Betulin: a novel triterpenoid anti-cancerous agent targeting cervical cancer through epigenetic proteins.

Worldwide, cervical cancer (CCa) is a major killer of women. As the conventional drugs used to treat cervical cancer are expensive and expose severe side effects, there is a growing demand to search for novel modifications. Therefore, in the current investigation employing a bioinformatic approach, we explored triterpenoids for their anti-cancer efficacy by targeting cervical cancer epigenetic proteins, namely DNMT3A, HDAC4, and KMT2C. The study utilized molecular docking, ADMET assay, Molecular Dynamic simulation, and DFT calculation to unveil Betulin (BE) as the potential lead compound. Comparative analysis with that standard drug indicated that BE has a better glide score with the target protein KM2TC (- 9.893 kcal/mol), HDAC4 (- 9.720 kcal/mol), and DNMT3A (- 7.811 kcal/mol), which depicts that BE could be a potent inhibitor of these three epigenetic proteins and exhibits favorable pharmacokinetic, pharmacodynamics and toxicity properties. Molecular Dynamics simulation revealed noteworthy structural stability and compactness. DFT analysis revealed higher molecular activity of BE and showed the most increased kinetic stability (δE = 0.254647 eV). Further, we employed In vitro analysis through MTT assay and found that BE has IC50 of 15 µg/ml. In conclusion, BE can potentially treat CCa upon further investigations using in vivo models for better understanding.

Exploratory drug discovery in breast cancer patients: A multimodal deep learning approach to identify novel drug candidates targeting RTK signaling.

Breast cancer, a highly formidable and diverse malignancy predominantly affecting women globally, poses a significant threat due to its intricate genetic variability, rendering it challenging to diagnose accurately. Various therapies such as immunotherapy, radiotherapy, and diverse chemotherapy approaches like drug repurposing and combination therapy are widely used depending on cancer subtype and metastasis severity. Our study revolves around an innovative drug discovery strategy targeting potential drug candidates specific to RTK signalling, a prominently targeted receptor class in cancer. To accomplish this, we have developed a multimodal deep neural network (MM-DNN) based QSAR model integrating omics datasets to elucidate genomic, proteomic expression data, and drug responses, validated rigorously. The results showcase an R2 value of 0.917 and an RMSE value of 0.312, affirming the model's commendable predictive capabilities. Structural analogs of drug molecules specific to RTK signalling were sourced from the PubChem database, followed by meticulous screening to eliminate dissimilar compounds. Leveraging the MM-DNN-based QSAR model, we predicted the biological activity of these molecules, subsequently clustering them into three distinct groups. Feature importance analysis was performed. Consequently, we successfully identified prime drug candidates tailored for each potential downstream regulatory protein within the RTK signalling pathway. This method makes the early stages of drug development faster by removing inactive compounds, providing a hopeful path in combating breast cancer.

A breast cancer-specific combinational QSAR model development using machine learning and deep learning approaches.

Breast cancer is the most prevalent and heterogeneous form of cancer affecting women worldwide. Various therapeutic strategies are in practice based on the extent of disease spread, such as surgery, chemotherapy, radiotherapy, and immunotherapy. Combinational therapy is another strategy that has proven to be effective in controlling cancer progression. Administration of Anchor drug, a well-established primary therapeutic agent with known efficacy for specific targets, with Library drug, a supplementary drug to enhance the efficacy of anchor drugs and broaden the therapeutic approach. Our work focused on harnessing regression-based Machine learning (ML) and deep learning (DL) algorithms to develop a structure-activity relationship between the molecular descriptors of drug pairs and their combined biological activity through a QSAR (Quantitative structure-activity relationship) model. 11 popularly known machine learning and deep learning algorithms were used to develop QSAR models. A total of 52 breast cancer cell lines, 25 anchor drugs, and 51 library drugs were considered in developing the QSAR model. It was observed that Deep Neural Networks (DNNs) achieved an impressive R2 (Coefficient of Determination) of 0.94, with an RMSE (Root Mean Square Error) value of 0.255, making it the most effective algorithm for developing a structure-activity relationship with strong generalization capabilities. In conclusion, applying combinational therapy alongside ML and DL techniques represents a promising approach to combating breast cancer.

Cytotoxicity and Proteasome Inhibition by Alkaloid Extract from Murraya koenigii Leaves in Breast Cancer Cells-Molecular Docking Studies.

Murraya koenigii (curry tree) leaves are rich in bioactive compounds such as flavonoids, alkaloids, and coumarins. Alkaloids from M. koenigii leaves have antianalgesic, antiulcerogenic, antiobesity, and antitumor activities. In this study, we tested the cytotoxic and proteasome-inhibitory potential of a total alkaloid extract (TAE) from M. koenigii leaves in the breast cancer cell line MDA-MB-231. The TAE decreased cell viability with an IC50 of 14.4 µg/mL and altered growth kinetics of breast cancer cells. TAE (32 µg/mL) arrested cells (35%) in the "S" phase of the cell cycle and induced apoptosis. The 26S proteasome, a multicatalytic protease complex, promotes tumor cell proliferation and protects tumor cells from apoptosis. The TAE and mahanine, a carbazole alkaloid present in M. koenigii leaves, preferentially inhibited the trypsin-like, but not the chymotrypsin-like proteolytic activity of the proteasome with an IC50 of 162 µg/mL and 287 µM, respectively. In silico analysis of 26 compounds from M. koenigii leaves revealed significant docking scores for mahanine and two other carbazole alkaloids with the ß2 and ß5 subunits of the catalytic 20S proteasome. Taken together, this study demonstrates that inhibition of the proteasome is an important biological activity of M. koenigii alkaloids, which may lead to cancer cell death.

A Study on Spectro-Analytical Aspects, DNA - Interaction, Photo-Cleavage, Radical Scavenging, Cytotoxic Activities, Antibacterial and Docking Properties of 3 - (1 - (6 - methoxybenzo [d] thiazol - 2 - ylimino) ethyl) - 6 - methyl - 3H - pyran - 2, 4 - dione and its Metal Complexes.

The focus of the present work is on the design, synthesis, characterization, DNA-interaction, photo-cleavage, radical scavenging, in-vitro cytotoxicity, antimicrobial, docking and kinetic studies of Cu (II), Cd (II), Ce (IV) and Zr (IV) metal complexes of an imine derivative, 3 - (1 - (6 - methoxybenzo [d] thiazol - 2 - ylimino) ethyl) - 6 - methyl - 3H - pyran - 2, 4 - dione. The investigation of metal ligand interactions for the determination of composition of metal complexes, corresponding kinetic studies and antioxidant activity in solution was carried out by spectrophotometric methods. The synthesized metal complexes were characterized by EDX analysis, Mass, IR, (1)H-NMR, (13)C-NMR and UV-Visible spectra. DNA binding studies of metal complexes with Calf thymus (CT) DNA were carried out at room temperature by employing UV-Vis electron absorption, fluorescence emission and viscosity measurement techniques. The results revealed that these complexes interact with DNA through intercalation. The results of in vitro antibacterial studies showed the enhanced activity of chelating agent in metal chelated form and thus inferring scope for further development of new therapeutic drugs. Cell viability experiments indicated that all complexes showed significant dose dependent cytotoxicity in selected cell lines. The molecular modeling and docking studies were carried out with energy minimized structures of metal complexes to identify the receptor to metal interactions.

Synthesis, characterization, antibacterial, DNA binding and cleavage studies of mixed ligand Cu(II), Co(II) complexes.

Mixed-ligand Cu(II), Co(II) complexes of formulae [Co(NSALT)(A.A)(H2O)](1), [Co(OHAPT)(A.A)H2O](2), and [Cu(ESALT)(ABPH)H2O] (3) were obtained by refluxing methanol solutions of copper, cobalt chlorides with the appropriate ligands. The complexes were characterized by the ESI-MASS, vibrational spectroscopy (Fourier transform-IR), (1)H-NMR spectroscopy, UV-vis spectroscopy, TGA, ESR, SEM and powder XRD. The preliminary DNA-binding activity of the complexes was studied by recording electronic absorption spectra of the complexes in presence of CT-DNA. The binding constants of three complexes towards calf thymus DNA (CT-DNA) [1.2 × 10(4) M(-1) for 1, 2.5 × 10(4) M(-1) for 2, and 3.0 × 10(4) M(-1) for 3] indicate strong interaction of 3. Changes in the fluorescence of ethidium bromide in the presence of DNA suggest intercalation into or electrostatic interactions with CT DNA. The quenching constants, KSV towards-DNA calculated through fluorescence spectra are 2.9 × 10(4) M(-1)for 1, 1.8 × 10(4) M(-1) for 2, and 3.2 × 10(4) M(-1) for 3. Docking studies on DNA complexes confirm the binding of 1 and 2 in the major groove of CT-DNA (CTP-1 Endonuclease). Moreover, the antibacterial effect of 1-3 against the five bacterial species was evaluated. The metal complexes have cleavage affinity towards PBR322 plasmid. Furthermore, the antioxidant activities of the complexes were determined by DPPH scavenging activity method.

Mitochondrial control region alterations and breast cancer risk: a study in South Indian population.

BACKGROUND: Mitochondrial displacement loop (D-loop) is the hot spot for mitochondrial DNA (mtDNA) alterations which influence the generation of cellular reactive oxygen species (ROS). Association of D-loop alterations with breast cancer has been reported in few ethnic groups; however none of the reports were documented from Indian subcontinent. METHODOLOGY: We screened the entire mitochondrial D-loop region (1124 bp) of breast cancer patients (n = 213) and controls (n = 207) of south Indian origin by PCR-sequencing analysis. Haplotype frequencies for significant loci, the standardized disequilibrium coefficient (D') for pair-wise linkage disequilibrium (LD) were assessed by Haploview Software. PRINCIPAL FINDINGS: We identified 7 novel mutations and 170 reported polymorphisms in the D-loop region of patients and/or controls. Polymorphisms were predominantly located in hypervariable region I (60%) than in II (30%) of D-loop region. The frequencies of 310'C' insertion (P = 0.018), T16189C (P = 0.0019) variants and 310'C'ins/16189C (P = 0.00019) haplotype were significantly higher in cases than in controls. Furthermore, strong LD was observed between nucleotide position 310 and 16189 in controls (D' = 0.49) as compared to patients (D' = 0.14). CONCLUSIONS: Mitochondrial D-loop alterations may constitute inherent risk factors for breast cancer development. The analysis of genetic alterations in the D-loop region might help to identify patients at high risk for bad progression, thereby helping to refine therapeutic decisions in breast cancer.

Mitochondrial genome variations in advanced stage endometriosis: a study in South Indian population.

BACKGROUND: Endometriosis is a chronic gynecological benign disease that shares several features similar to malignancy. Mitochondrial DNA (mtDNA) mutations have been reported in all most all types of tumors. However, it is not known as to whether mtDNA mutations are associated with endometriosis. METHODOLOGY: We sequenced the entire mitochondrial genome of analogous ectopic and eutopic endometrial tissues along with blood samples from 32 advanced stage endometriosis patients to analyze the role of somatic and germ-line mtDNA variations in pathogenesis of endometriosis. All ectopic tissues were screened for tumor-specific mtDNA deletions and microsatellite instability (MSI). We also performed mtDNA haplogrouping in 128 patients and 90 controls to identify its possible association with endometriosis risk. PRINCIPAL FINDINGS: We identified 51 somatic (novel: 31; reported: 20) and 583 germ-line mtDNA variations (novel: 53; reported: 530) in endometriosis patients. The A13603G, a novel missense mutation which leads to a substitution from serine to glycine at the codon 423 of ND5 gene showed 100% incidence in ectopic tissues. Interestingly, eutopic endometrium and peripheral leukocytes of all the patients showed heteroplasmy (A/G; 40-80%) at this locus, while their ectopic endometrium showed homoplasmic mutant allele (G/G). Superimposition of native and mutant structures of ND5 generated by homology modeling revealed no structural differences. Tumor-specific deletions and MSI were not observed in any of the ectopic tissues. Haplogrouping analysis showed a significant association between haplogroup M5 and endometriosis risk (P: 0.00069) after bonferroni correction. CONCLUSIONS: Our findings substantiate the rationale for exploring the mitochondrial genome as a biomarker for the diagnosis of endometriosis.