Recent Trends in Computer-aided Drug Design for Anti-cancer Drug Discovery.

Cancer is considered one of the deadliest diseases globally, and continuous research is being carried out to find novel potential therapies for myriad cancer types that affect the human body. Researchers are hunting for innovative remedies to minimize the toxic effects of conventional therapies being driven by cancer, which is emerging as pivotal causes of mortality worldwide. Cancer progression steers the formation of heterogeneous behavior, including self-sustaining proliferation, malignancy, and evasion of apoptosis, tissue invasion, and metastasis of cells inside the tumor with distinct molecular features. The complexity of cancer therapeutics demands advanced approaches to comprehend the underlying mechanisms and potential therapies. Precision medicine and cancer therapies both rely on drug discovery. In vitro drug screening and in vivo animal trials are the mainstays of traditional approaches for drug development; however, both techniques are laborious and expensive. Omics data explosion in the last decade has made it possible to discover efficient anti-cancer drugs via computational drug discovery approaches. Computational techniques such as computer-aided drug design have become an essential drug discovery tool and a keystone for novel drug development methods. In this review, we seek to provide an overview of computational drug discovery procedures comprising the target sites prediction, drug discovery based on structure and ligand-based design, quantitative structure-activity relationship (QSAR), molecular docking calculations, and molecular dynamics simulations with a focus on cancer therapeutics. The applications of artificial intelligence, databases, and computational tools in drug discovery procedures, as well as successfully computationally designed drugs, have been discussed to highlight the significance and recent trends in drug discovery against cancer. The current review describes the advanced computer-aided drug design methods that would be helpful in the designing of novel cancer therapies.

Biosynthesis of ZnO Nanoparticles Using Bacillus Subtilis: Characterization and Nutritive Significance for Promoting Plant Growth in Zea mays L.

Nano-fertilizer(s), an emerging field of agriculture, is alternate option for enhancement of plant growth replacing the synthetic fertilizers. Zinc oxide nanoparticles (ZnO NPs) can be used as the zinc source for plants. The present investigation was carried out to assess the role of ZnO NPs in growth promotion of maize plants. Biosynthesized ZnO NPs (using Bacillus sp) were characterized using Scanning Electron Microscope (SEM), Transmission Electron Microscope (TEM), X-ray diffraction (XRD) and Zeta potential. Different concentrations of ZnO NPs (2, 4, 8, 16 mg/L) were explored in pot culture experiment. Size of ZnO NPs ranged between 16 and 20 nm. A significant increase in growth parameters like shoot length (61.7%), root length (56.9%) and significantly higher level of protein was observed in the treated plants. The overall pattern for growth biomarkers including the protein contents was maximum at 8 mg/L of ZnO NPs. It was observed that application of biosynthesized ZnO NPs has improved majority of growth biomarkers including plant growth parameters, protein contents and leaf area. Therefore, biosynthesized ZnO NPs could be considered as an alternate source of nutrient in Zn deficient soils for promoting the modern agriculture.

Isolation and antibiotic susceptibility of E. coli from urinary tract infections in a tertiary care hospital.

OBJECTIVE: The study was conducted to isolate and determine the antibiotic resistance in E. coli from urinary tract infections in a tertiary care hospital, Lahore. METHODS: Urine samples (n=500) were collected from patients with signs and symptoms of Urinary tract infections. Bacteria were isolated and identified by conventional biochemical profile. Antibiotic resistance pattern of E. coli against different antibiotic was determined by Kirby-Baur method. RESULTS: Bacterial etiological agent was isolated from 402 samples with highest prevalence of E. coli (321, 80%) followed by Staphylococcus aureus (9.4%), Proteus species (5.4%) and Pseudomonas species (5.2%). The E. coli were highly resistant to penicillin (100%), amoxicillin (100%) and cefotaxime (89.7%), followed by intermediate level of resistance to ceftazidime (73.8%), cephradine (73.8%), tetracycline (69.4%), doxycycline (66.6%), augmentin (62.6%), gentamycin (59.8%), cefuroxime (58.2%), ciprofloxacin (54.2%), cefaclor (50%), aztreonam (44.8%), ceftriaxone (43.3%), imipenem (43.3%), and low level of resistance to streptomycin (30%), kanamycin (19.9%), tazocin (14%), amikacin (12.7%) and lowest to norfloxacin (11.2%). Out of 321 E. coli isolates, 261 (81%) were declared as multiple drug resistant and 5 (1.5%) were extensive drug resistant. CONCLUSION: It is concluded that most of the urinary tract infections in human are caused by multiple drug resistant E. coli.