Discovery of Novel and Cardioselective Diltiazem-like Calcium Channel Blockers via Virtual Screening

Carosati, Emanuele; Budriesi, Roberta; Ioan, Pierfranco; Ugenti, Maria Paola; Frosini, Maria; Fusi, Fabio; Corda, Gaetano; Cosimelli, Barbara; Spinelli, Domenico; Chiarini, Alberto; Cruciani, Gabriele

doi:10.1021/jm800151n

Cited by 26 publications

(24 citation statements)

References 64 publications

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“…Nifedipine data were calculated and reported as a comparison. The physico-chemical and pharmacokinetic descriptors (Table 2) were fully convincing since all values are in line with alert threshold values [24]. According to solubility predictions, 1 shows a better profile than molecules present in the original dataset whereas 2 could be ranked in an intermediate position ( Figure 5).…”

Section: Figuresupporting

confidence: 52%

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Ligand-based design, in silico ADME-Tox filtering, synthesis and biological evaluation to discover new soluble 1,4-DHP-based CFTR activators

Visentin¹,

Ermondi²,

Medana

et al. 2012

European Journal of Medicinal Chemistry

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Section: Figuresupporting

confidence: 52%

“…The ADME-Tox profiles of drug-likeness 1 and 2 were evaluated according to the rules proposed by Carosati et al [24]. Nifedipine data were calculated and reported as a comparison.…”

Section: Figurementioning

confidence: 99%

Ligand-based design, in silico ADME-Tox filtering, synthesis and biological evaluation to discover new soluble 1,4-DHP-based CFTR activators

Visentin¹,

Ermondi²,

Medana

et al. 2012

European Journal of Medicinal Chemistry

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“…GRID MIFs have been applied to many areas of drug discovery,15,87,88 including pK a and tautomer modeling,89,90 structure-based drug design,91 scaffold-hopping,92–94 3D-QSAR,95–98 docking,98,99 ADME and pharmacokinetic modeling,17,18,100 and metabolism prediction 101–102…”

Section: Resultsmentioning

confidence: 99%

A Novel Approach for Predicting P-Glycoprotein (ABCB1) Inhibition Using Molecular Interaction Fields

et al. 2011

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P-glycoprotein (Pgp or ABCB1) is an ABC transporter protein involved in intestinal absorption, drug metabolism and brain penetration, and its inhibition can seriously alter a drug's bioavailability and safety. In addition, inhibitors of Pgp can be used to overcome multidrug resistance. Given this dual-purpose, reliable in silico procedures to predict Pgp inhibition are of great interest. A large and accurate literature collection yielded more than 1200 structures; a model was then constructed using various MIF-based technologies, considering pharmacophoric features and those physico-chemical properties related to membrane partitioning. High accuracy was demonstrated internally, with two different validation sets, and moreover using a number of molecules, for which Pgp inhibition was not experimentally available but was evaluated `inhouse'. All the validations confirmed the robustness of the model and its suitability to help medicinal chemists in drug discovery. The information derived from the model was rationalized as a pharmacophore for competitive Pgp inhibition.

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“…The BioGPS approach is based on the software FLAP . FLAP uses a “Common Reference Framework” for ligands and proteins, enabling ligand and receptor GRID MIF comparison, with applications to virtual screening and alignment, pharmacophore elucidation, and 3D‐QSAR.…”

Section: Methodsmentioning

confidence: 99%

BioGPS: Navigating biological space to predict polypharmacology, off-targeting, and selectivity

et al. 2015

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The structural comparison of protein binding sites is increasingly important in drug design; identifying structurally similar sites can be useful for techniques such as drug repurposing, and also in a polypharmacological approach to deliberately affect multiple targets in a disease pathway, or to explain unwanted off-target effects. Once similar sites are identified, identifying local differences can aid in the design of selectivity. Such an approach moves away from the classical "one target one drug" approach and toward a wider systems biology paradigm. Here, we report a semiautomated approach, called BioGPS, that is based on the software FLAP which combines GRID Molecular Interactions Fields (MIFs) and pharmacophoric fingerprints. BioGPS comprises the automatic preparation of protein structure data, identification of binding sites, and subsequent comparison by aligning the sites and directly comparing the MIFs. Chemometric approaches are included to reduce the complexity of the resulting data on large datasets, enabling focus on the most relevant information. Individual site similarities can be analyzed in terms of their Pharmacophoric Interaction Field (PIF) similarity, and importantly the differences in their PIFs can be extracted. Here we describe the BioGPS approach, and demonstrate its applicability to rationalize off-target effects (ERα and SERCA), to classify protein families and explain polypharmacology (ABL1 kinase and NQO2), and to rationalize selectivity between subfamilies (MAP kinases p38α/ERK2 and PPARδ/PPARγ). The examples shown demonstrate a significant validation of the method and illustrate the effectiveness of the approach.

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Discovery of Novel and Cardioselective Diltiazem-like Calcium Channel Blockers via Virtual Screening

Cited by 26 publications

References 64 publications

Ligand-based design, in silico ADME-Tox filtering, synthesis and biological evaluation to discover new soluble 1,4-DHP-based CFTR activators

Ligand-based design, in silico ADME-Tox filtering, synthesis and biological evaluation to discover new soluble 1,4-DHP-based CFTR activators

A Novel Approach for Predicting P-Glycoprotein (ABCB1) Inhibition Using Molecular Interaction Fields

BioGPS: Navigating biological space to predict polypharmacology, off-targeting, and selectivity

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