476 research outputs found
Anti-parasitic drug discovery against Babesia microti by natural compounds: an extensive computational drug design approach
Tick-borne Babesiosis is a parasitic infection caused by Babesia microti that can infect both animals and humans and may spread by tick, blood transfusions, and organ transplantation. The current therapeutic options for B. microti are limited, and drug resistance is a concern. This study proposes using computational drug design approaches to find and design an effective drug against B. microti. The study investigated the potentiality of nine natural compounds against the pathogenic human B. microti parasite and identified Vasicinone and Evodiamine as the most promising drugs. The ligand structures were optimized using density functional theory, molecular docking, molecular dynamics simulations, quantum mechanics such as HOMO–LUMO, drug-likeness and theoretical absorption, distribution, metabolism, excretion, and toxicity (ADMET), and pharmacokinetics characteristics performed. The results showed that Vasicinone (−8.6 kcal/mol and −7.8 kcal/mol) and Evodiamine (−8.7 kcal/mol and −8.5 kcal/mol) had the highest binding energy and anti-parasitic activity against B. microti lactate dehydrogenase and B. microti lactate dehydrogenase apo form. The strongest binding energy was reported by Vasicinone and Evodiamine; the compounds were evaluated through molecular dynamics simulation at 100 ns, and their stability when they form complexes with the targeted receptors was determined. Finally, the pkCSM web server is employed to predict the ADMET qualities of specific molecules, which can help prevent negative effects that arise from taking the treatment. The SwissADME web server is used to assess the Lipinski rule of five and drug-likeness properties including topological polar surface area and bioavailability. The Lipinski rule is used to estimate significant drug-likeness. The theoretical pharmacokinetics analysis and drug-likeness of the selected compounds are confirmed to be accepted by the Lipinski rule and have better ADMET features. Thus, to confirm their experimental value, these mentioned molecules should be suggested to carry out in wet lab, pre-clinical, and clinical levels
A computational view on nanomaterial intrinsic and extrinsic features for nanosafety and sustainability
In recent years, an increasing number of diverse Engineered Nano-Materials (ENMs), such as nanoparticles and nanotubes, have been included in many technological applications and consumer products. The desirable and unique properties of ENMs are accompanied by potential hazards whose impacts are difficult to predict either qualitatively or in a quantitative and predictive manner.
Alongside established methods for experimental and computational characterisation, physics-based modelling tools like molecular dynamics are increasingly considered in Safe and Sustainability-by-design (SSbD) strategies that put user health and environmental impact at the centre of the design and development of new products. Hence, the further development of such tools can support safe and
sustainable innovation and its regulation.
This paper stems from a community effort and presents the outcome of a four-year-long discussion on the benefits, capabilities and limitations of adopting physics-based modelling for computing suitable features of nanomaterials that can be used for toxicity assessment of nanomaterials in combination with data-based models and experimental assessment of toxicity endpoints. We review modern multiscale physics-based models that generate advanced system-dependent (intrinsic) or timeand
environment-dependent (extrinsic) descriptors/features of ENMs (primarily, but not limited to nanoparticles, NPs), with the former being related to the bare NPs and the latter to their dynamic fingerprinting upon entering biological media. The focus is on (i) effectively representing all nanoparticle attributes for multicomponent nanomaterials, (ii) generation and inclusion of intrinsic nanoform properties, (iii) inclusion of selected extrinsic properties, (iv) the necessity of considering distributions of structural advanced features rather than only averages. This review enables us to identify and highlight a number of key challenges associated with ENMs’ data generation, curation,
representation and use within machine learning or other advanced data-driven models to ultimately enhance toxicity assessment. Finally, the set up of dedicated databases as well as the development of grouping and read-across strategies based on the mode of action of ENMs using omics methods are identified as emerging methodologies for safety assessment and reduction of animal testing
2D, 3D-QSAR study and docking of vascular endothelial growth factor receptor 3 (VEGFR3) inhibitors for potential treatment of retinoblastoma
Background: Retinoblastoma is currently the most common malignant tumor seen in newborns and children’s eyes worldwide, posing a life-threatening hazard. Chemotherapy is an integral part of retinoblastoma treatment. However, the chemotherapeutic agents used in clinics often lead to drug resistance. Thus there is a need to investigate new chemotherapy-targeted agents. VEGFR3 inhibitors are anti-tumour-growth and could be used to develop novel retinoblastoma-targeted agents.Objective: To predict drug activity, discover influencing factors and design new drugs by building 2D, 3D-QSAR models.Method: First, linear and non-linear QSAR models were built using heuristic methods and gene expression programming (GEP). The comparative molecular similarity indices analysis (COMISA) was then used to construct 3D-QSAR models through the SYBYL software. New drugs were designed by changing drug activity factors in both models, and molecular docking experiments were performed.Result: The best linear model created using HM had an R2, S2, and R2cv of 0.82, 0.02, and 0.77, respectively. For the training and test sets, the best non-linear model created using GEP had correlation coefficients of 0.83 and 0.72 with mean errors of 0.02 and 0.04. The 3D model designed using SYBYL passed external validation due to its high Q2 (0.503), R2 (0.805), and F-value (76.52), as well as its low standard error of SEE value (0.172). This demonstrates the model’s reliability and excellent predictive ability. Based on the molecular descriptors of the 2D model and the contour plots of the 3D model, we designed 100 new compounds using the best active compound 14 as a template. We performed activity prediction and molecular docking experiments on them, in which compound 14.d performed best regarding combined drug activity and docking ability.Conclusion: The non-linear model created using GEP was more stable and had a more substantial predictive power than the linear model built using the heuristic technique (HM). The compound 14.d designed in this experiment has the potential for anti-retinoblastoma treatment, which provides new design ideas and directions for retinoblastoma-targeted drugs
Multi-Fidelity Bayesian Optimization for Efficient Materials Design
Materials design is a process of identifying compositions and structures to achieve
desirable properties. Usually, costly experiments or simulations are required to evaluate
the objective function for a design solution. Therefore, one of the major challenges is how
to reduce the cost associated with sampling and evaluating the objective. Bayesian
optimization is a new global optimization method which can increase the sampling
efficiency with the guidance of the surrogate of the objective. In this work, a new
acquisition function, called consequential improvement, is proposed for simultaneous
selection of the solution and fidelity level of sampling. With the new acquisition function,
the subsequent iteration is considered for potential selections at low-fidelity levels, because
evaluations at the highest fidelity level are usually required to provide reliable objective
values. To reduce the number of samples required to train the surrogate for molecular
design, a new recursive hierarchical similarity metric is proposed. The new similarity
metric quantifies the differences between molecules at multiple levels of hierarchy
simultaneously based on the connections between multiscale descriptions of the structures.
The new methodologies are demonstrated with simulation-based design of materials and
structures based on fully atomistic and coarse-grained molecular dynamics simulations,
and finite-element analysis. The new similarity metric is demonstrated in the design of
tactile sensors and biodegradable oligomers. The multi-fidelity Bayesian optimization
method is also illustrated with the multiscale design of a piezoelectric transducer by
concurrently optimizing the atomic composition of the aluminum titanium nitride ceramic
and the device’s porous microstructure at the micrometer scale.Ph.D
A Tale of Two Approaches: Comparing Top-Down and Bottom-Up Strategies for Analyzing and Visualizing High-Dimensional Data
The proliferation of high-throughput and sensory technologies in various fields has led to a considerable increase in data volume, complexity, and diversity. Traditional data storage, analysis, and visualization methods are struggling to keep pace with the growth of modern data sets, necessitating innovative approaches to overcome the challenges of managing, analyzing, and visualizing data across various disciplines.
One such approach is utilizing novel storage media, such as deoxyribonucleic acid~(DNA), which presents efficient, stable, compact, and energy-saving storage option. Researchers are exploring the potential use of DNA as a storage medium for long-term storage of significant cultural and scientific materials.
In addition to novel storage media, scientists are also focussing on developing new techniques that can integrate multiple data modalities and leverage machine learning algorithms to identify complex relationships and patterns in vast data sets. These newly-developed data management and analysis approaches have the potential to unlock previously unknown insights into various phenomena and to facilitate more effective translation of basic research findings to practical and clinical applications.
Addressing these challenges necessitates different problem-solving approaches. Researchers are developing novel tools and techniques that require different viewpoints. Top-down and bottom-up approaches are essential techniques that offer valuable perspectives for managing, analyzing, and visualizing complex high-dimensional multi-modal data sets. This cumulative dissertation explores the challenges associated with handling such data and highlights top-down, bottom-up, and integrated approaches that are being developed to manage, analyze, and visualize this data. The work is conceptualized in two parts, each reflecting the two problem-solving approaches and their uses in published studies. The proposed work showcases the importance of understanding both approaches, the steps of reasoning about the problem within them, and their concretization and application in various domains
Computational and chemical approaches to drug repurposing
Drug repurposing, which entails discovering novel therapeutic applications for already existing drugs, provides numerous benefits compared to conventional drug discovery methods. This strategy can be pursued through two primary approaches: computational and chemical. Computational methods involve the utilization of data mining and bioinformatics techniques to identify potential drug candidates, while chemical approaches involve experimental screens oriented to finding new potential treatments based on existing drugs. Both computational and chemical methods have proven successful in uncovering novel therapeutic uses for established drugs. During my PhD, I participated in several experimental drug repurposing screens based on high-throughput phenotypic approaches. Finally, attracted by the potential of computational drug repurposing pipelines, I decided to contribute and generate a web platform focused on the use of transcriptional signatures to identify potential new treatments for human disease. A summary of these studies follows:
In Study I, we utilized the tetracycline repressor (tetR)-regulated mechanism to create a human osteosarcoma cell line (U2OS) with the ability to express TAR DNA-binding protein 43 (TDP-43) upon induction. TDP-43 is a protein known for its association with several neurodegenerative diseases. We implemented a chemical screening with this system as part of our efforts to repurpose approved drugs. While the screening was unsuccessful to identify modulators of TDP-43 toxicity, it revealed compounds capable of inhibiting the doxycyclinedependent TDP-43 expression. Furthermore, a complementary CRISPR/Cas9 screening using the same cell system identified additional regulators of doxycycline-dependent TDP43 expression. This investigation identifies new chemical and genetic modulators of the tetR system and highlights potential limitations of using this system for chemical or genetic screenings in mammalian cells. In Study II, our objective was to reposition compounds that could potentially reduce the toxic effects of a fragment of the Huntingtin (HTT) protein containing a 94 amino acid long glutamine stretch (Htt-Q94), a feature of Huntington's disease (HD). To achieve this, we carried out a high-throughput chemical screening using a varied collection of 1,214 drugs, largely sourced from a drug repurposing library. Through our screening process, we singled out clofazimine, an FDA-approved anti-leprosy drug, as a potential therapeutic candidate. Its effectiveness was validated across several in vitro models as well as a zebrafish model of polyglutamine (polyQ) toxicity. Employing a combination of computational analysis of transcriptional signatures, molecular modeling, and biochemical assays, we deduced that clofazimine is an agonist for the peroxisome proliferator-activated receptor gamma (PPARγ), a receptor previously suggested to be a viable therapeutic target for HD due to its role in promoting mitochondrial biogenesis. Notably, clofazimine was successful in alleviating the mitochondrial dysfunction triggered by the expression of Htt-Q94. These findings lend substantial support to the potential of clofazimine as a viable candidate for drug repurposing in the treatment of polyQ diseases.
In Study III, we explored the molecular mechanism of a previously identified repurposing example, the use of diethyldithiocarbamate-copper complex (CuET), a disulfiram metabolite, for cancer treatment. We found CuET effectively inhibits cancer cell growth by targeting the NPL4 adapter of the p97VCP segregase, leading to translational arrest and stress in tumor cells. CuET also activates ribosomal biogenesis and autophagy in cancer cells, and its cytotoxicity can be enhanced by inhibiting these pathways. Thus, CuET shows promise as a cancer treatment, especially in combination therapies.
In Study IV, we capitalized on the Molecular Signatures Database (MSigDB), one of the largest signature repositories, and drug transcriptomic profiles from the Connectivity Map (CMap) to construct a comprehensive and interactive drug-repurposing database called the Drug Repurposing Encyclopedia (DRE). Housing over 39.7 million pre-computed drugsignature associations across 20 species, the DRE allows users to conduct real-time drugrepurposing analysis. This can involve comparing user-supplied gene signatures with existing ones in the DRE, carrying out drug-gene set enrichment analyses (drug-GSEA) using submitted drug transcriptomic profiles, or conducting similarity analyses across all database signatures using user-provided gene sets. Overall, the DRE is an exhaustive database aimed at promoting drug repurposing based on transcriptional signatures, offering deep-dive comparisons across molecular signatures and species.
Drug repurposing presents a valuable strategy for discovering fresh therapeutic applications for existing drugs, offering numerous benefits compared to conventional drug discovery methods. The studies conducted in this thesis underscore the potential of drug repurposing and highlight the complementary roles of computational and chemical approaches. These studies enhance our understanding of the mechanistic properties of repurposed drugs, such as clofazimine and disulfiram, and reveal novel mechanisms for targeting specific disease pathways. Additionally, the development of the DRE platform provides a comprehensive tool to support researchers in conducting drug-repositioning analyses, further facilitating the advancement of drug repurposing studies
The Influence of Allostery Governing the Changes in Protein Dynamics Upon Substitution
The focus of this research is to investigate the effects of allostery on the function/activity of an enzyme, human immunodeficiency virus type 1 (HIV-1) protease, using well-defined statistical analyses of the dynamic changes of the protein and variants with unique single point substitutions 1. The experimental data1 evaluated here only characterized HIV-1 protease with one of its potential target substrates. Probing the dynamic interactions of the residues of an enzyme and its variants can offer insight of the developmental importance for allosteric signaling and their connection to a protein’s function. The realignment of the secondary structure elements can modulate the mobility along with the frequency of residue contacts as well as which residues are making contact together2-5. We postulate that if there are more contacts occurring within a structure the mobility is being constrained and therefore gaining novel contacts can negatively influence the function of a protein.
The evolutionary importance of protein dynamics is probed by analyzing the residue positions possessing significant correlations and the relationship between experimental information1 (variant activities). We propose that the correlated dynamics of residues observed to have considerable correlations, if disrupted, can be used to infer the function of HIV-1 protease and its variants. Given the robustness of HIV-1 protease the identification of any significant constraint imposed on the dynamics from a potential allosteric site found to disrupt the catalytic activity of the variant is not plainly evident. We also develop machine learning (ML) algorithms to predict the protein function/activity change caused by a single point substitution by using the DCC of each residue pair. Recognition of any substantial association between the dynamics of specific residues and allosteric communication or mechanism requires detailed examination of the dynamics of HIV-1 protease and its variants.
We also explore the non-linear dependency between each pair of residues using Mutual Information (MI) and how it can influence the dynamics of HIV-1 protease and its variants. We suggest that if the residues of a protein receive more or less information than that of the WT it will adversely impact the function of the protein and can be used to support the classification of a variant structure. Furthermore, using the MI of residues obtained from the MD simulations for the HIV-1 protease structure, we build a ML model to predict a protein’s change in function caused by a single point substitution. Effectively the mobility, dynamics, and non-linear features tested in these studies are found to be useful towards the prediction of potentially drug resistant substitutions related to the catalytic efficiency of HIV-1 protease and the variants
Re-emergence of Neglected Tropical Diseases amid the COVID-19 Pandemic : Epidemiology, Transmission, Mitigation Strategies, and Recent Advances in Chemotherapy and Vaccines
The current re-emergence of neglected tropical diseases (NTD) amid the global COVID-19 pandemic requires increased attention. These include communicable and vector-borne diseases caused by various fungi, bacteria (e.g. tuberculosis), viruses (e.g. dengue, Chikungunya fever, monkeypox, Marburg and Ebola virus disease, poliomyelitis, rabies), and parasites (e.g. filariasis, malaria, trypanosomiasis, leishmaniasis, schistosomiasis, onchocerciasis). Whilst the vast majority of such diseases remain endemic to specific regions of the world (e.g. tropical Africa), some - like those caused by the Ebola virus, the Marburg virus, and more recently the Monkeypox virus - have been reported elsewhere (e.g. Europe and America), forcing public health boards in various countries to take all necessary precautions to control such a spread. The Department for Control of Neglected Tropical Disease was created in 2005 by the World Health Organization (WHO) to tackle NTD. In 2021, the 74th World Health Assembly proposed a 9-year plan (2021-2030) intended to eradicate neglected diseases. Over the past three years, COVID-19 has had a significant impact on socio-economic activities and healthcare systems worldwide. With the WHO recently declaring the global monkeypox outbreak a Public Health Emergency of International Concern, a coordinated effort among high-income and low/middle-income countries is now more than ever recommended to address the threat posed by the worldwide re-emergence of some NTD. There is currently a lack of knowledge on understanding how such diseases are transmitted and what mitigation strategies should be put in place to control their spread. Better availability of diagnostic tests, vaccines, and drugs in affected countries is also required. In this Research Topic, we wish to address how to best tackle the re-emergence of NTD in the context of the COVID-19 pandemic. This collection welcomes a range of articles including opinion, commentary, systematic reviews, and original research articles on epidemiology, transmission, mitigation strategies, and recent advances in chemotherapy and vaccines for these NTD
Machine learning modelling in predicting and optimizing PLGA nanoparticle encapsulation efficiency and therapeutic efficacy
Nanoparticles (NP) have become a promising drug delivery system in the past few decades in pharmaceutics for its diversity in encapsulating different types of drugs, including proteins/peptides, nucleic acids and small molecule drugs for the treatment of a variety of diseases. Application in cancer cell NP-based drug delivery has been a majority focus because of NP’s capability in delivering effective treatment while keeping side effects low. Often, series of chemical and biological assays need to be carried out to pursue certain research goals. However, NP fabrication process is rather time-consuming and costly, consisting of material selection, formation, purification, and characterization.
As NP composition choices can directly influence the NP physicochemical properties and biological behaviors, it is crucial to find the optimized combination efficiently to achieve better NP performances. To ease the burden of conducting experiments manually, collaboration with artificial intelligence (AI) techniques is likely to be a promising choice. Machine learning (ML) as a sub-concept of AI has been a popular tool in many pharmaceutical sciences studies, such as prediction of protein molecular structures, drug discoveries, high throughput screening, and prediction of drug formulation compositions, etc. It has been of researchers’ great interests in implementing this emerging technique to a variety of tasks to speed up pharmaceutics development.
In this study, we formulated 32 doxorubicin (DOX) or docetaxel (DTX)-loaded NPs to train and test ML-based Gaussian Processes (GP) models that can estimate the underlying relationships between four NP composition physicochemical properties (e.g., poly (lactic-co-glycolic) acid (PLGA) molecular weight (MW), PLGA lactic acid: glycolic acid (LA/GA) ratio, PLGA: drug weight ratio, and drug lipophilicity) and the corresponding drug EE% and therapeutic efficacy in ovarian cancer cells. No universal relationships between the predictor and response variables can be concluded. Three GP models including EE% model, DOX NP IC50 model, and DTX NP IC50 model were evaluated for their prediction accuracies that were measured by normalized-RMSE in testing sets. The normalized RMSE are 0.187, 0.296, and 0.206, respectively. The EE% model has the highest prediction accuracy that may be attributed to the larger training dataset compared to the other two models. Furthermore, a simplified Bayesian Optimization (BO) model was built to output a set of x variable values that can potentially help to find formulations that optimize the NP EE% and therapeutic efficacy. In EE% model, the suggested formulation is 2mg drug with lipophilicity of 2.12 being loaded in 94mg of 20001 Da, 1.17:1 (LA/GA) PLGA NP. In DOX NP IC50 model, the suggested formulation is 2 mg DOX-loaded 68mg of 39997 Da, 1.53:1 (LA/GA) PLGA NP. In DTX NP IC50 model, the suggested formulation is 2 mg DTX-loaded 90 mg of 20008 Da, 1.70:1 (LA/GA) PLGA NP
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