Computational Identification of a Phospholipidosis Toxicophore Using
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Bioorganic & Medicinal Chemistry 22 (2014) 6706–6714 Contents lists available at ScienceDirect Bioorganic & Medicinal Chemistry journal homepage: www.elsevier.com/locate/bmc Computational identification of a phospholipidosis toxicophore using 13C and 15N NMR-distance based fingerprints q Svetoslav H. Slavov a, Jon G. Wilkes a, Dan A. Buzatu a, Naomi L. Kruhlak b, James M. Willard b, ⇑ Joseph P. Hanig b, Richard D. Beger a, a Division of Systems Biology, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Rd., Jefferson, AR 72079, USA b Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA article info abstract Article history: Modified 3D-SDAR fingerprints combining 13C and 15N NMR chemical shifts augmented with inter-atomic Received 25 June 2014 distances were used to model the potential of chemicals to induce phospholipidosis (PLD). A curated Revised 13 August 2014 dataset of 328 compounds (some of which were cationic amphiphilic drugs) was used to generate Accepted 18 August 2014 3D-QSDAR models based on tessellations of the 3D-SDAR space with grids of different density. Composite Available online 27 August 2014 PLS models averaging the aggregated predictions from 100 fully randomized individual models were gen- erated. On each of the 100 runs, the activities of an external blind test set comprised of 294 proprietary Keywords: chemicals were predicted and averaged to provide composite estimates of their PLD-inducing potentials Phospholipidosis (PLD+ if PLD is observed, otherwise PLD ). The best performing 3D-QSDAR model utilized a grid with a Toxicophore À Fingerprint density of 8 ppm  8 ppm in the C–C region, 8 ppm  20 ppm in the C–N region and 20 ppm  20 ppm in Quantitative spectral data–activity the N–N region. The classification predictive performance parameters of this model evaluated on the relationship (3D-QSDAR) basis of the external test set were as follows: accuracy = 0.70, sensitivity = 0.73 and specificity = 0.66. A Molecular modeling projection of the most frequently occurring bins on the standard coordinate space suggested a toxico- phore composed of an aromatic ring with a centroid 3.5–7.5 Å distant from an amino-group. The presence of a second aromatic ring separated by a 4–5 Å spacer from the first ring and at a distance of between 5.5 Å and 7 Å from the amino-group was also associated with a PLD+ effect. These models provide com- parable predictive performance to previously reported models for PLD with the added benefit of being based entirely on non-confidential, publicly available training data and with good predictive performance when tested in a rigorous, external validation exercise. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creative- commons.org/licenses/by-nc-nd/3.0/). 1. Introduction structures; the presence of which is considered a characteristic sign of PLD.3 PLD is a condition caused by an excess accumulation Since the first observation of phospholipidosis (PLD) by Nelson of phospholipids induced in several cell types by a large variety of and Fitzhugh in 1948,1 it has been noted that some drugs may cationic amphiphilic drugs (CADs).4 Typical sub-structural features cause the appearance of microscopic foamy macrophages or cyto- present in the molecules of CADs are: (i) a hydrophobic ring plasmic vacuoles in the cells of various tissues.2 Further investiga- fragment and (ii) a hydrophilic amine moiety. Various possible tions revealed that these bodies consist of concentric myelin-like mechanisms resulting in the build-up of phospholipids have been proposed: (i) inhibition of lysosomal phospholipase activity; (ii) inhibition of lysosomal enzyme transport; (iii) enhanced phospho- 5 Abbreviations: 3D-SDAR, three-dimensional spectral data–activity relationship; lipid biosynthesis and (iv) enhanced cholesterol biosynthesis. AM1, Austin model 1; CAD, cationic amphiphilic drug; FDA, food and drug Although each of these mechanisms is plausible, later research administration; PLD, phospholipidosis; PLS-DA, partial least squares—discriminant indicated that PLD is often species, strain (as evidenced by the var- analysis; PLWG, phospholipidosis working group; QSAR, quantitative structure– activity relationship. iation in PLD manifestation in Fischer 344 and Sprague Dawley q The views presented in this article are those of the authors and do not rats) and age, but not organ specific, thus implying a highly com- 6 necessarily reflect those of the US Food and Drug Administration. No official plex PLD mechanism. After more than 65 years of extensive endorsement is intended nor should be inferred. research, PLD remains an unresolved issue due to unclear molecu- ⇑ Corresponding author. Tel.: +1 (870) 543 7080; fax: +1 (870) 543 7686. lar causes, and its intermittent relationship with a variety of E-mail address: [email protected] (R.D. Beger). http://dx.doi.org/10.1016/j.bmc.2014.08.021 0968-0896/Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). S.H. Slavov et al. / Bioorg. Med. Chem. 22 (2014) 6706–6714 6707 adverse drug reactions.7 Although basic knowledge as to whether 2. Dataset the accumulation of phospholipids is harmful to human health8 is still lacking, PLD’s resemblance to lysosomal storage diseases A curated set of 331 compounds described by Goracci18 was such as Niemann-Pick type C (which may be fatal and can cause used to develop 3D-QSDAR models. These models were utilized severe toxicities9) warrants an increased attention to drugs with to carry out blind predictions for an external test set of 296 potential to cause PLD. proprietary chemicals contained in the PLWG database. The chem- Drug-induced phospholipidosis (DIPLD) is recognized as a ical names of the compounds comprising the modeling set as well recurring preclinical finding in the drug development process.8 as their experimental and predicted PLD class are listed in The process of drug development can be seriously affected by the Table SI1. The external test set compounds are proprietary and, occurrence of PLD which can cause not only delays, but potentially respectively, their chemical names are not disclosed, as per Federal a termination of a project.10 It has been estimated that approxi- Regulations. Chemical names will become available on request as mately 5% of the approved drugs cause DIPLD,4 which is of concern they become releasable when and if they enter the public domain. to both pharmaceutical companies and regulators. In light of a Because 3D-QSDAR utilizes fingerprints requiring at least 2 car- recent report establishing a relationship between the potential of bon atoms, chemicals with fewer carbons in their structures were a compound to cause PLD and QT prolongation,11 methods to pre- excluded. The excluded compounds were as follows: chloroform, dict the potential of drug candidates to cause PLD are desirable. hydrazine and carbon tetrachloride from the modeling set (all were An early attempt to model PLD by QSAR suggested that com- PLDÀ) and one compound from the external test set (PLD+). One 2 2 pounds with a pKa >8 and a logP >1 for which logP +pKa >90 have inactive chemical (PLDÀ) from the external test set for which the a potential to cause PLD.12 Similar models (involving more sophis- simulation of the 13C and 15N spectra failed was also excluded. ticated descriptors and algorithms), with somewhat broader appli- Information about the total number of PLD+ and PLDÀ compounds cability have been reported by Tomizawa et al.,13 Pelletier et al.,14 constituting the modeling and the external test sets is summarized Hanumegowda et al.15 and Choi et al.16 However, since all of the in Table 2. above models were based on whole molecule parameters such as the lipophilicity (logP), the ionization state (pKa) or various topo- 3. Methods logical descriptors, no specific structural fragments or mechanisms of action were pinpointed. More recent modeling attempts based 3.1. Fingerprint construction on the FDA PLWG database, which combines findings in a variety of tissue types collected from animals (and in some cases con- The 3D-QSDAR approach is a grid based approach, which firmed by electron microscopy of human tissues collected posthu- utilizes descriptors generated by tessellation of molecular finger- mously or from a biopsy) are summarized in Table 1. As a general prints constructed from NMR chemical shifts of pairs of atoms rule, well-balanced models for which the accuracy, the sensitivity (on the X and Y axes) and their corresponding inter-atomic dis- and the specificity are close to each-other are preferable. tances (on the Z-axis). Depending on the density of the grid, the Comparing the inconsistences between the reported PLD-induc- fingerprints’ tessellation generates large descriptor matrices often 18 ing potentials of seven published datasets Goracci et al. exceeding several thousand columns, which are then processed 4 constructed a curated subset of the PLWG database containing by PLS. Earlier versions of 3D-QSDAR relied only on 13C NMR chem- 331 compounds. ical shifts and carbon-to-carbon distances, which as highly sensi- To extend on these earlier studies, we attempted to identify tive to their immediate chemical environment allowed potential ‘‘toxicophores’’ in the structures of CADs using an construction of accurate 3D-QSDAR models.19–21 Because the enhanced 3D-QSDAR (three-dimensional spectral data–activity majority of the PLD chemicals contained at least one nitrogen atom 13 15 relationship) approach based on C and N chemical shifts and in their structures, the original methodology relying only on 13C atom-to-atom distances. Since the 3D-QSDAR description of the chemical shifts and carbon-to-carbon distances was extended to molecular structure is atom specific, the identification of particular include 15N chemical shifts and their associated inter-atomic atoms or functional groups and their spatial arrangement associ- distances.