Biomedical subjects

Milica Jovanović

Publications and source records attributed to Milica Jovanović.

8 recordsLinked to original sources

PubMed2006

An investigation into the usefulness of generalized regression neural network analysis in the development of level A in vitro-in vivo correlation.

Quantitative correlations between in vivo and in vitro data (IVIVC) reduce the number of human in vivo studies, thus decreasing the overall time and expenses necessary for the development of optimal drug product formulation. Although linear regression analysis represents the simplest relationship, it is recognized that IVIVC should not be limited to linear relationship. With regards to the implementation of non-linear IVIVC models and the ability of artificial neural network (ANN) computing to cope with non-linear relationships, the usefulness of ANN analysis in the development of IVIVC merits further evaluation. The present paper is an attempt to develop an IVIVC for model sustained release paracetamol matrix tablet formulations employing various correlation approaches based on linear and non-linear modeling of in vitro and in vivo data. Currently accepted compendial methodology was compared with the alternative approaches, involving general mixed effects model and generalized regression neural network (GRNN) analysis, in order to evaluate their usefulness for predicting the in vivo behavior of drug products. Although based on analogous approaches, data generated by GRNN were closer to those observed in vivo, leading to the higher level of IVIVC than obtained by convolution. It can be assumed that GRNN analysis was able to generalize complex relations between the output and input parameters and could account for the differences in drug release kinetics observed under various conditions in vitro, thus offering potential as a reliable and robust estimate of drug products in vivo behavior.

Acetaminophen↗

PubMed2005

Brain abscess due to Actinobacillus actinomycetemcomitans.

Actinobacillus actinomycetemcomitans, a constituent of the oral flora, is a rare cause of brain abscesses. We report the case of a 47-year-old male who presented with multiple brain abscesses due to this organism, presumably originating from his poor dentition. Problems met in isolating and identifying A. actinomycetemcomitans suggest that its true rate of isolation from non-oral samples may have been underestimated.

Actinobacillus Infections↗

PubMed2004

[Erythromycin-resistant Streptococcus pyogenes].

Streptococcus pyogenes is the most prevalent cause of tonsillopharyngitis in children. The drug of choice for infections caused by this microorganism is penicillin. The problem of treating such infections arises when erythromycin-resistant strains occur. The aim of the study was to determine the prevalence of Streptococcus pyogenes resistant to erythromycin. The organism was recovered from the pharynx of children hospitalized or treated on outpatient basis at the University Children's Hospital in Belgrade. Streptococcus pyogenes was identified on blood agar, using bacitracin disc, and confirmed by latex agglutination test (Slidex bioMerieux). Disc diffusion test was carried out to estimate the penicillin resistance. Erythromycin disc was used as screening method to detect erythromycin-resistant Streptococcus pyogenes. MIC for erythromycin was performed by broth dilution method. In the study period from January 2001 to December 2003, all 1100 isolates of Streptococcus pyogenes had usual level of penicillin sensitivity. In 2001, only 0.45% of isolates were erythromycin-resistant. In 2002, erythromycin resistance was 0.63%, while in 2003, it was 1.09%. MIC for erythromycin was from 1 to 128 mg/l. Three strains had constitutive and one strain had inducible resistance to clindamycin. According to the results, our conclusion is that, despite sensitivity to penicillin, resistance to macrolides is the emerging phenomenon. Reasonable use of macrolide antibiotics is necessary to maintain the resistance at the lowest level.

Anti-Bacterial Agents↗

PubMed2003

Development of the second-order derivative UV spectrophotometric method for direct determination of paracetamol in urine intended for biopharmaceutical characterisation of drug products.

Paracetamol is a widely used nonsalicylate analgesic and antipyretic drug. The existing methods for the determination of paracetamol in biological fluids are mainly HPLC techniques, although there are some reported methods based on spectrophotometric determinations. However, all these methods involve some extraction or derivatisation procedures. In the present study the UV spectra of investigated samples were recorded over the wavelength range 220-400 nm (lambda step 0.21 nm; scan speed 60 nm/min) and second-order derivative spectra were calculated. Second-order derivative spectra of different blank urine samples displayed the presence of a zero-crossing point at 245-247 nm defined as lambdazc. The zero-order absorption spectra of paracetamol in water displays maximum absorbance at 243 nm, while in second derivative spectra, a minimum peak at 246 nm was observed. Therefore, the application of zero-crossing technique to the second-derivative UV absorption spectrum should be useful for the determination of paracetamol using 2Dlambdazc. The proposed method enables determination of total paracetamol in urine directly and simply by reading the 2Dlambdazc of the diluted samples. The obtained results were in good accordance with published data on cumulative urinary excretion after per oral administration of paracetamol obtained applying different spectrophotometric methods of determination. It could be useful for biopharmaceutical characterisation of drug products (monitoring of the levels of paracetamol in urine in bioavailability testing, for the evaluation of in vitro-in vivo correlation and screening of different formulations during drug product development).

Acetaminophen↗

PubMed2003

Artificial neural networks in the modeling and optimization of aspirin extended release tablets with Eudragit L 100 as matrix substance.

The purpose of the present study was to model the effects of the concentration of Eudragit L 100 and compression pressure as the most important process and formulation variables on the in vitro release profile of aspirin from matrix tablets formulated with Eudragit L 100 as matrix substance and to optimize the formulation by artificial neural network. As model formulations, 10 kinds of aspirin matrix tablets were prepared. The amount of Eudragit L 100 and the compression pressure were selected as causal factors. In vitro dissolution time profiles at 4 different sampling times were chosen as responses. A set of release parameters and causal factors were used as tutorial data for the generalized regression neural network (GRNN) and analyzed using a computer. Observed results of drug release studies indicate that drug release rates vary widely between investigated formulations, with a range of 5 hours to more than 10 hours to complete dissolution. The GRNN model was optimized. The root mean square value for the trained network was 1.12%, which indicated that the optimal GRNN model was reached. Applying the generalized distance function method, the optimal tablet formulation predicted by GRNN was with 5% of Eudragit L 100 and tablet hardness 60N. Calculated difference (f1 2.465) and similarity (f2 85.61) factors indicate that there is no difference between predicted and experimentally observed drug release profiles for the optimal formulation. This work illustrates the potential for an artificial neural network, GRNN, to assist in development of extended release dosage forms.

Aspirin↗

PubMed2002

The application of generalized regression neural network in the modeling and optimization of aspirin extended release tablets with Eudragit RS PO as matrix substance.

The objective of this work is to use a generalized regression neural network (GRNN) in the design of extended-release aspirin tablets. As model formulations, 10 kinds of aspirin matrix tablets were prepared. Eudragit RS PO was used as matrix substance. The amount of Eudragit RS PO and compression pressure were selected as causal factors. In-vitro dissolution-time profiles at four different sampling times, as well as coefficients n (release order) and log k (release constant) from the Peppas equation were estimated as release parameters. A set of release parameters and causal factors were used as tutorial data for the GRNN and analyzing using a computer. A GRNN model was constructed. The optimized GRNN model was used for prediction of formulation with desired in vitro drug release. For two tested formulations there was very good agreement between the GRNN predicted and observed in vitro profiles and estimated coefficients. Calculated difference (f(1)) and similarity (f(2)) factors indicate that there is no difference between predicted and experimental observed drug release profiles. This work illustrates the potential for an artificial neural network, GRNN, to assist in development of extended-release dosage forms. This method can be employed to achieve a desired in vitro dissolution profile.

Acrylic Resins↗

PubMedDate not supplied

Biopharmaceutical characterization of sustained release matrix tablets based on novel carbomer polymers: formulation and in vivo investigation.

With the increased interest in modified release dosage forms and drug delivery systems, there is an increasing concern for biopharmaceutical characterization of the formulation in the early stages of drug product development. The main objectives of biopharmaceutical characterization are the in vitro and in vivo evaluation of the selected formulations in order to identify the factors influencing drug release; define the in vitro test methodology that would be predictive of drug products in vivo behavior and develop quantitative in vitro - in vivo correlation. The purpose of this study was to assess the potential of novel carbomer polymers, Carbopol 971P and Carbopol 71G, as a sustained release agents in matrix tablets containing high dosage drug substance. Although chemically identical, the two polymers exhibited substantially different drug release properties in vitro. Hypothetical in vivo drug release profiles were calculated by numerical deconvolution from cumulative urinary excretion data observed in vivo. The obtained results indicated that sound and reliable in vivo drug release profiles could be obtained from urinary excretion data and also, emphasized the need for in vitro testing under a range of experimental conditions in order to develop the biorelevant drug release methodology.

Acetaminophen↗

PubMedDate not supplied

An investigation into the factors influencing drug release from hydrophilic matrix tablets based on novel carbomer polymers.

Drug release from hydrophilic matrix tablets can be strongly influenced by the proportion of matrix forming polymer and the dimensions and geometry of the tablets. A complete two-factor, three-level factorial design, followed by multiple regression analysis and response surface methodology, was applied to investigate the influence of polymer level and tablet size on drug release kinetics from hydrophilic matrix tablets prepared with Carbopol 971P and Carbopol 71G. Tablet diameter, radius-to-height ratio, tablet surface area, and surface-area-to-volume ratio were evaluated as independent variables in terms of their applicability to characterize tablet size and geometry. The results indicate that it may be possible to control the rate of drug release by modifying the proportion of carbomer in tablets and tablet dimensions. The practical benefit of these simulations is to optimize the geometry and dimensions of a controlled release device and reduce the number of experiments involved in the development of new controlled release dosage forms.

Absorption↗