Polypharmacological <i>in Silico</i> Bioactivity Profiling and Experimental Validation Uncovers Sedative-Hypnotic Effects of Approved and Experimental Drugs in Rat

Drakakis, Georgios; Wafford, Keith A.; Brewerton, Suzanne; Bodkin, Michael J.; Evans, David A.; Bender, Andreas

doi:10.1021/acschembio.7b00209

Cited by 9 publications

(7 citation statements)

References 48 publications

(106 reference statements)

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“…Clearly, a classification system for neuropsychiatric medications that is based on the pharmacological and molecular action and the resulting neurobehavioral changes would be more appropriate. Many compounds interact with multiple targets leading to more complex modes of action 6 , 7 . Thereby, the localized molecular effects are not transformed to higher spatiotemporal scales in a trivial manner and potentiating effects at the molecular level may diminish or even have inhibitory impact on the network systems level.…”

Section: Introductionmentioning

confidence: 99%

Systemic neurotransmitter responses to clinically approved and experimental neuropsychiatric drugs

et al. 2018

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Neuropsychiatric disorders are the third leading cause of global disease burden. Current pharmacological treatment for these disorders is inadequate, with often insufficient efficacy and undesirable side effects. One reason for this is that the links between molecular drug action and neurobehavioral drug effects are elusive. We use a big data approach from the neurotransmitter response patterns of 258 different neuropsychiatric drugs in rats to address this question. Data from experiments comprising 110,674 rats are presented in the Syphad database [www.syphad.org]. Chemoinformatics analyses of the neurotransmitter responses suggest a mismatch between the current classification of neuropsychiatric drugs and spatiotemporal neurostransmitter response patterns at the systems level. In contrast, predicted drug–target interactions reflect more appropriately brain region related neurotransmitter response. In conclusion the neurobiological mechanism of neuropsychiatric drugs are not well reflected by their current classification or their chemical similarity, but can be better captured by molecular drug–target interactions.

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Section: Introductionmentioning

confidence: 99%

Systemic neurotransmitter responses to clinically approved and experimental neuropsychiatric drugs

et al. 2018

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“…In a final case study, we analyzed the aforementioned study of Drakakis et al (2017) , to illustrate a scenario where functional prediction would have added value to a computational study. In this work, target prediction profiles were related to prolonged sleep bouts, where changing functional effects on receptors was related to the change on the sleep effect of compounds.…”

Section: Resultsmentioning

confidence: 99%

“…In the absence of functional information for the respective target, and since activation of BMP signaling in prostate carcinoma cells is known to be cytostatic (hence its inactivation would not explain the observed phenotype) ( Wahdan-Alaswad et al, 2012 ), the authors were forced to hypothesize that cytostatic agents may activate BMP1. Another study rationalized the polypharmacology of sleep-inducing compounds in rat, (which, without functional annotation) were forced to stipulate that bioactive compounds with multi-target activity may elicit their synergistic sleep parameter activity through inhibition of Histamine Receptor H1 (HRH1) and activation of Cholinergic Receptor Muscarinic 4 (CHRM4) (since the biological evidence at hand for both targets advocates this rationalization) ( Drakakis et al, 2017 ). Sertindole, a withdrawn approved drug, was also experimentally determined within the study to changeover functional activity.…”

Section: Introductionmentioning

confidence: 99%

Extending in Silico Protein Target Prediction Models to Include Functional Effects

et al. 2018

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In silico protein target deconvolution is frequently used for mechanism-of-action investigations; however existing protocols usually do not predict compound functional effects, such as activation or inhibition, upon binding to their protein counterparts. This study is hence concerned with including functional effects in target prediction. To this end, we assimilated a bioactivity training set for 332 targets, comprising 817,239 active data points with unknown functional effect (binding data) and 20,761,260 inactive compounds, along with 226,045 activating and 1,032,439 inhibiting data points from functional screens. Chemical space analysis of the data first showed some separation between compound sets (binding and inhibiting compounds were more similar to each other than both binding and activating or activating and inhibiting compounds), providing a rationale for implementing functional prediction models. We employed three different architectures to predict functional response, ranging from simplistic random forest models (‘Arch1’) to cascaded models which use separate binding and functional effect classification steps (‘Arch2’ and ‘Arch3’), differing in the way training sets were generated. Fivefold stratified cross-validation outlined cascading predictions provides superior precision and recall based on an internal test set. We next prospectively validated the architectures using a temporal set of 153,467 of in-house data points (after a 4-month interim from initial data extraction). Results outlined Arch3 performed with the highest target class averaged precision and recall scores of 71% and 53%, which we attribute to the use of inactive background sets. Distance-based applicability domain (AD) analysis outlined that Arch3 provides superior extrapolation into novel areas of chemical space, and thus based on the results presented here, propose as the most suitable architecture for the functional effect prediction of small molecules. We finally conclude including functional effects could provide vital insight in future studies, to annotate cases of unanticipated functional changeover, as outlined by our CHRM1 case study.

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“…A number of traditional sedatives like benzodiazepines often have side effects of addiction and residual drowsiness, while other sedatives like melatonin receptor agonists often prove less effective than its contemporaries. Systems analysis of the mechanism of sleep was recently employed to find novel, more efficacious drugs for sleep disorders [119].…”

Section: Polypharmacologymentioning

confidence: 99%

Binding site matching in rational drug design: algorithms and applications

Naderi

Lemoine

Govindaraj

et al. 2018

Briefings in Bioinformatics

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Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning.

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Polypharmacological in Silico Bioactivity Profiling and Experimental Validation Uncovers Sedative-Hypnotic Effects of Approved and Experimental Drugs in Rat

Cited by 9 publications

References 48 publications

Systemic neurotransmitter responses to clinically approved and experimental neuropsychiatric drugs

Systemic neurotransmitter responses to clinically approved and experimental neuropsychiatric drugs

Extending in Silico Protein Target Prediction Models to Include Functional Effects

Binding site matching in rational drug design: algorithms and applications

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