Validation of a yellow fever vaccine model using data from primary vaccination in children and adults, re-vaccination and dose-response in adults and studies with immunocompromised individuals

Background: An effective yellow fever (YF) vaccine has been available since 1937. Nevertheless, questions regarding its use remain poorly understood, such as the ideal dose to confer immunity against the disease, the need for a booster dose, the optimal immunisation schedule for immunocompetent, immunosuppressed, and pediatric populations, among other issues. This work aims to demonstrate that computational tools can be used to simulate different scenarios regarding YF vaccination and the immune response of individuals to this vaccine, thus assisting the response of some of these open questions. Results: This work presents the computational results obtained by a mathematical model of the human immune response to vaccination against YF. Five scenarios were simulated: primovaccination in adults and children, booster dose in adult individuals, vaccination of individuals with autoimmune diseases under immunomodulatory therapy, and the immune response to different vaccine doses. Where data were available, the model was able to quantitatively replicate the levels of antibodies obtained experimentally. In addition, for those scenarios where data were not available, it was possible to qualitatively reproduce the immune response behaviours described in the literature. Conclusions: Our simulations show that the minimum dose to confer immunity against YF is half of the reference dose. The results also suggest that immunological immaturity in children limits the induction and persistence of long-lived plasma cells are related to the antibody decay observed experimentally. Finally, the decay observed in the antibody level after ten years suggests that a booster dose is necessary to keep immunity against YF

Modelagem computacional da resposta imune à vacina contra febre amarela

An effective yellow fever vaccine has been available since 1937. Nevertheless, questions regarding its use remain poorly understood, such as the ideal dose to confer immunity against the disease, the need for booster dose, the optimal immunization schedule for immunocompetent, immunosuppressed, and children, among other issues. The objective of this work is to demonstrate that computational tools can be used to simulate different scenarios regarding yellow fever vaccination and the immune response of the individuals to this vaccine, thus assisting the response of some of these open questions. In this context, this work presents a computational model of the human immune response to vaccination against yellow fever. The model takes into account important cells and molecules of the human immune system such as antigen presenting cells, B and T lymphocytes, memory cells and antibodies. The model was able to replicate the levels of antibodies obtained experimentally in different vaccination scenarios, allowing a quantitative validation with experimental data. In addition, some behaviors of the immune response described in the literature were reproduced qualitatively. The immune responses of primovacinated, revaccinated adult individuals with autoimmune diseases under immunomodulatory therapy were simulated. In addition, the behavior observed in the primary vaccination of children and the levels of antibodies produced by the administration of smaller doses of vaccines were reproduced, compared to those obtained by the reference dose.Desde 1937 está disponível uma vacina eficaz contra febre amarela. Ainda assim, questões relativas a seu uso permanecem pouco entendidas, como, por exemplo, a dose ideal capaz de conferir imunidade contra a doença, a necessidade de dose reforço, o esquema ideal de vacinação para indivíduos imunocompetentes, imunossuprimidos, e crianças, dentre outras questões. O objetivo deste trabalho é demonstrar que ferramentas computacionais podem ser utilizadas para simular diferentes cenários referentes à vacinação contra a febre amarela e à resposta imune dos indivíduos a esta vacina, auxiliando assim na busca pelas respostas de algumas destas questões em aberto. Neste contexto, este trabalho apresenta um modelo computacional da resposta imune humana à vacinação contra a febre amarela. O modelo leva em conta importantes células e moléculas do sistema imune humano como células apresentadoras de antígeno, linfócitos B e T, células de memória e anticorpos. O modelo foi capaz de reproduzir os níveis de anticorpos obtidos experimentalmente em diferentes cenários relativos à vacinação, permitindo uma validação quantitativa com dados experimentais. Adicionalmente foram reproduzidos qualitativamente alguns comportamentos da resposta imune descritos na literatura. Foram simuladas as respostas imunes de indivíduos adultos primovacinados, revacinados e portadores de doenças autoimunes sob uso de terapia imunomoduladora. Além disso, reproduziu-se o comportamento observado na primovacinação de crianças e os níveis de anticorpos produzidos pela administração de doses menores de vacinas, comparados aos obtidos pela dose referência

Modelagem matemático-computacional da resposta imune à vacina de febre amarela

An effective vaccine against yellow fever is available since 1937, but some issues regarding its use remain poorly understood, for example, the need for a booster dose every ten years. The objective of this study is to demonstrate that mathematical-computational tools can be used to simulate distinct scenarios related both to vaccination and individuals in order to assist the search for the answers to some of these open issues. In this context, this study presents a mathematical-computational model of the human immune response to vaccination against yellow fever. The model takes into account important cells of the innate and adaptive systems, such as antigen presenting cells, antibodies, B cells and T cells (CD4 + and CD8 +). Memory cell populations, important on the immunity induced by a vaccine, were also considered in the model. The model was able to generate antibodies curves which are in accordance with experimental data as well as to represent the behavior of several important populations of the immune system according to the results of the literature surveyed. This is the first step towards an ideal scenario where it will be possible to simulate distinct situations related to the use of yellow fever vaccine, as its application in immunodeficient individuals, different vaccination strategies, duration of immunity and the need for a booster dose.Desde 1937 está disponível uma vacina eficaz contra febre amarela. Ainda assim, questões relativas a seu uso permanecem pouco entendidas, como, por exemplo, a necessidade da dose reforço a cada dez anos. O objetivo deste trabalho é demonstrar que ferramentas matemático-computacionais podem ser utilizadas para simular diferentes cenários referentes à vacinação e aos indivíduos a fim de auxiliar a busca pelas respostas de algumas destas questões em aberto. Neste contexto, este trabalho apresenta um modelo matemático-computacional da resposta imune humana à vacinação contra febre amarela. O modelo leva em conta importantes células dos sistemas inato e adaptativo, como células apresentadoras de antígeno, anticorpos, células B e células T (CD4+ e CD8+). Também são consideradas populações de células de memória, importantes na aquisição da imunidade conferida pela vacina. O modelo foi capaz de gerar curvas de anticorpos que estão de acordo com dados experimentais, além de representar o comportamento de diversas populações importantes do sistema imune de acordo com o que é esperado pela literatura. Este é o início de um caminho que, em um cenário ideal, permitirá simular diferentes situações relacionadas ao emprego da vacina contra febre amarela, como sua aplicação em indivíduos com imunodeficiências, diferentes estratégias de vacinação, duração da imunidade e necessidade de dose reforço.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

Mathematical modeling based on ordinary differential equations: A promising approach to vaccinology

New contributions that aim to accelerate the development or to improve the efficacy and safety of vaccines arise from many different areas of research and technology. One of these areas is computational science, which traditionally participates in the initial steps, such as the pre-screening of active substances that have the potential to become a vaccine antigen. In this work, we present another promising way to use computational science in vaccinology: mathematical and computational models of important cell and protein dynamics of the immune system. A system of Ordinary Differential Equations represents different immune system populations, such as B cells and T cells, antigen presenting cells and antibodies. In this way, it is possible to simulate, in silico, the immune response to vaccines under development or under study. Distinct scenarios can be simulated by varying parameters of the mathematical model. As a proof of concept, we developed a model of the immune response to vaccination against the yellow fever. Our simulations have shown consistent results when compared with experimental data available in the literature. The model is generic enough to represent the action of other diseases or vaccines in the human immune system, such as dengue and Zika virus

Health-status outcomes with invasive or conservative care in coronary disease

BACKGROUND In the ISCHEMIA trial, an invasive strategy with angiographic assessment and revascularization did not reduce clinical events among patients with stable ischemic heart disease and moderate or severe ischemia. A secondary objective of the trial was to assess angina-related health status among these patients. METHODS We assessed angina-related symptoms, function, and quality of life with the Seattle Angina Questionnaire (SAQ) at randomization, at months 1.5, 3, and 6, and every 6 months thereafter in participants who had been randomly assigned to an invasive treatment strategy (2295 participants) or a conservative strategy (2322). Mixed-effects cumulative probability models within a Bayesian framework were used to estimate differences between the treatment groups. The primary outcome of this health-status analysis was the SAQ summary score (scores range from 0 to 100, with higher scores indicating better health status). All analyses were performed in the overall population and according to baseline angina frequency. RESULTS At baseline, 35% of patients reported having no angina in the previous month. SAQ summary scores increased in both treatment groups, with increases at 3, 12, and 36 months that were 4.1 points (95% credible interval, 3.2 to 5.0), 4.2 points (95% credible interval, 3.3 to 5.1), and 2.9 points (95% credible interval, 2.2 to 3.7) higher with the invasive strategy than with the conservative strategy. Differences were larger among participants who had more frequent angina at baseline (8.5 vs. 0.1 points at 3 months and 5.3 vs. 1.2 points at 36 months among participants with daily or weekly angina as compared with no angina). CONCLUSIONS In the overall trial population with moderate or severe ischemia, which included 35% of participants without angina at baseline, patients randomly assigned to the invasive strategy had greater improvement in angina-related health status than those assigned to the conservative strategy. The modest mean differences favoring the invasive strategy in the overall group reflected minimal differences among asymptomatic patients and larger differences among patients who had had angina at baseline

Initial invasive or conservative strategy for stable coronary disease

BACKGROUND Among patients with stable coronary disease and moderate or severe ischemia, whether clinical outcomes are better in those who receive an invasive intervention plus medical therapy than in those who receive medical therapy alone is uncertain. METHODS We randomly assigned 5179 patients with moderate or severe ischemia to an initial invasive strategy (angiography and revascularization when feasible) and medical therapy or to an initial conservative strategy of medical therapy alone and angiography if medical therapy failed. The primary outcome was a composite of death from cardiovascular causes, myocardial infarction, or hospitalization for unstable angina, heart failure, or resuscitated cardiac arrest. A key secondary outcome was death from cardiovascular causes or myocardial infarction. RESULTS Over a median of 3.2 years, 318 primary outcome events occurred in the invasive-strategy group and 352 occurred in the conservative-strategy group. At 6 months, the cumulative event rate was 5.3% in the invasive-strategy group and 3.4% in the conservative-strategy group (difference, 1.9 percentage points; 95% confidence interval [CI], 0.8 to 3.0); at 5 years, the cumulative event rate was 16.4% and 18.2%, respectively (difference, 121.8 percentage points; 95% CI, 124.7 to 1.0). Results were similar with respect to the key secondary outcome. The incidence of the primary outcome was sensitive to the definition of myocardial infarction; a secondary analysis yielded more procedural myocardial infarctions of uncertain clinical importance. There were 145 deaths in the invasive-strategy group and 144 deaths in the conservative-strategy group (hazard ratio, 1.05; 95% CI, 0.83 to 1.32). CONCLUSIONS Among patients with stable coronary disease and moderate or severe ischemia, we did not find evidence that an initial invasive strategy, as compared with an initial conservative strategy, reduced the risk of ischemic cardiovascular events or death from any cause over a median of 3.2 years. The trial findings were sensitive to the definition of myocardial infarction that was used