Development of a Phospholipidosis Database and Predictive Quantitative Structure-Activity Relationship (QSAR) Models

AMALPHI: A Machine Learning Platform for Predicting Drug-Induced PhospholIpidosis

Drug-induced phospholipidosis (PLD) involves the accumulation of phospholipids in cells of multiple tissues, particularly within lysosomes, and it is associated with prolonged exposure to druglike compounds, predominantly cationic amphiphilic drugs (CADs). PLD affects a significant portion of drugs currently in development and has recently been proven to be responsible for confounding antiviral data during drug repurposing for SARS-CoV-2. In these scenarios, it has become crucial to identify potential safe drug candidates in advance and distinguish them from those that may lead to false in vitro antiviral activity. In this work, we developed a series of machine learning classifiers with the aim of predicting the PLD-inducing potential of drug candidates. The models were built on a high-quality chemical collection comprising 545 curated small molecules extracted from ChEMBL v30. The most effective model, obtained using the balanced random forest algorithm, achieved high performance, including an AUC value computed in validation as high as 0.90. The model was made freely available through a user-friendly web platform named AMALPHI (https://www.ba.ic.cnr.it/softwareic/amalphiportal/), which can represent a valuable tool for medicinal chemists interested in conducting an early evaluation of PLD inducer potential.

Inhibition of lysosomal phospholipase A2 predicts drug-induced phospholipidosis

Phospholipidosis, the excessive accumulation of phospholipids within lysosomes, is a pathological response observed following exposure to many drugs across multiple therapeutic groups. A clear mechanistic understanding of the causes and implications of this form of drug toxicity has remained elusive. We previously reported the discovery and characterization of a lysosome-specific phospholipase A2 (PLA2G15) and later reported that amiodarone, a known cause of drug-induced phospholipidosis, inhibits this enzyme. Here, we assayed a library of 163 drugs for inhibition of PLA2G15 to determine whether this phospholipase was the cellular target for therapeutics other than amiodarone that cause phospholipidosis. We observed that 144 compounds inhibited PLA2G15 activity. Thirty-six compounds not previously reported to cause phospholipidosis inhibited PLA2G15 with IC₅₀ values less than 1 mM and were confirmed to cause phospholipidosis in an in vitro assay. Within this group, fosinopril was the most potent inhibitor (IC₅₀ 0.18 μM). Additional characterization of the inhibition of PLA2G15 by fosinopril was consistent with interference of PLA2G15 binding to liposomes. PLA2G15 inhibition was more accurate in predicting phospholipidosis compared with in silico models based on pKa and ClogP, measures of protonation, and transport-independent distribution in the lysosome, respectively. In summary, PLA2G15 is a primary target for cationic amphiphilic drugs that cause phospholipidosis, and PLA2G15 inhibition by cationic amphiphilic compounds provides a potentially robust screening platform for potential toxicity during drug development.

Photosensitized direct C-H fluorination and trifluoromethylation in organic synthesis

The importance of fluorinated products in pharmaceutical and medicinal chemistry has necessitated the development of synthetic fluorination methods, of which direct C-H fluorination is among the most powerful. Despite the challenges and limitations associated with the direct fluorination of unactivated C-H bonds, appreciable advancements in manipulating the selectivity and reactivity have been made, especially via transition metal catalysis and photochemistry. Where transition metal catalysis provides one strategy for C-H bond activation, transition-metal-free photochemical C-H fluorination can provide a complementary selectivity via a radical mechanism that proceeds under milder conditions than thermal radical activation methods. One exciting development in C-F bond formation is the use of small-molecule photosensitizers, allowing the reactions i) to proceed under mild conditions, ii) to be user-friendly, iii) to be cost-effective and iv) to be more amenable to scalability than typical photoredox-catalyzed methods. In this review, we highlight photosensitized C-H fluorination as a recent strategy for the direct and remote activation of C-H (especially C(sp3)-H) bonds. To guide the readers, we present the developing mechanistic understandings of these reactions and exemplify concepts to assist the future planning of reactions.

Characterizing Adversity of Lysosomal Accumulation in Nonclinical Toxicity Studies: Results from the 5th ESTP International Expert Workshop

Lysosomes have a central role in cellular catabolism, trafficking, and processing of foreign particles. Accumulation of endogenous and exogenous materials in lysosomes represents a common finding in nonclinical toxicity studies. Histologically, these accumulations often lack distinctive features indicative of lysosomal or cellular dysfunction, making it difficult to consistently interpret and assign adverse dose levels. To help address this issue, the European Society of Toxicologic Pathology organized a workshop where representative types of lysosomal accumulation induced by pharmaceuticals and environmental chemicals were presented and discussed. The expert working group agreed that the diversity of lysosomal accumulations requires a case-by-case weight-of-evidence approach and outlined several factors to consider in the adversity assessment, including location and type of cell affected, lysosomal contents, severity of the accumulation, and related pathological effects as evidence of cellular or organ dysfunction. Lysosomal accumulations associated with cytotoxicity, inflammation, or fibrosis were generally considered to be adverse, while those found in isolation (without morphologic or functional consequences) were not. Workshop examples highlighted the importance of thoroughly characterizing the biological context of lysosomal effects, including mechanistic data and functional in vitro readouts if available. The information provided here should facilitate greater consistency and transparency in the interpretation of lysosomal effects.

Detection of nanocarrier potentiation on drug induced phospholipidosis in cultured cells and primary hepatocyte spheroids by high content imaging and analysis

Considerable effort has been made to develop nanocarriers for controlled drug delivery over the last decade, while it remains unclear how the strength of adverse drug effect will be altered when a drug is loaded on the nanocarrier. Drug-induced phospholipidosis (DIP) is characterized with excessive accumulation of phospholipids in cells and is common for cationic amphiphilic drugs (CAD). Previously, we have reported that PEGylated graphene oxide (PEG-GO) loaded with several CAD can potentiate DIP. In current study, we extended our study on newly identified phospholipidosis (PLD) inducers that had been identified from the Library of Pharmacologically Active Compounds (LOPAC), to investigate if PEO-GO loaded with these CAD can alter DIP. Twenty-two CAD were respectively loaded on PEG-GO and incubated with RAW264.7, a macrophage cell line. The results showed that when a CAD was loaded on PEG-GO, its strength of PLD induction can be enhanced, unchanged or attenuated. PEG-GO loaded with Ifenprodil exhibited the highest PEG-GO potentiation effect compared to Ifenprodil treatment alone in RAW264.7 cells, and this effect was confirmed in human hepatocellular carcinoma HepG2, another cell line model for PLD induction. Primary hepatocyte culture and spheroids mimicking in vivo conditions were used to further validate nanocarrier potentiation on DIP by Ifenprodil. Stronger phospholipid accumulation was found in PEG-GO/Ifenprodil treated hepatocytes or spheroids than Ifenprodil treatment alone. Therefore, evidences were provided by us that nanocarriers may increase the adverse drug effects and guidance by regulatory agencies need to be drafted for the safe use of nanotechnology in drug delivery.

From the Cover: Potentiation of Drug-Induced Phospholipidosis In Vitro through PEGlyated Graphene Oxide as the Nanocarrier

Cationic amphiphilic drugs (CADs) are small molecules that can induce phospholipidosis (PLD), causing the intracellular accumulation of phospholipid in the lamellar bodies. Nanotechnology based drug delivery systems have been used widely, while it is unknown if drug-induced PLD (DIP) can be potentiated through drug retention by indigestible nanocarriers. Due to the high drug loading capacity of graphene, we investigated if PEGylated graphene oxide (PEG-GO) loaded with CAD could potentiate DIP. Tamoxifen induced the accumulation of NBD-PE, a fluorescence labeled phospholipid in human hepatoma HepG2 cells, while PEG-GO loaded with tamoxifen (PEG-GO/tamoxifen) further potentiated PLD. PEG-GO/tamoxifen induced more gene expression of PLD marker than tamoxifen alone. PEG-GO enhanced DIP was also observed for other CAD, indicating that nanocarrier potentiated DIP could be universal. More lamellar bodies were observed in PEG-GO/tamoxifen treated cells than tamoxifen alone by transmission electron microscopy. When compared with tamoxifen alone, PEG-GO/tamoxifen showed a delayed but potent PLD. In addition, the retarded PLD recovery by PEG-GO/tamoxifen indicated that the reversibility of DIP was interfered. Confocal microscopy revealed the increased number of lysosomes, greater expression of lysosomal associated membrane protein 2 (LAMP2) (a PLD marker), and an increase in the co-localization between lysosome/LAMP2 and NBD-PE by PEG-GO/tamoxifen rather than tamoxifen alone. Finally, we found that PEG-GO or/and tamoxifen-induced PLD seemed to have no correlation with autophagy. This research suggests pharmaceutical companies and regulatory agencies that if nanoparticles are used as the vectors for drug delivery, the adverse drug effects may be further potentiated probably through the long-term accumulation of nanocarriers.

Why are most phospholipidosis inducers also hERG blockers?

Recent reports have noted that a number of compounds that block the human Ether-à-go-go related gene (hERG) ion channel also induce phospholipidosis (PLD). To explore a hypothesis explaining why most PLD inducers are also hERG inhibitors, a modeling approach was undertaken with data sets comprised of 4096 compounds assayed for hERG inhibition and 5490 compounds assayed for PLD induction. To eliminate the chemical domain effect, a filtered data set of 567 compounds tested in quantitative high-throughput screening (qHTS) format for both hERG inhibition and PLD induction was constructed. Partial least squares (PLS) modeling followed by 3D-SDAR mapping of the most frequently occurring bins and projection on to the chemical structure suggested that both adverse effects are driven by similar structural features, namely two aromatic rings and an amino group forming a three-center toxicophore. Non-parametric U-tests performed on the original 3D-SDAR bins indicated that the distance between the two aromatic rings is the main factor determining the differences in activity; at distances of up to about 5.5 Å, a phospholipidotic compound would also inhibit hERG, while at longer distances, a sharp reduction of the PLD-inducing potential leaves only a well-pronounced hERG blocking effect. The hERG activity itself diminishes after the distance between the centroids of the two aromatic rings exceeds 12.5 Å. Further comparison of the two toxicophores revealed that the almost identical aromatic rings to amino group distances play no significant role in distinguishing between PLD and hERG activity. The hypothesis that the PLD toxicophore appears to be a subset of the hERG toxicophore explains why about 80% of all phospholipidotic chemicals (the remaining 20% are thought to act via a different mechanism) also inhibit the hERG ion channel. These models were further validated in large-scale qHTS assays testing 1085 chemicals for their PLD-inducing potential and 1570 compounds for hERG inhibition. After removal of the modeling and experimental inconclusive compounds, the area under the receiver-operating characteristic (ROC) curve was 0.92 for the PLD model and 0.87 for the hERG model. Due to the exceptional ability of these models to recognize safe compounds (negative predictive values of 0.99 for PLD and 0.94 for hERG were achieved), their use in regulatory settings might be particularly useful.

LRRK2 inhibitors and their potential in the treatment of Parkinson's disease: current perspectives

Major advances in understanding how genetics underlies Parkinson's disease (PD) have provided new opportunities for understanding disease pathogenesis and potential new targets for therapeutic intervention. One such target is leucine-rich repeat kinase 2 (LRRK2), an enigmatic enzyme implicated in both familial and idiopathic PD risk. Both academia and industry have promoted the development of potent and selective inhibitors of LRRK2, and these are currently being employed to assess the safety and efficacy of such compounds in preclinical models of PD. This review examines the evidence that LRRK2 kinase activity contributes to the pathogenesis of PD and outlines recent progress on inhibitor development and early results from preclinical safety and efficacy testing. This review also looks at some of the challenges remaining for translation of LRRK2 inhibitors to the clinic, if indeed this is ultimately warranted. As a disease with no current cure that is increasing in prevalence in line with an aging population, there is much need for developing new treatments for PD, and targeting LRRK2 is currently a promising option.

Toward a unifying strategy for the structure-based prediction of toxicological endpoints

Most computational methods used for the prediction of toxicity endpoints are based on the assumption that similar compounds have similar biological properties. This principle can be exploited using computational methods like read across or quantitative structure-activity relationships. However, there is no general agreement about which method is the most appropriate for quantifying compound similarity neither for exploiting the similarity principle in order to obtain reliable estimations of the compound properties. Moreover, optimal similarity metrics and modeling methods might depend on the characteristics of the endpoints and training series used in each case. This study describes a comparative analysis of the predictive performance of diverse similarity metrics and modeling methods in toxicological applications. A collection of two quantitative (n = 660, n = 1114) and three qualitative (n = 447, n = 905, n = 1220) datasets representing very different endpoints of interest in drug safety evaluation and rigorous methods were used to estimate the external predictive ability in each case. The results confirm that no single approach produces the best results in all instances, and the best predictions were obtained using different tools in different situations. The trends observed in this study were exploited to propose a unifying strategy allowing the use of the most suitable method for every compound. A comparison of the quality of the predictions obtained by the unifying strategy with those obtained by standard prediction methods confirmed the usefulness of the proposed approach.

Detection of phospholipidosis induction: a cell-based assay in high-throughput and high-content format

Drug-induced phospholipidosis is characterized by the accumulation of intracellular phospholipids in cells exposed to cationic amphiphilic drugs. The appearance of unicentric or multicentric multilamellar bodies viewed under an electron microscope (EM) is the morphological hallmark of phospholipidosis. Although the EM method is the gold standard for detecting cellular phospholipidosis, this method has its drawbacks, including low throughput, high cost, and unsuitability for screening a large chemical library. In this study, a cell-based phospholipidosis assay has been developed using the LipidTOX Red reagent in HepG2 cells and miniaturized into a 1536-well plate format. To validate this assay for high-throughput screening (HTS), the LOPAC library of 1280 compounds was screened using a quantitative HTS platform. A group of known phospholipidosis inducers, such as amiodarone, propranolol, chlorpromazine, desipramine, promazine, clomipramine, and amitriptyline, was identified by the screen, consistent with previous reports. Several novel phospholipidosis inducers, including NAN-190, ebastine, GR127935, and cis-(Z)-flupentixol, were identified in this study and confirmed using the EM method. These results demonstrate that this assay can be used to evaluate and profile large numbers of chemicals for drug-induced phospholipidosis.

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.

Drug induced phospholipidosis: an acquired lysosomal storage disorder

There is a strong association between lysosome enzyme deficiencies and monogenic disorders resulting in lysosomal storage disease. Of the more than 75 characterized lysosomal proteins, two thirds are directly linked to inherited diseases of metabolism. Only one lysosomal storage disease, Niemann-Pick disease, is associated with impaired phospholipid metabolism. However, other phospholipases are found in the lysosome but remain poorly characterized. A recent exception is lysosomal phospholipase A2 (group XV phospholipase A2). Although no inherited disorder of lysosomal phospholipid metabolism has yet been associated with a loss of function of this lipase, this enzyme may be a target for an acquired form of lysosomal storage, drug induced phospholipidosis. This article is part of a Special Issue entitled Phospholipids and Phospholipid Metabolism.

Structure based model for the prediction of phospholipidosis induction potential of small molecules

Drug-induced phospholipidosis (PLD), characterized by an intracellular accumulation of phospholipids and formation of concentric lamellar bodies, has raised concerns in the drug discovery community, due to its potential adverse effects. To evaluate the PLD induction potential, 4,161 nonredundant drug-like molecules from the National Institutes of Health Chemical Genomics Center (NCGC) Pharmaceutical Collection (NPC), the Library of Pharmacologically Active Compounds (LOPAC), and the Tocris Biosciences collection were screened in a quantitative high-throughput screening (qHTS) format. The potential of drug-lipid complex formation can be linked directly to the structures of drug molecules, and many PLD inducing drugs were found to share common structural features. Support vector machine (SVM) models were constructed by using customized atom types or Molecular Operating Environment (MOE) 2D descriptors as structural descriptors. Either the compounds from LOPAC or randomly selected from the entire data set were used as the training set. The impact of training data with biased structural features and the impact of molecule descriptors emphasizing whole-molecule properties or detailed functional groups at the atom level on model performance were analyzed and discussed. Rebalancing strategies were applied to improve the predictive power of the SVM models. Using the undersampling method, the consensus model using one-third of the compounds randomly selected from the data set as the training set achieved high accuracy of 0.90 in predicting the remaining two-thirds of the compounds constituting the test set, as measured by the area under the receiver operator characteristic curve (AUC-ROC).

Inroads to predict in vivo toxicology-an introduction to the eTOX Project

There is a widespread awareness that the wealth of preclinical toxicity data that the pharmaceutical industry has generated in recent decades is not exploited as efficiently as it could be. Enhanced data availability for compound comparison (“read-across”), or for data mining to build predictive tools, should lead to a more efficient drug development process and contribute to the reduction of animal use (3Rs principle). In order to achieve these goals, a consortium approach, grouping numbers of relevant partners, is required. The eTOX (“electronic toxicity”) consortium represents such a project and is a public-private partnership within the framework of the European Innovative Medicines Initiative (IMI). The project aims at the development of in silico prediction systems for organ and in vivo toxicity. The backbone of the project will be a database consisting of preclinical toxicity data for drug compounds or candidates extracted from previously unpublished, legacy reports from thirteen European and European operation-based pharmaceutical companies. The database will be enhanced by incorporation of publically available, high quality toxicology data. Seven academic institutes and five small-to-medium size enterprises (SMEs) contribute with their expertise in data gathering, database curation, data mining, chemoinformatics and predictive systems development. The outcome of the project will be a predictive system contributing to early potential hazard identification and risk assessment during the drug development process. The concept and strategy of the eTOX project is described here, together with current achievements and future deliverables.

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.

In silico toxicology models and databases as FDA Critical Path Initiative toolkits

In silico toxicology methods are practical, evidence-based and high throughput, with varying accuracy. In silico approaches are of keen interest, not only to scientists in the private sector and to academic researchers worldwide, but also to the public. They are being increasingly evaluated and applied by regulators. Although there are foreseeable beneficial aspects--including maximising use of prior test data and the potential for minimising animal use for future toxicity testing--the primary use of in silico toxicology methods in the pharmaceutical sciences are as decision support information. It is possible for in silico toxicology methods to complement and strengthen the evidence for certain regulatory review processes, and to enhance risk management by supporting a more informed decision regarding priority setting for additional toxicological testing in research and product development. There are also several challenges with these continually evolving methods which clearly must be considered. This mini-review describes in silico methods that have been researched as Critical Path Initiative toolkits for predicting toxicities early in drug development based on prior knowledge derived from preclinical and clinical data at the US Food and Drug Administration, Center for Drug Evaluation and Research.

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.

A strategy for risk management of drug-induced phospholipidosis

Drug-induced phospholipidosis (PL) is an excessive accumulation of phospholipids and drug in lysosomes. Phospholipidosis signals a change in cell membrane integrity and accumulation of intracellular drug or metabolite in tissues. The sensitivity and susceptibility of preclinical models to detect PL vary with therapeutic agents, and PL is expected to be reversible after discontinuation of drug treatment. The prevailing scientific opinion is that PL by itself is not adverse; however, some regulatory authorities consider PL to be adverse because a small number of chemicals are able to cause PL and concurrent organ toxicity. Until a greater understanding of PL emerges, a well-thought-out risk management strategy for PL will increase confidence in safety and improve selection and development of new drugs. This paper provides a tiered approach to risk management of drug-induced PL. It begins with use of in silico and in vitro tools to design and select compounds with reduced potential to produce PL. Early in vivo studies in two species are used to better characterize potential for toxicity and PL. Finally, routine risk management tools (i.e., translational biomarkers, assessment of reversibility) are used to support confidence in safety of compounds that induce PL in animals.

Screening for the drug-phospholipid interaction: correlation to phospholipidosis

Phospholipid bilayers represent a complex, anisotropic environment fundamentally different from bulk oil or octanol, for instance. Even "simple" drug association to phospholipid bilayers can only be fully understood if the slab-of-hydrocarbon approach is abandoned and the complex, anisotropic properties of lipid bilayers reflecting the chemical structures and organization of the constituent phospholipids are considered. The interactions of drugs with phospholipids are important in various processes, such as drug absorption, tissue distribution, and subcellular distribution. In addition, drug-lipid interactions may lead to changes in lipid-dependent protein activities, and further, to functional and morphological changes in cells, a prominent example being the phospholipidosis (PLD) induced by cationic amphiphilic drugs. Herein we briefly review drug-lipid interactions in general and the significance of these interactions in PLD in particular. We also focus on a potential causal connection between drug-induced PLD and steatohepatitis, which is induced by some cationic amphiphilic drugs.