29 results found
Thomas DX, Bajaj S, McRae-McKee K, et al., 2020, Association of TDP-43 proteinopathy, cerebral amyloid angiopathy, and Lewy bodies with cognitive impairment in individuals with or without Alzheimer's disease neuropathology, Scientific Reports, Vol: 10, ISSN: 2045-2322
Alzheimer's disease patients typically present with multiple co-morbid neuropathologies at autopsy, but the impact of these pathologies on cognitive impairment during life is poorly understood. In this study, we developed cognitive trajectories for patients with common co-pathologies in the presence and absence of Alzheimer's disease neuropathology. Cognitive trajectories were modelled in a Bayesian hierarchical regression framework to estimate the effects of each neuropathology on cognitive decline as assessed by the mini-mental state examination and the clinical dementia rating scale sum of boxes scores. We show that both TDP-43 proteinopathy and cerebral amyloid angiopathy associate with cognitive impairment of similar magnitude to that associated with Alzheimer's disease neuropathology. Within our study population, 63% of individuals given the 'gold-standard' neuropathological diagnosis of Alzheimer's disease in fact possessed either TDP-43 proteinopathy or cerebral amyloid angiopathy of sufficient severity to independently explain the majority of their cognitive impairment. This suggests that many individuals diagnosed with Alzheimer's disease may actually suffer from a mixed dementia, and therapeutics targeting only Alzheimer's disease-related processes may have severely limited efficacy in these co-morbid populations.
Wolters FJ, Chibnik LB, Waziry R, et al., 2020, Twenty-seven-year time trends in dementia incidence in Europe and the United States: The Alzheimer Cohorts Consortium, Neurology, Vol: 95, Pages: e519-e531, ISSN: 0028-3878
OBJECTIVE: To determine changes in the incidence of dementia between 1988 and 2015. METHODS: This analysis was performed in aggregated data from individuals >65 years of age in 7 population-based cohort studies in the United States and Europe from the Alzheimer Cohort Consortium. First, we calculated age- and sex-specific incidence rates for all-cause dementia, and then defined nonoverlapping 5-year epochs within each study to determine trends in incidence. Estimates of change per 10-year interval were pooled and results are presented combined and stratified by sex. RESULTS: Of 49,202 individuals, 4,253 (8.6%) developed dementia. The incidence rate of dementia increased with age, similarly for women and men, ranging from about 4 per 1,000 person-years in individuals aged 65-69 years to 65 per 1,000 person-years for those aged 85-89 years. The incidence rate of dementia declined by 13% per calendar decade (95% confidence interval [CI], 7%-19%), consistently across studies, and somewhat more pronouncedly in men than in women (24% [95% CI 14%-32%] vs 8% [0%-15%]). CONCLUSION: The incidence rate of dementia in Europe and North America has declined by 13% per decade over the past 25 years, consistently across studies. Incidence is similar for men and women, although declines were somewhat more profound in men. These observations call for sustained efforts to finding the causes for this decline, as well as determining their validity in geographically and ethnically diverse populations.
Hadjichrysanthou C, Evans S, Bajaj S, et al., 2020, The dynamics of biomarkers across the clinical spectrum of Alzheimer's disease, Alzheimers Research & Therapy, Vol: 12, Pages: 1-16, ISSN: 1758-9193
BackgroundQuantifying changes in the levels of biological and cognitive markers prior to the clinical presentation of Alzheimer’s disease (AD) will provide a template for understanding the underlying aetiology of the clinical syndrome and, concomitantly, for improving early diagnosis, clinical trial recruitment and treatment assessment. This study aims to characterise continuous changes of such markers and determine their rate of change and temporal order throughout the AD continuum.MethodsThe methodology is founded on the development of stochastic models to estimate the expected time to reach different clinical disease states, for different risk groups, and synchronise short-term individual biomarker data onto a disease progression timeline. Twenty-seven markers are considered, including a range of cognitive scores, cerebrospinal (CSF) and plasma fluid proteins, and brain structural and molecular imaging measures. Data from 2014 participants in the Alzheimer’s Disease Neuroimaging Initiative database is utilised.ResultsThe model suggests that detectable memory dysfunction could occur up to three decades prior to the onset of dementia due to AD (ADem). This is closely followed by changes in amyloid-β CSF levels and the first cognitive decline, as assessed by sensitive measures. Hippocampal atrophy could be observed as early as the initial amyloid-β accumulation. Brain hypometabolism starts later, about 14 years before onset, along with changes in the levels of total and phosphorylated tau proteins. Loss of functional abilities occurs rapidly around ADem onset. Neurofilament light is the only protein with notable early changes in plasma levels. The rate of change varies, with CSF, memory, amyloid PET and brain structural measures exhibiting the highest rate before the onset of ADem, followed by a decline. The probability of progressing to a more severe clinical state increases almost exponentially with age. In accordance with previous stu
Evans S, McRae-McKee K, Hadjichrysanthou C, et al., 2019, Alzheimer's disease progression and risk factors: A standardized comparison between six large data sets., Alzheimers & Dementia, Vol: 5, Pages: 515-523, ISSN: 1552-5260
There exist a large number of cohort studies that have been used to identify genetic and biological risk factors for developing Alzheimer's disease (AD). However, there is a disagreement between studies as to how strongly these risk factors affect the rate of progression through diagnostic groups toward AD. We have calculated the probability of transitioning through diagnostic groups in six studies and considered how uncertainty around the strength of the effect of these risk factors affects estimates of the distribution of individuals in each diagnostic group in an AD clinical trial simulator. In this work, we identify the optimal choice of widely collected variables for comparing data sets and calculating probabilities of progression toward AD. We use the estimated transition probabilities to inform stochastic simulations of AD progression that are based on a Markov model and compare predicted incidence rates to those in a community-based study, the Cardiovascular Health Study.
McRae-McKee K, Chinedu T, Udeh-Momoh CT, et al., 2019, Perspective: Clinical relevance of the dichotomous classification of Alzheimer’s disease biomarkers: Should there be a “grey zone”?, Alzheimers & Dementia, Vol: 15, Pages: 1348-1356, ISSN: 1552-5260
The 2018 National Institute on Aging and the Alzheimer's Association (NIA-AA) research framework recently redefined Alzheimer's disease (AD) as a biological construct, based on in vivo biomarkers reflecting key neuropathologic features. Combinations of normal/abnormal levels of three biomarker categories, based on single thresholds, form the AD signature profile that defines the biological disease state as a continuum, independent of clinical symptomatology. While single thresholds may be useful in defining the biological signature profile, we provide evidence that their use in studies with cognitive outcomes merits further consideration. Using data from the Alzheimer's Disease Neuroimaging Initiative with a focus on cortical amyloid binding, we discuss the limitations of applying the biological definition of disease status as a tool to define the increased likelihood of the onset of the Alzheimer's clinical syndrome and the effects that this may have on trial study design. We also suggest potential research objectives going forward and what the related data requirements would be.
McRae-McKee K, Evans S, Hadjichrysanthou C, et al., 2019, Combining hippocampal volume metrics to better understand Alzheimer's disease progression in at-risk individuals, Scientific Reports, Vol: 9, ISSN: 2045-2322
To date nearly all clinical trials of Alzheimer’s disease (AD) therapies have failed. These failures are, at least in part, attributable to poor endpoint choice and to inadequate recruitment criteria. Recently, focus has shifted to targeting at-risk populations in the preclinical stages of AD thus improved predictive markers for identifying individuals likely to progress to AD are crucial to help inform the sample of individuals to be recruited into clinical trials. We focus on hippocampal volume (HV) and assess the added benefit of combining HV and rate of hippocampal atrophy over time in relation to disease progression. Following the cross-validation of previously published estimates of the predictive value of HV, we consider a series of combinations of HV metrics and show that a combination of HV and rate of hippocampal atrophy characterises disease progression better than either measure individually. Furthermore, we demonstrate that the risk of disease progression associated with HV metrics does not differ significantly between clinical states. HV and rate of hippocampal atrophy should therefore be used in tandem when describing AD progression in at-risk individuals. Analyses also suggest that the effects of HV metrics are constant across the continuum of the early stages of the disease.
Overton CE, Broom M, Hadjichrysanthou C, et al., 2019, Methods for approximating stochastic evolutionary dynamics on graphs, Journal of Theoretical Biology, Vol: 468, Pages: 45-59, ISSN: 0022-5193
Population structure can have a significant effect on evolution. For some systems with sufficient symmetry, analytic results can be derived within the mathematical framework of evolutionary graph theory which relate to the outcome of the evolutionary process. However, for more complicated heterogeneous structures, computationally intensive methods are required such as individual-based stochastic simulations. By adapting methods from statistical physics, including moment closure techniques, we first show how to derive existing homogenised pair approximation models and the exact neutral drift model. We then develop node-level approximations to stochastic evolutionary processes on arbitrarily complex structured populations represented by finite graphs, which can capture the different dynamics for individual nodes in the population. Using these approximations, we evaluate the fixation probability of invading mutants for given initial conditions, where the dynamics follow standard evolutionary processes such as the invasion process. Comparisons with the output of stochastic simulations reveal the effectiveness of our approximations in describing the stochastic processes and in predicting the probability of fixation of mutants on a wide range of graphs. Construction of these models facilitates a systematic analysis and is valuable for a greater understanding of the influence of population structure on evolutionary processes.
Anderson RM, Hadjichrysanthou C, Evans S, et al., 2019, Unsuccessful trials of therapies for Alzheimer's disease Reply, LANCET, Vol: 393, Pages: 29-30, ISSN: 0140-6736
Anderson RM, Hadjichrysanthou C, Evans S, et al., 2019, Unsuccessful trials of therapies for Alzheimer's disease - Authors' reply, Lancet, Vol: 393, Pages: 29-30, ISSN: 0140-6736
Hadjichrysanthou C, McRae-McKee K, Evans S, et al., 2018, Potential factors associated with cognitive improvement of individuals diagnosed with mild cognitive impairment or dementia in longitudinal studies, Journal of Alzheimer's Disease, Vol: 66, Pages: 587-600, ISSN: 1387-2877
Despite the progressive nature of Alzheimer’s disease and other dementias, it is observed that many individuals that are diagnosed with mild cognitive impairment (MCI) in one clinical assessment, may return back to normal cognition (CN) in a subsequent assessment. Less frequently, such ‘back-transitions’ are also observed in people that had already been diagnosed with later stages of dementia. In this study, an analysis was performed on two longitudinal cohort datasets provided by 1) the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 2) the National Alzheimer’s Coordinating Centre (NACC). The focus is on the observed improvement of individuals’ clinical condition recorded in these datasets to explore potential associations with different factors. It is shown that, in both datasets, transitions from MCI to CN are significantly associated with younger age, better cognitive function, and the absence of ApoE ɛ4 alleles. Better cognitive function and in some cases the absence of ApoE ɛ4 alleles are also significantly associated with transitions from types of dementia to less severe clinical states. The effect of gender and education is not clear-cut in these datasets, although highly educated people who reach MCI tend to be more likely to show an improvement in their clinical state. The potential effect of other factors such as changes in symptoms of depression is also discussed. Although improved clinical outcomes can be associated with many factors, better diagnostic tools are required to provide insight into whether such improvements are a result of misdiagnosis, and if they are not, whether they are linked to improvements in the underlying neuropathological condition.
Ower AK, Hadjichrysanthou C, Gras L, et al., 2018, Temporal association patterns and dynamics of amyloid-β and tau in Alzheimer's disease, European Journal of Epidemiology, Vol: 33, Pages: 657-666, ISSN: 0393-2990
The elusive relationship between underlying pathology and clinical disease hampers diagnosis of Alzheimer's disease (AD) and preventative intervention development. We seek to understand the relationship between two classical AD biomarkers, amyloid-β1-42 (Aβ1-42) and total-tau (t-tau), and define their trajectories across disease development, as defined by disease onset at diagnosis of mild cognitive impairment (MCI). Using longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we performed a correlation analysis of biomarkers CSF Aβ1-42 and t-tau, and longitudinal quantile analysis. Using a mixed effects model, with MCI onset as an anchor, we develop linear trajectories to describe the rate of change across disease development. These trajectories were extended through the incorporation of data from cognitively normal, healthy adults (aged 20-62 years) from the literature, to fit sigmoid curves by means of non-linear least squares estimators, to create curves encompassing the 50 years prior to MCI onset. A strong right-angled relationship between the biomarkers Aβ1-42 and t-tau is detected, implying a highly non-linear relationship. The rate of change of Aβ1-42 is correlated with the baseline concentration per quantile, reflecting a reduction in the rate of loss across disease within subjects. Regression models reveal significant amyloid loss relative to MCI onset (- 2.35 pg/mL/year), compared to minimal loss relative to AD onset (- 0.97 pg/mL/year). Tau accumulates consistently relative to MCI and AD onset, (2.05 pg/mL/year) and (2.46 pg/mL/year), respectively. The fitted amyloid curve shows peak loss of amyloid 8.06 years prior to MCI diagnosis, while t-tau exhibits peak accumulation 14.17 years following MCI diagnosis, with the upper limit not yet reached 30 years post diagnosis. Biomarker trajectories aid unbiased, objective assessment of disease progression. Q
Evans S, McRae-McKee K, Wong MM, et al., 2018, The importance of endpoint selection: how effective does a drug need to be for success in a clinical trial of a possible Alzheimer's disease treatment?, European Journal of Epidemiology, Vol: 33, Pages: 635-644, ISSN: 0393-2990
To date, Alzheimer's disease (AD) clinical trials have been largely unsuccessful. Failures have been attributed to a number of factors including ineffective drugs, inadequate targets, and poor trial design, of which the choice of endpoint is crucial. Using data from the Alzheimer's Disease Neuroimaging Initiative, we have calculated the minimum detectable effect size (MDES) in change from baseline of a range of measures over time, and in different diagnostic groups along the AD development trajectory. The Functional Activities Questionnaire score had the smallest MDES for a single endpoint where an effect of 27% could be detected within 3 years in participants with Late Mild Cognitive Impairment (LMCI) at baseline, closely followed by the Clinical Dementia Rating Sum of Boxes (CDRSB) score at 28% after 2 years in the same group. Composite measures were even more successful than single endpoints with an MDES of 21% in 3 years. Using alternative cognitive, imaging, functional, or composite endpoints, and recruiting patients that have LMCI could improve the success rate of AD clinical trials.
Hadjichrysanthou C, Ower AK, de Wolf F, et al., 2018, The development of a stochastic mathematical model of Alzheimer's disease to help improve the design of clinical trials of potential treatments, PLOS ONE, Vol: 13, ISSN: 1932-6203
Alzheimer’sdisease(AD)is a neurodegenerative disordercharacterisedbya slowprogres-sivedeteriorationof cognitivecapacity.Drugsareurgentlyneededforthetreatmentof ADandunfortunatelyalmostallclinicaltrialsof ADdrugcandidateshavefailedor beendiscon-tinuedto date.Mathematical,computationalandstatisticaltoolscanbeemployedin theconstructionof clinicaltrialsimulatorsto assistin theimprovementof trialdesignandenhancethechancesof successof potentialnewtherapies.Basedontheanalysisof a setof clinicaldataprovidedbytheAlzheimer’sDiseaseNeuroimagingInitiative(ADNI)wedevelopeda simplestochasticmathematicalmodelto simulatethedevelopmentandpro-gressionof Alzheimer’sin a longitudinalcohortstudy.Weshowhowthismodellingframe-workcouldbeusedto assesstheeffectandthechancesof successof hypotheticaltreatmentsthatareadministeredat differentstagesanddelaydiseasedevelopment.Wedemonstratethatthedetectionof thetrueefficacyof anADtreatmentcanbeverychalleng-ing,evenif thetreatmentis highlyeffective.Animportantreasonbehindtheinabilitytodetectsignalsof efficacyin a clinicaltrialin thistherapyareacouldbethehighbetween-andwithin-individualvariabilityin themeasurement of diagnosticmarkersandendpoints,whichconsequentlyresultsin themisdiagnosisof anindividual’sdiseasestate.
Anderson RM, Hadjichrysanthou C, Evans S, et al., 2017, Why do so many clinical trials of therapies for Alzheimer's disease fail?, LANCET, Vol: 390, Pages: 2327-2329, ISSN: 0140-6736
Chibnik LB, Wolters FJ, Bäckman K, et al., 2017, Trends in the incidence of dementia: design and methods in the Alzheimer Cohorts Consortium., European Journal of Epidemiology, Vol: 32, Pages: 931-938, ISSN: 0393-2990
Several studies have reported a decline in incidence of dementia which may have large implications for the projected burden of disease, and provide important guidance to preventive efforts. However, reports are conflicting or inconclusive with regard to the impact of gender and education with underlying causes of a presumed declining trend remaining largely unidentified. The Alzheimer Cohorts Consortium aggregates data from nine international population-based cohorts to determine changes in the incidence of dementia since 1990. We will employ Poisson regression models to calculate incidence rates in each cohort and Cox proportional hazard regression to compare 5-year cumulative hazards across study-specific epochs. Finally, we will meta-analyse changes per decade across cohorts, and repeat all analysis stratified by sex, education and APOE genotype. In all cohorts combined, there are data on almost 69,000 people at risk of dementia with the range of follow-up years between 2 and 27. The average age at baseline is similar across cohorts ranging between 72 and 77. Uniting a wide range of disease-specific and methodological expertise in research teams, the first analyses within the Alzheimer Cohorts Consortium are underway to tackle outstanding challenges in the assessment of time-trends in dementia occurrence.
Hadjichrysanthou C, Broom M, Rychtář J, 2017, Models of kleptoparasitism on networks: the effect of population structure on food stealing behaviour, Journal of Mathematical Biology, Vol: 76, Pages: 1465-1488, ISSN: 0303-6812
The behaviour of populations consisting of animals that interact with each other for their survival and reproduction is usually investigated assuming homogeneity amongst the animals. However, real populations are non-homogeneous. We focus on an established model of kleptoparasitism and investigate whether and how much population heterogeneities can affect the behaviour of kleptoparasitic populations. We consider a situation where animals can either discover food items by themselves or attempt to steal the food already discovered by other animals through aggressive interactions. Representing the likely interactions between animals by a network, we develop pairwise and individual-based models to describe heterogeneities in both the population structure and other individual characteristics, including searching and fighting abilities. For each of the models developed we derive analytic solutions at the steady state. The high accuracy of the solutions is shown in various examples of populations with different degrees of heterogeneity. We observe that highly heterogeneous structures can significantly affect the food intake rate and therefore the fitness of animals. In particular, the more highly connected animals engage in more conflicts, and have a reduced food consumption rate compared to poorly connected animals. Further, for equivalent average level of connectedness, the average consumption rate of a population with heterogeneous structure can be higher.
Lawrence E, Vegvari C, Ower A, et al., 2017, A Systematic Review of Longitudinal Studies Which Measure Alzheimer's Disease Biomarkers, JOURNAL OF ALZHEIMERS DISEASE, Vol: 59, Pages: 1359-1379, ISSN: 1387-2877
Alzheimer’s disease (AD) is a progressive and fatal neurodegenerative disease, with no effective treatment orcure. A gold standard therapy would be treatment to slow or halt disease progression; however, knowledge of causationin the early stages of AD is very limited. In order to determine effective endpoints for possible therapies, a number ofquantitative surrogate markers of disease progression have been suggested, including biochemical and imaging biomarkers.The dynamics of these various surrogate markers over time, particularly in relation to disease development, are, however,not well characterized. We reviewed the literature for studies that measured cerebrospinal fluid or plasma amyloid-andtau, or took magnetic resonance image or fluorodeoxyglucose/Pittsburgh compound B-positron electron tomography scans,in longitudinal cohort studies. We summarized the properties of the major cohort studies in various countries, commonlyused diagnosis methods and study designs. We have concluded that additional studies with repeat measures over time in arepresentative population cohort are needed to address the gap in knowledge of AD progression. Based on our analysis, wesuggest directions in which research could move in order to advance our understanding of this complex disease, includingrepeat biomarker measurements, standardization and increased sample sizes.
Stylianou A, Hadjichrysanthou C, Truscott JE, et al., 2017, Developing a mathematical model for the evaluation of the potential impact of a partially efficacious vaccine on the transmission dynamics of Schistosoma mansoni in human communities, PARASITES & VECTORS, Vol: 10, ISSN: 1756-3305
Background:There is currently no vaccine available to protect humans against infection with the schistosome digenean parasites, although candidate formulations for Schistosoma mansoni are under trial in animal models, including rodents and primates. Current strategies for the control of infection are based on mass drug administration (MDA) targeted at school-aged children of age 5 to 14 years. This approach is unlikely to eliminate exposure to infection except in settings with very low levels of transmission.Methods:A deterministic mathematical model for the transmission dynamics of the parasite is described and employed to investigate community level outcomes. The model is defined to encompass two different delivery strategies for the vaccination of the population, namely, infant (cohort) and mass vaccination. However, in this paper the focus is on vaccination delivered in a cohort immunisation programme where infants are immunised within the first year of life before acquiring infection. An analysis of the parasite’s transmission dynamics following the administration of a partially protective vaccine is presented. The vaccine acts on parasite mortality, fecundity or/and establishment.Results:A vaccine with an efficacy of over 60% can interrupt transmission in low and moderate transmission settings. In higher transmission intensity areas, greater efficacy or higher infant vaccination coverage is required. Candidate vaccines that act either on parasite mortality, fecundity or establishment within the human host, can be similarly effective. In all cases, however, the duration of protection is important. The community level impact of vaccines with all modes of action, declines if vaccine protection is of a very short duration. However, durations of protection of 5–10 years or more are sufficient, with high coverage and efficacy levels, to halt transmission. The time taken to break transmission may be 18 years or more after the start of the cohort vaccinati
Vegvari C, Hadjichrysanthou C, Cauët E, et al., 2016, How Can Viral Dynamics Models Inform Endpoint Measures in Clinical Trials of Therapies for Acute Viral Infections?, PLOS One, Vol: 11, ISSN: 1932-6203
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.
Vegvari C, Cauët E, Hadjichrysanthou C, et al., 2016, Using Clinical Trial Simulators to Analyse the Sources of Variance in Clinical Trials of Novel Therapies for Acute Viral Infections., PLOS One, Vol: 11, ISSN: 1932-6203
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.
Hadjichrysanthou C, Cauët E, Lawrence E, et al., 2016, Understanding the within-host dynamics of influenza A virus: from theory to clinical implications, Journal of the Royal Society Interface, Vol: 13, ISSN: 1742-5689
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.
Hadjichrysanthou C, Sharkey KJ, 2014, Epidemic control analysis: designing targeted intervention strategies against epidemics propagated on contact networks
Hadjichrysanthou C, 2013, Evolutionary models in structured populations
Hadjichrysanthou C, Broom M, Kiss IZ, 2012, Approximating evolutionary dynamics on networks using a Neighbourhood Configuration model, JOURNAL OF THEORETICAL BIOLOGY, Vol: 312, Pages: 13-21, ISSN: 0022-5193
Hadjichrysanthou C, Broom M, 2012, When should animals share food? Game theory applied to kleptoparasitic populations with food sharing, BEHAVIORAL ECOLOGY, Vol: 23, Pages: 977-991, ISSN: 1045-2249
Hadjichrysanthou C, Broom M, Rychtar J, 2011, Evolutionary Games on Star Graphs Under Various Updating Rules, DYNAMIC GAMES AND APPLICATIONS, Vol: 1, Pages: 386-407, ISSN: 2153-0785
Broom M, Hadjichrysanthou C, Rychtář J, et al., 2010, Two results on evolutionary processes on general non-directed graphs, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 466, Pages: 2795-2798, ISSN: 1364-5021
Broom M, Hadjichrysanthou C, Rychtar J, 2010, Evolutionary games on graphs and the speed of the evolutionary process, PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, Vol: 466, Pages: 1327-1346, ISSN: 1364-5021
Hadjichrysanthou C, 2008, Including information spread and host awareness in an SIR model
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