Efficacy, nephrotoxicity and ototoxicity of aminoglycosides, mathematically modelled for modelling-supported therapeutic drug monitoring

Pharmacokinetic modelling to predict risk of ototoxicity with intravenous tobramycin treatment in cystic fibrosis

INTRODUCTION

Further optimization of therapeutic drug monitoring (TDM) for aminoglycosides (AGs) is urgently needed, especially in special populations such as those with cystic fibrosis (CF), >50% of whom develop ototoxicity if treated with multiple courses of IV AGs. This study aimed to empirically test a pharmacokinetic (PK) model using Bayesian estimation of drug exposure in the deeper body tissues to determine feasibility for prediction of ototoxicity.

MATERIALS AND METHODS

IV doses (n = 3645) of tobramycin and vancomycin were documented with precise timing from 38 patients with CF (aged 8-21 years), including total doses given and total exposure (cumulative AUC). Concentration results were obtained at 3 and 10 h for the central (C1) compartment. These variables were used in Bayesian estimation to predict trough levels in the secondary tissue compartments (C2 trough) and maximum concentrations (C2max). The C1 and C2 measures were then correlated with hearing levels in the extended high-frequency range.

RESULTS

Patients with more severe hearing loss were older and had a higher number of tobramycin C2max concentrations >2 mg/L than patients with normal or lesser degrees of hearing loss. These two factors together significantly predicted average high-frequency hearing level (r = 0.618, P < 0.001). Traditional metrics such as C1 trough concentrations were not predictive. The relative risk for hearing loss was 5.8 times greater with six or more tobramycin courses that exceeded C2max concentrations of 3 mg/L or higher, with sensitivity of 83% and specificity of 86%.

CONCLUSIONS

Advanced PK model-informed analysis predicted ototoxicity risk in patients with CF treated with tobramycin and demonstrated high test prediction.

The QSAR Paradigm in Fragment-Based Drug Discovery: From the Virtual Generation of Target Inhibitors to Multi-Scale Modeling

Fragment-Based Drug Design (FBDD) has established itself as a promising approach in modern drug discovery, accelerating and improving lead optimization, while playing a crucial role in diminishing the high attrition rates at all stages in the drug development process. On the other hand, FBDD has benefited from the application of computational methodologies, where the models derived from the Quantitative Structure-Activity Relationships (QSAR) have become consolidated tools. This mini-review focuses on the evolution and main applications of the QSAR paradigm in the context of FBDD in the last five years. This report places particular emphasis on the QSAR models derived from fragment-based topological approaches to extract physicochemical and/or structural information, allowing to design potentially novel mono- or multi-target inhibitors from relatively large and heterogeneous databases. Here, we also discuss the need to apply multi-scale modeling, to exemplify how different datasets based on target inhibition can be simultaneously integrated and predicted together with other relevant endpoints such as the biological activity against non-biomolecular targets, as well as in vitro and in vivo toxicity and pharmacokinetic properties. In this context, seminal papers are briefly analyzed. As huge amounts of data continue to accumulate in the domains of the chemical, biological and biomedical sciences, it has become clear that drug discovery must be viewed as a multi-scale optimization process. An ideal multi-scale approach should integrate diverse chemical and biological data and also serve as a knowledge generator, enabling the design of potentially optimal chemicals that may become therapeutic agents.

A Review on Drug-Induced Nephrotoxicity: Pathophysiological Mechanisms, Drug Classes, Clinical Management, and Recent Advances in Mathematical Modeling and Simulation Approaches

A variety of marketed drugs belonging to various therapeutic classes are known to cause nephrotoxicity. Nephrotoxicity can manifest itself in several forms depending on the specific site involved as well as the underlying pathophysiological mechanisms. As they often coexist with other pathophysiological conditions, the steps that can be taken to treat them are often limited. Thus, drug-induced nephrotoxicity remains a major clinical challenge. Prior knowledge of risk factors associated with special patient populations and specific classes of drugs, combined with early diagnosis, therapeutic drug monitoring with dose adjustments, as well as timely prospective treatments are essential to prevent and manage them better. Most incident drug-induced renal toxicity is reversible only if diagnosed at an early stage and treated promptly. Hence, diagnosis at an early stage is the need of the hour to counter it. Significant recent advances in the identification of novel early biomarkers of nephrotoxicity are not beyond limitations. In such a scenario, mathematical modeling and simulation (M&S) approaches may help to better understand and predict toxicities in a clinical setting. This review summarizes pathophysiological mechanisms of drug-induced nephrotoxicity, classes of nephrotoxic drugs, management, prevention, and diagnosis in clinics. Finally, it also highlights some of the recent advancements in mathematical M&S approaches that could be used to better understand and predict drug-induced nephrotoxicity.

Examining Mannitol Use in Kidney Cancer Surgery: A Cautionary Tale of Extrapolated Surgical Data

Mannitol for renal protection during partial nephrectomy is common but unsupported by evidence from clinical trials. The impact of mannitol on renal physiology includes both beneficial and harmful effects. Current understanding of renal ischemia reperfusion injury suggests potentially targetable processes that have a lower potential risk.

Generating Robust and Informative Nonclinical In Vitro and In Vivo Bacterial Infection Model Efficacy Data To Support Translation to Humans

In June 2017, the National Institute of Allergy and Infectious Diseases, part of the National Institutes of Health, organized a workshop entitled “Pharmacokinetics-Pharmacodynamics (PK/PD) for Development of Therapeutics against Bacterial Pathogens.” The aims were to discuss details of various PK/PD models and identify sound practices for deriving and utilizing PK/PD relationships to design optimal dosage regimens for patients. Workshop participants encompassed individuals from academia, industry, and government, including the United States Food and Drug Administration.

In June 2017, the National Institute of Allergy and Infectious Diseases, part of the National Institutes of Health, organized a workshop entitled “Pharmacokinetics-Pharmacodynamics (PK/PD) for Development of Therapeutics against Bacterial Pathogens.” The aims were to discuss details of various PK/PD models and identify sound practices for deriving and utilizing PK/PD relationships to design optimal dosage regimens for patients. Workshop participants encompassed individuals from academia, industry, and government, including the United States Food and Drug Administration. This and the accompanying review on clinical PK/PD summarize the workshop discussions and recommendations. Nonclinical PK/PD models play a critical role in designing human dosage regimens and are essential tools for drug development. These include in vitro and in vivo efficacy models that provide valuable and complementary information for dose selection and translation from the laboratory to human. It is crucial that studies be designed, conducted, and interpreted appropriately. For antibacterial PK/PD, extensive published data and expertise are available. These have been leveraged to develop recommendations, identify common pitfalls, and describe the applications, strengths, and limitations of various nonclinical infection models and translational approaches. Despite these robust tools and published guidance, characterizing nonclinical PK/PD relationships may not be straightforward, especially for a new drug or new class. Antimicrobial PK/PD is an evolving discipline that needs to adapt to future research and development needs. Open communication between academia, pharmaceutical industry, government, and regulatory bodies is essential to share perspectives and collectively solve future challenges.

Safety, Tolerability, and Pharmacokinetics of Single Ascending Doses of ELX-02, a Potential Treatment for Genetic Disorders Caused by Nonsense Mutations, in Healthy Volunteers

ELX-02 is an investigational synthetic eukaryotic ribosome-selective glycoside optimized as a translational read-through molecule that induces read through of nonsense mutations, resulting in normally localized full-length functional proteins. ELX-02 is being developed as a therapy for genetic diseases caused by nonsense mutations. Two phase 1a, randomized, double-blind placebo-controlled, single-ascending-dose studies were conducted in healthy human subjects to evaluate the safety and pharmacokinetics of ELX-02. Single subcutaneously injected doses of ELX-02 between 0.3 mg/kg and 7.5 mg/kg showed an acceptable safety profile without severe or serious drug-related adverse events, including a lack of renal and ototoxicity events. Injection of ELX-02 resulted in a rapid time to peak concentration and elimination phase, with complete elimination from the vascular compartment within 10 hours. ELX-02 area under the concentration-time curve to infinity showed dose-exposure linearity (24-fold increase for a 25-fold dose increase), and the maximum concentration showed dose proportionality (17-fold increase for a 25-fold increase). The mean apparent volume of distribution was dose dependent, suggesting an increased distribution and tissue uptake of ELX-02 at higher doses. The primary route of excretion was in the urine, with the majority of the compound excreted unchanged. These results support the evaluation of the safety, pharmacokinetics, and efficacy of repeat dosing in future studies.

GC-MS-based metabolomics analysis reveals L-aspartate enhances the antibiotic sensitivity of neomycin sulfate-resistant Aeromonas hydrophila

Neomycin sulfate, a kind of drug known as aminoglycoside antibiotic, can be used to treat external or internal bacterial infections. However, there are increasing signs that antibiotics use in aquaculture may have a long-term and permanent potential to select for antibiotic-resistant bacteria in the aquatic environment. In the present study, we aimed to identify key metabolic biomarker whose abundance is correlated with neomycin sulfate resistance in A. hydrophila by gas chromatography-mass spectrometry (GC-MS). Through bioinformatics analysis, L-aspartate was identified as the most crucial biomarker in neomycin sulfate-resistant A. hydrophila and a potential modulator of neomycin sulfate resistance. It was validated as a synergist that incubation of neomycin sulfate-susceptible or -resistant A. hydrophila with exogenous L-aspartate sensitized the bacteria to neomycin sulfate in vitro assay. Moreover, L-aspartate also significantly improved the bactericidal efficacy of neomycin sulfate in Carassius auratus, and increased the survival rate of fish after A. hydrophila challenge. This study presents a novel approach in fighting against antibiotic-resistant pathogens through exploration of antibiotic-resistant biomarkers.

The role of infection models and PK/PD modelling for optimising care of critically ill patients with severe infections

Critically ill patients with severe infections are at high risk of suboptimal antimicrobial dosing. The pharmacokinetics (PK) and pharmacodynamics (PD) of antimicrobials in these patients differ significantly from the patient groups from whose data the conventional dosing regimens were developed. Use of such regimens often results in inadequate antimicrobial concentrations at the site of infection and is associated with poor patient outcomes. In this article, we describe the potential of in vitro and in vivo infection models, clinical pharmacokinetic data and pharmacokinetic/pharmacodynamic models to guide the design of more effective antimicrobial dosing regimens. Individualised dosing, based on population PK models and patient factors (e.g. renal function and weight) known to influence antimicrobial PK, increases the probability of achieving therapeutic drug exposures while at the same time avoiding toxic concentrations. When therapeutic drug monitoring (TDM) is applied, early dose adaptation to the needs of the individual patient is possible. TDM is likely to be of particular importance for infected critically ill patients, where profound PK changes are present and prompt appropriate antibiotic therapy is crucial. In the light of the continued high mortality rates in critically ill patients with severe infections, a paradigm shift to refined dosing strategies for antimicrobials is warranted to enhance the probability of achieving drug concentrations that increase the likelihood of clinical success.

Acute kidney injury in the perioperative period and in intensive care units (excluding renal replacement therapies)

Acute kidney injury (AKI) is a syndrome that has progressed a great deal over the last 20 years. The decrease in urine output and the increase in classical renal biomarkers, such as blood urea nitrogen and serum creatinine, have largely been used as surrogate markers for decreased glomerular filtration rate (GFR), which defines AKI. However, using such markers of GFR as criteria for diagnosing AKI has several limits including the difficult diagnosis of non-organic AKI, also called "functional renal insufficiency" or "pre-renal insufficiency". This situation is characterized by an oliguria and an increase in creatininemia as a consequence of a reduction in renal blood flow related to systemic haemodynamic abnormalities. In this situation, "renal insufficiency" seems rather inappropriate as kidney function is not impaired. On the contrary, the kidney delivers an appropriate response aiming to recover optimal systemic physiological haemodynamic conditions. Considering the kidney as insufficient is erroneous because this suggests that it does not work correctly, whereas the opposite is occurring, because the kidney is healthy even in a threatening situation. With current definitions of AKI, normalization of volaemia is needed before defining AKI in order to avoid this pitfall.

Therapeutic drug monitoring to individualize the dosing of pazopanib: a pharmacokinetic feasibility study

BACKGROUND

Patients treated with the standard dose of pazopanib show a large interpatient variability in drug exposure defined as the area under the plasma concentration-time curve (AUC0-24h). The primary objective of this study was to evaluate the feasibility of pharmacokinetics (PK)-guided individualized dosing to reduce the interpatient variability in pazopanib exposure.

METHODS

Thirteen patients were treated with pazopanib for 3 consecutive periods of 2 weeks. During the first period, all patients received 800 mg of pazopanib once daily to reach steady-state exposure. During the second period, the patients either received a PK-guided individualized pazopanib dose or the registered fixed 800-mg dose. During the third period, these 2 dosing regimens were switched.

RESULTS

The interpatient variability in pazopanib AUC0-24h during fixed dosing (27.3 coefficient of variation) was not significantly different when compared with the variability in AUC0-24h during PK-guided dosing (24.8 coefficient of variation). The percentage of patients within the target window during PK-guided dosing (53.9%) was not significantly different from the percentage during fixed dosing (46.2%). Both Ctrough and C24 were significantly (P < 0.001) correlated to pazopanib AUC0-24h (R = 0.596 and R = 0.940, respectively). Pazopanib AUC0-24h decreased 17% over time.

CONCLUSIONS

PK-guided dosing did not reduce the interpatient variability in pazopanib exposure. In this study, the intrapatient variability in pazopanib exposure was relatively large compared with interpatient variability. This makes it challenging to achieve a target exposure within a predefined window. The causes of intrapatient variability must first be better understood and controlled, before PK-guided dosing can reduce the interpatient variability.

Stochastic process pharmacodynamics: dose timing in neonatal gentamicin therapy as an example

We consider dosing regimens designed to cure patients by eradicating colony forming units (CFU) such as bacteria. In the field of "population" pharmaco-kinetics/dynamics (PK/PD), inter-individual variability (IIV) of patients is estimated using model parameter statistical distributions. We consider a more probabilistic approach to IIV called stochastic process theory, motivated by the fact that tumor treatment planning uses both approaches. Stochastic process PD can supply additional insights and suggest different dosing regimens due to its emphasis on the probability of complete CFU eradication and its predictions on "pure chance" fluctuations of CFU number per patient when treatment has reduced this integer to less than ~100. To exemplify the contrast between stochastic process PD models and standard deterministic PD models, which track only average CFU number, we analyze, neglecting immune responses, neonatal intravenous gentamicin dosing regimens directed against Escherichia coli. Our stochastic calculations predict that the first dose is crucial for CFU eradication. For example, a single 6 mg/kg dose is predicted to have a higher eradication probability than four daily 4 mg/kg doses. We conclude: (1) neonatal gentamicin dosing regimens with larger first doses but smaller total doses deserve investigation; (2) in general, if standard PK/PD models predict average CFU number drops substantially below 100, the models should be modified to incorporate stochastic effects more accurately, and will then usually make more favorable, or less unfavorable, predictions for front boosting ("hit hard early"). Various caveats against over-interpreting the calculations are given.

Two mechanisms of killing of Pseudomonas aeruginosa by tobramycin assessed at multiple inocula via mechanism-based modeling

Bacterial resistance is among the most serious threats to human health globally, and many bacterial isolates have emerged that are resistant to all antibiotics in monotherapy. Aminoglycosides are often used in combination therapies against severe infections by multidrug-resistant bacteria. However, models quantifying different antibacterial effects of aminoglycosides are lacking. While the mode of aminoglycoside action on protein synthesis has often been studied, their disruptive action on the outer membrane of Gram-negative bacteria remains poorly characterized. Here, we developed a novel quantitative model for these two mechanisms of aminoglycoside action, phenotypic tolerance at high bacterial densities, and adaptive bacterial resistance in response to an aminoglycoside (tobramycin) against three Pseudomonas aeruginosa strains. At low-intermediate tobramycin concentrations (<4 mg/liter), bacterial killing due to the effect on protein synthesis was most important, whereas disruption of the outer membrane was the predominant killing mechanism at higher tobramycin concentrations (≥8 mg/liter). The extent of killing was comparable across all inocula; however, the rate of bacterial killing and growth was substantially lower at the 10(8.9) CFU/ml inoculum than that at the lower inocula. At 1 to 4 mg/liter tobramycin for strain PAO1-RH, there was a 0.5- to 6-h lag time of killing that was modeled via the time to synthesize hypothetical lethal protein(s). Disruption of the outer bacterial membrane by tobramycin may be critical to enhance the target site penetration of antibiotics used in synergistic combinations with aminoglycosides and thereby combat multidrug-resistant bacteria. The two mechanisms of aminoglycoside action and the new quantitative model hold great promise to rationally design novel, synergistic aminoglycoside combination dosage regimens.

A general ANN-based multitasking model for the discovery of potent and safer antibacterial agents

Bacteria have been one of the world's most dangerous and deadliest pathogens for mankind, nowadays giving rise to significant public health concerns. Given the prevalence of these microbial pathogens and their increasing resistance to existing antibiotics, there is a pressing need for new antibacterial drugs. However, development of a successful drug is a complex, costly, and time-consuming process. Quantitative Structure-Activity Relationships (QSAR)-based approaches are valuable tools for shortening the time of lead compound identification but also for focusing and limiting time-costly synthetic activities and in vitro/vivo evaluations. QSAR-based approaches, supported by powerful statistical techniques such as artificial neural networks (ANNs), have evolved to the point of integrating dissimilar types of chemical and biological data. This chapter reports an overview of the current research and potential applications of QSAR modeling tools toward the rational design of more efficient antibacterial agents. Particular emphasis is given to the setup of multitasking models along with ANNs aimed at jointly predicting different antibacterial activities and safety profiles of drugs/chemicals under diverse experimental conditions.

Chemoinformatics for medicinal chemistry: in silico model to enable the discovery of potent and safer anti-cocci agents

Background: Gram-positive cocci are increasingly antibiotic-resistant bacteria responsible for causing serious diseases. Chemoinformatics can help to rationalize the discovery of more potent and safer antibacterial drugs. We have developed a chemoinformatic model for simultaneous prediction of anti-cocci activities, and profiles involving absorption, distribution, metabolism, elimination and toxicity (ADMET). Results: A dataset containing 48,874 cases from many different chemicals assayed under dissimilar experimental conditions was created. The best model displayed accuracies around 93% in both training and prediction (test) sets. Quantitative contributions of several fragments to the biological effects were calculated and analyzed. Multiple biological effects of the investigational drug JNJ-Q2 were correctly predicted. Conclusion: Our chemoinformatic model can be used as powerful tool for virtual screening of promising anti-cocci agents.

Therapeutic drug monitoring of once daily aminoglycoside dosing: comparison of two methods and investigation of the optimal blood sampling strategy

PURPOSE

Therapeutic drug monitoring of patients receiving once daily aminoglycoside therapy can be performed using pharmacokinetic (PK) formulas or Bayesian calculations. While these methods produced comparable results, their performance has never been checked against full PK profiles. We performed a PK study in order to compare both methods and to determine the best time-points to estimate AUC0-24 and peak concentrations (C max).

METHODS

We obtained full PK profiles in 14 patients receiving a once daily aminoglycoside therapy. PK parameters were calculated with PKSolver using non-compartmental methods. The calculated PK parameters were then compared with parameters estimated using an algorithm based on two serum concentrations (two-point method) or the software TCIWorks (Bayesian method).

RESULTS

For tobramycin and gentamicin, AUC0-24 and C max could be reliably estimated using a first serum concentration obtained at 1 h and a second one between 8 and 10 h after start of the infusion. The two-point and the Bayesian method produced similar results. For amikacin, AUC0-24 could reliably be estimated by both methods. C max was underestimated by 10-20% by the two-point method and by up to 30% with a large variation by the Bayesian method.

CONCLUSIONS

The ideal time-points for therapeutic drug monitoring of once daily administered aminoglycosides are 1 h after start of a 30-min infusion for the first time-point and 8-10 h after start of the infusion for the second time-point. Duration of the infusion and accurate registration of the time-points of blood drawing are essential for obtaining precise predictions.

Simultaneous virtual prediction of anti-Escherichia coli activities and ADMET profiles: A chemoinformatic complementary approach for high-throughput screening

Escherichia coli remains one of the principal pathogens that cause nosocomial infections, medical conditions that are increasingly common in healthcare facilities. E. coli is intrinsically resistant to many antibiotics, and multidrug-resistant strains have emerged recently. Chemoinformatics has been a great ally of experimental methodologies such as high-throughput screening, playing an important role in the discovery of effective antibacterial agents. However, there is no approach that can design safer anti-E. coli agents, because of the multifactorial nature and complexity of bacterial diseases and the lack of desirable ADMET (absorption, distribution, metabolism, elimination, and toxicity) profiles as a major cause of disapproval of drugs. In this work, we introduce the first multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for simultaneous virtual prediction of anti-E. coli activities and ADMET properties of drugs and/or chemicals under many experimental conditions. The mtk-QSBER model was developed from a large and heterogeneous data set of more than 37800 cases, exhibiting overall accuracies of >95% in both training and prediction (validation) sets. The utility of our mtk-QSBER model was demonstrated by performing virtual prediction of properties for the investigational drug avarofloxacin (AVX) under 260 different experimental conditions. Results converged with the experimental evidence, confirming the remarkable anti-E. coli activities and safety of AVX. Predictions also showed that our mtk-QSBER model can be a promising computational tool for virtual screening of desirable anti-E. coli agents, and this chemoinformatic approach could be extended to the search for safer drugs with defined pharmacological activities.

Rational use of aminoglycosides--review and recommendations by the Swedish Reference Group for Antibiotics (SRGA)

The Swedish Reference Group for Antibiotics (SRGA) has carried out a risk-benefit analysis of aminoglycoside treatment based on clinical efficacy, antibacterial spectrum, and synergistic effect with beta-lactam antibiotics, endotoxin release, toxicity, and side effects. In addition, SRGA has considered optimal dosage schedules and advice on serum concentration monitoring, with respect to variability in volume of drug distribution and renal clearance. SRGA recommends that aminoglycoside therapy should be considered in the following situations: (1) progressive severe sepsis and septic shock, in combination with broad-spectrum beta-lactam antibiotics, (2) sepsis without shock, in combination with broad-spectrum beta-lactam antibiotics if the infection is suspected to be caused by multi-resistant Gram-negative pathogens, (3) pyelonephritis, in combination with a beta-lactam or quinolone until culture and susceptibility results are obtained, or as monotherapy if a serious allergy to beta-lactam or quinolone antibiotics exists, (4) serious infections caused by multi-resistant Gram-negative bacteria when other alternatives are lacking, and (5) endocarditis caused by difficult-to-treat pathogens when monotherapy with beta-lactam antibiotics is not sufficient. Amikacin is generally more active against extended-spectrum beta-lactamase (ESBL)-producing and quinolone-resistant Escherichia coli than other aminoglycosides, making it a better option in cases of suspected infection caused by multidrug-resistant Enterobacteriaceae. Based on their resistance data, local drug committees should decide on the choice of first-line aminoglycoside. Unfortunately, aminoglycoside use is rarely followed up with audiometry, and in Sweden we currently have no systematic surveillance of adverse events after aminoglycoside treatment. We recommend routine assessment of adverse effects, including hearing loss and impairment of renal function, if possible at the start and after treatment with aminoglycosides, and that these data should be included in hospital patient safety surveillance and national quality registries.