Machine learning to design antimicrobial combination therapies: Promises and pitfalls

Cantrell, Jennifer M; Chung, Carolina H; Chandrasekaran, Sriram

doi:10.1016/j.drudis.2022.04.006

Cited by 12 publications

(9 citation statements)

References 92 publications

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“…In an organism—animal or human—it is possible that the antimicrobial synergism of this drug combination develops and is favored by the participation of secondary metabolites due to metabolism [18]. The secondary metabolites of ciprofloxacin that could participate in an antimicrobial synergism are mainly three: diethylene ciprofloxacin, oxociprofloxacin, and formyl ciprofloxacin.…”

Section: Discussionmentioning

confidence: 99%

“…However, gentamicin does not have a secondary metabolite with antibacterial activity because it cannot be metabolized and is excreted unchanged in the urine [20]. It is important to note that more in vitro and animal studies are necessary to determine the optimal doses of a combination of antimicrobials that maintain the potentiation effect and minimize adverse effects [18].…”

Section: Discussionmentioning

confidence: 99%

“…Currently, there are computational methods for designing drug combinations that allow the identification of drug mixtures with synergistic antimicrobial potential [18]. However, few studies have documented the interaction between two antimicrobial drugs using the same methods and statistical analyses as this study.…”

Section: Discussionmentioning

confidence: 99%

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Isobolographic analysis of the ciprofloxacin–gentamicin combination against beta‐lactamase‐producing Staphylococcus aureus

Isiordia-Espinoza

Terán-Rosales

Serafín-Higuera

et al. 2023

Fundamemntal Clinical Pharma

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BackgroundBacterial multi‐resistance is a serious global problem that continues to worsen over time due to multiple factors. Among these factors, it is important to highlight the clinical misuse of antibiotics and the mechanisms that microorganisms have developed to protect themselves from these drugs. In this sense, Staphylococcus aureus (S. aureus) is a pathogen that has found a way to resist many of the drugs currently in use, so infections by this bacterium represent a serious clinical problem.ObjectivesThe purpose of this study was to determine the type of interaction between ciprofloxacin and gentamicin against beta‐lactamase‐producing S. aureus using isobolographic analysis.MethodsCiprofloxacin (0.5–0.05 mg/mL) and gentamicin (10–1 mg/mL) were used to make concentration‐dependent curves for each individual drug. Thereafter, the 50 inhibitory concentration (IC50) of each drug was obtained, and different proportions of the ciprofloxacin–gentamicin combination—0.5:0.5, 0.8:0.2, 0.2:0.8, 0.9:0.1, 0.1:0.9, 0.95:0.05, and 0.05:0.95—were evaluated. The isobolographic analysis and the interaction index were used to analyze the data.ResultsThe isobolographic evaluation of the combination showed that the ratios 0.5:0.5, 0.8:0.2, 0.2:0.8, and 0.9:0.1 produced a synergistic anti‐staphylococcal effect, and the 0.95:0.05 ratio induced an additive antibacterial effect. Finally, the 0.1:0.9 and 0.05:0.95 ratios of the combination presented antagonistic effects against S. aureus. On the other hand, the interaction index showed similar results to the isobolographic analysis.ConclusionThe isobolographic results of this in vitro assay show that the ciprofloxacin–gentamicin combination induces synergistic, additive, and antagonistic antimicrobial effects against S. aureus.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Isobolographic analysis of the ciprofloxacin–gentamicin combination against beta‐lactamase‐producing Staphylococcus aureus

Isiordia-Espinoza

Terán-Rosales

Serafín-Higuera

et al. 2023

Fundamemntal Clinical Pharma

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“…Ultimately, CARAMeL serves as a proof-of-concept of how computational approaches, such as systems-level metabolic modeling and machine learning, can be combined to create hybrid models that provide mechanistic insight into various biological processes [77][78][79] , in this case antimicrobial efficacy and resistance. Although the use of GEMs in CARAMeL offers major advantages with data compatibility, condition tunability, and mechanistic insight, it also introduces some limitations.…”

Section: Discussionmentioning

confidence: 99%

A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions

Chung

Chandrasekaran

2021

Preprint

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Drug combinations are a promising strategy to counter antibiotic resistance. However, current experimental and computational approaches do not account for the entire complexity involved in combination therapy design, such as the effect of the growth environment, drug order, and time interval. To address these limitations, we present an approach that uses genome-scale metabolic modeling and machine learning to explain and guide combination therapy design. Our approach (a) accommodates diverse data types, (b) accurately predicts drug interactions in various growth conditions, (c) accounts for time- and order-specific interactions, and (d) identifies mechanistic factors driving drug interactions. The entropy in bacterial stress response, time between treatments, and gluconeogenesis activation were the most predictive features of combination therapy outcomes across time scales and growth conditions. Analysis of the vast landscape of condition-specific drug interactions revealed promising new drug combinations and a tradeoff in the efficacy between simultaneous and sequential combination therapies.

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“…This approach is complemented by the discovery of novel antimicrobial agents based on metabolic or virulence targets, host-adapted screening approaches, biotransformation, as well as machine learning approaches, along with genetic information of biosynthesis modules that can be edited to synthesize novel compound [ 90 , 91 , 92 , 93 , 94 , 95 , 96 ]. Additional strategies include the combinatorial screening of existing drugs in order to enhance the efficiency of antimicrobial agents through synergistic effects [ 97 ]. The discovery of intrinsic antimicrobial resistance components [ 98 , 99 ] might help in the identification of novel drug targets [ 100 , 101 ].…”

Section: Innovative Strategies To Overcome Antimicrobial Resistancementioning

confidence: 99%

Innovative Strategies to Overcome Antimicrobial Resistance and Tolerance

et al. 2022

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Antimicrobial resistance and tolerance are natural phenomena that arose due to evolutionary adaptation of microorganisms against various xenobiotic agents. These adaptation mechanisms make the current treatment options challenging as it is increasingly difficult to treat a broad range of infections, associated biofilm formation, intracellular and host adapted microbes, as well as persister cells and microbes in protected niches. Therefore, novel strategies are needed to identify the most promising drug targets to overcome the existing hurdles in the treatment of infectious diseases. Furthermore, discovery of novel drug candidates is also much needed, as few novel antimicrobial drugs have been introduced in the last two decades. In this review, we focus on the strategies that may help in the development of innovative small molecules which can interfere with microbial resistance mechanisms. We also highlight the recent advances in optimization of growth media which mimic host conditions and genome scale molecular analyses of microbial response against antimicrobial agents. Furthermore, we discuss the identification of antibiofilm molecules and their mechanisms of action in the light of the distinct physiology and metabolism of biofilm cells. This review thus provides the most recent advances in host mimicking growth media for effective drug discovery and development of antimicrobial and antibiofilm agents.

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Machine learning to design antimicrobial combination therapies: Promises and pitfalls

Cited by 12 publications

References 92 publications

Isobolographic analysis of the ciprofloxacin–gentamicin combination against beta‐lactamase‐producing Staphylococcus aureus

Isobolographic analysis of the ciprofloxacin–gentamicin combination against beta‐lactamase‐producing Staphylococcus aureus

A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions

Innovative Strategies to Overcome Antimicrobial Resistance and Tolerance

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