Drug-induced phospholipidosis caused by combinations of common drugs in vitro.

Drug-induced phospholipidosis (DIPLD), characterized by the accumulation of phospholipids within lysosomes, is suspected to impair lysosomal function and considered an adverse side effect of the administered medication. The increasing use of polypharmacy and the resultant elevated risks of adverse drug reactions raise the need to explore the effects of drug combinations with respect to their influence on side effects, such as DIPLD. In this study, we utilized an in vitro assay to investigate DIPLD that was caused by 24 commonly used drugs applied alone and in binary combinations with each other. Moreover, we attempted to predict the extent of DIPLD resulting from the combinations using a simple additive approach based on the increase in phospholipid levels caused by the single drugs. The results suggest that DIPLD, which was caused by combinations of drugs, occurs in an additive manner, depending on total drug concentration. Furthermore, we show that the extent of DIPLD can be predicted from the DIPLD caused by the single drugs. Thus, the simultaneous use of multiple drugs with PLD-inducing properties increases the event risk, as well as the severity of drug-induced phospholipidosis. The findings underline the importance of considering the DIPLD-inducing properties of drugs, especially in the context of polypharmacy.

Functional inhibitors of acid sphingomyelinase (FIASMAs).

Sphingolipids are not only structural components of biological membranes, but also play an important role in cellular signalling and, thus, are involved in cell proliferation and differentiation but also stress and cell death. It is therefore of great clinical relevance to define inhibitors of the enzymes involved in sphingolipid metabolism. Here, we describe the state of the art of functional inhibitors of the acid sphingomyelinase. The acid sphingomyelinase converts sphingomyelin to ceramide, a compound often involved in cell stress. We describe the structural and physicochemical properties, the distribution, the pharmacokinetics, the pharmocodynamics and the clinical use of direct and functional inhibitors of the acid sphingomyelinase.

Identification of PPARgamma partial agonists of natural origin (I): development of a virtual screening procedure and in vitro validation.

BACKGROUND: Although there are successful examples of the discovery of new PPARγ agonists, it has recently been of great interest to identify new PPARγ partial agonists that do not present the adverse side effects caused by PPARγ full agonists. Consequently, the goal of this work was to design, apply and validate a virtual screening workflow to identify novel PPARγ partial agonists among natural products. METHODOLOGY/PRINCIPAL FINDINGS: We have developed a virtual screening procedure based on structure-based pharmacophore construction, protein-ligand docking and electrostatic/shape similarity to discover novel scaffolds of PPARγ partial agonists. From an initial set of 89,165 natural products and natural product derivatives, 135 compounds were identified as potential PPARγ partial agonists with good ADME properties. Ten compounds that represent ten new chemical scaffolds for PPARγ partial agonists were selected for in vitro biological testing, but two of them were not assayed due to solubility problems. Five out of the remaining eight compounds were confirmed as PPARγ partial agonists: they bind to PPARγ, do not or only moderately stimulate the transactivation activity of PPARγ, do not induce adipogenesis of preadipocyte cells and stimulate the insulin-induced glucose uptake of adipocytes. CONCLUSIONS/SIGNIFICANCE: We have demonstrated that our virtual screening protocol was successful in identifying novel scaffolds for PPARγ partial agonists.

Identification of drugs inducing phospholipidosis by novel in vitro data.

Drug-induced phospholipidosis (PLD) is a lysosomal storage disorder characterized by the accumulation of phospholipids within the lysosome. This adverse drug effect can occur in various tissues and is suspected to impact cellular viability. Therefore, it is important to test chemical compounds for their potential to induce PLD during the drug design process. PLD has been reported to be a side effect of many commonly used drugs, especially those with cationic amphiphilic properties. To predict drug-induced PLD in silico, we established a high-throughput cell-culture-based method to quantitatively determine the induction of PLD by chemical compounds. Using this assay, we tested 297 drug-like compounds at two different concentrations (2.5 µM and 5.0 µM). We were able to identify 28 previously unknown PLD-inducing agents. Furthermore, our experimental results enabled the development of a binary classification model to predict PLD-inducing agents based on their molecular properties. This random forest prediction system yields a bootstrapped validated accuracy of 86 %. PLD-inducing agents overlap with those that target similar biological processes; a high degree of concordance with PLD-inducing agents was identified for cationic amphiphilic compounds, small molecules that inhibit acid sphingomyelinase, compounds that cross the blood-brain barrier, and compounds that violate Lipinski's rule of five. Furthermore, we were able to show that PLD-inducing compounds applied in combination additively induce PLD.

Qualitative prediction of blood-brain barrier permeability on a large and refined dataset.

The prediction of blood-brain barrier permeation is vitally important for the optimization of drugs targeting the central nervous system as well as for avoiding side effects of peripheral drugs. Following a previously proposed model on blood-brain barrier penetration, we calculated the cross-sectional area perpendicular to the amphiphilic axis. We obtained a high correlation between calculated and experimental cross-sectional area (r = 0.898, n = 32). Based on these results, we examined a correlation of the calculated cross-sectional area with blood-brain barrier penetration given by logBB values. We combined various literature data sets to form a large-scale logBB dataset with 362 experimental logBB values. Quantitative models were calculated using bootstrap validated multiple linear regression. Qualitative models were built by a bootstrapped random forest algorithm. Both methods found similar descriptors such as polar surface area, pKa, logP, charges and number of positive ionisable groups to be predictive for logBB. In contrast to our initial assumption, we were not able to obtain models with the cross-sectional area chosen as relevant parameter for both approaches. Comparing those two different techniques, qualitative random forest models are better suited for blood-brain barrier permeability prediction, especially when reducing the number of descriptors and using a large dataset. A random forest prediction system (n(trees) = 5) based on only four descriptors yields a validated accuracy of 88%.

Identification of novel functional inhibitors of acid sphingomyelinase.

We describe a hitherto unknown feature for 27 small drug-like molecules, namely functional inhibition of acid sphingomyelinase (ASM). These entities named FIASMAs (Functional Inhibitors of Acid SphingoMyelinAse), therefore, can be potentially used to treat diseases associated with enhanced activity of ASM, such as Alzheimer's disease, major depression, radiation- and chemotherapy-induced apoptosis and endotoxic shock syndrome. Residual activity of ASM measured in the presence of 10 µM drug concentration shows a bimodal distribution; thus the tested drugs can be classified into two groups with lower and higher inhibitory activity. All FIASMAs share distinct physicochemical properties in showing lipophilic and weakly basic properties. Hierarchical clustering of Tanimoto coefficients revealed that FIASMAs occur among drugs of various chemical scaffolds. Moreover, FIASMAs more frequently violate Lipinski's Rule-of-Five than compounds without effect on ASM. Inhibition of ASM appears to be associated with good permeability across the blood-brain barrier. In the present investigation, we developed a novel structure-property-activity relationship by using a random forest-based binary classification learner. Virtual screening revealed that only six out of 768 (0.78%) compounds of natural products functionally inhibit ASM, whereas this inhibitory activity occurs in 135 out of 2028 (6.66%) drugs licensed for medical use in humans.

Conformation-dependent QSPR models: logPOW.

Quantitative structure-property relationships for predicting the water-octanol partition coefficient, logP(OW), are reported. The models are based on local properties calculated at the standard isodensity surface using semiempirical molecular orbital theory and use descriptors obtained as the areas of the surface found in each bin in a predefined binning scheme. The effect of conformation is taken into account but was found to have little effect on the predictive power of the models. A detailed error analysis suggests that the accuracy of the models is limited by that of the experimental data and that the best possible performance is approximately ±0.5 log units. The models yield a local hydrophobicity function at the surface of the molecules.

Functional Inhibitors of Acid Sphingomyelinase (FIASMAs): a novel pharmacological group of drugs with broad clinical applications.

Acid sphingomyelinase (ASM) is an important lipid-metabolizing enzyme cleaving sphingomyelin to ceramide, mainly within lysosomes. Acid ceramidase (AC) further degrades ceramide to sphingosine which can then be phosphorylated to sphingosine-1-phosphate. Ceramide and its metabolite sphingosine-1-phosphate have been shown to antagonistically regulate apoptosis, cellular differentiation, proliferation and cell migration. Inhibitors of ASM or AC therefore hold promise for a number of new clinical therapies, e.g. for Alzheimer's disease and major depression on the one hand and cancer on the other. Inhibitors of ASM have been known for a long time. Cationic amphiphilic substances induce the detachment of ASM protein from inner lysosomal membranes with its consecutive inactivation, thereby working as functional inhibitors of ASM. We recently experimentally identified a large number of hitherto unknown functional inhibitors of ASM and determined specific physicochemical properties of such cationic amphiphilic substances that functionally inhibit ASM. We propose the acronym "FIASMA" (Functional Inhibitor of Acid SphingoMyelinAse) for members of this large group of compounds with a broad range of new clinical indications. FIASMAs differ markedly with respect to molecular structure and current clinical indication. Most of the available FIASMAs are licensed for medical use in humans, are minimally toxic and may therefore be applied for disease states associated with increased activity of ASM.