Model-based learn and confirm: designing effective treatment regimens against multidrug resistant Gram-negative pathogens

Over the last decade, there has been a growing appreciation for the use of in vitro and in vivo infection models to generate robust and informative nonclinical PK/PD data to accelerate the clinical translation of treatment regimens. The objective of this study was to develop a model-based "learn and confirm" approach to help with the design of combination regimens using in vitro infection models to optimise the clinical utility of existing antibiotics. Static concentration time-kill studies were used to evaluate the PD activity of polymyxin B (PMB) and meropenem against two carbapenem-resistant Klebsiella pneumoniae (CRKP) isolates; BAA2146 (PMB-susceptible) and BRKP67 (PMB-resistant). A mechanism-based model (MBM) was developed to quantify the joint activity of PMB and meropenem. In silico simulations were used to predict the time-course of bacterial killing using clinically-relevant PK exposure profiles. The predictive accuracy of the model was further evaluated by validating the model predictions using a one-compartment PK/PD in vitro dynamic infection model (IVDIM). The MBM captured the reduction in bacterial burden and regrowth well in both the BAA2146 and BRKP67 isolate (R2 = 0.900 and 0.940, respectively). The bacterial killing and regrowth predicted by the MBM were consistent with observations in the IVDIM: sustained activity against BAA2146 and complete regrowth of the BRKP67 isolate. Differences observed in PD activity suggest that additional dose optimisation might be beneficial in PMB-resistant isolates. The model-based approach presented here demonstrates the utility of the MBM as a translational tool from static to dynamic in vitro systems to effectively perform model-informed drug optimisation.

Characterization of the broad-spectrum antibacterial activity of bacteriocin-like inhibitory substance-producing probiotics isolated from fermented foods

Antimicrobial peptides, such as bacteriocin, produced by probiotics have become a promising novel class of therapeutic agents for treating infectious diseases. Selected lactic acid bacteria (LAB) isolated from fermented foods with probiotic potential were evaluated for various tests, including exopolysaccharide production, antibiotic susceptibility, acid and bile tolerance, antibacterial activity, and cell adhesion and cytotoxicity to gastric cell lines. Six selected LAB strains maintained their high viability under gastrointestinal conditions, produced high exopolysaccharides, showed no or less cytotoxicity, and adhered successfully to gastric cells. Furthermore, three strains, Weissella confusa CYLB30, Lactiplantibacillus plantarum CYLB47, and Limosilactobacillus fermentum CYLB55, demonstrated a strong antibacterial effect against drug-resistant Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Salmonella enterica serovar Choleraesuis, Enterococcus faecium, and Staphylococcus aureus. Whole genome sequencing was performed on these three strains using the Nanopore platform; then, the results showed that all three strains did not harbor genes related to toxins, superantigens, and acquired antimicrobial resistance, in their genome. The bacteriocin gene cluster was found in CYLB47 genome, but not in CYLB30 and CYLB55 genomes. In SDS-PAGE, the extract of CYLB30 and CYLB47 bacteriocin-like inhibitory substance (BLIS) yielded a single band with a size of less than 10 kDa. These BLIS inhibited the growth and biofilm formation of drug-resistant P. aeruginosa and methicillin-resistant S. aureus (MRSA), causing membrane disruption and inhibiting adhesion ability to human skin HaCaT cells. Moreover, CYLB30 and CYLB47 BLIS rescued the larvae after being infected with P. aeruginosa and MRSA infections. In conclusion, CYLB30 and CYLB47 BLIS may be potential alternative treatment for multidrug-resistant bacteria infections.

Proof-of-concept for incorporating mechanistic insights from multi-omics analyses of polymyxin B in combination with chloramphenicol against Klebsiella pneumoniae

Carbapenemase-resistant Klebsiella pneumoniae (KP) resistant to multiple antibiotic classes necessitates optimized combination therapy. Our objective is to build a workflow leveraging omics and bacterial count data to identify antibiotic mechanisms that can be used to design and optimize combination regimens. For pharmacodynamic (PD) analysis, previously published static time-kill studies (J Antimicrob Chemother 70, 2015, 2589) were used with polymyxin B (PMB) and chloramphenicol (CHL) mono and combination therapy against three KP clinical isolates over 24 h. A mechanism-based model (MBM) was developed using time-kill data in S-ADAPT describing PMB-CHL PD activity against each isolate. Previously published results of PMB (1 mg/L continuous infusion) and CHL (C_max : 8 mg/L; bolus q6h) mono and combination regimens were evaluated using an in vitro one-compartment dynamic infection model against a KP clinical isolate (10⁸ CFU/ml inoculum) over 24 h to obtain bacterial samples for multi-omics analyses. The differentially expressed genes and metabolites in these bacterial samples served as input to develop a partial least squares regression (PLSR) in R that links PD responses with the multi-omics responses via a multi-omics pathway analysis. PMB efficacy was increased when combined with CHL, and the MBM described the observed PD well for all strains. The PLSR consisted of 29 omics inputs and predicted MBM PD response (R²  = 0.946). Our analysis found that CHL downregulated metabolites and genes pertinent to lipid A, hence limiting the emergence of PMB resistance. Our workflow linked insights from analysis of multi-omics data with MBM to identify biological mechanisms explaining observed PD activity in combination therapy.

Optimizing Antimicrobial Therapy by Integrating Multi-Omics With Pharmacokinetic/Pharmacodynamic Models and Precision Dosing

In the era of “Bad Bugs, No Drugs,” optimizing antibiotic therapy against multi-drug resistant (MDR) pathogens is crucial. Mathematical modelling has been employed to further optimize dosing regimens. These models include mechanism-based PK/PD models, systems-based models, quantitative systems pharmacology (QSP) and population PK models. Quantitative systems pharmacology has significant potential in precision antimicrobial chemotherapy in the clinic. Population PK models have been employed in model-informed precision dosing (MIPD). Several antibiotics require close monitoring and dose adjustments in order to ensure optimal outcomes in patients with infectious diseases. Success or failure of antibiotic therapy is dependent on the patient, antibiotic and bacterium. For some drugs, treatment responses vary greatly between individuals due to genotype and disease characteristics. Thus, for these drugs, tailored dosing is required for successful therapy. With antibiotics, inappropriate dosing such as insufficient dosing may put patients at risk of therapeutic failure which could lead to mortality. Conversely, doses that are too high could lead to toxicities. Hence, precision dosing which customizes doses to individual patients is crucial for antibiotics especially those with a narrow therapeutic index. In this review, we discuss the various strategies in optimizing antimicrobial therapy to address the challenges in the management of infectious diseases and delivering personalized therapy.

Multidrug Resistance (MDR): A Widespread Phenomenon in Pharmacological Therapies

Multidrug resistance is a leading concern in public health. It describes a complex phenotype whose predominant feature is resistance to a wide range of structurally unrelated cytotoxic compounds, many of which are anticancer agents. Multidrug resistance may be also related to antimicrobial drugs, and is known to be one of the most serious global public health threats of this century. Indeed, this phenomenon has increased both mortality and morbidity as a consequence of treatment failures and its incidence in healthcare costs. The large amounts of antibiotics used in human therapies, as well as for farm animals and even for fishes in aquaculture, resulted in the selection of pathogenic bacteria resistant to multiple drugs. It is not negligible that the ongoing COVID-19 pandemic may further contribute to antimicrobial resistance. In this paper, multidrug resistance and antimicrobial resistance are underlined, focusing on the therapeutic options to overcome these obstacles in drug treatments. Lastly, some recent studies on nanodrug delivery systems have been reviewed since they may represent a significant approach for overcoming resistance.

Predicting Antimicrobial Activity at the Target Site: Pharmacokinetic/Pharmacodynamic Indices versus Time-Kill Approaches

Antibiotic dosing strategies are generally based on systemic drug concentrations. However, drug concentrations at the infection site drive antimicrobial effect, and efficacy predictions and dosing strategies should be based on these concentrations. We set out to review different translational pharmacokinetic-pharmacodynamic (PK/PD) approaches from a target site perspective. The most common approach involves calculating the probability of attaining animal-derived PK/PD index targets, which link PK parameters to antimicrobial susceptibility measures. This approach is time efficient but ignores some aspects of the shape of the PK profile and inter-species differences in drug clearance and distribution, and provides no information on the PD time-course. Time–kill curves, in contrast, depict bacterial response over time. In vitro dynamic time–kill setups allow for the evaluation of bacterial response to clinical PK profiles, but are not representative of the infection site environment. The translational value of in vivo time–kill experiments, conversely, is limited from a PK perspective. Computational PK/PD models, especially when developed using both in vitro and in vivo data and coupled to target site PK models, can bridge translational gaps in both PK and PD. Ultimately, clinical PK and experimental and computational tools should be combined to tailor antibiotic treatment strategies to the site of infection.

Therapeutic Drug Monitoring of Antibiotics in the Elderly: A Narrative Review

PURPOSE

Antibiotic dosing adaptation in elderly patients is frequently complicated by age-related changes affecting the processes of drug absorption, distribution, metabolism, and/or elimination. These events eventually result in treatment failure and/or development of drug-related toxicity. Therapeutic drug monitoring (TDM) can prevent suboptimal antibiotic exposure in adult patients regardless of age. However, little data are available concerning the specific role of TDM in the elderly.

METHODS

This review is based on a PubMed search of the literature published in the English language. The search involved TDM studies of antibiotics in the elderly performed between 1990 and 2021. Additional studies were identified from the reference lists of the retrieved articles. Studies dealing with population pharmacokinetic modeling were not considered.

RESULTS

Only a few studies, mainly retrospective and with observational design, have specifically dealt with appropriate antibiotic dosing in the elderly based on TDM. Nevertheless, some clinical situations in which the selection of optimal antibiotic dosing in the elderly was successfully guided by TDM were identified.

CONCLUSIONS

Elderly patients are at an increased risk of bacterial infections and inadequate drug dosing compared to younger patients. Therefore, the availability of TDM services can improve the appropriateness of antibiotic prescriptions in this population.

Antibiotic pharmacokinetic/pharmacodynamic modelling: MIC, pharmacodynamic indices and beyond

The dramatic increase in antimicrobial resistance and the limited pharmacological treatment options highlight the urgent need to optimize therapeutic regimens of new and available anti-infectives. Several in-vitro and in-vivo infection models are employed to understand the relationship between drug exposure profiles in plasma or at the site of infection (pharmacokinetics) and the time course of therapeutic response (pharmacodynamics) to select and optimize dosage regimens for new and approved drugs. Well-designed preclinical studies, combined with mathematical-model-based pharmacokinetic/pharmacodynamic analysis and in-silico simulations, are critical for the effective translation of preclinical data and design of appropriate and successful clinical trials. Integration with population pharmacokinetic modelling and simulations allows for the incorporation of interindividual variability that occurs in both pharmacokinetics and pharmacodynamics, and helps to predict the probability of target attainment and treatment outcome in patients. This article reviews the role of pharmacokinetic/pharmacodynamic approaches in the optimization of dosage regimens to maximize antibacterial efficacy while minimizing toxicity and emergence of resistance, and to achieve a high likelihood of therapeutic success. Polymyxin B, an approved drug with a narrow therapeutic window, serves as an illustrative example to highlight the importance of pharmacokinetic/pharmacodynamic modelling in conjunction with experimentation, employing static time-kill studies followed by dynamic in-vitro or in-vivo models, or both, to learn and confirm mechanistic insights necessary for translation to the bedside.