Professor Ron S. Kenett, is CEO and founder of KPA and research professor at the University of Turin in Italy. He is past president of the European Network for Business and Industrial Statistics (ENBIS) and of the Israel Statistical Association (ISA). Ron was awarded in 2013 the Greenfield Medal by the English Royal Statistical Society for excellence in the development and application of statistical methods. He has co-authored 10 books (including Modern Industrial Statistics with applications in R, MINITAB and JMP, Wiley, 2014 and Statistical Methods in Health Care, Wiley, 2012) and over 170 publications in international journals. Anat Reiner-Benaim has been studying the topic of large data analysis for nearly 15 years. Her studies initially focused on the control of the false discovery rate (FDR) as a type I statistical error criterion under multiple testing, and on the statistical testing within high-throughput analysis and complex studies. She has been recently focusing on studying the combination of statistical models with machine learning algorithms in big data analytics, and is currently collaborating with industry on several projects. She obtained her PhD in statistics under the supervision of Prof. Yoav Benjamini from the Department of Statistics and Operations Research in Tel-Aviv University. She was a postdoctoral fellow in the Weizmann Institute and in Stanford University, and is currently a faculty member in the Department of Statistics in the University of Haifa and a senior consultant in the KPA group. David M. Steinberg is a Professor of Statistics in the Department of Statistics and Operations Research at Tel Aviv University. His primary field of research is the statistical design of experiments, including factorial experiments, Latin hypercubes, computer experiments, robust parameter design experiments and seismic networks. He has worked on numerous applications and has worked as a consultant with many different companies, in fields from marketing design to bio-technology. He served as Editor of Technometrics and was Section Editor for Experimental Design for the Wiley Encyclopedia of Statistics in Quality and Reliability. He is a Fellow of the American Statistical Association and the 2013 George Box Medalist of the European Network for Business and Industrial Statistics (ENBIS) for lifetime contributions to the field. David is also a consultant with KPA. Benny Yoskovich, PhD, is Senior Statistical Consultant in the KPA Group. He has more than 20 years of experience in the application of statistical methods in the pharmaceutical industry. He earned a PhD at Tel Aviv University with a thesis on “Dynamic Problems in Robust Design” that was supervised by Professor David Steinberg and got an MSc in quality assurance and reliability from the Technion in 1991. Benny has extensive experience as a teacher and consultant in topics such as Quality by Design (QbD), design of experiments (DOE), Statistical Process Control (SPC), Sampling Plans, Regression Models, Stability Studies, Bioequivalence Analysis and Dissolution Profiles. He is also an expert programmer and developed a range of simulators in R.
In our previous blog post, we wrote about using designed experiments to develop analytic methods. This post continues the discussion of analytic methods and shows how a new type of experimental design, the Definitive Screening Design[1] (DSD), can be used to assess and improve analytic methods. We begin with a
Development of measurement or analytic methods parallels the development of drug products. Understanding of the process monitoring and control requirements drives the performance criteria for analytical methods, including the process critical quality attributes (CQAs) and specification limits. Uncovering the characteristics of a drug substance that require control to ensure safety
Split-plot experiments are experiments with hard-to-change factors that are difficult to randomize and can only be applied at the block level. Once the level of a hard-to-change factor is set, we can run experiments with several other factors keeping that level fixed. To illustrate the idea, we refer in this
George Box and K.B. Wilson introduced the idea of response surface methodology in a famous article[1] in 1951. There were several novel and extremely useful ideas in the article: Designed experiments can be a great tool in experimentally optimizing conditions. When feedback is rapid, there are great benefits to breaking
Scientists in the pharmaceutical industry must often determine product formulations. The properties of a formulation, or mixture, are usually a function of the relative proportions of the ingredients rather than their absolute amounts. So, in experiments with mixtures, a factorʹs value is its proportion in the mixture, which must fall
In an earlier installment of The QbD Column titled A QbD factorial experiment, we described a case study where the focus was on modeling the effect of three process factors on one response, viscosity. Here, we expand on that case study to show how to optimize process parameters of a
The first two posts in this series described the principles and methods of Quality by Design (QbD) in the pharmaceutical industry. The focus now shifts to the role of experimental design in QbD. Quality by Design in the pharmaceutical industry is a systematic approach to development of drug products and
A quick review of QbD The first blog post in this series described Quality by Design (QbD) in the pharmaceutical industry as a systematic approach for developing drug products and drug manufacturing processes. Under QbD, statistically designed experiments are used to efficiently and effectively investigate how process and product factors
Developing new drugs is a complex, lengthy and expensive endeavor. When the process leads to an approved drug, the result is improved patient care and great benefits for the developers. But many promising drugs never live up to expectations. The US Food and Drug Administration (FDA), observing that new drug