In vitro efficacy and in silico analysis of cefixime-ofloxacin combination for Salmonella Typhi from bloodstream infection.

AIMS: Recently, the cefixime-ofloxacin combination is approved by Drug Controller General of India to treat typhoid fever. We sought to evaluate the antimicrobial activity of cefixime-ofloxacin combination against Salmonella Typhi. METHODS AND RESULTS: A total of 283 nonduplicate S. Typhi isolates collected during 2012-2014 were included in this study. Minimum inhibitory concentration (MIC) of cefixime and ofloxacin was determined by using broth microdilution method. Combinational testing was performed by using checkerboard assay. In checkerboard assay, synergistic activity was seen in 11% of isolates, while the majority of the isolate showed indifference and none of them showed antagonism. An in silico strategy, an alternative to the animal model, was carried out to understand drug interaction and toxicity. Molecular docking results elucidated that cefixime and ofloxacin are capable of inhibiting the cell wall synthesis and DNA replication, respectively. Computational ADMET analysis showed no toxicity and no drug-drug interaction between cefixime and ofloxacin. CONCLUSION: Cefixime-ofloxacin combination could be effective against moderately susceptible fluoroquinolone S. Typhi but not fluoroquinolone-resistant isolates. SIGNIFICANCE AND IMPACT OF THE STUDY: Cefixime-ofloxacin combination with no drug-drug interaction and nontoxic predicted through computational analysis did not show antagonism against S. Typhi in in vitro. Although this study showed no adverse effects with the cefixime-ofloxacin combination, further studies on pharmacokinetic and pharmacodynamic (PK-PD) parameters of cefixime and ofloxacin combination are warranted.

Elucidating the Mutational Landscape in Hepatocyte Nuclear Factor 1ß (HNF1B) by Computational Approach.

Transcription factors are the major gene-regulatory proteins that recognize specific nucleotide sequences and bind to them. Missense mutations in transcription factors play a significant role in misregulation of gene expression contributing to various diseases and disorders. Understanding their structural and functional impact of the disease-causing mutations becomes prime importance in treating a disease. Commonly associated defect with the mutations of hepatocyte nuclear factor 1 beta (HNF1B) protein, a transcription factor results in maturity-onset diabetes of the young-5 (MODY-5) leading to loss of function. In the study presented, we applied a series of computational strategies to analyze the effect of mutations on protein structure or function in protein-DNA complex. The mutations from publicly available databases were retrieved and subjected to an array of in silico prediction methods. Key implementation of the present study consists of a pipeline drawn using well established in silico prediction methods of different algorithms to explain the biochemical changes impaired upon mutations in the binding sites of protein-DNA complex using HNF1B. Prediction scores obtained from the in silico tools suggested H153N and A241T as the major nsSNPs involved in destabilizing the protein. Further, high-end microscopic computational study, such as molecular dynamics simulations was utilized to relate the structural and functional effects upon mutations. Although, both the mutations exhibited similar structural differences, we observed A241T with higher destabilizing effect on the protein. The presented work is a step toward understanding the genotype-phenotype relationships in transcription factor genes using fast and accurate computational approach.