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Controlling coverage of D-optimal onion designs and selections
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Research Group for Chemometrics)
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Research Group for Chemometrics)
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Research Group for Chemometrics)
2004 (English)In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 18, no 12, 548-557 p.Article in journal (Refereed) Published
Abstract [en]

Statistical molecular design (SMD) is a powerful approach for selection of compound sets in medicinal chemistry and quantitative structure-activity relationships (QSARs) as well as other areas. Two techniques often used in SMD are space-filling and D-optimal designs. Both on occasions lead to unwanted redundancy and replication. To remedy such shortcomings, a generalization of D-optimal selection was recently developed. This new method divides the compound candidate set into a number of subsets (layers or shells), and a D-optimal selection is made from each layer. This improves the possibility to select representative molecular structures throughout any property space independently of requested sample size. This is important in complex situations where any given model is unlikely to be valid over the whole investigated domain of experimental conditions. The number of selected molecules can be controlled by varying the number of subsets or by altering the complexity of the model equation in each layer and/or the dependency of previous layers. The new method, called D-optimal onion design (DOOD), will allow the user to choose the model equation complexity independently of sample size while still avoiding unwarranted redundancy. The focus of the present work is algorithmic improvements of DOOD in comparison with classical D-optimal design. As illustrations, extended DOODs have been generated for two applications by in-house programming, including some modifications of the D-optimal algorithm. The performances of the investigated approaches are expected to differ depending on the number of principal properties of the compounds in the design, sample sizes and the investigated model, i.e. the aim of the design. QSAR models have been generated from the selected compound sets, and root mean squared error of prediction (RMSEP) values have been used as measures of performance of the different designs.

Place, publisher, year, edition, pages
Chichester: Wiley & Sons , 2004. Vol. 18, no 12, 548-557 p.
Keyword [en]
statistical molecular design, space-filling design, D-optimal design, D-optimal onion designs, principal properties, PLS
URN: urn:nbn:se:umu:diva-14083DOI: 10.1002/cem.901OAI: diva2:153754
Available from: 2007-05-22 Created: 2007-05-22 Last updated: 2013-02-28Bibliographically approved
In thesis
1. Experimental Designs at the Crossroads of Drug Discovery
Open this publication in new window or tab >>Experimental Designs at the Crossroads of Drug Discovery
2006 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

New techniques and approaches for organic synthesis, purification and biological testing are enabling pharmaceutical industries to produce and test increasing numbers of compounds every year. Surprisingly, this has not led to more new drugs reaching the market, prompting two questions – why is there not a better correlation between their efforts and output, and can it be improved? One possible way to make the drug discovery process more efficient is to ensure, at an early stage, that the tested compounds are diverse, representative and of high quality. In addition the biological evaluation systems have to be relevant and reliable. The diversity of the tested compounds could be ensured and the reliability of the biological assays improved by using Design Of Experiments (DOE) more frequently and effectively. However, DOE currently offers insufficient options for these purposes, so there is a need for new, tailor-made DOE strategies. The aim of the work underlying this thesis was to develop and evaluate DOE approaches for diverse compound selection and efficient assay optimisation. This resulted in the publication of two new DOE strategies; D-optimal Onion Design (DOOD) and Rectangular Experimental Designs for Multi-Unit Platforms (RED-MUP), both of which are extensions to established experimental designs.

D-Optimal Onion Design (DOOD) is an extension to D-optimal design. The set of possible objects that could be selected is divided into layers and D-optimal selection is applied to each layer. DOOD enables model-based, but not model-dependent, selections in discrete spaces to be made, since the selections are not only based on the D-optimality criterion, but are also biased by the experimenter’s prior knowledge and specific needs. Hence, DOOD selections provide controlled diversity.

Assay development and optimisation can be a major bottleneck restricting the progress of a project. Although DOE is a recognised tool for optimising experimental systems, there has been widespread unwillingness to use it for assay optimisation, mostly because of the difficulties involved in performing experiments according to designs in 96-, 384- and 1536- well formats. The RED-MUP framework combines classical experimental designs orthogonally onto rectangular experimental platforms, which facilitates the execution of DOE on these platforms and hence provides an efficient tool for assay optimisation.

In combination, these two strategies can help uncovering the crossroads between biology and chemistry in drug discovery as well as lead to higher information content in the data received from biological evaluations, providing essential information for well-grounded decisions as to the future of the project. These two strategies can also help researchers identify the best routes to take at the crossroads linking biological and chemical elements of drug discovery programs.

Place, publisher, year, edition, pages
Umeå: Kemi, 2006. 80 p.
Chemometrics, Design of experiments, Experimental design, Multivariate data-analysis, D-optimal design, 96-well technology, Drug discovery, Assay optimisation, QSAR
National Category
Organic Chemistry
urn:nbn:se:umu:diva-693 (URN)91-7264-032-4 (ISBN)
Public defence
2006-03-04, KB3B1, KBC, Umeå Universitet, Umeå, 10:00
Available from: 2006-02-07 Created: 2006-02-07 Last updated: 2011-03-09Bibliographically approved

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