Real life Machine Learning Case Studies Data Analytics Platform used: http://letsexcel.in/apps/

1. Introduction PCA LDA PLS
Multivariate data analysis - set of statistical models that examine patterns in
multidimensional data by considering, at once, several data variables
• Doesn’t require you to make assumptions
• Can build predictive models to estimate value of unknown sample
Chemometrics is rapidly establishing itself as a tool in Pharmaceutical Industry
Transition from “quality-by-testing” to “quality-by-design”
FDA emphasized using PAT and QbD techniques in ICH Q8 guidelines
Pharmaceutical products and processes are complex systems by nature and can only
be effectively described by multi-factorial relationships
Multivariate data analysis comes into play - Development of calibration models to predict relevant
parameters and quality attributes in real-time
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Multivariate Data Analysis

2. Introduction PCA LDA PLS
PCA is a mathematical procedure that transforms a large set of variables into
a lower dimensional set of new variables designated as principal components.
Any data set, it is likely that the key information is contained in
• Dominating sources of variability
• other sources of variability (e.g. Noise)
Application in Pharmaceutical Industry:
• Can be used with other techniques (DoE or PLS) to determine in critical parameter
and study impact with CQA’s
• support investigation of root-cause of dissolution study
• Raw material classification
• Understanding differences in Positive and Negative control samples
• Support Process Analytical Technology Applications
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Principal Component Analysis

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Holt Melt Extrusion (HME)
Hot Melt Extrusion is used for
to uniformly disperse an API in a carrier
polymer:
• enhancement of solubility and
bioavailability of poorly soluble drugs
• control the delivery of an API
• taste masking of bitter API’s
Feeding API and polymer into an extruder
results in extrudate that contains stable,
uniformly dispersed, soluble API
HME has been used largely to manufacture
• granules
• pellets
• immediate and modified release tablets
• transmucosal/transdermal films
• implantable reservoir devices
Cautions:
• physicochemical properties of an API
are critical components of HME process
design
• API’s can undergo polymorphic changes
resulting in stability issues
• shear force and the cooling rate also
needs to be controlled

4. Introduction PCA LDA PLS
Paper Title: Data mining of solubility parameters for computational prediction of drug–
excipient miscibility
Predict the miscibility of drug and excipients using Hansen solubility parameters (HSPs)
• The energy from dispersion forces between molecules
• The energy from dipolar intermolecular force between molecules
• The energy from hydrogen bonds between molecules
In the study,
Drug Indomethacin’s miscibility was predicted with chemically diverse excipients using HSP
The miscibility was experimentally determined by Differential Scanning Calorimetry (DSC)
The algorithm results were then evaluated with DSC results and the prediction accuracy was
found at 94%
Paper reference: Alhalaweh, A., Alzghoul, A., & Kaialy, W. (2014). Data mining of solubility
parameters for computational prediction of drug–excipient miscibility. Drug development and
industrial pharmacy, 40(7), 904-909.
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Upload the file

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Model can be
saved for future
reference
Upload dataset
which needs to be
predicted

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18. Introduction PCA LDA PLS
LDA – Method to find linear combination of features that
characterizes or separates two or more classes of objects or events.
• Supervised Learning
• Closely related to ANOVA
Application
• Classification of in-process material based on Physicochemical
properties
• Quantitative structure activity relationship
• Classification of pass-fail batches
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Linear Discriminant Analysis

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Upload the file

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Check the tab, then select categorical and Numerical variables

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View the correlation between variables
+1: Positively Correlated
0: Not Correlated
-1: Negatively Correlated

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View Box plot of corresponding values of
the variables

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Save the Model
which can be used
for future prediction

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Upload the unknown
data file

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34. Introduction PCA LDA PLS
Technique that reduces the predictors to a smaller set of
uncorrelated components and performs least squares regression
on these components, instead of on the original data
Application in Pharmaceutical Industry :
• Widely used in Chemometrics and applied to spectroscopy data to obtain
measurements that enable real time release testing (PAT applications)
• Optimize dry granulation process – combine CMA and CPP to CQA’s
• Model the relationship between API and intermediate properties of a
formulation process
• Mine the historical data for a product and determine critical variables that
influence such variability
• Quantitative structure activity relationships
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Partial Least Square (PLS)

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Calibration and Test Data
of Sample from Raman
analysis of
Multicomponent Mixture
was clubbed together
•Since easyPLS
automatically divides
train and test samples
without bias
Mean Scatter Correction
was performed on the
spectra offline
Granulation drying stage
spectra is used as
unknown data
Bhavana, V., Chavan, R. B., Mannava, M. C., Nangia, A., & Shastri, N. R. (2019).
Quantification of niclosamide polymorphic forms–A comparative study by Raman,
NIR and MIR using chemometric techniques. Talanta, 199, 679-688.

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Upload the file

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Click at the
bottom to
select the data

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Validation
Select the RMSEP
Plot type

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Correlation Matrix
+1: Positively Correlated
0: Not Correlated
-1: Negatively Correlated

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Training data set

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Test data set

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The Spectra Profile of the data

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Model Summary

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Model can be
saved for future
reference

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Example
Upload dataset
which needs to be
predicted

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