Key Interactions Research Articles

Crystal structure of β-d-galactofuranosidase from Streptomyces sp. JHA19 in complex with an inhibitor provides insights into substrate specificity.

d-Galactofuranose (Galf) is widely distributed in glycoconjugates of pathogenic microbes. β-d-Galactofuranosidase (Galf-ase) from Streptomyces sp. JHA19 (ORF1110) belongs to glycoside hydrolase (GH) family 2 and is the first identified Galf-specific degradation enzyme. Here, the crystal structure of ORF1110 in complex with a mechanism-based potent inhibitor, d-iminogalactitol (Ki = 65 μm) was solved. ORF1110 binds to the C5-C6 hydroxy groups of d-iminogalactitol with an extensive and integral hydrogen bond network, a key interaction that discriminates the substrates. The active site structure of ORF1110 is largely different from those of β-glucuronidases and β-galactosidases in the same GH2 family. A C-terminal domain of ORF1110 is predicted to be a carbohydrate-binding module family 42 that may bind Galf. The structural insights into Galf-ase will contribute to the investigation of therapeutic tools against pathogens.

Transcriptional landscape of the interaction of human Mesenchymal Stem Cells with Glioblastoma in bioprinted co-cultures.

The interaction between mesenchymal stem cells (MSC) and Glioblastoma (GBM), although potentially of the highest importance, is ill-understood. This is due, in part, to the lack of relevant experimental models. The similarity between the in vitro situations and the in vivo situation can be improved by 3D co-culture as it reproduces key cell-cell interactions between the tumor microenvironment (TME) and cancer cells. MSC Can acquired characteristics of cancer associated fibroblasts (CAF) by being cultured with conditioned medium from GBM cultures and thus are called MSCCAF. We co Cultured MSCCAF with patient derived GBM in a scaffold 3D bioprinted model. We studied the response to current GBM therapy (e.g. Temozolomide + /Radiation) on the co cultures by bulk transcriptomic (RNA Seq) and epigenetic (ATAC Seq) analyses RESULTS: The transcriptomic modifications induced by standard GBM treatment in bioprinted scaffolds of mono- or co-cultures of GBM ± MSC can be analyzed. We found that mitochondrial encoded OXPHOS genes are overexpressed under these conditions and are modified by both co-culture and treatment (chemotherapy ± radiation). We have identified two new markers of MSC/GBM interactions, one epigenetically regulated (i.e. TREM-1) associated with an increased overall survival in GBM patients and another implicated in post-transcriptional regulation (i.e. the long non-coding RNA, miR3681HG), which is associated with a reduced overall survival in GBM patients.

Pseudo-trajectory inference for identifying essential regulations and molecules in cell fate decisions.

Cell fate decision is crucial in biological development and plays fundamental roles in normal development and functional maintenance of organisms. By identifying key regulatory interactions and molecules involved in these fate decisions, we can shed light on the intricate mechanisms underlying the cell fates. This understanding ultimately reveals the fundamental principles driving biological development and the origins of various diseases. In this study, we present an overarching framework which integrates pseudo-trajectory inference and differential analysis to determine critical regulatory interactions and molecules during cell fate transitions. To demonstrate feasibility and reliability of the approach, we employ the differentiation networks of hepatobiliary system and embryonic stem cells as representative model systems. By applying pseudo-trajectory inference to biological data, we aim to identify critical regulatory interactions and molecules during the cell fate transition processes. Consistent with experimental observations, the approach can allow us to infer dynamical cell fate decision processes and gain insights into the underlying mechanisms which govern cell state decisions.

Interplay of circular RNAs in gastric cancer - a systematic review

Circular RNAs (circRNAs) have gained prominence as important players in various biological processes such as gastric cancer (GC). Identification of several dysregulated circRNAs may serve as biomarkers for early diagnosis or as novel therapeutic targets. Predictive models can suggest potential new interactions and regulatory roles of circRNAs in GCs. Experimental validations of key interactions are being performed using in vitro models, confirming the significance of identified circRNA networks. The aim of this review is to highlight the important circRNAs associated with GC. On top of that an overview of the mechanistic details of the biogenesis and functionalities of the circRNAs are also presented. Furthermore, the potentialities of the circRNAs in the field of new drug discovery are deciphered.

In-Silico discovery of 17alpha-hydroxywithanolide-D as potential neuroprotective allosteric modulator of NMDA receptor targeting Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder marked by cognitive decline, memory impairment, and behavioral alterations. The N-methyl-D-aspartate (NMDA) receptor has emerged as a promising target for AD pharmacotherapy due to its role in the disease's pathogenesis. This study leverages advanced computational methods to screen 80 active constituents of Withania somnifera (Ashwagandha), a traditional herb known for its neuroprotective effects, against the NMDA receptor, using FDA-approved Ifenprodil as a reference. Our blind virtual screening results demonstrated that all tested compounds could bind to various domains of the NMDA receptor, with binding energies ranging from - 4.1 to -11.9kcal/mol, compared to Ifenprodil's -7.8kcal/mol. Binding preference analysis revealed 7 compounds bound to the A-chain, 37 to the B-chain, 7 to the C-chain, and 29 to the D-chain of the receptor. Notable binding was observed predominantly at the Amino Terminal Domain (ATD) core site, some at the ATD-Ligand Binding Domain (LBD) interface, and a few at the Transmembrane Domain (TMD). Particularly, 17alpha-hydroxywithanolide D, with a binding energy of -11.9kcal/mol, emerged as a prime candidate for further investigation. Molecular dynamics simulations of this compound revealed key interactions, including direct hydrogen bonding with residues ASP165, ARG431, THR433, LYS466, and TYR476 on the D-chain, as well as additional hydrophobic and water-bridging interactions. These simulations highlighted the compound's influence on dynamic conformational states of the GluN1b-GluN2B receptor complex, modulating interactions between GluN1b Lys178 and GluN2B Asn184. Furthermore, the compound affected the distance between LBD heterodimers and the tension within the LBD-M30 linker, demonstrating its potential to modulate NMDA receptor activity. This comprehensive study not only underscores the therapeutic promise of Withania somnifera derivatives for AD but also provides a detailed molecular basis for their efficacy, offering valuable insights for targeted drug development and innovative therapeutic strategies against Alzheimer's disease.

Abstract 4139195: Tipping Point Analysis Identifies C-terminal src kinase-Collagen 22 Regulation by Inflammation in the Pathogenesis of Early-Stage Pulmonary Arterial Hypertension

Inflammatory injury to pulmonary artery endothelial cells (PAECs) is a key pathogenetic event that drives fibrotic vascular remodeling in pulmonary arterial hypertension (PAH), a progressive disease that lacks early stage-specific therapeutic targets. In an inflammatory rodent model of PAH, we established early-stage disease based on abnormalities in right ventricular (RV) afterload, pulmonary vascular resistance, and RV cardiomyocyte phenotype that were evident without substantial elevation in pulmonary artery pressure. We performed RNA-Seq on PAEC mRNA isolated ex vivo from control, early-stage, and advanced-stage PAH rats and deployed a ‘tipping point’ methodology to identify key pulmonary endothelial protein-protein interactions (PPIs) that regulate the transition from control to early-stage PAH in silico . Building on prior data on the intersection between glucose and proline signaling in fibrosis, we interrogated our early-stage PAH network for convergent PPIs involving metabolism and fibrosis endotypes. C-terminal src kinase (Csk), a conserved inhibitor of Src, was predicted to regulate PAEC fibrosis and was increased in early-stage PAH. In human PAECs, inflammatory stimuli affected markers of Src activation as well as collagen 22 (Col22A1). We observed that pulmonary arterial Col22A1 expression increased progressively across all PAH stages in vivo and that arterial Col22A1 expression is upregulated in human PAH. Further, although inflammation upregulated total Csk mRNA and protein expression in HPAECs, it decreased phosphorylation of Src at tyrosine 527 (pTyr527Src), a Csk-dependent brake on Src kinase activity. Targeting Src through small molecule inhibition or adenovirus overexpression of Csk to increase pTyr527Src, in turn, attenuated inflammation-induced Col22A1 in vitro . Taken together, these data suggest that acquired inhibition of Csk is a novel molecular mechanism by which inflammation induces pulmonary endothelial dysfunction, Src activation, and fibrosis. In early-stage PAH, disrupted Csk inhibition of Src may induce transcriptional upregulation of Csk as a negative feedback response to Src activation. These data suggest that the Csk-Src-Col22A1 axis is a novel target for advancing early-stage therapeutics in patients with PAH subtypes characterized by inflammatory vascular injury.

Abstract 4136598: Osteochondrogenic transdifferentiation of vascular smooth muscle cells and microenvironmental dynamics in medial arterial calcification at single-cell resolution

Background: Medial arterial calcification (MAC) significantly contributes to cardiovascular mortality, yet effective treatments remain limited due to unclear molecular mechanisms. This study aimed to develop a mouse model of MAC using O-ring-induced transverse aortic constriction (OTAC) and to elucidate key genes, pathways, and cellular interactions involved in MAC formation through lineage tracing and single-cell RNA sequencing (scRNA-seq). Methods: An OTAC mouse model was established to replicate MAC. Adult C57BL/6J male mice underwent OTAC, and subsequent analyses were conducted at various time points. Histological and immunohistochemical assessments were performed to monitor changes in vascular smooth muscle cells (VSMCs). Lineage tracing was utilized to verify the origin of osteochondrogenic VSMCs. Furthermore, scRNA-seq was employed to capture dynamic changes in VSMCs and other cell types contributing to MAC development. Results: The OTAC model successfully induced osteochondrogenic transdifferentiation of VSMCs, leading to MAC. Immunohistochemical analysis demonstrated time-dependent decreases in α-SMA expression and increases in SOX9 and RUNX2 expression. Lineage tracing using Myh11CreERT2;ROSA26-EGFP mice confirmed that osteochondrogenic VSMCs originated from contractile VSMCs. scRNA-seq identified osteochondrogenic VSMCs and revealed dynamic changes in surrounding cells, including macrophages and fibroblasts, which were implicated in calcification-related and inflammatory processes. Conclusion: The OTAC method effectively mimicked human MAC pathology, offering comprehensive insights into the microenvironmental dynamics of MAC progression at the single-cell level. This model serves as a robust platform for future research on therapeutic interventions.

Inhibition mechanism of potential antituberculosis compound lansoprazole sulfide

Tuberculosis is one of the most common causes of death worldwide, with a rapid emergence of multi-drug-resistant strains underscoring the need for new antituberculosis drugs. Recent studies indicate that lansoprazole-a known gastric proton pump inhibitor and its intracellular metabolite, lansoprazole sulfide (LPZS)-are potential antituberculosis compounds. Yet, their inhibitory mechanism and site of action still remain unknown. Here, we combine biochemical, computational, and structural approaches to probe the interaction of LPZS with the respiratory chain supercomplex III2IV2 of Mycobacterium smegmatis, a close homolog of Mycobacterium tuberculosis supercomplex. We show that LPZS binds to the Qo cavity of the mycobacterial supercomplex, inhibiting the quinol substrate oxidation process and the activity of the enzyme. We solve high-resolution (2.6 Å) cryo-electron microscopy (cryo-EM) structures of the supercomplex with bound LPZS that together with microsecond molecular dynamics simulations, directed mutagenesis, and functional assays reveal key interactions that stabilize the inhibitor, but also how mutations can lead to the emergence of drug resistance. Our combined findings reveal an inhibitory mechanism of LPZS and provide a structural basis for drug development against tuberculosis.

Pyrazole Amides Towards Trailblazing Antimicrobial Research: Bridging in Vivo Insights and Molecular Docking Studies

Antimicrobial resistance presents a significant threat to contemporary healthcare systems, making it increasingly difficult to treat infections that were once easily managed. This study aims to assess the efficacy of pyrazole benzamide derivatives (M5a–M5o), which were synthesized for their antimicrobial and antifungal activities. These compounds were tested against methicillin resistant staphylococcus aureus (MRSA), vancomycin resistant staphylococcus aureus (VRSA), mycobacterium tuberculosis H37Rv and other resistant species. The minimum inhibitory concentrations (MIC) were determined using standardized protocols to evaluate their potency and effectiveness. Molecular docking studies were also conducted to explore potential interactions and binding affinities at the molecular level, providing further insights into their antimicrobial and anti-tubercular mechanisms. Compounds M5i, M5k, and M5b emerged as the most potent, displaying MIC value of 3.12 μg/ml. These compounds show great potential as new agents in addressing the growing problem of antimicrobial resistance. Molecular docking studies against Enoyl-[acyl-carrier-protein] reductase (4DRE), revealed key interactions, with compound M5n showing a docking energy of -9.8 kcal/mol and compound M5g at -9.6 kcal/mol. These compounds formed important hydrogen bonds and π-interactions with residues like Lys165, Gly14, Ser94, and Ile21. Overall, the findings indicate that specific pyrazole-benzamide derivatives exhibit promising antimicrobial and antifungal activities, underscoring their potential as therapeutic agents. Additionally, the integration of molecular docking studies proves valuable in guiding the development and optimization of these compounds for future therapeutic applications.

Mean-Field-Type Transformers

In this article, we present the mathematical foundations of generative machine intelligence and link them with mean-field-type game theory. The key interaction mechanism is self-attention, which exhibits aggregative properties similar to those found in mean-field-type game theory. It is not necessary to have an infinite number of neural units to handle mean-field-type terms. For instance, the variance reduction in error within generative machine intelligence is a mean-field-type problem and does not involve an infinite number of decision-makers. Based on this insight, we construct mean-field-type transformers that operate on data that are not necessarily identically distributed and evolve over several layers using mean-field-type transition kernels. We demonstrate that the outcomes of these mean-field-type transformers correspond exactly to the mean-field-type equilibria of a hierarchical mean-field-type game. Due to the non-convexity of the operators’ composition, gradient-based methods alone are insufficient. To distinguish a global minimum from other extrema—such as local minima, local maxima, global maxima, and saddle points—alternative methods that exploit hidden convexities of anti-derivatives of activation functions are required. We also discuss the integration of blockchain technologies into machine intelligence, facilitating an incentive design loop for all contributors and enabling blockchain token economics for each system participant. This feature is especially relevant to ensuring the integrity of factual data, legislative information, medical records, and scientifically published references that should remain immutable after the application of generative machine intelligence.

Structure-based design and synthesis of novel FXIa inhibitors targeting the S2' subsite for enhanced antithrombotic efficacy.

Factor XIa (FXIa), a key component of the intrinsic coagulation pathway, has recently been recognized as a safe and effective target for antithrombotic therapy. Research indicates that FXIa inhibitors can lower bleeding risk compared to novel oral anticoagulants. In this study, we designed and synthesized a series of novel FXIa inhibitors based on the structure of Asundexian, with a particular focus on optimizing the P2' region to enhance binding to the S2' subsite of FXIa. This strategy led to the discovery of compound F47, which demonstrated significantly greater FXIa inhibition (IC50 = 2.0nM) compared to Asundexian (IC50 = 5.0nM). F47 also showed excellent anticoagulant activity in the aPTT assay (EC2x = 0.4μM), with strong efficacy and minimal impact on the extrinsic coagulation pathway. Additionally, F47 exhibited inhibitory activity against plasma kallikrein (PKal), with selectivity comparable to that of Asundexian. The compound also displayed acceptable stability in human liver microsomal stability assays. Molecular modeling revealed that F47 binds tightly to the S1, S1', and S2' pockets of FXIa while maintaining key interactions; notably, its P2' moiety forms two additional π-π stacking interactions with the crucial amino acid TYR143. Further studies demonstrated that F47 exhibits dose-dependent antithrombotic efficacy in a rat FeCl3-induced thrombosis model. Ongoing research aims to further elucidate the potential of compound F47 as a promising lead in antithrombotic therapy.

Predictive modeling of preoperative acute heart failure in older adults with hypertension: a dual perspective of SHAP values and interaction analysis

BackgroundIn older adults with hypertension, hip fractures accompanied by preoperative acute heart failure significantly elevate surgical risks and adverse outcomes, necessitating timely identification and management to improve patient outcomes.Research objectiveThis study aims to enhance the early recognition of acute heart failure in older hypertensive adults prior to hip fracture surgery by developing a predictive model using logistic regression (LR) and machine learning methods, optimizing preoperative assessment and management.MethodsEmploying a retrospective study design, we analyzed hypertensive older adults who underwent hip fracture surgery at Hebei Medical University Third Hospital from January 2018 to December 2022. Predictive models were constructed using LASSO regression and multivariable logistic regression, evaluated via nomogram charts. Five additional machine learning methods were utilized, with variable importance assessed using SHAP values and the impact of key variables evaluated through multivariate correlation analysis and interaction effects.ResultsThe study included 1,370 patients. LASSO regression selected 18 key variables, including sex, age, coronary heart disease, pulmonary infection, ventricular arrhythmias, acute myocardial infarction, and anemia. The logistic regression model demonstrated robust performance with an AUC of 0.753. Although other models outperformed it in sensitivity and F1 score, logistic regression’s discriminative ability was significant for clinical decision-making. The Gradient Boosting Machine model, notable for a sensitivity of 95.2%, indicated substantial capability in identifying patients at risk, crucial for reducing missed diagnoses.ConclusionWe developed and compared efficacy of predictive models using logistic regression and machine learning, interpreting them with SHAP values and analyzing key variable interactions. This offers a scientific basis for assessing preoperative heart failure risk in older adults with hypertension and hip fractures, providing significant guidance for individualized treatment strategies and underscoring the value of applying machine learning in clinical settings.

Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning.

mRNA degradation is a central process that affects all gene expression levels, though it remains challenging to predict the stability of a mRNA from its sequence, due to the many coupled interactions that control degradation rate. Here, we carried out massively parallel kinetic decay measurements on over 50,000 bacterial mRNAs, using a learn-by-design approach to develop and validate a predictive sequence-to-function model of mRNA stability. mRNAs were designed to systematically vary translation rates, secondary structures, sequence compositions, G-quadruplexes, i-motifs, and RppH activity, resulting in mRNA half-lives from about 20 seconds to 20 minutes. We combined biophysical models and machine learning to develop steady-state and kinetic decay models of mRNA stability with high accuracy and generalizability, utilizing transcription rate models to identify mRNA isoforms and translation rate models to calculate ribosome protection. Overall, the developed model quantifies the key interactions that collectively control mRNA stability in bacterial operons and predicts how changing mRNA sequence alters mRNA stability, which is important when studying and engineering bacterial genetic systems.

Coamorphous systems of rebamipide: Selection of amino acid coformers based on protein-ligand docking, in vitro assessment and study of interactions by computational and multivariate analysis.

Three coamorphous systems of Rebamipide (REB) with the amino acids namely, Tryptophan (TRP), Phenylalanine (PHE) and Arginine (ARG) are reported. A unique approach for the virtual screening of amino acid coformers is presented by employing molecular docking studies based on interactions of the drug molecule with various amino acid fragments in the drug-receptor cocrystal structure. Successful formation of stable coamorphous systems with ARG, TRP and PHE served as the proof-of-concept along with negative benchmarking standards Histidine and Aspartic acid, wherein coamorphous systems could not be obtained despite multiple trials which resulted in crystalline physical mixtures. The coamorphous systems were characterized by a halo pattern in Powder XRD and a single glass transition temperature (Tg) in modulated DSC. Physical stability assessments of the coamorphous systems showed direct correlation of Tg with the observed stability of the amorphous phase which was found in the order ARGREB > TRPREB ≥ PHEREB. To determine the specific functional groups engaged in the interactions, multivariate analysis was performed on the FTIR spectra. These interactions were further validated by DFT and QTAIM analysis, which revealed key noncovalent interactions in the three coamorphous systems. All three coamorphous systems showed excellent release profiles of the API as demonstrated by the f2 and DE parameters in the order ARGREB ≥ TRPREB > PHEREB ≥ amorphous drug, far exceeding that of the crystalline drug. The interplay of multivariate analysis and QTAIM can be useful in estimating the interactions within the coamorphous systems which can further contribute to stability and physicochemical properties of the systems.

Cryo-EM reveals a phosphorylated R-domain envelops the NBD1 catalytic domain in an ABC transporter.

Many ATP-binding cassette transporters are regulated by phosphorylation on long and disordered loops which presents a challenge to visualize with structural methods. We have trapped an activated state of the regulatory domain (R-domain) of yeast cadmium factor 1 (Ycf1) by enzymatically enriching the phosphorylated state. A 3.23 Å cryo-EM structure reveals an R-domain structure with four phosphorylated residues and the position for the entire R-domain. The structure reveals key R-domain interactions including a bridging interaction between NBD1 and NBD2 and an interaction with the R-insertion, another regulatory region. We scanned these interactions by systematically replacing segments along the entire R-domain with scrambled combinations of alanine, glycine, and glutamine and probing function under cellular conditions that require the Ycf1 function. We find a close match with these interactions and interacting regions on our R-domain structure that points to the importance of most well-structured segments for function. We propose a model where the R-domain stabilizes a transport-competent state upon phosphorylation by enveloping NBD1 entirely.

Gesture2Text: A Generalizable Decoder for Word-Gesture Keyboards in XR Through Trajectory Coarse Discretization and Pre-Training.

Text entry with word-gesture keyboards (WGK) is emerging as a popular method and becoming a key interaction for Extended Reality (XR). However, the diversity of interaction modes, keyboard sizes, and visual feedback in these environments introduces divergent word-gesture trajectory data patterns, thus leading to complexity in decoding trajectories into text. Template-matching decoding methods, such as SHARK2 [32], are commonly used for these WGK systems because they are easy to implement and configure. However, these methods are susceptible to decoding inaccuracies for noisy trajectories. While conventional neural-network-based decoders (neural decoders) trained on word-gesture trajectory data have been proposed to improve accuracy, they have their own limitations: they require extensive data for training and deep-learning expertise for implementation. To address these challenges, we propose a novel solution that combines ease of implementation with high decoding accuracy: a generalizable neural decoder enabled by pre-training on large-scale coarsely discretized word-gesture trajectories. This approach produces a ready-to-use WGK decoder that is generalizable across mid-air and on-surface WGK systems in augmented reality (AR) and virtual reality (VR), which is evident by a robust average Top-4 accuracy of 90.4% on four diverse datasets. It significantly outperforms SHARK2 with a 37.2% enhancement and surpasses the conventional neural decoder by 7.4%. Moreover, the Pre-trained Neural Decoder's size is only 4 MB after quantization, without sacrificing accuracy, and it can operate in real-time, executing in just 97 milliseconds on Quest 3.

Rational design of some 1,3,4 trisubstituted pyrazole-thiazole derivatives to serve as MtInhA inhibitors using QSAR, ADMET, molecular docking, MM-GBSA, and molecular dynamics simulations approach

Using computational approaches, the potential efficacy and specificity of 1,3,4 trisubstituted pyrazole derivatives as MtInhA inhibitors which will aid in rational drug design for tubercular therapy were forecasted. QSARINS software was used to investigate the ability of compound to inhibit MtInhA. Three noteworthy descriptors with significant correlations and impressive statistical values were identified by the produced QSAR model: Correlation of coefficient(R2)= 0.8789, Cross-validation leave one out correlation coefficient (Q2LOO)= 0.8402, Cross-validation leave-many-out correlation coefficient(Q2LMO)=0.7321, Concordance Correlation Coefficient for cross-validation(CCCtr)=0.9355, CCCext =0.888. The descriptor generated by the QSAR model includes Centered Broto-Moreau autocorrelation weighted by Sanderson electronegativities (ATSC1e), Radial distribution functions at 15.0 and 2.0 Å inter-atomic distances weighted by relative van der Waals volumes (RDF150v), Radial distribution function – 145/weighted by relative I-state (RDF145s). Using these, three descriptor model was developed and the designed compounds were evaluated for their MtInhA inhibitory activity. Further, ADMET prediction and Molecular docking studies were carried out using Schrodinger's Software. ADMET prediction were used to evaluate drug likeliness and molecular docking was used to determine the interactions of designed compounds with the target protein. After the docking studies, the compounds were subjected for MM-GBSA calculations and MD simulation. Among the designed compounds, AP2 had the strongest binding affinity towards the MtInhA enzyme. The result of this work helps to understand the key interactions between 1,3,4 trisubstituted pyrazole derivatives and MtInhA protein that may be necessary to develop new lead compounds against tuberculosis.

An Early Career Perspective on the Value of Interdisciplinary Training Networks

As a global society, we face various challenges that threaten economic, social, and ecological stability and security. Tackling complex global challenges, such as antimicrobial resistance (AMR) and climate change, requires collaboration and the integration of diverse perspectives from across the full spectrum of disciplinary approaches. This requires an increasing number of effective interdisciplinary researchers. In this reflective article, I present a case study narrative based on my own experience to examine how my interactions with an interdisciplinary training scheme (the Medical Research Foundation’s National PhD Training Programme in Antimicrobial Resistance Research (MRF-PhD-AMR Programme)) helped me develop as an interdisciplinary early career researcher. I describe three key interactions I had with the MRF-PhD-AMR Programme during and after my doctoral studies, and how these interactions offered me different opportunities to develop the capacity to effectively navigate interdisciplinary spaces. My reflections help highlight the importance of supporting doctoral and postdoctoral researchers in engaging in training opportunities that cross disciplinary boundaries, to enable them to become more effective interdisciplinary researchers. Funding Acknowledgment My doctoral research exploring antimicrobial usage in UK livestock farming was funded by the Economic and Social Research Council [grant number ES/J500148/1]. My attendance at MRF-PhD-AMR Programme events was funded by the Medical Research Foundation [grant number MRF-145-0004-TPG-AVISO]. At the time of writing, I am employed as a Knowledge Exchange Fellow on the ACCESS (Advancing Capacity for Climate and Environment Social Science) project, which is funded by the Economic and Social Research Council [grant number ES/W00805X/1].

Evaluation of natural compounds as VEGFR-2 inhibitors for breast cancer therapy: insights from molecular docking and drug-likeness analysis

Breast cancer remains one of the most common cancers worldwide, with VEGFR-2 (KDR) playing a key role in tumor angiogenesis. Inhibiting VEGFR-2 is a promising therapeutic strategy. Natural compounds are increasingly studied for their potential to inhibit VEGFR-2. This study aims to assess the binding affinity of 11 natural compounds (andrographolide, alpha-mangostin, pinostrobin, pinocembrin, ethyl-p-methoxycinnamate (EPMS), xanthorrhizol, galangin, gamma-mangostin, curcumin, cinnamaldehyde, and alashanoid B) to the VEGFR-2 protein through molecular docking and Lipinski's rule analysis, identifying promising candidates for breast cancer treatment. Molecular docking simulations were performed for 11 compounds and sunitinib as a control, with binding energies and interactions analyzed. The compounds were also evaluated for drug-likeness using Lipinski’s rule of five. Curcumin showed the highest binding affinity to VEGFR-2 with a binding energy of -9.9 kcal/mol, surpassing sunitinib (-9.4 kcal/mol). Key interactions were observed with active site residues Cys919 and Asp1046. All tested compounds met the criteria for oral bioavailability per Lipinski’s rules. Curcumin demonstrates potential as a VEGFR-2 inhibitor due to its favorable binding affinity and drug-like properties. Enhancing curcumin’s bioavailability is recommended for effective therapeutic application.

Health-related quality of life dynamics: modeling insights from immunotherapy.

Understanding health-related quality of life (HRQoL) dynamics is essential for assessing and improving treatment experiences; however, clinical and observational studies struggle to capture their full complexity. We use simulation modeling and the case of Chimeric Antigen Receptor T-cell therapy-a type of cancer immunotherapy that can prolong survival, but carries life-threatening risks-to study HRQoL dynamics. We developed an exploratory system dynamics model with mathematical equations and parameter values informed by literature and expert insights.We refined its feedback structure and evaluated its dynamic behavior through iterative interviews. Model simulated HRQoL from treatment approval through six months post-infusion. Two strategies-reducing the delay to infusion and enhancing social support-were incorporated into the model. To dynamically evaluate the effect of these strategies, we developed four metrics: post-treatment HRQoL decline, recovery time to pre-treatment HRQoL, post-treatment HRQoL peak, and durability of the peak. Model captures key interactions within HRQoL, providing a nuanced analysis of its continuous temporal dynamics, particularly physical well-being, psychological well-being, tumor burden, receipt and efficacy of treatment, side effects, and their management. Model analysis shows reducing infusion delays enhanced HRQoL across all four metrics. While enhanced social support improved the first three metrics for patients who received treatment, it did not change durability of the peak. Simulation modeling can help explore the effects of strategies on HRQoL while also demonstrating the dynamic interactions between its key components, offering a powerful tool to investigate aspects of HRQoL that are difficult to assess in real-world settings.