PubMed Health⌕ Search

Biomedical subjects

R Hanus

Publications and source records attributed to R Hanus.

5 recordsLinked to original sources

Stability parameter estimation at ambient temperature from studies at elevated temperatures.

The determination of specific kinetic constants k(i) in pH-profile studies is often undertaken at ambient temperature. However, when dealing with a drug substance that is stable at ambient temperature, the pH-profile study is conducted at a chosen elevated temperature and the kinetic parameters are given at this particular elevated temperature. But in stability studies we generally need kinetic constants at ambient or storage temperature for practical reasons (information and storage conditions of formulation). To assess this ambient kinetic information from studies at elevated temperatures, cumulative sequential steps are usually employed with very few statistical concerns on the final estimates. The statistical problems on these final estimates in cumulative procedures are highlighted in many papers. Because these stability parameters are useful for drug formulation and storage conditions, good practical decisions have to be made on the basis of statistically unbiased identified parameters. We propose in this paper a nonlinear model that allows the direct determination of specific activation energies E(ai) that are linked to the specific kinetic constants k(i). Hence, a mathematical relationship between drug concentration C, pH, temperature T, and time t is obtained. Kinetic data from acetylsalicylic acid (ASA) hydrolysis (first-order kinetics) are used to validate the model. The results show that it is possible to obtain directly, by an extrapolation procedure, the kinetic parameters (specific kinetic constants k(i), specific activation energies E(ai), and dissociation constant pK(a)) at low temperature from data gathered at elevated temperatures using more meaningful statistics.

Data Interpretation, Statistical↗

Improved kinetic parameter estimation in pH-profile data treatment.

Statistical problems in temperature stability parameter estimation have been the subject of many papers whereas statistics in, pH-profile parameter estimation have focused little attention. However, the conventional two step method used in data treatment in both cases leads to identical statistical problems. The aim of this study is then to introduce a method that improves statistics in pH-profile parameter estimation. A one step non-linear method that takes into account the errors in drug content determination is proposed. A mathematical relationship between drug content C, pH and time t is tested. The proposed method allows the estimation of the specific kinetic constants and the dissociation constant (pK(a)) in a single run. The most likely experimental initial drug contents C(0j),. where j is the index of a given experiment, are also determined. This approach that takes into account all relevant experimental information for the estimation of kinetic parameters is more rigorous from a statistical viewpoint than the classical two step methods. Kinetic data from acetylsalicylic acid (ASA) hydrolysis was used for the tests.

Algorithms↗

Incorporating batch effects in the estimation of drug stability parameters using an Arrhenius model.

The nonlinear estimation of drug stability parameters (energy of activation Ea and shelf-life tY) by conventional approaches employs equations relating drug content determination C at time t and temperature T. The identification procedures lead to the determination of only one initial drug content C0 for several different experiments. However, it is well known that because of experimental concentration variation or of intentional modification of the experimental schedule, there are as many initial drug contents as experiments. For these reasons, a method which takes into account batch effects is proposed to determine stability parameters and also all initial drug contents C0j where j is the index of experiment in one step. This method is more accurate from a statistical viewpoint and is suitable for data treatment in pharmaceutical industries where the initial drug content of each batch entering the stability program can be checked a posteriori. The application of this method is shown on real kinetic data from the hydrolysis of acetylsalicylic acid (ASA).

Algorithms↗

Multidrug resistance modifies polyamines uptake in P388 murine lymphoma cells: experimental and modeling approach.

Polyamines (putrescine, spermidine and spermine) are ubiquitous compounds, essential for cell growth. This paper compares the polyamine transport between sensitive P388 murine lymphoma cells and two multidrug resistant P388 sublines with the assistance of an experimental model. This new model allows the characterisation of the whole polyamines uptake and efflux. Three parameters are identified by the model: two rate constants (K+ for the uptake and K- for the efflux) which are considered as physical constants specific to the transport of one polyamine in one cell type, and Ci(o) which represents the initial intracellular concentration. This model well describes our experimental results of polyamine transport across the P388 cell plasma membrane. Multidrug resistant P388 cells exhibit spermine uptake significantly higher than that of sensitive cells when on the opposite, putrescine enters more rapidly into the sensitive P388 cells. In conclusion, comparison of polyamine transport between sensitive and multidrug resistant P388 phenotypes shows large and significant differences.

Animals↗