Development, Use, and Validation of (Q)SARs for Predicting Genotoxicity and Carcinogenicity: Experiences from Italian National Institute of Health Activities.

In silico approaches are now increasingly accepted in several areas of toxicology to rapidly assess chemical hazard without the need for animal testing. Among in silico tools, quantitative and qualitative structure-activity approaches ((Q)SARs) are the most typically applied methods to predict hazard in the absence of experimental data. This paper provides an overview of different protocols that can be applied while dealing with (Q)SARs in different scenarios, namely, (Q)SAR development, use, and validation. Examples of protocols adopted in the three scenarios are reported, derived from the authors' experience in working at the Predictive Toxicology unit of the Italian National Institute of Health, focusing on the endpoints of carcinogenicity and genotoxicity.The illustrated activities are in line with the Institute's mission, the main center of research, control, and technical-scientific advice on public health in Italy.

Using ApE for In Silico Golden Gate Cloning.

Golden Gate cloning allows rapid and reliable assembly of multiple DNA fragments in a defined orientation. Golden Gate cloning requires careful design of the restriction fragment overhangs to minimize undesired products and to generate the desired junctions. The ApE (A plasmid Editor) software package can assist in silico design of input fragments or to generate expected assembly products.

An in-silico study of conventional and FLASH radiotherapy iso-effectiveness: potential impact of radiolytic oxygen depletion on tumor growth curves and tumor control probability.

Objective: This work aims to investigate the iso-effectiveness of conventional and FLASH radiotherapy on tumors through in-silico mathematical models. We focused on the role of radiolytic oxygen depletion (ROD), which has been argued as a possible factor to explain the FLASH effect. Approach: We used a spatiotemporal reaction-diffusion model, including ROD, to simulate tumor oxygenation and response. From those oxygen distributions we obtained surviving fractions (SFs) using the linear-quadratic (LQ) model with the oxygen enhancement ratios (OER). We then employed the calculated SFs to describe the evolution of preclinical tumor volumes through a mathematical model of tumor response, and we also extrapolated those results to calculate tumor control probabilities (TCPs) using the Poisson-LQ approach. Main results: Our study suggests that the ROD effect may cause differences in SF between FLASH and conventional radiotherapy, especially in low α/ß and poorly oxygenated cells. However, a statistical analysis showed that these changes in SF generally do not result in significant differences in the evolution of preclinical tumor growth curves when the sample size is small, because such differences in SF may not be noticeable in the heterogeneity of the population of animals}. Nonetheless, when extrapolating this effect to TCP curves, we observed important differences between both techniques (TCP is lower in FLASH radiotherapy). When analyzing the response of tumors with heterogeneous oxygenations, differences in TCP are more important for well oxygenated tumors. This apparent contradiction with the results obtained for homogeneously oxygenated cells is explained by the complex interplay between the heterogeneity of tumor oxygenation, the OER effect, and the ROD effect. Significance: This study supports the experimentally observed iso-effectiveness of FLASH and conventional radiotherapy when analyzing the volume evolution of preclinical tumors (that are far from control). However, this study also hints that tumor growth curves may be less sensitive to small variations in SF than tumor control probability: ROD may lead to increased SF in FLASH radiotherapy, which while not large enough to cause significant differences in tumor growth curves, could lead to important differences in clinical TCPs. Nonetheless, it cannot be discarded that other effects not modeled in this work, like radiation-induced immune effects, can contribute to tumor control and maintain the iso-effectiveness of FLASH radiotherapy. The study of tumor growth curves may not be the ideal experiment to test the iso-effectiveness of FLASH, and experiments reporting TCP or D50 may be preferred.

A novel peptide-based tau aggregation inhibitor as a potential therapeutic for Alzheimer's disease and other tauopathies.

INTRODUCTION: As aggregation underpins Tau toxicity, aggregation inhibitor peptides may have disease-modifying potential. They are therefore currently being designed and target either the 306VQIVYK311 aggregation-promoting hotspot found in all Tau isoforms or the 275VQIINK280 aggregation-promoting hotspot found in 4R isoforms. However, for any Tau aggregation inhibitor to potentially be clinically relevant for other tauopathies, it should target both hotspots to suppress aggregation of Tau isoforms, be stable, cross the blood-brain barrier, and rescue aggregation-dependent Tau phenotypes in vivo. METHODS: We developed a retro-inverso, stable D-amino peptide, RI-AG03 [Ac-rrrrrrrrGpkyk(ac)iqvGr-NH2], based on the 306VQIVYK311 hotspots which exhibit these disease-relevant attributes. RESULTS: Unlike other aggregation inhibitors, RI-AG03 effectively suppresses aggregation of multiple Tau species containing both hotspots in vitro and in vivo, is non-toxic, and suppresses aggregation-dependent neurodegenerative and behavioral phenotypes. DISCUSSION: RI-AG03 therefore meets many clinically relevant requirements for an anti-aggregation Tau therapeutic and should be explored further for its disease-modifying potential for Tauopathies. HIGHLIGHTS: Our manuscript describes the development of a novel peptide inhibitor of Tau aggregation, a retro-inverso, stable D-amino peptide called RI-AG03 that displays many clinically relevant attributes. We show its efficacy in preventing Tau aggregation in both in vitro and in vivo experimental models while being non-toxic to cells. RI-AG03 also rescues a biosensor cell line that stably expresses Tau repeat domains with the P301S mutation fused to Cer/Clo and rescues aggregation-dependent phenotypes in vivo, suppressing neurodegeneration and extending lifespan. Collectively our data describe several properties and attributes of RI-AG03 that make it a promising disease-modifying candidate to explore for reducing pathogenic Tau aggregation in Tauopathies such as Alzheimer's disease. Given the real interest in reducing Tau aggregation and the potential clinical benefit of using such agents in clinical practice, RI-AG03 should be investigated further for the treatment of Tauopathies after validation in mammalian models. Tau aggregation inhibitors are the obvious first choice as Tau-based therapies as much of Tau-mediated toxicity is aggregation dependent. Indeed, there are many research efforts focusing on this therapeutic strategy with aggregation inhibitors being designed against one of the two aggregation-promoting hotspots of the Tau protein. To our knowledge, RI-AG03 is the only peptide aggregation inhibitor that inhibits aggregation of Tau by targeting both aggregation-promoting hotspot motifs simultaneously. As such, we believe that our study will have a significant impact on drug discovery efforts in this arena.

Molecular docking and dynamics simulation of farnesol as a potential anticancer agent targeting mTOR pathway.

Farnesol is a natural acyclic sesquiterpene alcohol, found in various essential oils such as, lemon grass, citronella, tuberose, neroli, and musk. It has a molecular mass of 222.372 g/mol and chemical formula of C15H26O. The main objective of this study was to assess the effect of farnesol on mTOR and its two downstream effectors, p70S6K and eIF4E, which are implicated in the development of cancer, via molecular dynamic simulation, and docking analysis in an in silico study. A multilayer, primarily computer-based analysis was conducted to assess farnesol's anticancer potential, with a focus on primary cancer targets. From the calculations performed, farnesol showed a binding affinity of - 9.66 kcal/mol, followed by binding affinity of - 7.4 kcal/mol and - 7.8 kcal/mol for mTOR, p70S6K and eIF4E respectively. Rapamycin showed the binding affinity of - 10.45 kcal/mol for mTOR, for p70S6K and eIF4E the calculated binding affinity was - 10.65 kcal/mol and 8.16 kcal/mol respectively. The binding affinity of farnesol was comparable to the standard drug rapamycin indicating its potential as an mTOR inhibitor. Molecular dynamics simulations suggest that the ligands (farnesol and rapamycin) were well trapped within the active site of the protein over a time gap of 50 ns. It is clear that farnesol showed relatively stable MD simulation results, with minor fluctuations and maintains a consistent binding orientation, suggesting a strong and stable interaction with the target proteins when compared to simulation data of standard drug. This study explores the potential of farnesol as an anticancer agent through an in-silico approach, focusing on its interaction with mTOR and its downstream effectors. Inhibition of mTOR signaling pathway may be responsible for the anticancer effect of farnesol. As this pathway plays a crucial role in cell proliferation and survival, making it a significant target in cancer research.

From in vitro to in silico: a pipeline for generating virtual tissue simulations from real image data.

3D cell culture models replicate tissue complexity and aim to study cellular interactions and responses in a more physiologically relevant environment compared to traditional 2D cultures. However, the spherical structure of these models makes it difficult to extract meaningful data, necessitating advanced techniques for proper analysis. In silico simulations enhance research by predicting cellular behaviors and therapeutic responses, providing a powerful tool to complement experimental approaches. Despite their potential, these simulations often require advanced computational skills and significant resources, which creates a barrier for many researchers. To address these challenges, we developed an accessible pipeline using open-source software to facilitate virtual tissue simulations. Our approach employs the Cellular Potts Model, a versatile framework for simulating cellular behaviors in tissues. The simulations are constructed from real world 3D image stacks of cancer spheroids, ensuring that the virtual models are rooted in experimental data. By introducing a new metric for parameter optimization, we enable the creation of realistic simulations without requiring extensive computational expertise. This pipeline benefits researchers wanting to incorporate computational biology into their methods, even if they do not possess extensive expertise in this area. By reducing the technical barriers associated with advanced computational modeling, our pipeline enables more researchers to utilize these powerful tools. Our approach aims to foster a broader use of in silico methods in disease research, contributing to a deeper understanding of disease biology and the refinement of therapeutic interventions.

Isoxazole compounds: Unveiling the synthetic strategy, in-silico SAR & toxicity studies and future perspective as PARP inhibitor in cancer therapy.

Latest developments in cancer treatment have shed a light on the crucial role of PARP inhibitors that enhance the treatment effectiveness by modifying abnormal repair pathways. PARP inhibitors, such as Olaparib, Rucaparib, Niraparib, and Talazoparib have been approved in a number of cancers including BRCA 1/BRCA2 associated malignancies although there are many difficulties as therapeutical resistance. Besides the conventional synthetic drugs, natural compounds such as flavones and flavonoids have been found to be PARP inhibitors but only in preclinical studies. Isoxazole is very important class of potential candidates for medicinal chemistry with anti-cancer and other pharmacological activities. At present, there are no approved PARP inhibitors of isoxazole origin but their ability to hit many pathways inside the cancer cells points out on its importance for future treatments design. In drug development, isoxazoles are helpful because of the molecular design flexibility that may be enhanced using various synthetic approaches. This review highlights the molecular mechanisms of PARP inhibition, importance of isoxazole compounds and present advances in their synthetic strategies that demonstrate promise for these agents as new anticancer drugs. It emphasizes that isoxazole-based PARP inhibitors compounds could be novel anti-cancer drugs. Through this review, we hope to grow a curiosity in additional explorations of isoxazole-based PARP inhibitors and their applications in the trends of novel insights towards precision cancer therapy.

Structural Prediction and Antigenic Analysis of ROP18, MIC4, and SAG1 Proteins to Improve Vaccine Design against Toxoplasma gondii: An In silico Approach.

BACKGROUND: Toxoplasmosis is a cosmopolitan infectious disease in warm-blooded mammals that poses a serious worldwide threat due to the lack of effective medications and vaccines. AIMS: The purpose of this study was to design a multi-epitope vaccine using several bioinfor-matics approaches against the antigens of Toxoplasma gondii (T. gondii). METHODS: Three proteins of T. gondii, including ROP18, MIC4, and SAG1, were analyzed to predict the most dominant B- and T-cell epitopes. Finally, we designed a chimeric immunogen RMS (ROP18, MIC4, and SAG1) using some domains of ROP18 (N377-E546), MIC4 (D302-G471), and SAG1 (T130-L299) linked by rigid linker A (EAAAK) A. Physicochemical prop-erties, secondary and tertiary structures, antigenicity, and allergenicity of RMS were predicted utilizing immunoinformatic tools and servers. RESULTS: RMS protein had 545 amino acids with a molecular weight (MW) of 58,833.46 Da and a theoretical isoelectric point (IP) of 6.47. The secondary structure of RMS protein con-tained 21.28% alpha-helix, 24.59% extended strand, and 54.13% random coil. In addition, eval-uation of antigenicity and allergenicity showed the protein to be an immunogen and non-aller-gen. The results of the Ramachandran plot indicated that 76.4%, 12.9%, and 10.7% of amino acid residues were incorporated in the favored, allowed, and outlier regions, respectively. ΔG of the best-predicted mRNA secondary structure was -593.80 kcal/mol, which indicated that a stable loop was not formed at the 5' end. CONCLUSION: Finally, the accuracy and precision of the in silico analysis must be confirmed by successful heterologous expression and experimental studies.

Insight into Protein Engineering: From In silico Modelling to In vitro Synthesis.

Protein engineering alters the polypeptide chain to obtain a novel protein with improved functional properties. This field constantly evolves with advanced in silico tools and techniques to design novel proteins and peptides. Rational incorporating mutations, unnatural amino acids, and post-translational modifications increases the applications of engineered proteins and peptides. It aids in developing drugs with maximum efficacy and minimum side effects. Currently, the engineering of peptides is gaining attention due to their high stability, binding specificity, less immunogenic, and reduced toxicity properties. Engineered peptides are potent candidates for drug development due to their high specificity and low cost of production compared with other biologics, including proteins and antibodies. Therefore, understanding the current perception of designing and engineering peptides with the help of currently available in silico tools is crucial. This review extensively studies various in silico tools available for protein engineering in the prospect of designing peptides as therapeutics, followed by in vitro aspects. Moreover, a discussion on the chemical synthesis and purification of peptides, a case study, and challenges are also incorporated.

Design of Novel Imidazole Derivatives as Potential Non-nucleoside Reverse Transcriptase Inhibitors Using Molecular Docking and Dynamics Strategies.

Human Immunodeficiency Virus (HIV) has become an epidemic causing Acquired Immunodeficiency Syndrome (AIDS). Highly active antiretroviral therapy (HAART) consists of Nucleoside Reverse Transcriptase Inhibitors (NRTIS), Nucleotide Reverse Transcriptase Inhibitors (NtRTIS), and Non- Nucleoside Reverse Transcriptase Inhibitors (NNRTIS) with HIV Protease Inhibitors (HIV PIs). However, the emergence of resistant strains of NNRTIS necessitates the search for better HIV-1-RT inhibitors. METHODS: In this study, a series of novel imidazoles (SP01-SP30) was designed using molecular docking inside the non-nucleoside inhibitory binding pocket (NNIBP) of the HIV-1-RT (PDB ID-1RT2) using Glide v13.0.137, Autodock Vina, and FlexX v2.1.3. Prime MMGBSA was used to study the free energy of binding of the inhibitors with the target enzyme. Molecular dynamics simulation studies were carried out to discover the dynamic behavior of the protein as well as to unveil the role of the essential amino acids required for the better binding affinity of the inhibitor within the NNIBP of the enzyme. The QikProp software module of Schrodinger and online SwissADME were also used to evaluate the drug-likeliness of these compounds. RESULTS: The imidazole derivative SP08 is predicted to be the most promising design compound that can be considered for further synthetic exploitations to obtain a molecule with the highest therapeutic index against HIV-1-RT. CONCLUSION: The results of the current study demonstrate the robustness of our in-silico drug design strategy that can be used for the discovery of novel HIV-1-RT inhibitors.

In silico prediction of drug-induced nephrotoxicity: current progress and pitfalls.

INTRODUCTION: Due to its role in absorption and metabolism, the kidney is an important target for drug toxicity. Drug-induced nephrotoxicity (DIN) presents a significant challenge in clinical practice and drug development. Conventional methods for assessing nephrotoxicity have limitations, highlighting the need for innovative approaches. In recent years, in silico methods have emerged as promising tools for predicting DIN. AREAS COVERED: A literature search was performed using PubMed and Web of Science, from 2013 to February 2023 for this review. This review provides an overview of the current progress and pitfalls in the in silico prediction of DIN, which discusses the principles and methodologies of computational models. EXPERT OPINION: Despite significant advancements, this review identified issues accentuates the pivotal imperatives of data fidelity, model optimization, interdisciplinary collaboration, and mechanistic comprehension in sculpting the vista of DIN prediction. Integration of multiple data sources and collaboration between disciplines are essential for improving predictive models. Ultimately, a holistic approach combining computational, experimental, and clinical methods will enhance our understanding and management of DIN.

Computational Modelling of the Impact of Evaporation on In-Vitro Dermal Absorption.

PURPOSE: Volatiles are common in personal care products and dermatological drugs. Determining the impact of evaporation of volatiles on skin permeation is crucial to evaluate and understand their delivery, bioavailability, efficacy and safety. We aim to develop an in-silico model to simulate the impact of evaporation on the dermal absorption of volatiles. METHOD: The evaporation of volatile permeants was modelled using vapour pressure as the main factor. This model considers evaporation as a passive diffusion process driven by the concentration gradient between the air-vehicle interface and the ambient environment. The evaporation model was then integrated with a previously published physiologically based pharmacokinetic (PBPK) model of skin permeation and compared with published in vitro permeation test data from the Cosmetics Europe ADME Task Force. RESULTS: The evaporation-PBPK model shows improved predictions when evaporation is considered. In particular, good agreement has been obtained for the distributions in the evaporative loss, and the overall percutaneous absorption. The model is further compared with published in-silico models from the Cosmetics Europe ADME Task Force where favourable results are achieved. CONCLUSION: The evaporation of volatile permeants under finite dose in vitro permeation test conditions has been successfully predicted using a mechanistic model with the intrinsic volatility parameter vapour pressure. Integrating evaporation in PBPK modelling significantly improved the prediction of dermal delivery.

Structure-guided mutations in CDRs for enhancing the affinity of neutralizing SARS-CoV-2 nanobody.

The optimization of antibodies to attain the desired levels of affinity and specificity holds great promise for the development of next generation therapeutics. This study delves into the refinement and engineering of complementarity-determining regions (CDRs) through in silico affinity maturation followed by binding validation using isothermal titration calorimetry (ITC) and pseudovirus-based neutralization assays. Specifically, it focuses on engineering CDRs targeting the epitopes of receptor-binding domain (RBD) of the spike protein of SARS-CoV-2. A structure-guided virtual library of 112 single mutations in CDRs was generated and screened against RBD to select the potential affinity-enhancing mutations. Protein-protein docking analysis identified 32 single mutants of which nine mutants were selected for molecular dynamics (MD) simulations. Subsequently, biophysical ITC studies provided insights into binding affinity, and consistent with in silico findings, six mutations that demonstrated better binding affinity than native nanobody were further tested in vitro for neutralization activity against SARS-CoV-2 pseudovirus. Leu106Thr mutant was found to be most effective in virus-neutralization with IC50 values of ∼0.03 µM, as compared to the native nanobody (IC50 ∼0.77 µM). Thus, in this study, the developed computational pipeline guided by structure-aided interface profiles and thermodynamic analysis holds promise for the streamlined development of antibody-based therapeutic interventions against emerging variants of SARS-CoV-2 and other infectious pathogens.

Biomarkers From Discovery to Clinical Application: In Silico Pre-Clinical Validation Approach in the Face of Lung Cancer.

Background: Clinical biomarkers, allow better classification of patients according to their disease risk, prognosis, and/or response to treatment. Although affordable omics-based approaches have paved the way for quicker identification of putative biomarkers, validation of biomarkers is necessary for translation of discoveries into clinical application. Objective: Accordingly, in this study, we emphasize the potential of in silico approaches and have proposed and applied 3 novel sequential in silico pre-clinical validation steps to better identify the biomarkers that are truly desirable for clinical investment. Design: As protein biomarkers are becoming increasingly important in the clinic alongside other molecular biomarkers and lung cancer is the most common cause of cancer-related deaths, we used protein biomarkers for lung cancer as an illustrative example to apply our in silico pre-clinical validation approach. Methods: We collected the reported protein biomarkers for 3 cases (lung adenocarcinoma-LUAD, squamous cell carcinoma-LUSC, and unspecified lung cancer) and evaluated whether the protein biomarkers have cancer altering properties (i.e., act as tumor suppressors or oncoproteins and represent cancer hallmarks), are expressed in body fluids, and can be targeted by FDA-approved drugs. Results: We collected 3008 protein biomarkers for lung cancer, 1189 for LUAD, and 182 for LUSC. Of these protein biomarkers for lung cancer, LUAD, and LUSC, only 28, 25, and 6 protein biomarkers passed the 3 in silico pre-clinical validation steps examined, and of these, only 5 and 2 biomarkers were specific for lung cancer and LUAD, respectively. Conclusion: In this study, we applied our in silico pre-clinical validation approach the protein biomarkers for lung cancer cases. However, this approach can be applied and adapted to all cancer biomarkers. We believe that this approach will greatly facilitate the transition of cancer biomarkers into the clinical phase and offers great potential for future biomarker research.

Biomarkers, which are routinely used in clinics, allow better classification of patients according to their disease risk, prognosis, and/or response to treatment. Although affordable omics-based approaches have paved the way for quicker identification of putative biomarkers, validation of biomarkers is necessary for translation of discoveries into clinical application. This research article highlights the challenges of translating cancer biomarkers into clinical practice and summarizes feasible step toward "in silico pre-clinical validation" using the example of lung cancer types. Accordingly, protein biomarkers proposed for lung cancer are being investigated using the "in silico pre-clinical validation" approach to determine whether they have cancer altering properties (i.e., oncoprotein, tumor suppressor, and cancer hallmark), are expressed in body fluids (i.e., plasma/serum, saliva, urine, and bronchoalveolar lavage) and can be targeted with FDA-approved drugs. We believe that the step of in silico pre-clinical validation is the future of biomarker research for all professionals involved in clinical, biological, epidemiological, biostatistical and health research, and that it will greatly facilitate the transition of biomarkers to the clinical phase.

In silico vaccine design for Yersinia enterocolitica: A comprehensive approach to enhanced immunogenicity, efficacy and protection.

Yersinia enterocolitica, a foodborne pathogen, has emerged as a significant public health concern due to its increased prevalence and multidrug resistance. This study employed reverse vaccinology to identify novel vaccine candidates against Y. enterocolitica through comprehensive in silico analyses. The core genome's conserved protein translocase subunit SecY was selected as the target, and potential B-cell, MHC class I, and MHC class II epitopes were mapped. 3B-cell epitopes, 3 MHCI and 11 MHCII epitopes were acquired. A multi-epitope vaccine construct was designed by incorporating the identified epitopes, TLR4 Agonist was used as adjuvants to enhance the immunogenic response. EAAAK, CPGPG and AYY linkers were used to form a vaccine construct, followed by extensive computational evaluations. The vaccine exhibited desirable physicochemical properties, stable secondary and tertiary structures as evaluated by PDBSum and trRosetta. Moreover, favorable interactions with the human Toll-like receptor 4 (TLR4) was observed by ClusPro. Population coverage analysis estimated the vaccine's applicability across 99.74 % in diverse populations. In addition, molecular dynamics simulations and normal mode analysis confirmed the vaccine's structural stability and dynamics in a simulated biological environment. Furthermore, codon optimization and in silico cloning facilitated the evaluation of the vaccine's expression potential in E. coli and pET-28a was used a recombinant plasmid. This study provides a promising foundation for the development of an efficacious vaccine against Y. enterocolitica infections.

Toward widespread use of virtual trials in medical imaging innovation and regulatory science.

The rapid advancement in the field of medical imaging presents a challenge in keeping up to date with the necessary objective evaluations and optimizations for safe and effective use in clinical settings. These evaluations are traditionally done using clinical imaging trials, which while effective, pose several limitations including high costs, ethical considerations for repetitive experiments, time constraints, and lack of ground truth. To tackle these issues, virtual trials (aka in silico trials) have emerged as a promising alternative, using computational models of human subjects and imaging devices, and observer models/analysis to carry out experiments. To facilitate the widespread use of virtual trials within the medical imaging research community, a major need is to establish a common consensus framework that all can use. Based on the ongoing efforts of an AAPM Task Group (TG387), this article provides a comprehensive overview of the requirements for establishing virtual imaging trial frameworks, paving the way toward their widespread use within the medical imaging research community. These requirements include credibility, reproducibility, and accessibility. Credibility assessment involves verification, validation, uncertainty quantification, and sensitivity analysis, ensuring the accuracy and realism of computational models. A proper credibility assessment requires a clear context of use and the questions that the study is intended to objectively answer. For reproducibility and accessibility, this article highlights the need for detailed documentation, user-friendly software packages, and standard input/output formats. Challenges in data and software sharing, including proprietary data and inconsistent file formats, are discussed. Recommended solutions to enhance accessibility include containerized environments and data-sharing hubs, along with following standards such as CDISC (Clinical Data Interchange Standards Consortium). By addressing challenges associated with credibility, reproducibility, and accessibility, virtual imaging trials can be positioned as a powerful and inclusive resource, advancing medical imaging innovation and regulatory science.

Gamma-Decanolactone Increases Stress Resistance and Improves Toxicity Parameters on the Caenorhabditis elegans Alternative Model.

Gamma-decanolactone (GD) is a monoterpene compound with anticonvulsant, antiparkinsonian, and neuroprotective effects in preclinical trials. This study aimed to evaluate the toxicity and antioxidant profile of GD in silico and in the Caenorhabditis elegans (C. elegans) experimental model. The C. elegans was used to determine the median lethal concentration (LC50) of GD, as well as its effect on survival, development, reproduction, pharyngeal pumping, and stress resistance assays. The in silico study did not indicate hepatotoxic, cardiotoxic, or mutagenic potential to GD. It reduced the worms' survival, both at the L1 and L4 stages, in a concentration-dependent manner with an LC50 value of 212.16 ± 5.56 µmol/mL. GD did not alter the development, reproduction, and pharyngeal pumping under normal experimental conditions in the three concentrations tested (25, 50, and 100 µmol/mL). In the thermal stress assay, GD did not change the survival pattern of the worms. Hydrogen peroxide (H2O2) reduced the survival of C. elegans and decreased the number of pharyngeal pumping, with these effects being reversed by GD. Also, GD presents an antioxidant activity by modulation the expression of the stress response genes such as sod-3, ctl-1,2,3, and gst-4. In conclusion, GD showed low toxicity in the C. elegans model and antioxidant profile both in the in silico study and in vivo assays.

Review on Cassia alata Bioactive Compounds: In silico, in vitro, and in vivo Studies.

Cassia alata Linn is a popular herbal remedy in many countries, and its activities have been studied through many studies, starting from in silico, in vitro, and in vivo. This narrative review will focus more on secondary metabolites that are responsible for certain pharmacological activities that have undergone in vivo, in vitro, and in silico testing to determine the underlying mechanism. Twenty pharmacological activities have been identified, with the flavonoid group (emodin, kaempferol, quercetin) as the most prevalent secondary metabolite found in Cassia alata. There have been numerous studies looking at the role of flavonoids about specific diseases, and flavonoid testing is quite thorough because it covers three different study types. However, there has not been significant progress accomplished in terms of the evaluation of the dosage form so that test results for promising activities like antidiabetic, antifungal, and antiviral can be carried out into further research. Additionally, several disorders lack comprehensive investigation, particularly in silico studies, therefore further study is required to fill any gaps in the knowledge.

Implementation of an Automated System Using Machine Learning Models to Accelerate the Process of In Silico Identification of Small Molecules As Drug Candidates.

Drugs are commonly utilized to diagnose, cure, or prevent the occurrence of diseases, as well as to restore, alter, or change organic functions. Drug discovery is a time-consuming, costly, difficult, and inefficient process that yields very few medicinal breakthroughs. Drug research and design involves the capturing of structural information for biological targets and small molecules as well as various in silico methods, such as molecular docking and molecular dynamic simulation. This article proposes the idea of expediting computational drug development through a collaboration of scientists and universities, similar to the Human Genome Project using machine learning (ML) strategies. We envision an automated system where readily available or novel small molecules (chemical or plant-derived), as well as their biological targets, are uploaded to an online database, which is constantly updated. For this system to function, machine learning strategies have to be implemented, and high-quality datasets and high quality assurance of the ML models will be required. ML can be applied to all computational drug discovery fields, including hit discovery, target validation, lead optimization, drug repurposing, and data mining of small compounds and biomolecule structures. Researchers from various disciplines, such as bioengineers, bioinformaticians, geneticists, chemists, computer and software engineers, and pharmacists, are expected to collaborate to establish a solid workflow and certain parameters as well as constraints for a successful outcome. This automated system may help speed up the drug discovery process while also lowering the number of unsuccessful drug candidates. Additionally, this system will decrease the workload, especially in computational studies, and expedite the process of drug design. As a result, a drug may be manufactured in a relatively short time.

Identification of steroidal cardenolides from Calotropis procera as novel HIV-1 PR inhibitors: A molecular docking & molecular dynamics simulation study.

Background & objectives Despite advancements in antiretroviral therapy, drug-resistant strains of HIV (human immunodeficiency virus) remain a global health concern. Natural compounds from medicinal plants offer a promising avenue for developing new HIV-1 PR (protease) inhibitors. This study aimed to explore the potential of compounds derived from Calotropis procera, a medicinal plant, as inhibitors of HIV-1 PR. Methods This in silico study utilized natural compound information and the crystal structure of HIV-1 PR. Molecular docking of 17 steroidal cardenolides from Calotropis procera against HIV-1 PR was performed using AutoDock 4.2 to identify compounds with higher antiviral potential. A dynamic simulation study was performed to provide insights into the stability, binding dynamics, and potential efficacy of the top potential antiviral compound as an HIV-1 therapeutic. Results We found that all tested cardenolides had higher binding affinities than Amprenavir, indicating their potential as potent HIV-1 PR inhibitors. Voruscharin and uscharidin displayed the strongest interactions, forming hydrogen bonds and hydrophobic interactions with HIV-1 PR. Voruscharin showed improved stability with lower RMSD (Root Mean Square Deviation) values and reduced fluctuations in binding site residues but increased flexibility in certain regions. The radius of gyration analysis confirmed a stable binding pose between HIV-1 PR and Voruscharin. Interpretation & conclusions These findings suggest that Calotropis procera could potentially be a source of compounds for developing novel HIV-1 PR inhibitors, contributing to the efforts to combat HIV. Further studies and clinical trials are needed to evaluate the safety and efficacy of these compounds as potential drug candidates for the treatment of HIV-1 infection.