Predictive Modeling for Breast Cancer Classification in the Context of Bangladeshi Patients: A Supervised Machine Learning Approach with Explainable AI

Breast cancer has rapidly increased in prevalence in recent years, making it one of the leading causes of mortality worldwide. Among all cancers, it is by far the most common. Diagnosing this illness manually requires significant time and expertise. Since detecting breast cancer is a time-consuming process, preventing its further spread can be aided by creating machine-based forecasts. Machine learning and Explainable AI are crucial in classification as they not only provide accurate predictions but also offer insights into how the model arrives at its decisions, aiding in the understanding and trustworthiness of the classification results. In this study, we evaluate and compare the classification accuracy, precision, recall, and F-1 scores of five different machine learning methods using a primary dataset (500 patients from Dhaka Medical College Hospital). Five different supervised machine learning techniques, including decision tree, random forest, logistic regression, naive bayes, and XGBoost, have been used to achieve optimal results on our dataset. Additionally, this study applied SHAP analysis to the XGBoost model to interpret the model's predictions and understand the impact of each feature on the model's output. We compared the accuracy with which several algorithms classified the data, as well as contrasted with other literature in this field. After final evaluation, this study found that XGBoost achieved the best model accuracy, which is 97%.Comment: Accepted for the Scientific Reports (Nature) journal. 32 pages, 12 figure

The combination of multi-approach studies to explore the potential therapeutic mechanisms of imidazole derivatives as an MCF-7 inhibitor in therapeutic strategies

Breast cancer covers a large area of research because of its prevalence and high frequency all over the world. This study is based on drug discovery against breast cancer from a series of imidazole derivatives. A 3D-QSAR and activity atlas model was developed by exploring the dataset computationally, using the machine learning process of Flare. The dataset of compounds was divided into active and inactive compounds according to their biological and structural similarity with the reference drug. The obtained PLS regression model provided an acceptable r2 = 0.81 and q2 = 0.51. Protein-ligand interactions of active molecules were shown by molecular docking against six potential targets, namely, TTK, HER2, GR, NUDT5, MTHFS, and NQO2. Then, toxicity risk parameters were evaluated for hit compounds. Finally, after all these screening processes, compound C10 was recognized as the best-hit compound. This study identified a new inhibitor C10 against cancer and provided evidence-based knowledge to discover more analogs

Chemical composition and potential antioxidant, anti-inflammatory, and analgesic efficacy of Cistus albidus L.

This study aims to assess the chemical composition of the aqueous extract of Cistus albidus L. leaves, as well as the potential of aqueous and hydroethanol extracts of the leaves and seeds as analgesic, anti-inflammatory, and antioxidant agents. The contents of phenolics and inorganic constituents were determined in C. albidus seeds and leaves; antioxidant capacity was assessed by 3 complementary and diverse tests. The carrageenan-induced paw edema technique was used to investigate the anti-inflammatory effect in vivo, and albumin denaturation to evaluate the anti-inflammatory effect in vitro. The acetic acid-induced contortion test, the tail-flick test, and the plantar test were used to assess the analgesic efficacy in vivo. Chemical analysis was performed by UPLC-MS/MS to quantify several phenolic compounds including catechin (1,627.6 mg kg–1), quercitrin (1,235.8 mg kg–1) and gallic acid (628. 2 mg kg–1). The ICP analysis revealed that potassium and calcium were the main inorganic components in the seeds and leaves of C. albidus. The hydroethanolic extract of the leaves showed the highest content of polyphenols/flavonoids, whereas the highest value of proanthocyanidins was detected in the aqueous extract of the seeds. All extracts showed potent antioxidant activity related to different phenolic compounds (quercetin, gallic acid, astragalin, catechin, and rutin). The aqueous extract of the leaves strongly inhibited paw edema (76.1 %) after 6 h of treatment and showed maximal inhibition of protein denaturation (191.0 µg mL–1 for 50 % inhibition) and analgesic activity in different nociceptive models. The presented data reveal that C. albidus extracts potentially show antioxidant, anti-inflammatory, and analgesic activities that could confirm the traditional use of this plant

Discovery of novel natural products as dual MNK/PIM inhibitors for acute myeloid leukemia treatment: Pharmacophore modeling, molecular docking, and molecular dynamics studies

MNK-2 and PIM-2 kinases play an indispensable role in cell proliferation signaling pathways linked to tyrosine kinase inhibitors resistance. In this study, pharmacophore modeling studies have been conducted on the co-crystalized ligands of MNK-2 and PIM-2 enzyme crystal structures to determine the essential features required for the identification of potential dual inhibitors. The obtained pharmacophore features were then screened against a library of 270,540 natural products from the ZINC database. The matched natural molecules were docked into the binding sites of MNK-2 and PIM-2 enzymes. The compounds with high docking scores with the two enzymes were further subjected to MM-GBSA calculations and ADME prediction. This led to the identification of compound 1 (ZINC000085569211), compound 2 (ZINC000085569178), and compound 3 (ZINC000085569190), with better docking scores compared to the reference co-crystallized ligands of MNK-2 and PIM-2. Moreover, compounds 1‒3 displayed better MM-GBSA binding free energies compared to the reference ligands. Finally, molecular dynamics (MD) study was used to assess the interaction stability of the compounds with MNK-2. To this end, compounds 1 and 3 bound strongly to the target during the whole period of MD simulation. The findings of the current study may further help the researchers in the discovery of novel molecules against MNK-2 and PIM-2

Exploration of phenolic acid derivatives as inhibitors of SARS-CoV-2 main protease and receptor binding domain: potential candidates for anti-SARS-CoV-2 therapy

Severe acute respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) is the etiological virus of Coronavirus Disease 2019 (COVID-19) which has been a public health concern due to its high morbidity and high mortality. Hence, the search for drugs that incapacitate the virus via inhibition of vital proteins in its life cycle is ongoing due to the paucity of drugs in clinical use against the virus. Consequently, this study was aimed at evaluating the potentials of natural phenolics against the Main protease (Mpro) and the receptor binding domain (RBD) using molecular modeling techniques including molecular docking, molecular dynamics (MD) simulation, and density functional theory (DFT) calculations. To this end, thirty-five naturally occurring phenolics were identified and subjected to molecular docking simulation against the proteins. The results showed the compounds including rosmarinic acid, cynarine, and chlorogenic acid among many others possessed high binding affinities for both proteins as evident from their docking scores, with some possessing lower docking scores compared to the standard compound (Remdesivir). Further subjection of the hit compounds to drug-likeness, pharmacokinetics, and toxicity profiling revealed chlorogenic acid, rosmarinic acid, and chicoric acid as the compounds with desirable profiles and toxicity properties, while the study of their electronic properties via density functional theory calculations revealed rosmarinic acid as the most reactive and least stable among the sets of lead compounds that were identified in the study. Molecular dynamics simulation of the complexes formed after docking revealed the stability of the complexes. Ultimately, further experimental procedures are needed to validate the findings of this study

Identifying and validating MMP family members (MMP2, MMP9, MMP12, and MMP16) as therapeutic targets and biomarkers in kidney renal clear cell carcinoma (KIRC).

Kidney Renal Clear Cell Carcinoma (KIRC) is a malignant tumor that carries a substantial risk of morbidity and mortality. The MMP family assumes a crucial role in tumor invasion and metastasis. This study aimed to uncover the mechanistic relevance of the MMP gene family as a therapeutic target and diagnostic biomarker in Kidney Renal Clear Cell Carcinoma (KIRC) through a comprehensive approach encompassing both computational and molecular analyses. STRING, Cytoscape, UALCAN, GEPIA, OncoDB, HPA, cBioPortal, GSEA, TIMER, ENCORI, DrugBank, targeted bisulfite sequencing (bisulfite-seq), conventional PCR, Sanger sequencing, and RT-qPCR based analyses were used in the present study to analyze MMP gene family members to accurately determine a few hub genes that can be utilized as both therapeutic targets and diagnostic biomarkers for KIRC. By performing STRING and Cytohubba analyses of the 24 MMP gene family members, MMP2 (matrix metallopeptidase 2), MMP9 (matrix metallopeptidase 9), MMP12 (matrix metallopeptidase 12), and MMP16 (matrix metallopeptidase 16) genes were denoted as hub genes having highest degree scores. After analyzing MMP2, MMP9, MMP12, and MMP16 via various TCGA databases and RT-qPCR technique across clinical samples and KIRC cell lines, interestingly, all these hub genes were found significantly overexpressed at mRNA and protein levels in KIRC samples relative to controls. The notable effect of the up-regulated MMP2, MMP9, MMP12, and MMP16 was also documented on the overall survival (OS) of the KIRC patients. Moreover, targeted bisulfite-sequencing (bisulfite-seq) analysis revealed that promoter hypomethylation pattern was associated with up-regulation of hub genes (MMP2, MMP9, MMP12, and MMP16). In addition to this, hub genes were involved in various diverse oncogenic pathways. The MMP gene family members (MMP2, MMP9, MMP12, and MMP16) may serve as therapeutic targets and prognostic biomarkers in KIRC. [Abstract copyright: © 2024 Kunlun et al.

Porous and highly responsive polymeric fabricated nanometrices for solubility enhancement of acyclovir; characterization and toxicological evaluation

Solubility is one of the major factors which affects several therapeutic mioeties in terms of their therapeutic efficacy. In the current study, we presented a porous and amorphous nanometrices system for the enhancement of the solubility of acyclovir. The polymeric network was fabricated by crosslinking polyethylene glycol-6000, polycaprolactone, and β-cyclodextrin with methacrylic acid by optimizing free radical polymerization technique using methylene bisacrylamide as a crosslinking agent. The formulated nanometrices were then characterized by zetasizer, FTIR, PXRD, Scanning electron microscopy, Thermogravimetric analysis, swelling, sol-gel fraction, drug loading, stability, solubility, and in-vitro dissolution analysis. Since the formulated system has to be administered orally, therefore to determine the in-vivo biocompatibility, nanometrices were administered orally to experimental animals. SEM images provided a rough and porous structure while PXRD showed an amorphous diffractogram of the unloaded and loaded nanometrices. Moreover, the particle size of the optimum loaded formulation was 25 nm higher than unloaded nanometrices due to the repulsion of the loaded drug. A significant loading of the drug with enhanced solubility and dissolution profiles was observed for the poorly soluble drug. The dissolution profile was quite satisfactory as compared to the marketed brand of drug which depicted that the solubility of the drug has been enhanced. Toxicity study conducted on rabbits confirmed the biocompatibility of the nanometrices. The systematic method of preparation, enhanced solubility and high dissolution profile of the formulated nanometrices may be proved as a promising technique to enhance the solubility of poorly aqueous soluble therapeutic agents

Anti-parasitic drug discovery against Babesia microti by natural compounds: an extensive computational drug design approach

Tick-borne Babesiosis is a parasitic infection caused by Babesia microti that can infect both animals and humans and may spread by tick, blood transfusions, and organ transplantation. The current therapeutic options for B. microti are limited, and drug resistance is a concern. This study proposes using computational drug design approaches to find and design an effective drug against B. microti. The study investigated the potentiality of nine natural compounds against the pathogenic human B. microti parasite and identified Vasicinone and Evodiamine as the most promising drugs. The ligand structures were optimized using density functional theory, molecular docking, molecular dynamics simulations, quantum mechanics such as HOMO–LUMO, drug-likeness and theoretical absorption, distribution, metabolism, excretion, and toxicity (ADMET), and pharmacokinetics characteristics performed. The results showed that Vasicinone (−8.6 kcal/mol and −7.8 kcal/mol) and Evodiamine (−8.7 kcal/mol and −8.5 kcal/mol) had the highest binding energy and anti-parasitic activity against B. microti lactate dehydrogenase and B. microti lactate dehydrogenase apo form. The strongest binding energy was reported by Vasicinone and Evodiamine; the compounds were evaluated through molecular dynamics simulation at 100 ns, and their stability when they form complexes with the targeted receptors was determined. Finally, the pkCSM web server is employed to predict the ADMET qualities of specific molecules, which can help prevent negative effects that arise from taking the treatment. The SwissADME web server is used to assess the Lipinski rule of five and drug-likeness properties including topological polar surface area and bioavailability. The Lipinski rule is used to estimate significant drug-likeness. The theoretical pharmacokinetics analysis and drug-likeness of the selected compounds are confirmed to be accepted by the Lipinski rule and have better ADMET features. Thus, to confirm their experimental value, these mentioned molecules should be suggested to carry out in wet lab, pre-clinical, and clinical levels

Evaluation of Adenanthera pavonina-derived compounds against diabetes mellitus: insight into the phytochemical analysis and in silico assays

Adenanthera pavonina is a medicinal plant with numerous potential secondary metabolites showing a significant level of antidiabetic activity. The objective of the current study was to identify potential phytochemicals from the methanolic leaf extract of Adenanthera pavonina as therapeutic agents against diabetes mellitus using GC-MS and in silico methods. The GC-MS analysis of the leaf extract revealed a total of 17 phytochemicals. Molecular docking was performed using these phytochemicals, targeting the mutated insulin receptor tyrosine kinase (5hhw), which inhibits glucose uptake by cells. Diazoprogesterone (−9.2 kcal/mol), 2,4,4,7a-Tetramethyl-1-(3-oxobutyl)octahydro-1H-indene-2-carboxylic acid (−6.9 kcal/mol), and 2-Naphthalenemethanol, decahydro-.alpha.,.alpha.,4a-trimethyl-8-methylene-, [2R-(2.alpha.,4a.alpha.,8a.beta.)] (−6.6 kcal/mol) exhibited better binding with the target protein. The ADMET analysis was performed for the top three compounds with the best docking scores, which showed positive results with no observed toxicity in the AMES test. Furthermore, the molecular dynamics study confirmed the favorable binding of Diazoprogesterone, 2,4,4,7a-Tetramethyl-1-(3-oxobutyl)octahydro-1H-indene-2-carboxylic acid and 2-Naphthalenemethanol, decahydro-.alpha.,.alpha.,4a-trimethyl-8-methylene-, [2R-(2.alpha.,4a.alpha.,8a.beta.)] with the receptor throughout the 100 ns simulation period

Pharmacological activities of chemically characterized essential oils from Haplophyllum tuberculatum (Forssk.)

The present work aimed at characterizing the phytochemical composition of Haplophyllum tuberculatum essential oil (HTEO), assessing its antifungal activity against various fungal strains, evaluating its insecticidal and repulsive properties against Callosobruchus maculatus, and determine its antioxidant capacity. To this end, Gas chromatography-mass spectrometry analysis detected 34 compounds in HTEO, with β-Caryophyllene being the major constituent (36.94%). HTEO demonstrated predominantly modest antifungal effects, however, it sustains notable activity, particularly against Aspergillus flavus, with an inhibition rate of 76.50% ± 0.60%. Minimum inhibitory concentrations ranged from 20.53 ± 5.08 to 76.26 ± 5.08 mg/mL, effectively inhibiting fungal growth. Furthermore, the antifungal, and antioxidant activities of HTEO were evaluated in silico against the proteins Aspergillus flavus FAD glucose dehydrogenase, and beta-1,4-endoglucanase from Aspergillus niger, NAD(P)H Oxidase. Moreover, HTEO displayed strong insecticidal activity against C. maculatus, with contact and inhalation tests yielding LC50 values of 30.66 and 40.28 μL/100g, respectively, after 24 h of exposure. A dose of 5 μL/100g significantly reduced oviposition (48.85%) and inhibited emergence (45.15%) compared to the control group. Additionally, HTEO exhibited a high total antioxidant capacity of 758.34 mg AAE/g EO, highlighting its antioxidant potential. Insilico results showed that the antifungal activity of HTEO is mostly attributed to γ-Cadinene and p-Cymen-7-ol, while antioxidant is attributed to α-Terpinyl isobutyrate displayed. Overall, HTEO offers a sustainable and environmentally friendly alternative to synthetic products used to manage diseases