Search for an Optimal Design of a Bioprosthetic Venous Valve: In silico and in vitro Studies

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine

INTRODUCTION

Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties.

AREAS COVERED

The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged.

EXPERT OPINION

AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.

Predicting the risk of postsplenectomy thrombosis in patients with portal hypertension using computational hemodynamics models: A proof-of-concept study

BACKGROUND

The high incidence of thrombosis in the portal venous system following splenectomy (a frequently adopted surgery for treating portal hypertension in patients with splenomegaly and hypersplenism) is a critical clinical issue. The aim of this study was to address whether quantification of postsplenectomy hemodynamics has potential value for assessing the risk of postsplenectomy thrombosis.

METHODS

Computational models were constructed for three portal hypertensive patients treated with splenectomy based on their preoperative clinical data to quantify hemodynamics in the portal venous system before and after splenectomy, respectively. Each patient was followed up for three or five months after surgery and examined with CT to screen potential thrombosis.

FINDINGS

The area ratio of wall regions exposed to low wall shear stress was small before splenectomy in all patients, which increased markedly after splenectomy and exhibited enlarged inter-patient differences. The largest area ratio of low wall shear stress and most severe flow stagnation after splenectomy were predicted for the patient suffering from postsplenectomy thrombosis, with the wall regions exposed to low wall shear stress corresponding well with the CT-detected distribution of thrombus. Further analyses revealed that postoperative hemodynamic characteristics were considerably influenced by the anatomorphological features of the portal venous system.

INTERPRETATION

Postoperative hemodynamic conditions in the portal venous system are highly patient-specific and have a potential link to postsplenectomy thrombosis, which indicates that patient-specific hemodynamic studies may serve as a complement to routine clinical assessments for refining risk stratification and postoperative patient management.

Mathematical and Computational Modeling of Device-Induced Thrombosis

Given the extensive and routine use of cardiovascular devices, a major limiting factor to their success is the thrombotic rate that occurs. This both poses direct risk to the patient and requires counterbalancing with anticoagulation and other treatment strategies, contributing additional risks. Developing a better understanding of the mechanisms of device-induced thrombosis to aid in device design and medical management of patients is critical to advance the ubiquitous use and durability. Thus, mathematical and computational modelling of device-induced thrombosis has received significant attention recently, but challenges remain. Additional areas that need to be explored include microscopic/macroscopic approaches, reconciling physical and numerical timescales, immune/inflammatory responses, experimental validation, and incorporating pathologies and blood conditions. Addressing these areas will provide engineers and clinicians the tools to provide safe and effective cardiovascular devices.

Association study of hypertension susceptibility genes ITGA9, MOV10, and CACNB2 with preeclampsia in Chinese Han population

OBJECTIVE

Preeclampsia (PE) is a disorder that occurs during the pregnancy and could affect the maternal and perinatal mortality as well as morbidity. The aim of our study is to investigate the associations between the hypertension susceptibility genes ITGA9, MOV10 and CACNB2 with PE in Chinese Han population.

METHODS

A case-control study including 178 PE patients and 202 healthy controls was conducted to assess the associations between three loci (ITGA9 rs155524, MOV10 rs2932538 and CACNB2 rs4373814) and PE. The TaqMan probe assay was applied for genotyping in our study. Quantitative real-time PCR was performed to detect the mRNA expression levels of ITGA9, MOV10 and CACNB2. ELISA was carried out to detect the concentration of serum sFlt-1 or PLGF.

RESULTS

Our study detected no significant differences in allelic frequencies of three SNPs between PE patients and healthy controls. In the genetic model, the results showed that the patients with ITGA9 rs155524 GA or AA genotypes had a higher risk of PE development compared to those with GG genotype in codominant model. And PE patients had a higher frequency of GA + AA genotypes based on the dominant model. Subgroup analysis showed ITGA9 rs155524 was associated with early-onset PE but not with late-onset PE. No association was observed between MOV10 and CACNB2 with PE in any genetic model and subgroup analysis. Quantitative real-time PCR results showed that ITGA9 mRNA expression level was apparently increased in the placental tissues of PE patients. In addition, ITGA9 expression levels of GA + AA subjects were apparently higher than that in the genotype GG of placental tissues. sFlt-1/PLGF ratio was higher in GA + AA subjects than that in GG subjects. Regression analysis revealed that ratio of sFlt-1/PLGF was positively correlated with ITGA9 mRNA expression level.

CONCLUSION

This study has identified ITGA9 is a promising candidate susceptibility gene for early-onset PE. Our findings demonstrated that the high expression of ITGA9 might be associated with an increased risk of PE.

In silico trials: Verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products

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Regulators now consider also evidences produced in silico.

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We need accepted methods to evaluate the credibility of models.

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In this paper we describe the use of the ASME V&V-40 technical standard.

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We also discuss its application to various types of modelling methods.

Historically, the evidences of safety and efficacy that companies provide to regulatory agencies as support to the request for marketing authorization of a new medical product have been produced experimentally, either in vitro or in vivo. More recently, regulatory agencies started receiving and accepting evidences obtained in silico, i.e. through modelling and simulation. However, before any method (experimental or computational) can be acceptable for regulatory submission, the method itself must be considered “qualified” by the regulatory agency. This involves the assessment of the overall “credibility” that such a method has in providing specific evidence for a given regulatory procedure. In this paper, we describe a methodological framework for the credibility assessment of computational models built using mechanistic knowledge of physical and chemical phenomena, in addition to available biological and physiological knowledge; these are sometimes referred to as “biophysical” models. Using guiding examples, we explore the definition of the context of use, the risk analysis for the definition of the acceptability thresholds, and the various steps of a comprehensive verification, validation and uncertainty quantification process, to conclude with considerations on the credibility of a prediction for a specific context of use. While this paper does not provide a guideline for the formal qualification process, which only the regulatory agencies can provide, we expect it to help researchers to better appreciate the extent of scrutiny required, which should be considered early on in the development/use of any (new) in silico evidence.