In Silico Models

Design, synthesis, and evaluation of purine and pyrimidine-based KRAS G12D inhibitors: Towards potential anticancer therapy

Oncogenic RAS mutations, commonly observed in human tumors, affect approximately 30% of cancer cases and pose a significant challenge for effective cancer treatment. Current strategies to inhibit the KRAS G12D mutation have shown limited success, emphasizing the urgent need for new therapeutic approaches. In this study, we designed and synthesized several purine and pyrimidine analogs as inhibitors for the KRAS G12D mutation. Our synthesized compounds demonstrated potent anticancer activity against cell lines with the KRAS G12D mutation, effectively impeding their growth. They also exhibited low toxicity in normal cells, indicating their selective action against cancer cells harboring the KRAS G12D mutation. Notably, the lead compound, PU1-1 induced the programmed cell death of KRAS G12D-mutated cells and reduced the levels of active KRAS and its downstream signaling proteins. Moreover, PU1-1 significantly shrunk the tumor size in a pancreatic xenograft model induced by the KRAS G12D mutation, further validating its potential as a therapeutic agent. These findings highlight the potential of purine-based KRAS G12D inhibitors as candidates for targeted cancer therapy. However, further exploration and optimization of these compounds are essential to meet the unmet clinical needs of patients with KRAS-mutant cancers.

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

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

An agent-based model of cardiac allograft vasculopathy: toward a better understanding of chronic rejection dynamics

Cardiac allograft vasculopathy (CAV) is a coronary artery disease affecting 50% of heart transplant (HTx) recipients, and it is the major cause of graft loss. CAV is driven by the interplay of immunological and non-immunological factors, setting off a cascade of events promoting endothelial damage and vascular dysfunction. The etiology and evolution of tissue pathology are largely unknown, making disease management challenging. So far, in vivo models, mostly mouse-based, have been widely used to study CAV, but they are resource-consuming, pose many ethical issues, and allow limited investigation of time points and important biomechanical measurements. Recently, agent-based models (ABMs) proved to be valid computational tools for deciphering mechanobiological mechanisms driving vascular adaptation processes at the cell/tissue level, augmenting cost-effective in vivo lab-based experiments, at the same time guaranteeing richness in observation time points and low consumption of resources. We hypothesize that integrating ABMs with lab-based experiments can aid in vivo research by overcoming those limitations. Accordingly, this work proposes a bidimensional ABM of CAV in a mouse coronary artery cross-section, simulating the arterial wall response to two distinct stimuli: inflammation and hemodynamic disturbances, the latter considered in terms of low wall shear stress (WSS). These stimuli trigger i) inflammatory cell activation and ii) exacerbated vascular cell activities. Moreover, an extensive analysis was performed to investigate the ABM sensitivity to the driving parameters and inputs and gain insights into the ABM working mechanisms. The ABM was able to effectively replicate a 4-week CAV initiation and progression, characterized by lumen area decrease due to progressive intimal thickening in regions exposed to high inflammation and low WSS. Moreover, the parameter and input sensitivity analysis highlighted that the inflammatory-related events rather than the WSS predominantly drive CAV, corroborating the inflammatory nature of the vasculopathy. The proof-of-concept model proposed herein demonstrated its potential in deepening the pathology knowledge and supporting the in vivo analysis of CAV.

Significant shifts in preclinical and clinical neurotoxicology: a review and commentary

The ever-expanding prevalence of adverse neurotoxic reactions of the brain in response to therapeutic and recreational drugs, dietary supplements, environmental hazards, cosmetic ingredients, a spectrum of herbals, health status, and environmental stressors continues to prompt the development of novel cell-based assays to better determine neurotoxic hazard. Neurotoxicants may cause direct and epigenetic damage to the nervous tissue and alter the chemistry, structure, or normal activity of the nervous system. In severe neurotoxicity due to exposure to physical or psychosocial toxicants, neurons are disrupted or killed, and a consistent pattern of clinical neural dysfunction appears. In utero exposure to neurotoxicants can lead to altered development of the nervous system [developmental neurotoxicity (DNT)]. Patients with certain disorders and certain genomic makeup may be particularly susceptible to neurotoxicants. Traditional cytotoxicity measurements, like cell death, are easy to measure, but insufficient at identifying current routine biomarkers of toxicity including functional impairment in cell communication, which often occurs before or even in the absence of cell death. The present paper examines some of the limitations of existing neurotoxicology in light of the increasing need to develop tools to meet the challenges of achieving greater sensitivity in detection and developing and standardizing methods for exploring the toxicologic risk of such neurotoxic entities as engineered nanomaterials and even variables associated with poverty.

In silico evaluation of molecular virus-virus interactions taking place between Cotton leaf curl Kokhran virus- Burewala strain and Tomato leaf curl New Delhi virus

Background

Cotton leaf curl disease (CLCuD) is a disease of cotton caused by begomoviruses, leading to a drastic loss in the annual yield of the crop. Pakistan has suffered two epidemics of this disease leading to the loss of billions in annual exports. The speculation that a third epidemic of CLCuD may result as consequence of the frequent occurrence of Tomato leaf curl New Delhi virus (ToLCNDV) and Cotton leaf curl Kokhran Virus-Burewala Strain (CLCuKoV-Bu) in CLCuD infected samples, demand that the interactions taking between the two viruses be properly evaluated. This study is designed to assess virus-virus interactions at the molecular level and determine the type of co-infection taking place.

Methods

Based on the amino acid sequences of the gene products of both CLCuKoV-Bu and ToLCNDV, protein structures were generated using different software, i.e., MODELLER, I-TASSER, QUARKS, LOMETS and RAPTORX. A consensus model for each protein was selected after model quality assessment using ERRAT, QMEANDisCo, PROCHECK Z-Score and Ramachandran plot analysis. The active and passive residues in the protein structures were identified using the CPORT server. Protein–Protein Docking was done using the HADDOCK webserver, and 169 Protein–Protein Interaction (PPIs) were performed between the proteins of the two viruses. The docked complexes were submitted to the PRODIGY server to identify the interacting residues between the complexes. The strongest interactions were determined based on the HADDOCK Score, Desolvation energy, Van der Waals Energy, Restraint Violation Energy, Electrostatic Energy, Buried Surface Area and Restraint Violation Energy, Binding Affinity and Dissociation constant (K_d). A total of 50 ns Molecular Dynamic simulations were performed on complexes that exhibited the strongest affinity in order to validate the stability of the complexes, and to remove any steric hindrances that may exist within the structures.

Results

Our results indicate significant interactions taking place between the proteins of the two viruses. Out of all the interactions, the strongest were observed between the Replication Initiation protein (Rep) of CLCuKoV-Bu with the Movement protein (MP), Nuclear Shuttle Protein (NSP) of ToLCNDV (DNA-B), while the weakest were seen between the Replication Enhancer protein (REn) of CLCuKoV-Bu with the REn protein of ToLCNDV. The residues identified to be taking a part in interaction belonged to domains having a pivotal role in the viral life cycle and pathogenicity. It maybe deduced that the two viruses exhibit antagonistic behavior towards each other, and the type of infection may be categorised as a type of Super Infection Exclusion (SIE) or homologous interference. However, further experimentation, in the form of transient expression analysis, is needed to confirm the nature of these interactions and increase our understanding of the direct interactions taking place between two viruses.

In silico deceased donor intervention research: A potential accelerant for progress

Progress in deceased donor intervention research has been limited. Development of an in silico model of deceased donor physiology may elucidate potential therapeutic targets and provide an efficient mechanism for testing proposed deceased donor interventions. In this study, we report a preliminary in silico model of deceased kidney donor injury built, calibrated, and validated based on data from published animal and human studies. We demonstrate that the in silico model behaves like animal studies of brain death pathophysiology with respect to upstream markers of renal injury including hemodynamics, oxygenation, cytokines expression, and inflammation. Therapeutic hypothermia, a deceased donor intervention studied in human trials, is performed to demonstrate the model's ability to mimic an established clinical trial. Finally, future directions for developing this concept into a functional, clinically applicable model are discussed.