Process systems engineering tools in the pharmaceutical industry

Abstract

The purpose of this paper is to provide a summary of the current state of the application of process systems engineering tools in the pharmaceutical industry. In this paper, we present the compiled results of an industrial questionnaire submitted to pharmaceutical industry professionals. The topics covered in the questionnaire include process analytics, process monitoring, plant-wide information systems, unit operation modeling, quality control, and process optimization. A futuristic view of what process systems engineering tools will enable the pharmaceutical industry will be also be presented. While the industry is regularly using the traditional Design of Experiments approach to identify key parameters and to define control spaces, these approaches result in passive control strategies that do not attempt to compensate for disturbances. Special new approaches are needed for batch processes due to their essential dependence on time-varying conditions. Lastly, we briefly describe a novel data driven modeling approach, called Design of Dynamic Experiments that enables the optimization of batch processes with respect to time-varying conditions through an example of a simulated chemical reaction process. Many more approaches of this type are needed for the calculation of the design and control spaces of the process, and the effective design of feedback systems.

Introduction

Almost a decade has elapsed since the FDA publication “Pharmaceutical cGMPs for the 21st Century: A Risk-Based Approach” and almost eight years since “PAT – A Framework for innovative Pharmaceutical Manufacturing and Quality Assurance” were issued. Much progress and innovation in pharmaceutical manufacturing has occurred since the publication of these landmark documents. For example, pharmaceutical companies have readily adopted in-process measurements systems, such as near infrared spectroscopy for concentration, and focused beam reflectance measurements for estimation of particle size distribution. The application of multivariate process monitoring for real time fault detection and isolation has also found its way into pharmaceutical manufacturing. The industry has moved away from quality control strategies based on uni-variate parameters specifications, and towards the multivariate design space approach. While, tremendous progress has been achieved in the decade, there is work to be done to realize the full potential of the process systems engineering (PSE) toolbox.

The purpose of the paper is to describe the current state of the art of the application of PSE tools in the pharmaceutical industry. The sub areas of PSE discussed in this work are process analytical technology (PAT) measurement systems, process monitoring, plant wide information technology systems, process control, modeling, and optimization methodologies. This paper focuses on PSE applications primarily related to active pharmaceutical ingredient (API), and solid oral dosage manufacturing. Details on the application of PAT measurement systems, and process control in biologics are out of scope of this work, for readers interested in biologics PSE applications we are listing a few relevant review papers (Junker and Wang, 2006, Schugerl, 2001).

To augment information available in the open literature, we conducted an industrial benchmarking survey on the above-mentioned PSE sub areas that contained twenty-one questions in total¹. The survey was submitted to current pharmaceutical industry professionals in all areas of the industry: active pharmaceutical ingredient, solid oral dosage, and biologics, in both development and manufacturing. The companies that submitted responses to the survey are listed in Table 1.

The paper is organized as follows, for each of the sub areas of PSE covered; we provide a brief background on how PSE tools are currently used in the pharmaceutical industry. Where possible, literature references have been provided, with a preference towards papers published by pharmaceutical industry professionals. The questionnaire results pertaining to each PSE area are presented at the end of each section. We then discuss the impact of increased out-sourcing of product development and manufacturing and the prospect of continuous processes on the future utilization of PSE tools. Lastly, we present the application of a novel method for batch process optimization called dynamic design of experiments. A simulated API synthesis reaction process is used to explain the method.