Rational design of a cyclohexanone dehydrogenase for enhanced α,ß-desaturation and substrate specificity.

The selective α,ß-desaturation of cyclic carbonyl compounds, which are found in the core of many steroid and bioactive molecules, using green chemistry is highly desirable. To achieve this task, we have for the first time described and solved the de novo structure of a member of the cyclohexanone dehydrogenase class of enzymes. The breadth of substrate specificity was investigated by assaying the cyclohexanone dehydrogenase, from Alicycliphilus denitrificans, against several cyclic ketones, lactones and lactams. To investigate substrate binding, a catalytic variant, Y195F, was generated and used to obtain a crystallographic complex with the natural substrate, cyclohexanone. This revealed substrate-active site interactions, as well as the proximity of the cofactor, flavin adenine dinucleotide, and enabled us to propose a mechanistic function to key amino acids. We then used molecular dynamic simulations to guide design to add functionality to the cyclohexanone dehydrogenase enzyme. The resulting W113A variant had overall improved enzyme activity and substrate scope, i.e., accepting the bulkier carbonyl compound, dihydrocoumarin. Structural analysis of the W113A variant revealed a broader, more open active site, which helped explain the modified substrate specificity. This work paves the way for future bespoke regioselective α,ß-desaturation in the synthesis of important bioactive molecules via rational enzyme engineering.

A low-volume LC/MS method for highly sensitive monitoring of phenoxymethylpenicillin, benzylpenicillin, and probenecid in human serum.

Background: The optimization of antimicrobial dosing plays a crucial role in improving the likelihood of achieving therapeutic success while reducing the risks associated with toxicity and antimicrobial resistance. Probenecid has shown significant potential in enhancing the serum exposure of phenoxymethylpenicillin, thereby allowing for lower doses of phenoxymethylpenicillin to achieve similar pharmacokinetic/pharmacodynamic (PK/PD) targets. We developed a triple quadrupole liquid chromatography mass spectrometry (TQ LC/MS) analysis of, phenoxymethylpenicillin, benzylpenicillin and probenecid using benzylpenicillin-d7 and probenecid-d14 as IS in single low-volumes of human serum, with improved limit of quantification to support therapeutic drug monitoring. Methods: Sample clean-up was performed by protein precipitation using acetonitrile. Reverse phase chromatography was performed using TQ LC/MS. The mobile phase consisted of 55% methanol in water + 0.1% formic acid, with a flow rate of 0.4 mL min-1. Antibiotic stability was assessed at different temperatures. Results: Chromatographic separation was achieved within 2 minutes, allowing simultaneous measurement of phenoxymethylpenicillin, benzylpenicillin and probenecid in a single 15 µL blood sample. Validation indicated linearity over the range 0.0015-10 mg L-1, with accuracy of 96-102% and a LLOQ of 0.01 mg L-1. All drugs demonstrated good stability under different storage conditions. Conclusion: The developed method is simple, rapid, accurate and clinically applicable for the quantification of phenoxymethylpenicillin, benzylpenicillin and probenecid in tandem.

Management of Bacterial and Fungal Infections in the ICU: Diagnosis, Treatment, and Prevention Recommendations.

Bacterial and fungal infections are common issues for patients in the intensive care unit (ICU). Large, multinational point prevalence surveys have identified that up to 50% of ICU patients have a diagnosis of bacterial or fungal infection at any one time. Infection in the ICU is associated with its own challenges. Causative organisms often harbour intrinsic and acquired mechanisms of drug-resistance, making empiric and targeted antimicrobial selection challenging. Infection in the ICU is associated with worse clinical outcomes for patients. We review the epidemiology of bacterial and fungal infection in the ICU. We discuss risk factors for acquisition, approaches to diagnosis and management, and common strategies for the prevention of infection.

A rapid, simple, high-performance liquid chromatography method for the clinical measurement of beta-lactam antibiotics in serum and interstitial fluid.

Background: enhanced methods of therapeutic drug monitoring are required to support the individualisation of antibiotic dosing based on pharmacokinetics (PK) parameters. PK studies can be hampered by limited total serum volume, especially in neonates, or by sensitivity in the case of critically ill patients. We aimed to develop a liquid chromatography-mass spectrometry (LC/MS) analysis of benzylpenicillin, phenoxymethylpenicillin and amoxicillin in single low volumes of human serum and interstitial fluid (ISF) samples, with an improved limit of detection (LOD) and limit of quantification (LOQ), compared with previously published assays. Methods: sample clean-up was performed by protein precipitation using acetonitrile. Reverse phase chromatography was performed using triple quadrupole LC/MS. The mobile phase consisted of 55% methanol in water + 0.1% formic acid, with a flow rate of 0.4 mL min-1. Antibiotics stability was assessed at different temperatures. Results: chromatographic separation was achieved within 3 minutes for all analytes. Three common penicillins can now be measured in a single low-volume blood and ISF sample (15 µL) for the first time. Validation has demonstrated the method to be linear over the range 0.0015-10 mg L-1, with an accuracy of 93-104% and high sensitivity, with LOD ≈ 0.003 mg L-1 and LOQ ≈ 0.01 mg L-1 for all three analytes, which is critical for use in dose optimisation/individualisation. All evaluated penicillins indicated good stability at room temperature over 4 h, at (4 °C) over 24 h and at -80 °C for 6 months. Conclusion: the developed method is simple, rapid, accurate and clinically applicable for the quantification of three penicillin classes.

Triple quadrupole LC/MS method for the simultaneous quantitative measurement of cefiderocol and meropenem in serum.

Background: therapeutic drug monitoring is a crucial aspect of the management of hospitalized patients. The correct dosage of antibiotics is imperative to ensure their adequate exposure specially in critically ill patients. The aim of this study is to establish and validate a robust and fast liquid chromatography-tandem mass spectrometry (LC/MS) method for the simultaneous quantification of two important antibiotics in critically ill patients, cefiderocol and meropenem in human plasma. Methods: sample clean-up was performed by protein precipitation using acetonitrile. Reverse phase chromatography was performed using triple quadrupole LC/MS. The mobile phase was consisted of 55% methanol in water +0.1% formic acid, with flow rate of 0.4 ml min-1. Antibiotics stability was assessed at different temperatures. Serum protein binding was assessed using ultrafiltration devices. Results: chromatographic separation was achieved within 1.5 minutes for all analytes. Validation has demonstrated the method to be linear over the range 0.0025-50 mg L-1 for cefiderocol and 0.00028-50 mg L-1 for meropenem, with accuracy of 94-101% and highly sensitive, with LLOQ ≈ 0.02 mg L-1 and 0.003 mg L-1 for cefiderocol and meropenem, respectively. Both cefiderocol and meropenem showed a good stability at room temperature over 6 h, and at (4 °C) over 24 h. Cefiderocol and meropenem demonstrated a protein binding of 49-60% and 98%, respectively in human plasma. Conclusion: the developed method is simple, rapid, accurate and clinically applicable for the quantification of cefiderocol and meropenem.

Closed-loop control of continuous piperacillin delivery: An in silico study.

Background and objective: Sub-therapeutic dosing of piperacillin-tazobactam in critically-ill patients is associated with poor clinical outcomes and may promote the emergence of drug-resistant infections. In this paper, an in silico investigation of whether closed-loop control can improve pharmacokinetic-pharmacodynamic (PK-PD) target attainment is described. Method: An in silico platform was developed using PK data from 20 critically-ill patients receiving piperacillin-tazobactam where serum and tissue interstitial fluid (ISF) PK were defined. Intra-day variability on renal clearance, ISF sensor error, and infusion constraints were taken into account. Proportional-integral-derivative (PID) control was selected for drug delivery modulation. Dose adjustment was made based on ISF sensor data with a 30-min sampling period, targeting a serum piperacillin concentration between 32 and 64 mg/L. A single tuning parameter set was employed across the virtual population. The PID controller was compared to standard therapy, including bolus and continuous infusion of piperacillin-tazobactam. Results: Despite significant inter-subject and simulated intra-day PK variability and sensor error, PID demonstrated a significant improvement in target attainment compared to traditional bolus and continuous infusion approaches. Conclusion: A PID controller driven by ISF drug concentration measurements has the potential to precisely deliver piperacillin-tazobactam in critically-ill patients undergoing treatment for sepsis.

Addition of probenecid to oral ß-lactam antibiotics: a systematic review and meta-analysis.

OBJECTIVES: To explore the literature comparing the pharmacokinetic and clinical outcomes from adding probenecid to oral ß-lactams. METHODS: Medline and EMBASE were searched from inception to December 2021 for all English language studies comparing the addition of probenecid (intervention) with an oral ß-lactam [flucloxacillin, penicillin V, amoxicillin (±â€Šclavulanate), cefalexin, cefuroxime axetil] alone (comparator). ROBINS-I and ROB-2 tools were used. Data on antibiotic therapy, infection diagnosis, primary and secondary outcomes relating to pharmacokinetics and clinical outcomes, plus adverse events were extracted and reported descriptively. For a subset of studies comparing treatment failure between probenecid and control groups, meta-analysis was performed. RESULTS: Overall, 18/295 (6%) screened abstracts were included. Populations, methodology and outcome data were heterogeneous. Common populations included healthy volunteers (9/18; 50%) and those with gonococcal infection (6/18; 33%). Most studies were crossover trials (11/18; 61%) or parallel-arm randomized trials (4/18; 22%). Where pharmacokinetic analyses were performed, addition of probenecid to oral ß-lactams increased total AUC (7/7; 100%), Cmax (5/8; 63%) and serum t½ (6/8; 75%). Probenecid improved PTA (2/2; 100%). Meta-analysis of 3105 (2258 intervention, 847 control) patients treated for gonococcal disease demonstrated a relative risk of treatment failure in the random-effects model of 0.33 (95% CI 0.20-0.55; I2 = 7%), favouring probenecid. CONCLUSIONS: Probenecid-boosted ß-lactam therapy is associated with improved outcomes in gonococcal disease. Pharmacokinetic data suggest that probenecid-boosted oral ß-lactam therapy may have a broader application, but appropriately powered mechanistic and efficacy studies are required.

An R-Convolution Graph Kernel Based on Fast Discrete-Time Quantum Walk.

In this article, a novel R-convolution kernel, named the fast quantum walk kernel (FQWK), is proposed for unattributed graphs. In FQWK, the similarity of the neighborhood-pair substructure between two nodes is measured via the superposition amplitude of quantum walks between those nodes. The quantum interference in this kind of local substructures provides more information on the substructures so that FQWK can capture finer-grained local structural features of graphs. In addition, to efficiently compute the transition amplitudes of multistep discrete-time quantum walks, a fast recursive method is designed. Thus, compared with all the existing kernels based on the quantum walk, FQWK has the highest computation speed. Extensive experiments demonstrate that FQWK outperforms state-of-the-art graph kernels in terms of classification accuracy for unattributed graphs. Meanwhile, it can be applied to distinguish a larger family of graphs, including cospectral graphs, regular graphs, and even strong regular graphs, which are not distinguishable by classical walk-based methods.

Exploring the Pharmacokinetics of Phenoxymethylpenicillin (Penicillin-V) in Adults: A Healthy Volunteer Study.

This healthy volunteer study aimed to explore phenoxymethylpenicillin (penicillin-V) pharmacokinetics (PK) to support the planning of large dosing studies in adults. Volunteers were dosed with penicillin-V at steady state. Total and unbound penicillin-V serum concentrations were determined, and a base population PK model was fitted to the data.

Optimizing antimicrobial use: challenges, advances and opportunities.

An optimal antimicrobial dose provides enough drug to achieve a clinical response while minimizing toxicity and development of drug resistance. There can be considerable variability in pharmacokinetics, for example, owing to comorbidities or other medications, which affects antimicrobial pharmacodynamics and, thus, treatment success. Although current approaches to antimicrobial dose optimization address fixed variability, better methods to monitor and rapidly adjust antimicrobial dosing are required to understand and react to residual variability that occurs within and between individuals. We review current challenges to the wider implementation of antimicrobial dose optimization and highlight novel solutions, including biosensor-based, real-time therapeutic drug monitoring and computer-controlled, closed-loop control systems. Precision antimicrobial dosing promises to improve patient outcome and is important for antimicrobial stewardship and the prevention of antimicrobial resistance.

Supervised machine learning to support the diagnosis of bacterial infection in the context of COVID-19.

BACKGROUND: Bacterial infection has been challenging to diagnose in patients with COVID-19. We developed and evaluated supervised machine learning algorithms to support the diagnosis of secondary bacterial infection in hospitalized patients during the COVID-19 pandemic. METHODS: Inpatient data at three London hospitals for the first COVD-19 wave in March and April 2020 were extracted. Demographic, blood test and microbiology data for individuals with and without SARS-CoV-2-positive PCR were obtained. A Gaussian Naive Bayes, Support Vector Machine (SVM) and Artificial Neural Network were trained and compared using the area under the receiver operating characteristic curve (AUCROC). The best performing algorithm (SVM with 21 blood test variables) was prospectively piloted in July 2020. AUCROC was calculated for the prediction of a positive microbiological sample within 48 h of admission. RESULTS: A total of 15 599 daily blood profiles for 1186 individual patients were identified to train the algorithms; 771/1186 (65%) individuals were SARS-CoV-2 PCR positive. Clinically significant microbiology results were present for 166/1186 (14%) patients during admission. An SVM algorithm trained with 21 routine blood test variables and over 8000 individual profiles had the best performance. AUCROC was 0.913, sensitivity 0.801 and specificity 0.890. Prospective testing on 54 patients on admission (28/54, 52% SARS-CoV-2 PCR positive) demonstrated an AUCROC of 0.960 (95% CI: 0.90-1.00). CONCLUSIONS: An SVM using 21 routine blood test variables had excellent performance at inferring the likelihood of positive microbiology. Further prospective evaluation of the algorithms ability to support decision making for the diagnosis of bacterial infection in COVID-19 cohorts is underway.

Graph Embedding Using Frequency Filtering.

The target of graph embedding is to embed graphs in vector space such that the embedded feature vectors follow the differences and similarities of the source graphs. In this paper, a novel method named Frequency Filtering Embedding (FFE) is proposed which uses graph Fourier transform and Frequency filtering as a graph Fourier domain operator for graph feature extraction. Frequency filtering amplifies or attenuates selected frequencies using appropriate filter functions. Here, heat, anti-heat, part-sine and identity filter sets are proposed as the filter functions. A generalized version of FFE named GeFFE is also proposed by defining pseudo-Fourier operators. This method can be considered as a general framework for formulating some previously defined invariants in other works by choosing a suitable filter bank and defining suitable pseudo-Fourier operators. This flexibility empowers GeFFE to adapt itself to the properties of each graph dataset unlike the previous spectral embedding methods and leads to superior classification accuracy relative to the others. Utilizing the proposed part-sine filter set, which its members filter different parts of the spectrum in turn, improves the classification accuracy of GeFFE method. Additionally, GeFFE resolves the cospectrality problem entirely in tested datasets.

A Real-world Evaluation of a Case-based Reasoning Algorithm to Support Antimicrobial Prescribing Decisions in Acute Care.

BACKGROUND: A locally developed case-based reasoning (CBR) algorithm, designed to augment antimicrobial prescribing in secondary care was evaluated. METHODS: Prescribing recommendations made by a CBR algorithm were compared to decisions made by physicians in clinical practice. Comparisons were examined in 2 patient populations: first, in patients with confirmed Escherichia coli blood stream infections ("E. coli patients"), and second in ward-based patients presenting with a range of potential infections ("ward patients"). Prescribing recommendations were compared against the Antimicrobial Spectrum Index (ASI) and the World Health Organization Essential Medicine List Access, Watch, Reserve (AWaRe) classification system. Appropriateness of a prescription was defined as the spectrum of the prescription covering the known or most-likely organism antimicrobial sensitivity profile. RESULTS: In total, 224 patients (145 E. coli patients and 79 ward patients) were included. Mean (standard deviation) age was 66 (18) years with 108/224 (48%) female sex. The CBR recommendations were appropriate in 202/224 (90%) compared to 186/224 (83%) in practice (odds ratio [OR]: 1.24 95% confidence interval [CI]: .392-3.936; P = .71). CBR recommendations had a smaller ASI compared to practice with a median (range) of 6 (0-13) compared to 8 (0-12) (P < .01). CBR recommendations were more likely to be classified as Access class antimicrobials compared to physicians' prescriptions at 110/224 (49%) vs. 79/224 (35%) (OR: 1.77; 95% CI: 1.212-2.588; P < .01). Results were similar for E. coli and ward patients on subgroup analysis. CONCLUSIONS: A CBR-driven decision support system provided appropriate recommendations within a narrower spectrum compared to current clinical practice. Future work must investigate the impact of this intervention on prescribing behaviors more broadly and patient outcomes.

Development of a Minimally Invasive Microneedle-Based Sensor for Continuous Monitoring of ß-Lactam Antibiotic Concentrations in Vivo.

Antimicrobial resistance poses a global threat to patient health. Improving the use and effectiveness of antimicrobials is critical in addressing this issue. This includes optimizing the dose of antibiotic delivered to each individual. New sensing approaches that track antimicrobial concentration for each patient in real time could allow individualized drug dosing. This work presents a potentiometric microneedle-based biosensor to detect levels of ß-lactam antibiotics in vivo in a healthy human volunteer. The biosensor is coated with a pH-sensitive iridium oxide layer, which detects changes in local pH as a result of ß-lactam hydrolysis by ß-lactamase immobilized on the electrode surface. Development and optimization of the biosensor coatings are presented, giving a limit of detection of 6.8 µM in 10 mM PBS solution. Biosensors were found to be stable for up to 2 weeks at -20 °C and to withstand sterilization. Sensitivity was retained after application for 6 h in vivo. Proof-of-concept results are presented showing that penicillin concentrations measured using the microneedle-based biosensor track those measured using both discrete blood and microdialysis sampling in vivo. These preliminary results show the potential of this microneedle-based biosensor to provide a minimally invasive means to measure real-time ß-lactam concentrations in vivo, representing an important first step toward a closed-loop therapeutic drug monitoring system.

Microneedle biosensors for real-time, minimally invasive drug monitoring of phenoxymethylpenicillin: a first-in-human evaluation in healthy volunteers.

BACKGROUND: Enhanced methods of drug monitoring are required to support the individualisation of antibiotic dosing. We report the first-in-human evaluation of real-time phenoxymethylpenicillin monitoring using a minimally invasive microneedle-based ß-lactam biosensor in healthy volunteers. METHODS: This first-in-human, proof-of-concept study was done at the National Institute of Health Research/Wellcome Trust Imperial Clinical Research Facility (Imperial College London, London, UK). The study was approved by London-Harrow Regional Ethics Committee. Volunteers were identified through emails sent to a healthy volunteer database from the Imperial College Clinical Research Facility. Volunteers, who had to be older than 18 years, were excluded if they had evidence of active infection, allergies to penicillin, were at high risk of skin infection, or presented with anaemia during screening. Participants wore a solid microneedle ß-lactam biosensor for up to 6 h while being dosed at steady state with oral phenoxymethylpenicillin (five 500 mg doses every 6 h). On arrival at the study centre, two microneedle sensors were applied to the participant's forearm. Blood samples (via cannula, at -30, 0, 10, 20, 30, 45, 60, 90, 120, 150, 180, 210, 240 min) and extracellular fluid (ECF; via microdialysis, every 15 min) pharmacokinetic (PK) samples were taken during one dosing interval. Phenoxymethylpenicillin concentration data obtained from the microneedles were calibrated using locally estimated scatter plot smoothing and compared with free-blood and microdialysis (gold standard) data. Phenoxymethylpenicillin PK for each method was evaluated using non-compartmental analysis. Area under the concentration-time curve (AUC), maximum concentration, and time to maximum concentration were compared. Bias and limits of agreement were investigated with Bland-Altman plots. Microneedle biosensor limits of detection were estimated. The study was registered with ClinicalTrials.gov, number NCT03847610. FINDINGS: Ten healthy volunteers participated in the study. Mean age was 42 years (SD 14). Seven (70%) were men. Microdialysis and microneedle results were similar for phenoxymethylpenicillin ECF maximum concentration (0·74 mg/L vs 0·64 mg/L; 95% CI -0·24 to 0·44; p=0·53), time to maximum concentration (1·18 h vs 1·10 h; -0·52 to 0·67; p=0·79), and AUC (1·54 mg × h/L vs 1·67 mg × h/L; -1·10 to 0·85; p=0·79). In total, 440 time points were compared with mean difference between measurements -0·16 mg/L (95% CI -1·30 to 0·82). Mean phenoxymethylpenicillin AUCs for free serum and microneedle PK were similar (1·77 mg × h/L [SD 0·59] vs 1·67 mg × h/L [1·00]; -0·77 to 0·97; p=0·81). Median coefficient of variation between sensors within individuals was 7% (IQR 4-17). Limit of detection for the microneedles was estimated at 0·17 mg/L. INTERPRETATION: This study is proof-of-concept of real-time, microneedle sensing of penicillin in vivo. Future work will explore microneedle use in patient populations, their role in data generation to inform dosing recommendations, and their incorporation into closed-loop control systems for automated drug delivery. FUNDING: National Institute for Health Research Imperial Biomedical Research Centre, Mérieux Foundation.

Thermodynamic Analysis of Time Evolving Networks.

The problem of how to represent networks, and from this representation, derive succinct characterizations of network structure and in particular how this structure evolves with time, is of central importance in complex network analysis. This paper tackles the problem by proposing a thermodynamic framework to represent the structure of time-varying complex networks. More importantly, such a framework provides a powerful tool for better understanding the network time evolution. Specifically, the method uses a recently-developed approximation of the network von Neumann entropy and interprets it as the thermodynamic entropy for networks. With an appropriately-defined internal energy in hand, the temperature between networks at consecutive time points can be readily derived, which is computed as the ratio of change of entropy and change in energy. It is critical to emphasize that one of the main advantages of the proposed method is that all these thermodynamic variables can be computed in terms of simple network statistics, such as network size and degree statistics. To demonstrate the usefulness of the thermodynamic framework, the paper uses real-world network data, which are extracted from time-evolving complex systems in the financial and biological domains. The experimental results successfully illustrate that critical events, including abrupt changes and distinct periods in the evolution of complex networks, can be effectively characterized.

A centrality measure for cycles and subgraphs II.

In a recent work we introduced a measure of importance for groups of vertices in a complex network. This centrality for groups is always between 0 and 1 and induces the eigenvector centrality over vertices. Furthermore, its value over any group is the fraction of all network flows intercepted by this group. Here we provide the rigorous mathematical constructions underpinning these results via a semi-commutative extension of a number theoretic sieve. We then established further relations between the eigenvector centrality and the centrality proposed here, showing that the latter is a proper extension of the former to groups of nodes. We finish by comparing the centrality proposed here with the notion of group-centrality introduced by Everett and Borgatti on two real-world networks: the Wolfe's dataset and the protein-protein interaction network of the yeast Saccharomyces cerevisiae. In this latter case, we demonstrate that the centrality is able to distinguish protein complexes.

Generative Graph Prototypes from Information Theory.

In this paper we present a method for constructing a generative prototype for a set of graphs by adopting a minimum description length approach. The method is posed in terms of learning a generative supergraph model from which the new samples can be obtained by an appropriate sampling mechanism. We commence by constructing a probability distribution for the occurrence of nodes and edges over the supergraph. We encode the complexity of the supergraph using an approximate Von Neumann entropy. A variant of the EM algorithm is developed to minimize the description length criterion in which the structure of the supergraph and the node correspondences between the sample graphs and the supergraph are treated as missing data. To generate new graphs, we assume that the nodes and edges of graphs arise under independent Bernoulli distributions and sample new graphs according to their node and edge occurrence probabilities. Empirical evaluations on real-world databases demonstrate the practical utility of the proposed algorithm and show the effectiveness of the generative model for the tasks of graph classification, graph clustering and generating new sample graphs.

Approximate von Neumann entropy for directed graphs.

In this paper, we develop an entropy measure for assessing the structural complexity of directed graphs. Although there are many existing alternative measures for quantifying the structural properties of undirected graphs, there are relatively few corresponding measures for directed graphs. To fill this gap in the literature, we explore an alternative technique that is applicable to directed graphs. We commence by using Chung's generalization of the Laplacian of a directed graph to extend the computation of von Neumann entropy from undirected to directed graphs. We provide a simplified form of the entropy which can be expressed in terms of simple node in-degree and out-degree statistics. Moreover, we find approximate forms of the von Neumann entropy that apply to both weakly and strongly directed graphs, and that can be used to characterize network structure. We illustrate the usefulness of these simplified entropy forms defined in this paper on both artificial and real-world data sets, including structures from protein databases and high energy physics theory citation networks.