Prediction of skin penetration using machine learning methods

Improving predictions of the skin permeability coefficient is a difficult problem. It is also an important issue with the increasing use of skin patches as a means of drug delivery. In this work, we applyK-nearest-neighbour regression, single layer networks, mixture of experts and Gaussian processes to predict the permeability coefficient. We obtain a considerable improvement over the quantitative structureactivity relationship (QSARs) predictors. We show that using five features, which are molecular weight, solubility parameter, lipophilicity, the number of hydrogen bonding acceptor and donor groups, can produce better predictions than the one using only lipophilicity and the molecular weight. The Gaussian process regression with five compound features gives the best performance in this work

Development of an intelligent approach for delivering high performing training solutions

Predictive modelling is a state-of-the-art technique for which an objective variable is predicted given a set of environmental parameters. Many types of models exist and most of them can be built with different architectures. This project comes from the necessity to improve the measurement of biometric indicators and understand how team performance can affect their level in a defence training context. The biometric indicators of interest are the player’s heart rate and stress index, which are going to be related to information extracted from the GPS coordinates they have. The objective has been to study the evolution of the stress index during an exercise period, trying to determine how the other biometrical and positional factors influence its level. After reviewing existing work in the medical field for stress monitoring and team performance in an educational context, it has been observed that no literature involved both topics relating them to each other, let alone in a defence environment. A list of quality indicators has been defined to assess the quality of the provided raw datasets, the information from which has been managed to build a single dataset that could be used to train a model. The results of the quality assessment have shown that the recording frequency for the different indicators should be modified to a common value since the existing time difference between recordings has proven to be a complex issue to solve when building the model. Regression, neural network, and random forest models have been tested, with the latter being the one offering the best precision. The heart rate, the duration of the exercise, and the distance from the player to the opposite team were the variables that played a major role in the prediction. Overall, a valid prediction has been reached despite the missing gaps in the provided datasets. The key features to predict the stress index have been identified and recommendations in terms of data quality have been made so the predictions can be improve

Micellar chromatographic partition coefficients and their application in predicting skin permeability

The major goal for physicochemical screening of pharmaceuticals is to predict human drug absorption, distribution, elimination, excretion and toxicity. These are all dependent on the lipophilicity of the drug, which is expressed as a partition coefficient i.e. a measure of a drug’s preference for the lipophilic or hydrophilic phases. The most common method of determining a partition coefficient is the shake flask method using octanol and water as partitioning media. However, this system has many limitations when modeling the interaction of ionised compounds with membranes, therefore, unreliable partitioning data for many solutes has been reported. In addition to these concerns, the procedure is tedious and time consuming and requires a high level of solute and solvent purity. Micellar liquid chromatography (MLC) has been proposed as an alternative technique for measuring partition coefficients utilising surfactant aggregates, known as micelles. This thesis investigates the application of MLC in determining micelle-water partition coefficients (logPMW) of pharmaceutical compounds of varying physicochemical properties. The effect of mobile phase pH and column temperature on the partitioning of compounds was evaluated. Results revealed that partitioning of drugs solely into the micellar core was influenced by the interaction of charged and neutral species with the surface of the micelle. Furthermore, the pH of the mobile phase significantly influenced the partitioning behaviour and a good correlation of logPMW was observed with calculated distribution coefficient (logD) values. More interestingly, a significant change in partitioning was observed near the dissociation constant of each drug indicating an influence of ionised species on the association with the micelle and retention on the stationary phase. Elevated column temperatures confirmed partitioning of drugs considered in this study was enthalpically driven with a small change in the entropy of the system because of the change in the nature of hydrogen bonding. Finally, a quantitative structure property relationship was developed to evaluate biological relevance in terms of predicting skin permeability of the newly developed partition coefficient values. This study provides a better surrogate for predicting skin permeability based on an easy, fast and cheap experimental methodology, and the method holds the predictive capability for a wider population of drugs. In summary, it can be concluded that MLC has the ability to generate partition coefficient values in a shorter time with higher accuracy, and has the potential to replace the octanol-water system for pharmaceutical compounds