A Practical Framework Toward Prediction of Breaking Force and Disintegration of Tablet Formulations Using Machine Learning Tools Ilgaz Akseli, Jingjin.

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Presentation transcript:

A Practical Framework Toward Prediction of Breaking Force and Disintegration of Tablet Formulations Using Machine Learning Tools Ilgaz Akseli, Jingjin Xie, Leon Schultz, Nadia Ladyzhynsky, Tommasina Bramante, Xiaorong He, Rich Deanne, Keith R. Horspool, Robert Schwabe Journal of Pharmaceutical Sciences Volume 106, Issue 1, Pages 234-247 (January 2017) DOI: 10.1016/j.xphs.2016.08.026 Copyright © 2016 American Pharmacists Association® Terms and Conditions

Figure 1 Basic working principle of neural network. The input parameters in the input layer are represented by neurons. The values are forwarded to the neurons in the hidden layer for evaluation with weight and bias. The final decision was transmitted to the output layer. Note: there could be multiple inputs, hidden layer, and outputs in a typical neural network. Journal of Pharmaceutical Sciences 2017 106, 234-247DOI: (10.1016/j.xphs.2016.08.026) Copyright © 2016 American Pharmacists Association® Terms and Conditions

Figure 2 Flow chart of typical genetic algorithm. Journal of Pharmaceutical Sciences 2017 106, 234-247DOI: (10.1016/j.xphs.2016.08.026) Copyright © 2016 American Pharmacists Association® Terms and Conditions

Figure 3 Flow chart of typical support vector machine. Journal of Pharmaceutical Sciences 2017 106, 234-247DOI: (10.1016/j.xphs.2016.08.026) Copyright © 2016 American Pharmacists Association® Terms and Conditions

Figure 4 The flow chart for a typical random forest algorithm. Journal of Pharmaceutical Sciences 2017 106, 234-247DOI: (10.1016/j.xphs.2016.08.026) Copyright © 2016 American Pharmacists Association® Terms and Conditions

Figure 5 Schematics of the experimental setup for measuring ultrasonic properties of tablets (a). An example of a waveform captured using the developed setup (b). Journal of Pharmaceutical Sciences 2017 106, 234-247DOI: (10.1016/j.xphs.2016.08.026) Copyright © 2016 American Pharmacists Association® Terms and Conditions

Figure 6 The preparation of data for developing models. Journal of Pharmaceutical Sciences 2017 106, 234-247DOI: (10.1016/j.xphs.2016.08.026) Copyright © 2016 American Pharmacists Association® Terms and Conditions

Figure 7 Predicted tablet breaking force of 10 randomly selected tablets from internal data samples. The blue line depicts the experimental results and red line with square markers depicts predicted results. Each data point represents the mean value of 10 experimental data samples. The error bars are the ±1 standard deviation. Journal of Pharmaceutical Sciences 2017 106, 234-247DOI: (10.1016/j.xphs.2016.08.026) Copyright © 2016 American Pharmacists Association® Terms and Conditions

Figure 8 Predicted tablet breaking force of 10 randomly selected tablets from external data samples. The blue line depicts the experimental results and red line with square markers depicts predicted results. Each data point represents the mean value of 10 experimental data samples. The error bars are the ±1 standard deviation. Journal of Pharmaceutical Sciences 2017 106, 234-247DOI: (10.1016/j.xphs.2016.08.026) Copyright © 2016 American Pharmacists Association® Terms and Conditions

Figure 9 Plotted relative importance of inputs in the prediction of tablet breaking force using internal data samples. The evaluation was achieved using internal data (cross-validation) (a) and external data as the test set (b). Journal of Pharmaceutical Sciences 2017 106, 234-247DOI: (10.1016/j.xphs.2016.08.026) Copyright © 2016 American Pharmacists Association® Terms and Conditions

Figure 10 Predicted disintegration time of 10 randomly selected tablets from internal data samples. The blue line depicts the experimental results and red line with square markers depicts predicted results. Each data point represents the mean value of 10 experimental data samples. The error bars are the ±1 standard deviation. Journal of Pharmaceutical Sciences 2017 106, 234-247DOI: (10.1016/j.xphs.2016.08.026) Copyright © 2016 American Pharmacists Association® Terms and Conditions

Figure 11 Predicted disintegration time of 10 randomly selected tablets from external data samples. The blue line depicts the experimental results and red line with square markers depicts predicted results. Each data point represents the mean value of 10 experimental data samples. The error bars are the ±1 standard deviation. Journal of Pharmaceutical Sciences 2017 106, 234-247DOI: (10.1016/j.xphs.2016.08.026) Copyright © 2016 American Pharmacists Association® Terms and Conditions

Figure 12 Plotted variable importance in the prediction of tablet disintegration time: relative importance using internal data for predictions (a) and relative importance using external data for predictions (b). Journal of Pharmaceutical Sciences 2017 106, 234-247DOI: (10.1016/j.xphs.2016.08.026) Copyright © 2016 American Pharmacists Association® Terms and Conditions

Figure 13 Modeling flow and implementation of the predicted models. Journal of Pharmaceutical Sciences 2017 106, 234-247DOI: (10.1016/j.xphs.2016.08.026) Copyright © 2016 American Pharmacists Association® Terms and Conditions

Figure 14 Comparisons among different disintegration times from experiments and predictions. The comparison between experimental disintegration time and predicted disintegration time using the tablet breaking force obtained from experiments (a) and the comparison between experimental disintegration time and predicted disintegration time using the predicted tablet breaking force obtained with RF model (b). Journal of Pharmaceutical Sciences 2017 106, 234-247DOI: (10.1016/j.xphs.2016.08.026) Copyright © 2016 American Pharmacists Association® Terms and Conditions