scholarly journals In Silico Methods for Development of Generic Drug–Device Combination Orally Inhaled Drug Products

2019 ◽  
Vol 8 (6) ◽  
pp. 359-370 ◽  
Author(s):  
Ross L. Walenga ◽  
Andrew H. Babiskin ◽  
Liang Zhao
Keyword(s):  
Generic Drug ◽  
In Silico ◽  
Drug Products ◽  
Orally Inhaled Drug Products ◽  
In Silico Methods

scholarly journals Optimization of the Transwell® System for Assessing the Dissolution Behavior of Orally Inhaled Drug Products through In Vitro and In Silico Approaches

Pharmaceutics ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1109
Author(s):  
Elham Amini ◽  
Abhinav Kurumaddali ◽  
Sharvari Bhagwat ◽  
Simon M. Berger ◽  
Günther Hochhaus
Keyword(s):  
In Silico ◽  
Mass Effect ◽  
Dissolution Behavior ◽  
Experimental Conditions ◽  
Drug Products ◽  
Starting Point ◽  
Orally Inhaled Drug Products ◽  
Model Drug ◽  
The Impact

The aim of this study was to further evaluate and optimize the Transwell® system for assessing the dissolution behavior of orally inhaled drug products (OIDPs), using fluticasone propionate as a model drug. Sample preparation involved the collection of a relevant inhalable dose fraction through an anatomical mouth/throat model, resulting in a more uniform presentation of drug particles during the subsequent dissolution test. The method differed from previously published procedures by (1) using a 0.4 µm polycarbonate (PC) membrane, (2) stirring the receptor compartment, and (3) placing the drug-containing side of the filter paper face downwards, towards the PC membrane. A model developed in silico, paired with the results of in vitro studies, suggested that a dissolution medium providing a solubility of about 5 µg/mL would be a good starting point for the method’s development, resulting in mean transfer times that were about 10 times longer than those of a solution. Furthermore, the model suggested that larger donor/receptor and sampling volumes (3, 3.3 and 2 mL, respectively) will significantly reduce the so-called “mass effect”. The outcomes of this study shed further light on the impact of experimental conditions on the complex interplay of dissolution and diffusion within a volume-limited system, under non-sink conditions.

Elucidation of Phosphodiesterase-1 Inhibitory Effect of Some Selected Natural Polyphenolics Using In Vitro and In Silico Methods

2016 ◽  
Vol 17 (4) ◽  
pp. 412-417 ◽  
Author(s):  
Abdur Rauf ◽  
Ilkay Erdogan Orhan ◽  
Abdulselam Ertas ◽  
Hamdi Temel ◽  
Taibi Ben Hadda ◽  
...  
Keyword(s):  
In Silico ◽  
Inhibitory Effect ◽  
In Silico Methods

Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes

2019 ◽  
Vol 19 (5) ◽  
pp. 319-336 ◽  
Author(s):  
Alexander V. Dmitriev ◽  
Alexey A. Lagunin ◽  
Dmitry А. Karasev ◽  
Anastasia V. Rudik ◽  
Pavel V. Pogodin ◽  
...  
Keyword(s):  
In Silico ◽  
Computational Models ◽  
Field Analysis ◽  
Pharmaceutical Chemistry ◽  
Sources Of Information ◽  
Metabolizing Enzymes ◽  
Physiologically Based Pharmacokinetic ◽  
Pharmacophore Models ◽  
Drug Metabolising Enzymes ◽  
In Silico Methods

Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.

scholarly journals Innovating on Inhaled Bioequivalence: A Critical Analysis of the Current Limitations, Potential Solutions and Stakeholders of the Process

Pharmaceutics ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1051
Author(s):  
Jonattan Gallegos-Catalán ◽  
Zachary Warnken ◽  
Tania F. Bahamondez-Canas ◽  
Daniel Moraga-Espinoza
Keyword(s):  
New Technologies ◽  
Innovation Process ◽  
Cost Effective ◽  
Regulatory Agencies ◽  
Drug Products ◽  
Effective Strategies ◽  
Rate Analysis ◽  
Orally Inhaled Drug Products ◽  
Next Generation Impactor ◽  
First Time

Orally inhaled drug products (OIDPs) are an important group of medicines traditionally used to treat pulmonary diseases. Over the past decade, this trend has broadened, increasing their use in other conditions such as diabetes, expanding the interest in this administration route. Thus, the bioequivalence of OIDPs is more important than ever, aiming to increase access to affordable, safe and effective medicines, which translates into better public health policies. However, regulatory agencies leading the bioequivalence process are still deciding the best approach for ensuring a proposed inhalable product is bioequivalent. This lack of agreement translates into less cost-effective strategies to determine bioequivalence, discouraging innovation in this field. The Next-Generation Impactor (NGI) is an example of the slow pace at which the inhalation field evolves. The NGI was officially implemented in 2003, being the last equipment innovation for OIDP characterization. Even though it was a breakthrough in the field, it did not solve other deficiencies of the BE process such as dissolution rate analysis on physiologically relevant conditions, being the last attempt of transferring technology into the field. This review aims to reveal the steps required for innovation in the regulations defining the bioequivalence of OIDPs, elucidating the pitfalls of implementing new technologies in the current standards. To do so, we collected the opinion of experts from the literature to explain these trends, showing, for the first time, the stakeholders of the OIDP market. This review analyzes the stakeholders involved in the development, improvement and implementation of methodologies that can help assess bioequivalence between OIDPs. Additionally, it presents a list of methods potentially useful to overcome some of the current limitations of the bioequivalence standard methodologies. Finally, we review one of the most revolutionary approaches, the inhaled Biopharmaceutical Classification System (IBCs), which can help establish priorities and order in both the innovation process and in regulations for OIDPs.

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

Hydrogen ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 101-121
Author(s):  
Sergey P. Verevkin ◽  
Vladimir N. Emel’yanenko ◽  
Riko Siewert ◽  
Aleksey A. Pimerzin
Keyword(s):  
In Silico ◽  
Quantum Chemical ◽  
Structure Property ◽  
Chemical Methods ◽  
Key Technology ◽  
Structure Property Relationships ◽  
Quantum Chemical Methods ◽  
Dehydrogenation Reactions ◽  
Storage Technologies ◽  
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.

scholarly journals Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning

Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2505
Author(s):  
Raheem Remtulla ◽  
Sanjoy Kumar Das ◽  
Leonard A. Levin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
In Silico ◽  
Disulfide Bonds ◽  
Oral Absorption ◽  
In Vivo Studies ◽  
Drug Candidates ◽  
In Silico Methods

Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated in the downstream effects of axonal injury. A difficulty in using standard in silico methods for studying these drugs is that most computational tools are not designed for borane-containing compounds. Using in silico and machine learning methodologies, the absorption-distribution properties of these unique compounds were assessed. Features examined with in silico methods included cellular permeability, octanol-water partition coefficient, blood-brain barrier permeability, oral absorption and serum protein binding. The resultant neural networks demonstrated an appropriate level of accuracy and were comparable to existing in silico methodologies. Specifically, they were able to reliably predict pharmacokinetic features of known boron-containing compounds. These methods predicted that phosphine-borane compounds and their metabolites meet the necessary pharmacokinetic features for orally active drug candidates. This study showed that the combination of standard in silico predictive and machine learning models with neural networks is effective in predicting pharmacokinetic features of novel boron-containing compounds as neuroprotective drugs.

scholarly journals Glucocorticoid resistance conferring mutation in the C-terminus of GR alters the receptor conformational dynamics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anna Kaziales ◽  
Florian Rührnößl ◽  
Klaus Richter
Keyword(s):  
Glucocorticoid Receptor ◽  
Steroid Hormones ◽  
In Silico ◽  
Conformational Dynamics ◽  
Glucocorticoid Resistance ◽  
Physiological Processes ◽  
C Terminus ◽  
Hsp90 Chaperone ◽  
In Silico Methods

AbstractThe glucocorticoid receptor is a key regulator of essential physiological processes, which under the control of the Hsp90 chaperone machinery, binds to steroid hormones and steroid-like molecules and in a rather complicated and elusive response, regulates a set of glucocorticoid responsive genes. We here examine a human glucocorticoid receptor variant, harboring a point mutation in the last C-terminal residues, L773P, that was associated to Primary Generalized Glucocorticoid Resistance, a condition originating from decreased affinity to hormone, impairing one or multiple aspects of GR action. Using in vitro and in silico methods, we assign the conformational consequences of this mutation to particular GR elements and report on the altered receptor properties regarding its binding to dexamethasone, a NCOA-2 coactivator-derived peptide, DNA, and importantly, its interaction with the chaperone machinery of Hsp90.

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

2021 ◽  
Vol 9 (8) ◽  
pp. 1615
Author(s):  
Alfonso Torres-Sánchez ◽  
Jesús Pardo-Cacho ◽  
Ana López-Moreno ◽  
Ángel Ruiz-Moreno ◽  
Klara Cerk ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
In Silico ◽  
Ecological Impacts ◽  
Bacillus Species ◽  
Human Gut ◽  
Contaminant Exposure ◽  
Antimicrobial Effects ◽  
Human Gut Microbiota ◽  
Bacillus Spp ◽  
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.

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