Comparative evaluation of eight in vitro pharmacodynamic models of infection: Activity of moxifloxacin against Escherichia coli and Streptococcus pneumoniae as an exemplary example

Exposure of Escherichia coli to antibiotic-efflux pump inhibitor combinations in a pharmacokinetic model: impact on bacterial clearance and drug resistance

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.

Predicting microbe-drug associations with structure-enhanced contrastive learning and self-paced negative sampling strategy

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.

The pharmacodynamics of minocycline alone and in combination with rifampicin against Staphylococcus aureus studied in an in vitro pharmacokinetic model of infection

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.