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Investigating interventions in Alzheimer's disease with computer simulation models.

ABSTRACT: Progress in the development of therapeutic interventions to treat or slow the progression of Alzheimer's disease has been hampered by lack of efficacy and unforeseen side effects in human clinical trials. This setback highlights the need for new approaches for pre-clinical testing of possible interventions. Systems modelling is becoming increasingly recognised as a valuable tool for investigating molecular and cellular mechanisms involved in ageing and age-related diseases. However, there is still a lack of awareness of modelling approaches in many areas of biomedical research. We previously developed a stochastic computer model to examine some of the key pathways involved in the aggregation of amyloid-beta (A?) and the micro-tubular binding protein tau. Here we show how we extended this model to include the main processes involved in passive and active immunisation against A? and then demonstrate the effects of this intervention on soluble A?, plaques, phosphorylated tau and tangles. The model predicts that immunisation leads to clearance of plaques but only results in small reductions in levels of soluble A?, phosphorylated tau and tangles. The behaviour of this model is supported by neuropathological observations in Alzheimer patients immunised against A?. Since, soluble A?, phosphorylated tau and tangles more closely correlate with cognitive decline than plaques, our model suggests that immunotherapy against A? may not be effective unless it is performed very early in the disease process or combined with other therapies.

SUBMITTER: Proctor CJ

PROVIDER: S-EPMC3782376 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Investigating interventions in Alzheimer's disease with computer simulation models.

Proctor Carole J CJ Boche Delphine D Gray Douglas A DA Nicoll James A R JA

PloS one 20130916 9

Progress in the development of therapeutic interventions to treat or slow the progression of Alzheimer's disease has been hampered by lack of efficacy and unforeseen side effects in human clinical trials. This setback highlights the need for new approaches for pre-clinical testing of possible interventions. Systems modelling is becoming increasingly recognised as a valuable tool for investigating molecular and cellular mechanisms involved in ageing and age-related diseases. However, there is sti ...[more]

PMID: 24098635

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Project description:IntroductionDiabetes is a major public health problem and prediabetes (intermediate hyperglycaemia) is associated with a high risk of developing diabetes. With evidence supporting the use of preventive interventions for prediabetes populations and the discovery of novel biomarkers stratifying the risk of progression, there is a need to evaluate their cost-effectiveness across jurisdictions. In diabetes and prediabetes, it is relevant to inform cost-effectiveness analysis using decision models due to their ability to forecast long-term health outcomes and costs beyond the time frame of clinical trials. To support good implementation and reimbursement decisions of interventions in these populations, models should be clinically credible, based on best available evidence, reproducible and validated against clinical data. Our aim is to identify recent studies on computer simulation models and model-based economic evaluations of populations of individuals with prediabetes, qualify them and discuss the knowledge gaps, challenges and opportunities that need to be addressed for future evaluations.Methods and analysisA systematic review will be conducted in MEDLINE, Embase, EconLit and National Health Service Economic Evaluation Database. We will extract peer-reviewed studies published between 2000 and 2016 that describe computer simulation models of the natural history of individuals with prediabetes and/or decision models to evaluate the impact of interventions, risk stratification and/or screening on these populations. Two reviewers will independently assess each study for inclusion. Data will be extracted using a predefined pro forma developed using best practice. Study quality will be assessed using a modelling checklist. A narrative synthesis of all studies will be presented, focussing on model structure, quality of models and input data, and validation status.Ethics and disseminationThis systematic review is exempt from ethics approval because the work is carried out on published documents. The findings of the review will be disseminated in a related peer-reviewed journal and presented at conferences.Reviewregistration numberCRD42016047228.

Project description:In vivo and ex vivo sensors have the potential to aid tracking and anti-poaching endeavours and provide new insights into rhinoceros physiology and environment. However, the propagation of electromagnetic signals in rhinoceros tissue is currently not known. We present simulation and agar models of the rhinoceros that allow the investigation of electromagnetic propagation by in vivo and ex vivo devices without the need for surgery. Since the dielectric properties of rhinoceros tissue have not been documented, the conductivity and permittivity of the skin, fat, muscle, blood and other organs are first approximated by means of a meta-analysis that includes animals with similar physical properties. Subsequently, we develop anatomical models that include dermal layers, internal organs and a skeleton. We also develop a flank model that serves as an approximation of the anatomical model in certain situations. These models are used to determine the viability of communication between an in vivo device and an ex vivo device attached to the hind leg of the animal. Two types of antenna (microstrip-fed planar elliptical monopole antenna and printed inverted-F antenna) and three feasible implant locations (back, neck and chest) are considered. In addition to the computer models, phantom recipes using salt, sugar and agar are developed to match the dielectric properties of each tissue type at the industrial, scientific and medical (ISM) frequencies of 403MHz, 910MHz and 2.4GHz. The average error between the measured and theoretically predicted dielectric values was 6.22% over all recipes and 4.49% for the 2.4 GHz recipe specifically. When considering the predicted efficiency of the transmitting and receiving antennas, an agreement of 67.38% was demonstrated between the computer simulations and laboratory measurements using the agar rhinoceros flank models. Computer simulations using the anatomical model of the rhinoceros indicate that the chest is the optimal implant location and that best signal propagation is achieved using the planar inverted-F antenna (PIFA). Using this configuration, the simulations indicate that communication between the implant and an ex vivo device attached to the hind leg is challenging but possible. Furthermore, we find that the inclusion of factors such as the density and temperature of the phantom materials were found to be critical to the achievement of good agreement between practice and simulation.

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Investigating interventions in Alzheimer's disease with computer simulation models.

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Investigating interventions in Alzheimer's disease with computer simulation models.

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