QSAR Models for P-Glycoprotein Transport Based on a Highly Consistent Data Set

Broccatelli, Fabio

doi:10.1021/ci3002809

Cited by 42 publications

(41 citation statements)

References 74 publications

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“…As the most promiscuous efflux transporter, biliarily eliminated drugs are frequently P-gp substrates and therefore might be expected to exhibit physicochemical properties that overlap with those of P-gp substrates. Recently, Broccatelli determined that P-gp nonsubstrates have a calculated surface area (S)<400 Å 2 (6). Transport efficiency can be inhibited by xenobiotics, endogenous substrates, disease states, or genetic polymorphisms, resulting in decreased clearance of drugs and endogenous compounds such as bilirubin and bile salts and may result in unpredictable, possibly toxic exposure.…”

Section: Introductionmentioning

confidence: 99%

Predicting when Biliary Excretion of Parent Drug is a Major Route of Elimination in Humans

Hosey-Cojocari

Broccatelli

Benet

2014

AAPS J

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Abstract. Biliary excretion is an important route of elimination for many drugs, yet measuring the extent of biliary elimination is difficult, invasive, and variable. Biliary elimination has been quantified for few drugs with a limited number of subjects, who are often diseased patients. An accurate prediction of which drugs or new molecular entities are significantly eliminated in the bile may predict potential drug-drug interactions, pharmacokinetics, and toxicities. The Biopharmaceutics Drug Disposition Classification System (BDDCS) characterizes significant routes of drug elimination, identifies potential transporter effects, and is useful in understanding drug-drug interactions. Class 1 and 2 drugs are primarily eliminated in humans via metabolism and will not exhibit significant biliary excretion of parent compound. In contrast, class 3 and 4 drugs are primarily excreted unchanged in the urine or bile. Here, we characterize the significant elimination route of 105 orally administered class 3 and 4 drugs. We introduce and validate a novel model, predicting significant biliary elimination using a simple classification scheme. The model is accurate for 83% of 30 drugs collected after model development. The model corroborates the observation that biliarily eliminated drugs have high molecular weights, while demonstrating the necessity of considering route of administration and extent of metabolism when predicting biliary excretion. Interestingly, a predictor of potential metabolism significantly improves predictions of major elimination routes of poorly metabolized drugs. This model successfully predicts the major elimination route for poorly permeable/poorly metabolized drugs and may be applied prior to human dosing.

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Section: Introductionmentioning

confidence: 99%

Predicting when Biliary Excretion of Parent Drug is a Major Route of Elimination in Humans

Hosey-Cojocari

Broccatelli

Benet

2014

AAPS J

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“…The 95 % confidence intervals of accuracy for binary models based on the MPO score and our fourparameter model are only slightly overlapping (0,015) at difference of mean accuracy values 0.057. Nevertheless an accuracy value for our last model is rather far from typical values for perfect SAR classification models (see, for example,22,23).…”

Section: The Results Of Cns/non‐cns Classification Of 616 Considered mentioning

confidence: 68%

CNS Multiparameter Optimization Approach: Is it in Accordance with Occam’s Razor Principle?

Raevsky

2016

Molecular Informatics

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A detailed analysis of the possibility of using the Multiparameter Optimization approach (MPO) for CNS/non-CNS classification of drugs was carried out. This work has shown that MPO descriptors are able to describe only part of chemical transport in the CNS connected with transmembrane diffusion. Hence the "intuitive" CNS MPO approach with arbitrary selection of descriptors and calculations of score functions, search of thresholds of classification, and absence of any chemometric procedures, leads to rather modest accuracy of CNS/non-CNS classification models.

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“…P-gp generated a poorer model (inferior test accuracy) [23], with a higher rate of mispredictions than Ames and CYP450 models, which makes the task of defining a smooth AD profile considerably harder. The noise in P-gp data comes from the variable threshold used in various sources to consider a compound as being a substrate [37] as well as the very large level of experimental uncertainty [38]. Furthermore, P-gp binding is notably known as being a very complex phenomenon driven by outstanding polyspecificity [39], which makes it naturally prone to error or bias in the experimental data.…”

Section: Assessment Of the Ad Quality Using A Scoring Functionmentioning

confidence: 99%

A novel applicability domain technique for mapping predictive reliability across the chemical space of a QSAR: reliability-density neighbourhood

et al. 2016

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The ability to define the regions of chemical space where a predictive model can be safely used is a necessary condition to assure the reliability of new predictions. This implies that reliability must be determined across chemical space in the attempt to localize “safe” and “unsafe” regions for prediction. As a result we devised an applicability domain technique that addresses the data locally instead of handling it as a whole—the reliability-density neighbourhood (RDN). The main novelty aspect of this method is that it characterizes each single training instance according to the density of its neighbourhood in the training set, as well as its individual bias and precision. By scanning through the chemical space (by iteratively increasing the applicability domain area), it was observed that new test compounds are successively included into the applicability domain region in such a manner that strongly correlates to their predictive performance. This allows the mapping of local reliability across different locations in the training set space, and thus allows identifying regions where the model has low reliability. This method also showed matching profiles between two external sets, which is an indication that it performs robustly with new data. Another novel aspect in this technique is that it is paired with a specific feature selection algorithm. As a result, the impact of the feature set used was studied from which the top 20 features selected by ReliefF yielded the best results, as opposed to using the model’s features or the entire feature set as commonly done. As the third novel aspect, in this work we propose a new scoring function to help evaluate the quality of an applicability domain profile (i.e., the curve of accuracy vs the applicability domain measure in question). Overall, the RDN showed to be a promising method that can correctly sort new instances according to predictive performance. As a result, this technique can be received by an end-user as proof of concept for the performance of a QSAR model in new data, thus promoting the user’s trust on the QSAR output.Graphical abstract. Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0182-y) contains supplementary material, which is available to authorized users.

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QSAR Models for P-Glycoprotein Transport Based on a Highly Consistent Data Set

Cited by 42 publications

References 74 publications

Predicting when Biliary Excretion of Parent Drug is a Major Route of Elimination in Humans

Predicting when Biliary Excretion of Parent Drug is a Major Route of Elimination in Humans

CNS Multiparameter Optimization Approach: Is it in Accordance with Occam’s Razor Principle?

A novel applicability domain technique for mapping predictive reliability across the chemical space of a QSAR: reliability-density neighbourhood

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