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Additivity of inhibitory effects in multidrug combinations.

ABSTRACT: From natural ecology1-4 to clinical therapy5-8, cells are often exposed to mixtures of multiple drugs. Two competing null models are used to predict the combined effect of drugs: response additivity (Bliss) and dosage additivity (Loewe)9-11. Here, noting that these models diverge with increased number of drugs, we contrast their predictions with growth measurements of four phylogenetically distant microorganisms including Escherichia coli, Staphylococcus aureus, Enterococcus faecalis and Saccharomyces cerevisiae, under combinations of up to ten different drugs. In all species, as the number of drugs increases, Bliss maintains accuracy while Loewe systematically loses its predictive power. The total dosage required for growth inhibition, which Loewe predicts should be fixed, steadily increases with the number of drugs, following a square-root scaling. This scaling is explained by an approximation to Bliss where, inspired by R. A. Fisher's classical geometric model12, dosages of independent drugs add up as orthogonal vectors rather than linearly. This dose-orthogonality approximation provides results similar to Bliss, yet uses the dosage language as in Loewe and is hence easier to implement and intuit. The rejection of dosage additivity in favour of effect additivity and dosage orthogonality provides a framework for understanding how multiple drugs and stressors add up in nature and the clinic.

SUBMITTER: Russ D

PROVIDER: S-EPMC6295580 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Additivity of inhibitory effects in multidrug combinations.

Russ D D Kishony R R

Nature microbiology 20181015 12

From natural ecology1-4 to clinical therapy5-8, cells are often exposed to mixtures of multiple drugs. Two competing null models are used to predict the combined effect of drugs: response additivity (Bliss) and dosage additivity (Loewe)9-11. Here, noting that these models diverge with increased number of drugs, we contrast their predictions with growth measurements of four phylogenetically distant microorganisms including Escherichia coli, Staphylococcus aureus, ...[more]

PMID: 30323252

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Project description:Background and Objectives: Developing effective treatment outcomes for multidrug-resistant Acinetobacter baumannii (MRAB) infections, with colistin and tigecycline the current frontline therapies, is challenging, because of the risk of renal toxicity and low blood concentrations of active ingredients when administered intravenously. This study aimed to identify the effect of combination therapy using conventional antimicrobial agents that are used for treating drug-resistant bacteria and the additional synergistic effect of four probiotic culture extracts isolated from the human body and Lactobacillus preparations. Materials and Methods: The antimicrobial combination and synergistic effect of adding Lactobacillus extract against 33 strains of A. baumannii isolated from pus, urine, and other specimens submitted to the Department of Laboratory Medicine of a university hospital, located in Gyeonggi-do, Korea, was investigated over a 3-year period between January 2017 and December 2019. Results: Antimicrobial susceptibility tests on bacteria isolated in clinical practice demonstrated that 26 strains (79%) were MRAB, while multi-locus sequence typing indicated that ST191 was the predominant type (45%; n = 15). Checkerboard test results demonstrated that combination therapy using meropenem and colistin had the highest synergistic effect (fractional inhibitory concentration index = 0.5), while the time-kill assay test using Lactobacillus spp. culture extract exhibited an inhibitory effect within 1 h and complete inhibition of MRAB within 3 h. Lactobacillus paracasei exhibited the fastest antimicrobial reactivity and longest sustained antimicrobial activity. Conclusion: These findings provide useful foundational data for an appropriate combination of colistin with other antimicrobial agents for treating MRAB infection in clinical settings, and the use of various probiotic culture extracts to reduce the required dosage, and therefore toxicity of colistin.

Project description:Resistance to polymyxin antibiotics is increasing. Without new antibiotic classes, combination therapy is often required. We systematically investigated bacterial killing with polymyxin-based combinations against multidrug-resistant (including polymyxin-resistant), carbapenemase-producing Klebsiella pneumoniae Monotherapies and double- and triple-combination therapies were compared to identify the most efficacious treatment using static time-kill studies (24 h, six isolates), an in vitro pharmacokinetic/pharmacodynamic model (IVM; 48 h, two isolates), and the mouse thigh infection model (24 h, six isolates). In static time-kill studies, all monotherapies (polymyxin B, rifampin, amikacin, meropenem, or minocycline) were ineffective. Initial bacterial killing was enhanced with various polymyxin B-containing double combinations; however, substantial regrowth occurred in most cases by 24 h. Most polymyxin B-containing triple combinations provided greater and more sustained killing than double combinations. Standard dosage regimens of polymyxin B (2.5 mg/kg of body weight/day), rifampin (600 mg every 12 h), and amikacin (7.5 mg/kg every 12 h) were simulated in the IVM. Against isolate ATH 16, no viable bacteria were detected across 5 to 25 h with triple therapy, with regrowth to ∼2-log10 CFU/ml occurring at 48 h. Against isolate BD 32, rapid initial killing of ∼3.5-log10 CFU/ml at 5 h was followed by a slow decline to ∼2-log10 CFU/ml at 48 h. In infected mice, polymyxin B monotherapy (60 mg/kg/day) generally was ineffective. With triple therapy (polymyxin B at 60 mg/kg/day, rifampin at 120 mg/kg/day, and amikacin at 300 mg/kg/day), at 24 h there was an ∼1.7-log10 CFU/thigh reduction compared to the starting inoculum for all six isolates. Our results demonstrate that the polymyxin B-rifampin-amikacin combination significantly enhanced in vitro and in vivo bacterial killing, providing important information for the optimization of polymyxin-based combinations in patients.

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Additivity of inhibitory effects in multidrug combinations.

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Additivity of inhibitory effects in multidrug combinations.

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