Chemometrics for Analysis of NIR Spectra on Pharmaceutical Oral Dosages

Transcription

1 Chemometrics for Analysis of NIR Spectra on Pharmaceutical Oral Dosages William Welsh, Sastry Isukapalli, Rodolfo Romañach, Bozena Kohn-Michniak, Alberto Cuitino, Fernando Muzzio NATIONAL SCIENCE FOUNDATION A I R ACCELERATING INNOCATION RESEARCH PROGRAM NON-DESTRUCTIVE CHARACTERIZATION OF PHARMACEUTICAL PRODUCTS NSF ENGINEERING RESEARCH CENTER FOR STRUCTURE ORGANIC PARTICULATE SYSTEMS 1

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. 2

3 Diffuse NIR Spectrometry Detector for Reflectance NIR spectra tablet Detector for Transmission Use Chemometrics to Correlate NIR spectral features to sample properties 3

4 NIR Dataset Reflectance and Transmission Spectra 47 samples: API (acetaminophen); lactose; MgStearate (1%) Two dependent variables: %API (-30%), and Compaction Force (7 - kn) Output t 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 4

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 regression clustering group 1 group 2 classification inactive drug active drug Predict ted Value Actual Value 5

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

7 Clustering with Pooled Reflectance and Transmission Data SNV: clustering with 12 clusters [case snv-r-t] 1 Compac ction Force (kn) % Active Ingredient Unsupervised clustering Distinct clusters for low CF cases and for low %API cases Single sub-cluster for high CF-%API cases 7

8 Hierarchical Cluster Analysis Iterative agglomeration of clusters through distance similarity measures To estimate control variables from experimental conditions (CF, %API) Clustering of samples based on their spectra only moderately correlated to CF-%API groupings g Distance SF-1-17 SF SF-1-17 SF-1-17 SF-16-1 SF SF SF SF--17 SF-16-1 SF-24- SF-16-1 SF-1-17 SF SF--16 SF--17 SF SF SF--17 SF-30- SF-2- SF--17 SF SF-26- SF SF-24- SF SF--16 SF SF SF SF SF SF-26- SF SF SF SF SF SF--17 SF-- SF SF SF SF--12 SF SF SF--12 SF SF-1-16 SF SF-2-12 SF SF-2-13 SF SF SF-12- SF-1-11 SF--11 SF SF-2-12 SF SF SF--13 SF-12-9 SF-- SF-- SF SF SF-- SF-- SF-22- SF-1- SF-16- SF-- SF-16- SF Cluster Nodes

9 LASSO, Ridge, and Elastic Net Regressions Ordinary least squares regression models Y ƒ(x i ) 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 9

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 CART Regression Trees % Active Ingred dient (predicted by CART) pr redicted Compactio on Force (kn) (pre edicted by CART predicte ed Transmission Data only Regression Tree Modeling [case: snv-t] % Active Ingredient % Active Ingredient (actual) Reflectance Data only Regression Tree Modeling [case: snv-r] % Active Ingredient % Active Ingredient (actual) 30 All Data Pooled (including derivatives) Regression Tree Modeling [case: all-together] 35 % Active Ingredient % Active Ingredient (actual) actual actual actual Regression Tree Modeling [case: snv-t] Compaction Force 5 5 Compaction Force (kn) (actual) Regression Tree Modeling [case: snv-r] Compaction Force 5 5 Compaction Force (kn) (actual) Regression Tree Modeling [case: all-together] 5 5 Compaction Force (kn) (actual)

12 LASSO Regression 30 Predicted % Active Ingre edient predict ted All Data Pooled Transmission Data only Reflectance Data only (including derivatives) % A.I. LASSO Regression [Baseline T] edient Predicted % Active Ingre 30 LASSO Regression [SNV R] edient Predicted % Active Ingre 30 LASSO Regression [B/SNV/G1/G2: R + T combined] 1 16 Predicted Compac ction Force (kn) predic cted 12 Actual % Active Ingredient Compaction Force LASSO Regression [Baseline T] Actual % Active Ingredient LASSO Regression [SNV R] 30 Actual % Active Ingredient actual actual actual ction Force (kn) Predicted Compac paction Force (kn) Predicted Comp LASSO Regression [B/SNV/G1/G2: R + T combined] Actual Compaction Force (kn) Actual Compaction Force (kn) Actual Compaction Force (kn)

13 1 Actual [cross-validation] LASSO Regression [B/SNV/G1/G2: R only] Predicted oactual o predicted R only Reflectance Data LASSO Regression e paction (kn) Force Compacti % Active Ingredient 1 Compaction Forc ce (kn) on Force 16 Compactio 12 %API LASSO Regression [B/SNV/G1/G2: T only] Actual [cross-validation] Predicted oactual o predicted Tonly Transmission Data Simultaneous prediction of %API and Compaction Force Improvement when both reflectance and transmission data are used rce (kn) n Force Compaction For Co LASSO Regression [B/SNV/G1/G2: R + T combined] Actual [cross-validation] Predicted oactual o predicted Pooled Data R and T %API % Active Ingredient %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 Acknowledgments People 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