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Biomedical subjects

E Zintzaras

Publications and source records attributed to E Zintzaras.

30 records · Page 2Linked to original sources
PubMed1999

A comparison of amino acid distance measures using procrustes analysis.

Four methods to convert an amino acid similarity matrix into a metric distance matrix were compared using procrustes analysis. Procrustes analysis is a rotational fit technique, which assesses the degree of fit between two configurations after optimal matching of the two matrices under translation, rotation and scaling. The analysis shows that not all conversion methods are equivalent, but that the results of two methods are more similar than the others.

Amino Acids↗
PubMed1994

Growing a classification tree using the apparent misclassification rate.

A method to determine the size of a classification tree is proposed. This method is based on the change of the apparent misclassification rate (AMR) of the tree at each growing stage. The method is simple and fast compared to the other classification tree methods, which are based on minimizing a cost complexity function. To test the method, it was used to classify species of fungi, and the results are in good agreement with those obtained by linear discriminant analysis. Also, 21 proteins with known structures and functions were classified using the proposed method. For this purpose the coefficient of variation for several properties of the secondary structures of these proteins has been used. Again, the results were in good agreement with the classification obtained previously using dynamic programming.

Algorithms↗
PubMed1993

Characterization of the human T cell receptor alpha-chain repertoire and demonstration of a genetic influence on V alpha usage.

Despite considerable knowledge of the determinants and profile of the T cell receptor (TCR) beta-chain repertoire, very little is known about the repertoire of the V alpha and J alpha segments of the TCR in man or mouse. We have used the anchored polymerase chain reaction (PCR) to analyze the human TCR alpha-chain repertoire at the mRNA level in peripheral blood lymphocytes. We find marked differences in the usage of individual V alpha segments. The V alpha repertoire is determined both by differences in the use of individual V alpha gene segments at the time of gene rearrangement and by genetic influences on the selection of the expressed V alpha repertoire, presumably during thymic maturation. Analysis of the J alpha repertoire shows non-random usage of individual J alpha segments but no obvious relationship to chromosomal position. We also report three new V alpha segments, three new J alpha segments and possible allelic variation in V alpha gene segments. Two unusual TCR alpha transcripts are described which add new information on the nature of TCR alpha transcript assembly.

Amino Acid Sequence↗
PubMed1992

A statistical test of hypotheses on the organization and origin of the genetic code.

Theories of the origin of the genetic code assign different weights to amino acid properties such as polarity and precursor-product relationship. Previous statistical work on the origin of the genetic code has produced controversial results. We analyze relationships between various amino acid and tRNA properties by one and the same statistical method. It is shown that polarities as well as precursor-product relationships are both likely to have been important in shaping the genetic code, together with codon swapping that left protein sequences intact.

Amino Acids↗
PubMed1991

Apparent decline in fertility in heifers after repeated oestrus synchronisation with cloprostenol.

Over several years groups of heifers which were repeatedly treated with the PGF2 alpha analogue, cloprostenol, to synchronise oestrus were artificially inseminated. When sperm treatment was optimised, a reduction in conception rate was observed which was related to the number of synchronisation treatments the animals had received. Although factors such as a seasonal reduction in fertility may have contributed to the effect, there appeared to be a decrease in the proportion of animals becoming pregnant after successive synchronisations. Possible explanations for this observation are suggested.

Animals↗
PubMedDate not supplied

Bioequivalence studies: biometrical concepts of alternative designs and pooled analysis.

A bioequivalence study compares the bioavailability between a test and a reference drug product in terms of the rate and extent of drug absorption. Area under the plasma concentration-time curve (AUC) and maximum plasma concentration (Cmax) are the pharmacokinetic parameters that serve as characteristics for the assessment of the extent and rate of absorption, respectively. The experimental design of a bioequivalence study is usually a crossover and rarely a parallel or a paired comparative. The statistical assessment of bioequivalence is based on the 90% confidence interval for the ratio of the test mean to the reference mean for AUC and Cmax The aims of this paper are to: (i) investigate alternative designs to a crossover design for conducting bioequivalence studies; (ii) propose the statistical analysis of different designs for bioequivalence studies on the same products; and (iii) discuss their usefulness for the approval of new generic drug products. For this purpose, three case studies are illustrated and analysed. The first case study concerns the investigation of the merits of a crossover design relative to a parallel group design for highly variable drugs using as an example a bioequivalence study of tamoxifen products. The second case study concerns the pooled statistical analysis of two bioequivalent studies of the same levodopa products. The analyses of the individual studies failed to meet the regulatory criteria for bioequivalence. The one study design was a paired comparative and the other one a crossover. Under some assumptions the crossover design may be considered as a paired comparative and the data from the two studies may be analysed together as a paired comparative design. The third case study concerns the statistical pooled analysis of two bioequivalent studies of the same clodronate products. The one study was a three-period crossover pilot study and it was used to identify the variability of the active substance. Then, this variability was used to determine the number of subjects for the main pivotal study which was a two-period crossover. The pilot study design was converted into a two-period crossover design and the data from the two studies were analysed together as a two-period crossover design. The original data of the studies were modified accordingly.

Cross-Over Studies↗
PubMedDate not supplied

Biometrical evaluation of bioequivalence trials using a bootstrap individual direct curve comparison method.

Bioequivalence of two medicinal, or veterinary, products is established by comparing the mean of bioavailability measures, such as AUC and Cmax, following administration of the test (T) and reference (R) products. However, the use of these parameters has several drawbacks, e.g. they do not take into consideration the overall pharmacokinetic profile shape. Therefore, concerns have been raised regarding their appropriateness for assessment of bioequivalence. To overcome the limitations of these bioequivalence parameters, direct curve comparison metrics methods were recently proposed on an average basis. In this paper, an individual based direct curve comparison method for assessing bioequivalence is proposed. The bioequivalence of T and R in each subject is evaluated by a new curve comparison metrics delta. The metrics delta is the absolute sum of the difference between two curves. The significance of the metrics for each subject is assessed by bootstrapping. An overall bioequivalence of T and R may be considered if less than 25% of the subjects show statistically different profiles.

Area Under Curve↗
PubMedDate not supplied

A bioequivalence study of levothyroxine tablets versus an oral levothyroxine solution in healthy volunteers.

Probably for genetic reasons a substantial part of the Greek population requires Levothyroxine treatment. Since commercially available Levothyroxine was first marketed, the manufacture and storage of the drug in tablet form has been complicated and difficult; and as cases of therapeutic failure have frequently been reported following treatment with this medicinal agent, quality control is an essential factor. Due to the unreliability of Levothyroxine-based commercial products, in the present study we decided to follow the Food and Drug Administration (FDA) guidelines*, and use a Levothyroxine solution as reference product. The bioavailability of the Levothyroxine sodium tablet formulation THYROHORMONE/Ni-The Ltd (0.2 mg/tab) and that of a reference oral solution (0.3 mg/100 ml) under fasting conditions were compared in an open, randomized, single-dose two-way crossover study. Twenty four healthy Caucasian volunteers (M/F=15/9, mean age=32.9+/-7.4yr) participated in the study. Bioavailability was assessed by pharmacokinetic parameters such as the area under plasma concentration-time curve from time zero up to the measurable last time point (AUC(last)) and the maximum plasma concentration (Cmax). Heparinized venous blood samples were collected pre-dose and up to a 48-hour period post-dose. Levothyroxine sodium in plasma samples was assayed by a validated electrochemiluninescent immunoassay technique. Statistical analysis showed that the post-dose thyrotropin-stimulating hormone (TSH) levels decreased significantly (p<0.05). Regarding Levothyroxine (T4), the point estimate of the test formulation to the reference formulation ratios (T/R) for AUC(last) and Cmax was 0.92 with 90% confidence limits (0.90, 0.94) and 0.93 with 90% confidence limits (0.91, 0.94), respectively. Regarding triiodo-L-thyronine (T3), the point estimate for the T/R ratios of AUC(last) and Cmax was 0.92 with 90% confidence limits (0.90, 0.95) and 0.94 with 90% confidence limits (0.92, 0.95), respectively. The 90% confidence limits for the pharmacokinetic parameters AUC(last) and Cmax lie within the acceptance limits for bioequivalence (0.80, 1.25), for both T3 and T4.

Administration, Oral↗
PubMedDate not supplied

The existence of sequence effect in cross-over bioequivalence trials.

A generic drug product (T) in order to be approved for marketing authorization a bioequivalence trial is required. In the trial the generic product is compared to the innovator product (R) in terms of the pharmacokinetic parameters AUC and Cmax. The regulatory requirement for bioequivalence is that the 90% confidence intervals for the ratio (T/R) of the generic to innovator product pharmacokinetic parameter averages lies within the limits (80%, 125%). The design of the trial is usually a two-period crossover. This design has the limitation that if the statistical analysis reveal significant sequence effect then the bioequivalence results may be biased and their interpretation is difficult. The sequence effect is confounding with the unequal residual effect and with the formulation by period interaction. Since the existence of the sequence effect questions the quality of the trial, the applicant should provide possible explanations and information on the subjects, the trial conditions, the clinical settings and the assay methodology. An additional statistical analysis on the data from the first period of the trial may support the bioequivalence. If it is proven that the sequence effect is a true effect then the generic may be approved for marketing authorization.

Biological Availability↗
PubMedDate not supplied

Statistical aspects of bioequivalence testing between two medicinal products.

A generic drug product (test product) is bioequivalent to an innovator product (reference product) when their bioavailabilities in the same molar dose are similar. Bioavailability is expressed by pharmacokinetic parameters such as the area under plasma concentration-time curve (AUC), the maximum plasma concentration (Cmax) and the time of maximum plasma concentration (tmax). The assessment of bioequivalence is carried out by in vivo bioequivalence studies. This paper examines and appraises design issues for performing a bioequivalence study: the use of crossover, parallel, replicated, and add-on designs; and the determination of sample size. In addition, it presents the valid statistical approaches for proving bioequivalence: average bioequivalence on transformed and untransformed data; parametric and non-parametric analyses; moment based individual bioequivalence; direct curve comparison metrics.

Area Under Curve↗