Impact of Peptide Structure on Colonic Stability and Tissue Permeability

Most marketed peptide drugs are administered parenterally due to their inherent gastrointestinal (GI) instability and poor permeability across the GI epithelium. Several molecular design techniques, such as cyclisation and D-amino acid (D-AA) substitution, have been proposed to improve oral peptide drug bioavailability. However, very few of these techniques have been translated to the clinic. In addition, little is known about how synthetic peptide design may improve stability and permeability in the colon, a key site for the treatment of inflammatory bowel disease and colorectal cancer. In this study, we investigated the impact of various cyclisation modifications and D-AA substitutions on the enzymatic stability and colonic tissue permeability of native oxytocin and 11 oxytocin-based peptides. Results showed that the disulfide bond cyclisation present in native oxytocin provided an improved stability in a human colon model compared to a linear oxytocin derivative. Chloroacetyl cyclisation increased native oxytocin stability in the colonic model at 1.5 h by 30.0%, whereas thioether and N-terminal acetylated cyclisations offered no additional protection at 1.5 h. The site and number of D-AA substitutions were found to be critical for stability, with three D-AAs at Tyr, Ile and Leu, improving native oxytocin stability at 1.5 h in both linear and cyclic structures by 58.2% and 79.1%, respectively. Substitution of three D-AAs into native cyclic oxytocin significantly increased peptide permeability across rat colonic tissue; this may be because D-AA substitution favourably altered the peptide’s secondary structure. This study is the first to show how the strategic design of peptide therapeutics could enable their delivery to the colon via the oral route

The use of the SAEM algorithm in MONOLIX software for estimation of population pharmacokinetic-pharmacodynamic-viral dynamics parameters of maraviroc in asymptomatic HIV subjects

Using simulated viral load data for a given maraviroc monotherapy study design, the feasibility of different algorithms to perform parameter estimation for a pharmacokinetic-pharmacodynamic-viral dynamics (PKPD-VD) model was assessed. The assessed algorithms are the first-order conditional estimation method with interaction (FOCEI) implemented in NONMEM VI and the SAEM algorithm implemented in MONOLIX version 2.4. Simulated data were also used to test if an effect compartment and/or a lag time could be distinguished to describe an observed delay in onset of viral inhibition using SAEM. The preferred model was then used to describe the observed maraviroc monotherapy plasma concentration and viral load data using SAEM. In this last step, three modelling approaches were compared; (i) sequential PKPD-VD with fixed individual Empirical Bayesian Estimates (EBE) for PK, (ii) sequential PKPD-VD with fixed population PK parameters and including concentrations, and (iii) simultaneous PKPD-VD. Using FOCEI, many convergence problems (56%) were experienced with fitting the sequential PKPD-VD model to the simulated data. For the sequential modelling approach, SAEM (with default settings) took less time to generate population and individual estimates including diagnostics than with FOCEI without diagnostics. For the given maraviroc monotherapy sampling design, it was difficult to separate the viral dynamics system delay from a pharmacokinetic distributional delay or delay due to receptor binding and subsequent cellular signalling. The preferred model included a viral load lag time without inter-individual variability. Parameter estimates from the SAEM analysis of observed data were comparable among the three modelling approaches. For the sequential methods, computation time is approximately 25% less when fixing individual EBE of PK parameters with omission of the concentration data compared with fixed population PK parameters and retention of concentration data in the PD-VD estimation step. Computation times were similar for the sequential method with fixed population PK parameters and the simultaneous PKPD-VD modelling approach. The current analysis demonstrated that the SAEM algorithm in MONOLIX is useful for fitting complex mechanistic models requiring multiple differential equations. The SAEM algorithm allowed simultaneous estimation of PKPD and viral dynamics parameters, as well as investigation of different model sub-components during the model building process. This was not possible with the FOCEI method (NONMEM version VI or below). SAEM provides a more feasible alternative to FOCEI when facing lengthy computation times and convergence problems with complex models

Deciphering the connectivity structure of biological networks using MixNet

<p>Abstract</p> <p>Background</p> <p>As biological networks often show complex topological features, mathematical methods are required to extract meaningful information. Clustering methods are useful in this setting, as they allow the summary of the network's topology into a small number of relevant classes. Different strategies are possible for clustering, and in this article we focus on a model-based strategy that aims at clustering nodes based on their connectivity profiles.</p> <p>Results</p> <p>We present MixNet, the first publicly available computer software that analyzes biological networks using mixture models. We apply this method to various networks such as the <it>E. coli </it>transcriptional regulatory network, the macaque cortex network, a foodweb network and the <it>Buchnera aphidicola </it>metabolic network. This method is also compared with other approaches such as module identification or hierarchical clustering.</p> <p>Conclusion</p> <p>We show how MixNet can be used to extract meaningful biological information, and to give a summary of the networks topology that highlights important biological features. This approach is powerful as MixNet is adaptive to the network under study, and finds structural information without any a priori on the structure that is investigated. This makes MixNet a very powerful tool to summarize and decipher the connectivity structure of biological networks.</p

Microfabrication of a biomimetic arcade-like electrospun scaffold for cartilage tissue engineering applications

Designing and fabricating hierarchical geometries for tissue engineering (TE) applications is the major challenge and also the biggest opportunity of regenerative medicine in recent years, being the in vitro recreation of the arcade-like cartilaginous tissue one of the most critical examples due to the current inefficient standard medical procedures and the lack of fabrication techniques capable of building scaffolds with the required architecture in a cost and time effective way. Taking this into account, we suggest a feasible and accurate methodology that uses a sequential adaptation of an electrospinning-electrospraying set up to construct a system comprising both fibres and sacrificial microparticles. Polycaprolactone (PCL) and polyethylene glycol were respectively used as bulk and sacrificial biomaterials, leading to a bi-layered PCL scaffold which presented not only a depth-dependent fibre orientation similar to natural cartilage, but also mechanical features and porosity compatible with cartilage TE approaches. In fact, cell viability studies confirmed the biocompatibility of the scaffold and its ability to guarantee suitable cell adhesion, proliferation and migration throughout the 3D anisotropic fibrous network. Additionally, likewise the natural anisotropic cartilage, the PCL scaffold was capable of inducing oriented cell-material interactions since the morphology, alignment and density of the chondrocytes changed relatively to the specific topographic cues of each electrospun layer.publishe

Cardiovasc Diabetol

BACKGROUND: Advanced glycation end-products play a role in diabetic vascular complications. Their optical properties allow to estimate their accumulation in tissues by measuring the skin autofluorescence (SAF). We searched for an association between SAF and major adverse cardiovascular events (MACE) incidence in subjects with Type 1 Diabetes (T1D) during a 7 year follow-up. METHODS: During year 2009, 232 subjects with T1D were included. SAF measurement, clinical [age, sex, body mass index (BMI), comorbidities] and biological data (HbA1C, blood lipids, renal parameters) were recorded. MACE (myocardial infarction, stroke, lower extremity amputation or a revascularization procedure) were registered at visits in the center or by phone call to general practitioners until 2016. RESULTS: The participants were mainly men (59.5%), 51.5 +/- 16.7 years old, with BMI 25.0 +/- 4.1 kg/m(2), diabetes duration 21.5 +/- 13.6 years, HbA1C 7.6 +/- 1.1%. LDL cholesterol was 1.04 +/- 0.29 g/L, estimated Glomerular Filtration Rates (CKD-EPI): 86.3 +/- 26.6 ml/min/1.73 m(2). Among these subjects, 25.1% were smokers, 45.3% had arterial hypertension, 15.9% had elevated AER (>/= 30 mg/24 h), and 9.9% subjects had a history of previous MACE. From 2009 to 2016, 22 patients had at least one new MACE: 6 myocardial infarctions, 1 lower limb amputation, 15 revascularization procedures. Their SAF was 2.63 +/- 0.73 arbitrary units (AU) vs 2.08 +/- 0.54 for other patients (p = 0.002). Using Cox-model, after adjustment for age (as the scale time), sex, diabetes duration, BMI, hypertension, smoking status, albumin excretion rates, statin treatment and a previous history of MACE, higher baseline levels of SAF were significantly associated with an increased risk of MACE during follow-up (HR = 4.13 [1.30-13.07]; p = 0.02 for 1 AU of SAF) and Kaplan-Meier curve follow-up showed significantly more frequent MACE in group with SAF upper the median (p = 0.001). CONCLUSION: A high SAF predicts MACE in patients with T1D