Information

7.4: Interactions Between Drug and Host - Biology

7.4: Interactions Between Drug and Host - Biology


We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

7.4: Interactions Between Drug and Host

7.4: Interactions Between Drug and Host - Biology

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited.

Feature Papers represent the most advanced research with significant potential for high impact in the field. Feature Papers are submitted upon individual invitation or recommendation by the scientific editors and undergo peer review prior to publication.

The Feature Paper can be either an original research article, a substantial novel research study that often involves several techniques or approaches, or a comprehensive review paper with concise and precise updates on the latest progress in the field that systematically reviews the most exciting advances in scientific literature. This type of paper provides an outlook on future directions of research or possible applications.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to authors, or important in this field. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.


Biological Explanations

In looking at drug use, the field of biology focuses on two related major questions. First, how and why do drugs affect a person&rsquos behavior, mood, perception, and other qualities? Second, what biological factors explain why some people are more likely than others to use drugs?

Regarding the first question, the field of biology has an excellent understanding of how drugs work. The details of this understanding are beyond the scope of this chapter, but they involve how drugs affect areas in the brain and the neurotransmitters that cause a particular drug&rsquos effects. For example, cocaine produces euphoria and other positive emotions in part because it first produces an accumulation of dopamine, a neurotransmitter linked to feelings of pleasure and enjoyment.

Research on identical twins suggests that alcoholism has a genetic basis.

Michael Dorausch &ndash Identical Twins Jedward &ndash CC BY-SA 2.0.

Regarding the second question, biological research is more speculative, but it assumes that some people are particularly vulnerable to the effects of drugs. These people are more likely to experience very intense effects and to become physiologically and/or psychologically addicted to a particular drug. To the extent this process occurs, the people in question are assumed to have a biological predisposition for drug addiction that is thought to be a genetic predisposition.

Most research on genetic predisposition has focused on alcohol and alcoholism (Hanson et al., 2012). Studies of twins find that identical twins are more likely than fraternal twins (who are not genetically identical) to both have alcohol problems or not to have them. In addition, studies of children of alcoholic parents who are adopted by nonalcoholic parents find that these children are more likely than those born to nonalcoholic parents to develop alcohol problems themselves. Although a genetic predisposition for alcoholism might exist for reasons not yet well understood, there is not enough similar research on other types of drug addiction to assume that a genetic predisposition exists for these types. Many nonbiological factors also explain the use of, and addiction to, alcohol and other drugs. We now turn to these factors.


Mapping the Interactions of HBV cccDNA with Host Factors

Hepatitis B virus (HBV) infection is a major health problem affecting about 300 million people globally. Although successful administration of a prophylactic vaccine has reduced new infections, a cure for chronic hepatitis B (CHB) is still unavailable. Current anti-HBV therapies slow down disease progression but are not curative as they cannot eliminate or permanently silence HBV covalently closed circular DNA (cccDNA). The cccDNA minichromosome persists in the nuclei of infected hepatocytes where it forms the template for all viral transcription. Interactions between host factors and cccDNA are crucial for its formation, stability, and transcriptional activity. Here, we summarize the reported interactions between HBV cccDNA and various host factors and their implications on HBV replication. While the virus hijacks certain cellular processes to complete its life cycle, there are also host factors that restrict HBV infection. Therefore, we review both positive and negative regulation of HBV cccDNA by host factors and the use of small molecule drugs or sequence-specific nucleases to target these interactions or cccDNA directly. We also discuss several reporter-based surrogate systems that mimic cccDNA biology which can be used for drug library screening of cccDNA-targeting compounds as well as identification of cccDNA-related targets.

Keywords: Hepatitis B virus covalently closed circular DNA drug target host-virus interaction screening systems.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Schematic representation of the hepatitis…

Schematic representation of the hepatitis B virus (HBV) life cycle.

Summary of main classes of…

Summary of main classes of host factors that promote or inhibit covalently closed…


7.4: Interactions Between Drug and Host - Biology

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited.

Feature Papers represent the most advanced research with significant potential for high impact in the field. Feature Papers are submitted upon individual invitation or recommendation by the scientific editors and undergo peer review prior to publication.

The Feature Paper can be either an original research article, a substantial novel research study that often involves several techniques or approaches, or a comprehensive review paper with concise and precise updates on the latest progress in the field that systematically reviews the most exciting advances in scientific literature. This type of paper provides an outlook on future directions of research or possible applications.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to authors, or important in this field. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.


A Worldwide Yearly Survey of New Data in Adverse Drug Reactions

Cucnhat Walker PharmD, BCPS, MPH , Sidhartha D. Ray PhD, FACN , in Side Effects of Drugs Annual , 2019

Injection-site reaction

An in-vitro model (hyaluronic acid filled cartridges mimicking subcutaneous space) and an in-vivo model (mice) were used to study the mechanisms for injection-site reaction (i.e., injection site mass, erythema, atrophy) and draining lymph nodes (i.e., lymphadenopathy) associated with GA injection [ 71H ]. The in-vitro model showed that glatiramer–hyaluronic acid aggregates at the injection site and slows drug release into the system over 3 days. On day 3, approximately 30% of the drug was found at the injection site. The in-vivo model showed that the drug traveled to the draining lymph node and remained there for over 4 h.

A prospective observational study investigated post-injection reactions (PIR) of GA in patients with relapsing–remitting multiple sclerosis [ 72R ]. Typical reactions were palpitations, dyspnea, tachycardia, bronchospasm, and urticaria. Atypical symptoms were abdominal cramps, nausea/vomiting, diarrhea, shivering, fever, and other uncommon symptoms. Patients either received SQ 20 mg GA (n = 97) or 40 mg GA (n = 173). The 40 mg dose was significantly associated with atypical post-injection reactions. In this group, 22% reported atypical PIRs, of which 15% had recurrent reactions, and 8.1% experienced both typical and atypical PIRs. The 20 mg dose had only 2.1% responded with atypical PIRs.


Proteomics and Host—Pathogen Interactions

David G. Biron , . Philippe Holzmuller , in Genetics and Evolution of Infectious Disease , 2011

11.7.4 Environment and Host–Parasite Interactions

For host–pathogen interactions, the main assumption is that, over ecological time-scales, host susceptibility and pathogen virulence are fixed at the onset of the crosstalk ( Bull, 1994 Dieckmann et al., 2002 ). Also, environmental factors are traditionally viewed as “setting the scene” for the crosstalk rather than having any explicit role once it is underway. As a result, the effect of extrinsic factors on host susceptibility and pathogen virulence during a crosstalk has received little attention. However, it is common to find in populations of a pathogen species a substantial variation in the virulence, even when pathogens are collected in the same environment and at the same time. When a biological characteristic such as the virulence is variable for both genetic and environmental reasons, two individuals may differ because they differ in genotype, because they have had different environmental experiences, or both ( Elliot et al., 2002 ). Unfortunately, the extent to which different individual pathogens and pathogen ecotypes display different virulence abilities is poorly documented and deciphered.

Life-history traits of hosts and pathogens are shaped by coevolution processes ( Wolinska and King, 2009 ). Infections measured under laboratory conditions have shown that the environment in which hosts and pathogens interact may affect the range of host genotypes that can be infected with a given pathogen genotype in host pathogen associations (i.e. the specificity of selection ). Despite this important fact, environmental fluctuations are often excluded in surveys on host–pathogen interactions. Since most host–pathogen interactions are in heterogeneous environments, there is a crucial need to take into account environmental conditions in proteomics surveys. The population proteomics would be a promising prospect to resolve interesting issues specific to host–pathogen crosstalks in a varying environment ( Figure 11.8 ). This kind of survey would bring pioneer molecular data to decipher the reaction norm of a genotype and to understand why pathogens sometimes evolve in a given environment toward high virulence and hosts toward high resistance. Also, these surveys would permit to assess the fixity or not of host–parasite interactomes involved in a host–pathogen association in a varying environment.

Figure 11.8 . Host–pathogen interactions in a varying environment.


Methods

Cell culture

Human Caco-2 cells, derived from colon carcinoma, were obtained from the Deutsche Sammlung von Mikroorganismen und Zellkulturen (DSMZ AC169). The cell-authentification certificate from DSMZ is available and cells have been tested negative for mycoplasma infection.

Cells were grown at 37 °C in minimal essential medium (MEM) supplemented with 10% fetal bovine serum (FBS) and containing 100 IU/ml penicillin and 100 μg/ml streptomycin. All culture reagents were purchased from Sigma.

Virus preparation

SARS-CoV-2 was isolated from samples of travellers returning from Wuhan (China) to Frankfurt (Germany) using the human colon carcinoma cell line Caco-2 as described previously 6 . SARS-CoV-2 stocks used in the experiments had undergone one passage on Caco-2 cells and were stored at −80 °C. Virus titres were determined as TCID50/ml in confluent cells in 96-well microtitre plates.

Quantification of viral RNA

SARS-CoV-2 RNA from cell-culture supernatant samples was isolated using AVL buffer and the QIAamp Viral RNA Kit (Qiagen) according to the manufacturer’s instructions. Absorbance-based quantification of the RNA yield was performed using the Genesys 10S UV-Vis Spectrophotometer (Thermo Scientific). RNA was subjected to OneStep qRT–PCR analysis using the Luna Universal One-Step RT-qPCR Kit (New England Biolabs) and a CFX96 Real-Time System, C1000 Touch Thermal Cycler. Primers were adapted from the WHO protocol 31 targeting the open-reading frame for RNA-dependent RNA polymerase (RdRp): RdRP_SARSr-F2 (GTGARATGGTCATGTGTGGCGG) and RdRP_SARSr-R1 (CARATGTTAAASACACTATTAGCATA) using 0.4 μM per reaction. Standard curves were created using plasmid DNA (pEX-A128-RdRP) that contained the corresponding amplicon regions of the RdRP target sequence according to GenBank accession number NC_045512. For each condition three biological replicates were used. Mean ± s.d. were calculated for each group.

Antiviral and cell viability assays

Confluent layers of Caco-2 cells in 96-well plates were infected with SARS-CoV-2 at a MOI of 0.01. Virus was added together with drugs and incubated in MEM supplemented with 2% FBS with different drug dilutions. Cytopathogenic effects were assessed visually 48 h after infection. To assess the effects of drugs on Caco-2 cell viability, confluent cell layers were treated with different drug concentration in 96-well plates. The viability was measured using the Rotitest Vital (Roth) according to the manufacturer’s instructions. Data for each condition were collected for at least three biological replicates. For dose–response curves, data were fitted with all replicates using OriginPro 2020 with the following equation:

IC50 values were generated by Origin together with metrics for curve fits.

Detection of the nucleoprotein of SARS-CoV-2

Viral infection was assessed by staining of SARS-CoV-2 nucleoprotein. In brief, cells were fixed with acetone:methanol (40:60) solution and immunostaining was performed using a monoclonal antibody directed against the nucleoprotein of SARS-CoV-2 (1:500, Sinobiological, 40143-R019-100ul), which was detected with a peroxidase-conjugated anti-rabbit secondary antibody (1:1,000, Dianova), followed by addition of AEC substrate.

Isotope labelling and cell lysis

In brief, 2 h before collection, cells were washed twice with warm PBS to remove interfering medium and cultured for an additional 2 h with DMEM medium containing 84 mg/l l -arginine ( 13 C6 15 N4 (R10) Cambridge Isotope Laboratories, CNLM-539-H) and 146 mg/l l -lysine ( 13 C6 15 N2 (K8), Cambridge Isotope Laboratories, CNLM-291-H) to label nascent proteins. After labelling culture, the cells were washed three times with warm PBS and lysed with 95 °C hot lysis buffer (100 mM EPPS pH 8.2, 2% sodium deoxycholate, 1 mM TCEP, 4 mM 2-chloracetamide, protease inhibitor tablet mini EDTA-free (Roche)). Samples were then incubated for an additional 5min at 95 °C, followed by sonication for 30 s and a further 10-min incubation at 95 °C.

Sample preparation for LC–MS/MS

Samples were prepared as previously described 3 . In brief, proteins were precipitated using methanol:chloroform precipitation and resuspended in 8 M urea and 10 mM EPPS pH 8.2. Isolated proteins were digested with 1:50 w/w LysC (Wako Chemicals) and 1:100 w/w trypsin (Promega, Sequencing-grade) overnight at 37 °C after dilution to a final urea concentration of 1 M. Digests were then acidified (pH 2–3) using TFA. Peptides were purified using C18 (50 mg) SepPak columns (Waters) as previously described. Desalted peptides were dried and 25 μg of peptides were resuspended in TMT-labelling buffer (200 mM EPPS pH 8.2, 10% acetonitrile). Peptides were subjected to TMT labelling with 1:2 peptide TMT ratio (w/w) for1 h at room temperature. The labelling reaction was quenched by addition of hydroxylamine to a final concentration of 0.5% and incubation at room temperature for an additional 15 min. Labelled peptides were pooled and subjected to high pH reverse Phase fractionation with the HpH RP Fractionation kit (ThermoFisher Scientific) following the manufacturer’s instructions. All multiplex reactions were mixed with a bridge channel, which consists of a control sample labelled in one reaction and split to all multiplexed reactions in equimolar amounts.

LC–MS/MS

Peptides were resuspended in 0.1% formic acid and separated on an Easy nLC 1200 (ThermoFisher Scientific) and a 22-cm-long, 75-μm-inner-diameter fused-silica column, which had been packed in house with 1.9-μm C18 particles (ReproSil-Pur, Dr. Maisch), and kept at 45 °C using an integrated column oven (Sonation). Peptides were eluted by a nonlinear gradient from 5–38% acetonitrile over 120 min and directly sprayed into a QExactive HF mass spectrometer equipped with a nanoFlex ion source (ThermoFisher Scientific) at a spray voltage of 2.3 kV. Full-scan MS spectra (350–1,400 m/z) were acquired at a resolution of 120,000 at m/z 200, a maximum injection time of 100 ms and an AGC target value of 3 × 10 6 . Up to 20 most intense peptides per full scan were isolated using a 1 Th window and fragmented using higher-energy collisional dissociation (normalized collision energy of 35). MS/MS spectra were acquired with a resolution of 45,000 at m/z 200, a maximum injection time of 80 ms and an AGC target value of 1 × 10 5 . Ions with charge states of 1 and >6 as well as ions with unassigned charge states were not considered for fragmentation. Dynamic exclusion was set to 20 s to minimize repeated sequencing of already acquired precursors.

LC–MS/MS data analysis

Raw files were analysed using Proteome Discoverer 2.4 software (ThermoFisher Scientific). Spectra were selected using default settings and database searches performed using SequestHT node in Proteome Discoverer. Database searches were performed against a trypsin-digested Homo sapiens SwissProt database, the SARS-CoV-2 database (Uniprot pre-release) and FASTA files of common contaminants (‘contaminants.fasta’ provided with MaxQuant) for quality control. Fixed modifications were set as TMT6 at the N terminus and carbamidomethyl at cysteine residues. One search node was set up to search with TMT6 (K) and methionine oxidation as static modifications to search for light peptides and one search node was set up with TMT6+K8 (K, +237.177), Arg10 (R, +10.008) and methionine oxidation as static modifications to identify heavy peptides. Searches were performed using Sequest HT. After each search, posterior error probabilities were calculated and peptide spectrum matches (PSMs) filtered using Percolator using default settings. Consensus Workflow for reporter ion quantification was performed with default settings, except the minimal signal-to-noise ratio was set to 5. Results were then exported to Excel files for further processing. For proteome quantification all PSMs were summed intensity normalized, followed by IRS 32 and TMM 33 normalization and peptides corresponding to a given UniProt accession were summed, including all modification states.

For translatome measurements, Excel files were processed in Python, as previously described 3 . Python 3.6 was used together with the following packages: pandas 0.23.4 34 , numpy 1.15.4 35 and scipy 1.3.0. Excel files with normalized PSM data were read in and each channel was normalized to the lowest channel based on total intensity. For each peptide sequence, all possible modification states containing a heavy label were extracted and the intensities for each channel were averaged between all modified peptides. Baseline subtraction was performed by subtracting the measured intensities for the non-SILAC-labelled sample from all other values. Negative intensities were treated as zero. The heavy label incorporation at the protein level was calculated by summing the intensities of all peptide sequences belonging to one unique protein accession. These values were combined with the standard protein output of Proteome Discoverer 2.4 to add annotation data to the master protein accessions.

Hierarchical clustering and profile comparison

Hierarchical cluster analysis and comparison with viral protein profiles for all samples was performed using Perseus 36 software package (version 1.6.5.0) after centring and scaling of data (Z-scores). K-means pre-processing was performed with a cluster number of 12 and a maximum of 10 iterations. For the comparison of profiles, the viral profiles were Z-scored and averaged to generate reference profile. Profiles of all proteins were compared to the reference (Pearson), distances and FDRs were computed.

Network analysis

For network analysis, Cytoscape 3.7.1 37 software was used with the BiNGO 3.0.3 38 plugin for gene ontology analysis, EnrichmentMap 3.1.0 39 and ReactomeFI 6.1.0 40 . For gene ontology analyses, gene sets were extracted from data as indicated using fold change and significance cut-offs.

Statistical analysis

No statistical methods were used to predetermine sample size. Significance was, unless stated otherwise, tested using unpaired two-sided Student’s t-tests with equal variance assumed. Statistical analysis was performed using OriginPro 2020 analysis software. For network and gene ontology analysis all statistical computations were performed by the corresponding packages.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this paper.


Polymeric Nanoparticles in Targeting and Delivery of Drugs

7.5 Release Kinetics

The drug release mechanisms are equally important as the drug polymer formulation because of the proposed application in sustained drug delivery. For manipulation of the rate and the timing of the drug release from NPs, a good understanding of the mechanisms of drug release is desired. There are five possible methods of drug release:

Diffusion through the NPs matrix,

Desorption of drug bound to the surface,

A combined erosion–diffusion process,

Diffusion through the polymer wall of nanocapsules.

7.5.1 Zero-order release

In zero-order release, drug released from formulations doesn’t disaggregate and finally the drug release slowly which can be represented through following equations:

By rearranging the Eq. (7.1) gives

Here, Qt represents the amount of drug dissolved in time t, Q represents the initial concentration of drug and K0 is the zero-order release constant expressed in units of concentration/time ( Dash et al., 2010 ).

7.5.2 First-order release

First order release kinetics is generally used for absorption and elimination of some drugs. The release of drugs through first or der kinetics can be represented by the following equations:

Here, K represents the first-order rate constant expressed (time −1 ).

C0 is the initial concentration of drugs, K is the first-order rate constant, and t is the time ( Dash et al., 2010 ).

7.5.3 Higuchi release

The first model of drug release from matrix system was proposed by Higuchi, 1961a,b . This model is based on hypothesis of (1) drug concentration in the matrix system is more than drug solubility (2) diffusion of drug takes place only in single or uni-direction (3) diffusion of drug is constant (4) swelling and dissolution of matrix system is negligible (5) perfect sink conditions are always maintained in the release environment (6) size of drug particles are smaller than matrix system thickness ( Dash et al., 2010 ). Higuchi proposed the following equation ( Higuchi, 1961a,b ):

Here, Mt represents the amount of drug released at time t, A is the surface area of the matrix film, D is the diffusion of drug molecules in the matrix carrier, c0 represents the initial drug concentration, and cs represents the drug solubility in the matrix carrier ( Siepmann and Siepmann, 2008 ).

7.5.4 Weibull release model

The Weibull equation is best described as a triphasic or sigmoidal release curve. The Weibull equation is applied to model release study with the drug delivery systems that follow:

Erosion-dominated process coupled with least diffusive release

Zero to least initial burst release

Zero to least diffusion-mediated release

A Weibull equation was used to estimate the drug release from polymeric NPs at long and short duration of conditions.

Here, X is the drug release percentage at time t, Xinf is the 100% drug release, α is the scale factor equivalent to apparent release rate constant, and β is the shape factor.

In this equation, α explains the rate process and β explains the shape of the curve as exponential when β=1, sigmoidal or S-shaped with upward curvature continued with a turning point when β>1, and parabolic, with a higher initial slope and after that stable with exponential when β<1 ( D’Souza et al., 2005 ).

7.5.5 Hopfenberg model

Hopfenberg proposed a model to compare the drug release from the surface of polymer to how long a surface area remains constant and stable through the degradation. According to Hopfenberg the cumulative drug release at time t was explained as:

Here, k0 is the zero-order release rate constant which explain the drug release from polymer surface, CL is the initial amount of drug loading in the system, a is the radius of the system, and n is an exponent which varies with geometry, n=1 for flat geometry, n=2 for cylindrical geometry, and n=3 for spherical geometry ( Dash et al., 2010 ).

7.5.6 Hixson–Crowell model

Hixson and Crowell (1931) suggested that the particles area is proportional to the cube root of its volume. According to Hixson–Crowell:

Here, W0 represent the initial concentration of drug in NPs, Wt denotes the remaining concentration of drug in NPs at time t, and κ (kappa) is a constant for surface–volume relationship ( Dash et al., 2010 ).

7.5.7 Korsmeyer–Peppas model

Korsmeyer et al. (1983) derived a relationship that expressed the drug release from polymeric NPs system.

Here, Mt is the concentration of the drug in the release medium at time t, M is the equilibrium concentration of drug in the release medium, k is the drug release rate constant, and n is the release exponent ( Korsmeyer et al., 1983 Dash et al., 2010 ).

7.5.8 Baker–Lonsdale model

This release model was expressed by Baker and Lonsdale (1974) from the Higuchi model ( Baker and Lonsdale, 1974 ) and expressed the drug release from spherical matrices according to the following equation:

Here, Mt is the drug release at time t and M is the drug release at infinite time. The release rate constant k, corresponds to the slope. This equation has been used for linearization of release data from microcapsules or microspheres ( Costa and Lobo, 2001 Dash et al., 2010 ).

7.5.9 Gompertz model

The in vitro dissolution rate is mostly expressed by an exponential model, which is generally known as the Gompertz model.

Here, X(t) is the percent dissolved at time t divided by 100 and XMax is the maximum dissolution, α determines the undissolved proportion at time interval t, β is the dissolution rate/unit of time expressed as shape parameter. The Gompertz model is more beneficial for comparing the release rate of drugs having good solubility and intermediate release rate profile ( Dash et al., 2010 ).


Author information

These authors contributed equally: Sepideh Sadegh, Julian Matschinske.

Affiliations

Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany

Sepideh Sadegh, Julian Matschinske, David B. Blumenthal, Gihanna Galindez, Tim Kacprowski, Markus List, Reza Nasirigerdeh, Mhaned Oubounyt, Marisol Salgado-Albarrán, Julian Späth, Nina K. Wenke, Kevin Yuan & Jan Baumbach

Institute of Virology, TUM School of Medicine, Technical University of Munich, München, Germany

Andreas Pichlmair & Alexey Stukalov

LipiTUM, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany

Tim Daniel Rose & Josch K. Pauling

Natural Sciences Department, Universidad Autónoma Metropolitana-Cuajimalpa (UAM-C), 05300, Mexico City, Mexico

Computational Biomedicine Lab, Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark


Watch the video: pKa and Drug Solubility: Absorption and Distribution Pharmacokinetics PK. Lecturio (November 2022).