Simulating a virtual population's sensitivity to salt and uninephrectomy

MapperPlus: Agnostic clustering of high-dimension data for precision medicine

One of the goals of precision medicine is to classify patients into subgroups that differ in their susceptibility and response to a disease, thereby enabling tailored treatments for each subgroup. Therefore, there is a great need to identify distinctive clusters of patients from patient data. There are three key challenges to three key challenges of patient stratification: 1) the unknown number of clusters, 2) the need for assessing cluster validity, and 3) the clinical interpretability. We developed MapperPlus, a novel unsupervised clustering pipeline, that directly addresses these challenges. It extends the topological Mapper technique and blends it with two random-walk algorithms to automatically detect disjoint subgroups in patient data. We demonstrate that MapperPlus outperforms traditional agnostic clustering methods in key accuracy/performance metrics by testing its performance on publicly available medical and non-medical data set. We also demonstrate the predictive power of MapperPlus in a medical dataset of pediatric stem cell transplant patients where a number of cluster is unknown. Here, MapperPlus stratifies the patient population into clusters with distinctive survival rates. The MapperPlus software is open-source and publicly available.

The Combination of Cell Cultured Technology and In Silico Model to Inform the Drug Development

Human-derived in vitro models can provide high-throughput efficacy and toxicity data without a species gap in drug development. Challenges are still encountered regarding the full utilisation of massive data in clinical settings. The lack of translated methods hinders the reliable prediction of clinical outcomes. Therefore, in this study, in silico models were proposed to tackle these obstacles from in vitro to in vivo translation, and the current major cell culture methods were introduced, such as human-induced pluripotent stem cells (hiPSCs), 3D cells, organoids, and microphysiological systems (MPS). Furthermore, the role and applications of several in silico models were summarised, including the physiologically based pharmacokinetic model (PBPK), pharmacokinetic/pharmacodynamic model (PK/PD), quantitative systems pharmacology model (QSP), and virtual clinical trials. These credible translation cases will provide templates for subsequent in vitro to in vivo translation. We believe that synergising high-quality in vitro data with existing models can better guide drug development and clinical use.

Impact of sex and pathophysiology on optimal drug choice in hypertensive rats: quantitative insights for precision medicine

Less than half of all hypertensive patients receiving treatment are successful in normalizing their blood pressure. Despite the complexity and heterogeneity of hypertension, the current antihypertensive guidelines are not tailored to the individual patient. As a step toward individualized treatment, we develop a quantitative systems pharmacology model of blood pressure regulation in the spontaneously hypertensive rat (SHR) and generate sex-specific virtual populations of SHRs to account for the heterogeneity between the sexes and within the pathophysiology of hypertension. We then used the mechanistic model integrated with machine learning tools to study how variability in these mechanisms leads to differential responses in rodents to the four primary classes of antihypertensive drugs. We found that both the sex and the pathophysiological profile of the individual play a major role in the response to hypertensive treatments. These results provide insight into potential areas to apply precision medicine in human primary hypertension.

The interaction on hypertension between family history and diabetes and other risk factors

To explore the individual effect and interaction of diabetes and family history and other risk factors on hypertension in Han in Shanghai China. The method of case-control study with l:l matched pairs was used, 342 cases of hypertension and 342 controls were selected and investigate their exposed factors with face-to-face. The method of epidemiology research was used to explore the individual effect and interaction of diabetes and family history and other risk factors on hypertension. The individual effect of family history (OR = 4.103, 95%CI 2.660-6.330), diabetes (OR = 4.219, 95%CI 2.926-6.083), personal taste (OR = 1.256, 95%CI 1.091-1.593), drinking behavior (OR = 1.391, 95%CI 1.010-1.914) and smoking behavior (OR = 1.057, 95%CI 1.00-1.117) were significant (p < 0.05). But individual effect of sex, education, occupation, work/life pressure, environmental noise, sleeping time and sports habit were not significant (p > 0.05). The OR of interaction between FH and DM to hypertension was 16.537 (95%CI 10.070-21.157), between FH and drinking behavior was 4.0 (95%CI 2.461-6.502), FH and sport habit was 7.668 (95%CI 3.598-16.344), FH and personal taste was 6.521 (95%CI 3.858-11.024), FH and smoking behavior was 5.526 (95%CI 3.404-8.972), FH and work/life pressure was 4.087 (95%CI 2.144-7.788). The SI of FH and DM was 2.27, RERI was 8.68, AP was 52.48% and PAP was 55.86%. FH and DM, personal taste, smoking behavior had positive interaction on hypertension, but FH and sport habits, drinking behavior, work/life pressure had reverse interaction on hypertension. FH and diabetes were very important risk factors with significant effect for hypertension. FH and diabetes, personal taste, smoking behavior had positive interaction on hypertension, but FH and sport habits, drinking behavior, work/life pressure had reverse interaction on hypertension.

In silico trial of baroreflex activation therapy for the treatment of obesity-induced hypertension

Clinical trials evaluating the efficacy of chronic electrical stimulation of the carotid baroreflex for the treatment of hypertension (HTN) are ongoing. However, the mechanisms by which this device lowers blood pressure (BP) are unclear, and it is uncertain which patients are most likely to receive clinical benefit. Mathematical modeling provides the ability to analyze complicated interrelated effects across multiple physiological systems. Our current model HumMod is a large physiological simulator that has been used previously to investigate mechanisms responsible for BP lowering during baroreflex activation therapy (BAT). First, we used HumMod to create a virtual population in which model parameters (n = 335) were randomly varied, resulting in unique models (n = 6092) that we define as a virtual population. This population was calibrated using data from hypertensive obese dogs (n = 6) subjected to BAT. The resultant calibrated virtual population (n = 60) was based on tuning model parameters to match the experimental population in 3 key variables: BP, glomerular filtration rate, and plasma renin activity, both before and after BAT. In the calibrated population, responses of these 3 key variables to chronic BAT were statistically similar to experimental findings. Moreover, blocking suppression of renal sympathetic nerve activity (RSNA) and/or increased secretion of atrial natriuretic peptide (ANP) during BAT markedly blunted the antihypertensive response in the virtual population. These data suggest that in obesity-mediated HTN, RSNA and ANP responses are key factors that contribute to BP lowering during BAT. This modeling approach may be of value in predicting BAT responses in future clinical studies.

Testing Computer Models Predicting Human Responses to a High-Salt Diet

Recently, mathematical models of human integrative physiology, derived from Guyton's classic 1972 model of the circulation, have been used to investigate potential mechanistic abnormalities mediating salt sensitivity and salt-induced hypertension. We performed validation testing of 2 of the most evolved derivatives of Guyton's 1972 model, Quantitative Cardiovascular Physiology-2005 and HumMod-3.0.4, to determine whether the models accurately predict sodium balance and hemodynamic responses of normal subjects to increases in salt intake within the real-life range of salt intake in humans. Neither model, nor the 1972 Guyton model, accurately predicts the usual changes in sodium balance, cardiac output, and systemic vascular resistance that normally occur in response to clinically realistic increases in salt intake. Furthermore, although both contemporary models are extensions of the 1972 Guyton model, testing revealed major inconsistencies between model predictions with respect to sodium balance and hemodynamic responses of normal subjects to short-term and long-term salt loading. These results demonstrate significant limitations with the hypotheses inherent in the Guyton models regarding the usual regulation of sodium balance, cardiac output, and vascular resistance in response to increased salt intake in normal salt-resistant humans. Accurate understanding of the normal responses to salt loading is a prerequisite for accurately establishing abnormal responses to salt loading. Accordingly, the present results raise concerns about the interpretation of studies of salt sensitivity with the various Guyton models. These findings indicate a need for continuing development of alternative models that incorporate mechanistic concepts of blood pressure regulation fundamentally different from those in the 1972 Guyton model and its contemporary derivatives.