Application of Box–Behnken Design to Optimize the Osmotic Drug Delivery System of Metoprolol Succinate and its In Vivo Evaluation in Beagle Dogs

Development and pharmacokinetic evaluation of osmotically controlled drug delivery system of Valganciclovir HCl for potential application in the treatment of CMV retinitis

Valganciclovir HCl (VGH) is the widely used drug for the treatment of cytomegalovirus (CMV) retinitis infection with an induction dose of 900 mg per oral (p.o.) twice a day and a maintenance dose of 900 mg (p.o.). This required dose of the drug also leads to multiple side effects due to repeated administration. The research was highlighted to develop, formulate, optimize, and evaluate single-core osmotic pump (SCOP) tablet of VGH with the dose of 450 mg to reduce dosing frequency and associated side effects. The decrease in dose also minimizes the hepatic and nephrotic load. The optimized batch of the formulation was subjected to comparative in vitro and in vivo evaluation. The tablet core composition is the primary influencer of the drug delivery fraction in a zero order, whereas the membrane characteristics control the drug release rate. In vivo pharmacokinetic studies revealed that the newly developed osmotic formulation has controlled zero-order release for 24 h with a single dose of 450 mg while the marketed formulation requires twice administration within 24 h to maintain the plasma concentration in the therapeutic window. The pharmacokinetic study demonstrated that the developed formulation has the area under curve (AUC) of 58.415 µg h/ml with single dose while the marketed formulation shows the AUC of about 37.903 µg h/ml and 31.983 µg h/ml for first and second dose, respectively. The large AUC demonstrates the extended release of drug with a single dose and effective plasma concentration. Hence, the developed formulation can be a promising option for the treatment of CMV retinitis with the minimum dose and dosing frequency.

Combining gene expression programming and genetic algorithm as a powerful hybrid modeling approach for pear rootstocks tissue culture media formulation

BACKGROUND

Predicting impact of plant tissue culture media components on explant proliferation is important especially in commercial scale for optimizing efficient culture media. Previous studies have focused on predicting the impact of media components on explant growth via conventional multi-layer perceptron neural networks (MLPNN) and Multiple Linear Regression (MLR) methods. So, there is an opportunity to find more efficient algorithms such as Radial Basis Function Neural Network (RBFNN) and Gene Expression Programming (GEP). Here, a novel algorithm, i.e. GEP which has not been previously applied in plant tissue culture researches was compared to RBFNN and MLR for the first time. Pear rootstocks (Pyrodwarf and OHF) were used as case studies on predicting the effect of minerals and some hormones in the culture medium on proliferation indices.

RESULTS

Generally, RBFNN and GEP showed extremely higher performance accuracy than the MLR. Moreover, GEP models as the most accurate models were optimized using genetic algorithm (GA). The improvement was mainly due to the RBFNN and GEP strong estimation capability and their superior tolerance to experimental noises or improbability.

CONCLUSIONS

GEP as the most robust and accurate prospecting procedure to achieve the highest proliferation quality and quantity has also the benefit of being easy to use.