SWOT and Root Cause Analyses of Antimicrobial Resistance to Oral Antimicrobial Treatment of Cystitis

Nearly 150 million cases of urinary tract infections (UTIs) are reported each year, of which uncomplicated cystitis triggers > 25% of outpatient prescriptions of oral antimicrobial treatment (OAT). OAT aids immune cells infiltrating the urothelium in eliminating uropathogens capable of invading the urothelium and surviving hyperosmotic urine. This self-evident adaptability of uropathogens and the short interval between the introduction of Penicillin and the first report of antimicrobial resistance (AMR) implicate AMR as an evolutionary conserved heritable trait of mutant strains selected by the Darwinian principle to survive environmental threats through exponential proliferation. Therefore, AMR can only be countered by antimicrobial stewardship (AMS) following the principle of the five Ds-drug, dose, duration, drug route, and de-escalation. While convenient to administer, the onset of the minimum inhibitory concentration (MIC) for OAT in urine leaves a window of opportunity for uropathogens to survive the first contact with an antimicrobial and arm their descendant colonies with AMR for surviving subsequent higher urine antimicrobial levels. Meanwhile, the initial dose of intravesical antimicrobial treatment (IAT) may be well above the MIC. Therefore, the widespread clinical use of OAT for cystitis warrants an analysis of the strengths, weaknesses, opportunity, and threats (SWOTs) and a root cause analysis of the AMR associated with OAT and IAT.

Serine Deamination Is a New Acid Tolerance Mechanism Observed in Uropathogenic Escherichia coli

Escherichia coli associates with humans early in life and can occupy several body niches either as a commensal in the gut and vagina, or as a pathogen in the urinary tract. As such, E. coli has an arsenal of acid response mechanisms that allow it to withstand the different levels of acid stress encountered within and outside the host. Here, we report the discovery of an additional acid response mechanism that involves the deamination of l-serine to pyruvate by the conserved l-serine deaminases SdaA and SdaB. l-serine is the first amino acid to be imported in E. coli during growth in laboratory media. However, there remains a lack in knowledge as to how l-serine is utilized. Using a uropathogenic strain of E. coli, UTI89, we show that in acidified media, l-serine is brought into the cell via the SdaC transporter. We further demonstrate that deletion of the l-serine deaminases SdaA and SdaB renders E. coli susceptible to acid stress, similar to other acid stress deletion mutants. The pyruvate produced by l-serine deamination activates the pyruvate sensor BtsS, which in concert with the noncognate response regulator YpdB upregulates the putative transporter YhjX. Based on these observations, we propose that l-serine deamination constitutes another acid response mechanism in E. coli. IMPORTANCE The observation that l-serine uptake occurs as E. coli cultures grow is well established, yet the benefit E. coli garners from this uptake remains unclear. Here, we report a novel acid tolerance mechanism where l-serine is deaminated to pyruvate and ammonia, promoting survival of E. coli under acidic conditions. This study is important as it provides evidence of the use of l-serine as an acid response strategy, not previously reported for E. coli.

Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation

The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.

A pH-independent electrochemical aptamer-based biosensor supports quantitative, real-time measurement in vivo

An electrochemical aptamer-based sensor, enabling in vivo measurements of drug concentrations directly in the bladder of living rats under pH-variable conditions, was developed employing a π-extended tetrathiafulvalene (exTTF) as redox reported.

PBPK Simulation-Based Evaluation of Ganciclovir Crystalluria Risk Factors: Effect of Renal Impairment, Old Age, and Low Fluid Intake

Dosing guidance is often lacking for chronic kidney disease (CKD) due to exclusion of such patients from pivotal clinical trials. Physiologically based pharmacokinetic (PBPK) modelling supports model-informed dosing when clinical data are lacking, but application of these approaches to patients with impaired renal function is not yet at full maturity. In the current study, a ganciclovir PBPK model was developed for patients with normal renal function and extended to CKD population. CKD-related changes in tubular secretion were explored in the mechanistic kidney model and implemented either as proportional or non-proportional decline relative to GFR. Crystalluria risk was evaluated in different clinical settings (old age, severe CKD and low fluid intake) by simulating ganciclovir medullary collecting duct (MCD) concentrations. The ganciclovir PBPK model captured observed changes in systemic pharmacokinetic endpoints in mild-to-severe CKD; these trends were evident irrespective of assumed pathophysiological mechanism of altered active tubular secretion in the model. Minimal difference in simulated ganciclovir MCD concentrations was noted between young adult and geriatric populations with normal renal function and urine flow (1 mL/min), with lower concentrations predicted for severe CKD patients. High crystalluria risk was identified at reduced urine flow (0.1 mL/min) as simulated ganciclovir MCD concentrations exceeded its solubility (2.6–6 mg/mL), irrespective of underlying renal function. The analysis highlighted the importance of appropriate distribution of virtual subjects’ systems data in CKD populations. The ganciclovir PBPK model illustrates the ability of this translational tool to explore individual and combined effects of age, urine flow, and renal impairment on local drug renal exposure.

Challenges and solutions in polymer drug delivery for bacterial biofilm treatment: A tissue-by-tissue account

To tackle the emerging antibiotic resistance crisis, novel antimicrobial approaches are urgently needed. Bacterial communities (biofilms) are a particular concern in this context. Biofilms are responsible for most human infections and are inherently less susceptible to antibiotic treatments. Biofilms have been linked with several challenging chronic diseases, including implant-associated osteomyelitis and chronic wounds. The specific local environments present in the infected tissues further contribute to the rise in antibiotic resistance by limiting the efficacy of systemic antibiotic therapies and reducing drug concentrations at the infection site, which can lead to reoccurring infections. To overcome the shortcomings of systemic drug delivery, encapsulation within polymeric carriers has been shown to enhance antimicrobial efficacy, permeation and retention at the infection site. In this Review, we present an overview of current strategies for antimicrobial encapsulation within polymeric carriers, comparing challenges and solutions on a tissue-by-tissue basis. We compare challenges and proposed drug delivery solutions from the perspective of the local environments for biofilms found in oral, wound, gastric, urinary tract, bone, pulmonary, vaginal, ocular and middle/inner ear tissues. We will also discuss future challenges and barriers to clinical translation for these therapeutics. The following Review demonstrates there is a significant imbalance between the research focus being placed on different tissue types, with some targets (oral and wound biofims) being extensively more studied than others (vaginal and otitis media biofilms and endocarditis). Furthermore, the importance of the local tissue environment when selecting target therapies is demonstrated, with some materials being optimal choices for certain sites of bacterial infection, while having limited applicability in others.

Pharmacokinetics of caffeine self-administered in overdose in a Japanese patient admitted to hospital

Background

Caffeine (0.1 g) is used as a central nervous system stimulant and as a nontoxic phenotyping probe for cytochrome P450 1A2. However, an increasing number of suicide attempts by caffeine overdose have been recently reported.

Case presentation

A 25-year-old woman (body weight, 43 kg) who intentionally took an overdose of 5.9 g caffeine as a suicide attempt was emergently admitted to Kyoto Medical Center. The plasma concentrations of caffeine and its primary metabolite, N-demethylated paraxanthine, in the current case were 100 and 7.3 μg/mL, 81 and 9.9 μg/mL, 63 and 12 μg/mL, and 21 and 14 μg/mL, at 12, 20, 30, and 56 h after oral overdose, respectively. The observed apparent terminal elimination half-life of caffeine during days 1 and 2 of hospitalization was 27 h, which is several times longer than the reported normal value. This finding implied nonlinearity of caffeine pharmacokinetics over such a wide dose range, which could affect the accuracy of values simulated by a simplified physiologically based pharmacokinetic model founded on a normal dose of 100 mg. Low serum potassium levels (2.9 and 3.5 mM) on days 1 and 2 may have been caused by the caffeine overdose in the current case.

Conclusions

The patient underwent infusion with bicarbonate Ringer’s solution and potassium chloride and was discharged on the third day of hospitalization despite taking a potentially lethal dose of caffeine. The virtual plasma exposures of caffeine estimated using the current simplified PBPK model were higher than the measured values. The present results based on drug monitoring data and additional pharmacokinetic predictions could serve as a useful guide in cases of caffeine overdose.

Quantitative Proteomic Map of Enzymes and Transporters in the Human Kidney: Stepping Closer to Mechanistic Kidney Models to Define Local Kinetics

The applications of translational modeling of local drug concentrations in various organs had a sharp increase over the last decade. These are part of the model-informed drug development initiative, adopted by the pharmaceutical industry and promoted by drug regulatory agencies. With respect to the kidney, the models serve as a bridge for understanding animal vs. human observations related to renal drug disposition and any consequential adverse effects. However, quantitative data on key drug-metabolizing enzymes and transporters relevant for predicting renal drug disposition are limited. Using targeted and global quantitative proteomics, we determined the abundance of multiple enzymes and transporters in 20 human kidney cortex samples. Nine enzymes and 22 transporters were quantified (8 for the first time in the kidneys). In addition, > 4,000 proteins were identified and used to form an open database. CYP2B6, CYP3A5, and CYP4F2 showed comparable, but generally low expression, whereas UGT1A9 and UGT2B7 levels were the highest. Significant correlation between abundance and activity (measured by mycophenolic acid clearance) was observed for UGT1A9 (Rs = 0.65, P = 0.004) and UGT2B7 (Rs = 0.70, P = 0.023). Expression of P-gp ≈ MATE-1 and OATP4C1 transporters were high. Strong intercorrelations were observed between several transporters (P-gp/MRP4, MRP2/OAT3, and OAT3/OAT4); no correlation in expression was apparent for functionally related transporters (OCT2/MATEs). This study extends our knowledge of pharmacologically relevant proteins in the kidney cortex, with implications on more prudent use of mechanistic kidney models under the general framework of quantitative systems pharmacology and toxicology.

Prediction of Drug Clearance from Enzyme and Transporter Kinetics

Accurate estimation of in vivo clearance in human is pivotal to determine the dose and dosing regimen for drug development. In vitro-in vivo extrapolation (IVIVE) has been performed to predict drug clearance using empirical and physiological scalars. Multiple in vitro systems and mathematical modeling techniques have been employed to estimate in vivo clearance. The models for predicting clearance have significantly improved and have evolved to become more complex by integrating multiple processes such as drug metabolism and transport as well as passive diffusion. This chapter covers the use of conventional as well as recently developed methods to predict metabolic and transporter-mediated clearance along with the advantages and disadvantages of using these methods and the associated experimental considerations. The general approaches to improve IVIVE by use of appropriate scalars, incorporation of extrahepatic metabolism and transport and application of physiologically based pharmacokinetic (PBPK) models with proteomics data are also discussed. The chapter also provides an overview of the advantages of using such dynamic mechanistic models over static models for clearance predictions to improve IVIVE.

Physiologically-Based Pharmacokinetic Modelling of Creatinine-Drug Interactions in the Chronic Kidney Disease Population

Elevated serum creatinine (S_Cr ) caused by the inhibition of renal transporter(s) may be misinterpreted as kidney injury. The interpretation is more complicated in patients with chronic kidney disease (CKD) due to altered disposition of creatinine and renal transporter inhibitors. A clinical study was conducted in 17 patients with CKD (estimated glomerular filtration rate 15-59 mL/min/1.73 m² ); changes in S_Cr were monitored during trimethoprim treatment (100-200 mg/day), administered to prevent recurrent urinary infection, relative to the baseline level. Additional S_Cr -interaction data with trimethoprim, cimetidine, and famotidine in patients with CKD were collated from the literature. Our published physiologically-based creatinine model was extended to predict the effect of the CKD on S_Cr and creatinine-drug interaction. The creatinine-CKD model incorporated age/sex-related differences in creatinine synthesis, CKD-related glomerular filtration deterioration; change in transporter activity either proportional or disproportional to glomerular filtration rate (GFR) decline were explored. Optimized models successfully recovered baseline S_Cr from 64 patients with CKD (geometric mean fold-error of 1.1). Combined with pharmacokinetic models of inhibitors, the creatinine model was used to simulate transporter-mediated creatinine-drug interactions. Use of inhibitor unbound plasma concentrations resulted in 66% of simulated S_Cr interaction data within the prediction limits, with cimetidine interaction significantly underestimated. Assuming that transporter activity deteriorates disproportional to GFR decline resulted in higher predicted sensitivity to transporter inhibition in patients with CKD relative to healthy patients, consistent with sparse clinical data. For the first time, this novel modelling approach enables quantitative prediction of S_Cr in CKD and delineation of the effect of disease and renal transporter inhibition in this patient population.

A Novel Physiologically Based Model of Creatinine Renal Disposition to Integrate Current Knowledge of Systems Parameters and Clinical Observations

Creatinine is the most common clinical biomarker of renal function. As a substrate for renal transporters, its secretion is susceptible to inhibition by drugs, resulting in transient increase in serum creatinine and false impression of damage to kidney. Novel physiologically based models for creatinine were developed here and (dis)qualified in a stepwise manner until consistency with clinical data. Data from a matrix of studies were integrated, including systems data (common to all models), proteomics-informed in vitro-in vivo extrapolation of all relevant transporter clearances, exogenous administration of creatinine (to estimate endogenous synthesis rate), and inhibition of different renal transporters (11 perpetrator drugs considered for qualification during creatinine model development and verification on independent data sets). The proteomics-informed bottom-up approach resulted in the underprediction of creatinine renal secretion. Subsequently, creatinine-trimethoprim clinical data were used to inform key model parameters in a reverse translation manner, highlighting best practices and challenges for middle-out optimization of mechanistic models.

Mechanistic Models as Framework for Understanding Biomarker Disposition: Prediction of Creatinine-Drug Interactions

Creatinine is widely used as a biomarker of glomerular filtration, and, hence, renal function. However, transporter-mediated secretion also contributes to its renal clearance, albeit to a lesser degree. Inhibition of these transporters causes transient serum creatinine elevation, which can be mistaken as impaired renal function. The current study developed mechanistic models of creatinine kinetics within physiologically based framework accounting for multiple transporters involved in creatinine renal elimination, assuming either unidirectional or bidirectional-OCT2 transport (driven by electrochemical gradient). Robustness of creatinine models was assessed by predicting creatinine-drug interactions with 10 perpetrators; performance evaluation accounted for 5% intra-individual variability in serum creatinine. Models showed comparable predictive performances of the maximum steady-state effect regardless of OCT2 directionality assumptions. However, only the bidirectional-OCT2 model successfully predicted the minimal effect of ranitidine. The dynamic nature of models provides clear advantage to static approaches and most advanced framework for evaluating interplay between multiple processes in creatinine renal disposition.

Mechanistic PBPK Modeling of Urine pH Effect on Renal and Systemic Disposition of Methamphetamine and Amphetamine

The effect of urine pH on renal excretion and systemic disposition has been observed for many drugs and metabolites. When urine pH is altered, tubular ionization, passive reabsorption, renal clearance, and systemic exposure of drugs and metabolites may all change dramatically, raising clinically significant concerns. Surprisingly, the urine pH effect on drug disposition is not routinely explored in humans, and regulatory agencies have neither developed guidance on this issue nor required industry to conduct pertinent human trials. In this study, we hypothesized that physiologically based pharmacokinetic (PBPK) modeling could be used as a cost-effective method to examine potential urine pH effect on drug and metabolite disposition. Our previously developed and verified mechanistic kidney model was integrated with a full-body PBPK model to simulate renal clearance and area under the plasma concentration-time curve (AUC) with varying urine pH statuses using methamphetamine and amphetamine as model compounds. We first developed and verified drug models for methamphetamine and amphetamine under normal urine pH condition [absolute average fold error (AAFE) < 1.25 at study level]. Then, acidic and alkaline urine scenarios were simulated. Our simulation results show that the renal excretion and plasma concentration-time profiles for methamphetamine and amphetamine could be recapitulated under different urine pH (AAFE < 2 at individual level). The methamphetamine-amphetamine parent-metabolite full-body PBPK model also successfully simulated amphetamine plasma concentration-time profiles (AAFE < 1.25 at study level) and amphetamine/methamphetamine urinary concentration ratios (AAFE < 2 at individual level) after dosing methamphetamine. This demonstrates that our mechanistic PBPK model can predict urine pH effect on systemic and urinary disposition of drugs and metabolites. SIGNIFICANCE STATEMENT: Our study shows that integrating mechanistic kidney model with full-body physiologically based pharmacokinetic model can predict the magnitude of alteration in renal excretion and area under the plasma concentration-time curve (AUC) of drugs and metabolites when urine pH is changed. This provides a cost-effective method to evaluate the likelihood of renal and systemic disposition changes due to varying urine pH. This is important because multiple drugs and diseases can alter urine pH, leading to quantitatively and clinically significant changes in drug and metabolite disposition that may require adjustment of therapy.

A Physiologically Based in Silico Tool to Assess the Risk of Drug-Related Crystalluria

Drug precipitation in the nephrons of the kidney can cause drug-induced crystal nephropathy (DICN). To aid mitigation of this risk in early drug discovery, we developed a physiologically based in silico model to predict DICN in rats, dogs, and humans. At a minimum, the likelihood of DICN is determined by the level of systemic exposure to the molecule, the molecule's physicochemical properties and the unique physiology of the kidney. Accordingly, the proposed model accounts for these properties in order to predict drug exposure relative to solubility along the nephron. Key physiological parameters of the kidney were codified in a manner consistent with previous reports. Quantitative structure-activity relationship models and in vitro assays were used to estimate drug-specific physicochemical inputs to the model. The proposed model was calibrated against urinary excretion data for 42 drugs, and the utility for DICN prediction is demonstrated through application to 20 additional drugs.

Reducing deposition of encrustation in ureteric stents by changing the stent architecture: A microfluidic-based investigation

Ureteric stents are clinically deployed to retain ureteral patency in the presence of an obstruction of the ureter lumen. Despite the fact that multiple stent designs have been researched in recent years, encrustation and biofilm-associated infections remain significant complications of ureteral stenting, potentially leading to the functional failure of the stent. It has been suggested that "inactive" side-holes of stents may act as anchoring sites for encrusting crystals, as they are associated with low wall shear stress (WSS) levels. Obstruction of side-holes due to encrustation is particularly detrimental to the function of the stent, since holes provide a path for urine to by-pass the occlusion. Therefore, there is an unmet need to develop novel stents to reduce deposition of encrusting particles at side-holes. In this study, we employed a stent-on-chip microfluidic model of the stented and occluded ureter to investigate the effect of stent architecture on WSS distribution and encrustation over its surface. Variations in the stent geometry encompassed (i) the wall thickness and (ii) the shape of side-holes. Stent thickness was varied in the range 0.3-0.7 mm, while streamlined side-holes of triangular shape were evaluated (with a vertex angle in the range 45°-120°). Reducing the thickness of the stent increased WSS and thus reduced the encrustation rate at side-holes. A further improvement in performance was achieved by using side-holes with a triangular shape; notably, a 45° vertex angle showed superior performance compared to other angles investigated, resulting in a significant increase in WSS within "inactive" side-holes. In conclusion, combining the optimal stent thickness (0.3 mm) and hole vertex angle (45°) resulted in a ∼90% reduction in encrustation rate within side-holes, compared to a standard design. If translated to a full-scale ureteric stent, this optimised architecture has the potential for significantly increasing the stent lifetime while reducing clinical complications.