Semi-mechanistic pharmacokinetic-pharmacodynamic modelling of antibiotic drug combinations

Quantifying combined effects of colistin and ciprofloxacin against Escherichia coli in an in silico pharmacokinetic-pharmacodynamic model

Co-administering a low dose of colistin (CST) with ciprofloxacin (CIP) may improve the antibacterial effect against resistant Escherichia coli, offering an acceptable benefit-risk balance. This study aimed to quantify the interaction between ciprofloxacin and colistin in an in silico pharmacokinetic-pharmacodynamic model from in vitro static time-kill experiments (using strains with minimum inhibitory concentrations, MICCIP 0.023-1 mg/L and MICCST 0.5-0.75 mg/L). It was also sought to demonstrate an approach of simulating concentrations at the site of infection with population pharmacokinetic and whole-body physiologically based pharmacokinetic models to explore the clinical value of the combination when facing more resistant strains (using extrapolated strains with lower susceptibility). The combined effect in the final model was described as the sum of individual drug effects with a change in drug potency: for ciprofloxacin, concentration at half maximum killing rate (EC50) in combination was 160% of the EC50 in monodrug experiments, while for colistin, the change in EC50 was strain-dependent from 54.1% to 119%. The benefit of co-administrating a lower-than-commonly-administrated colistin dose with ciprofloxacin in terms of drug effect in comparison to either monotherapy was predicted in simulated bloodstream infections and pyelonephritis. The study illustrates the value of pharmacokinetic-pharmacodynamic modelling and simulation in streamlining rational development of antibiotic combinations.

Revisiting the Checkerboard to Inform Development of β-Lactam/β-Lactamase Inhibitor Combinations

A two-dimensional "checkerboard" array employing systematic titration (e.g., serial two-fold dilutions) is a well-established in vitro method for exploring the antibacterial effects of novel drug combinations. Minimum inhibitory concentrations (MICs) on the checkerboard are isoeffective points at which the antibiotic potency is the same. Representations of checkerboard MIC curves for a β-lactam and β-lactamase inhibitor combination are used in hypothetical "thought experiments" and reveal the ways in which current practices can be improved. Because different types of response (i.e., independence vs. additivity vs. one effective agent; interaction vs. noninteraction) produce different MIC curves, data from different strains/isolates should not be pooled indiscriminately, as the composition of a pooled dataset will influence any derived pharmacokinetic/pharmacodynamic (PK/PD) index. Because the β-lactamase inhibitor threshold concentration (CT) parameter is a function of the β-lactam partner dosing regimen, it is not possible to derive a universal PK/PD index target based on CT. Alternative susceptibility testing methods represent different planes through the checkerboard; a fixed ratio method is less prone to bias for all β-lactam and β-lactamase inhibitor combinations. Susceptibility test MICs will often not reflect the sensitivity of the strain/isolate to the β-lactamase inhibitor, so the use of these MICs to normalize PK/PD indices is inappropriate.

Translational PK/PD for the Development of Novel Antibiotics-A Drug Developer's Perspective

Antibiotic development traditionally involved large Phase 3 programs, preceded by Phase 2 studies. Recognizing the high unmet medical need for new antibiotics and, in some cases, challenges to conducting large clinical trials, regulators created a streamlined clinical development pathway in which a lean clinical efficacy dataset is complemented by nonclinical data as supportive evidence of efficacy. In this context, translational Pharmacokinetic/Pharmacodynamic (PK/PD) plays a key role and is a major contributor to a "robust" nonclinical package. The classical PK/PD index approach, proven successful for established classes of antibiotics, is at the core of recent antibiotic approvals and the current antibacterial PK/PD guidelines by regulators. Nevertheless, in the case of novel antibiotics with a novel Mechanism of Action (MoA), there is no prior experience with the PK/PD index approach as the basis for translating nonclinical efficacy to clinical outcome, and additional nonclinical studies and PK/PD analyses might be considered to increase confidence. In this review, we discuss the value and limitations of the classical PK/PD approach and present potential risk mitigation activities, including the introduction of a semi-mechanism-based PK/PD modeling approach. We propose a general nonclinical PK/PD package from which drug developers might choose the studies most relevant for each individual candidate in order to build up a "robust" nonclinical PK/PD understanding.

An improved PKPD modeling approach to characterize the pharmacodynamic interaction over time between ceftazidime/avibactam and colistin from in vitro time-kill experiments against multidrug-resistant Klebsiella pneumoniae isolates

In contrast to the checkerboard method, bactericidal experiments [time-kill curves (TKCs)] allow an assessment of pharmacodynamic (PD) interactions over time. However, TKCs in combination pose interpretation problems. The objective of this study was to characterize the PD interaction over time between ceftazidime/avibactam (CZA) and colistin (CST) using TKC against four multidrug-resistant Klebsiella pneumoniae susceptible to both antibiotics and expressing a widespread carbapenemase determinant KPC-3. In vitro TKCs were performed and analyzed using pharmacokinetic/pharmacodynamic (PKPD) modeling. The general pharmacodynamic interaction model was used to characterize PD interactions between drugs. The 95% confidence intervals (95%CIs) of the expected additivity and of the observed interaction were built using parametric bootstraps and compared to evaluate the in vitro PD interaction over time. Further simulations were conducted to investigate the effect of the combination at varying concentrations typically observed in patients. Regrowth was observed in TKCs at high concentrations of drugs alone [from 4 to 32× minimum inhibitory concentrations (MIC)], while the combination systematically prevented the regrowth at concentrations close to the MIC. Significant synergy or antagonism were observed under specific conditions but overall 95%CIs overlapped widely over time indicating an additive interaction between antibiotics. Moreover, simulations of typical PK profile at standard dosages indicated that the interaction should be additive in clinical conditions. The nature of the PD interaction varied with time and concentration in TKC. Against the four K. pneumoniae isolates, the bactericidal effect of CZA + CST combination was predicted to be additive and to prevent the emergence of resistance at clinical concentrations.

Model-informed dose optimisation of polymyxin-rifampicin combination therapy against multidrug-resistant Acinetobacter baumannii

OBJECTIVES

Antimicrobial resistance is a major global threat. Because of the stagnant antibiotic pipeline, synergistic antibiotic combination therapy has been proposed to treat rapidly emerging multidrug-resistant (MDR) pathogens. We investigated antimicrobial synergy of polymyxin/rifampicin combination against MDR Acinetobacter baumannii.

METHODS

In vitro static time-kill studies were performed over 48 h at an initial inoculum of ∼107 CFU/mL against three polymyxin-susceptible but MDR A. baumannii isolates. Membrane integrity was examined at 1 and 4 h post-treatment to elucidate the mechanism of synergy. Finally, a semi-mechanistic PK/PD model was developed to simultaneously describe the time course of bacterial killing and prevention of regrowth by mono- and combination therapies.

RESULTS

Polymyxin B and rifampicin alone produced initial killing against MDR A. baumannii but were associated with extensive regrowth. Notably, the combination showed synergistic killing across all three A. baumannii isolates with bacterial loads below the limit of quantification for up to 48 h. Membrane integrity assays confirmed the role of polymyxin-driven outer membrane remodelling in the observed synergy. Subsequently, the mechanism of synergy was incorporated into a PK/PD model to describe the enhanced uptake of rifampicin due to polymyxin-induced membrane permeabilisation. Simulations with clinically utilised dosing regimens confirmed the therapeutic potential of this combination, particularly in the prevention of bacterial regrowth. Finally, results from a neutropenic mouse thigh infection model confirmed the in vivo synergistic killing of the combination against A. baumannii AB5075.

CONCLUSION

Our results showed that polymyxin B combined with rifampicin is a promising option to treat bloodstream and tissue infection caused by MDR A. baumannii and warrants clinical evaluations.

Two-Tailed Dynamic Covalent Amphiphile Combats Bacterial Biofilms

Drug combination provides an efficient pathway to combat drug resistance in bacteria and bacterial biofilms. However, the facile methodology to construct the drug combinations and their applications in nanocomposites is still lacking. Here the two-tailed antimicrobial amphiphiles (T2 A2 ) composed of nitric oxide (NO)-donor (diethylenetriamine NONOate, DN) and various natural aldehydes are reported. T2 A2 self-assemble into nanoparticles due to their amphiphilic nature, with remarkably low critical aggregation concentration. The representative cinnamaldehyde (Cin)-derived T2 A2 (Cin-T2 A2 ) assemblies demonstrate excellent bactericidal efficacy, notably higher than free Cin and free DN. Cin-T2 A2 assemblies kill multidrug-resistant staphylococci and eradicate their biofilms via multiple mechanisms, as proved by mechanism studies, molecular dynamics simulations, proteomics, and metabolomics. Furthermore, Cin-T2 A2 assemblies rapidly eradicate bacteria and alleviate inflammation in the subsequent murine infection models. Together, the Cin-T2 A2 assemblies may provide an efficient, non-antibiotic alternative in combating the ever-increasing threat of drug-resistant bacteria and their biofilms.

The pharmacokinetic-pharmacodynamic modelling framework as a tool to predict drug resistance evolution

Pharmacokinetic-pharmacodynamic (PKPD) models, which describe how drug concentrations change over time and how that affects pathogen growth, have proven highly valuable in designing optimal drug treatments aimed at bacterial eradication. However, the fast rise of antimicrobial resistance calls for increased focus on an additional treatment optimization criterion: avoidance of resistance evolution. We demonstrate here how coupling PKPD and population genetics models can be used to determine treatment regimens that minimize the potential for antimicrobial resistance evolution. Importantly, the resulting modelling framework enables the assessment of resistance evolution in response to dynamic selection pressures, including changes in antimicrobial concentration and the emergence of adaptive phenotypes. Using antibiotics and antimicrobial peptides as an example, we discuss the empirical evidence and intuition behind individual model parameters. We further suggest several extensions of this framework that allow a more comprehensive and realistic prediction of bacterial escape from antimicrobials through various phenotypic and genetic mechanisms.

PK/PD modeling and simulation of the in vitro activity of the combinations of isavuconazole with echinocandins against Candida auris

In vitro combination of echinocandins and isavuconazole against the emerging species Candida auris is mainly synergistic. However, this combination has not been evaluated in clinical settings. A pharmacokinetic/pharmacodynamic modeling and simulation approach based on in vitro data may be helpful to further study the therapeutic potential of these combinations. Therefore, the aims of this study were to characterize the time course of growth and killing of C. auris in response to the combination of the three approved echinocandins and isavuconazole using a semimechanistic model and to perform model-based simulations in order to predict the in vivo response to combination therapy. In vitro static time-kill curve data for isavuconazole and echinocandins combinations against six blood isolates of C. auris were best modeled considering the total killing of the fungal population as dependent on the additive effects of both drugs. Once assessed, the predictive performance of the model using simulations of different dosing and fungal susceptibility scenarios were conducted. Model-based simulations revealed that none of the combinations at standard or higher dosages would be effective against the studied isolates of C. auris and it was predicted that the combinations of isavuconazole with anidulafungin or caspofungin would be effective for minimum inhibitory concentrations up to 0.03 and 0.06 mg/L respectively, whereas the combination with micafungin would lead to treatment failure. The current approach highlights the importance of bridging the in vitro results to the clinic.

Pharmacokinetic/Pharmacodynamic Index Linked to In Vivo Efficacy of the Ampicillin-Ceftriaxone Combination against Enterococcus faecalis

Combination therapy with ampicillin plus ceftriaxone (AMP+CRO) is the first-line therapy for treating severe infections due to Enterococcus faecalis. However, the pharmacokinetic/pharmacodynamic (PK/PD) index linked to the in vivo efficacy of the combination is not yet defined, hindering dose optimization in the clinic. Because classical PK/PD indices are not directly applicable to antimicrobial combinations, two novel indices were tested in the optimized murine model of infection by E. faecalis to delineate the potentiation of AMP by CRO: the time above the CRO threshold (T_>threshold) and the time above the AMP instantaneous MIC (T_>MICi). The potential clinical relevance was evaluated by simulating human doses of AMP and CRO. Hill's equation fitted well the exposure-response data in terms of T_>threshold, with a CRO threshold of 1 mg/L. The required exposures were 46%, 49%, and 52% for stasis and 1- and 2-log₁₀ killing, respectively. Human ceftriaxone doses of 2 g every 12 h (q12h) would reach the target in >90% of strains with thresholds ≤64 mg/L. The AMP T_>MICi index also fitted well, and the required exposures were 37%, 41%, and 46% for stasis and 1- and 2-log₁₀ killing, respectively. In humans, the addition of CRO would allow use of lower AMP doses to reach the same T_>MICi and to treat strains with higher MICs. This is the first report of the PK/PD indices and required magnitudes linked to AMP+CRO against E. faecalis; these results can be used as the basis to guide the design of clinical trials to improve combined therapy against enterococci.

Pharmacodynamics and Bactericidal Activity of Combination Regimens in Pulmonary Tuberculosis: Application to Bedaquiline-Pretomanid-Pyrazinamide

A critical barrier to codevelopment of tuberculosis (TB) regimens is a limited ability to identify optimal drug and dose combinations in early-phase clinical testing. While pharmacokinetic-pharmacodynamic (PKPD) target attainment is the primary tool for exposure-response optimization of TB drugs, the PD target is a static index that does not distinguish individual drug contributions to the efficacy of a multidrug combination. A PKPD model of bedaquiline-pretomanid-pyrazinamide (BPaZ) for the treatment of pulmonary TB was developed as part of a dynamic exposure-response approach to regimen development. The model describes a time course relationship between the drug concentrations in plasma and their individual as well as their combined effect on sputum bacillary load assessed by solid culture CFU counts and liquid culture time to positivity (TTP). The model parameters were estimated using data from the phase 2A studies NC-001-(J-M-Pa-Z) and NC-003-(C-J-Pa-Z). The results included a characterization of BPaZ activity as the most and least sensitive to changes in pyrazinamide and bedaquiline exposures, respectively, with antagonistic activity of BPa compensated by synergistic activity of BZ and PaZ. Simulations of the NC-003 study population with once-daily bedaquiline at 200 mg, pretomanid at 200 mg, and pyrazinamide at 1,500 mg showed BPaZ would require 3 months to attain liquid culture negativity in 90% of participants. These results for BPaZ were intended to be an example application with the general approach aimed at entirely novel drug combinations from a growing pipeline of new and repurposed TB drugs.

Pharmacokinetic/Pharmacodynamic Modeling and Application in Antibacterial and Antifungal Pharmacotherapy: A Narrative Review

Pharmacokinetics and pharmacodynamics are areas in pharmacology related to different themes in the pharmaceutical sciences, including therapeutic drug monitoring and different stages of drug development. Although the knowledge of these disciplines is essential, they have historically been treated separately. While pharmacokinetics was limited to describing the time course of plasma concentrations after administering a drug-dose, pharmacodynamics describes the intensity of the response to these concentrations. In the last decades, the concept of pharmacokinetic/pharmacodynamic modeling (PK/PD) emerged, which seeks to establish mathematical models to describe the complete time course of the dose-response relationship. The integration of these two fields has had applications in optimizing dose regimens in treating antibacterial and antifungals. The anti-infective PK/PD models predict the relationship between different dosing regimens and their pharmacological activity. The reviewed studies show that PK/PD modeling is an essential and efficient tool for a better understanding of the pharmacological activity of antibacterial and antifungal agents.

Pharmacokinetic-pharmacodynamic models for time courses of antibiotic effects: VSI: Antimicrobial Pharmacometrics

Pharmacokinetic-pharmacodynamic (PKPD) models have emerged as valuable tools for the characterisation and translation of antibiotic effects, and consequently for drug development and therapy. In contrast to traditional PKPD concepts for antibiotics like MIC and PKPD indices, PKPD models enable to describe the continuous, often species- or population-dependent time course of antimicrobial effects, commonly considering mechanistic pathogen- and drug-related knowledge. This review presents a comprehensive overview of previously published PKPD models describing repeated measurements of antibiotic effects. We conducted a literature review to identify PKPD models based on (i) antibiotic compounds, (ii) Gram-positive or Gram-negative pathogens, and (iii) in vitro or in vivo longitudinal colony forming unit data. We identified 132 publications released between 1963 and 2021, including models based on exposure with single antibiotics (n=92) and drug combinations (n=40), as well as different experimental settings (e.g., static/traditional dynamic/hollow-fibre/animal time-kill models, n=90/27/32/11). An interactive, fully searchable table summarises the details of each model, i.e. variants and mechanistic elements of PKPD submodels capturing observed bacterial growth, regrowth, drug effects, and interactions. Furthermore, the review highlights main purposes of PKPD model development, including the translation of preclinical PKPD to clinical settings and the assessment of varied dosing regimens and patient characteristics for their impact on clinical antibiotic effects. In summary, this comprehensive overview of PKPD models shall assist in identifying PKPD modelling strategies to describe growth, killing, regrowth and interaction patterns for pathogen-antibiotic combinations over time and ultimately facilitate model-informed antibiotic translation, dosing and drug development.

A New Hope in the Fight Against Antimicrobial Resistance with Artificial Intelligence

Recent years have witnessed the rise of artificial intelligence (AI) in antimicrobial resistance (AMR) management, implying a positive signal in the fight against antibiotic-resistant microbes. The impact of AI starts with data collection and preparation for deploying AI-driven systems, which can lay the foundation for some effective infection control strategies. Primary applications of AI include identifying potential antimicrobial molecules, rapidly testing antimicrobial susceptibility, and optimizing antibiotic combinations. Aside from their outstanding effectiveness, these applications also express high potential in narrowing the burden gap of AMR among different settings around the world. Despite these benefits, the interpretability of AI-based systems or models remains vague. Attempts to address this issue had led to two novel explanation techniques, but none have shown enough robustness or comprehensiveness to be widely applied in AI and AMR control. A multidisciplinary collaboration between the medical field and advanced technology is therefore needed to partially manage this situation and improve the AI systems’ performance and their effectiveness against drug-resistant pathogens, in addition to multiple equity actions for mitigating the failure risks of AI due to a global-scale equity gap.

Efficient simulation of clinical target response surfaces

Simulation of combination therapies is challenging due to computational complexity. Either a simple model is used to simulate the response for many combinations of concentration to generate a response surface but parameter variability and uncertainty are neglected and the concentrations are constant—the link to the doses to be administered is difficult to make—or a population pharmacokinetic/pharmacodynamic model is used to predict the response to combination therapy in a clinical trial taking into account the time‐varying concentration profile, interindividual variability (IIV), and parameter uncertainty but simulations are limited to only a few selected doses. We devised new algorithms to efficiently search for the combination doses that achieve a predefined efficacy target while taking into account the IIV and parameter uncertainty. The result of this method is a response surface of confidence levels, indicating for all dose combinations the likelihood of reaching the specified efficacy target. We highlight the importance to simulate across a population rather than focus on an individual. Finally, we provide examples of potential applications, such as informing experimental design.

Kinetics of bactericidal potency with synergistic combination of allicin and selected antibiotics

Synergistic therapy against the resurgence of bacterial pathogenesis is a modern trend for antibacterial chemotherapy. The phytochemical allicin, found in garlic extract is a commendable antimicrobial agent that can be used in synergistic combination with modern antibiotics. Determination of optimal antibacterial combination for the target species is vital for maximizing efficacy, lowering toxicity, total eradication of the bacterial cells and minimization of the risk of resistance generation. In this present investigation, Hill function-based pharmacodynamics models were employed to elaborate various time-kill kinetics parameters. The bactericidal potency of the synergistic combinations of allicin and individual antibiotic was assessed in comparison to their monotherapy application viz. using sole allicin and sole antibiotics (levofloxacin, ciprofloxacin, oxytetracycline, rifaximin, ornidazole and azithromycin) on actively growing Bacillus subtilis and Escherichia coli bacteria. Here, all the synergistic combinations showed significantly better (t-test p-value < 0.05) killing effect and biofilm reduction potential compared to their respective monotherapy application, where the highest killing effect was observed with rifaximin-allicin combination (kill rate was more than 5.5 h^-1). Moreover, the average inhibition potential to protein denaturation by the synergistic combination group was significantly higher (3.4 fold) than the sole antibiotic's group manifests reduction in the dose-related toxicity. The potential of synergism between antibiotics and allicin combination demonstrated greater killing efficiency at significantly lower concentration compared to monotherapy with increased kill rates in all cases.

Application of Semi-Mechanistic Pharmacokinetic and Pharmacodynamic Model in Antimicrobial Resistance

Antimicrobial resistance is a major public health issue. The pharmacokinetic/pharmacodynamic (PK/PD) model is an essential tool to optimize dosage regimens and alleviate the emergence of resistance. The semi-mechanistic PK/PD model is a mathematical quantitative tool to capture the relationship between dose, exposure, and response, in terms of the mechanism. Understanding the different resistant mechanisms of bacteria to various antibacterials and presenting this as mathematical equations, the semi-mechanistic PK/PD model can capture and simulate the progress of bacterial growth and the variation in susceptibility. In this review, we outline the bacterial growth model and antibacterial effect model, including different resistant mechanisms, such as persisting resistance, adaptive resistance, and pre-existing resistance, of antibacterials against bacteria. The application of the semi-mechanistic PK/PD model, such as the determination of PK/PD breakpoints, combination therapy, and dosage optimization, are also summarized. Additionally, it is important to integrate the PD effect, such as the inoculum effect and host response, in order to develop a comprehensive mechanism model. In conclusion, with the semi-mechanistic PK/PD model, the dosage regimen can be reasonably determined, which can suppress bacterial growth and resistance development.

In search for a synergistic combination against pandrug-resistant A. baumannii; methodological considerations

PURPOSE

Pending approval of new antimicrobials, synergistic combinations are the only treatment option against pandrug-resistant A. baumannii (PDRAB). Considering the lack of a standardized methodology, the aim of this manuscript is to systematically review the methodology and discuss unique considerations for assessing antimicrobial combinations against PDRAB.

METHODS

Post-hoc analysis of a systematic review (conducted in PubMed and Scopus from inception to April 2021) of studies evaluating antimicrobial combination against A. baumannii, based on antimicrobials that are inactive in vitro alone.

RESULTS

Eighty-four publications were reviewed, using a variety of synergy testing methods, including; gradient-based methods (n = 11), disk-based methods (n = 6), agar dilution (n = 2), checkerboard assay (n = 44), time-kill assay (n = 50), dynamic in vitro PK/PD models (n = 6), semi-mechanistic PK/PD models (n = 5), and in vivo animal models (n = 11). Several variations in definitions of synergy and interpretation of each method were observed and are discussed. Challenges related to testing combinations of antimicrobials that are inactive alone (with regards to concentrations at which the combinations are assessed), as well as other considerations (assessment of stasis vs killing, clinical relevance of re-growth in vitro after initial killing, role of in vitro vs in vivo conditions, challenges of clinical testing of antimicrobial combinations against PDRAB infections) are discussed.

CONCLUSION

This review demonstrates the need for consensus on a standardized methodology and clinically relevant definitions for synergy. Modifications in the methodology and definitions of synergy as well as a roadmap for further development of antimicrobial combinations against PDRAB are proposed.

Pharmacodynamics and the Bactericidal Activity of Bedaquiline in Pulmonary Tuberculosis

Bedaquiline is a diarylquinoline antimycobacterial drug and a key component of several regimens in clinical development for treatment of tuberculosis (TB), but with ongoing phase 3 trials that include assessment of simplified dosing. A pharmacokinetic-pharmacodynamic model of bedaquiline Mycobacterium tuberculosis killing kinetics in adults with pulmonary TB was developed to inform dose selection of bedaquiline-containing regimens. The model parameters were estimated with data from the 14-day early bactericidal activity (EBA) study TMC207-CL001 conducted in Cape Town, South Africa. The study included 60 adult males and females with drug-susceptible pulmonary TB, who were administered bedaquiline with loading doses on the first two days followed by once daily 100 mg, 200 mg, 300 mg, or 400 mg. The modeling results included expected values (mean±SD) for a maximum drug kill rate constant equal to 0.23±0.03 log₁₀ CFU/mL sputum/day, a half-maximum effect plasma concentration equal to 1.6±0.3 mg/L, and an average time to onset of activity equal to 40±7 h. Model simulations showed once daily 200 mg, 300 mg, and 400 mg (without loading doses) attained 40%, 50%, and 60%, respectively, of an expected maximum 14-day EBA equal to 0.18 log₁₀ CFU/mL/day, or 10 h/day assessed by liquid culture time to positivity (TTP). Additional simulations illustrated efficacy outcomes during eight weeks of treatment with the recommended and alternative dosages. The results demonstrate a general mathematical and statistical approach to analysis of EBA studies with broad application to TB regimen development.

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.

Systematic Review of Antimicrobial Combination Options for Pandrug-Resistant Acinetobacter baumannii

Antimicrobial combinations are at the moment the only potential treatment option for pandrug-resistant A. baumannii. A systematic review was conducted in PubMed and Scopus for studies reporting the activity of antimicrobial combinations against A. baumannii resistant to all components of the combination. The clinical relevance of synergistic combinations was assessed based on concentrations achieving synergy and PK/PD models. Eighty-four studies were retrieved including 818 eligible isolates. A variety of combinations (n = 141 double, n = 9 triple) were tested, with a variety of methods. Polymyxin-based combinations were the most studied, either as double or triple combinations with cell-wall acting agents (including sulbactam, carbapenems, glycopeptides), rifamycins and fosfomycin. Non-polymyxin combinations were predominantly based on rifampicin, fosfomycin, sulbactam and avibactam. Several combinations were synergistic at clinically relevant concentrations, while triple combinations appeared more active than the double ones. However, no combination was consistently synergistic against all strains tested. Notably, several studies reported synergy but at concentrations unlikely to be clinically relevant, or the concentration that synergy was observed was unclear. Selecting the most appropriate combinations is likely strain-specific and should be guided by in vitro synergy evaluation. Furthermore, there is an urgent need for clinical studies on the efficacy and safety of such combinations.

A personalised approach to antibiotic pharmacokinetics and pharmacodynamics in critically ill patients

Critically ill patients admitted to intensive care unit (ICU) with severe infections, or those who develop nosocomial infections, have poor outcomes with substantial morbidity and mortality. Such patients commonly have suboptimal antibiotic exposures at routinely used antibiotic doses related to an increased volume of distribution and altered clearance due to their underlying altered physiology. Furthermore, the use of extracorporeal devices such as renal replacement therapy and extracorporeal membrane oxygenation in these group of patients also has the potential to alter in vivo drug concentrations. Moreover, ICU patients are likely to be infected with less-susceptible pathogens. Therefore, one potential contributing cause to the poor outcomes observed in critically ill patients may be related to subtherapeutic antibiotic exposures. Newer concepts include the clinician considering optimised dosing based on a blood antibiotic exposure defined by pharmacokinetic modelling and therapeutic drug monitoring, combined with a knowledge of the antibiotic penetration into the site of infection, thereby achieving optimal bacterial killing. Such optimised dosing is likely to improve patient outcomes. The aim of this review is to highlight key aspects of antibiotic pharmacokinetics and pharmacodynamics (PK/PD) in critically ill patients and provide a PK/PD approach to tailor antibiotic dosing to the individual patient.

In Vitro Pharmacokinetic/Pharmacodynamic Modelling and Simulation of Amphotericin B against Candida auris

The aims of this study were to characterize the antifungal activity of amphotericin B against Candida auris in a static in vitro system and to evaluate different dosing schedules and MIC scenarios by means of semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) modelling and simulation. A two-compartment model consisting of a drug-susceptible and a drug-resistant subpopulation successfully characterized the time-kill data and a modified E_max sigmoidal model best described the effect of the drug. The model incorporated growth rate constants for both subpopulations, a death rate constant and a transfer constant between both compartments. Additionally, the model included a parameter to account for the delay in growth in the absence or presence of the drug. Amphotericin B displayed a concentration-dependent fungicidal activity. The developed PK/PD model was able to characterize properly the antifungal activity of amphotericin B against C. auris. Finally, simulation analysis revealed that none of the simulated standard dosing scenarios of 0.6, 1 and 1.5 mg/kg/day over a week treatment showed successful activity against C. auris infection. Simulations also pointed out that an MIC of 1 mg/L would be linked to treatment failure for C. auris invasive infections and therefore, the resistance rate to amphotericin B may be higher than previously reported.

A novel mechanism-based pharmacokinetic-pharmacodynamic (PKPD) model describing ceftazidime/avibactam efficacy against β-lactamase-producing Gram-negative bacteria

BACKGROUND

Diazabicyclooctanes (DBOs) are an increasingly important group of non β-lactam β-lactamase inhibitors, employed clinically in combinations such as ceftazidime/avibactam. The dose finding of such combinations is complicated using the traditional pharmacokinetic/pharmacodynamic (PK/PD) index approach, especially if the β-lactamase inhibitor has an antibiotic effect of its own.

OBJECTIVES

To develop a novel mechanism-based pharmacokinetic-pharmacodynamic (PKPD) model for ceftazidime/avibactam against Gram-negative pathogens, with the potential for combination dosage simulation.

METHODS

Four β-lactamase-producing Enterobacteriaceae, covering Ambler classes A, B and D, were exposed to ceftazidime and avibactam, alone and in combination, in static time-kill experiments. A PKPD model was developed and evaluated using internal and external evaluation, and combined with a population PK model and applied in dosage simulations.

RESULTS

The developed PKPD model included the effects of ceftazidime alone, avibactam alone and an 'enhancer' effect of avibactam on ceftazidime in addition to the β-lactamase inhibitory effect of avibactam. The model could describe an extensive external Pseudomonas aeruginosa data set with minor modifications to the enhancer effect, and the utility of the model for clinical dosage simulation was demonstrated by investigating the influence of the addition of avibactam.

CONCLUSIONS

A novel mechanism-based PKPD model for the DBO/β-lactam combination ceftazidime/avibactam was developed that enables future comparison of the effect of avibactam with other DBO/β-lactam inhibitors in simulations, and may be an aid in translating PKPD results from in vitro to animals and humans.

The Role of PK/PD Analysis in the Development and Evaluation of Antimicrobials

Pharmacokinetic/pharmacodynamic (PK/PD) analysis has proved to be very useful to establish rational dosage regimens of antimicrobial agents in human and veterinary medicine. Actually, PK/PD studies are included in the European Medicines Agency (EMA) guidelines for the evaluation of medicinal products. The PK/PD approach implies the use of in vitro, ex vivo, and in vivo models, as well as mathematical models to describe the relationship between the kinetics and the dynamic to determine the optimal dosing regimens of antimicrobials, but also to establish susceptibility breakpoints, and prevention of resistance. The final goal is to optimize therapy in order to maximize efficacy and minimize side effects and emergence of resistance. In this review, we revise the PK/PD principles and the models to investigate the relationship between the PK and the PD of antibiotics. Additionally, we highlight the outstanding role of the PK/PD analysis at different levels, from the development and evaluation of new antibiotics to the optimization of the dosage regimens of currently available drugs, both for human and animal use.

In Vitro Synergistic Interactions of Isavuconazole and Echinocandins against Candida auris

Candida auris is an emergent fungal pathogen that causes severe infectious outbreaks globally. The public health concern when dealing with this pathogen is mainly due to reduced susceptibility to current antifungal drugs. A valuable alternative to overcome this problem is to investigate the efficacy of combination therapy. The aim of this study was to determine the in vitro interactions of isavuconazole with echinocandins against C. auris. Interactions were determined using a checkerboard method, and absorbance data were analyzed with different approaches: the fractional inhibitory concentration index (FICI), Greco universal response surface approach, and Bliss interaction model. All models were in accordance and showed that combinations of isavuconazole with echinocandins resulted in an overall synergistic interaction. A wide range of concentrations within the therapeutic range were selected to perform time-kill curves. These confirmed that isavuconazole–echinocandin combinations were more effective than monotherapy regimens. Synergism and fungistatic activity were achieved with combinations that included isavuconazole in low concentrations (≥0.125 mg/L) and ≥1 mg/L of echinocandin. Time-kill curves revealed that once synergy was achieved, combinations of higher drug concentrations did not improve the antifungal activity. This work launches promising results regarding the combination of isavuconazole with echinocandins for the treatment of C. auris infections.

Model-Informed Drug Development for Anti-Infectives: State of the Art and Future

Model-informed drug development (MIDD) has a long and rich history in infectious diseases. This review describes foundational principles of translational anti-infective pharmacology, including choice of appropriate measures of exposure and pharmacodynamic (PD) measures, patient subpopulations, and drug-drug interactions. Examples are presented for state-of-the-art, empiric, mechanistic, interdisciplinary, and real-world evidence MIDD applications in the development of antibacterials (review of minimum inhibitory concentration-based models, mechanism-based pharmacokinetic/PD (PK/PD) models, PK/PD models of resistance, and immune response), antifungals, antivirals, drugs for the treatment of global health infectious diseases, and medical countermeasures. The degree of adoption of MIDD practices across the infectious diseases field is also summarized. The future application of MIDD in infectious diseases will progress along two planes; "depth" and "breadth" of MIDD methods. "MIDD depth" refers to deeper incorporation of the specific pathogen biology and intrinsic and acquired-resistance mechanisms; host factors, such as immunologic response and infection site, to enable deeper interrogation of pharmacological impact on pathogen clearance; clinical outcome and emergence of resistance from a pathogen; and patient and population perspective. In particular, improved early assessment of the emergence of resistance potential will become a greater focus in MIDD, as this is poorly mitigated by current development approaches. "MIDD breadth" refers to greater adoption of model-centered approaches to anti-infective development. Specifically, this means how various MIDD approaches and translational tools can be integrated or connected in a systematic way that supports decision making by key stakeholders (sponsors, regulators, and payers) across the entire development pathway.

Model-Informed Drug Development for Antimicrobials: Translational PK and PK/PD Modeling to Predict an Efficacious Human Dose for Apramycin

Apramycin represents a subclass of aminoglycoside antibiotics that has been shown to evade almost all mechanisms of clinically relevant aminoglycoside resistance. Model-informed drug development may facilitate its transition from preclinical to clinical phase. This study explored the potential of pharmacokinetic/pharmacodynamic (PK/PD) modeling to maximize the use of in vitro time-kill and in vivo preclinical data for prediction of a human efficacious dose (HED) for apramycin. PK model parameters of apramycin from four different species (mouse, rat, guinea pig, and dog) were allometrically scaled to humans. A semimechanistic PK/PD model was developed from the rich in vitro data on four Escherichia coli strains and subsequently the sparse in vivo efficacy data on the same strains were integrated. An efficacious human dose was predicted from the PK/PD model and compared with the classical PK/PD index methodology and the aminoglycoside dose similarity. One-compartment models described the PK data and human values for clearance and volume of distribution were predicted to 7.07 L/hour and 26.8 L, respectively. The required fAUC/MIC (area under the unbound drug concentration-time curve over MIC ratio) targets for stasis and 1-log kill in the thigh model were 34.5 and 76.2, respectively. The developed PK/PD model predicted the efficacy data well with strain-specific differences in susceptibility, maximum bacterial load, and resistance development. All three dose prediction approaches supported an apramycin daily dose of 30 mg/kg for a typical adult patient. The results indicate that the mechanistic PK/PD modeling approach can be suitable for HED prediction and serves to efficiently integrate all available efficacy data with potential to improve predictive capacity.

Can phenotypic data complement our understanding of antimycobacterial effects for drug combinations?

Objectives

To demonstrate how phenotypic cell viability data can provide insight into antimycobacterial effects for the isoniazid/rifampicin treatment backbone.

Methods

Data from a Mycobacterium komossense hollow-fibre infection model comprising a growth control group, rifampicin at three different exposures (C_max = 0.14, 0.4 and 1.47 mg/L with t_½ = 1.57 h and τ = 8 h) and rifampicin plus isoniazid (C_max rifampicin = 0.4 mg/L and C_max isoniazid = 1.2 mg/L with t_½ = 1.57 h and τ = 8 h) were used for this investigation. A non-linear mixed-effects modelling approach was used to fit conventional cfu data, quantified using solid-agar plating. Phenotypic proportions of respiring (alive), respiring but with damaged cell membrane (injured) and ‘not respiring’ (dead) cells data were quantified using flow cytometry and Sytox Green™ (Sigma–Aldrich, UK) and resazurin sodium salt staining and fitted using a multinomial logistic regression model.

Results

Isoniazid/rifampicin combination therapy displayed a decreasing overall antimicrobial effect with time (θTime1/2 = 438 h) on cfu data, in contrast to rifampicin monotherapy where this trend was absent. In the presence of isoniazid a phenotype associated with cell injury was displayed, whereas with rifampicin monotherapy a pattern of phenotypic cell death was observed. Bacterial killing onset time on cfu data correlated negatively (θTime50 = 28.9 h, θLAGRIF50 = 0.132 mg/L) with rifampicin concentration up to 0.165 mg/L and this coincided with a positive relationship between rifampicin concentration and the probability of phenotypic cell death.

Conclusions

Cell viability data provide structured information on the pharmacodynamic interaction between isoniazid and rifampicin that complements the understanding of the antibacillary effects of this mycobacterial treatment backbone.

Development of New Tuberculosis Drugs: Translation to Regimen Composition for Drug-Sensitive and Multidrug-Resistant Tuberculosis

Tuberculosis (TB) kills more people than any other infectious disease. Challenges for developing better treatments include the complex pathology due to within-host immune dynamics, interpatient variability in disease severity and drug pharmacokinetics-pharmacodynamics (PK-PD), and the growing emergence of resistance. Model-informed drug development using quantitative and translational pharmacology has become increasingly recognized as a method capable of drug prioritization and regimen optimization to efficiently progress compounds through TB drug development phases. In this review, we examine translational models and tools, including plasma PK scaling, site-of-disease lesion PK, host-immune and bacteria interplay, combination PK-PD models of multidrug regimens, resistance formation, and integration of data across nonclinical and clinical phases.We propose a workflow that integrates these tools with computational platforms to identify drug combinations that have the potential to accelerate sterilization, reduce relapse rates, and limit the emergence of resistance.

Combination of polymyxin B and minocycline against multidrug-resistant Klebsiella pneumoniae: interaction quantified by pharmacokinetic/pharmacodynamic modelling from in vitro data

Lack of effective treatment for multidrug-resistant Klebsiella pneumoniae (MDR-Kp) necessitates finding and optimising combination therapies of old antibiotics. The aims of this study were to quantify the combined effect of polymyxin B and minocycline by building an in silico semi-mechanistic pharmacokinetic/pharmacodynamic (PKPD) model and to predict bacterial kinetics when exposed to the drugs alone and in combination at clinically achievable unbound drug concentration-time profiles. A clinical K. pneumoniae strain resistant to polymyxin B [minimum inhibitory concentration (MIC) = 16 mg/L] and minocycline (MIC = 16 mg/L) was selected for extensive in vitro static time-kill experiments. The strain was exposed to concentrations of 0.0625-48 × MIC, with seven samples taken per experiment for viable counts during 0-28 h. These observations allowed the development of the PKPD model. The final PKPD model included drug-induced adaptive resistance for both drugs. Both the minocycline-induced bacterial killing and resistance onset rate constants were increased when polymyxin B was co-administered, whereas polymyxin B parameters were unaffected. Predictions at clinically used dosages from the developed PKPD model showed no or limited antibacterial effect with monotherapy, whilst combination therapy kept bacteria below the starting inoculum for >20 h at high dosages [polymyxin B 2.5 mg/kg + 1.5 mg/kg every 12 h (q12h); minocycline 400 mg + 200 mg q12h, loading + maintenance doses]. This study suggests that polymyxin B and minocycline in combination may be of clinical benefit in the treatment of infections by MDR-Kp and for isolates that are non-susceptible to either drug alone.

XDR-Klebsiella pneumoniae isolates harboring blaOXA-48: In vitro and in vivo evaluation using a murine thigh-infection model

Blood stream infection with extensively drug-resistant-carbapenamase producing Klebsiella (K.) pneumoniae usually represents a major threat with medical challenges among hospitalized cancer patients with poor functional status and underlying diseases. Accordingly, the aim of the study was to evaluate the efficacy of different antibiotics either alone or in combinations against extensively drug-resistant-OXA-48 producing K. pneumoniae clinical isolates that were previously recovered from febrile neutropenic pediatric cancer patients. The antimicrobial activity of amikacin, gentamicin, colistin, ertapenem, imipenem, meropenem and tigecycline was assessed by broth microdilution method. The results revealed that all the tested OXA-48 producing K. pneumoniae isolates exhibited extensively drug-resistant phenotype and all of them were susceptible to tigecycline. Checkerboard method was used to determine the fraction inhibitory concentration index, to further classify the effect of antibiotic combination as synergistic, additive, indifferent, or antagonistic effect. The results revealed that in vitro dual carbapenem combination of ertapenem with meropenem had shown synergistic effect against all of the tested isolates. Additionally, synergistic effect of meropenem with colistin was detected among three of four isolates tested. Herein we investigated the in vivo activity of colistin, meropenem alone and in combination in a rat thigh infection model. The results showed that addition of meropenem to colistin was not effective at reduction of bacterial count as compared to colistin alone at 24 h post treatment. Accordingly, we can conclude that in vitro antibiotic combinations of dual carbapenems (ertapenem plus meropenem) and meropenem plus colistin showed synergism in 100% and 75% of the tested isolates, respectively. Colistin alone had significantly reduced bacterial count while its combination with meropenem was not superior to monotherapy in murine thigh infection model.

Impact statement

The present study aimed to evaluate the effectiveness of various antibiotics both in vitro and in vivo using murine animal model either alone or in combination against various strains of extensively drug-resistant (XDR) Klebsiella pneumoniae, life-threatening pathogens of relevant medical importance isolated from febrile neutropenic pediatric cancer patients. This work also emphasizes how to select the appropriate antibiotics options and help the physicians to choice the appropriate antibiotic for the treatment of such superbugs (extensively drug-resistant (XDR) Klebsiella pneumoniae). The results showed that in vitro dual carbapenem combination of ertapenem with meropenem had shown synergistic effect against all of the tested XDR isolates. Antibiotic combinations of dual carbapenems and meropenem plus colistin showed synergism in 100% and 75% of the testes isolates, respectively. Results of the in vivo evaluation, colistin alone had significantly reduced bacterial count while its combination with meropenem was not superior to monotherapy.

Semimechanistic Pharmacodynamic Modeling of Aztreonam-Avibactam Combination to Understand Its Antimicrobial Activity Against Multidrug-Resistant Gram-Negative Bacteria

Aztreonam‐avibactam (ATM‐AVI) is a promising combination to treat serious infections caused by multidrug‐resistant (MDR) pathogens. Three distinct mechanisms of action have been previously characterized for AVI: inhibition of ATM degradation by β‐lactamases, proper bactericidal effect, and enhancement of ATM bactericidal activity. The aim of this study was to quantify the individual contribution of each of the three AVI effects. In vitro static time‐kill studies were performed on four MDREnterobacteriaceae with different β‐lactamase profiles. β‐Lactamase activity was characterized by measuring ATM concentrations over 27 hours. Data were analyzed by a semimechanistic pharmacodynamics modeling approach. Surprisingly, even though AVI prevented ATM degradation, the combined bactericidal activity was mostly explained by the enhancement of ATM effect within clinical range of ATM (5–125 mg/L) and AVI concentrations (0.9–22.5 mg/L). Therefore, when selecting a β‐lactamase inhibitor for combination with a β‐lactam, its capability to enhance the β‐lactam activity should be considered in addition to the spectrum of β‐lactamases inhibited.

Soup with or without meatballs: Impact of nutritional factors on the MIC, kill-rates and growth-rates

BACKGROUND

The Minimum Inhibitory Concentration (MIC) is a reference value for susceptibility testing of bacteria. However, the MIC is a net result of growth and killing after a certain duration of exposure under standardized favourable in vitro conditions. Killing and growth characteristics of a drug may yield more information on its activity and help to explain discrepancies between efficacy observed in vitro and in vivo.

METHODS

The MIC of meropenem was determined for P. aeruginosa ATCC 27853 both by microdilution and the E-test in dilutions of Mueller Hinton (MH) broth from 100% to 1%. Time-kill curves were obtained for twofold dilutions of meropenem. Growth rates and kill rates at each concentration and dilution were obtained by linear regression. The Hill equation was fit to the kill rates vs concentrations.

RESULTS

Growths rates decreased log linearly from 0.63/h at 100% to 0.29/h at 6% MH. Over the 100-6% MH dilution range, there was a log-linear decrease of the MIC of meropenem of both the E-test and microdilution. The EC50s decreased from 0.29 mg/L to 0.07 mg/L, which is in agreement with the MIC results. There was a log-linear relationship between MIC and EC50 for the various dilutions MH.

CONCLUSIONS

The availability of nutritional factors is related to the MIC, and a lower availability is related to both a lower growth rate and higher kill rate. Since nutritional factors are less abundantly available in vivo as compared to in vitro, this should be taken into account when translating in vitro to in vivo pharmacodynamics.

Polymyxin B in Combination with Enrofloxacin Exerts Synergistic Killing against Extensively Drug-Resistant Pseudomonas aeruginosa

Polymyxins are increasingly used as a last-resort class of antibiotics against extensively drug-resistant (XDR) Gram-negative bacteria. However, resistance to polymyxins can emerge with monotherapy. As nephrotoxicity is the major dose-limiting factor for polymyxin monotherapy, dose escalation to suppress the emergence of polymyxin resistance is not a viable option. Therefore, novel approaches are needed to preserve this last-line class of antibiotics. This study aimed to investigate the antimicrobial synergy of polymyxin B combined with enrofloxacin against Pseudomonas aeruginosa Static time-kill studies were conducted over 24 h with polymyxin B (1 to 4 mg/liter) and enrofloxacin (1 to 4 mg/liter) alone or in combination. Additionally, in vitro one-compartment model (IVM) and hollow-fiber infection model (HFIM) experiments were performed against P. aeruginosa 12196. Polymyxin B and enrofloxacin in monotherapy were ineffective against all of the P. aeruginosa isolates examined, whereas polymyxin B-enrofloxacin in combination was synergistic against P. aeruginosa, with ≥2 to 4 log₁₀ kill at 24 h in the static time-kill studies. In both IVM and HFIM, the combination was synergistic, and the bacterial counting values were below the limit of quantification on day 5 in the HFIM. A population analysis profile indicated that the combination inhibited the emergence of polymyxin resistance in P. aeruginosa 12196. The mechanism-based modeling suggests that the synergistic killing is a result of the combination of mechanistic and subpopulation synergy. Overall, this is the first preclinical study to demonstrate that the polymyxin-enrofloxacin combination is of considerable utility for the treatment of XDR P. aeruginosa infections and warrants future clinical evaluations.