Narrow-Spectrum Antibacterial Combinations for Resistant Pathogens
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Solution Overview
Problem
The spread of antimicrobial resistance has led to ineffective treatments for bacterial infections, particularly against multi-drug resistant Gram-negative pathogens, and current antibacterial therapies often harm healthy intestinal flora, leading to antibiotic resistance and adverse effects on the gut microbiome.
Innovation Solution
A method for systematically screening and assessing drug-drug interactions in clinically-relevant Gram-negative bacteria to identify synergistic or antagonistic pairs, including non-antibiotic compounds like vanillin, to develop narrow-spectrum antibacterial therapies that effectively target specific bacterial strains while minimizing harm to commensal bacteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If broad-spectrum antibiotics are used to treat bacterial infections, then the effectiveness against multi-drug resistant pathogens is improved, but the harm to healthy gut microbiota increases
Solution Approach 1:
The patent segments the antibacterial spectrum by combining drugs with different spectra of activity. The first drug typically has broad-spectrum activity to ensure coverage of resistant pathogens, while the second drug is selected to target specific bacterial groups or pathways, thereby segmenting the overall antibacterial effect to preserve beneficial microbiota while eliminating resistant pathogens.
Solution Approach 2:
The patent applies local quality by selecting drug combinations where the second drug provides targeted activity against specific bacterial taxa or metabolic pathways. This creates localized antibacterial pressure against resistant pathogens while leaving other bacterial populations unaffected, thus treating the infection locally without broad collateral damage to the microbiome.
2Adaptability or versatility
If antibiotic combinations are used to treat resistant infections, then the therapeutic solution space is expanded, but the complexity of interaction assessment increases
Solution Approach 1:
The patent employs preliminary action by using in silico computational models to predict drug-drug interactions before conducting physical experiments. The system pre-assesses potential combinations using machine learning algorithms trained on existing interaction data, thereby reducing the complexity of subsequent experimental assessment and prioritizing the most promising combinations for further study.
Solution Approach 2:
The patent introduces an intermediary computational layer that mediates between the vast space of possible drug combinations and the limited experimental resources. The in silico prediction system acts as an intermediary filter, translating the therapeutic solution space into a manageable set of high-probability combinations for empirical validation, thus reducing assessment complexity.
3Reliability
If novel antibiotic classes are developed to overcome resistance, then the activity against Gram-negative pathogens is improved, but the development time and cost increase
Solution Approach 1:
The patent merges existing approved drugs in novel combinations to achieve therapeutic effects against resistant Gram-negative pathogens. By combining drugs with different mechanisms of action that are already clinically approved, the invention bypasses the lengthy development process required for entirely new antibiotic classes while still providing effective treatment for resistant infections.
Solution Approach 2:
The patent uses in silico computational models that copy and simulate real-world drug interaction dynamics to predict combination outcomes. These virtual models replicate the complex biological interactions of drug combinations, allowing researchers to screen thousands of potential pairs computationally before selecting candidates for physical testing, thereby dramatically reducing development time.
Data Source
AI summary
The present invention relates to the field of therapeutics and, more in particular, to pharmaceutical compositions for the prevention and/or treatment of bacterial infections and antibacterial-induced dysfunctions. The compositions of the present invention demonstrate high species-specificity in inhibiting the growth of a small number of bacterial species, and most importantly are effective also against multi drug resistant (MDR) clinical isolate species. Interestingly, one of those combinations pairs a non-antibiotic drug, vanillin, with an antibiotic drug, spectinomycin, to demonstrate a surprisingly strong inhibitory effect on the growth of clinically relevant Gram-negative pathogenic and multi-drug resistant E. coli isolates. A second set of compounds combines the polymyxin colistin with loperamide, a rifamycin, or a macrolide. Importantly, this invention relates to combinations that enable narrow-spectrum antibacterial therapies, constituting a major effort of current and future drug development efforts in order to prevent major side effects of antibacterial strategies. This invention also relates to pharmaceutical combinations useful to prevent an adverse effect on the gut microbiome, induced by the use of antibacterial compounds.