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Chemometrics for Analysis of NIR Spectra

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Presentation on theme: "Chemometrics for Analysis of NIR Spectra"— Presentation transcript:

1 Chemometrics for Analysis of NIR Spectra
on Pharmaceutical Oral Dosages William Welsh, Sastry Isukapalli, Rodolfo Romañach, Bozena Kohn-Michniak, Alberto Cuitino, Fernando Muzzio

2 Destructive vs Non-Destructive Testing
Tablets – the most common drug delivery vehicle Dissolution tests: Key requirement for the development, registration, approval, and quality control of these tablets. Disadvantages of dissolution: Destructive, time consuming, expensive, and tedious. Need a fast, non-destructive and easy technique for tablet characterization. Near IR (NIR) spectroscopy serves this purpose.  Shearing Stress = a force that causes layers or parts of a substance to slide upon each other in opposite directions

3 Diffuse NIR Spectrometry
Detector for Reflectance NIR spectra tablet Page 22, Handbook of Near Infrared Analysis: Light that is scattered from the sample toward a detector on the opposite side of the sample is said to be detected in transmission. Light that is scattered from the sample toward a detector on the same side of the sample is said to be detected in reflection. If the angle of reflection is equal to the angle of incidence, the reflection is said to be specular. Radiation reflected at all other angles is diffuse. The sum of the specularly and diffusely reflected radiation is the remitted radiation. For samples with a matte finish, especially powdered samples, the specularly reflected radiation is generally of low intensity. Hence measurement of the radiation from this type of sample is frequently known as diffuse reflection spectrometry. Detector for Transmission Use Chemometrics to Correlate NIR spectral features to sample properties

4 NIR Dataset Reflectance and Transmission Spectra Chemometric Models
47 samples: API (acetaminophen); lactose; MgStearate (1%) Two dependent variables: %API (10-30%), and Compaction Force ( kN) Output data: Reflectance (R), Transmittance (T); Pooled R & T data, 1st and 2nd derivatives Chemometric Models Correlate %API and CF with NIR spectral data Standard Models: HCA, Regression Trees (CART), PLS Approaches for improved predictive ability: LASSO Regression, Ridge regression, Elastic Nets, Bayesian models

5 Chemometrics – Two General Approaches
Unsupervised Principal Component Analysis (PCA) Hierarchical Cluster Analysis (HCA) Supervised Partial Least Squares (PLS) regression Classification and Regression Tree (CART) Support Vector Machine (SVM) Artificial Neural Network (ANN) LASSO, Ridge Regression, and Elastic Nets clustering group 1 group 2 active drug inactive classification Predicted Value Actual Value regression 5 5

6 Chemometrics Many Common Methods Many Uses
Data Exploration & Clustering Classification & Discrimination Quantitative Prediction & Correlation Many Uses PCA HCA kNN ANN SVR Cooman’s plot OUTLIERS CLASS 1 CLASS 2 CLASS 1&2 ? Many Common Methods

7 Clustering with Pooled Reflectance and Transmission Data
Unsupervised clustering Distinct clusters for low CF cases and for low %API cases Single sub-cluster for high CF-%API cases

8 Hierarchical Cluster Analysis
Iterative agglomeration of clusters through “distance” similarity measures To estimate control variables from experimental conditions (CF, %API) Cluster Nodes Clustering of samples based on their spectra only moderately correlated to CF-%API groupings “Distance”

9 LASSO, Ridge, and Elastic Net Regressions
Ordinary ‘least squares’ regression models Y ≈ ƒ(Xi) tend to overfit the data, leading to poor predictive ability. The problem is over-determined: many more variables (spectral data) than solutions (property values; samples). A process called Regularization can be introduced to prevent overfitting and to provide models that are predictive (low bias) & robust (low variance). Examples of this approach are LASSO (Least Absolute Shrinkage and Selection Operator) Ridge regression Elastic Nets

10 LASSO, Ridge, and Elastic Net Regressions
Methods like LASSO penalize over-complex models, thereby leading to models with fewer terms (Occam’s Razor). Occam’s Razor: All other things being equal, simpler solutions are preferred over complex ones. Simpler models discern “hidden” structure, and may thus have better predictive performance. LASSO models are more easily interpretable; fewer variables.

11 All Data Pooled (including derivatives) Transmission Data only
CART Regression Trees All Data Pooled (including derivatives) Transmission Data only Reflectance Data only % Active Ingredient % Active Ingredient % Active Ingredient predicted actual actual actual Compaction Force Compaction Force predicted

12 All Data Pooled (including derivatives) Transmission Data only
LASSO Regression All Data Pooled (including derivatives) Transmission Data only Reflectance Data only LASSO Regression [Baseline T] 10 15 20 25 30 Actual % Active Ingredient Predicted % Active Ingredient LASSO Regression [SNV R] 10 15 20 25 30 Actual % Active Ingredient Predicted % Active Ingredient LASSO Regression [B/SNV/G1/G2: R + T combined] % A.I. 30 25 predicted Predicted % Active Ingredient 20 15 10 10 15 actual 20 30 25 Actual % Active Ingredient actual actual 8 10 12 14 16 18 Actual Compaction Force (kN) Predicted Compaction Force (kN) LASSO Regression [SNV R] 8 10 12 14 16 18 Actual Compaction Force (kN) Predicted Compaction Force (kN) LASSO Regression [B/SNV/G1/G2: R + T combined] 8 10 12 14 16 18 Actual Compaction Force (kN) Predicted Compaction Force (kN) LASSO Regression [Baseline T] Compaction Force predicted

13 LASSO Regression Simultaneous prediction of %API and Compaction Force
20 LASSO Regression [B/SNV/G1/G2: R only] o actual o predicted Actual [cross-validation] Predicted R only Reflectance Data 18 Simultaneous prediction of %API and Compaction Force Improvement when both reflectance and transmission data are used 16 14 Compaction Force Compaction Force (kN) 12 10 8 6 5 10 15 %API 20 25 30 35 5 10 15 20 25 30 35 % Active Ingredient 6 8 12 14 16 18 Compaction Force (kN) LASSO Regression [B/SNV/G1/G2: T only] Actual [cross-validation] % Active Ingredient 20 LASSO Regression [B/SNV/G1/G2: R + T combined] o actual o predicted Predicted T only o actual o predicted Actual [cross-validation] Predicted Pooled Data R and T Transmission Data 18 16 Compaction Force Compaction Force 14 Compaction Force (kN) 12 10 8 6 5 10 15 20 25 30 %API %API % Active Ingredient

14 Concluding Remarks Novel chemometrics methods can build relationships between non-destructive test data and biorelevant properties of tablets, including dissolution Pooling reflectance & transmission data advantageous LASSO regression delivers substantial model improvements, and identifies subset of information-rich variables (NIR features) Methods used in the analysis are easily scalable Thousands of dimensions/millions of rows Open source tool kits

15 Thank You Acknowledgments People Funding: NSF-AIR
Rutgers: Bozena Michniak-Kohn, Alberto Cuitino, Fernando Muzzio UPR-Mayaguez: Rodolfo J. Romañach Team at Rutgers’ ERC-Structured Organic Particulate Systems Snowdon: Sastry Isukapalli Funding: NSF-AIR Thank You

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