid intake and prostate cancer risk: a meta-analysis. BMJ open. 2013; 3(5):e002280. https://doi.org/10.1136/bmjopen-2012-002280
55. Seddon JM, Rosner B, Sperduto RD, Yannuzzi L, Haller JA, Blair NP, Willett W. Dietary fat and risk for advanced age-related macular degeneration. Archives of ophthalmology. 2001; 119(8):1191-9. https://doi.org/10.1001/archopht.119.8.1191
56. Austria JA, Richard MN, Chahine MN, Edel AL, Malcolmson LJ, Dupasquier CM, Pierce GN. Bioavailability of alpha-linolenic acid in subjects after ingestion of three different forms of flaxseed. Journal of the American College of Nutrition. 2008; 27(2):214-21. https://doi.org/10.1080/07315724.2008.10719693
57. Regensburger J, Knak A, Maisch T, Landthaler M, Bäumler W. Fatty acids and vitamins generate singlet oxygen under UVB irradiation. Experimental dermatology. 2012; 21(2):135-9. https://doi.org/10.1111/j.1600-0625.2011.01414.x