How Can Viral Dynamics Models Inform Endpoint Measures in Clinical Trials of Therapies for Acute Viral Infections?

Acute viral infections pose many practical challenges for the accurate assessment of the impact of novel therapies on viral growth and decay. Using the example of influenza A, we illustrate how the measurement of infection-related quantities that determine the dynamics of viral load within the human host, can inform investigators on the course and severity of infection and the efficacy of a novel treatment. We estimated the values of key infection-related quantities that determine the course of natural infection from viral load data, using Markov Chain Monte Carlo methods. The data were placebo group viral load measurements collected during volunteer challenge studies, conducted by Roche, as part of the oseltamivir trials. We calculated the values of the quantities for each patient and the correlations between the quantities, symptom severity and body temperature. The greatest variation among individuals occurred in the viral load peak and area under the viral load curve. Total symptom severity correlated positively with the basic reproductive number. The most sensitive endpoint for therapeutic trials with the goal to cure patients is the duration of infection. We suggest laboratory experiments to obtain more precise estimates of virological quantities that can supplement clinical endpoint measurements

Using Clinical Trial Simulators to Analyse the Sources of Variance in Clinical Trials of Novel Therapies for Acute Viral Infections.

BACKGROUND:About 90% of drugs fail in clinical development. The question is whether trials fail because of insufficient efficacy of the new treatment, or rather because of poor trial design that is unable to detect the true efficacy. The variance of the measured endpoints is a major, largely underestimated source of uncertainty in clinical trial design, particularly in acute viral infections. We use a clinical trial simulator to demonstrate how a thorough consideration of the variability inherent in clinical trials of novel therapies for acute viral infections can improve trial design. METHODS AND FINDINGS:We developed a clinical trial simulator to analyse the impact of three different types of variation on the outcome of a challenge study of influenza treatments for infected patients, including individual patient variability in the response to the drug, the variance of the measurement procedure, and the variance of the lower limit of quantification of endpoint measurements. In addition, we investigated the impact of protocol variation on clinical trial outcome. We found that the greatest source of variance was inter-individual variability in the natural course of infection. Running a larger phase II study can save up to $38 million, if an unlikely to succeed phase III trial is avoided. In addition, low-sensitivity viral load assays can lead to falsely negative trial outcomes. CONCLUSIONS:Due to high inter-individual variability in natural infection, the most important variable in clinical trial design for challenge studies of potential novel influenza treatments is the number of participants. 100 participants are preferable over 50. Using more sensitive viral load assays increases the probability of a positive trial outcome, but may in some circumstances lead to false positive outcomes. Clinical trial simulations are powerful tools to identify the most important sources of variance in clinical trials and thereby help improve trial design

Biomimetic mineralization of metal-organic frameworks as protective coatings for biomacromolecules

Enhancing the robustness of functional biomacromolecules is a critical challenge in biotechnology, which if addressed would enhance their use in pharmaceuticals, chemical processing and biostorage. Here we report a novel method, inspired by natural biomineralization processes, which provides unprecedented protection of biomacromolecules by encapsulating them within a class of porous materials termed metal-organic frameworks. We show that proteins, enzymes and DNA rapidly induce the formation of protective metal-organic framework coatings under physiological conditions by concentrating the framework building blocks and facilitating crystallization around the biomacromolecules. The resulting biocomposite is stable under conditions that would normally decompose many biological macromolecules. For example, urease and horseradish peroxidase protected within a metal-organic framework shell are found to retain bioactivity after being treated at 80 °C and boiled in dimethylformamide (153 °C), respectively. This rapid, low-cost biomimetic mineralization process gives rise to new possibilities for the exploitation of biomacromolecules.Kang Liang, Raffaele Ricco, Cara M. Doherty, Mark J. Styles, Stephen Bell, Nigel Kirby, Stephen Mudie, David Haylock, Anita J. Hill, Christian J. Doonan, Paolo Falcar

Understanding the within-host dynamics of influenza A virus: from theory to clinical implications

Mathematical models have provided important insights into acute viral dynamics within individual patients. In this paper, we study the simplest target cell-limited models to investigate the within-host dynamics of influenza A virus infection in humans. Despite the biological simplicity of the models, we show how these can be used to understand the severity of the infection and the key attributes of possible immunotherapy and antiviral drugs for the treatment of infection at different times post infection. Through an analytic approach, we derive and estimate simple summary biological quantities that can provide novel insights into the infection dynamics and the definition of clinical endpoints. We focus on nine quantities, including the area under the viral load curve, peak viral load, the time to peak viral load and the level of cell death due to infection. Using Markov chain Monte Carlo methods, we fitted the models to data collected from 12 untreated volunteers who participated in two clinical studies that tested the antiviral drugs oseltamivir and zanamivir. Based on the results, we also discuss various difficulties in deriving precise estimates of the parameters, even in the very simple models considered, when experimental data are limited to viral load measures and/or there is a limited number of viral load measurements post infection

Contextual factors influencing the equitable implementation of precision medicine in routine cancer care in Belgium

Background Precision medicine represents a paradigm shift in health systems, moving from a one-size-fits-all approach to a more individualized form of care, spanning multiple scientific disciplines including drug discovery, genomics, and health communication. This study aims to explore the contextual factors influencing the equitable implementation of precision medicine in Belgium for incorporating precision medicine into routine cancer care within the Belgian health&nbsp;system. Methods As part of a foresight study, our approach evaluates critical factors affecting the implementation of precision oncology. The study scrutinizes contextual, i.e. demographic, economic, societal, technological, environmental, and political/policy-related (DESTEP) factors, identified through a comprehensive literature review and validated by a multidisciplinary group at the Belgian Cancer Center, Sciensano. An expert survey further assesses the importance and likelihood of these factors, illuminating potential barriers and facilitators to&nbsp;implementation. Results Based on the expert survey, five key elements (rising cancer rates, dedicated healthcare reimbursement budgets, increasing healthcare expenditures, advanced information technology solutions for data transfer, and demand for high-quality data) are expected to influence the equitable implementation of precision medicine in routine cancer care in Belgium in the&nbsp;future. Conclusions This work contributes to the knowledge base on precision medicine in Belgium and public health foresight, exploring the implementation challenges and suggesting solutions with an emphasis on the importance of comparative analyses of health systems, evaluation of health technology assessment methods, and the exploration of ethical issues in data privacy and&nbsp;equity.</p

Accurate ground-state potential energy surfaces of the C2H2–Kr and C2H2–Xe van der Waals complexes

Accurate ab initio intermolecular potential energy surfaces (IPES) have been obtained for the first time for the ground electronic state of the C 2H2-Kr and C2H2-Xe van der Waals complexes. Extensive tests, including complete basis set and all-electron scalar relativistic results, support their calculation at the CCSD(T) level of theory, using small-core relativistic pseudopotentials for the rare-gas atoms and aug-cc-pVQZ basis sets extended with a set of 3s3p2d1f1g mid-bond functions. All results are corrected for the basis set superposition error. The importance of the scalar relativistic and rare-gas outer-core (n.1)d correlation effects is investigated. The calculated IPES, adjusted to analytical functions, are characterized by global minima corresponding to skew T-shaped geometries, in which the Jacobi vector positioning the rare-gas atom with respect to the center of mass of the C2H2 moiety corresponds to distances of 4.064 and 4.229Å, and angles of 65.22° and 68.67° for C 2H2-Kr and C2H2-Xe, respectively. The interaction energy of both complexes is estimated to be -151.88 (1.817 kJ mol-1) and -182.76 cm-1 (2.186 kJ mol-1), respectively. The evolution of the topology of the IPES as a function of the rare-gas atom, from He to Xe, is also discussed. © 2012 Taylor and Francis.SCOPUS: cp.jinfo:eu-repo/semantics/publishe

Unlocking the genomic landscape: Results of the Beyond 1 Million Genomes (B1MG) pilot in Belgium towards Genomic Data Infrastructure (GDI)

Abstract Genomic medicine has great potential to offer insights into how humans’ genetic variation can affect their health, prevention options and treatment responses. The Beyond 1 Million Genomes (B1MG) project was kicked off in 2020 with the aim of building a federated network of genomic data in Europe, in which Belgium took part as a piloting country. B1MG developed a framework to enable all interested countries to self-evaluate the level of maturity of national genomic medicine practices following a common matrix, called Maturity Level Model (MLM), that contained 49 indicators across eight domains: I. Governance and strategy; II. Investment and economic model; III. Ethics, legislation and policy; IV. Public awareness and acceptance; V. Workforce skills and organisation; VI. Clinical organisation, infrastructure and tools; VII. Clinical genomics guidelines and infrastructure; and VIII. Data management, standards and infrastructure. The ongoing Genomic Data Infrastructure (GDI) project aims to capitalise on the experience of B1MG piloting countries and their MLM results. In this paper, we present the qualitative and quantitative outcomes of B1MG MLM assessment in Belgium and discuss their relevance to GDI. The insights gained from this study can be helpful for steering future policy directions and interventions on genomics in Belgium and&nbsp;beyond.</p

Accessing Photoredox Transformations with an Iron(III) Photosensitizer and Green Light

Efficient excited-state electron transfer between an iron(III) photosensitizer and organic electron donors was realized with green light irradiation. This advance was enabled by the use of the previously reported iron photosensitizer, [Fe(phtmeimb)2]+ (phtmeimb = {phenyl[tris(3-methyl-imidazolin-2-ylidene)]borate}, that exhibited long-lived and luminescent ligand-to-metal charge-transfer (LMCT) excited states. A benchmark dehalogenation reaction was investigated with yields that exceed 90% and an enhanced stability relative to the prototypical photosensitizer [Ru(bpy)3]2+. The initial catalytic step is electron transfer from an amine to the photoexcited iron sensitizer, which is shown to occur with a large cage-escape yield. For LMCT excited states, this reductive electron transfer is vectorial and may be a general advantage of Fe(III) photosensitizers. In-depth time-resolved spectroscopic methods, including transient absorption characterization from the ultraviolet to the infrared regions, provided a quantitative description of the catalytic mechanism with associated rate constants and yields

Mechanistic investigation of a visible light mediated dehalogenation/cyclisation reaction using iron(iii), iridium(iii) and ruthenium(ii) photosensitizers

The mechanism of a visible light-driven dehalogenation/cyclization reaction was investigated using ruthenium(II), iridium(III) and iron(III) photosensitizers by means of steady-state photoluminescence, time-resolved infrared spectroscopy, and nanosecond/femtosecond transient absorption spectroscopy. The nature of the photosensitizer was found to influence the product distribution such that the dehalogenated, non-cyclized products were only detected for the iron photosensitizer. Strikingly, with the iron photosensitizer, large catalytic yields required a low dielectric solvent such as dichloromethane, consistent with a previous publication. This low dielectric solvent allowed ultrafast charge-separation to outcompete geminate charge recombination and improved cage escape efficiency. Further, the identification of reaction mechanisms unique to the iron, ruthenium, and iridium photosensitizer represents progress towards the long-sought goal of utilizing earth-abundant, first-row transition metals for emerging energy and environmental applications

Homologous and Heterologous Prime-Boost Vaccination: Impact on Clinical Severity of SARS-CoV-2 Omicron Infection among Hospitalized COVID-19 Patients in Belgium

We investigated effectiveness of (1) mRNA booster vaccination versus primary vaccination only and (2) heterologous (viral vector&ndash;mRNA) versus homologous (mRNA&ndash;mRNA) prime-boost vaccination against severe outcomes of BA.1, BA.2, BA.4 or BA.5 Omicron infection (confirmed by whole genome sequencing) among hospitalized COVID-19 patients using observational data from national COVID-19 registries. In addition, it was investigated whether the difference between the heterologous and homologous prime-boost vaccination was homogenous across Omicron sub-lineages. Regression standardization (parametric g-formula) was used to estimate counterfactual risks for severe COVID-19 (combination of severity indicators), intensive care unit (ICU) admission, and in-hospital mortality under exposure to different vaccination schedules. The estimated risk for severe COVID-19 and in-hospital mortality was significantly lower with an mRNA booster vaccination as compared to only a primary vaccination schedule (RR = 0.59 [0.33; 0.85] and RR = 0.47 [0.15; 0.79], respectively). No significance difference was observed in the estimated risk for severe COVID-19, ICU admission and in-hospital mortality with a heterologous compared to a homologous prime-boost vaccination schedule, and this difference was not significantly modified by the Omicron sub-lineage. Our results support evidence that mRNA booster vaccination reduced the risk of severe COVID-19 disease during the Omicron-predominant period