Combining population genomics and biophysical modelling to assess connectivity patterns in an Antarctic fish.

Connectivity is a fundamental process of population dynamics in marine ecosystems. In the last decade, with the emergence of new methods, combining different approaches to understand the patterns of connectivity among populations and their regulation has become increasingly feasible. The Western Antarctic Peninsula (WAP) is characterized by complex oceanographic dynamics, where local conditions could act as barriers to population connectivity. Here, the notothenioid fish Harpagifer antarcticus, a demersal species with a complex life cycle (adults with poor swim capabilities and pelagic larvae), was used to assess connectivity along the WAP by combining biophysical modelling and population genomics methods. Both approaches showed congruent patterns. Areas of larvae retention and low potential connectivity, observed in the biophysical model output, coincide with four genetic groups within the WAP: (1) South Shetland Islands, (2) Bransfield Strait, (3) the central and (4) the southern area of WAP (Marguerite Bay). These genetic groups exhibited limited gene flow between them, consistent with local oceanographic conditions, which would represent barriers to larval dispersal. The joint effect of geographic distance and larval dispersal by ocean currents had a greater influence on the observed population structure than each variable evaluated separately. The combined effect of geographic distance and a complex oceanographic dynamic would be generating limited levels of population connectivity in the fish H. antarcticus along the WAP. Based on this, population connectivity estimations and priority areas for conservation were discussed, considering the marine protected area proposed for this threatened region of the Southern Ocean.

UiO-66(Zr) as drug delivery system for non-steroidal anti-inflammatory drugs.

The toxicity for the human body of non-steroidal anti-inflammatory drugs (NSAIDs) overdoses is a consequence of their low water solubility, high doses, and facile accessibility to the population. New drug delivery systems (DDS) are necessary to overcome the bioavailability and toxicity related to NSAIDs. In this context, UiO-66(Zr) metal-organic framework (MOF) shows high porosity, stability, and load capacity, thus being a promising DDS. However, the adsorption and release capability for different NSAIDs is scarcely described. In this work, the biocompatible UiO-66(Zr) MOF was used to study the adsorption and release conditions of ibuprofen, naproxen, and diclofenac using a theoretical and experimental approximation. DFT results showed that the MOF-drug interaction was due to an intermolecular hydrogen bond between protons of the groups in the defect sites, (µ3 - OH, and - OH2) and a lone pair of oxygen carboxyl functional group of the NSAIDs. Also, the experimental results suggest that the solvent where the drug is dissolved affects the adsorption process. The adsorption kinetics are similar between the drugs, but the maximum load capacity differs for each drug. The release kinetics assay showed a solvent dependence kinetics whose maximum liberation capacity is affected by the interaction between the drug and the material. Finally, the biological assays show that none of the systems studied are cytotoxic for HMVEC. Additionally, the wound healing assay suggests that the UiO-66(Zr) material has potential application on the wound healing process. However, further studies should be done.

A Genuinely Hybrid, Multiscale 3D Cancer Invasion and Metastasis Modelling Framework.

We introduce in this paper substantial enhancements to a previously proposed hybrid multiscale cancer invasion modelling framework to better reflect the biological reality and dynamics of cancer. These model updates contribute to a more accurate representation of cancer dynamics, they provide deeper insights and enhance our predictive capabilities. Key updates include the integration of porous medium-like diffusion for the evolution of Epithelial-like Cancer Cells and other essential cellular constituents of the system, more realistic modelling of Epithelial-Mesenchymal Transition and Mesenchymal-Epithelial Transition models with the inclusion of Transforming Growth Factor beta within the tumour microenvironment, and the introduction of Compound Poisson Process in the Stochastic Differential Equations that describe the migration behaviour of the Mesenchymal-like Cancer Cells. Another innovative feature of the model is its extension into a multi-organ metastatic framework. This framework connects various organs through a circulatory network, enabling the study of how cancer cells spread to secondary sites.

In vitro platform to model the function of ionocytes in the human airway epithelium.

BACKGROUND: Pulmonary ionocytes have been identified in the airway epithelium as a small population of ion transporting cells expressing high levels of CFTR (cystic fibrosis transmembrane conductance regulator), the gene mutated in cystic fibrosis. By providing an infinite source of airway epithelial cells (AECs), the use of human induced pluripotent stem cells (hiPSCs) could overcome some challenges of studying ionocytes. However, the production of AEC epithelia containing ionocytes from hiPSCs has proven difficult. Here, we present a platform to produce hiPSC-derived AECs (hiPSC-AECs) including ionocytes and investigate their role in the airway epithelium. METHODS: hiPSCs were differentiated into lung progenitors, which were expanded as 3D organoids and matured by air-liquid interface culture as polarised hiPSC-AEC epithelia. Using CRISPR/Cas9 technology, we generated a hiPSCs knockout (KO) for FOXI1, a transcription factor that is essential for ionocyte specification. Differences between FOXI1 KO hiPSC-AECs and their wild-type (WT) isogenic controls were investigated by assessing gene and protein expression, epithelial composition, cilia coverage and motility, pH and transepithelial barrier properties. RESULTS: Mature hiPSC-AEC epithelia contained basal cells, secretory cells, ciliated cells with motile cilia, pulmonary neuroendocrine cells (PNECs) and ionocytes. There was no difference between FOXI1 WT and KO hiPSCs in terms of their capacity to differentiate into airway progenitors. However, FOXI1 KO led to mature hiPSC-AEC epithelia without ionocytes with reduced capacity to produce ciliated cells. CONCLUSION: Our results suggest that ionocytes could have role beyond transepithelial ion transport by regulating epithelial properties and homeostasis in the airway epithelium.

Use of quantitative in vitro to in vivo extrapolation (QIVIVE) for the assessment of non-combustible next-generation product aerosols.

With the use of in vitro new approach methodologies (NAMs) for the assessment of non-combustible next-generation nicotine delivery products, new extrapolation methods will also be required to interpret and contextualize the physiological relevance of these results. Quantitative in vitro to in vivo extrapolation (QIVIVE) can translate in vitro concentrations into in-life exposures with physiologically-based pharmacokinetic (PBPK) modelling and provide estimates of the likelihood of harmful effects from expected exposures. A major challenge for evaluating inhalation toxicology is an accurate assessment of the delivered dose to the surface of the cells and the internalized dose. To estimate this, we ran the multiple-path particle dosimetry (MPPD) model to characterize particle deposition in the respiratory tract and developed a PBPK model for nicotine that was validated with human clinical trial data for cigarettes. Finally, we estimated a Human Equivalent Concentration (HEC) and predicted plasma concentrations based on the minimum effective concentration (MEC) derived after acute exposure of BEAS-2B cells to cigarette smoke (1R6F), or heated tobacco product (HTP) aerosol at the air liquid interface (ALI). The MPPD-PBPK model predicted the in vivo data from clinical studies within a factor of two, indicating good agreement as noted by WHO International Programme on Chemical Safety (2010) guidance. We then used QIVIVE to derive the exposure concentration (HEC) that matched the estimated in vitro deposition point of departure (POD) (MEC cigarette = 0.38 puffs or 11.6 µg nicotine, HTP = 22.9 puffs or 125.6 µg nicotine) and subsequently derived the equivalent human plasma concentrations. Results indicate that for the 1R6F cigarette, inhaling 1/6th of a stick would be required to induce the same effects observed in vitro, in vivo. Whereas, for HTP it would be necessary to consume 3 sticks simultaneously to induce in vivo the effects observed in vitro. This data further demonstrates the reduced physiological potency potential of HTP aerosol compared to cigarette smoke. The QIVIVE approach demonstrates great promise in assisting human health risk assessments, however, further optimization and standardization are required for the substantiation of a meaningful contribution to tobacco harm reduction by alternative nicotine delivery products.

Improving olaparib exposure to optimize adverse effects management.

Background: Olaparib is an inhibitor of the human poly-(ADP-ribose)-polymerase enzymes (PARP1/2) needed to repair single-strand DNA breaks. It is used in breast, ovarian, prostate and pancreatic cancer. Objectives: This work aimed to describe the pharmacokinetics/pharmacodynamics (PK/PD) relationship between olaparib plasma concentrations and common adverse effects (i.e. anaemia and hypercreatininaemia), in a real-life setting, to propose a target concentration for therapeutic drug monitoring. Methods: Two PK/PD models describing the evolution of haemoglobinaemia and creatininaemia as a function of time were developed, based on data from, respectively, 38 and 37 patients receiving olaparib. The final model estimates were used to calculate the incidence of anaemia and creatinine increase according to plasma trough concentrations for 1000 virtual subjects to define target exposure. Results: The final models correctly described the temporal evolution of haemoglobinaemia and creatininaemia for all patients. The haemoglobinaemia PK/PD model is inspired by Friberg's model, and the creatininaemia PK/PD model is an indirect response model. Model parameters were in agreement with physiological values and close to literature values for similar models. The mean (population) plasma haemoglobin concentration at treatment initiation, as estimated by the model, was 11.62 g/dL, while creatinine concentration was 71.91 µmol/L. Using simulations, we have identified a target trough concentration of 3500-4000 ng/mL, above which more than 20% of patients would report grade ≥3 anaemia. Conclusion: Based on real-world data, we were able to properly describe the time course of haemoglobinaemia and plasma creatininaemia during olaparib treatment.

Hepatitis C screening in Lithuania: first-year results and scenarios for achieving WHO elimination targets.

BACKGROUND: The World Health Organization (WHO) has outlined a set of targets to achieve eliminating hepatitis C by 2030. In May 2022, Lithuanian health authorities initiated a hepatitis C virus (HCV) screening program to start working towards elimination. In the program, bonus was given to general practitioners (GPs) to promote and conduct anti-HCV tests for two situations: (1) one time testing for individuals born in 1945-1994 and (2) annual HCV testing for persons who inject drugs or are living with human immunodeficiency virus (HIV) regardless of age. This study aimed to model the current viral hepatitis C epidemiological status in Lithuania and to outline the requirements for WHO elimination targets using the first-year HCV screening results. METHODS: Individuals were invited to participate in the anti-HCV screening by GPs during routine visits. Patients who tested positive were then referred to a gastroenterologist or infectious disease doctor for further confirmatory testing. If a patient received a positive RNA test and a fibrosis staging result of ≥ F2, the doctor prescribed direct-acting antivirals. Information on the patients screened, diagnosed, and treated was obtained from the National Health Insurance Fund. The Markov disease progression model, developed by the CDA Foundation, was used to evaluate the screening program results and HCV elimination progress in Lithuania. RESULTS: Between May 2022 and April 2023, 790,070 individuals underwent anti-HCV testing, with 11,943 individuals (1.5%) receiving positive results. Anti-HCV seroprevalence was found to be higher among males than females, 1.9% and 1.2%, respectively. Within the risk population tested, 2087 (31.1%) seropositive individuals were identified. When comparing the screening program results to WHO elimination targets through modelling, 2180 patients still need to be treated annually until 2030, along with expanding fibrosis restrictions. If an elimination approach was implemented, 1000 new infections would be prevented, while saving 150 lives and averting 90 decompensated cirrhosis cases and 110 hepatocellular carcinoma cases. CONCLUSIONS: During the first year of the Lithuanian screening program, GPs were able to screen 44% of the target population. However, the country will not meet elimination targets as it currently stands without increasing treatment levels and lifting fibrosis restrictions.

The development and internal validation of a multivariable model predicting 6-month mortality for people with opioid use disorder presenting to community drug services in England: a protocol.

BACKGROUND: People with opioid use disorder have substantially higher standardised mortality rates compared to the general population; however, lack of clear individual prognostic information presents challenges to prioritise or target interventions within drug treatment services. Previous prognostic models have been developed to estimate the risk of developing opioid use disorder and opioid-related overdose in people routinely prescribed opioids but, to our knowledge, none have been developed to estimate mortality risk in people accessing drug services with opioid use disorder. Initial presentation to drug services is a pragmatic time to evaluate mortality risk given the contemporaneous routine collection of prognostic indicators and as a decision point for appropriate service prioritisation and targeted intervention delivery. This study aims to develop and internally validate a model to estimate 6-month mortality risk for people with opioid use disorder from prognostic indicators recorded at initial assessment in drug services in England. METHODS: An English national dataset containing records from individuals presenting to drug services between 1 April 2013 and 1 April 2023 (n > 800,000) (the National Drug Treatment Monitoring System (NDTMS)) linked to their lifetime hospitalisation and death records (Hospital Episode Statistics-Office of National Statistics (HES-ONS)). Twelve candidate prognostic indicator variables were identified based on literature review of demographic and clinical features associated with increased mortality for people in treatment for opioid use disorder. Variables will be extracted at initial presentation to drug services with mortality measured at 6 months. Two multivariable Cox regression models will be developed one for 6-month all-cause mortality and one for 6-month drug-related mortality using backward elimination with a fractional polynomial approach for continuous variables. Internal validation will be undertaken using bootstrapping methods. Discrimination of both models will be reported using Harrel's c and d-statistics. Calibration curves and slopes will be presented comparing expected and observed event rates. DISCUSSION: The models developed and internally validated in this study aim to improve clinical assessment of mortality risk for people with opioid use disorder presenting to drug services in England. External validation in different populations will be required to develop the model into a tool to assist future clinical decision-making.

Coupling an agent-based model and an ensemble Kalman filter for real-time crowd modelling.

Agent-based modelling has emerged as a powerful tool for modelling systems that are driven by discrete, heterogeneous individuals and has proven particularly popular in the realm of pedestrian simulation. However, real-time agent-based simulations face the challenge that they will diverge from the real system over time. This paper addresses this challenge by integrating the ensemble Kalman filter (EnKF) with an agent-based crowd model to enhance its accuracy in real time. Using the example of Grand Central Station in New York, we demonstrate how our approach can update the state of an agent-based model in real time, aligning it with the evolution of the actual system. The findings reveal that the EnKF can substantially improve the accuracy of agent-based pedestrian simulations by assimilating data as they evolve. This approach not only offers efficiency advantages over existing methods but also presents a more realistic representation of a complex environment than most previous attempts. The potential applications of this method span the management of public spaces under 'normality' to exceptional circumstances such as disaster response, marking a significant advancement for real-time agent-based modelling applications.

Scalable Risk Stratification for Heart Failure Using Artificial Intelligence applied to 12-lead Electrocardiographic Images: A Multinational Study.

Background: Current risk stratification strategies for heart failure (HF) risk require either specific blood-based biomarkers or comprehensive clinical evaluation. In this study, we evaluated the use of artificial intelligence (AI) applied to images of electrocardiograms (ECGs) to predict HF risk. Methods: Across multinational longitudinal cohorts in the integrated Yale New Haven Health System (YNHHS) and in population-based UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), we identified individuals without HF at baseline. Incident HF was defined based on the first occurrence of an HF hospitalization. We evaluated an AI-ECG model that defines the cross-sectional probability of left ventricular dysfunction from a single image of a 12-lead ECG and its association with incident HF. We accounted for the competing risk of death using the Fine-Gray subdistribution model and evaluated the discrimination using Harrel's c-statistic. The pooled cohort equations to prevent HF (PCP-HF) were used as a comparator for estimating incident HF risk. Results: Among 231,285 individuals at YNHHS, 4472 had a primary HF hospitalization over 4.5 years (IQR 2.5-6.6) of follow-up. In UKB and ELSA-Brasil, among 42,741 and 13,454 people, 46 and 31 developed HF over a follow-up of 3.1 (2.1-4.5) and 4.2 (3.7-4.5) years, respectively. A positive AI-ECG screen portended a 4-fold higher risk of incident HF among YNHHS patients (age-, sex-adjusted HR [aHR] 3.88 [95% CI, 3.63-4.14]). In UKB and ELSA-Brasil, a positive-screen ECG portended 13- and 24-fold higher hazard of incident HF, respectively (aHR: UKBB, 12.85 [6.87-24.02]; ELSA-Brasil, 23.50 [11.09-49.81]). The association was consistent after accounting for comorbidities and the competing risk of death. Higher model output probabilities were progressively associated with a higher risk for HF. The model's discrimination for incident HF was 0.718 in YNHHS, 0.769 in UKB, and 0.810 in ELSA-Brasil. Across cohorts, incorporating model probability with PCP-HF yielded a significant improvement in discrimination over PCP-HF alone. Conclusions: An AI model applied to images of 12-lead ECGs can identify those at elevated risk of HF across multinational cohorts. As a digital biomarker of HF risk that requires just an ECG image, this AI-ECG approach can enable scalable and efficient screening for HF risk.

A robust computational quest: Discovering potential hits to improve the treatment of pyrazinamide-resistant Mycobacterium tuberculosis.

The rise of pyrazinamide (PZA)-resistant strains of Mycobacterium tuberculosis (MTB) poses a major challenge to conventional tuberculosis (TB) treatments. PZA, a cornerstone of TB therapy, must be activated by the mycobacterial enzyme pyrazinamidase (PZase) to convert its active form, pyrazinoic acid, which targets the ribosomal protein S1. Resistance, often associated with mutations in the RpsA protein, complicates treatment and highlights a critical gap in the understanding of structural dynamics and mechanisms of resistance, particularly in the context of the G97D mutation. This study utilizes a novel integration of computational techniques, including multiscale biomolecular and molecular dynamics simulations, physicochemical and medicinal chemistry predictions, quantum computations and virtual screening from the ZINC and Chembridge databases, to elucidate the resistance mechanism and identify lead compounds that have the potential to improve treatment outcomes for PZA-resistant MTB, namely ZINC15913786, ZINC20735155, Chem10269711, Chem10279789 and Chem10295790. These computational methods offer a cost-effective, rapid alternative to traditional drug trials by bypassing the need for organic subjects while providing highly accurate insight into the binding sites and efficacy of new drug candidates. The need for rapid and appropriate drug development emphasizes the need for robust computational analysis to justify further validation through in vitro and in vivo experiments.

Two-way feedback between chromatin compaction and histone modification state explains Saccharomyces cerevisiae heterochromatin bistability.

Compact chromatin is closely linked with gene silencing in part by sterically masking access to promoters, inhibiting transcription factor binding and preventing polymerase from efficiently transcribing a gene. However, a broader hypothesis suggests that chromatin compaction can be both a cause and a consequence of the locus histone modification state, with a tight bidirectional interaction underpinning bistable transcriptional states. To rigorously test this hypothesis, we developed a mathematical model for the dynamics of the HMR locus in Saccharomyces cerevisiae, that incorporates activating histone modifications, silencing proteins, and a dynamic, acetylation-dependent, three-dimensional locus size. Chromatin compaction enhances silencer protein binding, which in turn feeds back to remove activating histone modifications, leading to further compaction. The bistable output of the model was in good agreement with prior quantitative data, including switching rates from expressed to silent states (and vice versa), and protein binding/histone modification levels within the locus. We then tested the model by predicting changes in switching rates as the genetic length of the locus was increased, which were then experimentally verified. Such bidirectional feedback between chromatin compaction and the histone modification state may be a widespread and important regulatory mechanism given the hallmarks of many heterochromatic regions: physical chromatin compaction and dimerizing (or multivalent) silencing proteins.

Improved assay development of pharmaceutical modalities using feedback-controlled liquid chromatography optimization.

Development of meaningful and reliable analytical assays in the (bio)pharmaceutical industry can often be challenging, involving tedious trial and error experimentation. In this work, an automated analytical workflow using an AI-based algorithm for streamlined method development and optimization is presented. Chromatographic methods are developed and optimized from start to finish by a feedback-controlled modeling approach using readily available LC instrumentation and software technologies, bypassing manual user intervention. With the use of such tools, the time requirement of the analyst is drastically minimized in the development of a method. Herein key insights on chromatography system control, automatic optimization of mobile phase conditions, and final separation landscape for challenging multicomponent mixtures are presented (e.g., small molecules drug, peptides, proteins, and vaccine products) showcased by a detailed comparison of a chiral method development process. The work presented here illustrates the power of modern chromatography instrumentation and AI-based software to accelerate the development and deployment of new separation assays across (bio)pharmaceutical modalities while yielding substantial cost-savings, method robustness, and fast analytical turnaround.

Dynamics of antibiotic resistance genes in the sediments of a water-diversion lake and its human exposure risk behaviour.

The dynamics and exposure risk behaviours of antibiotic resistance genes (ARGs) in the sediments of water-diversion lakes remain poorly understood. In this study, spatiotemporal investigations of ARG profiles in sediments targeting non-water (NWDP) and water diversion periods (WDP) were conducted in Luoma Lake, a typical water-diversion lake, and an innovative dynamics-based risk assessment framework was constructed to evaluate ARG exposure risks to local residents. ARGs in sediments were significantly more abundant in the WDP than in the NWDP, but there was no significant variation in their spatial distribution in either period. Moreover, the pattern of ARG dissemination in sediments was unchanged between the WDP and NWDP, with horizontal gene transfer (HGT) and vertical gene transfer (VGT) contributing to ARG dissemination in both periods. However, water diversion altered the pattern in lake water, with HGT and VGT in the NWDP but only HGT in the WDP, which were critical pathways for the dissemination of ARGs. The significantly lower ARG sediment-water partition coefficient in the WDP indicated that water diversion could shift the fate of ARGs and facilitate their aqueous partitioning. Risk assessment showed that all age groups faced a higher human exposure risk of ARGs (HERA) in the WDP than in the NWDP, with the 45-59 age group having the highest risk. Furthermore, HERA increased overall with the bacterial carrying capacity in the local environment and peaked when the carrying capacity reached three (NWDP) or four (WDP) orders of magnitude higher than the observed bacterial population. HGT and VGT promoted, whereas ODF covering gene mutation and loss mainly reduced HERA in the lake. As the carrying capacity increased, the relative contribution of ODF to HERA remained relatively stable, whereas the dominant mechanism of HERA development shifted from HGT to VGT.

Dual-energy X-ray absorptiometry derived knee shape may provide a useful imaging biomarker for predicting total knee replacement: Findings from a study of 37,843 people in UK Biobank.

Objective: We aimed to create an imaging biomarker for knee shape using knee dual-energy x-ray absorptiometry (DXA) scans and investigate its potential association with subsequent total knee replacement (TKR), independently of radiographic features of knee osteoarthritis and established risk factors. Methods: Using a 129-point statistical shape model, knee shape (expressed as a B-score) and minimum joint space width (mJSW) of the medial joint compartment (binarized as above or below the first quartile) were derived. Osteophytes were manually graded in a subset of images and an overall score was assigned. Cox proportional hazards models were used to examine the associations of B-score, mJSW and osteophyte score with TKR risk, adjusting for age, sex, height and weight. Results: The analysis included 37,843 individuals (mean age 63.7 years). In adjusted models, B-score was associated with TKR: each unit increase in B-score, reflecting one standard deviation from the mean healthy shape, corresponded to a hazard ratio (HR) of 2.25 (2.08, 2.43), while a lower mJSW had a HR of 2.28 (1.88, 2.77). Among the 6719 images scored for osteophytes, mJSW was replaced by osteophyte score in the most strongly predictive model for TKR. In ROC analyses, a model combining B-score, osteophyte score, and demographics outperformed a model including demographics alone (AUC â€‹= â€‹0.87 vs 0.73). Conclusions: Using statistical shape modelling, we derived a DXA-based imaging biomarker for knee shape that was associated with kOA progression. When combined with osteophytes and demographic data, this biomarker may help identify individuals at high risk of TKR, facilitating targeted interventions.

Telomerase is essential for cardiac differentiation and sustained metabolism of human cardiomyocytes.

Telomeres as the protective ends of linear chromosomes, are synthesized by the enzyme telomerase (TERT). Critically short telomeres essentially contribute to aging-related diseases and are associated with a broad spectrum of disorders known as telomeropathies. In cardiomyocytes, telomere length is strongly correlated with cardiomyopathies but it remains ambiguous whether short telomeres are the cause or the result of the disease. In this study, we employed an inducible CRISPRi human induced pluripotent stem cell (hiPSC) line to silence TERT expression enabling the generation of hiPSCs and hiPSC-derived cardiomyocytes with long and short telomeres. Reduced telomerase activity and shorter telomere lengths of hiPSCs induced global transcriptomic changes associated with cardiac developmental pathways. Consequently, the differentiation potential towards cardiomyocytes was strongly impaired and single cell RNA sequencing revealed a shift towards a more smooth muscle cell like identity in the cells with the shortest telomeres. Poor cardiomyocyte function and increased sensitivity to stress directly correlated with the extent of telomere shortening. Collectively our data demonstrates a TERT dependent cardiomyogenic differentiation defect, highlighting the CRISPRi TERT hiPSCs model as a powerful platform to study the mechanisms and consequences of short telomeres in the heart and also in the context of telomeropathies.

Effects of ranolazine on the arrhythmic substrate in hypertrophic cardiomyopathy.

Introduction: Hypertrophic cardiomyopathy (HCM) is a leading cause of lethal arrhythmias in the young. Although the arrhythmic substrate has been hypothesised to be amenable to late Na+ block with ranolazine, the specific mechanisms are not fully understood. Therefore, this study aimed to investigate the substrate mechanisms of safety and antiarrhythmic efficacy of ranolazine in HCM. Methods: Computational models of human tissue and ventricles were used to simulate the electrophysiological behaviour of diseased HCM myocardium for variable degrees of repolarisation impairment, validated against in vitro and clinical recordings. S1-S2 pacing protocols were used to quantify arrhythmic risk in scenarios of (i) untreated HCM-remodelled myocardium and (ii) myocardium treated with 3µM, 6µM and 10µM ranolazine, for variable repolarisation heterogeneity sizes and pacing rates. ECGs were derived from biventricular simulations to identify ECG biomarkers linked to antiarrhythmic effects. Results: 10µM ranolazine given to models manifesting ventricular tachycardia (VT) at baseline led to a 40% reduction in number of VT episodes on pooled analysis of >40,000 re-entry inducibility simulations. Antiarrhythmic efficacy and safety were dependent on the degree of repolarisation impairment, with optimal benefit in models with maximum JTc interval <370 ms. Ranolazine increased risk of VT only in models with severe-extreme repolarisation impairment. Conclusion: Ranolazine efficacy and safety may be critically dependent upon the degree of repolarisation impairment in HCM. For moderate repolarisation impairment, reductions in refractoriness heterogeneity by ranolazine may prevent conduction blocks and re-entry. With severe-extreme disease substrates, reductions of the refractory period can increase re-entry sustainability.

Development and experimental validation of 3D QSAR models for the screening of thyroid peroxidase inhibitors using integrated methods of computational chemistry.

The intricate network of glands and organs that makes up the endocrine system. Hormones are used to regulate and synchronize the nervous and physiological systems. The agents which perturbate an endocrine system are called endocrine disruptors and they can eventually affect cellular proliferation and differentiation in target tissues. A subclass of endocrine disruptors known as thyroid disruptors (TDs) or thyroid disrupting chemicals (TDCs) influence the hypothalamo-pituitary-thyroid axis or directly interfere with thyroid function by binding to thyroid hormone receptors. Thyroid hormone levels in circulation are now included in more test guidelines (OECD TG 441, 407, 408, 414, 421/422, 443/416). Although these might be adequate to recognize thyroid adversity, they are unable to explain the underlying mechanism of action. Thyroid peroxidase (TPO) and sodium iodide symporter (NIS), two proteins essential in the biosynthesis of thyroid hormones, are well-accepted molecular targets for inhibition. The screening of a large number of molecules using high throughput screening (HTS) requires a minimum quantity of sample, cost, and time consuming. Whereas 3-dimensional quantitative structure-activity relationship (3D-QSAR) analysis can screen the TDCs before synthesizing a compound. In the present study, the human TPO (hTPO) and NIS (hNIS) structures were modelled using homology modeling and the quality of the structures was validated satisfactorily using MD simulation for 100ns. Further, 190 human TPO inhibitors with IC50 were curated from Comptox and docked with the modelled structure of TPO using D238, H239 and D240 centric grid. The binding conformation of a molecule with low binding energy was used as a reference and the rest other molecules were aligned after generating the possible conformers. The activity-stratified partition was performed for aligned molecules and training set (139), test set (51) were defined. The machine learning models such as k Nearest Neighbor (kNN) and Random Forest (RF) models were built and validated using external experimental dataset containing 10 molecules. Among the 10 molecules, all 10 molecules were identified as TPO inhibitors and demonstrated 100 % accuracy qualitatively. To confirm the selective TPO inhibition all 10 molecules were docked with the modelled structure of hNIS and the results have demonstrated the selective TPO inhibition.

Understanding and computational design of genetic circuits of metabolic networks.

The expression of metabolic proteins is controlled by genetic circuits, matching metabolic demands and changing environmental conditions. Ideally, this regulation brings about a competitive level of metabolic fitness. Understanding how cells can achieve a robust (close-to-optimal) functioning of metabolism by appropriate control of gene expression aids synthetic biology by providing design criteria of synthetic circuits for biotechnological purposes. It also extends our understanding of the designs of genetic circuitry found in nature such as metabolite control of transcription factor activity, promoter architectures and transcription factor dependencies, and operon composition (in bacteria). Here, we review, explain and illustrate an approach that allows for the inference and design of genetic circuitry that steers metabolic networks to achieve a maximal flux per unit invested protein across dynamic conditions. We discuss how this approach and its understanding can be used to rationalize Escherichia coli's strategy to regulate the expression of its ribosomes and infer the design of circuitry controlling gene expression of amino-acid biosynthesis enzymes. The inferred regulation indeed resembles E. coli's circuits, suggesting that these have evolved to maximize amino-acid production fluxes per unit invested protein. We end by an outlook of the use of this approach in metabolic engineering applications.

Natural capital approaches for the optimal design of policies for nature recovery.

By embedding a spatially explicit ecosystem services modelling tool within a policy simulator we examine the insights that natural capital analysis can bring to the design of policies for nature recovery. Our study is illustrated through a case example of policies incentivising the establishment of new natural habitat in England. We find that a policy mirroring the current practice of offering payments per hectare of habitat creation fails to break even, delivering less value in improved flows of ecosystem services than public money spent and only 26% of that which is theoretically achievable. Using optimization methods, we discover that progressively more efficient outcomes are delivered by policies that optimally price activities (34%), quantities of environmental change (55%) and ecosystem service value flows (81%). Further, we show that additionally attaining targets for unmonetized ecosystem services (in our case, biodiversity) demands trade-offs in delivery of monetized services. For some policy instruments it is not even possible to achieve the targets. Finally, we establish that extending policy instruments to offer payments for unmonetized services delivers target-achieving and value-maximizing policy designs. Our findings reveal that policy design is of first-order importance in determining the efficiency and efficacy of programmes pursuing nature recovery. This article is part of the theme issue 'Bringing nature into decision-making'.