Application of Multivariate Tools in Pharmaceutical Product Development to Bridge Risk Assessment to Continuous Verification in a Quality by Design Environment

An important aspect of a quality by design approach to pharmaceutical product formulation and process development is continuous quality verification. This is an innovative way of validating the process where manufacturing performance is continuously monitored, evaluated and adjusted as necessary. For new drug products, the body of knowledge accumulated through the development cycle and formalised via risk assessment forms the natural basis of this activity. This paper shows how multivariate tools can be used as part of a continuous quality verification approach for a new drug product relying on the information that summarises the control strategy, i.e. the subset of critical variables selected via risk assessment and the related proven acceptable ranges determined during developmental studies.

[1]  C. Gustafsson,et al.  Multivariate methods in tablet formulation suitable for early drug development: Predictive models from a screening design of several linked responses , 2007 .

[2]  San Kiang,et al.  Can pharmaceutical process development become high tech , 2006 .

[3]  Roger Nosal,et al.  API Quality by Design Example from the Torcetrapib Manufacturing Process , 2007, Journal of Pharmaceutical Innovation.

[4]  R. H. Myers,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[5]  L. G. Blackwood Factor Analysis in Chemistry (2nd Ed.) , 1994 .

[6]  M. Khan,et al.  Quality by design: understanding the formulation variables of a cyclosporine A self-nanoemulsified drug delivery systems by Box-Behnken design and desirability function. , 2007, International journal of pharmaceutics.

[7]  Ron S. Kenett,et al.  Quality by Design applications in biosimilar pharmaceutical products , 2008 .

[8]  J. Macgregor,et al.  A Framework for the Development of Design and Control Spaces , 2008, Journal of Pharmaceutical Innovation.

[9]  Meiyu Shen,et al.  Three-Stage Sequential Statistical Dissolution Testing Rules , 2004, Journal of biopharmaceutical statistics.

[10]  Theodora Kourti,et al.  Process analysis, monitoring and diagnosis, using multivariate projection methods , 1995 .

[11]  John J. Peterson,et al.  A Bayesian Approach to the ICH Q8 Definition of Design Space , 2008, Journal of biopharmaceutical statistics.

[12]  T. Lundstedt,et al.  Optimization of a granulation and tabletting process by sequential design and multivariate analysis , 1998 .

[13]  Russ Somma,et al.  Development Knowledge Can Increase Manufacturing Capability and Facilitate Quality by Design , 2007, Journal of Pharmaceutical Innovation.

[14]  Nils-Olof Lindberg,et al.  Multivariate methods in pharmaceutical applications , 2002 .

[15]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[16]  Y. Heyden,et al.  Parallel co-ordinate geometry and principal component analysis for the interpretation of large multi-response experimental designs , 2002 .

[17]  Chris Potter,et al.  PQLI Application of Science- and Risk-based Approaches (ICH Q8, Q9, and Q10) to Existing Products , 2009, Journal of Pharmaceutical Innovation.

[18]  André I. Khuri,et al.  Response surface methodology , 2010, International Encyclopedia of Statistical Science.

[19]  López del Val Ja,et al.  Principal components analysis , 1993 .

[20]  Michael Sjöström,et al.  Multivariate Methods in the Development of a New Tablet Formulation , 2003, Drug development and industrial pharmacy.

[21]  D. Massart,et al.  The Mahalanobis distance , 2000 .

[22]  Svend Havelund,et al.  Quality by design - Spray drying of insulin intended for inhalation. , 2008, European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V.