Multivariate Analysis in the Pharmaceutical Industry

Ana Patricia Ferreira,Jose C. Menezes,Mike Tobyn

• Publisher: Academic Press
• Year: 2018
• ISBN:
  9780128110669
• Topic: Medicine
• Subtopic: Pharmacology

Multivariate Analysis in the Pharmaceutical Industry provides industry practitioners with guidance on multivariate data methods and their applications over the lifecycle of a pharmaceutical product, from process development, to routine manufacturing, focusing on the challenges specific to each step. It includes an overview of regulatory guidance specific to the use of these methods, along with perspectives on the applications of these methods that allow for testing, monitoring and controlling products and processes. The book seeks to put multivariate analysis into a pharmaceutical context for the benefit of pharmaceutical practitioners, potential practitioners, managers and regulators.

Users will find a resources that addresses an unmet need on how pharmaceutical industry professionals can extract value from data that is routinely collected on products and processes, especially as these techniques become more widely used, and ultimately, expected by regulators.

• Targets pharmaceutical industry practitioners and regulatory staff by addressing industry specific challenges
• Includes case studies from different pharmaceutical companies and across product lifecycle of to introduce readers to the breadth of applications
• Contains information on the current regulatory framework which will shape how multivariate analysis (MVA) is used in years to come

Section II

Applications in Pharmaceutical Development and Manufacturing

Outline

• Chapter 7 Multivariate Analysis Supporting Pharmaceutical Research
• Chapter 8 Multivariate Data Analysis for Enhancing Process Understanding, Monitoring, and Control—Active Pharmaceutical Ingredient Manufacturing Case Studies
• Chapter 9 Applications of MVDA and PAT for Drug Product Development and Manufacturing
• Chapter 10 Applications of Multivariate Analysis to Monitor and Predict Pharmaceutical Materials Properties
• Chapter 11 Mining Information From Developmental Data
• Chapter 12 A Systematic Approach to Process Data Analytics in Pharmaceutical Manufacturing: The Data Analytics Triangle and Its Application to the Manufacturing of a Monoclonal Antibody
• Chapter 13 Model Maintenance
• Chapter 14 Lifecycle Management of PAT Procedures: Applications to Batch and Continuous Processes
• Chapter 15 Applications of MVA for Product Quality Management: Continued Process Verification and Continuous Improvement
• Chapter 16 The Role of Multivariate Statistical Process Control in the Pharma Industry
• Chapter 17 Application of Multivariate Process Modeling for Monitoring and Control Applications in Continuous Pharmaceutical Manufacturing

Chapter 7

Multivariate Analysis Supporting Pharmaceutical Research

Johan Bøtker and Jukka Rantanen, University of Copenhagen, Copenhagen, Denmark

Abstract

The challenge of product design with pharmaceuticals is characterized by large diversity—compounds covering broadly the chemical space need to be formulated and manufactured at a large scale for specific routes of administration. This means that designing a pharmaceutical product is a complex interplay of chemistry and physics spiced up with a careful consideration of biology and engineering aspects. A path to a successful product can be difficult to follow and related decision-making processes can be challenging to document. Part of this decision-making is an experience-based and nonlinear process resulting from a huge amount of data. This situation will be further complicated in the future by the increasing need for more personalized medicinal products. This chapter is intended to provide the reader with an overview of the applications where multivariate analysis can be encountered. Many of these specific applications will be discussed in more detail in the following chapters.

Keywords

Process analytical technologies (PAT); Quality by Design (QbD); high-throughput screening (HTS); chemical imaging; quantitative structure–activity relationship (QSAR)

7.1 Overview of Multivariate Analysis as a Part of Pharmaceutical Product Design

The classical approach to the development of pharmaceutical products is all too often based on univariate thinking. There might be a huge arsenal of state-of-the-art analytical tools and a broad expert team, but still at the end of the day, the critical decision-making will be performed using experience-based and intuitive processes. Documentation of this type of decision-making can turn out to be a lengthy and difficult-to-follow process. An alternative approach would be the utilization of well-defined multivariate methods as supportive decision-making tools for combining the huge amount of data and precisely documenting the extraction of information from the huge amount of development data. The pharmaceutical business area is often a late adaptor of new thinking, but there are more and more published examples of the use of multivariate analysis (MVA) as a part of the drug development process. This type of holistic approach is a crucial element of the Quality by Design (QbD)-based development process. This chapter will introduce a broad range of examples of using MVA as a part of innovative product design.

Different MVA methods have a huge potential for diverse use throughout the whole drug development process. Examples of the MVA-based approaches are often related to exploring the chemical space and range from early phases of development (physicochemical parameters) to commercial manufacturing (process analytics). Physicochemical parameters, such as aqueous solubility, partitioning between different phases (e.g., logP), and biological barrier permeation are examples of critical parameters that can be explored and predicted using the multivariate approach. Classical examples are based on the use of molecular descriptors for cluster analysis and predictive models. Quantitative structure–activity relationship (QSAR) models can be used for predicting the relationship between a high number of molecular descriptors and the selected response (e.g., aqueous solubility). Bergström et al. (2003) utilized this approach for exploring the chemical space of the orally administered drugs selected from the World Health Organization (WHO)’s list of essential drugs. In this work (Bergström et al., 2003), the prediction of both aqueous drug solubility and drug permeability based on multivariate tools is suggested as an approach supporting the early phase decision-making related to the oral drug absorption. Multivariate methods will be increasingly important when aiming for personalized therapies. A central element of precision medicine will be the incorporation of individual characteristics, such as genomic and metabolic capacity-related data. This type of big data can be explored using basic multivariate methods (Trygg, Holmes, & Lundstedt, 2007). Similar relationships can be used as a part of the risk management strategy (e.g., evaluation of toxicity), reducing the number of experiments (e.g., evaluation of solvent similarity as a part of experimental polymorph screening), and identifying the relationship between material properties and processing behavior (quantitative structure–property relationship, QSPR).

MVA is a well-established part of many analytical methods and chemical imaging is an example of an analytical method, where the amount of data can rapidly become enormous and the logical approach to extract relevant information is the use of multivariate methods (Ravn, Skibsted, & Bro, 2008). Another trend in the drug development process is the increasing use of different high-throughput screening (HTS) methods. The HTS approach is based on maximizing the number of experiments by performing them in a smaller and smaller scale, e.g., using well-plate-based technologies (Aaltonen et al., 2009). This approach coupled with faster and faster analytical methods results again in a huge amount of data, which is underpinning the need for multivariate methods. It can be a very efficient approach to start by clustering a large experimental data set with MVA and identifying trends and main factors affecting the system behavior. At the other end of the broader drug development work, process analytics is an area where multivariate methods are becoming a standard solution. Near-infrared (NIR) spectroscopy is a well-established method for understan...