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!
Annals of Biomedical Engineering: An In Silico Modelling Approach to Predict Hemodynamic Outcomes in Diabetic and Hypertensive Kidney Disease.
Ning Wang et al
Abstract
Early diagnosis of kidney disease remains an unmet clinical challenge, preventing timely and effective intervention. Diabetes and hypertension are two main causes of kidney disease, can often appear together, and can only be distinguished by invasive biopsy. In this study, we developed a modelling approach to simulate blood velocity, volumetric flow rate, and pressure wave propagation in arterial networks of ageing, diabetic, and hypertensive virtual populations. The model was validated by comparing our predictions for pressure, volumetric flow rate and waveform-derived indexes with in vivo data on ageing populations from the literature. The model simulated the effects of kidney disease, and was calibrated to align quantitatively with in vivo data on diabetic and hypertensive nephropathy from the literature. Our study identified some potential biomarkers extracted from renal blood flow rate and flow pulsatility. For typical patient age groups, resistive index values were 0.69 (SD 0.05) and 0.74 (SD 0.02) in the early and severe stages of diabetic nephropathy, respectively. Similar trends were observed in the same stages of hypertensive nephropathy, with a range from 0.65 (SD 0.07) to 0.73 (SD 0.05), respectively. Mean renal blood flow rate through a single diseased kidney ranged from 329 (SD 40, early) to 317 (SD 38, severe) ml/min in diabetic nephropathy and 443 (SD 54, early) to 388 (SD 47, severe) ml/min in hypertensive nephropathy, showing potential as a biomarker for early diagnosis of kidney disease. This modelling approach demonstrated its potential application in informing biomarker identification and facilitating the setup of clinical trials.
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Clinical and Translational Science: Increasing acceptance of AI-generated digital twins through clinical trial applications.
Anna A. Vidovszky et al
Abstract
Today's approach to medicine requires extensive trial and error to determine the proper treatment path for each patient. While many fields have benefited from technological breakthroughs in computer science, such as artificial intelligence (AI), the task of developing effective treatments is actually getting slower and more costly. With the increased availability of rich historical datasets from previous clinical trials and real-world data sources, one can leverage AI models to create holistic forecasts of future health outcomes for an individual patient in the form of an AI-generated digital twin. This could support the rapid evaluation of intervention strategies in silico and could eventually be implemented in clinical practice to make personalized medicine a reality. In this work, we focus on uses for AI-generated digital twins of clinical trial participants and contend that the regulatory outlook for this technology within drug development makes it an ideal setting for the safe application of AI-generated digital twins in healthcare. With continued research and growing regulatory acceptance, this path will serve to increase trust in this technology and provide momentum for the widespread adoption of AI-generated digital twins in clinical practice.
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The Lancet Digital Health: Virtual pregnancies: predicting and preventing pregnancy complications with digital twins.
Adrienne K Scott, Michelle L Oyen
Abstract
Global progress in reducing mortality and morbidity in people who give birth has been unacceptably slow. In 2020, 223 maternal deaths per 100 000 live births occurred worldwide. Furthermore, poor health outcomes in people who give birth reflect severe socioeconomic, racial, and ethnic disparities in health care. For example, in the USA, non-Hispanic Black women are approximately three times more likely to die as a result of pregnancy-related complications than non-Hispanic White women. Similar statistics are seen in other countries, such as in the UK. To improve outcomes, considering all associated risk factors that contribute to poor health outcomes in people who give birth is crucial, including social factors, pre-existing conditions, and direct obstetric complications (eg, pre-eclampsia, infection, and postpartum haemorrhage).
Studying the rapid physiological changes of pregnancy throughout 9 months is especially difficult as the reproductive anatomy and physiology of animals used in research substantially differ to that of humans. The inherent challenges of studying this multifactorial and dynamic system might explain the slow advancements in treating pregnancy complications.
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Theranostics: Theranostic digital twins: Concept, framework and roadmap towards personalized radiopharmaceutical therapies.
Hamid Abdollah et al
Abstract
Radiopharmaceutical therapy (RPT) is a rapidly developing field of nuclear medicine, with several RPTs already well established in the treatment of several different types of cancers. However, the current approaches to RPTs often follow a somewhat inflexible “one size fits all” paradigm, where patients are administered the same amount of radioactivity per cycle regardless of their individual characteristics and features. This approach fails to consider inter-patient variations in radiopharmacokinetics, radiation biology, and immunological factors, which can significantly impact treatment outcomes. To address this limitation, we propose the development of theranostic digital twins (TDTs) to personalize RPTs based on actual patient data. Our proposed roadmap outlines the steps needed to create and refine TDTs that can optimize radiation dose to tumors while minimizing toxicity to organs at risk. The TDT models incorporate physiologically-based radiopharmacokinetic (PBRPK) models, which are additionally linked to a radiobiological optimizer and an immunological modulator, taking into account factors that influence RPT response. By using TDT models, we envisage the ability to perform virtual clinical trials, selecting therapies towards improved treatment outcomes while minimizing risks associated with secondary effects. This framework could empower practitioners to ultimately develop tailored RPT solutions for subgroups and individual patients, thus improving the precision, accuracy, and efficacy of treatments while minimizing risks to patients. By incorporating TDT models into RPTs, we can pave the way for a new era of precision medicine in cancer treatment.
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npj Digital Medicine - Nature: From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling
Hanwen Wang et al
Abstract
Virtual patients and digital patients/twins are two similar concepts gaining increasing attention in health care with goals to accelerate drug development and improve patients’ survival, but with their own limitations. Although methods have been proposed to generate virtual patient populations using mechanistic models, there are limited number of applications in immuno-oncology research. Furthermore, due to the stricter requirements of digital twins, they are often generated in a study-specific manner with models customized to particular clinical settings (e.g., treatment, cancer, and data types). Here, we discuss the challenges for virtual patient generation in immuno-oncology with our most recent experiences, initiatives to develop digital twins, and how research on these two concepts can inform each other.
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