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MacGowan AP, Attwood MLG, Noel AR, Barber R, Aron Z, Opperman TJ, Grimsey E, Stone J, Ricci V, Piddock LJV. Exposure of Escherichia coli to antibiotic-efflux pump inhibitor combinations in a pharmacokinetic model: impact on bacterial clearance and drug resistance. J Antimicrob Chemother 2023; 78:2869-2877. [PMID: 37837411 DOI: 10.1093/jac/dkad320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 09/22/2023] [Indexed: 10/16/2023] Open
Abstract
BACKGROUND Efflux pump inhibitors (EPIs) offer an attractive therapeutic option when combined with existing classes. However, their optimal dosing strategies are unknown. METHODS MICs of ciprofloxacin (CIP)+/-chlorpromazine, phenylalanine-arginine β naphthylamide (PAβN) and a developmental molecule MBX-4191 were determined and the pharmacodynamics (PD) was studied in an in vitro model employing Escherichia coli MG1655 and its isogenic MarR mutant (I1147). Exposure ranging experiments were performed initially then fractionation. Changes in bacterial load and population profiles were assessed. Strains recovered after EPI simulations were studied by WGS. RESULTS The CIPMICs for E. coli MG1655 and I1147 were 0.08 and 0.03 mg/L. Chlorpromazine at a concentration of 60 mg/L, PAβN concentrations of 30 mg/L and MBX-4191 concentrations of 0.5-1.0 mg/L reduced CIP MICs for I1147 and enhanced bacterial killing. Using CIP at an AUC of 1.2 mg·h/L, chlorpromazine AUC was best related to reduction in bacterial load at 24 h, however, when the time drug concentration was greater than 25 mg/L (T > 25 mg/L) chlorpromazine was also strongly related to the effect. For PaβN with CIP AUC, 0.6 mg·h/L PaβN AUC was best related to a reduction in bacterial load. MBX-4191T > 0.5-0.75 mg·h/L was best related to reduction in bacterial load. Changes in population profiles were not seen in experiments of ciprofloxacin + EPIs. WGS of recovered strains from simulations with all three EPIs showed mutations in gyrA, gyrB or marR. CONCLUSIONS AUC was the pharmacodynamic driver for chlorpromazine and PAβN while T > threshold was the driver for MBX-4191 and important in the activity of chlorpromazine and PAβN. Changes in population profiles did not occur with combinations of ciprofloxacin + EPIs, however, mutations in gyrA, gyrB and marR were detected.
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Affiliation(s)
- Alasdair P MacGowan
- Bristol Centre for Antimicrobial Research & Evaluation (BCARE), Infection Sciences, Southmead Hospital, Westbury-on-Trym, Bristol BS10 5NB, UK
| | - M L G Attwood
- Bristol Centre for Antimicrobial Research & Evaluation (BCARE), Infection Sciences, Southmead Hospital, Westbury-on-Trym, Bristol BS10 5NB, UK
| | - Alan R Noel
- Bristol Centre for Antimicrobial Research & Evaluation (BCARE), Infection Sciences, Southmead Hospital, Westbury-on-Trym, Bristol BS10 5NB, UK
| | - R Barber
- Microbiotix Inc, 1 Innovation Drive, Worcester, MA 01605, USA
| | - Zachary Aron
- Microbiotix Inc, 1 Innovation Drive, Worcester, MA 01605, USA
| | | | - Elizabeth Grimsey
- Antimicrobials Research Group, Institute of Microbiology and Infection, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Jack Stone
- Antimicrobials Research Group, Institute of Microbiology and Infection, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Vito Ricci
- Antimicrobials Research Group, Institute of Microbiology and Infection, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - L J V Piddock
- Antimicrobials Research Group, Institute of Microbiology and Infection, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
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Tian Z, Yu Y, Fang H, Xie W, Guo M. Predicting microbe-drug associations with structure-enhanced contrastive learning and self-paced negative sampling strategy. Brief Bioinform 2023; 24:7009077. [PMID: 36715986 DOI: 10.1093/bib/bbac634] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/19/2022] [Accepted: 12/29/2022] [Indexed: 01/31/2023] Open
Abstract
MOTIVATION Predicting the associations between human microbes and drugs (MDAs) is one critical step in drug development and precision medicine areas. Since discovering these associations through wet experiments is time-consuming and labor-intensive, computational methods have already been an effective way to tackle this problem. Recently, graph contrastive learning (GCL) approaches have shown great advantages in learning the embeddings of nodes from heterogeneous biological graphs (HBGs). However, most GCL-based approaches don't fully capture the rich structure information in HBGs. Besides, fewer MDA prediction methods could screen out the most informative negative samples for effectively training the classifier. Therefore, it still needs to improve the accuracy of MDA predictions. RESULTS In this study, we propose a novel approach that employs the Structure-enhanced Contrastive learning and Self-paced negative sampling strategy for Microbe-Drug Association predictions (SCSMDA). Firstly, SCSMDA constructs the similarity networks of microbes and drugs, as well as their different meta-path-induced networks. Then SCSMDA employs the representations of microbes and drugs learned from meta-path-induced networks to enhance their embeddings learned from the similarity networks by the contrastive learning strategy. After that, we adopt the self-paced negative sampling strategy to select the most informative negative samples to train the MLP classifier. Lastly, SCSMDA predicts the potential microbe-drug associations with the trained MLP classifier. The embeddings of microbes and drugs learning from the similarity networks are enhanced with the contrastive learning strategy, which could obtain their discriminative representations. Extensive results on three public datasets indicate that SCSMDA significantly outperforms other baseline methods on the MDA prediction task. Case studies for two common drugs could further demonstrate the effectiveness of SCSMDA in finding novel MDA associations. AVAILABILITY The source code is publicly available on GitHub https://github.com/Yue-Yuu/SCSMDA-master.
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Affiliation(s)
- Zhen Tian
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Yue Yu
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Haichuan Fang
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Weixin Xie
- Institute of Intelligent System and Bioinformatics, College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150000, China
| | - Maozu Guo
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, 100044, Beijing, China
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Noel AR, Attwood M, Bowker KE, MacGowan AP. The pharmacodynamics of minocycline alone and in combination with rifampicin against Staphylococcus aureus studied in an in vitro pharmacokinetic model of infection. J Antimicrob Chemother 2021; 76:1840-1844. [PMID: 33975336 DOI: 10.1093/jac/dkab112] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 03/10/2021] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Tetracyclines are widely used as oral therapy of MRSA infection, however, the pharmacodynamic underpinning is absent. OBJECTIVES We employed an in vitro pharmacokinetic model to study the pharmacodynamics of minocycline alone and in combination with rifampicin. METHODS An exposure-ranging design was used to establish fAUC/MIC targets for static, -1 log drop and -2 log drop effects against Staphylococcus aureus for minocycline and in combination with rifampicin. We then simulated 7-10 day human dosing of minocycline and the combination. RESULTS The minocycline fAUC/MIC for 24 h static effect and -1 log drop in bacterial load were 12.5 ± 7.1 and 23.3 ± 12.4. fAUC/MIC targets for static and -1 log drop were greater at 48 and 72 h. The addition of simulated free rifampicin associated with dosing 300 mg q12h reduced the 24 h minocycline fAUC/MICs. Simulations performed over 7-10 days exposure indicated that for minocycline standard human doses there was a 1-3 log reduction in viable count and no changes in population profiles. Addition of rifampicin resulted in larger reductions in staphylococcal load but emergence of resistance to rifampicin. There was no resistance to minocycline. CONCLUSIONS An fAUC/MIC minocycline target of 12-36 is appropriate for S. aureus. Addition of rifampicin decreases bacterial load but results in emergence of resistance to rifampicin. Unusually, there was no emergence of resistance to minocycline.
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Affiliation(s)
- A R Noel
- Bristol Centre for Antimicrobial Research & Evaluation (BCARE), North Bristol NHS Trust, Department of Infection Sciences, Pathology Sciences Building-Phase 2, Southmead Hospital, Westbury-on-Trym, Bristol BS10 5NB, UK
| | - M Attwood
- Bristol Centre for Antimicrobial Research & Evaluation (BCARE), North Bristol NHS Trust, Department of Infection Sciences, Pathology Sciences Building-Phase 2, Southmead Hospital, Westbury-on-Trym, Bristol BS10 5NB, UK
| | - K E Bowker
- Bristol Centre for Antimicrobial Research & Evaluation (BCARE), North Bristol NHS Trust, Department of Infection Sciences, Pathology Sciences Building-Phase 2, Southmead Hospital, Westbury-on-Trym, Bristol BS10 5NB, UK
| | - A P MacGowan
- Bristol Centre for Antimicrobial Research & Evaluation (BCARE), North Bristol NHS Trust, Department of Infection Sciences, Pathology Sciences Building-Phase 2, Southmead Hospital, Westbury-on-Trym, Bristol BS10 5NB, UK
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