Aleš Belič

Molecular Interactions between NAFLD and Xenobiotic Metabolism

Non-alcoholic fatty liver disease (NAFLD), the hepatic manifestation of the metabolic syndrome, is a complex multifactorial disease characterized by metabolic deregulations that include accumulation of lipids in the liver, lipotoxicity, and insulin resistance. The progression of NAFLD to non-alcoholic steatohepatitis and cirrhosis, and ultimately to carcinomas, is governed by interplay of pro-inflammatory pathways, oxidative stress, as well as fibrogenic and apoptotic cues. As the liver is the major organ of biotransformation, deregulations in hepatic signaling pathways have effects on both, xenobiotic and endobiotic metabolism. Several major nuclear receptors involved in the transcription and regulation of phase I and II drug metabolizing enzymes and transporters also have endobiotic ligands including several lipids. Hence, hepatic lipid accumulation in steatosis and NAFLD, which leads to deregulated activation patterns of nuclear receptors, may result in altered drug metabolism capacity in NAFLD patients. On the other hand, genetic and association studies have indicated that a malfunction in drug metabolism can affect the prevalence and severity of NAFLD. This review focuses on the complex interplay between NAFLD pathogenesis and drug metabolism. A better understanding of these relationships is a prerequisite for developing improved drug dosing algorithms for the pharmacotherapy of patients with different stages of NAFLD.

Tableting process optimisation with the application of fuzzy models

A quality-by-design (QbD) principle, including process analytical technology, is becoming the principal idea in drug development and manufacturing. The implementation of QbD into product development and manufacturing requires larger resources, both human and financial, however, large-scale production can be established in a more cost-effective manner and with improved product quality. The objective of the present work was to study the influence of particle size distribution in powder mixture for tableting, and the settings of the compression parameters on the tablet quality described by the capping coefficient, standard deviations of mass and crushing strength of compressed tablets. Fuzzy models were used for modelling of the effects of the particle size distribution and the tableting machine settings on the tablet quality. The results showed that the application of mathematical models, based on the contemporary routinely measured quantities, can significantly improve the trial-and-error procedures.

Minimisation of the capping tendency by tableting process optimisation with the application of artificial neural networks and fuzzy models

The pharmaceutical industry is increasingly aware of the advantages of implementing a quality-by-design (QbD) principle, including process analytical technology, in drug development and manufacturing. Although the implementation of QbD into product development and manufacturing inevitably requires larger resources, both human and financial, large-scale production can be established in a more cost-effective manner and with improved efficiency and product quality. The objective of the present work was to study the influence of particle size (and indirectly, the influence of dry granulation process) and the settings of the tableting parameters on the tablet capping tendency. Artificial neural network and fuzzy models were used for modelling the effect of the particle size and the tableting machine settings on the capping coefficient. The suitability of routinely measured quantities for the prediction of tablet quality was tested. Results showed that model-based expert systems based on the contemporary routinely measured quantities can significantly improve the trial-and-error procedures; however, they cannot completely replace them. The modelling results also suggest that in cases where it is not possible to obtain sufficient number of measurements to uniquely identify the model, it is beneficial to use several modelling techniques to identify the quality of model prediction.

Correlation of in vitro and in vivo paracetamol availability from layered excipient suppositories

An in vivo investigation of paracetamol availability was carried out on eight healthy volunteers, comparing two paracetamol suppository formulations prepared using two different gliceride bases, a fast drug-releasing one and a slow drug-releasing one, i.e. Witepsol H15 and W35, respectively. The formulations were selected on the basis of a previous in vitro drug release study, which showed that, by superimposing the excipients in two layers within the same suppository, the drug release kinetics could be modulated using different ratios between the two layers. The comparison between the two different formulations in terms of plasma profiles and total amounts of drug excreted in urine revealed an increase in the extent of drug absorption from the layered excipient suppository. As the W35 has a higher monoglyceride content than the H15, this improved paracetamol availability could be ascribed to the absorption-enhancing effect of the monoglycerides. Moreover, the W35 has also a higher viscosity, which could possibly cause the suppository to be retained for a longer time in the lower part of the rectum, where the blood is drained directly to the systemic circulation. It was therefore hypothesized that the enhanced paracetamol availability could be also due to a liver bypass mechanism. For a further examination of the paracetamol absorption kinetics after rectal administration, a one-compartment model was fitted to the drug plasma concentration data. This approach allowed to draw absorption versus time profiles, which showed that a retardation actually occurred in paracetamol absorption when using suppositories containing the slow drug releasing excipient W35. These absorption data were then employed for an A level in vitro-in vivo correlation testing, and a linear relationship was found between in vitro release rate and in vivo absorption rate, both for fast releasing and for the layered excipient suppositories.

Identification of the phase code in an EEG during gripping-force tasks: A possible alternative approach to the development of the brain-computer interfaces

BACKGROUND The subject of brain-computer interfaces (BCIs) represents a vast and still mainly undiscovered land, but perhaps the most interesting part of BCIs is trying to understand the information exchange and coding in the brain itself. According to some recent reports, the phase characteristics of the signals play an important role in the information transfer and coding. The mechanism of phase shifts, regarding the information processing, is also known as the phase coding of information. OBJECTIVE The authors would like to show that electroencephalographic (EEG) signals, measured during the performance of different gripping-force control tasks, carry enough information for the successful prediction of the gripping force, as applied by the subjects, when using a methodology based on the phase demodulation of EEG data. Since the presented methodology is non-invasive it could be used as an alternative approach for the development of BCIs. MATERIALS AND METHODS In order to predict the gripping force from the EEG signals we used a methodology that uses subsequent signal processing methods: simplistic filtering methods, for extracting the appropriate brain rhythm; principal component analysis, for achieving the linear independence and detecting the source of the signal; and the phase-demodulation method, for extracting the phase-coded information about the gripping force. A fuzzy inference system is then used to predict the gripping force from the processed EEG data. RESULTS The proposed methodology has clearly demonstrated that EEG signals carry enough information for a successful prediction of the subjects performance. Moreover, a cross-validation showed that information about the gripping force is encoded in a very similar way between the subjects tested. As for the development of BCIs, considering the computational time to pre-process the data and train the fuzzy model, a real-time online analysis would be possible if the real-time non-causal limitations of the methodology could be overcome. CONCLUSION The study has shown that phase coding in the human brain is a possible mechanism for information coding or transfer during visuo-motor tasks, while the phase-coded content about the gripping forces can be successfully extracted using the phase-demodulation approach. Since the methodology has proven to be appropriate for the case of this study it could also be used as an alternative approach for the development of BCIs for similar tasks.

Virtual race as an examination test: models, solutions, experiences

In this paper, an idea is presented on how to motivate students to learn an exacting subject such as computer-aided control systems design (CACSD). The idea is that the exam is a virtual race or competition meaning that the students are graded according to the success achieved in the competition. The aim of the competition is set by the professor, and the students have to design a controller to achieve the best possible result they can. Four competition subjects, those used in the last four years of CACSD education at the Faculty of Electrical Engineering, University of Ljubljana, are reviewed with emphasis on the fourth one-the surf regatta. The mathematical model of the surf is then presented. In all four cases, the criterion for the competition was the shortest time. The paper concludes with the description of solutions applied by students and the faculty experiences with the course.

Clinical-pharmacist intervention reduces clinically relevant drug–drug interactions in patients with heart failure: A randomized, double-blind, controlled trial

BACKGROUND Incidence of drug-drug interactions (DDIs) increases with complexity of treatment and comorbidities, as in heart failure (HF). This randomized, double-blind study evaluated the intervention of the pharmacist on prevalence of clinically relevant DDIs (NCT01855165). METHODS Patients admitted with HF were screened for clinically relevant DDIs, and randomized to control or intervention. All attending physicians received standard advice about pharmacological therapy; those in the intervention group also received alerts about clinically relevant DDIs. Primary endpoint was DDI at discharge and secondary were re-hospitalization or death during follow-up. RESULTS Of 213 patients, 51 (mean age, 79 ± 6 years; male, 47%) showed 66 clinically relevant DDIs and were randomized. For intervention (n=26) versus control (n=25), the number of patients with and the number of DDIs were significantly lower at discharge: 8 vs. 18 and 10 vs. 31; p=0.003 and 0.0049, respectively. Over a 6 month follow-up period, 11 control and 9 intervention patients were re-hospitalized or died (p>0.2 for all). No significant differences were seen between control and intervention for patients with eGFR <60 mL/min/1.73 m(2) (78%) for re-hospitalization or death (10 vs. 7; p=0.74). CONCLUSIONS Pharmacist intervention significantly reduces the number of patients with clinically relevant DDIs, but not clinical endpoints 6 months from discharge.

Gripping-force identification using EEG and phase-demodulation approach.

In this paper we investigate the fuzzy identification of brain-code during simple gripping-force control tasks. Since the synchronized oscillatory activity and the phase dynamics between the brain areas are two important mechanisms in the brains function and information transfer, we decided to examine whether it is possible to extract the encoded information from the EEG signals using the phase-demodulation approach. The EEG was measured during the performance of different visuomotor tasks and the information we were trying to decode was the gripping force as applied by the subjects. The study revealed that it is possible, by using simple beta-rhythm filtering, phase demodulation, principal component analysis and a fuzzy model, to estimate the gripping-force response by using EEG signals as the inputs for the proposed model. The presented study has shown that even though EEG signals represent a superposition of all the active neurons, it is still possible to decode some information about the current activity of the brain centers. Furthermore, the cross-validation showed that the information about the gripping force is encoded in a very similar way for all the examined subjects. Thus, the phase shifts of the EEG signals seem to have a key role during activity and information transfer in the brain, while the phase-demodulation method proved to be a crucial step in the signal processing.

SteatoNet: The First Integrated Human Metabolic Model with Multi-layered Regulation to Investigate Liver- Associated Pathologies

Current state-of-the-art mathematical models to investigate complex biological processes, in particular liver-associated pathologies, have limited expansiveness, flexibility, representation of integrated regulation and rely on the availability of detailed kinetic data. We generated the SteatoNet, a multi-pathway, multi-tissue model and in silico platform to investigate hepatic metabolism and its associated deregulations. SteatoNet is based on object-oriented modelling, an approach most commonly applied in automotive and process industries, whereby individual objects correspond to functional entities. Objects were compiled to feature two novel hepatic modelling aspects: the interaction of hepatic metabolic pathways with extra-hepatic tissues and the inclusion of transcriptional and post-transcriptional regulation. SteatoNet identification at normalised steady state circumvents the need for constraining kinetic parameters. Validation and identification of flux disturbances that have been proven experimentally in liver patients and animal models highlights the ability of SteatoNet to effectively describe biological behaviour. SteatoNet identifies crucial pathway branches (transport of glucose, lipids and ketone bodies) where changes in flux distribution drive the healthy liver towards hepatic steatosis, the primary stage of non-alcoholic fatty liver disease. Cholesterol metabolism and its transcription regulators are highlighted as novel steatosis factors. SteatoNet thus serves as an intuitive in silico platform to identify systemic changes associated with complex hepatic metabolic disorders.

Analysis of the steady-state relations and control-algorithm characterisation in a mathematical model of cholesterol biosynthesis

Elevated levels of cholesterol are known to be a risk factor for cardiovascular diseases. As a result, several treatment strategies and drugs have been developed to control these elevated cholesterol levels, but they are not always successful. Statins are now the most widely used cholesterol-lowering drugs; however, not all the mechanisms of their action are understood and this can sometimes lead to adverse effects. A dynamic mathematical model of the cholesterol biosynthesis network was developed with aim to understand the key mechanisms of cholesterol biosynthesis. In this article we show that in spite of a serious lack of experimental data, the model can be used to study the concepts of possible mechanisms of cholesterol biosynthesis and drug interactions. If only steady-state data is used for the model’s identification, the model can predict the steady-state relations after perturbation correctly, while the dynamical properties are not necessarily related to the real system. The control mechanism for the cholesterol levels through the SREBF-2 transcription factor was identified as the PI control algorithm. The comparison of model simulations and performed biological experiments indicated that the substances LK-980 and Atorvastatin most likely trigger the same indirect mechanism of cholesterol biosynthesis control, although they interact with the network in different ways.