Artificial Neural Networks to Optimize Formulation Components of a Fixed- Dose Combination of Rifampicin, Isoniazid and Pyrazinamide in a Microemulsion

Abstract

The aim of this study to design a stable microemulsion formulation to deliver a combination of rifampicin, isoniazid and pyrazinamide in quantities suitable for administration to a paediatric population. The chemical stability of rifampicin, isoniazid and pyrazinamide alone and in various combinations was investigated in different solvents, solubilizing agents and surfactants. An artificial neural network was used to model data from the stability studies and a sensitivity analysis was applied to optimize the selection of the formulation components. Imwitor 308 and Crillet 3, exhibiting the highest overall positive sensitivity were selected to formulate the stable microemulsion. Due to drug dose specifications and solubility limitations, the final formulation contained only rifampicin and isoniazid, since the solubility of pyrazinamide in the lipid and aqueous components of the microemulsion did not achieve the required dose. The stability and solubility of rifampicin were improved in the formulation. Solubilization of the rifampicin in the lipid droplets of the internal phase and lipophilic chains of the surfactants increased the quantity of rifampicin that can be incorporated, while protecting it from oxidative degradation and also limited its contact with isoniazid, which has been shown to affect its stability. The results of this study indicate that the Artificial Neural Network can be successfully used to optimize the choice of solvents, solubilizing agents and surfactants prior to formulation of the microemulsion, limiting the amount of experiments, thus reducing the costs during the preformulation study.

Artificial Neural Networks and Neuro-Fuzzy Models: Applications in Pharmaceutical Product Development

“…It came about that the ANNs prevalence over the regression models. Glass and coauthors [67] considered details of formulation of fixed-partition portion blends of rifampicin, isoniazid, and pyrazinamide in microemulsions. ANNs demonstrate that the dependability and affectability investigation was connected to advance the determination of details.…”

Artificial Neural Networks: Functioningandapplications in Pharmaceutical Industry

“…It addresses the multi-objective oriented concurrent optimization issues in the pharmaceutical industry to establish the relationship between reaction factors and insignificant factors [25]. The prediction of pharmaceutical responses in the polynomial equation and response surface methodology (RSM) has been broadly used as a part of formulation optimization [26][27] However; this prediction is small scale due to a low success rate of estimation.…”

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

“…were able to test 14 APIsafter optimizing the formulation base using self-organizing maps in order to expand the fast-release tablet database and study the effect of API on the tablet properties [50]. Moreover ANNs also have been successfully used in design of stable formulations for multiple active components, such as rifampicin and isoniazid microemulsions [51]. ANNs are certainly very useful in the preformulation design and would help reduce the cost and length of preformulation study.…”