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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 on theme: "A Practical Framework Toward Prediction of Breaking Force and Disintegration of Tablet Formulations Using Machine Learning Tools  Ilgaz Akseli, Jingjin."— Presentation transcript:

1 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 (January 2017) DOI: /j.xphs Copyright © 2016 American Pharmacists Association® Terms and Conditions

2 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  , DOI: ( /j.xphs ) Copyright © 2016 American Pharmacists Association® Terms and Conditions

3 Figure 2 Flow chart of typical genetic algorithm.
Journal of Pharmaceutical Sciences  , DOI: ( /j.xphs ) Copyright © 2016 American Pharmacists Association® Terms and Conditions

4 Figure 3 Flow chart of typical support vector machine.
Journal of Pharmaceutical Sciences  , DOI: ( /j.xphs ) Copyright © 2016 American Pharmacists Association® Terms and Conditions

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

6 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  , DOI: ( /j.xphs ) Copyright © 2016 American Pharmacists Association® Terms and Conditions

7 Figure 6 The preparation of data for developing models.
Journal of Pharmaceutical Sciences  , DOI: ( /j.xphs ) Copyright © 2016 American Pharmacists Association® Terms and Conditions

8 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  , DOI: ( /j.xphs ) Copyright © 2016 American Pharmacists Association® Terms and Conditions

9 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  , DOI: ( /j.xphs ) Copyright © 2016 American Pharmacists Association® Terms and Conditions

10 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  , DOI: ( /j.xphs ) Copyright © 2016 American Pharmacists Association® Terms and Conditions

11 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  , DOI: ( /j.xphs ) Copyright © 2016 American Pharmacists Association® Terms and Conditions

12 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  , DOI: ( /j.xphs ) Copyright © 2016 American Pharmacists Association® Terms and Conditions

13 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  , DOI: ( /j.xphs ) Copyright © 2016 American Pharmacists Association® Terms and Conditions

14 Figure 13 Modeling flow and implementation of the predicted models.
Journal of Pharmaceutical Sciences  , DOI: ( /j.xphs ) Copyright © 2016 American Pharmacists Association® Terms and Conditions

15 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  , DOI: ( /j.xphs ) Copyright © 2016 American Pharmacists Association® Terms and Conditions

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