A precision medicine approach to personalized prescribing using genetic and nongenetic factors for clinical decision-making.

Screening potential drug-drug interactions, drug-gene interactions, contraindications, and other factors is crucial in clinical practice. However, implementing these screening concepts in real-world settings poses challenges. This work proposes an approach towards precision medicine that combines genetic and nongenetic factors to facilitate clinical decision-making.

Determination of paracetamol and orphenadrine citrate in pharmaceutical tablets by modeling of spectrophotometric data using partial least-squares and artificial neural networks.

The estimation of paracetamol and orphenadrine citrate in a multicomponent pharmaceutical dosage form by spectrophotometric method has been reported. Because of highly interference in the spectra and the presence of non-linearity caused by the analyte concentrations which deviate from Beer and Lambert's law, partial least-squares (PLS) and artificial neural networks (ANN) techniques were used for the calibration. A validation set of spiked samples was employed for testing the accuracy and precision of the methods.