Inference of alveolar capillary network connectivity from blood flow dynamics.

The intricate lung structure is crucial for gas exchange within the alveolar region. Despite extensive research, questions remain about the connection between capillaries and the vascular tree. We propose a computational approach combining three-dimensional morphological modeling with computational fluid dynamics simulations to explore alveolar capillary network connectivity based on blood flow dynamics.We developed three-dimensional sheet-flow models to accurately represent alveolar capillary morphology and conducted simulations to predict flow velocities and pressure distributions. Our approach leverages functional features to identify plausible system architectures. Given capillary flow velocities and arteriole-to-venule pressure drops, we deduced arteriole connectivity details. Preliminary analyses for non-human species indicate a single alveolus connects to at least two 20 µm arterioles or one 30 µm arteriole. Hence, our approach narrows down potential connectivity scenarios, but a unique solution may not always be expected.Integrating our blood flow model results into our previously published gas exchange application, Alvin, we linked these scenarios to gas exchange efficiency. We found that increased blood flow velocity correlates with higher gas exchange efficiency.Our study provides insights into pulmonary microvasculature structure by evaluating blood flow dynamics, offering a new strategy to explore the morphology-physiology relationship that is applicable to other tissues and organs. Future availability of experimental data will be crucial in validating and refining our computational models and hypotheses.

Role of rheology in formulation and process design of hot melt extruded amorphous solid dispersions.

Hot melt extrusion (HME) has been widely used as a continuous and highly flexible pharmaceutical manufacturing process for the production of a variety of dosage forms. In particular, HME enables preparation of amorphous solid dispersions (ASDs) which can improve bioavailability of poorly water-soluble drugs. The rheological properties of drug-polymer mixtures can significantly influence the processability of drug formulations via HME and eventually the end-use product properties such as physical stability and drug release. The objective of this review is to provide an overview of various rheological techniques and properties that can be used to evaluate the flow behavior and processability of the drug-polymer mixtures as well as formulation characteristics such as drug-polymer interactions, miscibility/solubility, and plasticization to improve the HME processability. An overview of the thermodynamics and kinetics of ASD processing by HME is also provided, as well as aspects of scale-up and process modeling, highlighting rheological properties on formulation design and process development. Overall, this review provides valuable insights into critical rheological properties which can be used as a predictive tool to optimize the HME processing conditions.

Drug Delivery from A Ring Implant Attached to Intraocular Lens: An In-Silico Investigation.

Multiple iterations required to design ocular implants, which will last for the desired operational period of months or even years, necessitate the use of in-silico models for ocular drug delivery. In this study, we developed an in-silico model to simulate the flow of Aqueous Humor (AH) and drug delivery from an implant to the Trabecular Meshwork (TM). The implant, attached to the side of the intraocular lens (IOL), and the TM are treated as porous media, with their effects on AH flow accounted for using the Darcy equation. This model accurately predicts the physiological values of Intraocular Pressure (IOP) for both healthy individuals and glaucoma patients, as reported in the literature. Results reveal that the effective diffusivity of the drug within the implant is the critical parameter that can alter the bioavailability time period (BTP) from a few days to months. Intuitively, BTP should increase as effective diffusivity decreases. However, we discovered that with lower levels of initial drug loading, BTP declines when effective diffusivity falls below a specific threshold. Our findings further reveal that, while AH flow has a minimal effect on the drug release profile at the implant site, it significantly impacts drug availability at the TM.

The emerging challenge of Enterococcus faecalis endocarditis after transcatheter aortic valve implantation: time for innovative treatment approaches.

SUMMARYInfective endocarditis (IE) is a life-threatening infection that has nearly doubled in prevalence over the last two decades due to the increase in implantable cardiac devices. Transcatheter aortic valve implantation (TAVI) is currently one of the most common cardiac procedures. TAVI usage continues to exponentially rise, inevitability increasing TAVI-IE. Patients with TAVI are frequently nonsurgical candidates, and TAVI-IE 1-year mortality rates can be as high as 74% without valve or bacterial biofilm removal. Enterococcus faecalis, a historically less common IE pathogen, is the primary cause of TAVI-IE. Treatment options are limited due to enterococcal intrinsic resistance and biofilm formation. Novel approaches are warranted to tackle current therapeutic gaps. We describe the existing challenges in treating TAVI-IE and how available treatment discovery approaches can be combined with an in silico "Living Heart" model to create solutions for the future.

Transcriptional and secretome analysis of Rasamsonia emersonii lytic polysaccharide mono-oxygenases.

The current study is the first to describe the temporal and differential transcriptional expression of two lytic polysaccharide monooxygenase (LPMO) genes of Rasamsonia emersonii in response to various carbon sources. The mass spectrometry based secretome analysis of carbohydrate active enzymes (CAZymes) expression in response to different carbon sources showed varying levels of LPMOs (AA9), AA3, AA7, catalase, and superoxide dismutase enzymes pointing toward the redox-interplay between the LPMOs and auxiliary enzymes. Moreover, it was observed that cello-oligosaccharides have a negative impact on the expression of LPMOs, which has not been highlighted in previous reports. The LPMO1 (30 kDa) and LPMO2 (47 kDa), cloned and expressed in Pichia pastoris, were catalytically active with (kcat/Km) of 6.6×10-2 mg-1 ml min-1 and 1.8×10-2 mg-1 ml min-1 against Avicel, respectively. The mass spectrometry of hydrolysis products of Avicel/carboxy methyl cellulose (CMC) showed presence of C1/C4 oxidized oligosaccharides indicating them to be Type 3 LPMOs. The 3D structural analysis of LPMO1 and LPMO2 revealed distinct arrangements of conserved catalytic residues at their active site. The developed enzyme cocktails consisting of cellulase from R. emersonii mutant M36 supplemented with recombinant LPMO1/LPMO2 resulted in significantly enhanced saccharification of steam/acid pretreated unwashed rice straw slurry from PRAJ industries (Pune, India). The current work indicates that LPMO1 and LPMO2 are catalytically efficient and have a high degree of thermostability, emphasizing their usefulness in improving benchmark enzyme cocktail performance. KEY POINTS: • Mass spectrometry depicts subtle interactions between LPMOs and auxiliary enzymes. • Cello-oligosaccharides strongly downregulated the LPMO1 expression. • Developed LPMO cocktails showed superior hydrolysis in comparison to CellicCTec3.

The effects of airway disease on the deposition of inhaled drugs.

INTRODUCTION: The deposition of inhaled medications is the first step in the pulmonary pharmacokinetic process to produce a therapeutic response. Not only lung dose but more importantly the distribution of deposited drug in the different regions of the lung determines local bioavailability, efficacy, and clinical safety. Assessing aerosol deposition patterns has been the focus of intense research that combines the fields of physics, radiology, physiology, and biology. AREAS COVERED: The review covers the physics of aerosol transport in the lung, experimental, and in-silico modeling approaches to determine lung dose and aerosol deposition patterns, the effect of asthma, chronic obstructive pulmonary disease, and cystic fibrosis on aerosol deposition, and the clinical translation potential of determining aerosol deposition dose. EXPERT OPINION: Recent advances in in-silico modeling and lung imaging have enabled the development of realistic subject-specific aerosol deposition models, albeit mainly in health. Accurate modeling of lung disease still requires additional refinements in existing imaging and modeling approaches to better characterize disease heterogeneity in peripheral airways. Nevertheless, recent patient-centric innovation in inhaler device engineering and the incorporation of digital technology have led to more consistent lung deposition and improved targeting of the distal airways, which better serve the clinical needs of patients.

GC-MS/MS Analysis and Wound Repair Potential of Urtica dioica Essential Oil: In silico Modeling and In vivo Study in Rats.

BACKGROUND: The study aimed to assess the antioxidant and wound healing properties of Urtica dioica essential oil (UDEO) through a comprehensive evaluation involving in silico, in vitro, and in vivo analyses. The phytochemistry of UDEO was also investigated to identify trace compounds crucial. METHODS: Various injection methods of the multimode inlet (MMI) in chromatography were investigated to attain lower instrumental detection limits. Subsequently, in silico studies were employed to delve deeper into the potential biological activities of the identified compounds. Standard antioxidative tests, encompassing ABTS•+ and TAC, were performed. In vivo tests centered on wound healing were implemented using rat models. The rats were randomly allocated to four groups: saline solution, vaseline vehicle, cytol centella, and 5% UDEO ointment. Wound healing progress was evaluated through a chromatic study. RESULTS: Gas chromatography combined with triple quadrupole mass spectrometry (GC-MS/MS) analysis revealed the presence of 97 thermolabile compounds in UDEO. Subsequent in silico studies unveiled the potential of identified compounds to inhibit COX-2, TNF-α, and IL-6, suggesting a possible enhancement of anti-inflammatory responses and healing processes. In vitro tests elucidated the notable antioxidant capacity of UDEO, a finding reinforced by wound healing data, revealing a substantial closure rate of 89% following the topical application of UDEO. Notably, fibrinogen and C-reactive protein (CRP) levels were significantly reduced, indicating minimized oxidative stress damage compared to control. Additionally, UDEO exhibited an increase in antioxidant enzyme activities compared to control. CONCLUSION: The study concludes that UDEO possesses significant antioxidant and wound-healing properties, supported by its rich phytochemical composition. The findings suggest its potential application in therapeutic interventions for oxidative stress and inflammatory conditions.

In Silico Molecular Modeling to Identify the Role of Enzymes Involved in Mitochondriogenesis Upon Malvidin-3-Glucoside Effect.

Aim: Mitochondriogenesis refers to the process of creating and maintaining mitochondria, which plays an essential role in cellular metabolism. Mitochondrial processes such as energy generation, the response to oxidative stress, and cell death are all tightly regulated by enzymes. The flavonoid molecule malvidin-3-glucoside (M3G), which may be found in a wide variety of fruits and vegetables, has been shown to improve mitochondrial activity. However, the precise enzymes that mediate M3G's effect on mitochondriogenesis are yet unknown. Method: Here, we used in silico molecular modeling tools to look at how enzymes contribute to mitochondriogenesis after M3G administration. We used computational methods to discover candidate target enzymes known to interact with M3G and play important roles in mitochondrial physiology. Molecular docking was conducted to measure the binding affinity and stability of the M3G-enzyme complexes. The found enzymes' structural and functional features were analyzed using bioinformatics techniques, and the predicted functional implications of their interaction with M3G were formulated. Result: Our goal in doing these studies was to understand better how M3G regulates mitochondriogenesis by the action of altering SIRT-1, AMPK, and PGC-1α via M3G. Conclusion: In sum, our findings provide light on the molecular pathways by which M3G influences mitochondriogenesis. Furthermore, experimental validation of the discovered enzymes and their interactions with M3G may aid in the development of therapeutic approaches to improve mitochondrial function and cellular health.

Elucidation of the Reversible Self-Association Interface of a Diabody-Interleukin Fusion Protein Using Hydrogen-Exchange Mass Spectrometry and In Silico Modeling.

Reversible self-association (RSA) of therapeutic proteins presents major challenges in the development of high-concentration formulations, especially those intended for subcutaneous administration. Understanding self-association mechanisms is therefore critical to the design and selection of candidates with acceptable developability to advance to clinical trials. The combination of experiments and in silico modeling presents a powerful tool to elucidate the interface of self-association. RSA of monoclonal antibodies has been studied extensively under different solution conditions and have been shown to involve interactions for both the antigen-binding fragment and the crystallizable fragment. Novel modalities such as bispecific antibodies, antigen-binding fragments, single-chain-variable fragments, and diabodies constitute a fast-growing class of antibody-based therapeutics that have unique physiochemical properties compared to monoclonal antibodies. In this study, the RSA interface of a diabody-interleukin 22 fusion protein (FP-1) was studied using hydrogen-deuterium exchange coupled with mass spectrometry (HDX-MS) in combination with in silico modeling. Taken together, the results show that a complex solution behavior underlies the self-association of FP-1 and that the interface thereof can be attributed to a specific segment in the variable light chain of the diabody. These findings also demonstrate that the combination of HDX-MS with in silico modeling is a powerful tool to guide the design and candidate selection of novel biotherapeutic modalities.

Therapeutic peptides for coronary artery diseases: in silico methods and current perspectives.

Many drug formulations containing small active molecules are used for the treatment of coronary artery disease, which affects a significant part of the world's population. However, the inadequate profile of these molecules in terms of therapeutic efficacy has led to the therapeutic use of protein and peptide-based biomolecules with superior properties, such as target-specific affinity and low immunogenicity, in critical diseases. Protein‒protein interactions, as a consequence of advances in molecular techniques with strategies involving the combined use of in silico methods, have enabled the design of therapeutic peptides to reach an advanced dimension. In particular, with the advantages provided by protein/peptide structural modeling, molecular docking for the study of their interactions, molecular dynamics simulations for their interactions under physiological conditions and machine learning techniques that can work in combination with all these, significant progress has been made in approaches to developing therapeutic peptides that can modulate the development and progression of coronary artery diseases. In this scope, this review discusses in silico methods for the development of peptide therapeutics for the treatment of coronary artery disease and strategies for identifying the molecular mechanisms that can be modulated by these designs and provides a comprehensive perspective for future studies.

Exploring the Interactions between two Ligands, UCB-J and UCB-F, and Synaptic Vesicle Glycoprotein 2 Isoforms.

In silico modeling was applied to study the efficiency of two ligands, namely, UCB-J and UCB-F, to bind to isoforms of the synaptic vesicle glycoprotein 2 (SV2) that are involved in the regulation of synaptic function in the nerve terminals, with the ultimate goal to understand the selectivity of the interaction between UCB-J and UCB-F to different isoforms of SV2. Docking and large-scale molecular dynamics simulations were carried out to unravel various binding patterns, types of interactions, and binding free energies, covering hydrogen bonding and nonspecific hydrophobic interactions, water bridge, π-π, and cation-π interactions. The overall preference for bonding types of UCB-J and UCB-F with particular residues in the protein pockets can be disclosed in detail. A unique interaction fingerprint, namely, hydrogen bonding with additional cation-π interaction with the pyridine moiety of UCB-J, could be established as an explanation for its high selectivity over the SV2 isoform A (SV2A). Other molecular details, primarily referring to the presence of π-π interactions and hydrogen bonding, could also be analyzed as sources of selectivity of the UCB-F tracer for the three isoforms. The simulations provide atomic details to support future development of new selective tracers targeting synaptic vesicle glycoproteins and their associated diseases.

In silico study of the mechanisms of hypoxia and contractile dysfunction during ischemia and reperfusion of hiPSC cardiomyocytes.

Interconnected mechanisms of ischemia and reperfusion (IR) has increased the interest in IR in vitro experiments using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). We developed a whole-cell computational model of hiPSC-CMs including the electromechanics, a metabolite-sensitive sarcoplasmic reticulum Ca2+-ATPase (SERCA) and an oxygen dynamics formulation to investigate IR mechanisms. Moreover, we simulated the effect and action mechanism of levosimendan, which recently showed promising anti-arrhythmic effects in hiPSC-CMs in hypoxia. The model was validated using hiPSC-CM and in vitro animal data. The role of SERCA in causing relaxation dysfunction in IR was anticipated to be comparable to its function in sepsis-induced heart failure. Drug simulations showed that levosimendan counteracts the relaxation dysfunction by utilizing a particular Ca2+-sensitizing mechanism involving Ca2+-bound troponin C and Ca2+ flux to the myofilament, rather than inhibiting SERCA phosphorylation. The model demonstrates extensive characterization and promise for drug development, making it suitable for evaluating IR therapy strategies based on the changing levels of cardiac metabolites, oxygen and molecular pathways.

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.

Importance of Considering Fed-State Gastrointestinal Physiology in Predicting the Reabsorption of Enterohepatic Circulation of Drugs.

PURPOSE: The purpose of this study was to develop a simulation model for the pharmacokinetics (PK) of drugs undergoing enterohepatic circulation (EHC) with consideration to the environment in the gastrointestinal tract in the fed state in humans. The investigation particularly focused on the necessity of compensating for the permeability rate constant in the reabsorption process in consideration of drug entrapment in bile micelles. METHODS: Meloxicam and ezetimibe were used as model drugs. The extent of the entrapment of drugs inside bile micelles was evaluated using the solubility ratio of Fed State Simulated Intestinal Fluid version 2 (FeSSIF-V2) to Fasted State Simulated Intestinal Fluid version 2 (FaSSIF-V2). Prediction accuracy was evaluated using the Mean Absolute Percentage Error (MAPE) value, calculated from the observed and predicted oral PK profiles. RESULTS: The solubilization of ezetimibe by bile micelles was clearly observed while that of meloxicam was not. Assuming that only drugs in the free fraction of micelles permeate through the intestinal membrane, PK simulation for ezetimibe was performed in both scenarios with and without compensation by the permeation rate constant. The MAPE value of Zetia® tablet, containing ezetimibe, was lower with compensation than without compensation. By contrast, Mobic® tablet, containing meloxicam, showed a relatively low MAPE value even without compensation. CONCLUSION: For drugs which undergo EHC and can be solubilized by bile micelles, compensating for the permeation rate constant in the reabsorption process based on the free fraction ratio appears an important factor in increasing the accuracy of PK profile prediction.

Left atrial appendage occlusion: On the need of a numerical model to simulate the implant procedure.

Left atrial appendage occlusion (LAAO) is a percutaneous procedure to prevent thromboembolism in patients affected by atrial fibrillation. Despite its demonstrated efficacy, the LAA morphological complexity hinders the procedure, resulting in postprocedural drawbacks (device-related thrombus and peri-device leakage). Local anatomical features may cause difficulties in the device's positioning and affect the effectiveness of the device's implant. The current work proposes a detailed FE model of the LAAO useful to investigate implant scenarios and derive clinical indications. A high-fidelity model of the Watchman FLX device and simplified parametric conduits mimicking the zone of the LAA where the device is deployed were developed. Device-conduit interactions were evaluated by looking at clinical indicators such as device-wall gap, possible cause of leakage, and device protrusion. As expected, the positioning of the crimped device before the deployment was found to significantly affect the implant outcomes: clinician's choices can be improved if FE models are used to optimize the pre-operative planning. Remarkably, also the wall mechanical stiffness plays an important role. However, this parameter value is unknown for a specific LAA, a crucial point that must be correctly defined for developing an accurate FE model. Finally, numerical simulations outlined how the device's configuration on which the clinician relies to assess the implant success (i.e., the deployed configuration with the device still attached to the catheter) may differ from the actual final device's configuration, relevant for achieving a safe intervention.

Ab initio modeling of human IRS1 protein to find novel target to dock with drug MH to mitigate T2DM diabetes by insulin signaling.

IRS1 is a cytoplasmic adaptor protein that helps in cellular growth, glucose metabolism, proliferation, and differentiation. Highly disordered (insulin receptor substrate 1) IRS1 protein sequence (mol.wt- 131,590.97 da) has been used to develop model using ab initio modeling technique by I-Tassar tool and Discovery Studio/ DogSite Server to decipher a novel active site. The constructed protein model has been submitted with PMDB Id- PM0082210. GRAVY index of IRS1 model ( - 0.675) indicated surface protein-water interaction. Protparam tool instability index (75.22) demonstrated disorderedness combined with loops owing to prolines/glycines. After refinement, the Ramachandran plot showed that 88 percent of AAs were present in the allowed region and only 0.5% in the disallowed region. Novel IRS1 model protein has 10 α-helices, 22 ß-sheets, 20 ß-hairpins, 5 ß-bulges, 47 strands, 105 ß-turns, and 8 γ-turns. Docking of IRS1 with drug MH demonstrated interaction of Ser-70, Thr-18, and Pro-69 with C-H bonds; Gln-71, and Glu-113 with hydrogen bonds; while both Glu-114 and Glu-113 with salt-bridge connection. Permissible 1.0-1.5 Å range of RMSD fluctuation between 20 and 45 ns was obtained in simulation of IRS1 and IRS1-met complex confirmed that both complexes were stable during whole simulation process. RMSF result showed that except positions 57AA and 114AA, the binding of drug had no severe effects on the flexibility of the IRS1 and IRS1-met complex. The RoG value of compactness and rigidity showed little change in IRS1 protein. SASA value of IRS1 indicated non-significant fluctuation between IRS1 and drug MH means ligand (drug) and IRS1 receptor form stable structure. Hydrogen bond strength of IRS1 and IRS1-met was 81.2 and 76.4, respectively, which suggested stable interaction.

Expression and Purification of Cp3GT: Structural Analysis and Modeling of a Key Plant Flavonol-3-O Glucosyltransferase from Citrus paradisi.

Glycosyltransferases (GTs) are pivotal enzymes in the biosynthesis of various biological molecules. This study focuses on the scale-up, expression, and purification of a plant flavonol-specific 3-O glucosyltransferase (Cp3GT), a key enzyme from Citrus paradisi, for structural analysis and modeling. The challenges associated with recombinant protein production in Pichia pastoris, such as proteolytic degradation, were addressed through the optimization of culture conditions and purification processes. The purification strategy employed affinity, anion exchange, and size exclusion chromatography, leading to greater than 95% homogeneity for Cp3GT. In silico modeling, using D-I-TASSER and COFACTOR integrated with the AlphaFold2 pipeline, provided insights into the structural dynamics of Cp3GT and its ligand binding sites, offering predictions for enzyme-substrate interactions. These models were compared to experimentally derived structures, enhancing understanding of the enzyme's functional mechanisms. The findings present a comprehensive approach to produce a highly purified Cp3GT which is suitable for crystallographic studies and to shed light on the structural basis of flavonol specificity in plant GTs. The significant implications of these results for synthetic biology and enzyme engineering in pharmaceutical applications are also considered.

The Multienzyme Complex Nature of Dehydroepiandrosterone Sulfate Biosynthesis.

Dehydroepiandrosterone (DHEA), a precursor of steroid sex hormones, is synthesized by steroid 17-alpha-hydroxylase/17,20-lyase (CYP17A1) with the participation of microsomal cytochrome b5 (CYB5A) and cytochrome P450 reductase (CPR), followed by sulfation by two cytosolic sulfotransferases, SULT1E1 and SULT2A1, for storage and transport to tissues in which its synthesis is not available. The involvement of CYP17A1 and SULTs in these successive reactions led us to consider the possible interaction of SULTs with DHEA-producing CYP17A1 and its redox partners. Text mining analysis, protein-protein network analysis, and gene co-expression analysis were performed to determine the relationships between SULTs and microsomal CYP isoforms. For the first time, using surface plasmon resonance, we detected interactions between CYP17A1 and SULT2A1 or SULT1E1. SULTs also interacted with CYB5A and CPR. The interaction parameters of SULT2A1/CYP17A1 and SULT2A1/CYB5A complexes seemed to be modulated by 3'-phosphoadenosine-5'-phosphosulfate (PAPS). Affinity purification, combined with mass spectrometry (AP-MS), allowed us to identify a spectrum of SULT1E1 potential protein partners, including CYB5A. We showed that the enzymatic activity of SULTs increased in the presence of only CYP17A1 or CYP17A1 and CYB5A mixture. The structures of CYP17A1/SULT1E1 and CYB5A/SULT1E1 complexes were predicted. Our data provide novel fundamental information about the organization of microsomal CYP-dependent macromolecular complexes.

A fast in silico model for preoperative risk assessment of paravalvular leakage.

In silico simulations can be used to evaluate and optimize the safety, quality, efficacy and applicability of medical devices. Furthermore, in silico modeling is a powerful tool in therapy planning to optimally tailor treatment for each patient. For this purpose, a workflow to perform fast preoperative risk assessment of paravalvular leakage (PVL) after transcatheter aortic valve replacement (TAVR) is presented in this paper. To this end, a novel, efficient method is introduced to calculate the regurgitant volume in a simplified, but sufficiently accurate manner. A proof of concept of the method is obtained by comparison of the calculated results with results obtained from in vitro experiments. Furthermore, computational fluid dynamics (CFD) simulations are used to validate more complex stenosis scenarios. Comparing the simplified leakage model to CFD simulations reveals its potential for procedure planning and qualitative preoperative risk assessment of PVL. Finally, a 3D device deployment model and the efficient leakage model are combined to showcase the application of the presented leakage model, by studying the effect of stent size and the degree of stenosis on the regurgitant volume. The presented leakage model is also used to visualize the leakage path. To generalize the leakage model to a wide range of clinical applications, further validation on a large cohort of patients is needed to validate the accuracy of the model's prediction under various patient-specific conditions.

In silico λ-dynamics predicts protein binding specificities to modified RNAs.

RNA modifications shape gene expression through a smorgasbord of chemical changes to canonical RNA bases. Although numbering in the hundreds, only a few RNA modifications are well characterized, in part due to the absence of methods to identify modification sites. Antibodies remain a common tool to identify modified RNA and infer modification sites through straightforward applications. However, specificity issues can result in off-target binding and confound conclusions. This work utilizes in silico λ-dynamics to efficiently estimate binding free energy differences of modification-targeting antibodies between a variety of naturally occurring RNA modifications. Crystal structures of inosine and N6-methyladenosine (m6A) targeting antibodies bound to their modified ribonucleosides were determined and served as structural starting points. λ-Dynamics was utilized to predict RNA modifications that permit or inhibit binding to these antibodies. In vitro RNA-antibody binding assays supported the accuracy of these in silico results. High agreement between experimental and computed binding propensities demonstrated that λ-dynamics can serve as a predictive screen for antibody specificity against libraries of RNA modifications. More importantly, this strategy is an innovative way to elucidate how hundreds of known RNA modifications interact with biological molecules without the limitations imposed by in vitro or in vivo methodologies.