Click here to read some interesting recently published papers from our community. If you have published an article in the field of in silico medicine, send it to us: we will include it in this section of the newsletter!
Nature - Scientific Reports: Model-informed health and socio-economic benefits of enhancing global equity and access to Covid-19 vaccines
Matteo Italia et al
Abstract
We take a model-informed approach to the view that a global equitable access (GEA) to Covid-19 vaccines is the key to bring this pandemic to an end. We show that the equitable redistribution (proportional to population size) of the currently available vaccines is not sufficient to stop the pandemic, whereas a 60% increase in vaccine access (the global share of vaccinated people) would have allowed the current distribution to stop the pandemic in about a year of vaccination, saving millions of people in poor countries. We then investigate the interplay between access to vaccines and their distribution among rich and poor countries, showing that the access increase to stop the pandemic gets minimized at + 32% by the equitable distribution (− 36% in rich countries and + 60% in poor ones). To estimate the socio-economic benefits of a vaccination campaign with enhanced global equity and access (eGEA), we compare calibrated simulations of the current scenario with a hypothetical, vaccination-intensive scenario that assumes high rollouts (shown however by many rich and poor countries during the 2021–2022 vaccination campaign) and an improved equity from the current 2.5:1 to a 2:1 rich/poor-ratio of the population fractions vaccinated per day. Assuming that the corresponding + 130% of vaccine production is made possible by an Intellectual Property waiver, we show that the money saved on vaccines globally by the selected eGEA scenario overcomes the 5-year profit of the rights holders in the current situation. This justifies compensation mechanisms in exchange for the necessary licensing agreements. The good news is that the benefits of this eGEA scenario are still relevant, were we ready to implement it now.
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Biorxiv (pre-print): Insights into Intestinal P-glycoprotein Function using Talinolol: A PBPK Modeling Approach
Beatrice Stemmer Mallol et al
Abstract
Talinolol is a cardioselective beta-blocker that was previously used to treat heart failure and myocardial infarction. Following the development of new, more effective beta-blockers with better study results, talinolol is now only used clinically for the treatment of arterial hypertension. In basic science, talinolol continues to be used as a test substance due to its pharmacokinetics. Its intestinal absorption is determined by uptake by the organic anion transporting polypeptide 2B1 (OATP2B1) and efflux via P-glycoprotein (P-gp). Talinolol can be taken up via OATP1B1 in the liver, where it enters the enterohepatic circulation. Talinolol is excreted unchanged in the urine and feces. Talinolol is widely used as a probe drug for the intestinal efflux transporter P-gp, which plays a critical role in protecting against potentially toxic substances and facilitating the elimination of xenobiotics. In this work, an extensive database of talinolol pharmacokinetics was established and used to develop and validate a physiologically based pharmacokinetic (PBPK) model of talinolol for P-gp phenotyping. The model was used to investigate the influence of several factors on talinolol pharmacokinetics: (i) inhibition of P-gp via drug-drug interaction; (i) genetic polymorphisms of P-gp; (iii) activity of OATP2B1 and OATP1B1; (iv) effect of comorbidity, namely hepatic and renal impairment; and (v) site-specific distribution of P-gp and OATP2B1 in the intestine. The model accurately predicts the concentration-time profile of talinolol after oral or intravenous administration of single and multiple dosing. Furthermore, the model accurately describes the effect of genetic variants of P-gp on the pharmacokinetics of talinolol, the effect of inhibition of P-gp, the effect of renal impairment, as well as site-specific infusion of talinolol in the intestine. The detailed description of the intestinal absorption of talinolol and the predictions of talinolol pharmacokinetics as a function of hepatorenal impairment provide valuable clinical insights for metabolic phenotyping with talinolol. Both the model and the database are freely available for reuse.
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Intelligence-Based Cardiology and Cardiac Surgery: Chapter 36 - Digital twin in cardiovascular medicine and surgery
G. Hamilton Baker, Matthew Davis
Abstract
The interest in digital twin technology in healthcare is rapidly growing, particularly regarding its application in cardiovascular medicine and surgery. A digital twin is a virtual model of a physical system that simulates its behavior and responses to various stimuli, allowing for predictive analysis and optimization. In cardiovascular medicine, digital twins can be used at many scales, from microscopic, such as modeling cellular interactions, to macroscopic, such as modeling an entire healthcare enterprise. This chapter explores the current state of digital twin technology in cardiovascular medicine and surgery, including its potential applications, limitations, and future directions. It also discusses challenges and opportunities associated with integrating digital twin technology into healthcare.
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Physical and Engineering Sciences in Medicine - Springer: A novel computer modeling and simulation technique for bronchi motion tracking in human lungs under respiration
Byeong-Jun Kim et al
Abstract
In this work, we proposed a novel computer modeling and simulation technique for motion tracking of lung bronchi (or tumors) under respiration using 9 cases of computed tomography (CT)-based patient-specific finite element (FE) models and Ogden's hyperelastic model. In the fabrication of patient-specific FE models for the respiratory system, various organs such as the mediastinum, diaphragm, and thorax that could affect the lung motions during breathing were considered. To describe the nonlinear material behavior of lung parenchyma, the comparative simulation for biaxial tension-compression of lung parenchyma was carried out using several hyperelastic models in ABAQUS, and then, Ogden's model was adopted as an optimal model. Based on the aforementioned FE models and Ogden's material model, the 9 cases of respiration simulation were carried out from exhalation to inhalation, and the motion of lung bronchi (or tumors) was tracked. In addition, the changes in lung volume, lung cross-sectional area on the axial plane during breathing were calculated. Finally, the simulation results were quantitatively compared to the inhalation/exhalation CT images of 9 subjects to validate the proposed technique. Through the simulation, it was confirmed that the average relative errors of simulation to clinical data regarding to the displacement of 258 landmarks in the lung bronchi branches of total subjects were 1.10%~2.67%. In addition, the average relative errors of those with respect to the lung cross-sectional area changes and the volume changes in the superior-inferior direction were 0.20%~5.00% and 1.29 ~ 9.23%, respectively. Hence, it was considered that the simulation results were coincided well with the clinical data. The novelty of the present study is as follows: (1) The framework from fabrication of the human respiratory system to validation of the bronchi motion tracking is provided step by step. (2) The comparative simulation study for nonlinear material behavior of lung parenchyma was carried out to describe the realistic lung motion. (3) Various organs surrounding the lung parenchyma and restricting its motion were considered in respiration simulation. (4) The simulation results such as landmark displacement, lung cross-sectional area/volume changes were quantitatively compared to the clinical data of 9 subjects.
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SSRN: Chatgpt: How Many Data Protection Principles Do You Comply with?
Meszaros, Janos and Preuveneers, Davy and Biasin, Elisabetta and Marquet, Enzo and Erdogan-Peter, Irmak and Rosal Santos, Isabela Maria and Vranckaert, Koen and Belkadi, Lydia and Menéndez, Natalia
Abstract
Large Language Models (LLMs) such as ChatGPT have revolutionized the field of AI, offering unprecedented capabilities in natural language processing. However, their extensive data requirements and potential for unintended data exposure have raised significant concerns regarding privacy and compliance with the General Data Protection Regulation (GDPR).This paper critically assesses the intricate relationship between the GDPR data protection principles and LLMs, considering both legal and technical implications. Additionally, the technical analysis delves into potential solutions for enhancing privacy in LLMs, emphasizing the feasibility and benefits of hosting LLMs offline.While the GDPR provides a robust framework for safeguarding privacy, it is challenging for the data processing principles to prevail in their traditional form when applied to LLMs.
