Review of Predicting Synergistic Drug Combinations

HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction

Drug synergy therapy is a promising strategy for cancer treatment. However, the extensive variety of available drugs and the time-intensive process of determining effective drug combinations through clinical trials pose significant challenges. It requires a reliable method for the rapid and precise selection of drug synergies. In response, various computational strategies have been developed for predicting drug synergies, yet the exploitation of heterogeneous biological network features remains underexplored. In this study, we construct a heterogeneous graph that encompasses diverse biological entities and interactions, utilizing rich data sets from sources, such as DrugCombDB, PubChem, UniProt, and cancer cell line encyclopedia (CCLE). We initialize node feature representations and introduce a novel virtual node to enhance drug representation. Our proposed method, the heterogeneous graph attention network for drug-drug synergy prediction (HANSynergy), has been experimentally validated to demonstrate that the heterogeneous graph attention network can extract key node features, efficiently harness the diversity of information, and further enhance network functionality through the incorporation of a multihead attention mechanism. In the comparative experiment, the highest accuracy (Acc) and area under the curve (AUC) are 0.877 and 0.947, respectively, in DrugCombDB_early data set, demonstrating the superiority of HANSynergy over the competing methods. Moreover, protein-protein interactions are important in understanding the mechanism of action of drugs. The heterogeneous attention mechanism facilitates protein-protein interaction analysis. By analyzing the changes of attention weight before and after heterogeneous network training, we investigated proteins that may be associated with drug combinations. Additionally, case studies align our findings with existing research, underscoring the potential of HANSynergy in drug synergy prediction. This advancement not only contributes to the burgeoning field of drug synergy prediction but also holds the potential to provide valuable insights and uncover new drug synergies for combating cancer.

Environment by environment interactions (ExE) differ across genetic backgrounds (ExExG)

While the terms "gene-by-gene interaction" (GxG) and "gene-by-environment interaction" (GxE) are commonplace within the field of quantitative and evolutionary genetics, "environment-by-environment interaction" (ExE) is a term used less often. However, in this study, we find that environment-by-environment interactions are common and differ for different genotypes (ExExG). To reach this conclusion, we analyzed a large dataset of roughly 1,000 mutant yeast strains with varying degrees of resistance to different antifungal drugs. Many researchers endeavor to predict combinations of drugs that are more lethal than either single drug. But we show that the effectiveness of a drug combination, relative to the effectiveness of single drugs, often varies across different drug resistant mutants. Even mutants that differ by only a single nucleotide change can have dramatically different drug x drug (ExE) interactions. Studying how ExE interactions change across genotypes (ExExG) is not only important when modeling the evolution of pathogenic microbes. High throughput screens of GxG and GxE have taught us about the basic cell biology and gene regulatory networks underlying genetic interactions. ExExG has been omitted but stands to impart similar lessons about the architecture of living systems. In this study, we call attention to ExExG, measure its prevalence, introduce a new framework that in some instances better predicts its direction and magnitude, and make the case for further study of this type of genetic interaction.

Synergistic effects of nano curcumin mediated photodynamic inactivation and nano-silver@colistin against Pseudomonas aeruginosa biofilms

BACKGROUND

Patients with burn injuries colonized by multidrug-resistant Pseudomonas aeruginosa face increased mortality risk. The efficacy of colistin, a last-resort treatment, is declining as resistance levels rise. P. aeruginosa's robust biofilm exacerbates antibiotic resistance. Photodynamic Inactivation (PDI) shows promise in fighting biofilm.

MATERIALS AND METHODS

Nano curcumin (nCur) particles were synthesized, and their chemical characteristics were determined using zeta potential (ZP), dynamic light scattering analysis (DLS), energy-dispersive X-ray (EDX) analysis, and fourier transform infrared (FTIR). We conducted an MTT assay to assess the cytotoxicity of nCur-mediated PDI in combination with nanosilver colistin. The fractional biofilm inhibitory concentration (FBIC) of two P. aeruginosa clinical isolates and P. aeruginosa ATCC 27853 during nCur-mediated PDI@AgNPs@CL was determined using a 3-dimensional (3-D) checkerboard assay. To study the effect of nCur-mediated PDI@AgNPs@CL on lasI, lasR, rhlI, rhlR, pelA, and pslA gene expression, Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was conducted at each isolate's FBIC. The impact of treatments was also investigated using scanning electron microscopy (SEM).

RESULTS

The ZP and mean DLS values of the nCur were 10.3 mV and 402.6 ± 24.6 nm, respectively. The distinct functional groups of nCur corresponded with the peaks of FTIR absorption. Moreover, the EDX analysis showed the ratios of different metals in nCur. Cell viability percentages of nCur-mediated PDI@AgNPs@CL at FBIC concentrations of clinical isolates Nos. 30, 354, and P. aeruginosa ATCC 27853 were 91.36 %, 83.20 %, and 92.48 %, respectively. nCur-mediated PDI@AgNPs@CL treatment showed synergistic effects in clinical isolates and P. aeruginosa ATCC 27853 in a 3-D checkerboard assay. All six of the investigated genes showed down-regulation after nCur-mediated PDI@AgNPs@CL treatment. The most suppressed gene during nCur-mediated PDI@AgNPs@CL treatment was the rhlR gene (-11.9-fold) of P. aeruginosa ATCC 27853. The SEM micrographs further proved the connecting cement reduction and biofilm mass mitigation following nCur-mediated PDI@AgNPs@CL treatments.

CONCLUSIONS

The combined effect of nCur-mediated PDI and AgNPs@CL synergistically reduce the formation of biofilm in P. aeruginosa. This may be attributable to the suppression of the genes responsible for regulating the production of biofilms.