Long COVID quality of life and healthcare experiences in the UK: a mixed method online survey.

PURPOSE: The complexity of long COVID and its diverse symptom profile contributes to unprecedented challenges for patients, clinicians, and healthcare services. The threat of long COVID remains ignored by Governments, the media and public health messaging, and patients' experiences must be heard through understanding of the lived experience. This study aimed to understand the lived experience of those living with long COVID. METHODS: An online web-based survey was designed using Patient and Public Involvement and Engagement (PPIE) to increase understanding of the lived experiences of long COVID, and was distributed through PPIE groups, social media, and word of mouth. The survey used closed and open questions relating to demographics, pre- and post-COVID-19 health quality of life, daily activities and long COVID experiences. RESULTS: Within our sample of 132 people living with long COVID, the findings highlight that individuals are being severely impacted by their symptoms and are unable to or limited in participating in their daily activities, reducing quality of life. Long COVID places strain on relationships, the ability to live life fully and is detrimental to mental health. Varying health care experiences are described by participants, with reports of medical gaslighting and inadequate support received. CONCLUSIONS: Long COVID has a severe impact on the ability to live life fully, and strains mental health. The appropriate mechanisms and support services are needed to support those living with long COVID and manage symptoms.

A systematic review of allometric scaling exponents for IgG mAbs.

Increasing complexity of mAbs in development creates challenges in predicting human pharmacokinetic (PK) parameters from preclinical data. The aim of this analysis was to identify optimal allometric scaling exponents.Data were extracted from literature to create a central database (currently the largest available published database) of two-compartment model parameters for mAbs (n = 59) in cynomolgus monkey (CM) and human.Global allometric exponents were calculated and drug-dependent factors were investigated as potential variables in determining the optimal scaling factor.The global exponents for scaling CM mAb PK data were 0.74 (CL), 0.80 (CL with Fc-modified mAbs excluded), 0.44 (CL with Fc-modified mAbs only), 0.71 (Q), 1.12 (V1), and 0.99 (V2). These values are in line with previously published literature values.

The evolution and changing ecology of the African hominid oral microbiome.

The oral microbiome plays key roles in human biology, health, and disease, but little is known about the global diversity, variation, or evolution of this microbial community. To better understand the evolution and changing ecology of the human oral microbiome, we analyzed 124 dental biofilm metagenomes from humans, including Neanderthals and Late Pleistocene to present-day modern humans, chimpanzees, and gorillas, as well as New World howler monkeys for comparison. We find that a core microbiome of primarily biofilm structural taxa has been maintained throughout African hominid evolution, and these microbial groups are also shared with howler monkeys, suggesting that they have been important oral members since before the catarrhine-platyrrhine split ca. 40 Mya. However, community structure and individual microbial phylogenies do not closely reflect host relationships, and the dental biofilms of Homo and chimpanzees are distinguished by major taxonomic and functional differences. Reconstructing oral metagenomes from up to 100 thousand years ago, we show that the microbial profiles of both Neanderthals and modern humans are highly similar, sharing functional adaptations in nutrient metabolism. These include an apparent Homo-specific acquisition of salivary amylase-binding capability by oral streptococci, suggesting microbial coadaptation with host diet. We additionally find evidence of shared genetic diversity in the oral bacteria of Neanderthal and Upper Paleolithic modern humans that is not observed in later modern human populations. Differences in the oral microbiomes of African hominids provide insights into human evolution, the ancestral state of the human microbiome, and a temporal framework for understanding microbial health and disease.

Ecology, Not Host Phylogeny, Shapes the Oral Microbiome in Closely Related Species.

Host-associated microbiomes are essential for a multitude of biological processes. Placed at the contact zone between external and internal environments, the little-studied oral microbiome has important roles in host physiology and health. Here, we investigate the roles of host evolutionary relationships and ecology in shaping the oral microbiome in three closely related gorilla subspecies (mountain, Grauer's, and western lowland gorillas) using shotgun metagenomics of 46 museum-preserved dental calculus samples. We find that the oral microbiomes of mountain gorillas are functionally and taxonomically distinct from the other two subspecies, despite close evolutionary relationships and geographic proximity with Grauer's gorillas. Grauer's gorillas show intermediate bacterial taxonomic and functional, and dietary profiles. Altitudinal differences in gorilla subspecies ranges appear to explain these patterns, suggesting a close connection between dental calculus microbiomes and the environment, likely mediated through diet. This is further supported by the presence of gorilla subspecies-specific phyllosphere/rhizosphere taxa in the oral microbiome. Mountain gorillas show a high abundance of nitrate-reducing oral taxa, which may promote adaptation to a high-altitude lifestyle by modulating blood pressure. Our results suggest that ecology, rather than evolutionary relationships and geographic distribution, shape the oral microbiome in these closely related species.

Quantitative Systems Pharmacology and Machine Learning: A Match Made in Heaven or Hell?

As pharmaceutical development moves from early-stage in vitro experimentation to later in vivo and subsequent clinical trials, data and knowledge are acquired across multiple time and length scales, from the subcellular to whole patient cohort scale. Realizing the potential of this data for informing decision making in pharmaceutical development requires the individual and combined application of machine learning (ML) and mechanistic multiscale mathematical modeling approaches. Here we outline how these two approaches, both individually and in tandem, can be applied at different stages of the drug discovery and development pipeline to inform decision making compound development. The importance of discerning between knowledge and data are highlighted in informing the initial use of ML or mechanistic quantitative systems pharmacology (QSP) models. We discuss the application of sensitivity and structural identifiability analyses of QSP models in informing future experimental studies to which ML may be applied, as well as how ML approaches can be used to inform mechanistic model development. Relevant literature studies are highlighted and we close by discussing caveats regarding the application of each approach in an age of constant data acquisition. SIGNIFICANCE STATEMENT: We consider when best to apply machine learning (ML) and mechanistic quantitative systems pharmacology (QSP) approaches in the context of the drug discovery and development pipeline. We discuss the importance of prior knowledge and data available for the system of interest and how this informs the individual and combined application of ML and QSP approaches at each stage of the pipeline.

Radiation in Combination with Immune Checkpoint Blockade and DNA Damage Response Inhibitors in Mice: Dosage Optimization in MC38 Syngeneic Tumors via Modelling and Simulation.

Clinical trials assessing the impact of radiotherapy (RT) in combination with DNA damage response pathway inhibitors (DDRis) and/or immune checkpoint blockade are currently ongoing. However, current methods for optimizing dosage and schedule are limited. A mathematical model was developed to capture the impacts of RT in combination with DDRi and/or anti-PD-L1 [immune checkpoint inhibitor (ICI)] on tumor immune interactions in the MC38 syngeneic tumor model. The model was fitted to datasets that assessed the impact of RT in combination with the DNA protein kinase inhibitor (DNAPKi) AZD7648. The model was further fitted to datasets from studies that were used to assess both RT/ICI combinations as well as RT/ICI combinations followed by concurrent administration of the poly ADP ribose polymerase inhibitor (PARPi) olaparib. Nonlinear mixed-effects modeling was performed followed by internal validation with visual predictive checks (VPC). Simulations of alternative dosage regimens and scheduling were performed to identify optimal candidate dosage regimens of RT/DNAPKi and RT/PARPi/ICI. Model fits and VPCs confirmed a successful internal validation for both datasets and demonstrated very small differences in the median, lower, and upper percentile values of tumor diameters between RT/ICI and RT/PARPi/ICI, which indicated that the triple combination of RT/PARPi/ICI at the given dosage and schedule does not provide additional benefit compared with ICI in combination with RT. Simulation of alternative dosage regimens indicated that lowering the dosage of ICI to between 2 and 4 mg/kg could induce similar benefits to the full dosage regimen, which could be of translational benefit. SIGNIFICANCE STATEMENT: This work provides a mixed-effects model framework to quantify the effects of combination radiotherapy/DNA damage response pathway inhibitors/immune checkpoint inhibitors in preclinical tumor models and identify optimal dosage regimens, which could be of translational benefit.

Free Drug Theory - No Longer Just a Hypothesis?

The Free Drug Hypothesis is a well-established concept within the scientific lexicon pervading many areas of Drug Discovery and Development, and yet it is poorly defined by virtue of many variations appearing in the literature. Clearly, unbound drug is in dynamic equilibrium with respect to absorption, distribution, metabolism, elimination, and indeed, interaction with the desired pharmacological target. Binding interactions be they specific (e.g. high affinity) or nonspecific (e.g. lower affinity/higher capacity) are governed by the same fundamental physicochemical tenets including Hill-Langmuir Isotherms, the Law of Mass Action and Drug Receptor Theory. With this in mind, it is time to recognise a more coherent version and consider it the Free Drug Theory and a hypothesis no longer. Today, we have the experimental and modelling capabilities, pharmacological knowledge, and an improved understanding of unbound drug distribution (e.g. Kpuu) to raise the bar on our understanding and analysis of experimental data. The burden of proof should be to rule out mechanistic possibilities and/or experimental error before jumping to the conclusion that any observations contradict these fundamentals.

In situ solid-state nanopore fabrication.

Nanopores in solid-state membranes are promising for a wide range of applications including DNA sequencing, ultra-dilute analyte detection, protein analysis, and polymer data storage. Techniques to fabricate solid-state nanopores have typically been time consuming or lacked the resolution to create pores with diameters down to a few nanometres, as required for the above applications. In recent years, several methods to fabricate nanopores in electrolyte environments have been demonstrated. These in situ methods include controlled breakdown (CBD), electrochemical reactions (ECR), laser etching and laser-assisted controlled breakdown (la-CBD). These techniques are democratising solid-state nanopores by providing the ability to fabricate pores with diameters down to a few nanometres (i.e. comparable to the size of many analytes) in a matter of minutes using relatively simple equipment. Here we review these in situ solid-state nanopore fabrication techniques and highlight the challenges and advantages of each method. Furthermore we compare these techniques by their desired application and provide insights into future research directions for in situ nanopore fabrication methods.

Understanding Electrical Conduction and Nanopore Formation During Controlled Breakdown.

Controlled breakdown has recently emerged as a highly appealing technique to fabricate solid-state nanopores for a wide range of biosensing applications. This technique relies on applying an electric field of approximately 0.4-1 V nm-1 across the membrane to induce a current, and eventually, breakdown of the dielectric. Although previous studies have performed controlled breakdown under a range of different conditions, the mechanism of conduction and breakdown has not been fully explored. Here, electrical conduction and nanopore formation in SiNx membranes during controlled breakdown is studied. It is demonstrated that for Si-rich SiNx , oxidation reactions that occur at the membrane-electrolyte interface limit conduction across the dielectric. However, for stoichiometric Si3 N4 the effect of oxidation reactions becomes relatively small and conduction is predominately limited by charge transport across the dielectric. Several important implications resulting from understanding this process are provided which will aid in further developing controlled breakdown in the coming years, particularly for extending this technique to integrate nanopores with on-chip nanostructures.

Targeting Cellular DNA Damage Responses in Cancer: An In Vitro-Calibrated Agent-Based Model Simulating Monolayer and Spheroid Treatment Responses to ATR-Inhibiting Drugs.

We combine a systems pharmacology approach with an agent-based modelling approach to simulate LoVo cells subjected to AZD6738, an ATR (ataxia-telangiectasia-mutated and rad3-related kinase) inhibiting anti-cancer drug that can hinder tumour proliferation by targeting cellular DNA damage responses. The agent-based model used in this study is governed by a set of empirically observable rules. By adjusting only the rules when moving between monolayer and multi-cellular tumour spheroid simulations, whilst keeping the fundamental mathematical model and parameters intact, the agent-based model is first parameterised by monolayer in vitro data and is thereafter used to simulate treatment responses in in vitro tumour spheroids subjected to dynamic drug delivery. Spheroid simulations are subsequently compared to in vivo data from xenografts in mice. The spheroid simulations are able to capture the dynamics of in vivo tumour growth and regression for approximately 8 days post-tumour injection. Translating quantitative information between in vitro and in vivo research remains a scientifically and financially challenging step in preclinical drug development processes. However, well-developed in silico tools can be used to facilitate this in vitro to in vivo translation, and in this article, we exemplify how data-driven, agent-based models can be used to bridge the gap between in vitro and in vivo research. We further highlight how agent-based models, that are currently underutilised in pharmaceutical contexts, can be used in preclinical drug development.

A Robust Framework for Microbial Archaeology.

Microbial archaeology is flourishing in the era of high-throughput sequencing, revealing the agents behind devastating historical plagues, identifying the cryptic movements of pathogens in prehistory, and reconstructing the ancestral microbiota of humans. Here, we introduce the fundamental concepts and theoretical framework of the discipline, then discuss applied methodologies for pathogen identification and microbiome characterization from archaeological samples. We give special attention to the process of identifying, validating, and authenticating ancient microbes using high-throughput DNA sequencing data. Finally, we outline standards and precautions to guide future research in the field.

Identifying and characterising the impact of excitability in a mathematical model of tumour-immune interactions.

We study a five-compartment mathematical model originally proposed by Kuznetsov et al. (1994) to investigate the effect of nonlinear interactions between tumour and immune cells in the tumour microenvironment, whereby immune cells may induce tumour cell death, and tumour cells may inactivate immune cells. Exploiting a separation of timescales in the model, we use the method of matched asymptotics to derive a new two-dimensional, long-timescale, approximation of the full model, which differs from the quasi-steady-state approximation introduced by Kuznetsov et al. (1994), but is validated against numerical solutions of the full model. Through a phase-plane analysis, we show that our reduced model is excitable, a feature not traditionally associated with tumour-immune dynamics. Through a systematic parameter sensitivity analysis, we demonstrate that excitability generates complex bifurcating dynamics in the model. These are consistent with a variety of clinically observed phenomena, and suggest that excitability may underpin tumour-immune interactions. The model exhibits the three stages of immunoediting - elimination, equilibrium, and escape, via stable steady states with different tumour cell concentrations. Such heterogeneity in tumour cell numbers can stem from variability in initial conditions and/or model parameters that control the properties of the immune system and its response to the tumour. We identify different biophysical parameter targets that could be manipulated with immunotherapy in order to control tumour size, and we find that preferred strategies may differ between patients depending on the strength of their immune systems, as determined by patient-specific values of associated model parameters.

Micro Methods for Megafauna: Novel Approaches to Late Quaternary Extinctions and Their Contributions to Faunal Conservation in the Anthropocene.

Drivers of Late Quaternary megafaunal extinctions are relevant to modern conservation policy in a world of growing human population density, climate change, and faunal decline. Traditional debates tend toward global solutions, blaming either dramatic climate change or dispersals of Homo sapiens to new regions. Inherent limitations to archaeological and paleontological data sets often require reliance on scant, poorly resolved lines of evidence. However, recent developments in scientific technologies allow for more local, context-specific approaches. In the present article, we highlight how developments in five such methodologies (radiocarbon approaches, stable isotope analysis, ancient DNA, ancient proteomics, microscopy) have helped drive detailed analysis of specific megafaunal species, their particular ecological settings, and responses to new competitors or predators, climate change, and other external phenomena. The detailed case studies of faunal community composition, extinction chronologies, and demographic trends enabled by these methods examine megafaunal extinctions at scales appropriate for practical understanding of threats against particular species in their habitats today.

Complete Substrate Inhibition of Cytochrome P450 2C8 by AZD9496, an Oral Selective Estrogen Receptor Degrader.

AZD9496 ((E)-3-(3,5-difluoro-4-((1R,3R)-2-(2-fluoro-2-methylpropyl)-3-methyl-2,3,4,9-tetrahydro-1H-pyrido[3,4-b]indol-1-yl)phenyl)acrylic acid) is an oral selective estrogen receptor degrader currently in clinical development for treatment of estrogen receptor-positive breast cancer. In a first-in-human phase 1 study, AZD9496 exhibited dose nonlinear pharmacokinetics, the mechanistic basis of which was investigated in this study. The metabolism kinetics of AZD9496 were studied using human liver microsomes (HLMs), recombinant cytochrome P450s (rP450s), and hepatocytes. In addition, modeling approaches were used to gain further mechanistic insights. CYP2C8 was predominantly responsible for biotransformation of AZD9496 to its two main metabolites whose rate of formation with increasing AZD9496 concentrations exhibited complete substrate inhibition in HLM, rCYP2C8, and hepatocytes. Total inhibition by AZD9496 of amodiaquine N-deethylation, a specific probe of CYP2C8 activity, confirmed the completeness of this inhibition. The commonly used substrate inhibition model analogous to uncompetitive inhibition fit poorly to the data. However, using the same model but without constraints on the number of molecules occupying the inhibitory binding site (i.e., nS1ES) provided a significantly better fit (F test, P< 0.005). With the improved model, up to three AZD9496 molecules were predicted to bind the inhibitory site of CYP2C8. In contrast to previous studies showing substrate inhibition of P450s to be partial, our results demonstrate complete substrate inhibition of CYP2C8 via binding of more than one molecule of AZD9496 to the inhibitory site. As CYP2C8 appears to be the sole isoform catalyzing formation of the main metabolites, the substrate inhibition might explain the observed dose nonlinearity in the clinic at higher doses.

Structural identifiability for mathematical pharmacology: models of myelosuppression.

Structural identifiability is an often overlooked, but essential, prerequisite to the experiment design stage. The application of structural identifiability analysis to models of myelosuppression is used to demonstrate the importance of its considerations. It is shown that, under certain assumptions, these models are structurally identifiable and so drug and system specific parameters can truly be separated. Further it is shown via a meta-analysis of the literature that because of this the reported system parameter estimates for the "Friberg" or "Uppsala" model are consistent in the literature.

Oxygen-Driven Tumour Growth Model: A Pathology-Relevant Mathematical Approach.

Xenografts--as simplified animal models of cancer-differ substantially in vasculature and stromal architecture when compared to clinical tumours. This makes mathematical model-based predictions of clinical outcome challenging. Our objective is to further understand differences in tumour progression and physiology between animal models and the clinic. To achieve that, we propose a mathematical model based upon tumour pathophysiology, where oxygen--as a surrogate for endocrine delivery--is our main focus. The Oxygen-Driven Model (ODM), using oxygen diffusion equations, describes tumour growth, hypoxia and necrosis. The ODM describes two key physiological parameters. Apparent oxygen uptake rate (k'R) represents the amount of oxygen cells seem to need to proliferate. The more oxygen they appear to need, the more the oxygen transport. k'R gathers variability from the vasculature, stroma and tumour morphology. Proliferating rate (kp) deals with cell line specific factors to promote growth. The KH,KN describe the switch of hypoxia and necrosis. Retrospectively, using archived data, we looked at longitudinal tumour volume datasets for 38 xenografted cell lines and 5 patient-derived xenograft-like models. Exploration of the parameter space allows us to distinguish 2 groups of parameters. Group 1 of cell lines shows a spread in values of k'R and lower kp, indicating that tumours are poorly perfused and slow growing. Group 2 share the value of the oxygen uptake rate (k'R) and vary greatly in kp, which we interpret as having similar oxygen transport, but more tumour intrinsic variability in growth. However, the ODM has some limitations when tested in explant-like animal models, whose complex tumour-stromal morphology may not be captured in the current version of the model. Incorporation of stroma in the ODM will help explain these discrepancies. We have provided an example. The ODM is a very simple -and versatile- model suitable for the design of preclinical experiments, which can be modified and enhanced whilst maintaining confidence in its predictions.

Development of 1RM Prediction Equations for Bench Press in Moderately Trained Men.

Macht, JW, Abel, MG, Mullineaux, DR, and Yates, JW. Development of 1RM prediction equations for bench press in moderately trained men. J Strength Cond Res 30(10): 2901-2906, 2016-There are a variety of established 1 repetition maximum (1RM) prediction equations, however, very few prediction equations use anthropometric characteristics exclusively or in part, to estimate 1RM strength. Therefore, the purpose of this study was to develop an original 1RM prediction equation for bench press using anthropometric and performance characteristics in moderately trained male subjects. Sixty male subjects (21.2 ± 2.4 years) completed a 1RM bench press and were randomly assigned a load to complete as many repetitions as possible. In addition, body composition, upper-body anthropometric characteristics, and handgrip strength were assessed. Regression analysis was used to develop a performance-based 1RM prediction equation: 1RM = 1.20 repetition weight + 2.19 repetitions to fatigue - 0.56 biacromial width (cm) + 9.6 (R = 0.99, standard error of estimate [SEE] = 3.5 kg). Regression analysis to develop a nonperformance-based 1RM prediction equation yielded: 1RM (kg) = 0.997 cross-sectional area (CSA) (cm) + 0.401 chest circumference (cm) - 0.385%fat - 0.185 arm length (cm) + 36.7 (R = 0.81, SEE = 13.0 kg). The performance prediction equations developed in this study had high validity coefficients, minimal mean bias, and small limits of agreement. The anthropometric equations had moderately high validity coefficient but larger limits of agreement. The practical applications of this study indicate that the inclusion of anthropometric characteristics and performance variables produce a valid prediction equation for 1RM strength. In addition, the CSA of the arm uses a simple nonperformance method of estimating the lifter's 1RM. This information may be used to predict the starting load for a lifter performing a 1RM prediction protocol or a 1RM testing protocol.

An early cytoplasmic step of peptidoglycan synthesis is associated to MreB in Bacillus subtilis.

MreB proteins play a major role during morphogenesis of rod-shaped bacteria by organizing biosynthesis of the peptidoglycan cell wall. However, the mechanisms underlying this process are not well understood. In Bacillus subtilis, membrane-associated MreB polymers have been shown to be associated to elongation-specific complexes containing transmembrane morphogenetic factors and extracellular cell wall assembly proteins. We have now found that an early intracellular step of cell wall synthesis is also associated to MreB. We show that the previously uncharacterized protein YkuR (renamed DapI) is required for synthesis of meso-diaminopimelate (m-DAP), an essential constituent of the peptidoglycan precursor, and that it physically interacts with MreB. Highly inclined laminated optical sheet microscopy revealed that YkuR forms uniformly distributed foci that exhibit fast motion in the cytoplasm, and are not detected in cells lacking MreB. We propose a model in which soluble MreB organizes intracellular steps of peptidoglycan synthesis in the cytoplasm to feed the membrane-associated cell wall synthesizing machineries.

Systems toxicology: modelling biomarkers of glutathione homeostasis and paracetamol metabolism.

One aim of systems toxicology is to deliver mechanistic, mathematically rigorous, models integrating biochemical and pharmacological processes that result in toxicity to enhance the assessment of the risk posed to humans by drugs and other xenobiotics. The benefits of such 'in silico' models would be in enabling the rapid and robust prediction of the effects of compounds over a range of exposures, improving in vitro-in vivo correlations and the translation from preclinical species to humans. Systems toxicology models of organ toxicities that result in high attrition rates during drug discovery and development, or post-marketing withdrawals (e.g., drug-induced liver injury (DILI)) should facilitate the discovery of safe new drugs. Here, systems toxicology as applied to the effects of paracetamol (acetaminophen, N-acetyl-para-aminophenol (APAP)) is used to exemplify the potential of the approach.