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Pharmacogenetics of CYP1A2, novel polymorphisms and haplotypes in three distinct Asian populations

Lim JS, Singh O, Ramasamy RD, Ramasamy S, Subramanian K, Lee EJ, et al.

Drug Metab. Pharmacokinet., 2010;25(6):616-23.
PMID: 20930417

CYP1A2 play an important role in the metabolism of many carcinogens and clinically important drugs. CYP1A2 activity has been found to be influenced by the presence of polymorphic variants which were reported to display wide interethnic variation. This study investigates the frequency distribution and linkage disequilibrium patterns of CYP1A2 genetic polymorphisms, and characterize their haplotype structures in three healthy Asian populations in Singapore (Chinese, Malay, and Indian). The entire CYP1A2 gene was screened in 126 healthy subjects from all three ethnic groups (N=42 each). A total of 25 polymorphisms was identified, of which nine were novel. The polymorphisms, -2467delT and -163C>A were detected at high frequencies in all Asian ethnic groups. Significant interethnic differences were observed in the genotypic frequency distribution of IVS2-99G>A (P<0.01) and 1548C>T (P=0.05) across the three ethnic groups while -163C>A (P=0.02) was found to differ between Chinese and Malays. Haplotype analyses revealed four to six major haplotypes in each ethnic population which accounted for more than 60% of the cumulative haplotype frequencies. Future studies should be done to investigate the functional roles of these haplotypes.
A review of the Drepanosticta carmichaeli-group, with the description of D. wildermuthi sp. nov. from the Central Highlands of Vietnam (Odonata: Zygoptera: Platystictidae)

Phan QT, Yokoi N, Makbun N, Joshi S, Subramanian KA, Ngo QP, et al.

Zootaxa, 2021 Nov 10;5067(2):187-210.
PMID: 34810750 DOI: 10.11646/zootaxa.5067.2.2

A modified and expanded definition of the Drepanosticta carmichaeli-group is given. This includes the species: D. annandalei Fraser, 1924, D. brownelli Tinkham, 1938, D. carmichaeli (Laidlaw, 1915), D. emtrai Dow, Kompier Phan, 2018, D. hongkongensis Wilson, 1997, D. jurzitzai Hmlinen, 1999, D. sumatrana Sasamoto Karube, 2007, D. tenella Lieftinck, 1935, D. vietnamica Asahina, 1997 and D. wildermuthi sp. nov.). The species of the group are reviewed and in most cases illustrated, diagnostic notes are given wherever possible. Drepanosticta polychromatica Fraser, 1931 is considered to be a junior synonym of D. carmichaeli and variation in D. carmichaeli is discussed. Drepanosticta wildermuthi sp. nov. from the Central Highlands of Vietnam (holotype male from Bao Loc district, Lam Dong Province) is described. The female of D. jurzitzai Hmlinen, 1999 is described for the first time. A key to the males of the Drepanosticta carmichaeli-group is provided.
Fulltext Biological Activity of Cyclic Peptide Extracted from Sphaeranthus amaranthoides Using De Novo Sequencing Strategy by Mass Spectrometry for Cancer

Yanamadala S, Shanthirappan S, Kannan S, Chiterasu N, Subramanian K, Al-Keridis LA, et al.

Biology (Basel), 2023 Mar 07;12(3).
PMID: 36979104 DOI: 10.3390/biology12030412

Though there are several advancements and developments in cancer therapy, the treatment remains challenging. In recent years, the antimicrobial peptides (AMPs) from traditional herbs are focused for identifying and developing potential anticancer molecules. In this study, AMPs are identified from Sphaeranthus amaranthoides, a natural medicinal herb widely used as a crucial immune stimulant in Indian medicine. A total of 86 peptide traces were identified using liquid-chromatography-electrospray-ionisation mass spectrometry (LC-ESI-MS). Among them, three peptides were sequenced using the manual de novo sequencing technique. The in-silico prediction revealed that SA923 is a cyclic peptide with C-N terminal interaction of the carbon atom of ASP7 with the nitrogen atom of GLU1 (1ELVFYRD7). Thus, SA923 is presented under the orbitides class of peptides, which lack the disulfide bonds for cyclization. In addition, SA923, steered with the physicochemical properties and support vector machine (SVM) algorithm mentioned for the segment, has the highest in silico anticancer potential. Further, the in vitro cytotoxicity assay revealed the peptide has anti-proliferative activity, and toxicity studies were demonstrated in Danio rerio (zebrafish) embryos.
Fulltext Isolation of anticancer bioactive secondary metabolites from the sponge-derived endophytic fungi Penicillium sp. and in-silico computational docking approach

Kaliaperumal K, Salendra L, Liu Y, Ju Z, Sahu SK, Elumalai S, et al.

Front Microbiol, 2023;14:1216928.
PMID: 37849927 DOI: 10.3389/fmicb.2023.1216928

INTRODUCTION: Fungus-derived secondary metabolites are fascinating with biomedical potential and chemical diversity. Mining endophytic fungi for drug candidates is an ongoing process in the field of drug discovery and medicinal chemistry. Endophytic fungal symbionts from terrestrial plants, marine flora, and fauna tend to produce interesting types of secondary metabolites with biomedical importance of anticancer, antiviral, and anti-tuberculosis properties.
METHODS: An organic ethyl acetate extract of Penicillium verruculosum sponge-derived endophytic fungi from Spongia officinalis yielded seven different secondary metabolites which are purified through HPLC. The isolated compounds are of averufin (1), aspergilol-A (2), sulochrin (3), monomethyl sulochrin (4), methyl emodin (5), citreorosein (6), and diorcinol (7). All the seven isolated compounds were characterized by high-resolution NMR spectral studies. All isolated compounds', such as anticancer, antimicrobial, anti-tuberculosis, and antiviral, were subjected to bioactivity screening.
RESULTS: Out of seven tested compounds, compound (1) exhibits strong anticancer activity toward myeloid leukemia. HL60 cell lines have an IC50 concentration of 1.00μm, which is nearly significant to that of the standard anticancer drug taxol. A virtual computational molecular docking approach of averufin with HL60 antigens revealed that averufin binds strongly with the protein target alpha, beta-tubulin (1JFF), with a -10.98 binding score. Consecutive OSIRIS and Lipinski ADME pharmacokinetic validation of averufin with HL60 antigens revealed that averufin has good pharmacokinetic properties such as drug score, solubility, and mutagenic nature. Furthermore, aspergilol-A (2) is the first report on the Penicillium verruculosum fungal strain.
DISCUSSION: We concluded that averufin (1) isolated from Penicillium verruculosum can be taken for further preliminary clinical trials like animal model in-vivo studies and pharmacodynamic studies. A future prospect of in-vivo anticancer screening of averufin can be validated through the present experimental findings.
Fulltext In-vivo anti-hyperglycemic effect of herbal extracts Tribulus terrestris (L) and Curcuma amada (R) on streptozotocin-induced diabetic rats and its associated histopathological studies

Kaliaperumal K, Bhat BA, Subramanian K, Ramakrishnan T, Chakravarthy E, Al-Keridis LA, et al.

Heliyon, 2024 Jan 15;10(1):e24009.
PMID: 38230238 DOI: 10.1016/j.heliyon.2024.e24009

Dia/betes is a serious health concern in many countries with high blood glucose, obesity, and multiple organ failures in late stages. Treating diabetes with effective drugs is still a challenging issue since most of the available diabetic drugs are not effective in combating diabetes, especially in secondary disease complications like obesity, retinopathy, and nephropathy associated with diabetes. Hence search for effective antidiabetic medication, especially from natural sources is mandatory with no adverse side effects. In the present study, a combined herbal aqueous extract of Tribulus terrestris and Curcuma amada was administered to diabetic-induced rats for 37 days. During experimentation, the mean blood glucose level was estimated and at the end of the experiment on the 37th day, the animal was sacrificed and observed for weight gain, plasma insulin, glycogen, glycated hemoglobin, urea, and creatinine level. The results revealed that TT and CA extract-treated diabetic groups significantly lowered the mean blood glucose level followed by increased glycogen and insulin level. Urea, creatinine, and HbA1c levels were considerably reduced in TT and CA-treated diabetic animals as compared to that of antidiabetic drug Glibenclamide-treated groups. TT and CA-treated diabetic animals showed considerable net body weight gain at the end of the experimental day. A concluding remark of the study shows that TT and CA herbal extract is effective against diabetes and it can be considered as an antidiabetic agent in ayurvedic medicine practice.
Fulltext Cardiovascular diseases prediction by machine learning incorporation with deep learning

Subramani S, Varshney N, Anand MV, Soudagar MEM, Al-Keridis LA, Upadhyay TK, et al.

Front Med (Lausanne), 2023;10:1150933.
PMID: 37138750 DOI: 10.3389/fmed.2023.1150933

It is yet unknown what causes cardiovascular disease (CVD), but we do know that it is associated with a high risk of death, as well as severe morbidity and disability. There is an urgent need for AI-based technologies that are able to promptly and reliably predict the future outcomes of individuals who have cardiovascular disease. The Internet of Things (IoT) is serving as a driving force behind the development of CVD prediction. In order to analyse and make predictions based on the data that IoT devices receive, machine learning (ML) is used. Traditional machine learning algorithms are unable to take differences in the data into account and have a low level of accuracy in their model predictions. This research presents a collection of machine learning models that can be used to address this problem. These models take into account the data observation mechanisms and training procedures of a number of different algorithms. In order to verify the efficacy of our strategy, we combined the Heart Dataset with other classification models. The proposed method provides nearly 96 percent of accuracy result than other existing methods and the complete analysis over several metrics has been analysed and provided. Research in the field of deep learning will benefit from additional data from a large number of medical institutions, which may be used for the development of artificial neural network structures.