Humans are animals, but are animals human enough? A systematic review and meta-analysis on interspecies differences in renal drug clearance

Interspecies variability in protein binding of antibiotics basis for translational PK/PD studies-a case study using cefazolin

For antimicrobial agents in particular, plasma protein binding (PPB) plays a pivotal role in deciphering key properties of drug candidates. Animal models are generally used in the preclinical development of new drugs to predict their effects in humans using translational pharmacokinetics/pharmacodynamics (PK/PD). Thus, we compared the protein binding (PB) of cefazolin as well as bacterial growth under various conditions in vitro. The PB extent of cefazolin was studied in human, bovine, and rat plasmas at different antibiotic concentrations in buffer and media containing 20-70% plasma or pure plasma using ultrafiltration (UF) and equilibrium dialysis (ED). Moreover, bacterial growth and time-kill assays were performed in Mueller Hinton Broth (MHB) containing various plasma percentages. The pattern for cefazolin binding to plasma proteins was found to be similar for both UF and ED. There was a significant decrease in cefazolin binding to bovine plasma compared to human plasma, whereas the pattern in rat plasma was more consistent with that in human plasma. Our growth curve analysis revealed considerable growth inhibition of Escherichia coli at 70% bovine or rat plasma compared with 70% human plasma or pure MHB. As expected, our experiments with cefazolin at low concentrations showed that E. coli grew slightly better in 20% human and rat plasma compared to MHB, most probably due to cefazolin binding to proteins in the plasma. Based on the example of cefazolin, our study highlights the interspecies differences of PB with potential impact on PK/PD. These findings should be considered before preclinical PK/PD data can be extrapolated to human patients.

Advancements in kidney organoids and tubuloids to study (dys)function

The rising prevalence of kidney diseases urges the need for novel therapies. Kidney organoids and tubuloids are advanced in vitro models and have recently been described as promising tools to study kidney (patho)physiology. Recent developments have shown their application in disease modeling, drug screening, and nephrotoxicity. These applications rely on their ability to mimic (dys)function in vitro including endocrine activity and drug, electrolyte, and water transport. This review provides an overview of these emerging kidney models and focuses on the most recent developments that utilize their functional capabilities. In addition, we cover current limitations and provide future perspectives for this rapidly evolving field, including what these functional properties mean for translational and personalized medicine now and in the future.

Complex or not too complex? One size does not fit all in next generation microphysiological systems

In the attempt to overcome the increasingly recognized shortcomings of existing in vitro and in vivo models, researchers have started to implement alternative models, including microphysiological systems. First examples were represented by 2.5D systems, such as microfluidic channels covered by cell monolayers as blood vessel replicates. In recent years, increasingly complex microphysiological systems have been developed, up to multi-organ on chip systems, connecting different 3D tissues in the same device. However, such an increase in model complexity raises several questions about their exploitation and implementation into industrial and clinical applications, ranging from how to improve their reproducibility, robustness, and reliability to how to meaningfully and efficiently analyze the huge amount of heterogeneous datasets emerging from these devices. Considering the multitude of envisaged applications for microphysiological systems, it appears now necessary to tailor their complexity on the intended purpose, being academic or industrial, and possibly combine results deriving from differently complex stages to increase their predictive power.

Plasma Pharmacokinetics of N-Acetylgalactosamine-Conjugated Small-Interfering Ribonucleic Acids (GalNAc-Conjugated siRNAs)

Small-interfering ribonucleic acids (siRNAs) with N-acetylgalactosamine (GalNAc) conjugation for improved liver uptake represent an emerging class of drugs that modulate liver-expressed therapeutic targets. The pharmacokinetics of GalNAc-siRNAs are characterized by a rapid distribution from plasma to tissue (hours) and a long terminal plasma half-life, analyzed in the form of the antisense strand, driven by redistribution from tissue (weeks). Understanding how clinical pharmacokinetics relate to the dose and type of siRNA chemical stabilizing method used is critical, e.g., to design studies, to investigate safety windows, and to predict the pharmacokinetics of new preclinical assets. To this end, we collected and analyzed pharmacokinetic data from the literature regarding nine GalNAc-siRNAs. Based on this analysis, we showed that the clinical plasma pharmacokinetics of GalNAc-siRNAs are approximately dose proportional and similar between chemical stabilizing methods. This holds for both the area under the concentration-time curve (AUC) and the maximum plasma concentration (Cmax). Corresponding rat and monkey pharmacokinetic data for a subset of the nine GalNAc-siRNAs show dose-proportional Cmax, supra-dose-proportional AUC, and similar pharmacokinetics between chemical stabilizing methods​. Together, the animal and human pharmacokinetic data indicate that plasma clearance divided by bioavailability follows allometric principles and scales between species with an exponent of 0.75. Finally, the clinical plasma concentration-time profiles can be empirically described by standard one-compartment kinetics with first-order absorption up to 24 h after subcutaneous dosing, and by three-compartment kinetics with first-order absorption in general. To describe the system more mechanistically, we report a corrected and unambiguously defined version of a previously published physiologically based pharmacokinetic model.

Antibiotic perturbations to the gut microbiome

Antibiotic-mediated perturbation of the gut microbiome is associated with numerous infectious and autoimmune diseases of the gastrointestinal tract. Yet, as the gut microbiome is a complex ecological network of microorganisms, the effects of antibiotics can be highly variable. With the advent of multi-omic approaches for systems-level profiling of microbial communities, we are beginning to identify microbiome-intrinsic and microbiome-extrinsic factors that affect microbiome dynamics during antibiotic exposure and subsequent recovery. In this Review, we discuss factors that influence restructuring of the gut microbiome on antibiotic exposure. We present an overview of the currently complex picture of treatment-induced changes to the microbial community and highlight essential considerations for future investigations of antibiotic-specific outcomes. Finally, we provide a synopsis of available strategies to minimize antibiotic-induced damage or to restore the pretreatment architectures of the gut microbial community.

The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery

Drug discovery and development are aimed at identifying new chemical molecular entities (NCEs) with desirable pharmacokinetic profiles for high therapeutic efficacy. The plasma concentrations of NCEs are a biomarker of their efficacy and are governed by pharmacokinetic processes such as absorption, distribution, metabolism, and excretion (ADME). Poor ADME properties of NCEs are a major cause of attrition in drug development. ADME screening is used to identify and optimize lead compounds in the drug discovery process. Computational models predicting ADME properties have been developed with evolving model-building technologies from a simplified relationship between ADME endpoints and physicochemical properties to machine learning, including support vector machines, random forests, and convolution neural networks. Recently, in the field of in silico ADME research, there has been a shift toward evaluating the in vivo parameters or plasma concentrations of NCEs instead of using predictive results to guide chemical structure design. Another research hotspot is the establishment of a computational prediction platform to strengthen academic drug discovery. Bioinformatics projects have produced a series of in silico ADME models using free software and open-access databases. In this review, we introduce prediction models for various ADME parameters and discuss the currently available academic drug discovery platforms.

Impact of Blast Overpressure on the Pharmacokinetics of Various Antibiotics in Sprague Dawley Rats

INTRODUCTION

Combat injuries are complex and multimodal. Most injuries to the extremities occur because of explosive devices such as improvised explosive devices. Blast exposure dramatically increases the risk of infection in combat wounds, and there is limited available information on the best antibiotic treatments for these injuries. We previously demonstrated that mice exposed to blast displayed a delayed clearance of cefazolin from the plasma and liver; further semi-mechanistic modeling determined that cefazolin concentrations in the skin of these mice were reduced. Our objective was to investigate the effects of blast on the pharmacokinetics of antibiotics of different types used for the treatment of combat wounds in the rat model.

MATERIALS AND METHODS

Male Sprague Dawley rats were exposed to blast overpressure followed by injection of a bolus of animal equivalent doses of an antibiotic (cefazolin, cefepime, ertapenem, or clindamycin) into the tail vein at 1-hour post-blast exposure. Blood was collected at predetermined time points via repeated sampling from the tail vein. Animals were also euthanized at predetermined time points, at which time liver, kidney, skin, and blood via cardiac puncture were collected. Antibiotic concentrations were determined by ultra-performance liquid chromatography-tandem mass spectrometry.

RESULTS

Blast-exposed rats exhibited a similar rate of clearance compared to non-blasted rats in the blood, liver, kidney, and skin, which is inconsistent with the data regarding cefazolin in blast-exposed mice.

CONCLUSIONS

Our results in rats do not recapitulate our previous observation of delayed cefazolin clearance in mice following the blast overpressure exposure. Although using rats permitted us to collect multiple blood samples from the same animals, rats may not be a suitable model for measuring the pharmacokinetics of antibiotics following blast. The interpretation of the results may be challenging because of variation in data among rat subjects in the same sample groups.

The application of organ-on-chip models for the prediction of human pharmacokinetic profiles during drug development

Organ-on-chip (OoC) technology has led to in vitro models with many new possibilities compared to conventional in vitro and in vivo models. In this review, the potential of OoC models to improve the prediction of human oral bioavailability and intrinsic clearance is discussed, with a focus on the functionality of the models and the application in current drug development practice. Multi-OoC models demonstrating the application for pharmacokinetic (PK) studies are summarized and existing challenges are identified. Physiological parameters for a minimal viable platform of a multi-OoC model to study PK are provided, together with PK specific read-outs and recommendations for relevant reference compounds to validate the model. Finally, the translation to in vivo PK profiles is discussed, which will be required to routinely apply OoC models during drug development.

In Vitro-In Vivo Extrapolation and Scaling Factors for Clearance of Human and Preclinical Species with Liver Microsomes and Hepatocytes

In vitro-in vivo extrapolation ((IVIVE) and empirical scaling factors (SF) of human intrinsic clearance (CL_int) were developed using one of the largest dataset of 455 compounds with data from human liver microsomes (HLM) and human hepatocytes (HHEP). For extended clearance classification system (ECCS) class 2/4 compounds, linear SFs (SF_lin) are approximately 1, suggesting enzyme activities in HLM and HHEP are similar to those in vivo under physiological conditions. For ECCS class 1A/1B compounds, a unified set of SFs was developed for CL_int. These SFs contain both SF_lin and an exponential SF (SF_β) of fraction unbound in plasma (f_u,p). The unified SFs for class 1A/1B eliminate the need to identify the transporters involved prior to clearance prediction. The underlying mechanisms of these SFs are not entirely clear at this point, but they serve practical purposes to reduce biases and increase prediction accuracy. Similar SFs have also been developed for preclinical species. For HLM-HHEP disconnect (HLM > HHEP) ECCS class 2/4 compounds that are mainly metabolized by cytochrome P450s/FMO, HLM significantly overpredicted in vivo CL_int, while HHEP slightly underpredicted and geometric mean of HLM and HHEP slightly overpredicted in vivo CL_int. This observation is different than in rats, where rat liver microsomal CL_int correlates well with in vivo CL_int for compounds demonstrating permeability-limited metabolism. The good CL_int IVIVE developed using HLM and HHEP helps build confidence for prospective predictions of human clearance and supports the continued utilization of these assays to guide structure-activity relationships to improve metabolic stability.

CORAL Models for Drug-Induced Nephrotoxicity

Drug-induced nephrotoxicity is a major cause of kidney dysfunction with potentially fatal consequences. The poor prediction of clinical responses based on preclinical research hampers the development of new pharmaceuticals. This emphasises the need for new methods for earlier and more accurate diagnosis to avoid drug-induced kidney injuries. Computational predictions of drug-induced nephrotoxicity are an attractive approach to facilitate such an assessment and such models could serve as robust and reliable replacements for animal testing. To provide the chemical information for computational prediction, we used the convenient and common SMILES format. We examined several versions of so-called optimal SMILES-based descriptors. We obtained the highest statistical values, considering the specificity, sensitivity and accuracy of the prediction, by applying recently suggested atoms pairs proportions vectors and the index of ideality of correlation, which is a special statistical measure of the predictive potential. Implementation of this tool in the drug development process might lead to safer drugs in the future.

Human iPSC-Cardiomyocytes as an Experimental Model to Study Epigenetic Modifiers of Electrophysiology

The epigenetic landscape and the responses to pharmacological epigenetic regulators in each human are unique. Classes of epigenetic writers and erasers, such as histone acetyltransferases, HATs, and histone deacetylases, HDACs, control DNA acetylation/deacetylation and chromatin accessibility, thus exerting transcriptional control in a tissue- and person-specific manner. Rapid development of novel pharmacological agents in clinical testing—HDAC inhibitors (HDACi)—targets these master regulators as common means of therapeutic intervention in cancer and immune diseases. The action of these epigenetic modulators is much less explored for cardiac tissue, yet all new drugs need to be tested for cardiotoxicity. To advance our understanding of chromatin regulation in the heart, and specifically how modulation of DNA acetylation state may affect functional electrophysiological responses, human-induced pluripotent stem-cell-derived cardiomyocyte (hiPSC-CM) technology can be leveraged as a scalable, high-throughput platform with ability to provide patient-specific insights. This review covers relevant background on the known roles of HATs and HDACs in the heart, the current state of HDACi development, applications, and any adverse cardiac events; it also summarizes relevant differential gene expression data for the adult human heart vs. hiPSC-CMs along with initial transcriptional and functional results from using this new experimental platform to yield insights on epigenetic control of the heart. We focus on the multitude of methodologies and workflows needed to quantify responses to HDACis in hiPSC-CMs. This overview can help highlight the power and the limitations of hiPSC-CMs as a scalable experimental model in capturing epigenetic responses relevant to the human heart.

Advanced human mucosal tissue models are needed to improve preclinical testing of vaccines

There is a need for better models to improve preclinical testing of vaccines. This Perspective article argues that advanced mucosal human tissue models could present a solution to this pressing problem in the future.

Are treatment effects consistent with hypothesized mechanisms of action proposed for postoperative delirium interventions? Reanalysis of systematic reviews

Aim: Postoperative delirium (POD) is associated with increased morbidity and is poorly understood. The aim of this review was to identify putative mechanisms through re-analysis of randomized trials on treatment or prevention of POD. Materials & methods: A systematic review was performed to identify systematic reviews of treatments for POD. Constituent randomized controlled trials were identified, and interventions were grouped according to hypothesized mechanisms of action. Effects were meta-analyzed by hypothesized mechanism and timing of intervention. Results: A total of 116 randomized controlled trials described 47 individual interventions for POD, with nine mechanisms identified. The largest effects were observed for postoperative inflammation reduction, and preoperative reinforcement of sleep-wake cycle. Conclusion: This approach identifies treatments focused on mechanisms of action that may be front runners for future trials and interventions.

Meta-Assessment of Metformin Absorption and Disposition Pharmacokinetics in Nine Species

The objective of this study was to systematically assess literature datasets and quantitatively analyze metformin PK in plasma and some tissues of nine species. The pharmacokinetic (PK) parameters and profiles of metformin in nine species were collected from the literature. Based on a simple allometric scaling, the systemic clearances (CL) of metformin in these species highly correlate with body weight (BW) (R2 = 0.85) and are comparable to renal plasma flow in most species except for rabbit and cat. Reported volumes of distribution (VSS) varied appreciably (0.32 to 10.1 L/kg) among species. Using the physiological and anatomical variables for each species, a minimal physiologically based pharmacokinetic (mPBPK) model consisting of blood and two tissue compartments (Tissues 1 and 2) was used for modeling metformin PK in the nine species. Permeability-limited distribution (low fd1 and fd2) and a single tissue-to-plasma partition coefficient (Kp) value for Tissues 1 and 2 were applied in the joint mPBPK fitting. Nonlinear regression analysis for common tissue distribution parameters along with species-specific CL values reasonably captured the plasma PK profiles of metformin across most species, except for rat and horse with later time deviations. In separate fittings of the mPBPK model to each species, Tissue 2 was considered as slowly-equilibrating compartment consisting of muscle and skin based on in silico calculations of the mean transit times through tissues. The well-fitted mPBPK model parameters for absorption and disposition PK of metformin for each species were compared with in vitro/in vivo results found in the literature with regard to the physiological details and physicochemical properties of metformin. Bioavailability and absorption rates decreased with the increased BW among the species. Tissues such as muscle dominate metformin distribution with low permeability and partitioning while actual tissue concentrations found in rats and mice show likely transporter-mediated uptake in liver, kidney, and gastrointestinal tissues. Metformin has diverse pharmacologic actions, and this assessment revealed allometric relationships in its absorption and renal clearance but considerable variability in actual and modeled tissue distribution probably caused by transporter differences.

An Overview of Biological and Computational Methods for Designing Mechanism-Informed Anti-biofilm Agents

Bacterial biofilms are complex and highly antibiotic-resistant aggregates of microbes that form on surfaces in the environment and body including medical devices. They are key contributors to the growing antibiotic resistance crisis and account for two-thirds of all infections. Thus, there is a critical need to develop anti-biofilm specific therapeutics. Here we discuss mechanisms of biofilm formation, current anti-biofilm agents, and strategies for developing, discovering, and testing new anti-biofilm agents. Biofilm formation involves many factors and is broadly regulated by the stringent response, quorum sensing, and c-di-GMP signaling, processes that have been targeted by anti-biofilm agents. Developing new anti-biofilm agents requires a comprehensive systems-level understanding of these mechanisms, as well as the discovery of new mechanisms. This can be accomplished through omics approaches such as transcriptomics, metabolomics, and proteomics, which can also be integrated to better understand biofilm biology. Guided by mechanistic understanding, in silico techniques such as virtual screening and machine learning can discover small molecules that can inhibit key biofilm regulators. To increase the likelihood that these candidate agents selected from in silico approaches are efficacious in humans, they must be tested in biologically relevant biofilm models. We discuss the benefits and drawbacks of in vitro and in vivo biofilm models and highlight organoids as a new biofilm model. This review offers a comprehensive guide of current and future biological and computational approaches of anti-biofilm therapeutic discovery for investigators to utilize to combat the antibiotic resistance crisis.

City of Waste—Importance of Scale

By 2050, the world population is expected to reach 9.7 billion, almost 90% of which will live in urban areas. With such a fast growth in population and urbanization, it is anticipated that the annual waste generation will increase by 70% in comparison with current levels, and will reach 3.40 billion tons in 2050. A key question regarding the sustainability of the planet is the effect of city size on waste production. Are larger cities more efficient at generating waste than smaller cities? Do larger cities show economies of scale over waste? This article examines the allometric relationship between the amount of municipal waste (total and per capita) and the populations, city area, density, and wealth of city residents. The scope of the research concerned 930 Polish cities. Using the allometric equation, the waste scaling factors were calculated for selected parameters, and the Hellwig method was used to optimize their selection for cities with more than 50,000 inhabitants. The calculations show that the parameter population (1.059) and then the city area (0.934) are important elements influencing the scaling of the amount of municipal waste in cities of all sizes, but none came close to the value of the animal metabolism model (0.75). In response to the question of whether larger cities show benefits from economies of scale, it should be stated that, for the model of city size in Poland, such a regularity does not exist.