Luís F. Gouveia

Lymphatic Uptake of Pulmonary Delivered Radiolabelled Solid Lipid Nanoparticles

Lymphatic drainage plays an important role in the uptake of particulates in the respiratory system, being also associated to the spreading of lung cancer through metastasis development. In recent years solid lipid nanoparticles (SLN) have been proposed as carriers of anti-tumoural drugs, for their low toxicity and surface characteristics make them suitable for either imaging (gamma-scintigraphy) or therapy upon encapsulation of cytotoxic drugs. Assessment of inhaled radiolabelled SLN biodistribution is described in the present work. Methods : Nanoparticles (200 nm) were radiolabelled with 99m Tc using the lipophilic chelator d, l -hexamehylpropyleneamine oxime (HMPAO). Biodistribution studies were carried out following aerosolisation and administration of a 99m Tc-HMPAO-SLN suspension to a group of adult male Wistar rats. A 60 min dynamic image acquisition was performed in a gamma-camera, followed by static image collection at 30 min intervals up to 4 h postinhalation. Radiation counting was performed in organ samples, collected after the animals were sacrificed. Results : The data show an important and significant uptake of the radiolabelled SLN into the lymphatics after inhalation, and a high rate of distribution in periaortic, axillar and inguinal lymph nodes. Conclusion Results indicate that SLN could be effective colloidal carriers for lymphoscintigraphy or therapy upon pulmonary delivery.

Information-governing dynamics of attacker–defender interactions in youth rugby union

Abstract Previous work on dynamics of interpersonal interactions in 1 vs. 1 sub-phases of basketball has identified changes in interpersonal distance between an attacker and defender as a potential control parameter for influencing organizational states of attacker–defender dyads. Other studies have reported the constraining effect of relative velocity between an attacker and defender in 1 vs. 1 dyads. To evaluate the relationship between these candidate control parameters, we compared the impact of both interpersonal distance and relative velocity on the pattern-forming dynamics of attacker–defender dyads in the sport of rugby union. Results revealed that when interpersonal distance achieved a critical value of less than 4 m, and relative velocity values increased or were maintained above 1 m · s−1, a successful outcome (i.e. clean attempt) for an attacker was predicted. Alternatively, when values of relative velocity suddenly decreased below this threshold, at the same critical value of interpersonal distance, a successful outcome for the defender was predicted. Data demonstrated how the coupling of these two potential, nested control parameters moved the dyadic system to phase transitions, characterized as a try or a tackle. Observations suggested that relative velocity increased its influence on the organization of attacker–defender dyads in rugby union over time as spatial proximity to the try line increased.

Interpersonal Pattern Dynamics and Adaptive Behavior in Multiagent Neurobiological Systems: Conceptual Model and Data

ABSTRACT Ecological dynamics characterizes adaptive behavior as an emergent, self-organizing property of interpersonal interactions in complex social systems. The authors conceptualize and investigate constraints on dynamics of decisions and actions in the multiagent system of team sports. They studied coadaptive interpersonal dynamics in rugby union to model potential control parameter and collective variable relations in attacker–defender dyads. A videogrammetry analysis revealed how some agents generated fluctuations by adapting displacement velocity to create phase transitions and destabilize dyadic subsystems near the try line. Agent interpersonal dynamics exhibited characteristics of chaotic attractors and informational constraints of rugby union boxed dyadic systems into a low dimensional attractor. Data suggests that decisions and actions of agents in sports teams may be characterized as emergent, self-organizing properties, governed by laws of dynamical systems at the ecological scale. Further research needs to generalize this conceptual model of adaptive behavior in performance to other multiagent populations.

Binary search in flow titration employing photometric end-point detection

A binary search strategy is proposed and implemented in a continuous flow system to find the end-point titration by employing spectrophotometric detection. It takes advantage of the binary sampling process under a constant flow-rate. For this task an automated flow set-up based on solenoid valves was designed. A 386 microcomputer was employed to control the valves, and to perform data acquisition and data processing. The accuracy level can be previously settled as a software parameter. Samples with concentrations ranging within two orders of magnitude could be titrated by making use of the same flow set-up. Titration of hydrochloric acid with sodium hydroxide was performed to demonstrate the feasibility of the proposal. Titrations in triplicate, with 99.99% precision were carried out in 3 min with a solution consumption of 2 ml.

Prediction of the human oral bioavailability by using in vitro and in silico drug related parameters in a physiologically based absorption model

Estimates of the human oral absolute bioavailability were made by using a physiological-based pharmacokinetic model of absorption and the drug solubility at the gastrointestinal pH range 1.5-7.5, the apparent permeability (P(app)) in Caco-2 cells and the intrinsic clearance (Cl(int)) in human hepatocytes suspensions as major drug related parameters. The predictive ability of this approach was tested in 164 drugs divided in four levels of input data: (i) in vitro data for both P(app) and Cl(int); (ii) in vitro data for Cl(int) only; (iii) in vitro data for P(app) only and (iv) in silico data for both P(app) and Cl(int). In all scenarios, solubility was estimated in silico. Excellent predictive abilities were observed when in vitro data for both P(app) and Cl(int) were used, with 84% of drugs with oral bioavailability predictions within a±20% interval of the correct value. This predictive ability is reduced with the introduction of the in silico estimated parameters, particularly when Cl(int) is used. Performance of the model using only in silico data provided 53% of drugs with bioavailability predictions within a±20% acceptance interval. However, 74% of drugs in the same scenario resulted in bioavailability predictions within a±35% interval, which indicates that a qualitative prediction of the absolute bioavailability is still possible. This approach is a valuable way to estimate a fundamental pharmacokinetic parameter, using data typically collected in the drug discovery and early development phases, providing also mechanistic information of the limiting bioavailability steps of the drug.

The influence of instructions and body-scaling as constraints on decision-making processes in team sports.

Abstract Team games conceptualized as dynamical systems engender a view of emergent decision-making behaviour under constraints, although specific effects of instructional and body-scaling constraints have yet to be verified empirically. For this purpose, we studied the effects of task and individual constraints on decision-making processes in basketball. Eleven experienced female players performed 350 trials in 1 vs. 1 sub-phases of basketball in which an attacker tried to perturb the stable state of a dyad formed with a defender (i.e. break the symmetry). In Experiment 1, specific instructions (neutral, risk taking or conservative) were manipulated to observe effects on emergent behaviour of the dyadic system. When attacking players were given conservative instructions, time to cross court mid-line and variability of the attackers trajectory were significantly greater. In Experiment 2, body-scaling of participants was manipulated by creating dyads with different height relations. When attackers were considerably taller than defenders, there were fewer occurrences of symmetry-breaking. When attackers were considerably shorter than defenders, time to cross court mid-line was significantly shorter than when dyads were composed of athletes of similar height or when attackers were considerably taller than defenders. The data exemplify how interacting task and individual constraints can influence emergent decision-making processes in team ball games.

Prediction of the in vitro permeability determined in caco-2 cells by using artificial neural networks

Caco-2 cells are currently the most used in vitro tool for prediction of the potential oral absorption of new drugs. The existence of computational models based on this data may potentiate the early selection process of new drugs, but the current models are based on a limited number of cases or on a reduced molecular space. We present an artificial neural network based only on calculated molecular descriptors for modelling 296 in vitro Caco-2 apparent permeability (P(app)) drug values collected in the literature using also a pruning procedure for reducing the descriptors space. LogP(app) values were divided into a training group of 192 drugs for network optimization and a testing group of another 59 drugs for early stop and internal validation resulting in correlations of 0.843 and 0.702 and RMSE of 0.546 and 0.791 for the training and testing group, respectively. External validation was made with an additional group of 45 drugs with a correlation of 0.774 and RMSE of 0.601. The selected molecular descriptors encode information related to the lipophilicity, electronegativity, size, shape and flexibility characteristics of the molecules, which are related to drug absorption. This model may be a valuable tool for prediction and simulation in the drug development process, as it allows the in silico estimation of the in vitro Caco-2 apparent permeability.

Prediction of the in vitro intrinsic clearance determined in suspensions of human hepatocytes by using artificial neural networks

Use of in vitro suspensions of human hepatocytes is currently accepted as one of the most promising tools for prediction of metabolic clearance in new drugs. The possibility of creating computational models based on this data may potentiate the early selection process of new drugs. We present an artificial neural network for modelling human hepatocyte intrinsic clearances (CL(int)) based only on calculated molecular descriptors. In vitro CL(int) data obtained in human hepatocytes suspensions was divided into a train group of 71 drugs for network optimization and a test group of another 18 drugs for early-stop and internal validation resulting in correlations of 0.953 and 0.804 for the train and test group respectively. The model applicability was tested with 112 drugs by comparing the in silico predicted CL(int) with the in vivo CL(int) estimated by the well-stirred model based on the in vivo hepatic clearance (CL(H)). Acceptable correlations were observed with r values of 0.508 and 63% of drugs within a 10-fold difference when considering blood binding in acidic drugs only. This model may be a valuable tool for prediction and simulation in the drug development process, allowing the in silico estimation of the human in vivo hepatic clearance.

Prediction of drug distribution within blood.

Drug distribution in blood, defined as drug blood-to-plasma concentration ratio (R(b)), is a fundamental pharmacokinetic parameter. It relates the plasma clearance to the blood clearance, enabling the physiological interpretation of this parameter. Although easily experimentally determined, R(b) values are lacking for the vast majority of drugs. We present a systematic approach using mechanistic, partial least squares (PLS) regression and artificial neural network (ANN) models to relate various in vitro and in silico molecular descriptors to a dataset of 93 drug R(b) values collected in the literature. The ANN model resulted in the best overall approach, with r(2)=0.927 and r(2)=0.871 for the train and the test sets, respectively. PLS regression presented r(2)=0.557 for the train and r(2)=0.656 for the test set. The mechanistic model provided the worst results, with r(2)=0.342 and, additionally, is limited to drugs with a basic ionised group with pKa<7. The ANN model for drug distribution in blood can be a valuable tool in clinical pharmacokinetics as well as in new drug design, providing predictions of R(b) with a percentage of correct values within a 1.25-fold error of 86%, 84% and 87% in the train, test and validation set of data.

Interpersonal dynamics in sport: The role of artificial neural networks and 3-D analysis

In previous attempts to identify dynamical systems properties in patterns of play in team sports, only 2-D analysis methods have been used, implying that the plane of motion must be preselected and that movements out of the chosen plane are ignored. In the present study, we examined the usefulness of 3-D methods of analysis for establishing the presence of dynamical systems properties, such as phase transitions and symmetry-breaking processes in the team sport of rugby. Artificial neural networks (ANN s) were employed to reconstruct the 3-D performance space in a typical one-versus-one subphase of rugby. Results confirm that ANs are reliable tools for reconstructing a 3-D performance space and may be instrumental in identifying pattern formation in team sports generally.