clozapine and Lipidoses

clozapine has been researched along with Lipidoses* in 2 studies

Other Studies

2 other study(ies) available for clozapine and Lipidoses

Article	Year
In silico assay for assessing phospholipidosis potential of small druglike molecules: training, validation, and refinement using several data sets. Journal of medicinal chemistry, 2012, Jan-12, Volume: 55, Issue:1 Phospholipidosis (PLD) is a lysosomal storage disorder induced by compounds, notably cationic amphiphilic drugs, which although reversible interferes with cellular phospholipids.The in silico method described utilizes the amphiphilic moment ΔΔG(AM) (kJ/mol) together with basic pK(a) values to assign PLD inducing potential to a compound. The new model was accurate and sensitive (85% and 82%, respectively) when compared to other data sets. Therefore, the parallel in vitro assay for PLD was discontinued. The data reinforce our view that the amphiphilic moment is far more informative for determining a compound's potential to induce PLD than the combined use of basic pK(a) and ClogP values. Topics: Animals; Cattle; Cells, Cultured; Computer Simulation; Cornea; Drug-Related Side Effects and Adverse Reactions; Fibroblasts; Lipidoses; Lysosomal Storage Diseases; Models, Molecular; Pharmaceutical Preparations; Phospholipids; Structure-Activity Relationship; Thermodynamics	2012
Predicting phospholipidosis using machine learning. Molecular pharmaceutics, 2010, Oct-04, Volume: 7, Issue:5 Phospholipidosis is an adverse effect caused by numerous cationic amphiphilic drugs and can affect many cell types. It is characterized by the excess accumulation of phospholipids and is most reliably identified by electron microscopy of cells revealing the presence of lamellar inclusion bodies. The development of phospholipidosis can cause a delay in the drug development process, and the importance of computational approaches to the problem has been well documented. Previous work on predictive methods for phospholipidosis showed that state of the art machine learning methods produced the best results. Here we extend this work by looking at a larger data set mined from the literature. We find that circular fingerprints lead to better models than either E-Dragon descriptors or a combination of the two. We also observe very similar performance in general between Random Forest and Support Vector Machine models. Topics: Animals; Artificial Intelligence; Databases, Factual; Drug Discovery; Humans; Lipidoses; Models, Biological; Phospholipids; Support Vector Machine	2010

Article

Year

In silico assay for assessing phospholipidosis potential of small druglike molecules: training, validation, and refinement using several data sets.

Journal of medicinal chemistry, 2012, Jan-12, Volume: 55, Issue:1

Phospholipidosis (PLD) is a lysosomal storage disorder induced by compounds, notably cationic amphiphilic drugs, which although reversible interferes with cellular phospholipids.The in silico method described utilizes the amphiphilic moment ΔΔG(AM) (kJ/mol) together with basic pK(a) values to assign PLD inducing potential to a compound. The new model was accurate and sensitive (85% and 82%, respectively) when compared to other data sets. Therefore, the parallel in vitro assay for PLD was discontinued. The data reinforce our view that the amphiphilic moment is far more informative for determining a compound's potential to induce PLD than the combined use of basic pK(a) and ClogP values.

Topics: Animals; Cattle; Cells, Cultured; Computer Simulation; Cornea; Drug-Related Side Effects and Adverse Reactions; Fibroblasts; Lipidoses; Lysosomal Storage Diseases; Models, Molecular; Pharmaceutical Preparations; Phospholipids; Structure-Activity Relationship; Thermodynamics

2012

Predicting phospholipidosis using machine learning.

Molecular pharmaceutics, 2010, Oct-04, Volume: 7, Issue:5

Phospholipidosis is an adverse effect caused by numerous cationic amphiphilic drugs and can affect many cell types. It is characterized by the excess accumulation of phospholipids and is most reliably identified by electron microscopy of cells revealing the presence of lamellar inclusion bodies. The development of phospholipidosis can cause a delay in the drug development process, and the importance of computational approaches to the problem has been well documented. Previous work on predictive methods for phospholipidosis showed that state of the art machine learning methods produced the best results. Here we extend this work by looking at a larger data set mined from the literature. We find that circular fingerprints lead to better models than either E-Dragon descriptors or a combination of the two. We also observe very similar performance in general between Random Forest and Support Vector Machine models.

Topics: Animals; Artificial Intelligence; Databases, Factual; Drug Discovery; Humans; Lipidoses; Models, Biological; Phospholipids; Support Vector Machine

2010