Silico Methods Research Articles

Use of In Silico Methods for Regulatory Toxicological Assessment of Pharmaceutical Impurities.

The use of novel non-testing methodologies to support the toxicological assessment of drug impurities is having a growing impact in the regulatory framework for pharmaceutical development and marketed products. For DNA reactive (mutagenic) impurities specific recommendations for the use of in silico structure-based approaches (namely (Q)SAR methodologies) are provided in the ICH M7 guideline. In 2018 a draft reflection paper has been published by EMA addressing open issues in the qualification approach of non-genotoxic impurities (NGI) according to the ICH Q3A/Q3B guidelines, and proposing the use of alternative testing strategies, including TTC, (Q)SAR, read-across, and in vitro approaches, to gather impurity-specific safety information.In the present chapter we describe a workflow to perform the safety assessment of drug impurities based on non-testing in silico methodologies. The proposed approach consists of a stepwise decision scheme including three key phases: PHASE 1: assessment of bacterial mutagenicity and consequent classification of impurities according to ICH M7; PHASE 2: risk characterization of mutagenic impurities (Classes 1, 2 or 3); PHASE 3: qualification of non-mutagenic impurities (Classes 4 or 5). The proposed decision scheme offers the possibility to acquire impurity-specific data, also if testing is not feasible, and to decide on further in vitro testing, besides meeting 3R's principle.

In Silico Methods for Carcinogenicity Assessment.

Screening compounds for potential carcinogenicity is of major importance for prevention of environmentally induced cancers. A large sequence of predictive models, ranging from short-term biological assays (e.g., mutagenicity tests) to theoretical models, has been attempted in this field. Theoretical approaches such as (Q)SAR are highly desirable for identifying carcinogens, since they actively promote the replacement, reduction, and refinement of animal tests. This chapter reports and describes some of the most noted (Q)SAR models based on human expert knowledge and statistical approaches, aiming at predicting the carcinogenicity of chemicals. Additionally, the performance of the selected models has been evaluated, and the results are interpreted in details by applying these predictive models to some pharmaceutical molecules.

In Silico Methods for Environmental Risk Assessment: Principles, Tiered Approaches, Applications, and Future Perspectives.

This chapter aims to introduce the reader to the basic principles of environmental risk assessment of chemicals and highlights the usefulness of tiered approaches within weight of evidence approaches in relation to problem formulation i.e., data availability, time and resource availability. In silico models are then introduced and include quantitative structure-activity relationship (QSAR) models, whichsupport filling data gaps when no chemical property or ecotoxicological data are available. In addition, biologically-based models can be applied in more data rich situations and these include generic or species-specific models such as toxicokinetic-toxicodynamic models, dynamic energy budget models, physiologically based models, and models for ecosystem hazard assessment i.e.species sensitivity distributionsand ultimately for landscape assessment i.e.landscape-based modeling approaches. Throughout this chapter, particular attention is given to provide practical examples supporting the application ofsuch in silico models in real-world settings. Future perspectives are discussed to address environmental risk assessment in a more holistic manner particularly for relevant complex questions, such as the risk assessment of multiple stressors and the development of harmonized approaches to ultimately quantify the relative contribution and impactof singlechemicals, multiple chemicals and multiple stressors on living organisms.

In Silico Methods for Secretory Production of a Fungal Hydrophobin (HYPAI) in Yeast to Serve as a Promising Target for Drug Delivery

In Silico Methods for Secretory Production of a Fungal Hydrophobin (HYPAI) in Yeast to Serve as a Promising Target for Drug Delivery

Comparative Assessment of In Vitro and In Silico Methods for Aerodynamic Characterization of Powders for Inhalation.

In vitro assessment of dry powders for inhalation (DPIs) aerodynamic performance is an inevitable test in DPI development. However, contemporary trends in drug development also implicate the use of in silico methods, e.g., computational fluid dynamics (CFD) coupled with discrete phase modeling (DPM). The aim of this study was to compare the designed CFD-DPM outcomes with the results of three in vitro methods for aerodynamic assessment of solid lipid microparticle DPIs. The model was able to simulate particle-to-wall sticking and estimate fractions of particles that stick or bounce off the inhaler’s wall; however, we observed notable differences between the in silico and in vitro results. The predicted emitted fractions (EFs) were comparable to the in vitro determined EFs, whereas the predicted fine particle fractions (FPFs) were generally lower than the corresponding in vitro values. In addition, CFD-DPM predicted higher mass median aerodynamic diameter (MMAD) in comparison to the in vitro values. The outcomes of different in vitro methods also diverged, implying that these methods are not interchangeable. Overall, our results support the utility of CFD-DPM in the DPI development, but highlight the need for additional improvements in these models to capture all the key processes influencing aerodynamic performance of specific DPIs.

Development of “on-demand” thermo-responsive hydrogels for anti-cancer drugs sustained release: Rational design, in silico prediction and in vitro validation in colon cancer models

A rational design accurate based on the use of Statistical Design of the Experiments (DoE) and Molecular Dynamics Simulations Studies allows the prediction and the understanding of thermo-responsive hydrogels prepared regarding their gelation temperature and anti-cancer drug release rate. N-isopropylacrilamide (NIPAM) modified with specific co-monomers and crosslinkers, can be used to prepare “on-demand” thermo-responsive hydrogels with the ideal properties for clinical applications in which local sustained release of drugs is crucial. Two preferential formulations resulting from the predictive studies of DoE and In Silico methods were synthesized by radical polymerization, fully characterized, and loaded with the anticancer drug Doxorubicin (Dox). The hydrogel formulations were characterized by swelling rate, turbidity, FTIR, 1H NMR, SEM, gelation time, rheology, and biocompatibility assays. Both formulations demonstrated adequate morphologic, rheological, and biocompatibility properties; however, important differences in terms of drug retention were detected. As demonstrated by a Dox cumulative release study and posteriorly confirmed by an efficacy assay in an in vitro colorectal cancer model, the formulation composed by NIPAM and 4-penten-1-ol crosslinked with poly(ethylene glycol) diacrylate (PEGDA) (PNiPenPH) present a slow release over the time, presenting ideal properties to become and ideal depot system for the local sustained release of anticancer drugs as adjuvant therapy or in the case of non-resectable tumors.

In Silico Methods for the Identification of Diagnostic and Favorable Prognostic Markers in Acute Myeloid Leukemia

Acute myeloid leukemia (AML), the most common type of acute leukemia in adults, is mainly asymptomatic at early stages and progresses/recurs rapidly and frequently. These attributes necessitate the identification of biomarkers for timely diagnosis and accurate prognosis. In this study, differential gene expression analysis was performed on large-scale transcriptomics data of AML patients versus corresponding normal tissue. Weighted gene co-expression network analysis was conducted to construct networks of co-expressed genes, and detect gene modules. Finally, hub genes were identified from selected modules by applying network-based methods. This robust and integrative bioinformatics approach revealed a set of twenty-four genes, mainly related to cell cycle and immune response, the diagnostic significance of which was subsequently compared against two independent gene expression datasets. Furthermore, based on a recent notion suggesting that molecular characteristics of a few, unusual patients with exceptionally favorable survival can provide insights for improving the outcome of individuals with more typical disease trajectories, we defined groups of long-term survivors in AML patient cohorts and compared their transcriptomes versus the general population to infer favorable prognostic signatures. These findings could have potential applications in the clinical setting, in particular, in diagnosis and prognosis of AML.

Investigation of Bemethyl Biotransformation Pathways by Combination of LC-MS/HRMS and In Silico Methods.

Bemethyl is an actoprotector, an antihypoxant, and a moderate psychostimulant. Even though the therapeutic effectiveness of bemethyl is well documented, there is a gap in knowledge regarding its metabolic products and their quantitative and qualitative characteristics. Since 2018, bemethyl is included to the Monitoring Program of the World Anti-Doping Agency, which highlights the challenge of identifying its urinary metabolites. The objective of the study was to investigate the biotransformation pathways of bemethyl using a combination of liquid chromatography-high-resolution mass spectrometry and in silico studies. Metabolites were analyzed in a 24 h rat urine collected after oral administration of bemethyl at a single dose of 330 mg/kg. The urine samples were prepared for analysis by a procedure developed in the present work and analyzed by high performance liquid chromatography–tandem mass spectrometry. For the first time, nine metabolites of bemethyl with six molecular formulas were identified in rat urine. The most abundant metabolite was a benzimidazole–acetylcysteine conjugate; this biotransformation pathway is associated with the detoxification of xenobiotics. The BioTransformer and GLORY computational tools were used to predict bemethyl metabolites in silico. The molecular docking of bemethyl and its derivatives to the binding site of glutathione S-transferase has revealed the mechanism of bemethyl conjugation with glutathione. The findings will help to understand the pharmacokinetics and pharmacodynamics of actoprotectors and to improve antihypoxant and adaptogenic therapy.

Antimicrobial Effects of Potential Probiotics of Bacillus spp. Isolated from Human Microbiota: In Vitro and In Silico Methods.

The variable taxa components of human gut microbiota seem to have an enormous biotechnological potential that is not yet well explored. To investigate the usefulness and applications of its biocompounds and/or bioactive substances would have a dual impact, allowing us to better understand the ecology of these microbiota consortia and to obtain resources for extended uses. Our research team has obtained a catalogue of isolated and typified strains from microbiota showing resistance to dietary contaminants and obesogens. Special attention was paid to cultivable Bacillus species as potential next-generation probiotics (NGP) together with their antimicrobial production and ecological impacts. The objective of the present work focused on bioinformatic genome data mining and phenotypic analyses for antimicrobial production. In silico methods were applied over the phylogenetically closest type strain genomes of the microbiota Bacillus spp. isolates and standardized antimicrobial production procedures were used. The main results showed partial and complete gene identification and presence of polyketide (PK) clusters on the whole genome sequences (WGS) analysed. Moreover, specific antimicrobial effects against B. cereus, B. circulans, Staphylococcus aureus, Streptococcus pyogenes, Escherichia coli, Serratia marcescens, Klebsiella spp., Pseudomonas spp., and Salmonella spp. confirmed their capacity of antimicrobial production. In conclusion, Bacillus strains isolated from human gut microbiota and taxonomic group, resistant to Bisphenols as xenobiotics type endocrine disruptors, showed parallel PKS biosynthesis and a phenotypic antimicrobial effect. This could modulate the composition of human gut microbiota and therefore its functionalities, becoming a predominant group when high contaminant exposure conditions are present.

A Review of Current In Silico Methods for Repositioning Drugs and Chemical Compounds.

Drug repositioning is a new way of applying the existing therapeutics to new disease indications. Due to the exorbitant cost and high failure rate in developing new drugs, the continued use of existing drugs for treatment, especially anti-tumor drugs, has become a widespread practice. With the assistance of high-throughput sequencing techniques, many efficient methods have been proposed and applied in drug repositioning and individualized tumor treatment. Current computational methods for repositioning drugs and chemical compounds can be divided into four categories: (i) feature-based methods, (ii) matrix decomposition-based methods, (iii) network-based methods, and (iv) reverse transcriptome-based methods. In this article, we comprehensively review the widely used methods in the above four categories. Finally, we summarize the advantages and disadvantages of these methods and indicate future directions for more sensitive computational drug repositioning methods and individualized tumor treatment, which are critical for further experimental validation.

Estudos In silico sobre as atividades anticancerígenas do Eugenol presente no Cravo Da Índia (Syzygium aromaticum)

Objetivo: Investigar as propriedades anticancerígenas do eugenol usando métodos In silico. Metodologia: é um artigo original utilizando a metodologia In silico atraves dos softwares PASS (Prediction of activity spectra for substances) online e GUSAR disponíveis de forma gratuita e livre. Resultados: Os resultados do sistema operacional online PASS demonstraram diversas atividades biológicas positiva, tais quais efeito antimutagênico, estimulante de caspase, tratamento pré neoplásico e agonista de apoptose tendo uma probabilidade de atividade (PA) bem superior a 0,700. Tais achados reforçam aos descritos na literatura que evidenciam um potencial antineoplásico devido o componente ter efeitos antioxidante, e estar vinculado a expressão de genes envolvidos na apoptose e inflamação. Tal como efeitos na regulação negativa de Bcl-2, COX-2 e IL-1ß, e ações na regulação positiva de P53, Bax e expressão ativa de Caspase-3, caspase-9 além de induzir a via da apoptose mitocondrial. Já pelo software GUSAR, foi observado a toxicidade, demonstrando que na categoria 4, estão as vias de administração intravenosa, oral, e subcutânea, indicando que é pouco tóxico. Já na categoria 5 enquadra-se a via intraperitoneal é relativamente atóxico. Tal achado ainda é obscuro e possui poucos artigos científicos para o embasamento. Conclusão: O Eugenol possui efeitos favoráveis para o tratamento e prevenção de diversos canceres e células mutagênicas, porém ainda há pouco conhecimento sobre a toxicidade deste componente. Assim, necessita de um maior aprofundamento sobre a temática.

Investigation of the Differences in Antithrombin to Heparin Binding among Antithrombin Budapest 3, Basel, and Padua Mutations by Biochemical and In Silico Methods.

Antithrombin (AT) is a serine protease inhibitor, its activity is highly accelerated by heparin. Mutations at the heparin-binding region lead to functional defect, type II heparin-binding site (IIHBS) AT deficiency. The aim of this study was to investigate and compare the molecular background of AT Budapest 3 (p.Leu131Phe, ATBp3), AT Basel (p.Pro73Leu), and AT Padua (p.Arg79His) mutations. Advanced in silico methods and heparin-binding studies of recombinant AT proteins using surface plasmon resonance method were used. Crossed immunoelectrophoresis and Differential Scanning Fluorimetry (NanoDSF) were performed in plasma samples. Heparin affinity of AT Padua was the lowest (KD = 1.08 × 10−6 M) and had the most severe consequences affecting the allosteric pathways of activation, moreover significant destabilizing effects on AT were also observed. KD values for AT Basel, ATBp3 and wild-type AT were 7.64 × 10−7 M, 2.15 × 10−8 M and 6.4 × 10−10 M, respectively. Heparin-binding of AT Basel was slower, however once the complex was formed the mutation had only minor effect on the secondary and tertiary structures. Allosteric activation of ATBp3 was altered, moreover decreased thermostability in ATBp3 homozygous plasma and increased fluctuations in multiple regions of ATBp3 were observed by in silico methods suggesting the presence of a quantitative component in the pathogenicity of this mutation due to molecular instability.

Predicting Potential Endocrine Disrupting Chemicals Binding to Estrogen Receptor α (ERα) Using a Pipeline Combining Structure-Based and Ligand-Based in Silico Methods

The estrogen receptors α (ERα) are transcription factors involved in several physiological processes belonging to the nuclear receptors (NRs) protein family. Besides the endogenous ligands, several other chemicals are able to bind to those receptors. Among them are endocrine disrupting chemicals (EDCs) that can trigger toxicological pathways. Many studies have focused on predicting EDCs based on their ability to bind NRs; mainly, estrogen receptors (ER), thyroid hormones receptors (TR), androgen receptors (AR), glucocorticoid receptors (GR), and peroxisome proliferator-activated receptors gamma (PPARγ). In this work, we suggest a pipeline designed for the prediction of ERα binding activity. The flagged compounds can be further explored using experimental techniques to assess their potential to be EDCs. The pipeline is a combination of structure based (docking and pharmacophore models) and ligand based (pharmacophore models) methods. The models have been constructed using the Environmental Protection Agency (EPA) data encompassing a large number of structurally diverse compounds. A validation step was then achieved using two external databases: the NR-DBIND (Nuclear Receptors DataBase Including Negative Data) and the EADB (Estrogenic Activity DataBase). Different combination protocols were explored. Results showed that the combination of models performed better than each model taken individually. The consensus protocol that reached values of 0.81 and 0.54 for sensitivity and specificity, respectively, was the best suited for our toxicological study. Insights and recommendations were drawn to alleviate the screening quality of other projects focusing on ERα binding predictions.

Screenning Bioactive Compounds from Allium sativum as HER2 Inhibitors Targeting Breast Cancer by Docking Methods

Screenning Bioactive Compounds from Allium sativum as HER2 Inhibitors Targeting Breast Cancer by Docking Methods

Energetics of LOHC: Structure-Property Relationships from Network of Thermochemical Experiments and in Silico Methods

The storage of hydrogen is the key technology for a sustainable future. We developed an in silico procedure, which is based on the combination of experimental and quantum-chemical methods. This method was used to evaluate energetic parameters for hydrogenation/dehydrogenation reactions of various pyrazine derivatives as a seminal liquid organic hydrogen carriers (LOHC), that are involved in the hydrogen storage technologies. With this in silico tool, the tempo of the reliable search for suitable LOHC candidates will accelerate dramatically, leading to the design and development of efficient materials for various niche applications.

Genome Analysis of Bacillus aryabhattai to Identify Biosynthetic Gene Clusters and In Silico Methods to Elucidate its Antimicrobial Nature

Biosurfactants such as lipopeptides, lipoketides, and peptides produced by Bacillus species exhibit antimicrobial properties. In the present study, genome analysis was carried out to identify the possible lipopeptides produced by the bacterial strain of Bacillus aryabhattai. Iturin A, Surfactin A, Syringomycin E, Bacitracin A, Tryrocidine A, and Fengycin were identified as the possible lipopeptides produced by Bacillus aryabhattai. The identified lipopeptides were further studied by the application of in silico drug discovery tools like molecular docking and molecular dynamics. The target (1,3)-β-D-glucan synthase from Candida albicans was selected. The molecular docking studies showed that Iturin A revealed the best binding affinity against the target with the binding energy of −10.4 kcal/mol. The complex of (1,3)-β-D-glucan synthase- Iturin A was further subjected to molecular dynamics simulation under physiological conditions for the duration of 50 ns, and the results showed, ligand conformation during the simulation stayed close to original conformation state. The genome analysis and in silico studies suggest that Bacillus aryabhattai could be explored for the production and characterization of biosurfactants relevant to the biopharmaceutical industry.

The Application of In Silico Methods on Umami Taste Receptor.

The umami taste receptor is a heterodimer composed of two members of the T1R taste receptor family: T1R1 (taste receptor type 1 member 1) and T1R3 (taste receptor type 1 member 3). Taste receptor T1R1-T1R3 can be activated, or modulated, by binding to several natural ligands, such as L-glutamate, inosine-5'-monophosphate (IMP), and guanosine-5'-monophosphate (GMP). Because no structure of the umami taste receptor has been solved until now, in silico techniques, such as homology modelling, molecular docking, and molecular dynamics (MD) simulations, are used to generate a 3D structure model of this receptor and to understand its molecular mechanisms. The purpose of this chapter is to highlight how computational methods can provide a better deciphering of the mechanisms of action of umami ligands in activating the umami taste receptors leading to advancements in the taste research field.

Designing and Screening of New Schiff Bases of Isatins for Antibacterial Activity byIn Silico Methods and Docking Studies.

Isatin or tribulin is an indole derivative; the compound was first obtained by Erdmann [1] and Laurent [2] and Erdmann in 1841 as a product from the oxidation of indigo dye by nitric acid and chromic acids. The compound is found in many plants, such as Isatis tinctoria, Calanthe discolor, and Couroupita guianensis. Schiff bases of isatin are investigated for their pharmaceutical properties. Isatins have been found to have various activities such as antiviral, antibacterial, anti-inflammatory, analgesic, anticonvulsan, antidepressant anti-HIV, fungicidal, etc. In this study, we focus on isatin derivatives for antibacterial activity. Isatin derivatives are docked on two targets, glucoseamine-6-phosphate synthase (PDB ID:2VF%) and dihydropteroate synthase (PDB ID: 1AJ0) enzymes that are potential targets for antibacterial and antifungal agents. The in silico results and docking scores of the isatin derivatives were compared with standard drugs.

Using In Silico Methods to Search the Helicobacter Pylori Genome for Potential Vaccine Candidates

Using In Silico Methods to Search the Helicobacter Pylori Genome for Potential Vaccine Candidates

Predicting Volume of Distribution in Humans: Performance of In Silico Methods for a Large Set of Structurally Diverse Clinical Compounds.

Volume of distribution at steady state (VD,ss) is one of the key pharmacokinetic parameters estimated during the drug discovery process. Despite considerable efforts to predict VD,ss, accuracy and choice of prediction methods remain a challenge, with evaluations constrained to a small set (<150) of compounds. To address these issues, a series of in silico methods for predicting human VD,ss directly from structure were evaluated using a large set of clinical compounds. Machine learning (ML) models were built to predict VD,ss directly and to predict input parameters required for mechanistic and empirical VD,ss predictions. In addition, log D, fraction unbound in plasma (fup), and blood-to-plasma partition ratio (BPR) were measured on 254 compounds to estimate the impact of measured data on predictive performance of mechanistic models. Furthermore, the impact of novel methodologies such as measuring partition (Kp) in adipocytes and myocytes (n = 189) on VD,ss predictions was also investigated. In predicting VD,ss directly from chemical structures, both mechanistic and empirical scaling using a combination of predicted rat and dog VD,ss demonstrated comparable performance (62%–71% within 3-fold). The direct ML model outperformed other in silico methods (75% within 3-fold, r2 = 0.5, AAFE = 2.2) when built from a larger data set. Scaling to human from predicted VD,ss of either rat or dog yielded poor results (<47% within 3-fold). Measured fup and BPR improved performance of mechanistic VD,ss predictions significantly (81% within 3-fold, r2 = 0.6, AAFE = 2.0). Adipocyte intracellular Kp showed good correlation to the VD,ss but was limited in estimating the compounds with low VD,ss.SIGNIFICANCE STATEMENTThis work advances the in silico prediction of VD,ss directly from structure and with the aid of in vitro data. Rigorous and comprehensive evaluation of various methods using a large set of clinical compounds (n = 956) is presented. The scale of techniques evaluated is far beyond any previously presented. The novel data set (n = 254) generated using a single protocol for each in vitro assay reported in this study could further aid in advancing VD,ss prediction methodologies.