Predicting the mechanism of phospholipidosis.

The mechanism of phospholipidosis is still not well understood. Numerous different mechanisms have been proposed, varying from direct inhibition of the breakdown of phospholipids to the binding of a drug compound to the phospholipid, preventing breakdown. We have used a probabilistic method, the Parzen-Rosenblatt Window approach, to build a model from the ChEMBL dataset which can predict from a compound's structure both its primary pharmaceutical target and other targets with which it forms off-target, usually weaker, interactions. Using a small dataset of 182 phospholipidosis-inducing and non-inducing compounds, we predict their off-target activity against targets which could relate to phospholipidosis as a side-effect of a drug. We link these targets to specific mechanisms of inducing this lysosomal build-up of phospholipids in cells. Thus, we show that the induction of phospholipidosis is likely to occur by separate mechanisms when triggered by different cationic amphiphilic drugs. We find that both inhibition of phospholipase activity and enhanced cholesterol biosynthesis are likely to be important mechanisms. Furthermore, we provide evidence suggesting four specific protein targets. Sphingomyelin phosphodiesterase, phospholipase A2 and lysosomal phospholipase A1 are shown to be likely targets for the induction of phospholipidosis by inhibition of phospholipase activity, while lanosterol synthase is predicted to be associated with phospholipidosis being induced by enhanced cholesterol biosynthesis. This analysis provides the impetus for further experimental tests of these hypotheses.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

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

A metabolomics cell-based approach for anticipating and investigating drug-induced liver injury

In preclinical stages of drug development, anticipating potential adverse drug effects such as toxicity is an important issue for both saving resources and preventing public health risks. Current in vitro cytotoxicity tests are restricted by their predictive potential and their ability to provide mechanistic information. This study aimed to develop a metabolomic mass spectrometry-based approach for the detection and classification of drug-induced hepatotoxicity. To this end, the metabolite profiles of human derived hepatic cells (i.e., HepG2) exposed to different well-known hepatotoxic compounds acting through different mechanisms (i.e., oxidative stress, steatosis, phospholipidosis, and controls) were compared by multivariate data analysis, thus allowing us to decipher both common and mechanism-specific altered biochemical pathways. Briefly, oxidative stress damage markers were found in the three mechanisms, mainly showing altered levels of metabolites associated with glutathione and γ-glutamyl cycle. Phospholipidosis was characterized by a decreased lysophospholipids to phospholipids ratio, suggestive of phospholipid degradation inhibition. Whereas, steatosis led to impaired fatty acids β-oxidation and a subsequent increase in triacylglycerides synthesis. The characteristic metabolomic profiles were used to develop a predictive model aimed not only to discriminate between non-toxic and hepatotoxic drugs, but also to propose potential drug toxicity mechanism(s)

ADDME – Avoiding Drug Development Mistakes Early: central nervous system drug discovery perspective

The advent of early absorption, distribution, metabolism, excretion, and toxicity (ADMET) screening has increased the attrition rate of weak drug candidates early in the drug-discovery process, and decreased the proportion of compounds failing in clinical trials for ADMET reasons. This paper reviews the history of ADMET screening and its place in pharmaceutical development, and central nervous system drug discovery in particular. Assays that have been developed in response to specific needs and improvements in technology that result in higher throughput and greater accuracy of prediction of human mechanisms of absorption and toxicity are discussed. The paper concludes with the authors' forecast of new models that will better predict human efficacy and toxicity

薬剤誘発性リン脂質症のバイオマーカーとしての血漿中馬尿酸とフェニルアセチルグリシンに関する研究

筑波大学 (University of Tsukuba)201

Studies on the mechanisms and consequences of drug-induced perturbations of lysosomal structure and function

From a clinical perspective, a drug's pharmacokinetic properties (e.g., the volume of distribution, clearance, and half-life) are vitally important as these parameters are used to establish the proper dosing regimen necessary to achieve a desired drug exposure. Failure to properly account for changes to a drug's expected pharmacokinetic properties, whether perpetrated by pharmacological insult (drug-drug interaction) or a disease state, can result in an improper dosing regimen that either fails to achieve therapeutic drug levels or, conversely, can result in drug levels that reach toxic concentrations. Identifying conditions that can alter the expected pharmacokinetic properties of drugs is therefore immensely important. Previous work in our lab has shown that lysosomotropic drugs, i.e., drugs that preferentially accumulate within lysosomes due to an ion trapping-type mechanism, cause a marked expansion in the volume of lysosomes which can result in a drug-drug interaction involving lysosomes. Although this novel drug-drug interaction was well characterized the mechanistic basis explaining how it developed was not established. Within this work we have explored the cellular mechanisms underlying the development of the drug-induced expansion in lysosomal volume and the ensuing drug-drug interaction. Our data shows that the drug-induced expansion in lysosomal volume is achieved through a combination of reduced vesicle-mediated trafficking out of the lysosomes, the induction of autophagy, and the activation of lysosome biogenesis. We have additionally explored a structure activity relationship which implicates a drug's amphiphilicity and lysosomotropic properties as important features that correlate with their propensity to induce an expansion in lysosomal volume. Overall, the data presented in this work can be used to help explain the sources of variability seen in the pharmacokinetic properties of lysosomotropic drugs. In parallel to these studies we have also explored potential treatment strategies that can reduce the bloated lysosomal volume seen in cells with dysfunctional lysosomes. Using two cell models that exhibit dysfunctional lysosomes (lysosomotropic drug-treated cells or lysosomal storage diseased cells), we have examined how vitamin E helps to recover lysosomal volume. Our data indicates that vitamin E reduces both ion trapping-dependent (aqueous volume of lysosomes) and ion trapping-independent (lipid binding) drug accumulation mechanisms within cells. To our knowledge, this is the first report detailing that vitamin E reduces the aqueous volume of lysosomes. This finding is important as it helps to more fully explain how vitamin E is eliciting its positive effects within cells. Additionally, we have also examined a structure activity relationship of vitamin E and its ability to reduce lysosomal volume. Our data indicates that the physical structure of vitamin E, rather than its antioxidant properties, is the primary feature of vitamin E that correlates with its ability to reduce the bloated lysosomal volume seen in cells with dysfunctional lysosomes. Overall, this data can be used as a foundation to stage additional studies that could ultimately lead to the development of more potent and better drug-like molecules that could be used to treat lysosomal storage diseases and the toxic effects of lysosomotropic drug-treatments

Defining molecular initiating events in the adverse outcome pathway framework for risk assessment.

Consumer and environmental safety decisions are based on exposure and hazard data, interpreted using risk assessment approaches. The adverse outcome pathway (AOP) conceptual framework has been presented as a logical sequence of events or processes within biological systems which can be used to understand adverse effects and refine current risk assessment practices in ecotoxicology. This framework can also be applied to human toxicology and is explored on the basis of investigating the molecular initiating events (MIEs) of compounds. The precise definition of the MIE has yet to reach general acceptance. In this work we present a unified MIE definition: an MIE is the initial interaction between a molecule and a biomolecule or biosystem that can be causally linked to an outcome via a pathway. Case studies are presented, and issues with current definitions are addressed. With the development of a unified MIE definition, the field can look toward defining, classifying, and characterizing more MIEs and using knowledge of the chemistry of these processes to aid AOP research and toxicity risk assessment. We also present the role of MIE research in the development of in vitro and in silico toxicology and suggest how, by using a combination of biological and chemical approaches, MIEs can be identified and characterized despite a lack of detailed reports, even for some of the most studied molecules in toxicology.The authors acknowledge the financial support of Unilever.This is the accepted manuscript. The final published version is available from ACS at http://dx.doi.org/10.1021/tx500345

Managing the challenge of drug-induced liver injury: a roadmap for the development and deployment of preclinical predictive models

Drug-induced liver injury (DILI) is a patient-specific, temporal, multifactorial pathophysiological process that cannot yet be recapitulated in a single in vitro model. Current preclinical testing regimes for the detection of human DILI thus remain inadequate. A systematic and concerted research effort is required to address the deficiencies in current models and to present a defined approach towards the development of new or adapted model systems for DILI prediction. This Perspective defines the current status of available models and the mechanistic understanding of DILI, and proposes our vision of a roadmap for the development of predictive preclinical models of human DILI