Comparing the Efficacy of Two Generations of EGFR-TKIs: An Integrated Drug-Disease Mechanistic Model Approach in EGFR-Mutated Lung Adenocarcinoma.

Mutationsin epidermal growth factor receptor (EGFR) are found in approximately 48% of Asian and 19% of Western patients with lung adenocarcinoma (LUAD), leading to aggressive tumor growth. While tyrosine kinase inhibitors (TKIs) like gefitinib and osimertinib target this mutation, treatments often face challenges such as metastasis and resistance. To address this, we developed physiologically based pharmacokinetic (PBPK) models for both drugs, simulating their distribution within the primary tumor and metastases following oral administration. These models, combined with a mechanistic knowledge-based disease model of EGFR-mutated LUAD, allow us to predict the tumor's behavior under treatment considering the diversity within the tumor cells due to different mutations. The combined model reproduces the drugs' distribution within the body, as well as the effects of both gefitinib and osimertinib on EGFR-activation-induced signaling pathways. In addition, the disease model encapsulates the heterogeneity within the tumor through the representation of various subclones. Each subclone is characterized by unique mutation profiles, allowing the model to accurately reproduce clinical outcomes, including patients' progression, aligning with RECIST criteria guidelines (version 1.1). Datasets used for calibration came from NEJ002 and FLAURA clinical trials. The quality of the fit was ensured with rigorous visual predictive checks and statistical tests (comparison metrics computed from bootstrapped, weighted log-rank tests: 98.4% (NEJ002) and 99.9% (FLAURA) similarity). In addition, the model was able to predict outcomes from an independent retrospective study comparing gefitinib and osimertinib which had not been used within the model development phase. This output validation underscores mechanistic models' potential in guiding future clinical trials by comparing treatment efficacies and identifying patients who would benefit most from specific TKIs. Our work is a step towards the design of a powerful tool enhancing personalized treatment in LUAD. It could support treatment strategy evaluations and potentially reduce trial sizes, promising more efficient and targeted therapeutic approaches. Following its consecutive prospective validations with the FLAURA2 and MARIPOSA trials (validation metrics computed from bootstrapped, weighted log-rank tests: 94.0% and 98.1%, respectively), the model could be used to generate a synthetic control arm.

A quantitative systems pharmacology workflow toward optimal design and biomarker stratification of atopic dermatitis clinical trials.

BACKGROUND: The development of atopic dermatitis (AD) drugs is challenged by many disease phenotypes and trial design options, which are hard to explore experimentally. OBJECTIVE: We aimed to optimize AD trial design using simulations. METHODS: We constructed a quantitative systems pharmacology model of AD and standard of care (SoC) treatments and generated a phenotypically diverse virtual population whose parameter distribution was derived from known relationships between AD biomarkers and disease severity and calibrated using disease severity evolution under SoC regimens. RESULTS: We applied this workflow to the immunomodulator OM-85, currently being investigated for its potential use in AD, and calibrated the investigational treatment model with the efficacy profile of an existing trial (thereby enriching it with plausible marker levels and dynamics). We assessed the sensitivity of trial outcomes to trial protocol and found that for this particular example the choice of end point is more important than the choice of dosing regimen and patient selection by model-based responder enrichment could increase the expected effect size. A global sensitivity analysis revealed that only a limited subset of baseline biomarkers is needed to predict the drug response of the full virtual population. CONCLUSIONS: This AD quantitative systems pharmacology workflow built around knowledge of marker-severity relationships as well as SoC efficacy can be tailored to specific development cases to optimize several trial protocol parameters and biomarker stratification and therefore has promise to become a powerful model-informed AD drug development and personalized medicine tool.

Empirical methods for the validation of time-to-event mathematical models taking into account uncertainty and variability: application to EGFR + lung adenocarcinoma.

BACKGROUND: Over the past several decades, metrics have been defined to assess the quality of various types of models and to compare their performance depending on their capacity to explain the variance found in real-life data. However, available validation methods are mostly designed for statistical regressions rather than for mechanistic models. To our knowledge, in the latter case, there are no consensus standards, for instance for the validation of predictions against real-world data given the variability and uncertainty of the data. In this work, we focus on the prediction of time-to-event curves using as an application example a mechanistic model of non-small cell lung cancer. We designed four empirical methods to assess both model performance and reliability of predictions: two methods based on bootstrapped versions of parametric statistical tests: log-rank and combined weighted log-ranks (MaxCombo); and two methods based on bootstrapped prediction intervals, referred to here as raw coverage and the juncture metric. We also introduced the notion of observation time uncertainty to take into consideration the real life delay between the moment when an event happens, and the moment when it is observed and reported. RESULTS: We highlight the advantages and disadvantages of these methods according to their application context. We have shown that the context of use of the model has an impact on the model validation process. Thanks to the use of several validation metrics we have highlighted the limit of the model to predict the evolution of the disease in the whole population of mutations at the same time, and that it was more efficient with specific predictions in the target mutation populations. The choice and use of a single metric could have led to an erroneous validation of the model and its context of use. CONCLUSIONS: With this work, we stress the importance of making judicious choices for a metric, and how using a combination of metrics could be more relevant, with the objective of validating a given model and its predictions within a specific context of use. We also show how the reliability of the results depends both on the metric and on the statistical comparisons, and that the conditions of application and the type of available information need to be taken into account to choose the best validation strategy.

Knowledge-based mechanistic modeling accurately predicts disease progression with gefitinib in EGFR-mutant lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is associated with a low survival rate at advanced stages. Although the development of targeted therapies has improved outcomes in LUAD patients with identified and specific genetic alterations, such as activating mutations on the epidermal growth factor receptor gene (EGFR), the emergence of tumor resistance eventually occurs in all patients and this is driving the development of new therapies. In this paper, we present the In Silico EGFR-mutant LUAD (ISELA) model that links LUAD patients' individual characteristics, including tumor genetic heterogeneity, to tumor size evolution and tumor progression over time under first generation EGFR tyrosine kinase inhibitor gefitinib. This translational mechanistic model gathers extensive knowledge on LUAD and was calibrated on multiple scales, including in vitro, human tumor xenograft mouse and human, reproducing more than 90% of the experimental data identified. Moreover, with 98.5% coverage and 99.4% negative logrank tests, the model accurately reproduced the time to progression from the Lux-Lung 7 clinical trial, which was unused in calibration, thus supporting the model high predictive value. This knowledge-based mechanistic model could be a valuable tool in the development of new therapies targeting EGFR-mutant LUAD as a foundation for the generation of synthetic control arms.

A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems.

The Network Slice Selection Function (NSSF) in heterogeneous technology environments is a complex problem, which still does not have a fully acceptable solution. Thus, the implementation of new network selection strategies represents an important issue in development, mainly due to the growing demand for applications and scenarios involving 5G and future networks. This work presents an integrated solution for the NSSF problem, called the Network Slice Selection Function Decision-Aid Framework (NSSF DAF), which consists of a distributed solution in which a part is executed on the user's equipment (for example, smartphones, Unmanned Aerial Vehicles, IoT brokers) functioning as a transparent service, and another at the Edge of the operator or service provider. It requires a low consumption of computing resources from mobile devices and offers complete independence from the network operator. For this purpose, protocols and software tools are used to classify slices, employing the following four multicriteria methods to aid decision making: VIKOR (Visekriterijumska Optimizacija i Kompromisno Resenje), COPRAS (Complex Proportional Assessment), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and Promethee II (Preference Ranking Organization Method for Enrichment Evaluations). The general objective is to verify the similarity among these methods and applications to the slice classification and selection process, considering a specific scenario in the framework. It also uses machine learning through the K-means clustering algorithm, adopting a hybrid solution in the implementation and operation of the NSSF service in multi-domain slicing environments of heterogeneous mobile networks. Testbeds were conducted to validate the proposed framework, mapping the adequate quality of service requirements. The results indicate a real possibility of offering a complete solution to the NSSF problem that can be implemented in Edge, in Core, or even in the 5G Radio Base Station itself, without the incremental computational cost of the end user's equipment, allowing for an adequate quality of experience.

Integration of Heterogeneous Biological Data in Multiscale Mechanistic Model Calibration: Application to Lung Adenocarcinoma.

Mechanistic models are built using knowledge as the primary information source, with well-established biological and physical laws determining the causal relationships within the model. Once the causal structure of the model is determined, parameters must be defined in order to accurately reproduce relevant data. Determining parameters and their values is particularly challenging in the case of models of pathophysiology, for which data for calibration is sparse. Multiple data sources might be required, and data may not be in a uniform or desirable format. We describe a calibration strategy to address the challenges of scarcity and heterogeneity of calibration data. Our strategy focuses on parameters whose initial values cannot be easily derived from the literature, and our goal is to determine the values of these parameters via calibration with constraints set by relevant data. When combined with a covariance matrix adaptation evolution strategy (CMA-ES), this step-by-step approach can be applied to a wide range of biological models. We describe a stepwise, integrative and iterative approach to multiscale mechanistic model calibration, and provide an example of calibrating a pathophysiological lung adenocarcinoma model. Using the approach described here we illustrate the successful calibration of a complex knowledge-based mechanistic model using only the limited heterogeneous datasets publicly available in the literature.

Qualidade da água em um sistema de reservatórios em cascata - um estudo de caso no semiárido brasileiro / Water quality in a cascade reservoir system - a case study in the Brazilian semiarid region

RESUMO A escassez hídrica e o aumento da demanda de água para usos múltiplos incentivaram a construção de diversos reservatórios e sistemas de transferências hídricas entre bacias hidrográficas em diversas partes do mundo, principalmente em regiões áridas e semiáridas. Apesar de essas medidas mitigarem os aspectos quantitativos do abastecimento, a diminuição da qualidade da água disponível pode se tornar uma limitação importante. Este estudo teve por objetivo avaliar a qualidade da água no sistema de transferência que abastece a Região Metropolitana de Fortaleza, Ceará. Foram analisados 20 parâmetros de qualidade de água ao longo do sistema. Aplicou-se em seguida a análise de agrupamento utilizando a distância euclidiana associada ao método de ligação de Ward para agrupar os 13 pontos de coleta que foram estabelecidos conforme suas similaridades. A piora da qualidade da água ficou evidente ao longo do sistema, com o aumento da concentração de cianobactérias, pH, nitrogênio total, oxigênio dissolvido, turbidez e clorofila-a. Com relação às cianobactérias, duas espécies mostraram-se dominantes — Oscillatoria sp. e Microcystis sp. — e sete abundantes — Merismopedia sp., Cylindrospermopsis sp., Pseudanabaena sp., Anabaena sp., Aphanizomenon sp., Aphanocapsa sp. e Dolichospermum sp. A presença de cianobactérias potencialmente tóxicas e a piora da qualidade da água tornam a realização de monitoramentos frequentes e o desenvolvimento de sistemas de alerta essenciais para a adequada gestão dos recursos hídricos, tanto do ponto de vista ambiental quanto do de saúde pública.

ABSTRACT Water scarcity and increasing water demand for multiple uses encourage the construction of reservoirs and water transfer systems between watersheds in various parts of the world, especially in arid and semi-arid regions. Although these measures may mitigate the quantitative aspects of water supply, the quality decrease of the available water may become an important limitation. This study aimed to evaluate the water quality in the water transfer system that supplies the metropolitan region of Fortaleza, Ceará. Twenty water quality parameters were analyzed throughout the system followed by the application of the Euclidean distance and Ward's method to group the 13 sampling points according to their similarities. The worsening of the water quality was evident with increasing cyanobacterial concentration, pH, total nitrogen, dissolved oxygen, turbidity, and chlorophyll-a. Regarding cyanobacteria, two species were dominant: Oscillatoria sp. and Microcystis sp. while seven were abundant: Merismopedia sp., Cylindrospermopsis sp., Pseudanabaena sp., Anabaena sp., Aphanizomenon sp., Aphanocapsa sp. e Dolichospermum sp. The presence of potentially toxic cyanobacteria and the worsening of water quality make frequent monitoring and the development of alert systems essential for the management of water resources, both from an environmental and public health point of view.

Quantum neuron with real weights.

This paper proposes a new model of a real weights quantum neuron exploiting the so-called quantum parallelism which allows for an exponential speedup of computations. The quantum neurons were trained in a classical-quantum approach, considering the delta rule to update the values of the weights in an image database of three distinct patterns. We performed classical simulations and also executed experiments in an actual small-scale quantum processor. The results of the experiments show that the proposed quantum real neuron model has a good generalisation capacity, demonstrating better accuracy than the traditional binary quantum perceptron model.

Impact of an inpatient multidisciplinary glucose control management program.

OBJECTIVE: Glycemic control has been increasingly recognized as a critical element in inpatient care, but optimal management of blood glucose in the hospital setting remains challenging. The aims of this study were to describe and evaluate the impact of the implementation of an inpatient multidisciplinary glucose control management program on glucose control in hospitalized patients. MATERIALS AND METHODS: Retrospective analysis of medical records and glucose monitoring data obtained by point- of-care testing (POCT) in hospitalized patients before (May 2014) and after (June 2015 and May 2017) the implementation of the program. RESULTS: We analyzed 6888, 7290, and 7669 POCTs from 389, 545, and 475 patients in May 2014, June 2015, and May 2017, respectively. Hyperglycemia (≥ 180 mg/ dL) occurred in 23.5%, 19.6%, and 19.3% POCTs in May 2014, June 2015, and May/2017, respectively (p < 0.001), while severe hyperglycemia (≥ 300 mg/dL) was observed in 2.5%, 2.2%, and 1.8% of them, respectively (p = 0.003). Hyperglycemia (≥ 180 mg/dL) reduced significantly from May 2014 to June 2015 (16.3%, p < 0.001) and from May 2014 to May 2017 (178%, p < 0.001). No significant changes occurred in hypoglycemic parameters. CONCLUSIONS: The implementation of an inpatient multidisciplinary glucose control management program led to significant reductions in hyperglycemic events. The key elements for this achievement were the development of institutional inpatient glycemic control protocols, establishment of a multidisciplinary team, and continuing educational programs for hospital personnel. Altogether, these actions resulted in improvements in care processes, patient safety, and clinical outcomes of hospitalized patients.

Impact of an inpatient multidisciplinary glucose control management program

ABSTRACT Objective: Glycemic control has been increasingly recognized as a critical element in inpatient care, but optimal management of blood glucose in the hospital setting remains challenging. The aims of this study were to describe and evaluate the impact of the implementation of an inpatient multidisciplinary glucose control management program on glucose control in hospitalized patients. Materials and methods: Retrospective analysis of medical records and glucose monitoring data obtained by point- of-care testing (POCT) in hospitalized patients before (May 2014) and after (June 2015 and May 2017) the implementation of the program. Results: We analyzed 6888, 7290, and 7669 POCTs from 389, 545, and 475 patients in May 2014, June 2015, and May 2017, respectively. Hyperglycemia (≥ 180 mg/ dL) occurred in 23.5%, 19.6%, and 19.3% POCTs in May 2014, June 2015, and May/2017, respectively (p < 0.001), while severe hyperglycemia (≥ 300 mg/dL) was observed in 2.5%, 2.2%, and 1.8% of them, respectively (p = 0.003). Hyperglycemia (≥ 180 mg/dL) reduced significantly from May 2014 to June 2015 (16.3%, p < 0.001) and from May 2014 to May 2017 (178%, p < 0.001). No significant changes occurred in hypoglycemic parameters. Conclusions: The implementation of an inpatient multidisciplinary glucose control management program led to significant reductions in hyperglycemic events. The key elements for this achievement were the development of institutional inpatient glycemic control protocols, establishment of a multidisciplinary team, and continuing educational programs for hospital personnel. Altogether, these actions resulted in improvements in care processes, patient safety, and clinical outcomes of hospitalized patients.

Permanent Pacing After Transcatheter Aortic Valve Implantation: Incidence, Predictors and Evolution of Left Ventricular Function / Marca-passo Definitivo após Implante Valvar Aórtico Transcateter: Incidência, Preditores e Evolução da Função Ventricular Esquerda

Abstract Background: Transcatheter aortic valve implantation (TAVI) is a well-established procedure; however, atrioventricular block requiring permanent pacemaker implantation (PPI) is a common complication. Objectives: To determine the incidence, predictors and clinical outcomes of PPI after TAVI, focusing on how PPI affects left ventricular ejection fraction (LVEF) after TAVI. Methods: The Brazilian Multicenter TAVI Registry included 819 patients submitted to TAVI due to severe aortic stenosis from 22 centers from January/2008 to January/2015. After exclusions, the predictors of PPI were assessed in 670 patients by use of multivariate regression. Analysis of the ROC curve was used to measure the ability of the predictors; p < 0.05 was the significance level adopted. Results: Within 30 days from TAVI, 135 patients (20.1%) required PPI. Those patients were older (82.5 vs. 81.1 years; p = 0.047) and mainly of the male sex (59.3% vs 45%; p = 0.003). Hospital length of stay was longer in patients submitted to PPI (mean = 15.7 ± 25.7 vs. 11.8 ± 22.9 days; p < 0.001), but PPI affected neither all-cause death (26.7% vs. 25.6%; p = 0.80) nor cardiovascular death (14.1% vs. 14.8%; p = 0.84). By use of multivariate analysis, the previous presence of right bundle-branch block (RBBB) (OR, 6.19; 3.56-10.75; p ≤ 0.001), the use of CoreValve® prosthesis (OR, 3.16; 1.74-5.72; p ≤ 0.001) and baseline transaortic gradient > 50 mm Hg (OR, 1.86; 1.08-3.2; p = 0.025) were predictors of PPI. The estimated risk of PPI ranged from 4%, when none of those predictors was present, to 63%, in the presence of all of them. The model showed good ability to predict the need for PPI: 0.69 (95%CI: 0.64 - 0.74) in the ROC curve. The substudy of 287 echocardiograms during the 1-year follow-up showed worse LVEF course in patients submitted to PPI (p = 0.01). Conclusion: BRD prévio, gradiente aórtico médio > 50 mmHg e CoreValve® são preditores independentes de implante de MPD pós-TAVI. Ocorreu implante de MPD em aproximadamente 20% dos casos de TAVI, o que prolongou a internação hospitalar, mas não afetou a mortalidade. O implante de MPD afetou negativamente a FEVE pós-TAVI.

Resumo Fundamento: O implante valvar aórtico transcateter (TAVI) está bem estabelecido, porém, o bloqueio atrioventricular, com necessidade de marca-passo definitivo (MPD), é complicação frequente. Objetivo: Determinar a incidência, preditores e desfechos clínicos de implante de MPD após TAVI, focando em como a evolução da fração de ejeção do ventrículo esquerdo (FEVE), após o TAVI, é afetada pelo MPD. Métodos: No registro brasileiro foram incluídos 819 pacientes submetidos a TAVI por estenose aórtica severa em 22 centros entre janeiro/2008 e janeiro/2015. Após exclusões, os preditores de implante de MPD foram avaliados em 670 pacientes por regressão multivariada. Análise da curva ROC foi utilizada para medir a habilidade dos preditores; p < 0,05 foi considerado significativo. Resultados: Aos 30 dias, 135 pacientes (20,1%) necessitaram de MPD. Tais pacientes eram mais velhos (82,5 vs. 81,1 anos; p = 0,047) e predominantemente homens (59,3% vs 45%; p = 0,003). A permanência hospitalar foi maior no Grupo MPD (média= 15,7 ± 25,7 vs. 11,8 ± 22,9 dias; p < 0,001), mas o implante não afetou morte por qualquer causa (26,7% vs. 25,6%; p = 0,80) nem morte cardiovascular (14,1% vs. 14,8%; p = 0,84). Por análise multivariada, a presença prévia de bloqueio de ramo direito (BRD) (OR, 6,19; 3,56-10,75; p ≤ 0,001), o uso da prótese CoreValve® (OR, 3,16; 1,74-5,72; p ≤ 0,001) e gradiente transaórtico basal > 50 mmHg (OR, 1,86; 1,08-3,2; p= 0,025) foram preditores de implante de MPD. O risco estimado de implante de MPD foi 4%, quando nenhum dos fatores de risco estava presente, e 63% na presença de todos. O modelo mostrou boa habilidade de prever a necessidade de MPD: 0,69 (IC95%: 0,64 - 0,74) na curva ROC. Subestudo de 287 ecocardiogramas durante o seguimento de 1 ano mostrou pior evolução da FEVE no Grupo MPD (p = 0,01). Conclusão: Em crianças de 6 a 11 anos, circunferência da cintura aumentada está associada à PA elevada, mesmo quando o IMC é normal. (Arq Bras Cardiol. 2017; [online].ahead print, PP.0-0)

Permanent Pacing After Transcatheter Aortic Valve Implantation: Incidence, Predictors and Evolution of Left Ventricular Function.

BACKGROUND: Transcatheter aortic valve implantation (TAVI) is a well-established procedure; however, atrioventricular block requiring permanent pacemaker implantation (PPI) is a common complication. OBJECTIVES: To determine the incidence, predictors and clinical outcomes of PPI after TAVI, focusing on how PPI affects left ventricular ejection fraction (LVEF) after TAVI. METHODS: The Brazilian Multicenter TAVI Registry included 819 patients submitted to TAVI due to severe aortic stenosis from 22 centers from January/2008 to January/2015. After exclusions, the predictors of PPI were assessed in 670 patients by use of multivariate regression. Analysis of the ROC curve was used to measure the ability of the predictors; p < 0.05 was the significance level adopted. RESULTS: Within 30 days from TAVI, 135 patients (20.1%) required PPI. Those patients were older (82.5 vs. 81.1 years; p = 0.047) and mainly of the male sex (59.3% vs 45%; p = 0.003). Hospital length of stay was longer in patients submitted to PPI (mean = 15.7 ± 25.7 vs. 11.8 ± 22.9 days; p < 0.001), but PPI affected neither all-cause death (26.7% vs. 25.6%; p = 0.80) nor cardiovascular death (14.1% vs. 14.8%; p = 0.84). By use of multivariate analysis, the previous presence of right bundle-branch block (RBBB) (OR, 6.19; 3.56-10.75; p ≤ 0.001), the use of CoreValve® prosthesis (OR, 3.16; 1.74-5.72; p ≤ 0.001) and baseline transaortic gradient > 50 mm Hg (OR, 1.86; 1.08-3.2; p = 0.025) were predictors of PPI. The estimated risk of PPI ranged from 4%, when none of those predictors was present, to 63%, in the presence of all of them. The model showed good ability to predict the need for PPI: 0.69 (95%CI: 0.64 - 0.74) in the ROC curve. The substudy of 287 echocardiograms during the 1-year follow-up showed worse LVEF course in patients submitted to PPI (p = 0.01). CONCLUSION: BRD prévio, gradiente aórtico médio > 50 mmHg e CoreValve® são preditores independentes de implante de MPD pós-TAVI. Ocorreu implante de MPD em aproximadamente 20% dos casos de TAVI, o que prolongou a internação hospitalar, mas não afetou a mortalidade. O implante de MPD afetou negativamente a FEVE pós-TAVI.

O Brasil em 2035: tendências e incertezas para a área social

No Brasil, tem havido grandes dificuldades em se realizar um planejamento que ultrapasse o período de quatro anos estabelecidos nos planos plurianuais (PPAs). Por isso, o debate sobre planejamento de longo prazo no país, ainda que de interesse de diversos setores, detém pouco espaço na agenda política e das burocracias. Com base nessa consideração, este texto propõe iniciar um novo ciclo de debates sobre a temática apresentando questões e fatores que subsidiem estratégias de longo prazo. A partir do ferramental metodológico da prospectiva estratégica, expõem-se tendências e incertezas para a dimensão social no caso brasileiro, contemplando temáticas como as mudanças demográficas no Brasil, as desigualdades, a infraestrutura social urbana, o aumento nos anos de escolaridade da população e o uso da tecnologia na educação. O texto se insere no âmbito do projeto Brasil 2035, cujo objetivo foi gerar subsídios para a formulação de estratégias de desenvolvimento para o Brasil, a partir da construção de cenários prospectivos. Trata-se, portanto, da apresentação de uma etapa prévia do processo prospectivo de identificação e análise das principais variáveis. Não se trata de ser conclusivo, ao contrário, procura-se apresentar alguns fatores e perguntas-chave que impactarão o desenvolvimento brasileiro até 2035.