A hybrid framework of artificial intelligence-based neural network model (ANN) and central composite design (CCD) in quality by design formulation development of orodispersible moxifloxacin tablets: Physicochemical evaluation, compaction analysis, and its in-silico PBPK modeling

Publication: J Drug Deliv Sci Technol
Software: GastroPlus®


The objective was to apply the CCD-ANN system to design a QbD-based orodispersible tablet (ODT) of Moxifloxacin. The data sets of the trial formulations obtained from CCD were utilized for the training of the ANN-based model to obtain the optimized formulation. Three independent variables i.e. Acdisol, sodium bicarbonate, and compression force were selected to study their effect on critical dependent variables. The response variables were used for the supervised training using Holdback input randomization to develop a multi-layer perceptron (MLP) based ANN model for Moxifloxacin 400 mg ODT. The optimized formulation A generated by the prediction profiler was cross-validated by the CCD-based optimized formulation B using graphical and numerical methods. ANOVA findings revealed that there is no significant difference between the formulations A and B. These formulations were subjected to accelerated stability and shelf life was 31.380 and 25.475 months respectively. In-silico PBPK model exhibited comparative relative bioavailability of formulations A and B with the reference Moxifloxacin IR tablet. It can be concluded that ANN supported by CCD is a multi-objective simultaneous optimization technique, for pharmaceutical product development, especially when there is existing a nonlinear relationship between input and critical response variables.

By Momina Zarish Khan, Rabia Ismail Yousuf, Muhammad Harris Shoaib, Farrukh Rafiq Ahmed, Muhammad Talha Saleem, Fahad Siddiqui & Syed Adnan Rizvi