Chemogenomics and orthology‐based design of antibiotic combination therapies

Abstract Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical–genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli. Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug‐interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less‐studied pathogens by leveraging chemogenomics data in model organisms

Fabrication of a Dual-Drug-Loaded Smart Niosome-g-Chitosan Polymeric Platform for Lung Cancer Treatment

Changes in weather conditions and lifestyle lead to an annual increase in the amount of lung cancer, and therefore it is one of the three most common types of cancer, making it important to find an appropriate treatment method. This research aims to introduce a new smart nano-drug delivery system with antibacterial and anticancer capabilities that could be applied for the treatment of lung cancer. It is composed of a niosomal carrier containing curcumin as an anticancer drug and is coated with a chitosan polymeric shell, alongside Rose Bengal (RB) as a photosensitizer with an antibacterial feature. The characterization results confirmed the successful fabrication of lipid-polymeric carriers with a size of nearly 80 nm and encapsulation efficiency of about 97% and 98% for curcumin and RB, respectively. It had the Korsmeyer–Peppas release pattern model with pH and temperature responsivity so that nearly 60% and 35% of RB and curcumin were released at 37 °C and pH 5.5. Moreover, it showed nearly 50% toxicity against lung cancer cells over 72 h and antibacterial activity against Escherichia coli. Accordingly, this nanoformulation could be considered a candidate for the treatment of lung cancer; however, in vivo studies are needed for better confirmation

Large-Scale Identification and Analysis of Suppressive Drug Interactions

One drug may suppress the effects of another. Although knowledge of drug suppression is vital to avoid efficacy-reducing drug interactions or discover countermeasures for chemical toxins, drug-drug suppression relationships have not been systematically mapped. Here, we analyze the growth response of Saccharomyces cerevisiae to anti-fungal compound ("drug") pairs. Among 440 ordered drug pairs, we identified 94 suppressive drug interactions. Using only pairs not selected on the basis of their suppression behavior, we provide an estimate of the prevalence of suppressive interactions between anti-fungal compounds as 17%. Analysis of the drug suppression network suggested that Bromopyruvate is a frequently suppressive drug and Staurosporine is a frequently suppressed drug. We investigated potential explanations for suppressive drug interactions, including chemogenomic analysis, coaggregation, and pH effects, allowing us to explain the interaction tendencies of Bromopyruvate