Synchronized Seed Germination and Seedling Growth of Black Cumin

Black cumin (Nigella sativa) is an important medicinal plant in the pharmacological industry. It is cultivated on a commercial scale, but its seeds have a slow, unsynchronized germination rate. Enhancing seed germination is crucial for improving the production of black cumin. The influence of presowing treatments [gibberellic acid (GA3), potassium nitrate, salicylic acid, and stratification at 4° C] on seed germination was assessed. Seed germination was determined daily for 30 days, and germination parameters, including final germination percentage (FGP), corrected germination rate, number of days to reach 50% of FGP, and seedling length vigor index, were evaluated. Endogenous contents of GA3 and abscisic acid (ABA) in nonstratified and stratified seeds were estimated using high-performance liquid chromatography (HPLC) and seedling growth was determined in 45-day-old seedlings. All presowing treatments tended to boost early germination for the first 10 days compared with the control. Low concentrations of GA3 at 0.25 g·L-1 also increased FGP (80%) compared with the control group (65.55%). Stratification for 4 weeks provided the greatest FGP value at 95.56%, and stratification for 3 weeks proved to be the most effective treatment for optimal seedling growth. Sodium dodecyl sulphate–polyacrylamide gel electrophoresis patterns of stratified seeds revealed the alteration in intensities of 13 bands and the appearance of a new band (180 kDa) indicating a change in the synthesis of proteins during stratification. Moreover, stratification modulated the endogenous GA3 and ABA contents of black cumin seeds, which alleviated the physiological dormancy and resulted in high and synchronized seed germination

A pragmatic approach to the design of population pharmacokinetic studies

The publication of a seminal article on nonlinear mixed-effect modeling led to a revolution in pharmacokinetics (PKs) with the introduction of the population approach. Since then, interest in obtaining accurate and precise estimates of population PK parameters has led to work on population PK study design that extended previous work on optimal sampling designs for individual PK parameter estimation. The issues and developments in the design of population PK studies are reviewed as a prelude to investigating, via simulation, the performance of 2 approaches (population Fisher information matrix D-optimal design and informative block [profile] randomized [IBR] design) for designing population PK studies. The results of our simulation study indicate that the designs based on the 2 approaches yielded efficient parameter estimates. The designs based on the 2 approaches performed similarly, and in some cases designs based on the IBR approach were slightly better. The ease with which the IBR designs can be generated makes them preferable in drug development, where pragmatism and time are of great consideration. We, therefore, refer to the IBR designs as pragmatic designs. Pragmatic designs that achieve high efficiency in the estimation parameters should be used in the design of population PK studies, and simulation should be used to determine the efficiency of the designs

Simultaneous population optimal design for pharmacokinetic-pharmacodynamic experiments

Multiple outputs or measurement types are commonly gathered in biological experiments. Often, these experiments are expensive (such as clinical drug trials) or require careful design to achieve the desired information content. Optimal experimental design protocols could help alleviate the cost and increase the accuracy of these experiments. In general, optimal design techniques ignore between-individual variability, but even work that incorporates it (population optimal design) has treated simultaneous multiple output experiments separately by computing the optimal design sequentially, first finding the optimal design for one output (eg, a pharmacokinetic [PK] measurement) and then determining the design for the second output (eg, a pharmacodynamic [PD] measurement). Theoretically, this procedure can lead to biased and imprecise results when the second model parameters are also included in the first model (as in PK-PD models). We present methods and tools for simultaneous population D-optimal experimental designs, which simultaneously compute the design of multiple output experiments, allowing for correlation between model parameters. We then apply these methods to simulated PK-PD experiments. We compare the new simultaneous designs to sequential designs that first compute the PK design, fix the PK parameters, and then compute the PD design in an experiment. We find that both population designs yield similar results in designs for low sample number experiments, with simultaneous designs being possibly superior in situations in which the number of samples is unevenly distributed between outputs. Simultaneous population D-optimality is a potentially useful tool in the emerging field of experimental design