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- Ahlberg, Ernst, et al.
(författare)
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Using conformal prediction to prioritize compound synthesis in drug discovery
- 2017
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Ingår i: Proceedings of Machine Learning Research. - Stockholm : Machine Learning Research. ; , s. 174-184
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Konferensbidrag (refereegranskat)abstract
- The choice of how much money and resources to spend to understand certain problems is of high interest in many areas. This work illustrates how computational models can be more tightly coupled with experiments to generate decision data at lower cost without reducing the quality of the decision. Several different strategies are explored to illustrate the trade off between lowering costs and quality in decisions.AUC is used as a performance metric and the number of objects that can be learnt from is constrained. Some of the strategies described reach AUC values over 0.9 and outperforms strategies that are more random. The strategies that use conformal predictor p-values show varying results, although some are top performing.The application studied is taken from the drug discovery process. In the early stages of this process compounds, that potentially could become marketed drugs, are being routinely tested in experimental assays to understand the distribution and interactions in humans.
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| 2. |
- Chen, Hongming, et al.
(författare)
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In silico prediction of unbound brain-to-plasma concentration ratio using machine learning algorithms
- 2011
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Ingår i: Journal of Molecular Graphics and Modelling. - : Elsevier BV. - 1093-3263 .- 1873-4243. ; 29:8, s. 985-995
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Tidskriftsartikel (refereegranskat)abstract
- Distribution over the blood-brain barrier (BBB) is an important parameter to consider for compounds that will be synthesized in a drug discovery project. Drugs that aim at targets in the central nervous system (CNS) must pass the BBB. In contrast, drugs that act peripherally are often optimised to minimize the risk of CNS side effects by restricting their potential to reach the brain. Historically, most prediction methods have focused on the total compound distribution between the blood plasma and the brain. However, recently it has been proposed that the unbound brain-to-plasma concentration ratio (K(p,uu,brain)) is more relevant. In the current study, quantitative K(p,uu,brain) prediction models have been built on a set of 173 in-house compounds by using various machine learning algorithms. The best model was shown to be reasonably predictive for the test set of 73 compounds (R(2) = 0.58). When used for qualitative prediction the model shows an accuracy of 0.85 (Kappa = 0.68). An additional external test set containing 111 marketed CNS active drugs was also classified with the model and 89% of these drugs were correctly predicted as having high brain exposure.
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| 3. |
- Fridén, Markus, et al.
(författare)
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Structure-brain exposure relationships in rat and human using a novel data set of unbound drug concentrations in brain interstitial and cerebrospinal fluids
- 2009
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Ingår i: Journal of Medicinal Chemistry. - : American Chemical Society (ACS). - 0022-2623 .- 1520-4804. ; 52:20, s. 6233-6243
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Tidskriftsartikel (refereegranskat)abstract
- New experimental methodologies were applied to measure the unbound brain-to-plasma concentration ratio (K(p,uu,brain)) and the unbound CSF-to-plasma concentration ratio (K(p,uu,CSF)) in rats for 43 structurally diverse drugs. The relationship between chemical structure and K(p,uu,brain) was dominated by hydrogen bonding. Contrary to popular understanding based on the total brain-to-plasma concentration ratio (logBB), lipophilicity was not a determinant of unbound brain exposure. Although changing the number of hydrogen bond acceptors is a useful design strategy for optimizing K(p,uu,brain), future improvement of in silico prediction models is dependent on the accommodation of active drug transport. The structure-brain exposure relationships found in the rat also hold for humans, since the rank order of the drugs was similar for human and rat K(p,uu,CSF). This cross-species comparison was supported by K(p,uu,CSF) being within 3-fold of K(p,uu,brain) in the rat for 33 of 39 drugs. It was, however, also observed that K(p,uu,CSF) overpredicts K(p,uu,brain) for highly effluxed drugs, indicating lower efflux capacity of the blood-cerebrospinal fluid barrier compared to the blood-brain barrier.
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