Structural basis for raccoon dog receptor recognition by SARS-CoV-2.

Since the COVID-19 outbreak, raccoon dogs have been suggested as a potential intermediary in transmitting SARS-CoV-2 to humans. To understand their role in the COVID-19 pandemic and the species barrier for SARS-CoV-2 transmission to humans, we analyzed how their ACE2 protein interacts with SARS-CoV-2 spike protein. Biochemical data showed that raccoon dog ACE2 is an effective receptor for SARS-CoV-2 spike protein, though not as effective as human ACE2. Structural comparisons highlighted differences in the virus-binding residues of raccoon dog ACE2 compared to human ACE2 (L24Q, Y34H, E38D, T82M, R353K), explaining their varied effectiveness as receptors for SARS-CoV-2. These variations contribute to the species barrier that exists between raccoon dogs and humans regarding SARS-CoV-2 transmission. Identifying these barriers can help assess the susceptibility of other mammals to SARS-CoV-2. Our research underscores the potential of raccoon dogs as SARS-CoV-2 carriers and identifies molecular barriers that affect the virus's ability to jump between species.

Ribocentre-switch: a database of riboswitches.

Riboswitches are regulatory elements found in the untranslated regions (UTRs) of certain mRNA molecules. They typically comprise two distinct domains: an aptamer domain that can bind to specific small molecules, and an expression platform that controls gene expression. Riboswitches work by undergoing a conformational change upon binding to their specific ligand, thus activating or repressing the genes downstream. This mechanism allows gene expression regulation in response to metabolites or small molecules. To systematically summarise riboswitch structures and their related ligand binding functions, we present Ribocentre-switch, a comprehensive database of riboswitches, including the information as follows: sequences, structures, functions, ligand binding pockets and biological applications. It encompasses 56 riboswitches and 26 orphan riboswitches from over 430 references, with a total of 89 591 sequences. It serves as a good resource for comparing different riboswitches and facilitating the identification of potential riboswitch candidates. Therefore, it may facilitate the understanding of RNA structural conformational changes in response to ligand signaling. The database is publicly available at https://riboswitch.ribocentre.org.

Structural evolution of SARS-CoV-2 omicron in human receptor recognition.

Understanding the evolutionary strategies of the SARS-CoV-2 omicron variant is crucial for comprehending the COVID-19 pandemic and preventing future coronavirus pandemics. In this study, we determined the crystal structures of the receptor-binding domains (RBDs) from currently circulating omicron subvariants XBB.1 and XBB.1.5 (also the emerging XBB.1.9.1), each complexed with human ACE2. We studied how individual RBD residues evolved structurally in omicron subvariants, specifically how they adapted to human ACE2. Our findings revealed that residues 493 and 496, which exhibited good human ACE2 adaptation in pre-omicron variants, evolved to poor adaptation in early omicron subvariants (but with good adaption to mouse ACE2) and then reverted to good adaptation in recent omicron subvariants. This result is consistent with the hypothesis that non-human animals facilitated the evolution of early omicron subvariants. Additionally, residue 486, which exhibited good human ACE2 adaptation in early omicron subvariants, evolved to poor adaptation in later omicron subvariants and then returned to good adaptation in recent omicron subvariants. This result is consistent with the hypothesis that immune evasion facilitated the evolution of later omicron subvariants. Thus, our study suggests that both non-human animals and immune evasion may have contributed to driving omicron evolution at different stages of the pandemic. IMPORTANCE The sudden emergence and continued evolution of the SARS-CoV-2 omicron variant have left many mysteries unanswered, such as the origin of early omicron subvariants and the factors driving omicron evolution. To address these questions, we studied the crystal structures of human ACE2-bound receptor-binding domains (RBDs) from omicron subvariants XBB.1 and XBB.1.5 (XBB.1.9.1). Our in-depth structural analysis sheds light on how specific RBD mutations adapt to either human or mouse ACE2 and suggests non-human animals and immune evasion may have influenced omicron evolution during different stages of the pandemic. These findings provide valuable insights into the mechanisms underlying omicron evolution, deepen our understanding of the COVID-19 pandemic, and have significant implications for preventing future coronavirus pandemics.

Discovery of Nanosota-2, -3, and -4 as super potent and broad-spectrum therapeutic nanobody candidates against COVID-19.

IMPORTANCE: The COVID-19 pandemic exposed limitations of conventional antibodies as therapeutics, including high cost, limited potency, ineffectiveness against new viral variants, and primary reliance on injection-only delivery. Nanobodies are single-domain antibodies with therapeutic potentials. We discovered three anti-SARS-CoV-2 nanobodies, named Nanosota-2, -3, and -4, from an immunized alpaca. Nanosota-2 is super potent against prototypic SARS-CoV-2, Nanosota-3 is highly potent against the omicron variant, and Nanosota-4 is effective against both SARS-CoV-1 and SARS-CoV-2. In addition to their super potency and combined broad antiviral spectrum, these nanobodies are cost-effective, can be easily adapted to new viral variants through phage display, and can potentially be administered as inhalers. The Nanosota series are powerful therapeutic candidates to combat circulating SARS-CoV-2 and prepare for possible future coronavirus pandemics.

Nerve Growth Factor/Tyrosine Kinase A Receptor Pathway Enhances Analgesia in an Experimental Mouse Model of Bone Cancer Pain by Increasing Membrane Levels of δ-Opioid Receptors.

BACKGROUND: The role of nerve growth factor (NGF)/tyrosine kinase A receptor (TrKA) signaling, which is activated in a variety of pain states, in regulating membrane-associated δ-opioid receptor (mDOR) expression is poorly understood. The hypothesis was that elevated NGF in bone cancer tumors could upregulate mDOR expression in spinal cord neurons and that mDOR agonism might alleviate bone cancer pain. METHODS: Bone cancer pain (BCP) was induced by inoculating Lewis lung carcinoma cells into the femoral marrow cavity of adult C57BL/6J mice of both sexes. Nociceptive behaviors were evaluated by the von Frey and Hargreaves tests. Protein expression in the spinal dorsal horn of animals was measured by biochemical analyses, and excitatory synaptic transmission was recorded in miniature excitatory synaptic currents. RESULTS: The authors found that mDOR expression was increased in BCP mice (BCP vs. sham, mean ± SD: 0.18 ± 0.01 g vs. mean ± SD: 0.13 ± 0.01 g, n = 4, P < 0.001) and that administration of the DOR agonist deltorphin 2 (Del2) increased nociceptive thresholds (Del2 vs. vehicle, median [25th, 75th percentiles]: 1.00 [0.60, 1.40] g vs. median [25th, 75th percentiles]: 0.40 [0.16, 0.45] g, n = 10, P = 0.001) and reduced miniature excitatory synaptic current frequency in lamina II outer neurons (Del2 vs. baseline, mean ± SD: 2.21 ± 0.81 Hz vs. mean ± SD: 2.43 ± 0.90 Hz, n = 12, P < 0.001). Additionally, NGF expression was increased in BCP mice (BCP vs. sham, mean ± SD: 0.36 ± 0.03 vs. mean ± SD: 0.16 ± 0.02, n = 4, P < 0.001), and elevated NGF was associated with enhanced mDOR expression via TrKA signaling. CONCLUSIONS: Activation of mDOR produces analgesia that is dependent on the upregulation of the NGF/TrKA pathway by increasing mDOR levels under conditions of BCP in mice.

Inferring HIV transmission patterns from viral deep-sequence data via latent typed point processes.

Viral deep-sequencing data play a crucial role toward understanding disease transmission network flows, providing higher resolution compared to standard Sanger sequencing. To more fully utilize these rich data and account for the uncertainties in outcomes from phylogenetic analyses, we propose a spatial Poisson process model to uncover human immunodeficiency virus (HIV) transmission flow patterns at the population level. We represent pairings of individuals with viral sequence data as typed points, with coordinates representing covariates such as gender and age and point types representing the unobserved transmission statuses (linkage and direction). Points are associated with observed scores on the strength of evidence for each transmission status that are obtained through standard deep-sequence phylogenetic analysis. Our method is able to jointly infer the latent transmission statuses for all pairings and the transmission flow surface on the source-recipient covariate space. In contrast to existing methods, our framework does not require preclassification of the transmission statuses of data points, and instead learns them probabilistically through a fully Bayesian inference scheme. By directly modeling continuous spatial processes with smooth densities, our method enjoys significant computational advantages compared to previous methods that rely on discretization of the covariate space. We demonstrate that our framework can capture age structures in HIV transmission at high resolution, bringing valuable insights in a case study on viral deep-sequencing data from Southern Uganda.

Bayesian safety surveillance with adaptive bias correction.

Postmarket safety surveillance is an integral part of mass vaccination programs. Typically relying on sequential analysis of real-world health data as they accrue, safety surveillance is challenged by sequential multiple testing and by biases induced by residual confounding in observational data. The current standard approach based on the maximized sequential probability ratio test (MaxSPRT) fails to satisfactorily address these practical challenges and it remains a rigid framework that requires prespecification of the surveillance schedule. We develop an alternative Bayesian surveillance procedure that addresses both aforementioned challenges using a more flexible framework. To mitigate bias, we jointly analyze a large set of negative control outcomes that are adverse events with no known association with the vaccines in order to inform an empirical bias distribution, which we then incorporate into estimating the effect of vaccine exposure on the adverse event of interest through a Bayesian hierarchical model. To address multiple testing and improve on flexibility, at each analysis timepoint, we update a posterior probability in favor of the alternative hypothesis that vaccination induces higher risks of adverse events, and then use it for sequential detection of safety signals. Through an empirical evaluation using six US observational healthcare databases covering more than 360 million patients, we benchmark the proposed procedure against MaxSPRT on testing errors and estimation accuracy, under two epidemiological designs, the historical comparator and the self-controlled case series. We demonstrate that our procedure substantially reduces Type 1 error rates, maintains high statistical power and fast signal detection, and provides considerably more accurate estimation than MaxSPRT. Given the extensiveness of the empirical study which yields more than 7 million sets of results, we present all results in a public R ShinyApp. As an effort to promote open science, we provide full implementation of our method in the open-source R package EvidenceSynthesis.

Chronic nicotine exposure elicits pain hypersensitivity through activation of dopaminergic projections to anterior cingulate cortex.

BACKGROUND: Cigarette smoking is commonly reported among chronic pain patients in the clinic. Although chronic nicotine exposure is directly linked to nociceptive hypersensitivity in rodents, underlying neurobiological mechanisms remain unknown. METHODS: Multi-tetrode recordings in freely moving mice were used to test the activity of dopaminergic projections from the ventral tegmental area (VTA) to pyramidal neurones in the anterior cingulate cortex (ACC) in chronic nicotine-treated mice. The VTA→ACC dopaminergic pathway was inhibited by optogenetic manipulation to detect chronic nicotine-induced allodynia (pain attributable to a stimulus that does not normally provoke pain) assessed by von Frey monofilaments (force units in g). RESULTS: Allodynia developed concurrently with chronic (28-day) nicotine exposure in mice (0.36 g [0.0141] vs 0.05 g [0.0018], P<0.0001). Chronic nicotine activated dopaminergic projections from the VTA to pyramidal neurones in the ACC, and optogenetic inhibition of VTA dopaminergic terminals in the ACC alleviated chronic nicotine-induced allodynia in mice (0.06 g [0.0064] vs 0.28 g [0.0428], P<0.0001). Moreover, optogenetic inhibition of Drd2 dopamine receptor signalling in the ACC attenuated nicotine-induced allodynia (0.07 g [0.0082] vs 0.27 g [0.0211], P<0.0001). CONCLUSIONS: These findings implicate a role of Drd2-mediated dopaminergic VTA→ACC pathway signalling in chronic nicotine-elicited allodynia.

Effect of a fall within three months of admission on delirium in critically Ill elderly patients: a population-based cohort study.

BACKGROUND: Delirium is common among elderly patients in the intensive care unit (ICU) and is associated with prolonged hospitalization, increased healthcare costs, and increased risk of death. Understanding the potential risk factors and early prevention of delirium is critical to facilitate timely intervention that may reverse or mitigate the harmful consequences of delirium. AIM: To clarify the effects of pre-admission falls on ICU outcomes, primarily delirium, and secondarily pressure injuries and urinary tract infections. METHODS: The study relied on data sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Statistical tests (Wilcoxon rank-sum or chi-squared) compared cohort characteristics. Logistic regression was employed to investigate the association between a history of falls and delirium, as well as secondary outcomes, while Kaplan-Meier survival curves were used to assess short-term survival in delirium and non-delirium patients. RESULTS: Study encompassed 22,547 participants. Delirium incidence was 40%, significantly higher in patients with a history of falls (54.4% vs. 34.5%, p < 0.001). Logistic regression, controlling for confounders, not only confirmed that a history of falls elevates the odds of delirium (OR: 2.11; 95% CI: 1.97-2.26; p < 0.001) but also showed it increases the incidence of urinary tract infections (OR:1.50; 95% CI:1.40-1.62; p < 0.001) and pressure injuries (OR:1.36; 95% CI:1.26-1.47; p < 0.001). Elderly delirium patients exhibited lower 30-, 180-, and 360-day survival rates than non-delirium counterparts (all p < 0.001). CONCLUSIONS: The study reveals that history of falls significantly heighten the risk of delirium and other adverse outcomes in elderly ICU patients, leading to decreased short-term survival rates. This emphasizes the critical need for early interventions and could inform future strategies to manage and prevent these conditions in ICU settings.

Bio-Inspired Trace Hydroxyl-Rich Electrolyte Additives for High-Rate and Stable Zn-Ion Batteries at Low Temperatures.

High-rate and stable Zn-ion batteries working at low temperatures are highly desirable for practical applications, but are challenged by sluggish kinetics and severe corrosion. Herein, inspired by frost-resistant plants, we report trace hydroxyl-rich electrolyte additives that implement a dual remodeling effect for high-performance low-temperature Zn-ion batteries. The additive with high Zn absorbability not only remodels Zn2+ primary solvent shell by alternating H2 O molecules, but also forms a shielding layer thus remodeling the Zn surface, which effectively enhances fast Zn2+ de-solvation reaction kinetics and prohibits Zn anode corrosion. Taking trace α-D-glucose (αDG) as a demonstration, the electrolyte obtains a low freezing point of -55.3 °C, and the Zn//Zn cell can stably cycle for 2000 h at 5 mA cm-2 under -25 °C, with a high cumulative capacity of 5000 mAh cm-2 . A full battery that stably operates for 10000 cycles at -50 °C is also demonstrated.

Assessment of three-dimensional RNA structure prediction in CASP15.

The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty-two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and x-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as noncanonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.

Stroke-Induced Respiratory Dysfunction Is Associated With Cognitive Decline.

BACKGROUND: Respiratory dysfunction is a common complication of stroke, with an incidence of over 60%. Despite the high prevalence of stroke-induced respiratory dysfunction, how disordered breathing influences recovery and cognitive outcomes after ischemic stroke is unknown. We hypothesized that stroke induces chronic respiratory dysfunction, breathing instability, and apnea in mice, which would contribute to higher mortality and greater poststroke cognitive deficits. METHODS: Mice were subjected to a 60-minute transient middle cerebral artery occlusion or permanent distal middle cerebral artery occlusion. Whole body plethysmography was performed on C57BL/6 young (2-3 months) and aged (20 months) male and female mice. Animals were exposed to a variety of gas conditions to assess the contribution of peripheral and central chemoreceptors. A battery of cognitive tests was performed to examine behavioral function. RESULTS: Middle cerebral artery occlusion led to disordered breathing characterized by hypoventilation and apneas. Cognitive decline correlated with the severity of disordered breathing. Distal permanent middle cerebral artery occlusion, which produces a smaller cortical infarct, also produced breathing disorders and cognitive impairment but only in aged mice. CONCLUSIONS: Our data suggest that poststroke apnea is associated with cognitive decline and highlights the influence of aging on breathing disorders after stroke. Therefore, the treatment of respiratory instability may be a viable approach to improving cognitive outcomes after stroke.

Ultraviolet-Assisted Printing of Flexible Solid-State Zn-Ion Battery with a Heterostructure Electrolyte.

Flexible solid-state Zn-ion batteries (ZIBs) have garnered considerable attention for next-generation power sources, but the corrosion, dendrite growth, and interfacial problems severely hinder their practical applications. Herein, a high-performance flexible solid-state ZIB with a unique heterostructure electrolyte is facilely fabricated through ultraviolet-assisted printing strategy. The solid polymer/hydrogel heterostructure matrix not only isolates water molecules and optimizes electric field distribution for dendrite-free anode, but also facilitates fast and in-depth Zn2+ transport in the cathode. The in situ ultraviolet-assisted printing creates cross-linked and well-bonded interfaces between the electrodes and the electrolyte, enabling low ionic transfer resistance and high mechanical stability. As a result, the heterostructure electrolyte based ZIB outperforms single-electrolyte based cells. It not only delivers a high capacity of 442.2 mAh g-1 with long cycling life of 900 cycles at 2 A g-1 , but also maintains stable operation under mechanical bending and high-pressure compression in a wide temperature range (-20 °C to 100 °C).

Cocrystallization-Driven Double-Optimized Stratagem toward Directional Self-Assembly for the First Ternary Salt Cocrystal of Cardiotonic Drug Milrinone with Different Phenolic Acids Exhibits Optimal In Vitro/Vivo Biopharmaceutical Peculiarities.

The current research leverages the structural features and property superiorities along with benefits in protecting cardiovascular system of gallic acid (GLC) and gentisic acid (HGA) to optimize in vitro/vivo peculiarities of cardiotonic drug milrinone (MIL) through developing a stratagem of cocrystallization-driven double-optimized ternary salt cocrystal. This strategy assembles MIL ternary salt cocrystal by shaping a cocrystallization moiety relying on noncovalent interplays with GLC to obtain permeability advancement and molding a salt segment via the salification of proton transfer between HGA and MIL molecules to facilitate solubility enhancement. While the ameliorative in vitro properties further modulate the in vivo pharmacokinetic behaviors, thus fulfilling a dual optimization of MIL's biopharmaceutical characteristics on both in vitro and in vivo aspects. Along this line, the first MIL ternary salt cocrystal, viz., [HMIL+-GA-]-MIL-GLC-H2O (denoted as MTSC hereinafter), has been satisfactorily constructed and precisely structurally identified by diversified techniques. The single-crystal X-ray diffraction experiment validates that a molecular salt [HMIL+-GA-] species cocrystallizes with one neutral MIL, two GLC, and five solvent water molecules, among which the organic constituents compose laminated hydrogen bond networks, and then are self-assembled by water molecules to a 3D supramolecular structure. The unique structural feature and stacking pattern of MTSC make both the permeability and solubility be respectively enhanced by 9.69 times and 5.17- to 6.03-fold compared with the parent drug per se. The experimental outcomes are powerfully supported by associated calculations based on density functional theory. Intriguingly, these optimal in vitro physicochemical natures of MTSC have been potently converted into strengths of in vivo pharmacokinetics, showcasing the elevated drug plasma concentration, elongated half-life, alongside advanced bioavailability. Consequently, this presentation not just contributes a brand-new crystalline form with utility values, but ushers in a new dimension of ternary salt cocrystals for improving in vitro/vivo limitations of poor drug bioavailability.

Adjusting for both sequential testing and systematic error in safety surveillance using observational data: Empirical calibration and MaxSPRT.

Post-approval safety surveillance of medical products using observational healthcare data can help identify safety issues beyond those found in pre-approval trials. When testing sequentially as data accrue, maximum sequential probability ratio testing (MaxSPRT) is a common approach to maintaining nominal type 1 error. However, the true type 1 error may still deviate from the specified one because of systematic error due to the observational nature of the analysis. This systematic error may persist even after controlling for known confounders. Here we propose to address this issue by combing MaxSPRT with empirical calibration. In empirical calibration, we assume uncertainty about the systematic error in our analysis, the source of uncertainty commonly overlooked in practice. We infer a probability distribution of systematic error by relying on a large set of negative controls: exposure-outcome pairs where no causal effect is believed to exist. Integrating this distribution into our test statistics has previously been shown to restore type 1 error to nominal. Here we show how we can calibrate the critical value central to MaxSPRT. We evaluate this novel approach using simulations and real electronic health records, using H1N1 vaccinations during the 2009-2010 season as an example. Results show that combining empirical calibration with MaxSPRT restores nominal type 1 error. In our real-world example, adjusting for systematic error using empirical calibration has a larger impact than, and hence is just as essential as, adjusting for sequential testing using MaxSPRT. We recommend performing both, using the method described here.

Development and validation of a risk prediction model for frailty in patients with diabetes.

BACKGROUND: Frailty is the third most common complication of diabetes after macrovascular and microvascular complications. The aim of this study was to develop a validated risk prediction model for frailty in patients with diabetes. METHODS: The research used data from the China Health and Retirement Longitudinal Study (CHARLS), a dataset representative of the Chinese population. Twenty-five indicators, including socio-demographic variables, behavioral factors, health status, and mental health parameters, were analyzed in this study. The study cohort was randomly divided into a training set and a validation set at a ratio of 70 to 30%. LASSO regression analysis was used to screen the variables for the best predictors of the model based on a 10-fold cross-validation. The logistic regression model was applied to explore the associated factors of frailty in patients with diabetes. A nomogram was constructed to develop the prediction model. Calibration curves were applied to evaluate the accuracy of the nomogram model. The area under the receiver operating characteristic curve and decision curve analysis were conducted to assess predictive performance. RESULTS: One thousand four hundred thirty-six patients with diabetes from the CHARLS database collected in 2013 (n = 793) and 2015 (n = 643) were included in the final analysis. A total of 145 (10.9%) had frailty symptoms. Multivariate logistic regression analysis showed that marital status, activities of daily living, waist circumference, cognitive function, grip strength, social activity, and depression as predictors of frailty in people with diabetes. These factors were used to construct the nomogram model, which showed good concordance and accuracy. The AUC values of the predictive model and the internal validation set were 0.912 (95%CI 0.887-0.937) and 0.881 (95% CI 0.829-0.934). Hosmer-Lemeshow test values were P = 0.824 and P = 0.608 (both > 0.05). Calibration curves showed significant agreement between the nomogram model and actual observations. ROC and DCA indicated that the nomogram had a good predictive performance. CONCLUSIONS: Comprehensive nomogram constructed in this study was a promising and convenient tool to evaluate the risk of frailty in patients with diabetes, and contributed clinicians to screening the high-risk population.

Association between bidirectional intergenerational support and successful aging in China: Evidence from CHARLS 2018.

Aging has gradually accelerated in China, and achieving successful aging of older adults has become a public health concern. Intergenerational support is crucial for Chinese older adults in later life due to the culture of filial piety. However, the association between successful aging and intergenerational support remains poorly understood in China. This study aimed to examine the association between patterns of intergenerational support and successful aging of older adults in China. The present study is a secondary analysis of data obtained from the follow-up survey of the China Health and Retirement Longitudinal Study 2018. Data were analyzed using descriptive statistics and logistic regressions. Bidirectional intergenerational support was associated with successful aging in the participants. In addition, there was an association between different intergenerational financial, caring, and emotional support patterns and elements of successful aging.

Population pharmacokinetics and dosing optimization of mezlocillin in neonates and young infants.

OBJECTIVES: Mezlocillin is used in the treatment of neonatal infectious diseases. However, due to the absence of population pharmacokinetic studies in neonates and young infants, dosing regimens differ considerably in clinical practice. Hence, this study aimed to describe the pharmacokinetic characteristics of mezlocillin in neonates and young infants, and propose the optimal dosing regimen based on the population pharmacokinetic model of mezlocillin. METHODS: A prospective, open-label pharmacokinetic study of mezlocillin was carried out in newborns. Blood samples were collected using an opportunistic sampling method. HPLC was used to measure the plasma drug concentrations. A population pharmacokinetic model was developed using NONMEM software. RESULTS: Ninety-five blood samples from 48 neonates and young infants were included. The ranges of postmenstrual age and birth weight were 29-40 weeks and 1200-4000 g, respectively, including term and preterm infants. A two-compartment model with first-order elimination was developed to describe the population pharmacokinetics of mezlocillin. Postmenstrual age, current weight and serum creatinine concentration were the most important covariates. Monte Carlo simulation results indicated that the current dose of 50 mg/kg q12h resulted in 89.2% of patients achieving the therapeutic target, when the MIC of 4 mg/L was used as the breakpoint. When increasing the dosing frequency to q8h, a dose of 20 mg/kg resulted in 74.3% of patients achieving the therapeutic target. CONCLUSIONS: A population pharmacokinetic model of mezlocillin in neonates and young infants was established. Optimal dosing regimens based on this model were provided for use in neonatal infections.

Optimal dose of meropenem for the treatment of neonatal sepsis: Dosing guideline variations and clinical practice deviations.

AIMS: Meropenem is increasingly used to treat neonatal sepsis. There are several guidelines recommending different dosing regimens of meropenem in neonates. Furthermore, deviations from these guidelines regularly occur in daily clinical practice. Therefore, the current study aimed to evaluate the variations of meropenem dosing guidelines and compare the difference between guideline and clinical practice in terms of the probability of target attainment. METHODS: This study is based on a population pharmacokinetic model. After defining the predictive performance of the model, Monte Carlo simulations were used to calculate the probability of target attainment of the currently existing dosing guidelines of meropenem and their use in daily clinical practice. RESULTS: Two guidelines and two labels were included in the Monte Carlo simulations. For 70% fT>MIC (fraction of time when the free meropenem concentration exceeded the minimum inhibitory concentration during the dosing interval), the probability of target attainment of four recommended doses ranged from 59% to 88% (MIC = 2 mg·L-1 ) and from 17% to 47% (MIC = 8 mg·L-1 ). At the clinical practice evaluation, only 20% of patients attained target exposure for the MIC of 8 mg·L-1 with 70% fT>MIC , which was much less than those found in the Food and Drug Administration labels (40%). CONCLUSION: This model-based population pharmacokinetics simulation showed that improper guidelines and/or clinical practice deviations will result in low probability of target attainment for patients infected with resistant bacteria and critically ill patients. It is important to develop and adhere to evidence-based and clinically pragmatic guidelines.