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Experimental Designs at the Crossroads of Drug Discovery
Umeå University, Faculty of Science and Technology, Department of Chemistry.
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.
Keyword [en]
Chemometrics, Design of experiments, Experimental design, Multivariate data-analysis, D-optimal design, 96-well technology, Drug discovery, Assay optimisation, QSAR
National Category
Organic Chemistry
Identifiers
URN: urn:nbn:se:umu:diva-693ISBN: 91-7264-032-4 (print)OAI: oai:DiVA.org:umu-693DiVA: diva2:144247
Public defence
2006-03-04, KB3B1, KBC, Umeå Universitet, Umeå, 10:00
Opponent
Supervisors
Available from: 2006-02-07 Created: 2006-02-07 Last updated: 2011-03-09Bibliographically approved
List of papers
1. D-optimal onion designs in statistical molecular design
Open this publication in new window or tab >>D-optimal onion designs in statistical molecular design
2004 (English)In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, Vol. 73, no 1, 37-46 p.Article in journal (Refereed) Published
Abstract [en]

Statistical molecular design (SMD) is a technique for selecting a representative (diverse) set of substances in combinatorial chemistry and QSAR, as well as other areas depending on optimising chemical structure. Two approaches often used in SMD are space filling (SF) and D-optimal (DO) designs.

Space-filling designs provide good coverage of the physicochemical space but are not explicitly based on a model. For small design sizes, they perform similar to D-optimal designs, which maximize the determinant of the variance–covariance matrix. This leads to selection of the most extreme points of the candidate set and gives a minimal set of selected compounds with maximal diversity. However, the inner regions of the experimental domain are not well sampled by DO or small SF designs.

We have developed and evaluated an approach to remedy the shortcomings of SF and DO designs in SMD. This new approach divides the candidate set into a number of subsets (“shells” or “layers”), and a D-optimal selection is made from each layer. This makes it possible to select representative sets of molecular structures throughout any property space, e.g., the physicochemical space, with reasonable design sizes. The number of selected molecules is easily controlled by varying (a) the number of layers and (b) the model on which the design is based.

We outline here this new approach, the D-optimal onion design (DOOD). It is tested on two molecular data sets with varying size and compared with SF designs and ordinary DO designs. The designs have been evaluated with parameters, such as condition number, determinant, Tanimoto coefficients and Euclidean distances, as well as external evaluation of the resulting projection to latent structures (PLS) model.

Keyword
Statistical molecular design, D-optimal design, Space-filling design
Identifiers
urn:nbn:se:umu:diva-14076 (URN)10.1016/j.chemolab.2004.04.001 (DOI)
Available from: 2007-05-22 Created: 2007-05-22 Last updated: 2013-02-28Bibliographically approved
2. Controlling coverage of D-optimal onion designs and selections
Open this publication in new window or tab >>Controlling coverage of D-optimal onion designs and selections
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
Keyword
statistical molecular design, space-filling design, D-optimal design, D-optimal onion designs, principal properties, PLS
Identifiers
urn:nbn:se:umu:diva-14083 (URN)10.1002/cem.901 (DOI)
Available from: 2007-05-22 Created: 2007-05-22 Last updated: 2017-12-14Bibliographically approved
3. Design, Synthesis and Biological Evaluation of a Set of Type III Secretion Inhibitors
Open this publication in new window or tab >>Design, Synthesis and Biological Evaluation of a Set of Type III Secretion Inhibitors
Show others...
Manuscript (Other academic)
Abstract
Identifiers
urn:nbn:se:umu:diva-4944 (URN)
Available from: 2006-02-07 Created: 2006-02-07 Last updated: 2017-05-24Bibliographically approved
4. Rational DOE-protocols for 96-well plates
Open this publication in new window or tab >>Rational DOE-protocols for 96-well plates
Show others...
2006 (English)In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 83, no 1, 66-74 p.Article in journal (Refereed) Published
Abstract [en]

The use of 96-well plates for chemical and biological applications has rapidly increased as new applicable domains have been discovered and new laboratory instruments developed. There are 96, 384, 1536, etc. plates customized for diverse applications such as biological assays, sample preparation, solid-phase extraction and crystallization. Multi-pipettes as well as automated pipette systems accelerate the preparation of plates resulting in even faster evaluation systems. A bottleneck in the use of multi-unit plates is method development and optimization. By applying rational experimental design, the optimization could be made more efficient and less time-consuming. Unfortunately, the workload related to manual preparation of multi-unit plates according to an experimental design is often considered overwhelming. The present study introduces a new approach for experimental design in 96-well plates that minimizes the manual workload without compromising the quality of the experimental design. This approach is scalable to larger rectangular formats such as 384- and 1536-well plates. The optimal combinations will be delineated and applied experimentally to a reporter-gene assay.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2006
Keyword
DOE, 96-well technology, RED-MUP, Assay optimisation
Identifiers
urn:nbn:se:umu:diva-4945 (URN)10.1016/j.chemolab.2006.01.005 (DOI)
Available from: 2006-02-07 Created: 2006-02-07 Last updated: 2017-12-14Bibliographically approved

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