Predicting Phospholipidosis Using Machine Learning

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.</p

Simulation-based cheminformatic analysis of organelle-targeted molecules: lysosomotropic monobasic amines

Cell-based molecular transport simulations are being developed to facilitate exploratory cheminformatic analysis of virtual libraries of small drug-like molecules. For this purpose, mathematical models of single cells are built from equations capturing the transport of small molecules across membranes. In turn, physicochemical properties of small molecules can be used as input to simulate intracellular drug distribution, through time. Here, with mathematical equations and biological parameters adjusted so as to mimic a leukocyte in the blood, simulations were performed to analyze steady state, relative accumulation of small molecules in lysosomes, mitochondria, and cytosol of this target cell, in the presence of a homogenous extracellular drug concentration. Similarly, with equations and parameters set to mimic an intestinal epithelial cell, simulations were also performed to analyze steady state, relative distribution and transcellular permeability in this non-target cell, in the presence of an apical-to-basolateral concentration gradient. With a test set of ninety-nine monobasic amines gathered from the scientific literature, simulation results helped analyze relationships between the chemical diversity of these molecules and their intracellular distributions

Clams - Transplanting

Transplantinghttps://digitalmaine.com/dmr_images/3081/thumbnail.jp

Uniform procedure of 1H NMR analysis of rat urine and toxicometabonomics Part II : Comparison of NMR profiles classification of hepatotoxicity

A procedure of nuclear magnetic resonance (NMR) urinalysis using pattern recognition is proposed for early detection of toxicity of investigational compounds in rats. The method is applied to detect toxicity upon administration of 13 toxic reference compounds and one nontoxic control compound (mianserine) in rats. The toxic compounds are expected to induce necrosis (bromobenzene, paracetamol, carbon tetrachloride, iproniazid, isoniazid, thioacetamide), cholestasis (α-naphthylisothiocyanate (ANIT), chlorpromazine, ethinylestradiol, methyltestosterone, ibuprofen), or steatosis (phenobarbital, tetracycline). Animals were treated daily for 2 or 4 days except for paracetamol and bromobenzene (1 and 2 days) and carbon tetrachloride (1 day only). Urine was collected 24 h after the first and second treatment. The animals were sacrificed 24 h after the last treatment, and NMR data were compared with liver histopathology as well as blood and urine biochemistry. Pathology and biochemistry showed marked toxicity in the liver at high doses of bromobenzene, paracetamol, carbon tetrachloride, ANIT, and ibuprofen. Thioacetamide and chlorpromazine showed less extensive changes, while the influences of iproniazid, isoniazid, phenobarbital, ethinylestradiol, and tetracycline on the toxic parameters were marginal or for methyltestosterone and mianserine negligible. NMR spectroscopy revealed significant changes upon dosing in 88 NMR biomarker signals preselected with the Procrustus Rotation method on principal component discriminant analysis (PCDA) plots. Further evaluation of the specific changes led to the identification of biomarker patterns for the specific types of liver toxicity. Comparison of our rat NMR PCDA data with histopathological changes reported in humans and/or rats suggests that rat NMR urinalysis can be used to predict hepatotoxicity. © The Author 2007. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved

Establishing bioequivalence in complete and incomplete data designs using AUCs.

Nonclinical in vivo animal studies have to be completed before starting clinical studies of the pharmacokinetic behavior of a drug in humans. The drug exposure in animal studies is often measured by the area under the concentration versus time curve (AUC). The classical complete data design where each animal is sampled for analysis at every time point is applicable for large animals only. In the case of small animals, where blood sampling is restricted, the batch design or the serial sampling design need to be considered. In batch designs, samples are taken more than once from each animal, but not at all time points. In serial sampling designs, only one sample is taken from each animal. In this article we derive the asymptotic distribution for the ratio of two AUCs and construct different confidence intervals, which are frequently used to assess bioequivalence. The performance of these intervals is then evaluated between the different designs in a simulation study. Additionally, the sample sizes required for the different designs are compared