Statistical applications in pharmaceutical and chemical field

PRINCIPAL COMPONENT ANALYSIS AND CLUSTER ANALYSIS AS STATISTICAL TOOLS FOR A MULTIVARIATE CHARACTERIZATION OF PHARMACEUTICAL RAW MATERIALS

12/14/2020

Abstract

Numerous factors contribute to the variability of the pharmaceutical industry processes and among these the raw materials play a primary role as they often come from different sources that use different production processes.
Raw materials characterization therefore plays a fundamental role in terms of Quality which, by its nature, is "the enemy of variability".
Multivariate Statistical Analysis of Data (MVDA), beyond of its complex mathematical, is here presented as a powerful and practical tool for the study and classification of raw materials.
Thanks to the use of multivariate techniques such as Principal Component Analysis (PCA) or Cluster Analysis (CA), it is possible to graphically represent each lot, defined by the values of the different analytical parameters that characterize it, as a point in a Cartesian diagram whose coordinates are the principal components. Since these components are built to intercept the variability in the data, these graphs reveal characteristics which would escape other types of surveys and therefore allow to catalog the lots based on the degree of intrinsic homogeneity that defines them and identify any anomalous behavior. This approach can therefore be used both initially, to characterize the incoming raw materials, and subsequently, in the case of any anomalies, to see how the raw materials of the batches under investigation were located compared to those that had not given problems.
The techniques that have been detailed here can also be extended to other typical situations in the pharmaceutical industry such as, for instance:
• comparative evaluation of finished product lots, for example for the purposes of Annual Product Quality Review (APQR).
• comparative evaluation of series of measurements performed by different operators, etc.
Once again, statistical methods show how it is possible to "simplify complexity" and extract practical and "ready-to-use" knowledge from complex datasets by capturing their information content.

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MULTIPLE LINEAR REGRESSION: A POWERFUL STATISTICAL TOOL TO UNDERSTAND AND IMPROVE APIs MANUFACTURING PROCESSES

10/26/2020

Abstract

It is known that, over time, all production processes tend to deviate from their initial conditions, and this happens because of many different reasons such as changes in materials, personnel, environment, etc.
This variability in the processes, which often goes unnoticed, is instead well intercepted by the data that Quality Control systematically collects for batch release purposes.
If these data are analyzed using Multiple Linear Regression (MLR), they reveal a lot regarding the manufacturing processes that generated them.
This product knowledge is of great practical use to the Company as it allows to:
• understand which are the parameters that most affect the product quality and how they interact with each other,
• establish whether the parameters that are controlled are really the ones we need or, instead, which ones would be better to consider,
• define / improve a product control strategy based on experimental data and quantitative models rather than speculation,
• define and graphically represent the design space (ICH Q8) inherent to the production process considered,
• identify possible ways to improve process performance and scientifically pilot this improvement,
• mitigate the Regulatory impact in case of changes.
In this post is detailed, step by step, how this ready-to-use process knowledge can be obtained from experimental data easily available.

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QUALITY METRICS AND DATA CONSISTENCY – Part 2

08/01/2020

Abstract

This second part is the continuation and completion of the previous one.
In this second post the points dealt with are:

• CASE STUDY 3:
  Capability Analysis: metrics for stable/mature processes (Cp and Cpk) and metrics for new processes (Pp and Ppk)
  
  
• CASE STUDIES 4/5:
  Probabilistic methods for a quick evaluation of the manufacturing process (Standardized Normal distribution, Poisson and Binomial distributions)
  
  
• CASE STUDY 6:
  - Processes non-normally distributed: impurities content, microbial counts, Particle Size Distributions (PSD), black particles (or black specs)
  - Normalization of non-normal data using mathematical transformations (logarithm, square root, reverse or reciprocal)
  - Johnson Transformations
  
  
• CASE STUDY 7:
  Multivariate methods: a different way to look at Quality Control data!
  
  
• CONCLUSIONS:
  - Quality Metrics are ease of use quantitative indicators that allow to intercept the variability of products / processes, quantify it and therefore ensure Quality.
  - Quality Metrics provide a “quantitative knowledge” of the process that allows to manage (preventing or justifying them) anomalous events (OOT, OOS, deviations, etc.) and communicate awareness in what is done and reliability in the processes used.
  All this is summed up in two words: ECONOMIC ADVANTAGE!

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QUALITY METRICS AND DATA CONSISTENCY – Part 1

08/01/2020

Abstract

In 2002, FDA launched the “Pharmaceutical cGMPs for the 21st Century” initiative with the aim of promoting a modern production approach, risk- and science-based. In 2015, always in that context, FDA asked the industry for inputs to define a “FDA Quality Metrics program” and in December 2019 announced that the implementation of a “Quality Metrics Program” has become a priority. Taking its cue from these FDA stimuli, this post and the next deal with the use of quantitative tools (or Quality Metrics) for understanding, monitoring and possibly improving pharmaceutical manufacturing processes. Real case studies that show the practical application of Quality Metrics to typical QA / QC topics are discussed and their statistical analysis detailed step by step. In practice it is shown how, from data normally available at the company, it is possible to easily extract useful information on the state of the processes and, above all, predict their possible outcome. It is exactly this combination of two aspects, one descriptive and the other predictive, which allows to really know a given process, control it and possibly even improve it. This knowledge is also useful for managing issues like OOS, OOT, deviations, etc. In fact, a poor knowledge of the process and of its quality indicators can lead to consider anomalous what is not. Given the number of Quality Metrics considered and the breadth of the case studies discussed, the topic was splitted in two parts. In this first post the points dealt with are:

• INTRODUCTION:
  What are Quality Metrics?
  Understanding variability as «Quality is inversely proportional to variability»
  State of the art
  
  
• PURPOSE:
  Create knowledge from available data and therefore:
  - manage possible anomalous or risky situations (OOS, OOT, deviations, etc.)
  - communicate awareness in what is done and in the reliability of the processes used
  
  
• CASE STUDY 1:
  Use of graphical methods
  Normal distribution, Normality test and Hypothesis test (α, P-value)
  Anscombe’s quartet: Graphics reveal data!
  
  
• CASE STUDY 2:
  Control Charts (I-MR Chart, Run Chart, Xbar Chart
  Structures in data: clustering, trending, etc.
  ANOVA, t-test, 2-Variances test and comparison between two series of data
  Example: evaluation of Supplier data
  Bland-Altman (or Tukey Mean-Difference) plot
  Central Limit Theorem: regardless of the shape of parent population, the distribution of means quickly approaches the normal distribution
  

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Basics of Statistical Risk Analysis

07/23/2020

Abstract

Risk is an essential part of daily life and even the society, as a whole, needs to take risks to continue growing and developing. Risk management is the process of identifying, analyzing and responding to risk factors. According to ICH Q9, Risk Assessment consists of the identification of hazards and the analysis and evaluation of risks associated with exposure to those hazards. Apart from a few exceptions (e.g., quantitative FTA), most of the risk analysis tools commonly used in the pharmaceutical field (e.g., FMEA, etc.) are basically subjective. However, in some cases, there are statistical techniques that allow us to assess the extent of the risk associated with some decisions. A typical example of this is, for example, the decision regarding the conformity, or not, of a lot based on the analysis of a sample of it. In such a decision two figures must be considered, the PRODUCER and the CUSTOMER (or CONSUMER), who run two different types of risk. The PRODUCER runs the risk of rejecting a “good lot” while the CUSTOMER (or CONSUMER) that of accepting a “not compliant” or a “poor quality” product. This post briefly addresses this topic.

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Regulatory Technical Writing - Labor Ergo Scribo!

07/17/2020

Abstract

Those who work must necessarily write! The aims are many: to communicate the results of one's studies, to give operating instructions, to respond to requests, etc. In all cases, however, if the message contained in the writing does not reach the recipient, the entire communication process is frustrated and the consequences of this can be significant. For this purpose, it is sufficient to think that at least a third of the time of an executive is spent in writing documents and that the quality of a given job, the choice to continue it, interrupt it, finance it, etc. are often determined solely by the document that illustrates it! The focus of this presentation is therefore to analyze the structure of a technical document and provide practical suggestions for its preparation. Writing, however, is still much more than this and therefore the presentation considers, more generally, the "what it means to write and how to do it".

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Solvents Classification using a Multivariate Approach: Cluster Analysis.

07/16/2018

Abstract

This post continues and completes the analysis of a database consisting of 64 solvents, each described by eight physico-chemical descriptors, initiated in the previous post. Subject matter of this study is the application of Cluster Analysis with the intention of finding groups in data, i.e., identifying which observations are alike and categorize them in groups, or clusters. As clustering is a broad set of techniques, this study focuses just on the so-called hard clustering methods, i.e., those assigning observations with similar properties to the same group and dissimilar data points to different groups. Two types of algorithms have been considered: hierarchical and partitional. Quite apart from the chosen technique, the experimental evidence indicates the presence, in the database, of: • three main groups, each consisting of individuals categorized as similar among them and • a few isolated individuals dissimilar from the others. A similar finding was also obtained in the previous post using 2d-contour plots. A closer examination of these three main groups of solvent shows a finer structure consisting of smaller groups of individuals highly similar among them (e.g., members of a given chemical family (e.g., alcohols, chlorinated hydrocarbons) or of chemical entities sharing common characteristics (e.g., aprotic dipolar solvents).

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Solvents Classification using a Multivariate Approach: Correlation and Principal Component Data Analysis.

06/01/2018

Abstract

The identification of data-driven criteria to make a conscious choice of solvents for practical applications is a rather old issue in the chemical field. Solvents, in fact, are mainly selected based on Chemist’s experience and intuition driven by parameters such as polarity, basicity and acidity. At least two research groups, already in 1985, approached the issue of solvent selection using multivariate statistical methods. These Scientists, using different databases, each based on different types of physicochemical descriptors, obtained different classification patterns. In this post, it has been chosen one of those databases and the data analysis process has been repeated detailing it systematically. This post deals with the first part of the process and it covers the intercorrelation among the physicochemical descriptors used to characterize the solvents under study and Principal Component Analysis. The correlation found allows to capture 70% of the initial data variability just using two principal components the first of which is related to “polarity/polarizability” and “lipophilicity” of molecules and the second to “strength of intermolecular forces”. The use of these two principal components suggests the possibility of grouping solvents into aggregates (or clusters) of similar individuals and this aspect will be covered in the following post.

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A different way to look at pharmaceutical Quality Control data: multivariate instead of univariate.

05/09/2018

Abstract

In the pharmaceutical industry, Quality Control (QC) data are typically arranged in data tables each row of which refers to a specific production lot and contains the results from different types of measurements (chemical and microbiological). As for each active chemical entity, or dosage form, there is a specific data table and since all lots listed therein are manufactured using the same approved process, the data table contains the “analytical fingerprint” of that specific manufacturing process. In spite of their table form, QC data are usually reviewed, evaluated and trended in a univariate mode, i.e., each type of data is analyzed individually using statistical tools such as control charts, box plots, etc. The dataset is therefore studied “ by columns ”. In this post, it is proposed a different way to analyze QC data, i.e., by using a multivariate approach that improves upon separate univariate analyses of each variable by using information about the relationships between the variables. Moreover, the combination of multivariate methods with the power of the programming language R and its unsurpassed graphic tools, allows analyzing data mainly relying on graphics and, as stated by Chambers et al., “there is no statistical tool that is as powerful as a well-chosen graph”. This post shows how using R for combined multivariate data analysis and visualization, the information contained in QC chemical dataset can be easily extracted and converted into “knowledge ready to use”.

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