Pharmaceutical Perspectives of Nonlinear QSAR Strategies

Michielan, Lisa; Moro, Stefano

doi:10.1021/ci100072z

Cited by 47 publications

(28 citation statements)

References 140 publications

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“…We shall discuss some of their key strengths and weaknesses and, in particular, we consider the relative merits of linear and non-linear modeling approaches. The following discussion of QSAR is by no means exhaustive, so readers are referred elsewhere for greater detail on this topic [23,90,122,[143][144][145][146][147][148][149][150][151][152].…”

Section: Qsar Modeling Methodsmentioning

confidence: 99%

“…For example, drug-induced phospholipidosis, the potentially toxic excessive accumulation of phospholipids in cells/tissues, may be described with simple descriptors, such as the presence of a positive charge/basic substituent and high lipophilicity [19] (although more sophisticated approaches have also been investigated [20]). In contrast, P450 inhibition is clearly a complex receptor-mediated process, which arguably requires a complex chemical description such as that afforded by receptor docking, pharmacophores [21,22] or multiple descriptors in conjunction with complex non-linear modeling methods [23] (and which is further complicated by the flexibility and promiscuity of the proteins involved). In an effort to conform with the OECD QSAR regulatory guidelines [18], in silico methods to predict skin sensitization, which is believed to be a toxic response due to covalent modification of unknown proteins in the epidermis, rely on mechanistically interpretable models, at least in terms of their encoding of chemical reactivity.…”

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confidence: 99%

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The Challenges Involved in Modeling Toxicity Data In Silico: A Review

Gleeson¹,

Modi²,

Bender³

et al. 2012

CPD

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The percentage of failures in late pharmaceutical development due to toxicity has increased dramatically over the last decade or so, resulting in increased demand for new methods to rapidly and reliably predict the toxicity of compounds. In this review we discuss the challenges involved in both the building of in silico models on toxicology endpoints and their practical use in decision making. In particular, we will reflect upon the predictive strength of a number of different in silico models for a range of different endpoints, different approaches used to generate the models or rules, and limitations of the methods and the data used in model generation. Given that there exists no unique definition of a 'good' model, we will furthermore highlight the need to balance model complexity/interpretability with predictability, particularly in light of OECD/REACH guidelines. Special emphasis is put on the data and methods used to generate the in silico toxicology models, and their strengths and weaknesses are discussed. Switching to the applied side, we next review a number of toxicity endpoints, discussing the methods available to predict them and their general level of predictability (which very much depends on the endpoint considered). We conclude that, while in silico toxicology is a valuable tool to drug discovery scientists, much still needs to be done to, firstly, understand more completely the biological mechanisms for toxicity and, secondly, to generate more rapid in vitro models to screen compounds. With this biological understanding, and additional data available, our ability to generate more predictive in silico models should significantly improve in the future.

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Section: Qsar Modeling Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

The Challenges Involved in Modeling Toxicity Data In Silico: A Review

Gleeson¹,

Modi²,

Bender³

et al. 2012

CPD

View full text Add to dashboard Cite

show abstract

“…PLS algorithm was chosen since it is largely used in medicinal chemistry [17]. Machine learning tools on the other hand are largely used by biologists and have also been shown to have potential utility in the modelling of pharmaceutical problems [18]. For these reasons we also experiment a machine learning strategy based on the SVR algorithm.…”

Section: Introductionmentioning

confidence: 99%

Prediction and interpretation of the lipophilicity of small peptides

Visconti

Ermondi

Esposito

2015

J Comput Aided Mol Des

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Peptide-based drug discovery has considerably expanded and solid in silico tools for the prediction of physico-chemical properties of peptides are urgently needed. In this work we tested some combinations of descriptors/algorithms to find the best model to predict log D oct of a series of peptides. To do that we evaluate the models statistical performances but also their skills in providing a reliable deconvolution of the balance of intermolecular forces governing the partitioning phenomenon. Results prove that a PLS model based on VolSurf+ descriptors is the best tool to predict log D oct of neutral and ionised peptides. The mechanistic interpretation also reveals that the inclusion in the chemical structure of a HBD group is more efficient in decreasing lipophilicity than the inclusion of a HBA group.

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“…In practice, correlations between these parameters often are non-linear. Therefore, the need for more advanced non-linear regression models such as support vector machine, random forest or artificial neural networks has been recognised and such models are now being developed and applied widely in drug discovery [102,103].…”

Section: Introduction To Computational Modelling Approachesmentioning

confidence: 99%

Strategies for Minimisation of the Cholestatic Liver Injury Liability Posed by Drug-Induced Bile Salt Export Pump (BSEP) Inhibition

Kenna¹,

Stahl

Noeske

2013

Topics in Medicinal Chemistry

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Hepatobiliary uptake and efflux transporter proteins play key roles in the formation of bile, which is a vital function of the liver. The ATP-dependent bile salt export pump (BSEP) excretes bile salts from hepatocytes into bile. Inherited BSEP mutations in humans cause intrahepatic accumulation of bile salts, which results in cholestatic liver injury. Furthermore, inhibition of BSEP activity is considered one of a number of key initiating mechanisms by which drugs may cause liver injury (drug-induced liver injury, DILI) in the human population. DILI is an important cause of serious drug-induced illness and is a leading cause of drug attrition during development and of drug withdrawal and restrictive labelling post-marketing. In this chapter we summarise the evidence that BSEP inhibition is a drug-related DILI risk factor, we describe experimental approaches (in silico, in vitro and in vivo) which may be used to predict and quantify this process during drug discovery and development and we discuss data interpretation. We also outline an approach by which assessment of BSEP inhibition in drug discovery can be used to reduce the likelihood that DILI may arise during development. In addition, we consider the current state of computational predictive modelling of BSEP inhibition and discuss the influence of physicochemical parameters.

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Pharmaceutical Perspectives of Nonlinear QSAR Strategies

Cited by 47 publications

References 140 publications

The Challenges Involved in Modeling Toxicity Data In Silico: A Review

The Challenges Involved in Modeling Toxicity Data In Silico: A Review

Prediction and interpretation of the lipophilicity of small peptides

Strategies for Minimisation of the Cholestatic Liver Injury Liability Posed by Drug-Induced Bile Salt Export Pump (BSEP) Inhibition

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