Prediction of Linearity and Non-Linearity in Pharmaceutical Optimization Studies with Python

  • T. Bhanuteja VIT University, Vellore, Tamil Nadu, India.
  • A. Lakshmana Rao V. V. Institute of Pharmaceutical Sciences, Gudlavalleru, Andhra Pradesh, India.
  • T.E.G.K. Murthy Bapatla College of Pharmacy, Bapatla, Andhra Pradesh, India.
  • T. Pallavi KL University, Guntur, Andhra Pradesh, India.
Keywords: Pharmaceutical formulations, Optimization, Variables, Experimental Design.


Novel simple user-friendly python programme was developed to predict linearity and non-linearity in pharmaceutical optimization. Optimization is the process of obtaining optimum formulation. There are independent and dependent variables in optimization techniques regarding pharmaceutical formulations. The number of levels of independent factor is usually selected based on the linear/ non-linear relationship existing between the dependent and independent variable. The programme is run after entering the independent and dependent variables. The program is used to detect the best fitted model based on the observed correlation between dependent and independent factors, to predict the outcome against the input (independent variables). The program output is the regression coefficients, regression equations, predicted dependent variable and standard error of point estimate. The model offering the low error of point estimate is assumed to be the best fitted model for the given data. The model is applied successfully for both linear and non-linear data.


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How to Cite
T. Bhanuteja, A. Lakshmana Rao, T.E.G.K. Murthy, T. Pallavi. Prediction of Linearity and Non-Linearity in Pharmaceutical Optimization Studies with Python. IJRAPS [Internet]. 2022May18 [cited 2024Mar.4];6(3):610-3. Available from: