Multivariate design

Wold, Svante; Sjöstróm, Michael; Carlson, Rolf; Lundstedt, Torbjörn; Hellberg, Sven; Skagerberg, Bert; Wikström, Christina; Öhman, Jerker

doi:10.1016/s0003-2670(00)86294-7

Cited by 120 publications

(66 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multivariate design (MVD) 21,22 is a combination of multivariate characterization (MVC), [23][24][25] principal component analysis (PCA), and Statistical Experimental Design (SED) to select a diverse set of objects that represents all objects, that is, spans the variation. There is a number of different experimental designs that can be applied to span the variation in a…”

Section: Multivariate Designmentioning

confidence: 99%

“…To be able to do this, we have to characterize the objects with both measured and observed descriptors. This often includes setting up specific inclusion and exclusion criteria for the study, such as age span (e.g., 18-45 years), body mass index (e.g., [20][21][22][23][24][25][26][27][28][29][30], medicinal chemistry profiles (e.g., lipids, glucose), gender, tobacco habits, and use of drugs. In addition to those criteria, additional information regarding each object is collected by questionnaires that include life style factors, food and drinking habits, social situation, and so on.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Chemometrics in Metabonomics

2006

Self Cite

View full text Add to dashboard Cite

We provide an overview of how the underlying philosophy of chemometrics is integrated throughout metabonomic studies. Four steps are demonstrated: (1) definition of the aim, (2) selection of objects, (3) sample preparation and characterization, and (4) evaluation of the collected data. This includes the tools applied for linear modeling, for example, Statistical Experimental Design (SED), Principal Component Analysis (PCA), Partial least-squares (PLS), Orthogonal-PLS (OPLS), and dynamic extensions thereof. This is illustrated by examples from the literature.

show abstract

Section: Multivariate Designmentioning

confidence: 99%

mentioning

confidence: 99%

Chemometrics in Metabonomics

2006

Self Cite

View full text Add to dashboard Cite

show abstract

“…A representative selection of compounds that well span the chemical domain of interest should be included in this set. One way to accomplish a representative training set is through multivariate design (Wold et al 1986). This methodology is also frequently used in medicinal chemistry and combinatorial approaches and is known as statistical molecular design (SMD).…”

Section: Representativitymentioning

confidence: 99%

“…A representative selection of compounds that well span the chemical domain of interest should be included in these sets. One way to accomplish a representative selection of compounds is through SMD (Wold et al 1986). With this approach, test series of compounds are defined in which all major structural and chemical properties are systematically varied at the same time.…”

Section: Acceptability Criteriamentioning

confidence: 99%

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

Eriksson

Jaworska

Worth

et al. 2003

Environ Health Perspect

1,184

847

View full text Add to dashboard Cite

This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation.

show abstract

“…Multivariate design, or experimental design in principal properties as it is often referred to, is a combination of experimental design and PCA [14]. Instead of normal variables, principal properties are used in the design.…”

Section: Multivariate Designmentioning

confidence: 99%

Multivariate methods in pharmaceutical applications

Gabrielsson

Lindberg

Lundstedt

2002

Journal of Chemometrics

182

View full text Add to dashboard Cite

This review covers material published within the field of pharmacy in the last five years. Articles concerning experimental design, optimization and applications of multivariate techniques have been published, from factorial designs to multivariate data analysis, and the combination of the two in multivariate design. The number of publications on this topic testifies to the good results obtained in the studies. Much of the published material highlights the usefulness of experimental design, with many articles dealing with optimization, where much effort is spent on getting useful results. Examples of multivariate data analysis are comparatively few, but these methods are gaining in use. The employment of multivariate techniques in different applications has been reviewed. The examples in this review represent just a few of the possible applications with different aims within pharmaceutical applications. A number of companies are using experimental design as a standard tool in preformulation and in combination with response surface modeling. The properties of e.g. a tablet can be optimized to fulfill a well-specified aim such as a specific release profile, hardness, disintegration time etc. However, none of the companies apply multivariate methods in all steps of the drug development process. As this is still very much a growing field, it is only a question of time before experimental design, optimization and multivariate data analysis are implemented throughout the entire formulation process, from performulation to multivariate process control.

show abstract

Multivariate design

Cited by 120 publications

References 15 publications

Chemometrics in Metabonomics

Chemometrics in Metabonomics

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

Multivariate methods in pharmaceutical applications

Contact Info

Product

Resources

About