CYP1A2 expression rather than genotype is associated with olanzapine concentration in psychiatric patients.

Olanzapine is a commonly prescribed atypical antipsychotic agent for treatment of patients with schizophrenia and bipolar disorders. Previous in vitro studies using human liver microsomes identified CYP1A2 and CYP2D6 enzymes being responsible for CYP-mediated metabolism of olanzapine. The present work focused on the impact of CYP1A2 and CYP2D6 genetic polymorphisms as well as of CYP1A2 metabolizing capacity influenced by non-genetic factors (sex, age, smoking) on olanzapine blood concentration in patients with psychiatric disorders (N = 139). CYP2D6 genotype-based phenotype appeared to have negligible contribution to olanzapine metabolism, whereas a dominant role of CYP1A2 in olanzapine exposure was confirmed. However, CYP1A2 expression rather than CYP1A2 genetic variability was demonstrated to be associated with olanzapine concentration in patients. Significant contribution of - 163C > A (rs762551), the most common SNP (single nucleotide polymorphism) in CYP1A2 gene, to enhanced inducibility was confirmed by an increase in CYP1A2 mRNA expression in smokers carrying - 163A, and smoking was found to have appreciable impact on olanzapine concentration normalized by the dose/bodyweight. Furthermore, patients' olanzapine exposure was in strong association with CYP1A2 expression; therefore, assaying CYP1A2 mRNA level in leukocytes can be an appropriate tool for the estimation of patients' olanzapine metabolizing capacity and may be relevant in optimizing olanzapine dosage.

Selection and Evaluation of mRNA and miRNA Reference Genes for Expression Studies (qPCR) in Archived Formalin-Fixed and Paraffin-Embedded (FFPE) Colon Samples of DSS-Induced Colitis Mouse Model.

The choice of appropriate reference genes is essential for correctly interpreting qPCR data and results. However, the majority of animal studies use a single reference gene without any prior evaluation. Therefore, many qPCR results from rodent studies can be misleading, affecting not only reproducibility but also translatability. In this study, the expression stability of reference genes for mRNA and miRNA in archived FFPE samples of 117 C57BL/6JOlaHsd mice (males and females) from 9 colitis experiments (dextran sulfate sodium; DSS) were evaluated and their expression analysis was performed. In addition, we investigated whether normalization reduced/neutralized the influence of inter/intra-experimental factors which we systematically included in the study. Two statistical algorithms (NormFinder and Bestkeeper) were used to determine the stability of reference genes. Multivariate analysis was made to evaluate the influence of normalization with different reference genes on target gene expression in regard to inter/intra-experimental factors. Results show that archived FFPE samples are a reliable source of RNA and imply that the FFPE procedure does not change the ranking of stability of reference genes obtained in fresh tissues. Multivariate analysis showed that the histological picture is an important factor affecting the expression levels of target genes.

Phenoconversion of CYP2D6 by inhibitors modifies aripiprazole exposure.

The efficacy of aripiprazole therapy and the risk of adverse reactions are influenced by substantial inter-individual variability in aripiprazole metabolizing capacity. In vitro studies assigned the potential role in aripiprazole metabolism to CYP2D6 and CYP3A enzymes; therefore, the association between the steady-state aripiprazole plasma concentrations and patients' CYP2D6 and CYP3A statuses (CYP2D6, CYP3A4, and CYP3A5 genotypes, and CYP3A4 expression) and/or co-medication with CYP function modifying medications has been investigated in 93 psychiatric patients on stable aripiprazole therapy. The patients' CYP2D6 genotype had a major effect on aripiprazole plasma concentrations, whereas contribution of CYP3A genotypes and CYP3A4 expression to aripiprazole clearance were considered to be minor or negligible. The role of CYP3A4 expression in aripiprazole metabolism did not predominate even in the patients with nonfunctional CYP2D6 alleles. Furthermore, dehydroaripiprazole exposure was also CYP2D6 genotype-dependent. Dehydroaripiprazole concentrations were comparable with aripiprazole levels in patients with functional CYP2D6 alleles, and 35% or 22% of aripiprazole concentrations in patients with one or two non-functional CYP2D6 alleles, respectively. The concomitant intake of CYP2D6 inhibitors, risperidone, metoprolol, or propranolol was found to increase aripiprazole concentrations in patients with at least one wild-type CYP2D6*1 allele. Risperidone and 9-hydroxy-risperidone inhibited both dehydrogenation and hydroxylation of aripiprazole, whereas metoprolol and propranolol blocked merely the formation of the active dehydroaripiprazole metabolite, switching towards the inactivation pathways. Patients' CYP2D6 genotype and co-medication with CYP2D6 inhibitors can be considered to be the major determinants of aripiprazole pharmacokinetics. Taking into account CYP2D6 genotype and co-medication with CYP2D6 inhibitors may improve the outcomes of aripiprazole therapy.

Optimization of process conditions for mammalian fed-batch cell culture in automated micro-bioreactor system using genetic algorithm.

Recombinant proteins produced by mammalian cell culture technology represent an important segment of therapeutic molecules. Development of their manufacturing processes is a time- and resource-consuming task. A wide array of process conditions, e.g. physico-chemical parameters, medium composition, feeding strategy, needs to be optimized to design a commercially feasible process with the desired productivity and product characteristics. Traditionally, statistical experimental designs, i.e. design-of-experiments methodology, have been used for such optimizations. However, statistical design approach has several limitations related to high dimensionality of the explored parameter space originating from the complexity of the mammalian cell culture processes. An alternative is therefore desired to overcome these limitations. In this study, we have successfully used a simple genetic algorithm as a method of experimental design for optimization of mammalian cell culture processes for two recombinant cell lines, one expressing a monoclonal antibody and one an Fc-fusion protein. Harnessing the automation capability of a robotically driven micro-bioreactor system to execute the genetic algorithm-derived experiments, a set of 14 process parameters was optimized within 132 experiments per cell line (six generations of 22 experiments), showing the feasibility of this approach as an alternative to classical statistical experimental designs.

Modular Serial Flow Through device for pulsed electric field treatment of the liquid samples.

In biotechnology, medicine, and food processing, simple and reliable methods for cell membrane permeabilization are required for drug/gene delivery into the cells or for the inactivation of undesired microorganisms. Pulsed electric field treatment is among the most promising methods enabling both aims. The drawback in current technology is controllable large volume operation. To address this challenge, we have developed an experimental setup for flow through electroporation with online regulation of the flow rate with feedback control. We have designed a modular serial flow-through co-linear chamber with a smooth inner surface, the uniform cross-section geometry through the majority of the system's length, and the mesh in contact with the electrodes, which provides uniform electric field distribution and fluid velocity equilibration. The cylindrical cross-section of the chamber prevents arching at the active treatment region. We used mathematical modeling for the evaluation of electric field distribution and the flow profile in the active region. The system was tested for the inactivation of Escherichia coli. We compared two flow-through chambers and used a static chamber as a reference. The experiments were performed under identical experimental condition (product and similar process parameters). The data were analyzed in terms of inactivation efficiency and specific energy consumption.

Potential Role of Patients' CYP3A-Status in Clozapine Pharmacokinetics.

Background: The atypical antipsychotic clozapine is effective in treatment-resistant schizophrenia; however, the success or failure of clozapine therapy is substantially affected by the variables that impact the clozapine blood concentration. Thus, elucidating the inter-individual differences in clozapine pharmacokinetics can facilitate the personalized therapy. Methods: Since a potential role in clozapine metabolism is assigned to CYP1A2, CYP2C19, CYP2D6 and CYP3A enzymes, the association between the patients' CYP status (CYP genotypes, CYP expression) and clozapine clearance was evaluated in 92 psychiatric patients. Results: The patients' CYP2C19 or CYP2D6 genotypes and CYP1A2 expression seemed to have no effect on clozapine serum concentration, whereas CYP3A4 expression significantly influenced the normalized clozapine concentration (185.53±56.53 in low expressers vs 78.05±29.57 or 66.52±0.25 (ng/mL)/(mg/kg) in normal or high expressers, P<.0001), in particular that the patients expressed CYP1A2 at a relatively low level. The functional CYP3A5*1 allele seemed to influence clozapine concentrations in those patients who expressed CYP3A4 at low levels. The dose requirement for the therapeutic concentration of clozapine was substantially lower in low CYP3A4 expresser patients than in normal/high expressers (2.18±0.64 vs 4.98±1.40 mg/kg, P<.0001). Furthermore, significantly higher plasma concentration ratios of norclozapine/clozapine and clozapine N-oxide/clozapine were observed in the patients displaying normal/high CYP3A4 expression than in the low expressers. Conclusion: Prospective assaying of CYP3A-status (CYP3A4 expression, CYP3A5 genotype) may better identify the patients with higher risk of inefficiency or adverse reactions and may facilitate the improvement of personalized clozapine therapy; however, further clinical studies are required to prove the benefit of CYP3A testing for patients under clozapine therapy.

Beyond aphasia: Altered EEG connectivity in Broca's patients during working memory task.

Broca's region and adjacent cortex presumably take part in working memory (WM) processes. Electrophysiologically, these processes are reflected in synchronized oscillations. We present the first study exploring the effects of a stroke causing Broca's aphasia on these processes and specifically on synchronized functional WM networks. We used high-density EEG and coherence analysis to map WM networks in ten Broca's patients and ten healthy controls during verbal WM task. Our results demonstrate that a stroke resulting in Broca's aphasia also alters two distinct WM networks. These theta and gamma functional networks likely reflect the executive and the phonological processes, respectively. The striking imbalance between task-related theta synchronization and desynchronization in Broca's patients might represent a disrupted balance between task-positive and WM-irrelevant functional networks. There is complete disintegration of left fronto-centroparietal gamma network in Broca's patients, which could reflect the damaged phonological loop.

Optimization of Clonazepam Therapy Adjusted to Patient's CYP3A Status and NAT2 Genotype.

BACKGROUND: The shortcomings of clonazepam therapy include tolerance, withdrawal symptoms, and adverse effects such as drowsiness, dizziness, and confusion leading to increased risk of falls. Inter-individual variability in the incidence of adverse events in patients partly originates from the differences in clonazepam metabolism due to genetic and nongenetic factors. METHODS: Since the prominent role in clonazepam nitro-reduction and acetylation of 7-amino-clonazepam is assigned to CYP3A and N-acetyl transferase 2 enzymes, respectively, the association between the patients' CYP3A status (CYP3A5 genotype, CYP3A4 expression) or N-acetyl transferase 2 acetylator phenotype and clonazepam metabolism (plasma concentrations of clonazepam and 7-amino-clonazepam) was evaluated in 98 psychiatric patients suffering from schizophrenia or bipolar disorders. RESULTS: The patients' CYP3A4 expression was found to be the major determinant of clonazepam plasma concentrations normalized by the dose and bodyweight (1263.5±482.9 and 558.5±202.4ng/mL per mg/kg bodyweight in low and normal expressers, respectively, P<.0001). Consequently, the dose requirement for the therapeutic concentration of clonazepam was substantially lower in low-CYP3A4 expresser patients than in normal expressers (0.029±0.011 vs 0.058±0.024mg/kg bodyweight, P<.0001). Furthermore, significantly higher (about 2-fold) plasma concentration ratio of 7-amino-clonazepam and clonazepam was observed in the patients displaying normal CYP3A4 expression and slower N-acetylation than all the others. CONCLUSION: Prospective assaying of CYP3A4 expression and N-acetyl transferase 2 acetylator phenotype can better identify the patients with higher risk of adverse reactions and can facilitate the improvement of personalized clonazepam therapy and withdrawal regimen.

Cytochrome P450 metabolism of the post-lanosterol intermediates explains enigmas of cholesterol synthesis.

Cholesterol synthesis is among the oldest metabolic pathways, consisting of the Bloch and Kandutch-Russell branches. Following lanosterol, sterols of both branches are proposed to be dedicated to cholesterol. We challenge this dogma by mathematical modeling and with experimental evidence. It was not possible to explain the sterol profile of testis in cAMP responsive element modulator tau (Crem τ) knockout mice with mathematical models based on textbook pathways of cholesterol synthesis. Our model differs in the inclusion of virtual sterol metabolizing enzymes branching from the pathway. We tested the hypothesis that enzymes from the cytochrome P450 (CYP) superfamily can participate in the catalysis of non-classical reactions. We show that CYP enzymes can metabolize multiple sterols in vitro, establishing novel branching points of cholesterol synthesis. In conclusion, sterols of cholesterol synthesis can be oxidized further to metabolites not dedicated to production of cholesterol. Additionally, CYP7A1, CYP11A1, CYP27A1, and CYP46A1 are parts of a broader cholesterol synthesis network.

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.

Transcriptome study and identification of potential marker genes related to the stable expression of recombinant proteins in CHO clones.

BACKGROUND: Chinese hamster ovary (CHO) cells have become the host of choice for the production of recombinant proteins, due to their capacity for correct protein folding, assembly, and posttranslational modifications. The most widely used system for recombinant proteins is the gene amplification procedure that uses the CHO-Dhfr expression system. However, CHO cells are known to have a very unstable karyotype. This is due to chromosome rearrangements that can arise from translocations and homologous recombination, especially when cells with the CHO-Dhfr expression system are treated with methotrexate hydrate. The present method used in the industry for testing clones for their long-term stability of recombinant protein production is empirical, and it involves their cultivation over extended periods of time prior to the selection of the most suitable clone for further bioprocess development. The aim of the present study was the identification of marker genes that can predict stable expression of recombinant genes in particular clones early in the development stage. RESULTS: The transcriptome profiles of CHO clones with stable and unstable recombinant protein production were investigated over 10-weeks of cultivation, using a DNA microarray. We identified 14 genes that were differentially expressed between the stable and unstable clones already at 2 weeks from the beginning of the cultivation. Their expression was validated by reverse-transcription quantitative real-time PCR (RT-qPCR). Furthermore, the k-nearest neighbour algorithm approach shows that the combination of the gene expression patterns of only five of these 14 genes is sufficient to predict stable recombinant protein production in clones in the early phases of cell-line development. CONCLUSIONS: The exact molecular mechanisms that cause unstable recombinant protein production are not fully understood. However, the expression profiles of some genes in clones with stable and unstable recombinant protein production allow prediction of such instability early in the cell-line development stage. We have thus developed a proof-of-concept for a novel approach to eliminate unstable clones in the CHO-Dhfr expression system, which saves time and labour-intensive work in cell-line development.

The electrophysiological correlates of the working memory subcomponents: evidence from high-density EEG and coherence analysis.

Synchronization between prefrontal (executive) and posterior (association) cortices seems a plausible mechanism for temporary maintenance of information. However, while EEG studies reported involvement of (pre)frontal midline structures in synchronization, functional neuroimaging elucidated the importance of lateral prefrontal cortex (PFC) in working memory (WM). Verbal and spatial WM rely on lateralized subsystems (phonological loop and visuospatial sketchpad, respectively), yet only trends for hemispheric dissociation of networks supporting rehearsal of verbal and spatial information were identified by EEG. As oscillatory activity is WM load dependent, we applied an individually tailored submaximal load for verbal (V) and spatial (S) task to enhance synchronization in the relevant functional networks. To map these networks, we used high-density EEG and coherence analysis. Our results imply that the synchronized activity is limited to highly specialized areas that correspond well with the areas identified by functional neuroimaging. In both V and S task, two independent networks of theta synchronization involving dorsolateral PFC of each hemisphere were revealed. In V task, left prefrontal and left parietal areas were functionally coupled in gamma frequencies. Theta synchronization thus provides the necessary interface for storage and manipulation of information, while left-lateralized gamma synchronization could represent the EEG correlate of the phonological loop.

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.

Dehydroepiandrosterone post-transcriptionally modifies CYP1A2 induction involving androgen receptor.

The pharmacological dosage of dehydroepiandrosterone (DHEA) protects against chemically induced carcinogenesis. The chemoprotective activity of DHEA is attributed to its inhibitory potential for the expression of CYP1A enzymes, which are highly responsible for metabolic activation of several mutagenic and carcinogenic chemicals. The present work investigated whether the chemoprevention by DHEA was due to diminished transcriptional activation of CYP1A genes or to the post-transcriptional modulation of CYP1A expression. In primary human hepatocytes, DHEA diminished the increase in CYP1A activities (7-ethoxyresorufin O-dealkylation and phenacetin O-dealkylation) and in CYP1A2 mRNA level induced by 3-methylcholanthrene, but did not alter the amount of CYP1A1 and CYP1B1 mRNA. The androgen receptor seemed to be involved in DHEA-mediated diminishment of CYP1A2 induction, which was attenuated in the presence of bicalutamide, the androgen receptor antagonist. The potential role of the glucocorticoid receptor and estrogen receptor in DHEA-mediated decrease in CYP1A2 induction was excluded. The developed computational model of CYP1A2 induction kinetics and CYP1A2 mRNA degradation proposed that a post-transcriptional mechanism was likely to be the primary mechanism of the DHEA-mediated diminishment of CYP1A2 induction. The hypothesis was confirmed by the results of actinomycin D-chase experiments in MCF-7 and LNCaP cells, displaying that the degradation rates of CYP1A2 mRNA were significantly higher in the cells exposed to DHEA. The novel findings on DHEA-mediated modulation of CYP1A2 mRNA stability may account for the beneficial effects of DHEA by decreasing the metabolic activation of pro-carcinogenic compounds.

An algorithm for rapid computational construction of metabolic networks: a cholesterol biosynthesis example.

Alternative pathways of metabolic networks represent the escape routes that can reduce drug efficacy and can cause severe adverse effects. In this paper we introduce a mathematical algorithm and a coding system for rapid computational construction of metabolic networks. The initial data for the algorithm are the source substrate code and the enzyme/metabolite interaction tables. The major strength of the algorithm is the adaptive coding system of the enzyme-substrate interactions. A reverse application of the algorithm is also possible, when optimisation algorithm is used to compute the enzyme/metabolite rules from the reference network structure. The coding system is user-defined and must be adapted to the studied problem. The algorithm is most effective for computation of networks that consist of metabolites with similar molecular structures. The computation of the cholesterol biosynthesis metabolic network suggests that 89 intermediates can theoretically be formed between lanosterol and cholesterol, only 20 are presently considered as cholesterol intermediates. Alternative metabolites may represent links with other metabolic networks both as precursors and metabolites of cholesterol. A possible cholesterol-by-pass pathway to bile acids metabolism through cholestanol is suggested.

Assessment of the haptic robot as a new tool for the study of the neural control of reaching.

Current experimental methods for the study of reaching in the MRI environment do not exactly mimic actual reaching, due to constrains in movement which are imposed by the MRI machine itself. We tested a haptic robot (HR) as such a tool. Positive results would also be promising for combined use of fMRI and EEG to study reaching. Twenty right-handed subjects performed reaching tasks with their right hand with and without the HR. Reaction time, movement time (MT), accuracy, event-related potentials (ERPs) and event-related desynchronisation/synchronisation (ERD/ERS) were studied. Reaction times and accuracies did not differ significantly between the two tasks, while the MT was significantly longer in HR reaching (959 vs. 447 ms). We identified two positive and two negative ERP peaks across all leads in both tasks. The latencies of the P1 and N2 peaks were significantly longer in HR reaching, while there were no significant differences in the P3 and N4 latencies. ERD/ERS topographies were similar between tasks and similar to other reaching studies. Main difference was in ERS rebound which was observed only in actual reaching. Probable reason was significantly larger MT. We found that reaching with the HR engages similar neural structures as in actual reaching. Although there are some constrains, its use may be superior to other techniques used for reaching studies in the MRI environment, where freedom of movement is limited.

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.

Sniffing-related motor cortical potential: topography and possible generators.

This study estimated the whole-scalp topography and possible generators of the cortical potential associated with volitional self-paced inspirations (sniffs). In 17 healthy subjects we recorded a 32-channel electroencephalogram (EEG) during sniffing, for comparison during finger flexions. We averaged the EEG with respect to movement onset, and performed current source density and principal component analysis on the grand averaged data. We identified an early negative sniffing-related cortical potential starting ∼1.5s before movement at the vertex, which, in its time-course and dipole orientation, closely resembled Bereitshaftspotential preceding finger flexions. Around the movement onset, its topography became unique with three negative current sources: one at the vertex, and two bilaterally over the fronto-temporal derivations. We conclude that sequential cortical activation in preparation for sniffing is similar to other volitional movements. The current sources at sniff onset at the vertex likely reflect somatotopic motor representation of the diaphragm, neck and intercostal muscles, whereas current sources over fronto-temporal derivations likely reflect the somatotopic representation of the orofacial muscles.

Estimation of drug-metabolizing capacity by cytochrome P450 genotyping and expression.

Many undesired side effects or therapeutic failures of drugs are the result of differences or changes in drug metabolism, primarily depending on the levels and activities of cytochrome P450 (P450) enzymes. To assess whether P450 expression profiles can reflect hepatic drug metabolism, we compared P450 mRNA levels in the liver or peripheral leukocytes with the corresponding hepatic P450 activities. A preliminary P450 genotyping for the most frequent polymorphisms in white populations (CYP2C9*2, CYP2C9*3, CYP2C19*2, CYP2C19*3, CYP2D6*3, CYP2D6*4, CYP2D6*6, and CYP3A5*3) was carried out before P450 phenotyping, excluding the donors with nonfunctional alleles of CYP2C9, CYP2C19, and CYP2D6 and those with a functional CYP3A5*1 allele from a correlation analysis. The hepatic mRNA levels of CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP2D6, and CYP3A4 displayed a strong association with P450 activities in the liver, whereas the expression of CYP1A2, CYP2C9, CYP2C19, and CYP3A4 in leukocytes was proven to reflect the hepatic activities of these P450 species. The leukocytes were found to be inappropriate cells for the assessment of hepatic CYP2B6 and CYP2D6 activities. Combining the results of P450 genotyping and phenotyping analyses, patients' drug-metabolizing capacities can be estimated by the P450 expression in the liver and in leukocytes with some limitations. Patients' genetic and nongenetic variations in P450 status can guide the appropriate selection of drugs and the optimal dose, minimizing the risk of harmful side effects and ensuring a successful outcome of drug therapy.

Brain-computer interface analysis of a dynamic visuo-motor task.

BACKGROUND: The area of brain-computer interfaces (BCIs) represents one of the more interesting fields in neurophysiological research, since it investigates the development of the machines that perform different transformations of the brain's "thoughts" to certain pre-defined actions. Experimental studies have reported some successful implementations of BCIs; however, much of the field still remains unexplored. According to some recent reports the phase coding of informational content is an important mechanism in the brain's function and cognition, and has the potential to explain various mechanisms of the brain's data transfer, but it has yet to be scrutinized in the context of brain-computer interface. Therefore, if the mechanism of phase coding is plausible, one should be able to extract the phase-coded content, carried by brain signals, using appropriate signal-processing methods. In our previous studies we have shown that by using a phase-demodulation-based signal-processing approach it is possible to decode some relevant information on the current motor action in the brain from electroencephalographic (EEG) data. OBJECTIVE: In this paper the authors would like to present a continuation of their previous work on the brain-information-decoding analysis of visuo-motor (VM) tasks. The present study shows that EEG data measured during more complex, dynamic visuo-motor (dVM) tasks carries enough information about the currently performed motor action to be successfully extracted by using the appropriate signal-processing and identification methods. The aim of this paper is therefore to present a mathematical model, which by means of the EEG measurements as its inputs predicts the course of the wrist movements as applied by each subject during the task in simulated or real time (BCI analysis). However, several modifications to the existing methodology are needed to achieve optimal decoding results and a real-time, data-processing ability. The information extracted from the EEG could, therefore, be further used for the development of a closed-loop, non-invasive, brain-computer interface. MATERIALS AND METHODS: For the case of this study two types of measurements were performed, i.e., the electroencephalographic (EEG) signals and the wrist movements were measured simultaneously, during the subject's performance of a dynamic visuo-motor task. Wrist-movement predictions were computed by using the EEG data-processing methodology of double brain-rhythm filtering, double phase demodulation and double principal component analyses (PCA), each with a separate set of parameters. For the movement-prediction model a fuzzy inference system was used. RESULTS: The results have shown that the EEG signals measured during the dVM tasks carry enough information about the subjects' wrist movements for them to be successfully decoded using the presented methodology. Reasonably high values of the correlation coefficients suggest that the validation of the proposed approach is satisfactory. Moreover, since the causality of the rhythm filtering and the PCA transformation has been achieved, we have shown that these methods can also be used in a real-time, brain-computer interface. The study revealed that using non-causal, optimized methods yields better prediction results in comparison with the causal, non-optimized methodology; however, taking into account that the causality of these methods allows real-time processing, the minor decrease in prediction quality is acceptable. CONCLUSION: The study suggests that the methodology that was proposed in our previous studies is also valid for identifying the EEG-coded content during dVM tasks, albeit with various modifications, which allow better prediction results and real-time data processing. The results have shown that wrist movements can be predicted in simulated or real time; however, the results of the non-causal, optimized methodology (simulated) are slightly better. Nevertheless, the study has revealed that these methods should be suitable for use in the development of a non-invasive, brain-computer interface.