Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence

Today, a growing number of computational aids and simulations are shaping model-informed drug development. Artificial intelligence, a family of self-learning algorithms, is only the latest emerging trend applied by academic researchers and the pharmaceutical industry. Nanomedicine successfully conquered several niche markets and offers a wide variety of innovative drug delivery strategies. Still, only a small number of patients benefit from these advanced treatments, and the number of data sources is very limited. As a consequence, “big data” approaches are not always feasible and smart combinations of human and artificial intelligence define the research landscape. These methodologies will potentially transform the future of nanomedicine and define new challenges and limitations of machine learning in their development. In our review, we present an overview of modeling and artificial intelligence applications in the development and manufacture of nanomedicines. Also, we elucidate the role of each method as a facilitator of breakthroughs and highlight important limitations

Aquaporins: important but elusive drug targets.

The aquaporins (AQPs) are a family of small, integral membrane proteins that facilitate water transport across the plasma membranes of cells in response to osmotic gradients. Data from knockout mice support the involvement of AQPs in epithelial fluid secretion, cell migration, brain oedema and adipocyte metabolism, which suggests that modulation of AQP function or expression could have therapeutic potential in oedema, cancer, obesity, brain injury, glaucoma and several other conditions. Moreover, loss-of-function mutations in human AQPs cause congenital cataracts (AQP0) and nephrogenic diabetes insipidus (AQP2), and autoantibodies against AQP4 cause the autoimmune demyelinating disease neuromyelitis optica. Although some potential AQP modulators have been identified, challenges associated with the development of better modulators include the druggability of the target and the suitability of the assay methods used to identify modulators

Transmembrane protein PERP is a component of tessellate junctions and of other junctional and non-junctional plasma membrane regions in diverse epithelial and epithelium-derived cells

Protein PERP (p53 apoptosis effector related to PMP-22) is a small (21.4 kDa) transmembrane polypeptide with an amino acid sequence indicative of a tetraspanin character. It is enriched in the plasma membrane and apparently contributes to cell-cell contacts. Hitherto, it has been reported to be exclusively a component of desmosomes of some stratified epithelia. However, by using a series of newly generated mono- and polyclonal antibodies, we show that protein PERP is not only present in all kinds of stratified epithelia but also occurs in simple, columnar, complex and transitional epithelia, in various types of squamous metaplasia and epithelium-derived tumors, in diverse epithelium-derived cell cultures and in myocardial tissue. Immunofluorescence and immunoelectron microscopy allow us to localize PERP predominantly in small intradesmosomal locations and in variously sized, junction-like peri- and interdesmosomal regions (“tessellate junctions”), mostly in mosaic or amalgamated combinations with other molecules believed, to date, to be exclusive components of tight and adherens junctions. In the heart, PERP is a major component of the composite junctions of the intercalated disks connecting cardiomyocytes. Finally, protein PERP is a cobblestone-like general component of special plasma membrane regions such as the bile canaliculi of liver and subapical-to-lateral zones of diverse columnar epithelia and upper urothelial cell layers. We discuss possible organizational and architectonic functions of protein PERP and its potential value as an immunohistochemical diagnostic marker

Social Phobia in an Italian region: do Italian studies show lower frequencies than community surveys conducted in other European countries?

BACKGROUND: The lifetime prevalence of Social Phobia (SP) in European countries other than Italy has been estimated to range from 3.5% to 16.0%. The aim of this study was to assess the frequency of SP in Sardinia (Italy) in order to verify the evidence of a lower frequency of SP in Italy observed in previous studies (from 1.0% to 3.1%). METHODS: A randomised cross sample of 1040 subjects, living in Cagliari, in rural areas, and in a mining district in Sardinia were interviewed using a Simplified version of the Composite International Diagnostic Interview (CIDIS). Diagnoses were made according to the 10(th )International Classification of Diseases (ICD-10). RESULTS: Lifetime prevalence of SP was 2.2% (males: 1.5%, females: 2.8%) whereas 6-month prevalence resulted in 1.5% (males: 0.9%, females: 2.1%). Mean age at onset was 16.2 ± 9.3 years. A statistically significant association was found with Depressive Episode, Dysthymia and Generalized Anxiety Disorder. CONCLUSIONS: The study is consistent with findings reported in several previous studies of a lower prevalence of SP in Italy. Furthermore, the results confirm the fact that SP, due to its early onset, might constitute an ideal target for early treatment aimed at preventing both the accumulation of social disabilities and impairments caused by anxiety and avoidance behaviour, as well as the onset of more serious, associated complications in later stages of the illness

A finite element method model to simulate laser interstitial thermo therapy in anatomical inhomogeneous regions

BACKGROUND: Laser Interstitial ThermoTherapy (LITT) is a well established surgical method. The use of LITT is so far limited to homogeneous tissues, e.g. the liver. One of the reasons is the limited capability of existing treatment planning models to calculate accurately the damage zone. The treatment planning in inhomogeneous tissues, especially of regions near main vessels, poses still a challenge. In order to extend the application of LITT to a wider range of anatomical regions new simulation methods are needed. The model described with this article enables efficient simulation for predicting damaged tissue as a basis for a future laser-surgical planning system. Previously we described the dependency of the model on geometry. With the presented paper including two video files we focus on the methodological, physical and mathematical background of the model. METHODS: In contrast to previous simulation attempts, our model is based on finite element method (FEM). We propose the use of LITT, in sensitive areas such as the neck region to treat tumours in lymph node with dimensions of 0.5 cm – 2 cm in diameter near the carotid artery. Our model is based on calculations describing the light distribution using the diffusion approximation of the transport theory; the temperature rise using the bioheat equation, including the effect of microperfusion in tissue to determine the extent of thermal damage; and the dependency of thermal and optical properties on the temperature and the injury. Injury is estimated using a damage integral. To check our model we performed a first in vitro experiment on porcine muscle tissue. RESULTS: We performed the derivation of the geometry from 3D ultrasound data and show for this proposed geometry the energy distribution, the heat elevation, and the damage zone. Further on, we perform a comparison with the in-vitro experiment. The calculation shows an error of 5% in the x-axis parallel to the blood vessel. CONCLUSIONS: The FEM technique proposed can overcome limitations of other methods and enables an efficient simulation for predicting the damage zone induced using LITT. Our calculations show clearly that major vessels would not be damaged. The area/volume of the damaged zone calculated from both simulation and in-vitro experiment fits well and the deviation is small. One of the main reasons for the deviation is the lack of accurate values of the tissue optical properties. In further experiments this needs to be validated

Are Long-Range Structural Correlations Behind the Aggregration Phenomena of Polyglutamine Diseases?

We have characterized the conformational ensembles of polyglutamine peptides of various lengths (ranging from to ), both with and without the presence of a C-terminal polyproline hexapeptide. For this, we used state-of-the-art molecular dynamics simulations combined with a novel statistical analysis to characterize the various properties of the backbone dihedral angles and secondary structural motifs of the glutamine residues. For (i.e., just above the pathological length for Huntington's disease), the equilibrium conformations of the monomer consist primarily of disordered, compact structures with non-negligible -helical and turn content. We also observed a relatively small population of extended structures suitable for forming aggregates including - and -strands, and - and -hairpins. Most importantly, for we find that there exists a long-range correlation (ranging for at least residues) among the backbone dihedral angles of the Q residues. For polyglutamine peptides below the pathological length, the population of the extended strands and hairpins is considerably smaller, and the correlations are short-range (at most residues apart). Adding a C-terminal hexaproline to suppresses both the population of these rare motifs and the long-range correlation of the dihedral angles. We argue that the long-range correlation of the polyglutamine homopeptide, along with the presence of these rare motifs, could be responsible for its aggregation phenomena

Using Plant Functional Traits to Explain Diversity–Productivity Relationships

Background: The different hypotheses proposed to explain positive species richness–productivity relationships, i.e. selection effect and complementarity effect, imply that plant functional characteristics are at the core of a mechanistic understanding of biodiversity effects. Methodology/Principal Findings: We used two community-wide measures of plant functional composition, (1) community- weighted means of trait values (CWM) and (2) functional trait diversity based on Rao’s quadratic diversity (FDQ) to predict biomass production and measures of biodiversity effects in experimental grasslands (Jena Experiment) with different species richness (2, 4, 8, 16 and 60) and different functional group number and composition (1 to 4; legumes, grasses, small herbs, tall herbs) four years after establishment. Functional trait composition had a larger predictive power for community biomass and measures of biodiversitity effects (40–82% of explained variation) than species richness per se (,1–13% of explained variation). CWM explained a larger amount of variation in community biomass (80%) and net biodiversity effects (70%) than FDQ (36 and 38% of explained variation respectively). FDQ explained similar proportions of variation in complementarity effects (24%, positive relationship) and selection effects (28%, negative relationship) as CWM (27% of explained variation for both complementarity and selection effects), but for all response variables the combination of CWM and FDQ led to significant model improvement compared to a separate consideration of different components of functional trait composition. Effects of FDQ were mainly attributable to diversity in nutrient acquisition and life-history strategies. The large spectrum of traits contributing to positive effects of CWM on biomass production and net biodiversity effects indicated that effects of dominant species were associated with different trait combinations. Conclusions/Significance: Our results suggest that the identification of relevant traits and the relative impacts of functional identity of dominant species and functional diversity are essential for a mechanistic understanding of the role of plant diversity for ecosystem processes such as aboveground biomass production

Polyglutamine Induced Misfolding of Huntingtin Exon1 is Modulated by the Flanking Sequences

Polyglutamine (polyQ) expansion in exon1 (XN1) of the huntingtin protein is linked to Huntington's disease. When the number of glutamines exceeds a threshold of approximately 36–40 repeats, XN1 can readily form amyloid aggregates similar to those associated with disease. Many experiments suggest that misfolding of monomeric XN1 plays an important role in the length-dependent aggregation. Elucidating the misfolding of a XN1 monomer can help determine the molecular mechanism of XN1 aggregation and potentially help develop strategies to inhibit XN1 aggregation. The flanking sequences surrounding the polyQ region can play a critical role in determining the structural rearrangement and aggregation mechanism of XN1. Few experiments have studied XN1 in its entirety, with all flanking regions. To obtain structural insights into the misfolding of XN1 toward amyloid aggregation, we perform molecular dynamics simulations on monomeric XN1 with full flanking regions, a variant missing the polyproline regions, which are hypothesized to prevent aggregation, and an isolated polyQ peptide (Qn). For each of these three constructs, we study glutamine repeat lengths of 23, 36, 40 and 47. We find that polyQ peptides have a positive correlation between their probability to form a β-rich misfolded state and their expansion length. We also find that the flanking regions of XN1 affect its probability to^x_page_count=28 form a β-rich state compared to the isolated polyQ. Particularly, the polyproline regions form polyproline type II helices and decrease the probability of the polyQ region to form a β-rich state. Additionally, by lengthening polyQ, the first N-terminal 17 residues are more likely to adopt a β-sheet conformation rather than an α-helix conformation. Therefore, our molecular dynamics study provides a structural insight of XN1 misfolding and elucidates the possible role of the flanking sequences in XN1 aggregation