Biophysics and Thermodynamics: The Scientific Building Blocks of Bio-inspired Drug Delivery Nano Systems

Biophysics and thermodynamics are considered as the scientific milestones for investigating the properties of materials. The relationship between the changes of temperature with the biophysical variables of biomaterials is important in the process of the development of drug delivery systems. Biophysics is a challenge sector of physics and should be used complementary with the biochemistry in order to discover new and promising technological platforms (i.e., drug delivery systems) and to disclose the ‘silence functionality’ of bio-inspired biological and artificial membranes. Thermal analysis and biophysical approaches in pharmaceuticals present reliable and versatile tools for their characterization and for the successful development of pharmaceutical products. The metastable phases of self-assembled nanostructures such as liposomes should be taken into consideration because they represent the thermal events can affect the functionality of advanced drug delivery nano systems. In conclusion, biophysics and thermodynamics are characterized as the building blocks for design and development of bio-inspired drug delivery systems.KEY WORDS: biophysics, drug delivery nano systems, pharmaceutics, thermal analysis, thermodynamics     

Distinctive Behaviors of Druggable Proteins in Cellular Networks

The interaction environment of a protein in a cellular network is important in defining the role that the protein plays in the system as a whole, and thus its potential suitability as a drug target. Despite the importance of the network environment, it is neglected during target selection for drug discovery. Here, we present the first systematic, comprehensive computational analysis of topological, community and graphical network parameters of the human interactome and identify discriminatory network patterns that strongly distinguish drug targets from the interactome as a whole. Importantly, we identify striking differences in the network behavior of targets of cancer drugs versus targets from other therapeutic areas and explore how they may relate to successful drug combinations to overcome acquired resistance to cancer drugs. We develop, computationally validate and provide the first public domain predictive algorithm for identifying druggable neighborhoods based on network parameters. We also make available full predictions for 13,345 proteins to aid target selection for drug discovery. All target predictions are available through canSAR.icr.ac.uk. Underlying data and tools are available at https://cansar.icr.ac.uk/cansar/publications/druggable_network_neighbourhoods/.     

Changes in plasma amino acid levels in a euryhaline fish exposed to different environmental salinities

Previous studies have shown that Senegalese sole is partially euryhaline in the juvenile phase, being able to adapt to a wide range of salinities in a short-time period, due to changes at the osmoregulatory and metabolic level. This study aimed to assess the effects of acclimation of sole to a wide range of salinities, with a special emphasis on the role of plasma amino acids during this process. Sole juveniles were acclimated for 2 weeks to different salinities: 5, 15, 25, 38, and 55 g L⁻¹. Plasma levels of cortisol, glucose, osmolality, and free amino acids were assessed at the end. Changes in plasma levels of cortisol, glucose, and amino acids indicate that fish reared at 5 and 55 g L⁻¹ were facing extra energy costs. Amino acids seem to play an important role during salinity acclimation, either as energy sources or as important osmolytes for cell volume regulation.     

Improving the <Emphasis Type="Italic">i</Emphasis>MM904 <Emphasis Type="Italic">S. cerevisiae</Emphasis> metabolic model using essentiality and synthetic lethality data

Background  

Saccharomyces cerevisiae is the first eukaryotic organism for which a multi-compartment genome-scale metabolic model was constructed. Since then a sequence of improved metabolic reconstructions for yeast has been introduced. These metabolic models have been extensively used to elucidate the organizational principles of yeast metabolism and drive yeast strain engineering strategies for targeted overproductions. They have also served as a starting point and a benchmark for the reconstruction of genome-scale metabolic models for other eukaryotic organisms. In spite of the successive improvements in the details of the described metabolic processes, even the recent yeast model (i.e., i MM904) remains significantly less predictive than the latest E. coli model (i.e., i AF1260). This is manifested by its significantly lower specificity in predicting the outcome of grow/no grow experiments in comparison to the E. coli model.     

Genomic distance entrained clustering and regression modelling highlights interacting genomic regions contributing to proliferation in breast cancer

Background  

Genomic copy number changes and regional alterations in epigenetic states have been linked to grade in breast cancer. However, the relative contribution of specific alterations to the pathology of different breast cancer subtypes remains unclear. The heterogeneity and interplay of genomic and epigenetic variations means that large datasets and statistical data mining methods are required to uncover recurrent patterns that are likely to be important in cancer progression.     

OptForce: An Optimization Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions

Computational procedures for predicting metabolic interventions leading to the overproduction of biochemicals in microbial strains are widely in use. However, these methods rely on surrogate biological objectives (e.g., maximize growth rate or minimize metabolic adjustments) and do not make use of flux measurements often available for the wild-type strain. In this work, we introduce the OptForce procedure that identifies all possible engineering interventions by classifying reactions in the metabolic model depending upon whether their flux values must increase, decrease or become equal to zero to meet a pre-specified overproduction target. We hierarchically apply this classification rule for pairs, triples, quadruples, etc. of reactions. This leads to the identification of a sufficient and non-redundant set of fluxes that must change (i.e., MUST set) to meet a pre-specified overproduction target. Starting with this set we subsequently extract a minimal set of fluxes that must actively be forced through genetic manipulations (i.e., FORCE set) to ensure that all fluxes in the network are consistent with the overproduction objective. We demonstrate our OptForce framework for succinate production in Escherichia coli using the most recent in silico E. coli model, iAF1260. The method not only recapitulates existing engineering strategies but also reveals non-intuitive ones that boost succinate production by performing coordinated changes on pathways distant from the last steps of succinate synthesis.     

Piezotolerant small-colony variants with increased thermotolerance, antibiotic susceptibility, and low invasiveness in a clonal Staphylococcus aureus population

Following a pressure treatment of a clonal Staphylococcus aureus culture with 400 MPa for 30 min, piezotolerant variants were isolated. Among 21 randomly selected survivors, 9 were piezotolerant and all formed small colonies on several agar media. The majority of the isolates showed increased thermotolerance, impaired growth, and reduced antibiotic resistance compared to the wild type. However, several nonpiezotolerant isolates also demonstrated impaired growth and the small-colony phenotype. In agglutination tests for the detection of protein A and fibrinogen, the piezotolerant variants showed weaker agglutination reactions than the wild type and the other isolates. All variants also showed defective production of the typical S. aureus golden color, a characteristic which has previously been linked with virulence. They were also less able to invade intestinal epithelial cells than the wild type. These S. aureus variants showed phenotypic similarities to previously isolated Listeria monocytogenes piezotolerant mutants that contained mutations in ctsR. Because of these similarities, possible alterations in the ctsR hypermutable regions of the S. aureus variants were investigated through amplified fragment length polymorphism analysis. No mutations were identified, and subsequently we sequenced the ctsR and hrcA genes of three representative variants, finding no mutations. This work demonstrates that S. aureus probably possesses a strategy resulting in an abundance of multiple-stress-resistant variants within clonal populations. This strategy, however, seems to involve genes and regulatory mechanisms different from those previously reported for L. monocytogenes. We are in the process of identifying these mechanisms.     

Resistance to glyphosate in the cyanobacterium <Emphasis Type="Italic">Microcystis aeruginosa</Emphasis> as result of pre-selective mutations

Adaptation of Microcystis aeruginosa (Cyanobacteria) to resist the herbicide glyphosate was analysed by using an experimental model. Growth of wild-type, glyphosate-sensitive (G^s) cells was inhibited when they were cultured with 120 ppm glyphosate, but after further incubation for several weeks, occasionally the growth of rare cells resistant (G^r) to the herbicide was found. A fluctuation analysis was carried out to distinguish between resistant cells arising from rare spontaneous mutations and resistant cells arising from other mechanisms of adaptation. Resistant cells arose by rare spontaneous mutations prior to the addition of glyphosate, with a rate ranging from 3.1 × 10⁻⁷ to 3.6 × 10⁻⁷ mutants per cell per generation in two strains of M. aeruginosa; the frequency of the G^r allele ranged from 6.14 × 10⁻⁴ to 6.54 × 10⁻⁴. The G^r mutants are slightly elliptical in outline, whereas the G^s cells are spherical. Since G^r mutants have a diminished growth rate, they may be maintained in uncontaminated waters as the result of a balance between new resistants arising from spontaneous mutation and resistants eliminated by natural selection. Thus, rare spontaneous pre-selective mutations may allow the survival of M. aeruginosa in glyphosate-polluted waters via G^r clone selection.