A Bayesian Approach to Kinetic Modeling of Accelerated Stability Studies and Shelf Life Determination.

Kinetic modeling of accelerated stability data serves an important purpose in the development of pharmaceutical products, providing support for shelf life claims and expediting the path to clinical implementation. In this context, a Bayesian kinetic modeling framework is considered, accommodating different types of nonlinear kinetics with temperature and humidity dependent rates of degradation and accounting for the humidity conditions within the packaging to predict the shelf life. In comparison to kinetic modeling based on nonlinear least-squares regression, the Bayesian approach allows for interpretable posterior inference, flexible error modeling and the opportunity to include prior information based on historical data or expert knowledge. While both frameworks perform comparably for high-quality data from well-designed studies, the Bayesian approach provides additional robustness when the data are sparse or of limited quality. This is illustrated by modeling accelerated stability data from two solid dosage forms and is further examined by means of artificial data subsets and simulated data.

Solid State Kinetics of Nitrosation Using Native Sources of Nitrite.

While many reactive species are known to cause N-nitrosation, trace nitrite (NO2-), which may be present in several excipients, is a source of nitrosating agents in pharmaceutical formulations. In this study we have found that the salt form of NO2- can influence the favored nitrosation conditions and final amount of nitrosamine being formed. Using native levels of NO2-, most likely present as ammonium nitrite (NH4NO2), in microcrystalline cellulose, we have determined the kinetics of nitrosamine formation in solid state with dimethylamine substrate present in metformin, used as model compound. It was found that the competing degradation of NH4NO2 into N2 and H2O limited the amount of nitrosamine formation to a great extent. Empirically modelling the kinetic data predicted reaching at maximum 1.6% conversion over a hypothetical 3-year shelf-life. These results also showed that using other sources of NO2- as spiking reagents, such as NaNO2, may lead to unrealistic worst-case situations when the main form of NO2- in the drug product (DP) under evaluation may be NH4NO2. As well, measuring NO2- in freshly manufactured excipients containing NO2- potentially as NH4NO2 may lead to biased high NO2- content, which is not representative of the actual amounts present at the time of DP manufacture.

A novel high-throughput method for kinetic characterisation of anaerobic bioproduction strains, applied to Clostridium kluyveri.

Hexanoic acid (HA), also called caproic acid, can be used as an antimicrobial agent and as a precursor to various chemicals, such as fuels, solvents and fragrances. HA can be produced from ethanol and acetate by the mesophilic anaerobic bacterium Clostridium kluyveri, via two successive elongation steps over butyrate. A high-throughput anaerobic growth curve technique was coupled to a data analysis framework to assess growth kinetics for a range of substrate and product concentrations. Using this method, growth rates and several kinetic parameters were determined for C. kluyveri. A maximum growth rate (µmax) of 0.24 ± 0.01 h-1 was found, with a half-saturation index for acetic acid (KS,AA) of 3.8 ± 0.9 mM. Inhibition by butyric acid occurred at of 124.7 ± 5.7 mM (KI,BA), while the final product, HA, linearly inhibited growth with complete inhibition above 91.3 ± 10.8 mM (KHA of 10.9*10-3 ± 1.3*10-3 mM-1) at pH = 7, indicating that the hexanoate anion also exerts toxicity. These parameters were used to create a dynamic mass-balance model for bioproduction of HA. By coupling data collection and analysis to this modelling framework, we have produced a powerful tool to assess the kinetics of anaerobic micro-organisms, demonstrated here with C. kluyveri, in order further explore the potential of micro-organisms for chemicals production.

Application of iterative robust model-based optimal experimental design for the calibration of biocatalytic models.

The aim of model calibration is to estimate unique parameter values from available experimental data, here applied to a biocatalytic process. The traditional approach of first gathering data followed by performing a model calibration is inefficient, since the information gathered during experimentation is not actively used to optimize the experimental design. By applying an iterative robust model-based optimal experimental design, the limited amount of data collected is used to design additional informative experiments. The algorithm is used here to calibrate the initial reaction rate of an ω-transaminase catalyzed reaction in a more accurate way. The parameter confidence region estimated from the Fisher Information Matrix is compared with the likelihood confidence region, which is not only more accurate but also a computationally more expensive method. As a result, an important deviation between both approaches is found, confirming that linearization methods should be applied with care for nonlinear models. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1278-1293, 2017.