Making priors a priority
Summary This article discusses a critical issue that the community needs to address address in order to use the predictive…
In this example we will use the Profile Builder in StarDrop’s MPO Explorer module to derive a multi-parameter scoring profile, based on a data set initially described by Wager et al. [ACS Chem. Neurosci. 1 p. 435 (2010)].
Wager et al. used this data set to develop a multi-parameter optimisation method for selection of compounds intended for CNS indications. Wager’s ‘CNS MPO score’ is calculated as the sum of the values of desirability functions for six physicochemical parameters, calculated logP (clogP), calculated logD at pH 7.4 (clogD), molecular weight (MW), topological polar surface area (TPSA), number of hydrogen bond donors (HBD) and the pKa of the most basic center (pKa), resulting in a value between 0 and 6.
The authors compared the CNS MPO score for a set of 119 marketed drugs for CNS targets with 108 Pfizer CNS candidates and found that 74% of the marketed drugs achieved a desirability score of 4 compared with only 60% of the Pfizer candidates. The scoring profile derived by MPO Explorer will contain one or more rules that indicate combinations of properties that significantly increase the chances of identifying a drug and we will compare this with the results of the Wager et al. CNS MPO score.
Would you like to try it yourself?
To try this example yourself, download the example project file and follow the step-by-step guidelines.
Please note that the tutorial explores core features of StarDrop and the MPO Explorer module. If you don’t have access to StarDrop and would like to arrange a trial, please email us at info@optibrium.com
More MPO resources
Finding balance in drug discovery through multi-parameter optimisation
Successful drugs require a delicate balance of many properties, such as potency, ADME and toxicity, to meet a project’s therapeutic objective. To make decisions about compound progression and assay selection, the available data must be assessed against project-specific criteria. However, the data on which we base our decisions often come from different sources and can vary in quality, so how can we use this information to make confident decisions? In addition, how can we be sure that the criteria we’re using are the most appropriate?
Addressing toxicity risk in multi-parameter optimisation
In this example we will illustrate how knowledge-based predictions of toxicity can be used within a MPO environment to guide the selection and design of compounds with a good balance of properties and reduced risk of toxicity.
