Two-dimensional perfusion angiography permits direct visualization of redistribution of flow in hepatocellular carcinoma during b-TACE

OBJECTIVES

To demonstrate in vivo redistribution of the blood flow towards HCC's lesions by utilizing two-dimensional perfusion angiography in b-TACE procedures.

MATERIAL AND METHODS

In total, 30 patients with 35 HCC nodules treated in the period between January 2019 and November 2021. For each patient, a post-processing software leading to a two-dimensional perfusion angiography was applied on each angiography performed via balloon microcatheter, before and after inflation. On the colour map obtained, reflecting the evolution of contrast intensity change over time, five regions of interests (ROIs) were assessed: one on the tumour (ROI-t), two in the immediate peritumoural healthy liver parenchyma (ROI-ihl) and two in the peripheral healthy liver parenchyma (ROI-phl). The results have been interpreted with a novel in silico model that simulates the hemodynamics of the hepatic arterial system.

RESULTS

Among the ROIs drawn inside the same segment of target lesion, the time-to-peak of the ROI-t and of the ROI-ihl have a significantly higher mean value when the balloon was inflated compared with the ROIs obtained with deflated balloon (10.33 ± 3.66 s vs 8.87 ± 2.60 s (p = 0.015) for ROI-t; 10.50 ± 3.65 s vs 9.23 ± 2.70 s (p = 0.047) for ROI-ihl). The in silico model prediction time-to-peak delays when balloon was inflated, match with those observed in vivo. The numerical flow analysis shows how time-to-peak delays are caused by the obstruction of the balloon-occluded artery and the opening of intra-hepatic collateral.

CONCLUSION

The measurements identify predictively the flow redistribution in the hepatic arteries during b-TACE, supporting a proper positioning of the balloon microcatheter.

In silico modeling for ex vivo placenta perfusion of nicotine

Nicotine readily crosses the placenta to reach fetuses. However, membrane transporters, e.g., organic cation transporters (OCTs) play a role in the clearance of nicotine from the fetal to the maternal side, and this is rarely investigated clinically. In this work, we use an in silico model to simulate an ex vivo placenta perfusion experiment, which is the gold standard for measuring the transplacental permeability of compounds, including nicotine. The model consists of a system of seven ordinary differential equations (ODEs), where each equation represents the nicotine concentration in compartments that emulate the ex vivo experiment setup. The transport role of OCTs is simulated bi-directionally at the placenta's basal membrane (the fetal side). We show that the model can not only reproduce the actual ex vivo experiment results, but also predict the likely maternal and fetal nicotine concentrations when the OCT transporters are inhibited, which leads to a ∼12% increase in fetal nicotine concentration after 2 hours of OCT modulated nicotine perfusion. In conclusion, a first in silico model is proposed in this paper that can be used to simulate some subtle features of trans-placental properties of nicotine.

A new workflow for the effective curation of membrane permeability data from open ADME information

Membrane permeability is an in vitro parameter that represents the apparent permeability (Papp) of a compound, and is a key absorption, distribution, metabolism, and excretion parameter in drug development. Although the Caco-2 cell lines are the most used cell lines to measure Papp, other cell lines, such as the Madin-Darby Canine Kidney (MDCK), LLC-Pig Kidney 1 (LLC-PK1), and Ralph Russ Canine Kidney (RRCK) cell lines, can also be used to estimate Papp. Therefore, constructing in silico models for Papp estimation using the MDCK, LLC-PK1, and RRCK cell lines requires collecting extensive amounts of in vitro Papp data. An open database offers extensive measurements of various compounds covering a vast chemical space; however, concerns were reported on the use of data published in open databases without the appropriate accuracy and quality checks. Ensuring the quality of datasets for training in silico models is critical because artificial intelligence (AI, including deep learning) was used to develop models to predict various pharmacokinetic properties, and data quality affects the performance of these models. Hence, careful curation of the collected data is imperative. Herein, we developed a new workflow that supports automatic curation of Papp data measured in the MDCK, LLC-PK1, and RRCK cell lines collected from ChEMBL using KNIME. The workflow consisted of four main phases. Data were extracted from ChEMBL and filtered to identify the target protocols. A total of 1661 high-quality entries were retained after checking 436 articles. The workflow is freely available, can be updated, and has high reusability. Our study provides a novel approach for data quality analysis and accelerates the development of helpful in silico models for effective drug discovery. Scientific Contribution: The cost of building highly accurate predictive models can be significantly reduced by automating the collection of reliable measurement data. Our tool reduces the time and effort required for data collection and will enable researchers to focus on constructing high-performance in silico models for other types of analysis. To the best of our knowledge, no such tool is available in the literature.

Radiomics Multifactorial in Silico Model for Spatial Prediction of Glioblastoma Progression and Recurrence: A Proof-of-Concept

BACKGROUND

Radiomics-based prediction of glioblastoma spatial progression and recurrence may improve personalized strategies. However, most prototypes are based on limited monofactorial Gompertzian models of tumor growth. The present study consists of a proof of concept on the accuracy of a radiomics multifactorial in silico model in predicting short-term spatial growth and recurrence of glioblastoma.

METHODS

A radiomics-based biomathematical multifactorial in silico model was developed using magnetic resonance imaging (MRI) data from a 53-year-old patient with newly diagnosed glioblastoma of the right supramarginal gyrus. Raw and optimized models were derived from the MRI at diagnosis and matched to the preoperative MRI obtained 28 days after diagnosis to test the accuracy in predicting the short-term spatial growth of the tumor. An additional optimized model was derived from the early postoperative MRI and matched to the MRI documenting tumor recurrence to test spatial accuracy in predicting the location of recurrence. The spatial prediction accuracy of the model was reported as an average Jaccard index.

RESULTS

Optimized models yielded an average Jaccard index of 0.69 and 0.26 for short-term tumor growth and long-term recurrence site, respectively.

CONCLUSIONS

The present radiomics-based multifactorial in silico model was feasible, reliable, and accurate for short-term spatial prediction of glioblastoma progression. The predictive value for the spatial location of recurrence was still low, and refinements in the description of tissue reorganization in the peritumoral and resected areas may be critical to optimize accuracy further.

Understanding the ideal wound healing mechanistic behavior using in silico modelling perspectives: A review

Complexity of the entire body precludes an accurate assessment of the specific contributions of tissues or cells during the healing process, which might be expensive and time consuming. Because of this, controlling the wound's size, depth, and dimensions may be challenging, and there is not yet an efficient and reliable chronic wound model representation. Furthermore, given the inherent challenges associated with conducting non-invasive in vivo investigations, it becomes peremptory to explore alternative methodologies for studying wound healing. In this context, biologically-realistic mathematical and computational models emerge as a valuable framework that can effectively address this need. Therefore, it might improve our approach to understanding the process at its core. This article will examines all facets of wound healing, including the kinds, pathways, and most current developments in wound treatment worldwide, particularly in silico modelling utilizing both mathematical and structure-based modelling techniques. It may be helpful to identify the crucial traits through the feedback loop of computer models and experimental investigations in order to build innovative therapies to cure wounds. Hence the effectiveness of personalised medicine and more targeted therapy in the healing of wounds may be enhanced by this interdisciplinary expertise.

Thoracic Stent Graft Numerical Models To Virtually Simulate Thoracic Endovascular Aortic Repair: A Scoping Review

OBJECTIVE

Pre-procedural planning of thoracic endovascular aortic repair (TEVAR) may implement computational adjuncts to predict technical and clinical outcomes. The aim of this scoping review was to explore the currently available TEVAR procedure and stent graft modelling options.

DATA SOURCES

PubMed (MEDLINE), Scopus, and Web of Science were systematically searched (English language, up to 9 December 2022) for studies presenting a virtual thoracic stent graft model or TEVAR simulation.

REVIEW METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) was followed. Qualitative and quantitative data were extracted, compared, grouped, and described. Quality assessment was performed using a 16 item rating rubric.

RESULTS

Fourteen studies were included. Among the currently available in silico simulations of TEVAR, severe heterogeneity exists in study characteristics, methodological details, and evaluated outcomes. Ten studies (71.4%) were published during the last five years. Eleven studies (78.6%) included heterogeneous clinical data to reconstruct patient specific aortic anatomy and disease (e.g., type B aortic dissection, thoracic aortic aneurysm) from computed tomography angiography imaging. Three studies (21.4%) constructed idealised aortic models with literature input. The applied numerical methods consisted of computational fluid dynamics analysing aortic haemodynamics in three studies (21.4%) and finite element analysis analysing structural mechanics in the others (78.6%), including or excluding aortic wall mechanical properties. The thoracic stent graft was modelled as two separate components (e.g., graft, nitinol) in 10 studies (71.4%), as a one component homogenised approximation (n = 3, 21.4%), or including nitinol rings only (n = 1, 7.1%). Other simulation components included the catheter for virtual TEVAR deployment and numerous outcomes (e.g., Von Mises stresses, stent graft apposition, drag forces) were evaluated.

CONCLUSION

This scoping review identified 14 severely heterogeneous TEVAR simulation models, mostly of intermediate quality. The review concludes there is a need for continuous collaborative efforts to improve the homogeneity, credibility, and reliability of TEVAR simulations.

Making in silico predictive models for toxicology FAIR

In silico predictive models for toxicology include quantitative structure-activity relationship (QSAR) and physiologically based kinetic (PBK) approaches to predict physico-chemical and ADME properties, toxicological effects and internal exposure. Such models are used to fill data gaps as part of chemical risk assessment. There is a growing need to ensure in silico predictive models for toxicology are available for use and reproducible. This paper describes how the FAIR (Findable, Accessible, Interoperable, Reusable) principles, developed for data sharing, have been applied to in silico predictive models. In particular, this investigation has focussed on how the FAIR principles could be applied to improved regulatory acceptance of predictions from such models. Eighteen principles have been developed that cover all aspects of FAIR. It is intended that FAIRification of in silico predictive models for toxicology will increase their use and acceptance.

Brain physiome: A concept bridging in vitro 3D brain models and in silico models for predicting drug toxicity in the brain

In the last few decades, adverse reactions to pharmaceuticals have been evaluated using 2D in vitro models and animal models. However, with increasing computational power, and as the key drivers of cellular behavior have been identified, in silico models have emerged. These models are time-efficient and cost-effective, but the prediction of adverse reactions to unknown drugs using these models requires relevant experimental input. Accordingly, the physiome concept has emerged to bridge experimental datasets with in silico models. The brain physiome describes the systemic interactions of its components, which are organized into a multilevel hierarchy. Because of the limitations in obtaining experimental data corresponding to each physiome component from 2D in vitro models and animal models, 3D in vitro brain models, including brain organoids and brain-on-a-chip, have been developed. In this review, we present the concept of the brain physiome and its hierarchical organization, including cell- and tissue-level organizations. We also summarize recently developed 3D in vitro brain models and link them with the elements of the brain physiome as a guideline for dataset collection. The connection between in vitro 3D brain models and in silico modeling will lead to the establishment of cost-effective and time-efficient in silico models for the prediction of the safety of unknown drugs.

Folic acid pretreatment and its sustained delivery for chondrogenic differentiation of MSCs

Dietary uptake of folic acid (FA) improves cartilage regeneration. In this work, we discovered that three days of FA treatment is highly effective for promoting chondrogenic differentiation of tonsil-derived mesenchymal stem cells (TMSCs). In a three-dimensional pellet culture, the levels of typical chondrogenic biomarkers, sulfated glycosaminoglycan, proteoglycan, type II collagen (COL II), SRY box transcription factor 9 (SOX 9), cartilage oligomeric matrix protein (COMP), and aggrecan (ACAN) increased significantly in proportion to FA concentration up to 30 μM. At the mRNA expression level, COL II, SOX 9, COMP, and ACAN increased 3.6-6.0-fold with FA treatment at 30 μM compared with the control system that did not receive FA treatment, and the levels with FA treatment were 1.6-2.5 times greater than those in the kartogenin-treated positive control system. FA treatment did not increase type I collagen α1 (COL I α1), an osteogenic biomarker which is a concern with most chondrogenic promoters. At the high FA concentration of 100 μM, significant decreases in chondrogenic biomarkers were observed, which might be related to DNA methylation. A thermogel system incorporating TMSCs and FA provided sustained release of FA over several days, similar to the FA treatment. The thermogel system confirmed the efficacy of FA in promoting chondrogenic promotion of TMSCs. The increased nuclear translocation of core-binding factor β subunit (CBFβ) and the runt-related transcription factor 1 (RUNX1) expression after FA treatment, together with molecular docking studies, suggest that the chondrogenic enhancement mechanism of FA is mediated by CBFβ and RUNX1.

Imputation of sensory properties using deep learning

Predicting the sensory properties of compounds is challenging due to the subjective nature of the experimental measurements. This testing relies on a panel of human participants and is therefore also expensive and time-consuming. We describe the application of a state-of-the-art deep learning method, Alchemite™, to the imputation of sparse physicochemical and sensory data and compare the results with conventional quantitative structure-activity relationship methods and a multi-target graph convolutional neural network. The imputation model achieved a substantially higher accuracy of prediction, with improvements in R² between 0.26 and 0.45 over the next best method for each sensory property. We also demonstrate that robust uncertainty estimates generated by the imputation model enable the most accurate predictions to be identified and that imputation also more accurately predicts activity cliffs, where small changes in compound structure result in large changes in sensory properties. In combination, these results demonstrate that the use of imputation, based on data from less expensive, early experiments, enables better selection of compounds for more costly studies, saving experimental time and resources.

Mechanistic Models for USP2 Dissolution Apparatus, Including Fluid Hydrodynamics and Sedimentation

Drug product dissolution is a key input to Physiologically Based Biopharmaceutics Models (PBBM) to be able to predict in vivo dissolution. The integration of product dissolution in PBBMs for immediate release drug products should be mechanistic, i.e. allow to capture the main determinants of the in vitro dissolution experiment, and extract product batch specific parameter(s). This work focussed on the Product Particle Size Distribution (P-PSD), which was previously shown to integrate the effect of dose, volume, solubility (pH), size and concentration of micelles in the calculation of a batch specific input to PBBMs, and proposed new hydrodynamic (HD) models, which integrate the effect of USP2 apparatus paddle rotation speed and medium viscosity on dissolution. In addition, new models are also proposed to estimate the quantitative impact of formulation and drug sedimentation or "coning" on dissolution. Model "HDC-1" predicts coning in the presence of formulation insoluble excipients and "HDC-2" predicts the sedimentation of the drug substance only. These models were parameterized and validated on 166 dissolution experiments and 18 different drugs. The validation showed that the HD model average fold errors (AFE) for dissolution rate prediction of immediate release formulations, is comprised between 0.85 and 1.15, and the absolute average fold errors (AAFE) are comprised between 1.08 and 1.28, which shows satisfactory predictive power. For experiments where coning was suspected, the HDC-1 model improved the precision of the prediction (defined as ratio of "AAFE-1"values) by 2.46 fold compared to HD model. The calculation of a P-PSD integrating the impact of USP2 paddle rotation, medium viscosity and coning, will improve the PBBM predictions, since these parameters could have an influence on in vitro dissolution, and could open the way to better prediction of the effect of prandial state on human exposure, by developing new in silico tools which could integrate variation of velocity profiles due to the chyme viscosity.

Validation of the computational model of a coronary stent: a fundamental step towards in silico trials

The proof of the reliability of a numerical model is becoming of paramount importance in the era of in silico clinical trials. When dealing with a coronary stenting procedure, the virtual scenario should be able to replicate the real device, passing through the different stages of the procedure, which has to maintain the atherosclerotic vessel opened. Nevertheless, most of the published studies adopted commercially resembling geometries and generic material parameters, without a specific validation of the employed numerical models. In this work, a workflow for the generation and validation of the computational model of a coronary stent was proposed. Possible sources of variability in the results, such as the inter-batches variability in the material properties and the choice of proper simulation strategies, were accounted for and discussed. Then, a group of in vitro tests, representative of the device intended use was used as a comparator to validate the model. The free expansion simulation, which is the most used simulation in the literature, was shown to be only partially useful for stent model validation purposes. On the other hand, the choice of proper additional experiments, as the suggested uniaxial tensile tests on the stent and deployment tests into a deformable tube, could provide further suitable information to prove the efficacy of the numerical approach.

A corneal-PAMPA-based in silico model for predicting corneal permeability

The capability to predict corneal permeability based on physicochemical parameters has always been a desirable objective of ophthalmic drug development. However, previous work has been limited to cases where either the diversity of compounds used was lacking or the performance of the models was poor. Our study provides extensive quantitative structure-property relationship (QSPR) models for corneal permeability predictions. The models involved in vitro corneal permeability measurements of 189 diverse compounds. Preliminary analysis of data showed that there is no significant correlation between corneal-PAMPA (Parallel Artificial Membrane Permeability Assay) permeability values and other pharmacokinetically relevant in silico drug transport parameters like Caco-2, jejunal permeability and blood-brain partition coefficient (logBB). Two different QSPR models were developed: one for corneal permeability and one for corneal membrane retention, based on experimental corneal-PAMPA permeability data. Partial least squares regression was applied for producing the models, which contained classical molecular descriptors and ECFP fingerprints in combination. A complex validation protocol (including internal and external validation) was carried out to provide robust and appropriate predictions for the permeability and membrane retention values. Both models had an overall fit of R² > 0.90, including R²-values not lower than 0.85 for validation runs, and provide quick and accurate predictions of corneal permeability values for a diverse set of compounds.

In silico health effect prioritization of environmental chemicals through transcriptomics data exploration from a chemo-centric view

With the explosive growth of synthetic compounds, the health effects caused by exogenous chemical exposure have attracted more and more public attention. The prediction of health effect is a never-ending story. Collective resource of transcriptomics data offers an opportunity to understand and identify the multiple health effects of small molecule. Inspired by the fact that environmental chemicals of high health risk frequently share both similar gene expression profile and common structural feature of certain drugs, we here propose a novel computational effect prioritization method for environmental chemicals through transcriptomics data exploration from a chemo-centric view. Specifically, non-negative matrix factorization (NMF) method has been adopted to get the association network linking structural features with transcriptomics characteristics of drugs with specific effects. The model yields 13 pivotal types of effects, so-called components, that represent drug categories with common chemo- and geno- type features. Moreover, the established model effectively prioritizes potential toxic effects for the external chemicals from the endocrine disruptor screening program (EDSP) for their potential estrogenicity and other verified risks. Even if only the highest priority is set for the estrogenic effect, the precision and recall can reach 0.76 and 0.77 respectively for these chemicals. Our effort provides a successful endeavor as to profile potential toxic effects simultaneously for environmental chemicals using both chemical and omics data.

Effects of local coronary blood flow dynamics on the predictions of a model of in-stent restenosis

Computational models are increasingly used to study cardiovascular disease. However, models of coronary vessel remodelling usually make some strong assumptions about the effects of a local narrowing on the flow through the narrowed vessel. Here, we test the effects of local flow dynamics on the predictions of an in-stent restenosis (ISR) model. A previously developed 2D model of ISR is coupled to a 1D model of coronary blood flow. Then, two different assumptions are tested. The first assumption is that the vasculature is always able to adapt, and the volumetric flow rate through the narrowed vessel is kept constant. The second, alternative, assumption is that the vasculature does not adapt at all, and the ratio of the pressure drop to the flow rate (hydrodynamic resistance) stays the same throughout the whole process for all vessels unaffected by the stenosis, and aortic or venous blood pressure does not change either. Then, the dynamics are compared for different locations in coronary tree for two different reendothelization scenarios. The assumptions of constant volumetric flow rate (absolute vascular adaptation) versus constant aortic pressure drop and no adaptation do not significantly affect the growth dynamics for most locations in the coronary tree, and the differences can only be observed at the locations where a strong alternative flow pathway is present. On the other hand, the difference between locations is significant, which is consistent with small vessel size being a risk factor for restenosis. These results suggest that the assumption of a constant flow is a good approximation for ISR models dealing with the typical progression of ISR in the most often stented locations such as the proximal parts of left anterior descending (LAD) and left circumflex (LCX) arteries.

An agent-based model of prostate Cancer bone metastasis progression and response to Radium223

BACKGROUND

Bone metastasis is the most frequent complication in prostate cancer patients and associated outcome remains fatal. Radium223 (Rad223), a bone targeting radioisotope improves overall survival in patients (3.6 months vs. placebo). However, clinical response is often followed by relapse and disease progression, and associated mechanisms of efficacy and resistance are poorly understood. Research efforts to overcome this gap require a substantial investment of time and resources. Computational models, integrated with experimental data, can overcome this limitation and drive research in a more effective fashion.

METHODS

Accordingly, we developed a predictive agent-based model of prostate cancer bone metastasis progression and response to Rad223 as an agile platform to maximize its efficacy. The driving coefficients were calibrated on ad hoc experimental observations retrieved from intravital microscopy and the outcome further validated, in vivo.

RESULTS

In this work we offered a detailed description of our data-integrated computational infrastructure, tested its accuracy and robustness, quantified the uncertainty of its driving coefficients, and showed the role of tumor size and distance from bone on Rad223 efficacy. In silico tumor growth, which is strongly driven by its mitotic character as identified by sensitivity analysis, matched in vivo trend with 98.3% confidence. Tumor size determined efficacy of Rad223, with larger lesions insensitive to therapy, while medium- and micro-sized tumors displayed up to 5.02 and 152.28-fold size decrease compared to control-treated tumors, respectively. Eradication events occurred in 65 ± 2% of cases in micro-tumors only. In addition, Rad223 lost any therapeutic effect, also on micro-tumors, for distances bigger than 400 μm from the bone interface.

CONCLUSIONS

This model has the potential to be further developed to test additional bone targeting agents such as other radiopharmaceuticals or bisphosphonates.

New QSAR models to predict chromosome damaging potential based on the in vivo micronucleus test

A large number of computer-based prediction methods to determine the potential of chemicals to induce mutations at the gene level has been developed over the last decades. Conversely, only few such methods are currently available to predict potential structural and numerical chromosome aberrations. Even fewer of these are based on the preferred testing method for this endpoint, i.e. the micronucleus test. For the present work, in vivo micronucleus test results of 718 structurally diverse compounds were collected and applied for the construction of new models by means of the freely available SARpy in silico model building software. Multiple QSAR models were created using parameter variation and manual verification of (non-) alerting structures. To this extent, the original set of 718 compounds was split into a training (80 %) and a test (20 %) set. SARpy was applied on the training set to automatically extract sets of rules by generating and selecting substructures based on their prediction performance whereas the test set was used to evaluate model performance. Five different splits were made randomly, each of which had a similar balance between positive and negative substances compared to the full dataset. All generated models were characterised by an overall better performance than existing free and commercial models for the same endpoint, while demonstrating high coverage.

The role of hypofractionated radiotherapy in the management of head and neck cancer - a modelling approach

INTRODUCTION

Cancer stem cells (CSCs) and hypoxia are key contributors towards radioresistance and they influence the choice of radiotherapy schedule for optimal tumour control. Since hypofractionation is becoming more popular in head and neck cancer (HNC) management, the aim of this work is to use a modelling approach to evaluate the efficacy of hypofractionated radiotherapy on both early stage and advanced tumours.

METHODS

An in silico HNC was developed starting from one CSC. For a biologically indorsed tumour, CSCs generate all heterogeneous cell lineages with a 1.9% probability of symmetrical division, 33 h mean cell cycle time and 52 days volume doubling time. The simulated schedules include conventional, hyperfractionated, and hypofractionated radiotherapy and they target tumours with various oxygenation levels.

RESULTS

Oxic and mildly hypoxic tumours can benefit from hypofractionation, which reduces treatment time without increasing adverse events. Advanced tumours are only controlled by hyperfractionation, however a tumour with oxygen levels below 6 mmHg and 5.9% pre-treatment CSCs, needs either a dose greater than 81.6 Gy to be eradicated or the addition of adjuvant therapies.

CONCLUSIONS

Hypofractionation is suited for early stage tumours, whereas aggressive HNC require hyperfractionation. The interplay between CSCs and hypoxia dictates the optimal treatment strategy.

Improving prediction of torsadogenic risk in the CiPA in silico model by appropriately accounting for clinical exposure

Any adverse event is reliant on three properties: the appropriate pharmacology to trigger the event, the appropriate exposure of compound, and intrinsic patient factors. Each alone is necessary but insufficient to predict the event. The Comprehensive in vitro Proarrhythmia Assessment (CiPA) initiative attempts to predict the risk of torsade de pointes (TdP) by focusing on an in-silico model with thresholds determined at modest multiples of the therapeutic exposure for the parent molecule. This emphasizes the pharmacologic properties necessary for TdP but does not account for situations where clinical exposure may be higher, or where hERG potassium channel active metabolites are involved. Could accounting for clinical worst-case scenarios and metabolites, as is already standard practice in thorough QTc studies, improve the prediction algorithm? Terfenadine, a drug classed as "Intermediate" risk by CiPA, was assessed differently in the in-silico model validation. The clinical concentration of terfenadine used for the model was the exposure in the presence of metabolic inhibition representing a 14 to 40-fold increase in exposure compared to the therapeutic plasma concentration. However, several other "Intermediate" risk compounds are also known to be sensitive to metabolic inhibition and/or to have therapeutically active major metabolites, some of which are known to block hERG. Risperidone and astemizole are relevant examples. If only parent exposure is used to calculate a therapeutic window, risperidone has a relatively large multiple between clinical exposure and the hERG potency. Using this exposure of risperidone, the drug borders the "Intermediate" and "Low/No" risk categories for the CiPA in-silico model's TdP metric. The desmethyl metabolite of astemizole likely contributes significantly to the effects on cardiac repolarization, being equipotent on hERG but circulating at much higher levels than parent. Recalculating the TdP metric and margin values for terfenadine, risperidone and astemizole using the unbound concentration normally associated with treatment and a clinical worst case changes the qNet metric to higher risk values and illustrates the potential benefit to the algorithm of consistently using a clinical high exposure scenario accounting for all "hERG-active species". This exercise suggests repeating the model qualification accounting for clinical exposures and metabolites under 'stressed' scenarios would improve prediction of the TdP risk.

Computational determination of hERG-related cardiotoxicity of drug candidates

Background

Drug candidates often cause an unwanted blockage of the potassium ion channel of the human ether-a-go-go-related gene (hERG). The blockage leads to long QT syndrome (LQTS), which is a severe life-threatening cardiac side effect. Therefore, a virtual screening method to predict drug-induced hERG-related cardiotoxicity could facilitate drug discovery by filtering out toxic drug candidates.

Result

In this study, we generated a reliable hERG-related cardiotoxicity dataset composed of 2130 compounds, which were carried out under constant conditions. Based on our dataset, we developed a computational hERG-related cardiotoxicity prediction model. The neural network model achieved an area under the receiver operating characteristic curve (AUC) of 0.764, with an accuracy of 90.1%, a Matthews correlation coefficient (MCC) of 0.368, a sensitivity of 0.321, and a specificity of 0.967, when ten-fold cross-validation was performed. The model was further evaluated using ten drug compounds tested on guinea pigs and showed an accuracy of 80.0%, an MCC of 0.655, a sensitivity of 0.600, and a specificity of 1.000, which were better than the performances of existing hERG-toxicity prediction models.

Conclusion

The neural network model can predict hERG-related cardiotoxicity of chemical compounds with a high accuracy. Therefore, the model can be applied to virtual high-throughput screening for drug candidates that do not cause cardiotoxicity. The prediction tool is available as a web-tool at http://ssbio.cau.ac.kr/CardPred.

In silico approach to quantify nucleus self-deformation on micropillared substrates

Considering the major role of confined cell migration in biological processes and diseases, such as embryogenesis or metastatic cancer, it has become increasingly important to design relevant experimental set-ups for in vitro studies. Microfluidic devices have recently presented great opportunities in their respect since they offer the possibility to study all the steps from a suspended to a spread, and eventually crawling cell or a cell with highly deformed nucleus. Here, we focus on the nucleus self-deformation over a micropillared substrate. Actin networks have been observed at two locations in this set-up: above the nucleus, forming the perinuclear actin cap (PAC), and below the nucleus, surrounding the pillars. We can then wonder which of these contractile networks is responsible for nuclear deformation. The cytoplasm and the nucleus are represented through the superposition of a viscous and a hyperelastic material and follow a series of processes. First, the suspended cell settles on the pillars due to gravity. Second, an adhesive spreading force comes into play, and then, active deformations contract one or both actin domains and consequently the nucleus. Our model is first tested on a flat substrate to validate its global behaviour before being confronted to a micropillared substrate. Overall, the nucleus appears to be mostly pulled towards the pillars, while the mechanical action of the PAC is weak. Eventually, we test the influence of gravity and prove that the gravitational force does not play a role in the final deformation of the nucleus.

Can the inhibition of cytochrome P450 in aquatic invertebrates due to azole fungicides be estimated with in silico and in vitro models and extrapolated between species?

Azole fungicides, designed to halt fungal growth by specific inhibition of fungal cytochrome P450 (CYP51), inhibit cytochrome P450s involved in the metabolism of xenobiotics in several non-target organisms thus raising environmental concern. The present study investigates the degree by which inhibition strengths of azoles toward cytochrome P450 in rat liver, the insect Chironomus riparius larvae and the snail Lymnaea stagnalis can be extrapolated from estimated in silico affinities. Azoles' affinities toward human cytochrome P450 isoforms involved in xenobiotic metabolism (CYP3A4, CYP2C9 and CYP2D6) as well as fungal CYP51 were estimated with a ligand-protein docking model based on the ChemScore scoring function. Estimated affinities toward the selected enzymatic structures correlated strongly with measured inhibition strengths in rat liver (ChemScore vs. logIC₅₀ among cytochrome P450 isoforms: -0.662 < r < -0.891, n = 17 azoles), while weaker correlations were found for C. riparius larvae (-0.167 < r < -0.733, n = 9) and L. stagnalis (-0.084 < r < -0.648, n = 8). Inhibition strengths toward C. riparius and rat liver activities were found to be highly correlated to each other (r: 0.857) while no significant relationship was found between either of the species and L. stagnalis. The inhibition of cytochrome P450 due to azole fungicides could be estimated in vitro and to a lesser extent in silico for C. riparius but not for L. stagnalis, possibly due to different enzymatic susceptibility toward azole inhibition among the species.

In silico prediction of potential chemical reactions mediated by human enzymes

Background

Administered drugs are often converted into an ineffective or activated form by enzymes in our body. Conventional in silico prediction approaches focused on therapeutically important enzymes such as CYP450. However, there are more than thousands of different cellular enzymes that potentially convert administered drug into other forms.

Result

We developed an in silico model to predict which of human enzymes including metabolic enzymes as well as CYP450 family can catalyze a given chemical compound. The prediction is based on the chemical and physical similarity between known enzyme substrates and a query chemical compound. Our in silico model was developed using multiple linear regression and the model showed high performance (AUC = 0.896) despite of the large number of enzymes. When evaluated on a test dataset, it also showed significantly high performance (AUC = 0.746). Interestingly, evaluation with literature data showed that our model can be used to predict not only enzymatic reactions but also drug conversion and enzyme inhibition.

Conclusion

Our model was able to predict enzymatic reactions of a query molecule with a high accuracy. This may foster to discover new metabolic routes and to accelerate the computational development of drug candidates by enabling the prediction of the potential conversion of administered drugs into active or inactive forms.

In Silico Models for Acute Systemic Toxicity

In this chapter, we give an overview of the regulatory requirements for acute systemic toxicity information in the European Union, and we review the availability of structure-based computational models that are available and potentially useful in the assessment of acute systemic toxicity. The most recently published literature models for acute systemic toxicity are also discussed, and perspectives for future developments in this field are offered.

The Comprehensive in Vitro Proarrhythmia Assay (CiPA) initiative - Update on progress

The implementation of the ICH S7B and E14 guidelines has been successful in preventing the introduction of potentially torsadogenic drugs to the market, but it has also unduly constrained drug development by focusing on hERG block and QT prolongation as essential determinants of proarrhythmia risk. The Comprehensive in Vitro Proarrhythmia Assay (CiPA) initiative was established to develop a new paradigm for assessing proarrhythmic risk, building on the emergence of new technologies and an expanded understanding of torsadogenic mechanisms beyond hERG block. An international multi-disciplinary team of regulatory, industry and academic scientists are working together to develop and validate a set of predominantly nonclinical assays and methods that eliminate the need for the thorough-QT study and enable a more precise prediction of clinical proarrhythmia risk. The CiPA effort is led by a Steering Team that provides guidance, expertise and oversight to the various working groups and includes partners from US FDA, HESI, CSRC, SPS, EMA, Health Canada, Japan NIHS, and PMDA. The working groups address the three pillars of CiPA that evaluate drug effects on: 1) human ventricular ionic channel currents in heterologous expression systems, 2) in silico integration of cellular electrophysiologic effects based on ionic current effects, the ion channel effects, and 3) fully integrated biological systems (stem-cell-derived cardiac myocytes and the human ECG). This article provides an update on the progress of the initiative towards its target date of December 2017 for completing validation.

Restriction in hip internal rotation is associated with an increased risk of ACL injury

PURPOSE

Evidence suggests that femoroacetabular impingement (FAI) in athletes may increase the risk of anterior cruciate ligament (ACL) injury. This study correlates ACL injury with hip range of motion in a consecutive series of elite, contact athletes and tests the hypothesis that a restriction in the available hip axial rotation in a dynamic in silico model of a simulated pivot landing would increase ACL strain and the risk of ACL rupture.

METHODS

Three hundred and twenty-four football athletes attending the 2012 NFL National Invitational Camp were examined. Hip range of internal rotation was measured and correlated with a history of ACL injury and surgical repair. An in silico biomechanical model was used to study the effect of FAI on the peak relative ACL strain developed during a simulated pivot landing.

RESULTS

The in vivo results demonstrated that a reduction in internal rotation of the left hip was associated with a statistically significant increased odds of ACL injury in the ipsilateral or contralateral knee (OR 0.95, p = 0.0001 and p < 0.0001, respectively). A post-estimation calculation of odds ratio for ACL injury based on deficiency in hip internal rotation demonstrated that a 30-degree reduction in left hip internal rotation was associated with 4.06 and 5.29 times greater odds of ACL injury in the ipsilateral and contralateral limbs, respectively. The in silico model demonstrated that FAI systematically increased the peak ACL strain predicted during the pivot landing.

CONCLUSION

FAI may be associated with ACL injury because of the increased resistance to femoral internal axial rotation during a dynamic maneuver such as a pivot landing. This insight may lead to better interventions to prevent ACL injury and improved understanding of ACL reconstruction failure.

LEVEL OF EVIDENCE

Cohort study, Level IV.

An in silico model to predict the potential of dietary proteins as sources of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides

An in silico approach was developed to predict the potential of 72 dietary proteins to act as a source of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides. The model takes 68 DPP-IV inhibitory peptides (having an IC50 value <2000 μM) and the specific contribution of their amino acids into account. Bovine α-lactalbumin (α-La) and κ-casein (CN) displayed the highest protein coverage (PC, 43.9%) and potency index (PI, 17.9 10(-6) μM(-1)g(-1)), respectively for DPP-IV inhibitory peptides. Sequence alignment of 39 DPP-IV inhibitory peptides having IC50's<200 μM revealed the frequent occurrence of Trp at the N-terminus and Pro at position 2. Canola, chicken egg, oat and wheat were identified as potential sources of DPP-IV inhibitory peptides. In silico approaches may assist in the selection of food proteins for the enzymatic release of DPP-IV inhibitory peptides. The results are relevant to the generation of biofunctional ingredients for glycaemic management.

Mathematical modeling of regenerative processes

In many animals, regenerative processes can replace lost body parts. Organ and tissue regeneration consequently also hold great medical promise. The regulation of regenerative processes is achieved through concerted actions of multiple organizational levels of the organism, from diffusing molecules and cellular gene expression patterns up to tissue mechanics. Our intuition is usually not adapted well to this degree of complexity and the quantitative aspects of the regulation of regenerative processes remain poorly understood. One way out of this dilemma lies in the combination of experimentation and mathematical modeling within an iterative process of model development/refinement, model predictions for novel experimental conditions, quantitative experiments testing these predictions, and subsequent model refinement. This interdisciplinary approach has already provided key insights into smaller scale processes during embryonic development and a so-far limited number of more complex regeneration processes. This review discusses selected theoretical and interdisciplinary studies and is structured along the three phases of regeneration: (1) initiation of a regeneration response, (2) tissue patterning during regenerate growth, (3) arresting the regeneration response. Moreover, we highlight the opportunities provided by extensions of mathematical models from developmental processes toward the study of related regenerative processes.