With the cost of developing a new molecule estimated to have reached $1 billion, expiring patents, increasing competition among generics manufacturers and the growing power of low-cost manufacturing countries, almost all players in the pharmaceutical value chain are facing unprecedented pressures to reduce costs. At the same time, a growing middle class in many developing nations, an aging demographic across the western world, and rapid advances in technology offer massive opportunities for the industry.
Pharmaceutical and biopharmaceutical manufacturing includes products from raw material identification and characterization, chemical manufacture of intermediates, and manufacturing steps to the measurement of content uniformity. Moreover, the US FDA, European Union and other regulatory bodies have issued guidelines with a focus on Good Manufacturing Practices for the 21st Century and process analytical technology throughout the product development lifecycle.
Multivariate analysis tools can be used by pharmaceutical manufacturers across the entire product lifecycle from development through to production, including:
With the average time of drug discovery estimated to be four years, pharmaceutical companies must find ways of reducing this time in order to bring new discoveries to market before their competition. Each year of discovery results in approximately 80 million dollars of research effort in the overall scheme of development. Being able to reduce this time with high throughput analysis coupled with state of the art multivariate analysis will help realize new drugs in a faster and more scientifically understood way.
The second stage of new drug development is clinical trials. This stage accounts for 7 years in the development cycle and is by far the most time consuming. Multivariate methods have been relatively underutilized to date for clinical trial data, but the use of methods such as L-PLS allow data analysts to not only capture traditional patient information on drug efficacy, but also allow the incorporation of demographic and patient history into the model to provide an overall picture of the drugs action and also any effects that may be related to demographic and social status information. Multivariate analysis tools offer the clinical trials statistician the necessary tools for targeting certain groups and better understanding the action of new drugs on a variety of people and may lead to more scientific, risk based approaches to better trials. The use of Designed Experiments is also a key aspect of good clinical trials.
The tools of multivariate analysis can be used to provide compelling evidence of drug efficacy and action. Any further data can help reduce the time for drug registration, which currently accounts for approximately 2 years in the development lifecycle.
Real-time measurements using advanced control techniques facilitate online prediction and classification of critical process parameters (CPP’s) that most influence critical quality attributes (CQA’s). This allows the definition of the process signature, i.e. the path a process should take to lead to the desired state of the product. If deviations are detected, the process can be adjusted in real-time within the design space to bring the process back into a state of control, and hence maintaining quality. The ability to make measurements in real-time can also reduce delays in manufacturing as materials can be released to the next unit operation without the need for off-line analysis and can also minimize waste and energy consumption allowing a manufacturer to optimize equipment usage and increase production without factory expansion.
A process centric approach to quality starts at the early design stages (right back to drug discovery and formulation) of the process and continues throughout the entire life-cycle of a product. The use of PAT tools and in particular multivariate analysis has been isolated in the PAT framework guidance as being a vital tool for gaining process understanding, which therefore leads to less regulatory oversight. Real-time prediction and classification of products and processes brings manufacturers one step closer to mastering their process and product control and also points to ways of implementing continuous improvement initiatives.
Gone are the days of three batch validation procedures that cannot continuously monitor the performance of a process for manufacturing quality drug products. Continuous verification and process validation using modern analysis technology and scientific, risk based approaches are now seen as the future. Multivariate models are key here in defining a Pharmaceutical Quality System (PQS), which captures real time data, detects deviations and allows a manufacturer to use the data obtained to improve. This follows the philosophy Define, Design Analyse, Implement Improve through the guiding principles of ICH Q8, Q9 and Q10.
As pioneers in MVA, our software has been tried and tested in the pharmaceutical and biopharmaceutical sectors for over 25 years, used by both large innovator companies through to small and mid-sized generics manufacturers. Our solutions offer the power of advanced analysis and predictions but are elegantly simple and easy to use, with the flexibility and adaptability to meet your specific needs.