Overview of the Current Situation and Challenges about Neuromyelitis Optica Spectrum Disorders in the Republic of Macedonia

BACKGROUND: Neuromyelitis optica spectrum disorders (NMOSD) are rare, progressive inflammatory disorders of the central nervous system characterized by severe, immune-mediated demyelination targeting optic nerves and spinal cord. Prior establishment of diagnostic criteria, patients were often misdiagnosed which led to delayed/inappropriate treatment and disability. Current practice involving immunotherapies is insufficient. Recent data are encouraging since the novel treatments allow effective prevention. AIM: The primary objective was to evaluate the current situation to identify challenges and develop intervention that might improve the current state as secondary objectives. METHODS: Standard questionnaire containing 22 questions was developed. Collected data were analyzed and descriptive report was created. RESULTS: Current estimated prevalence is approximately 20 NMOSD patients; trend is unknown due unavailability of patient registry. Six neurologists from one health-care institution are responsible for the whole management. Despite physician’s insufficient experience, ~80% of them are willing to switch patients into innovative treatments once available. Aquaporin-4-IgG testing is not routinely available resulting in ~30% testing rate. Approximately 80–90% of patients are on maintenance treatment with immunosuppressant, corticosteroids are used for acute relapse. Lack of novel innovative medications is evident. CONCLUSION: Current NMOSD management is challenging with significant unmet needs. Highest priorities that might provide improvement are: APQ4-IgG testing availability, establishment of patient registry, and availability of novel treatments

The association of C3435T single-nucleotide polymorphism, Pgp-glycoprotein gene expression levels and carbamazepine maintenance dose in patients with epilepsy

The ABCB1 gene encodes the P-glycoprotein (Pgp) protein, which is thought to transport various antiepileptic drugs. The single nucleotide polymorphism (SNP) (C3435T) in exon 26 of this gene correlates with the altered expression levels of P-glycoprotein, range of drug response and clinical conditions. In order to investigate the influence of this polymorphism on the susceptibility to and efficacy of carbamazepine therapy, we evaluated the allelic frequency and genotype distribution of this variant in 162 epilepsy patients from the Republic of Macedonia. Statistically significant differences were detected neither in the allelic frequency and genotype distribution between carbamazepine-resistant and carbamazepine-responsive epilepsy patients nor between the subgroups of carbamazepine (CBZ)-responsive patients treated with different CBZ doses. However, the T-allele was enriched in CBZ-responsive patients who required higher maintenance CBZ doses, This observation was substantiated by the findings that the median total plasma levels were the lowest in patients with CC (20 μmol/L) followed by CT (23 μmol/L) and TT (29 μmol/L) genotypes. Patients with a CC genotype also had a higher likelihood of response compared to patients with CT or TT genotypes over a wide range (400–1000 mg/day) of initial doses of CBZ. The T allele showed a reduced expression of ~5% compared to the C allele in peripheral blood mononuclear cells in heterozygotes for the variant. This difference might be translated into ~10% difference in homozygotes for the variant, which would explain the trend towards a dose-dependent efficacy of the CBZ treatment in patients with different genotypes. A larger prospective study is warranted to clarify the clinical utility of a genotypespecific individualized CBZ therapy

Application of Counter-propagation Artificial Neural Networks in Prediction of Topiramate Concentration in Patients with Epilepsy

Purpose: The application of artificial neural networks in the pharmaceutical sciences is broad, ranging from drug discovery to clinical pharmacy. In this study, we explored the applicability of counter-propagation artificial neural networks (CPANNs), combined with genetic algorithm (GA) for prediction of topiramate (TPM) serum levels based on identified factors important for its prediction. Methods: The study was performed on 118 TPM measurements obtained from 78 adult epileptic patients. Patients were on stable TPM dosing regimen for at least 7 days; therefore, steady-state was assumed. TPM serum concentration was determined by high performance liquid chromatography with fluorescence detection. The influence of demographic, biochemical parameters and therapy characteristics of the patients on TPM levels were tested. Data analysis was performed by CPANNs. GA was used for optimal CPANN parameters, variable selection and adjustment of relative importance. Results: Data for training included 88 measured TPM concentrations, while remaining were used for validation. Among all factors tested, TPM dose, renal function (eGFR) and carbamazepine dose significantly influenced TPM level and their relative importance were 0.7500, 0.2813, 0.0625, respectively. Relative error and root mean squared relative error (%) and their corresponding 95% confidence intervals for training set were 2.14 [(-2.41) - 6.70] and 21.5 [18.5 - 24.1]; and for test set were 6.21 [(-21.2) - 8.77] and 39.9 [31.7 - 46.7], respectively. Conclusions: Statistical parameters showed acceptable predictive performance. Results indicate the feasibility of CPANNs combined with GA to predict TPM concentrations and to adjust relative importance of identified variability factors in population of adult epileptic patients

Evaluation of APOE Genotype and Vascular Risk Factors As Prognostic and Risk Factors for Alzheimer’s Disease and Their Influence On Age of Symptoms Onset

BACKGROUND: Alzheimer’s disease (AD), the most common cause of dementia, is evolving to become a threatening epidemy of the 21st century. Only 21% of the predicted number of AD patients in Macedonia have been diagnosed and treated, which means that almost 80% are underdiagnosed or misdiagnosed. Apolipoprotein E gene (APOE) is recognised as the strongest genetic risk factor for sporadic AD. Whether and when Alzheimer’s disease develops, depends on the very complex interaction between genetic and modifiable risk factors. It has been known that vascular factors like hypertension, diabetes mellitus, hypercholesterolemia and obesity increase the risk of developing both AD, vascular dementia and mixed AD and vascular pathology AIM: This study aims to evaluate the influence of APOEε4 allele presence and modifiable vascular risk factors (hypertension, diabetes mellitus and dyslipidemia) as prognostic and risk factors for AD and their influence on the age of onset of AD symptoms among 144 AD patients from Macedonia. MATERIAL AND METHODS: Study group of a total of 144 patients diagnosed with AD was evaluated. APOE genotyping was performed using APOE haplotype-specific sequence specific-primer (SSP)-PCR (Polymerase Chain Reaction) methodology. The non-standardized questionnaire was used to obtain information about demographics, lifestyle and modifiable risk factors that could influence disease onset and phenotype. RESULTS: Statistically significant association was found between the presences of APOEε4 allele in AD group versus controls. The presence of APOEε4 allele increases the risk of developing AD in a 3-fold manner. The average age of disease onset in the ε4 carrier group was 67.2 ± 8.3 and in the ε4 non-carrier group 69.7 ± 9.4. This confirms that the presence of APOEε4 allele shifts towards earlier disease onset, though the difference is not statistically significant. Out of the vascular risk factors, only hypertension was significantly associated with earlier AD onset. Out of total 144 patients, in 22.9% the first symptom onset was before the age of 65, that can be considered as early onset Alzheimer’s Disease (EOAD), which is much higher than 5% for EOAD as most of the studies report. CONCLUSIONS: The average age of disease onset of 68.4 years could be considered earlier than the average age of AD onset worldwide. Out of all the vascular risk factors analysed in this study, only hypertension and dyslipidemia were found to significantly increase the risk for developing AD and only the presence of hypertension influences the age of onset, shifting towards earlier disease onset. Public awareness campaigns should be organised to influence general population knowledge about Alzheimer’s disease, early recognition and the influence of modifiable vascular risk factors

Идетификација и изолација на некои флавоноиди и фенолни киселини од Verbascum scardicolum Bornm. и Melampyrum scardicum Wettst. со некои хроматографски методи

Заради бројни предности, како што се видовата специфичност и едноставната детекција (особено со поновите хроматографски техники), флавоноидите се погодни како таксномски маркери. Познавањето на флавоноидниот состав овозможува хемотаксономски квалитативно да се испита евентуалното сродство помеѓу одредени растителни групи. За разлика од другите припрадници на Verbascum или Melampyrum, не постојат литературни податоци за хемискиот состав на ендемичните видови Verbascum scardicolum и Melampyrum scardicum. Со цел овие видови да се доведат во врска со други видови од истиот род, испитувани се за присуството на дваесет и еден фалвоноид и две фолни киселини. Притоа, употребена е реверзно-фазна високоефикацна течна хроматографија, а компонентите се детектирани преку споредба на ретенционите времиња и податоците од ултравиолетовата спектроскопија со соодветни податоци за стандардните примероци

Prediction of toxicity and data exploratory analysis of estrogen-active endocrine disruptors using counter-propagation artificial neural networks

In this work, a novel algorithm for optimization of counter-propagation artificial neural networks has been used for development of quantitative structure-activity relationships model for prediction of the estrogenic activity of endocrine-disrupting chemicals. The search for the best model was performed using genetic algorithms. Genetic algorithms were used not only for selection of the most suitable descriptors for modeling, but also for automatic adjustment of their relative importance. Using our recently developed algorithm for automatic adjustment of the relative importance of the input variables, we have developed simple models with very good generalization performances using only few interpretable descriptors. One of the developed models is in details discussed in this article. The simplicity of the chosen descriptors and their relative importance for this model helped us in performing a detailed data exploratory analysis which gave us an insight in the structural features required for the activity of the estrogenic endocrine-disrupting chemicals

Prediction of aqueous solubility of drug-like molecules using a novel algorithm for automatic adjustment of relative importance of descriptors implemented in counter-propagation artificial neural networks

Copyright © 2012 Elsevier B.V. All rights reserved.In this work, we present a novel approach for the development of models for prediction of aqueous solubility, based on the implementation of an algorithm for the automatic adjustment of descriptor's relative importance (AARI) in counter-propagation artificial neural networks (CPANN). Using this approach, the interpretability of the models based on artificial neural networks, which are traditionally considered as "black box" models, was significantly improved. For the development of the model, a data set consisting of 374 diverse drug-like molecules, divided into training (n=280) and test (n=94) sets using self-organizing maps, was used. Heuristic method was applied in preselecting a small number of the most significant descriptors to serve as inputs for CPANN training. The performances of the final model based on 7 descriptors for prediction of solubility were satisfactory for both training (RMSEP(train)=0.668) and test set (RMSEP(test)=0.679). The model was found to be a highly interpretable in terms of solubility, as well as rationalizing structural features that could have an impact on the solubility of the compounds investigated. Therefore, the proposed approach can significantly enhance model usability by giving guidance for structural modifications of compounds with the aim of improving solubility in the early phase of drug discovery.Peer reviewe

Optimization and validation of bioanalytical SPE – HPLC method for the simultaneous determination of carbamazepine and its main metabolite, carbamazepine-10, 11-epoxide, in plasma

Carbamazepine is widely used as an antiepileptic drug in the treatment of partial and generalized tonic-clonic seizures. Carbamazepine 10,11-epoxide is the most important metabolite of carbamazepine, because it is a pharmacologically active compound with anticonvulsant properties. According to that, the routine analysis of carbamazepine 10,11-epoxide along with carbamazepine may provide optimal therapeutic monitoring of carbamazepine treatment. The aim of this study was to optimize and validate a simple and reliable solid - phase extraction method followed by RP-HPLC for the simultaneous determination of plasma levels of carbamazepine and carbamazepine-10,11-epoxide, in order to assure the implementation of the method for therapeutic monitoring. The extraction of the analytes from the plasma samples was performed by means of a solid-phase extraction procedure. The separation was carried out on a reversed-phase column using isocratic elution with acetonitrile and water (35:65, v/v) as a mobile phase. The temperature was 30°C and UV detection was set at 220 nm. The extraction yield values were more than 98% for all analytes, measured at four concentration levels of the linear concentration range. The method displayed excellent selectivity, sensitivity, linearity, precision and accuracy. Stability studies indicate that stock solutions and plasma samples were stabile under different storage conditions at least during the observed period. The method was successfully applied to determine the carbamazepine and carbamazepine-10,11-epoxide in plasma of epileptic patients treated with carbamazepine as monotherapy and in polytherapy. In conclusion, the proposed method is suitable for application in therapeutic drug monitoring of epileptic patients undergoing treatment with carbamazepine