Identification Of Off-targets Research Articles

Dynamic Comparison of Binding Pocket of Berberine Protein Crystals

Introduction: Berberine is an important isoquinoline alkaloid that has various pharmacological properties. The comparison of the binding pocket similarity of berberine is regarded as the starting point for deciphering various activities. Eight berberine protein crystals were clustered and studied by molecular dynamics (MD) simulations to investigate common features of berberine binding pockets. Method: Root Mean Square Deviation (RMSD) results showed that berberine was able to bind to each protein in a stable manner. Residue analysis showed that the stability of residue composition of different protein pockets varied. This is also consistent with the results of the pocket similarity analysis: PS-score curves of most proteins fluctuated to varying degrees. The binding pocket of 3BTI in homogeneous protein analysis exhibited high stability (PS-scoremean = 0.703 and PS-scoremin = 0.5664). Pocket similarity analysis between two heterologous proteins showed that most of PS-score values were in the interval of 0.3-0.35, and PS-score values of 3D6Y were relatively high when compared with the other three proteins. Results: Pocket residue matching analysis showed that GLU145/VAL147/ILE182/TYR229/GLU253 in 3D6Y can be matched structurally to the corresponding residues in 1JUM, 2QVD, and 5Y0V, respectively, which can be considered as an important pocket feature for the berberine binding. Nevertheless, the obtained matched residues are limited to the category of pocket structural similarity. Conclusion: This was the first study in which dynamic comparison of berberine binding pockets were used to discover pocket patterns. These results were of great significance for the polypharmacological study, the identification of potential off-targets, and the repurposing of berberine. conclusion: This was the first study in which dynamic comparisons of berberine binding pockets were used to discover interaction patterns. These results were of great significance for the polypharmacological study, the identification of potential off-targets, and the repurposing of berberine.

STPP-UP: An alternative method for drug target identification using protein thermal stability

Thermal proteome profiling (TPP) has significantly advanced the field of drug discovery by facilitating proteome-wide identification of drug targets and off-targets. However, TPP has not been widely applied for high-throughput drug screenings, since the method is labor intensive and requires a lot of measurement time on a mass spectrometer. Here, we present Single-tube TPP with Uniform Progression (STPP-UP), which significantly reduces both the amount of required input material and measurement time, while retaining the ability to identify drug targets for compounds of interest. By using incremental heating of a single sample, changes in protein thermal stability across a range of temperatures can be assessed, while alleviating the need to measure multiple samples heated to different temperatures. We demonstrate that STPP-UP is able to identify the direct interactors for anticancer drugs in both human and mice cells. In summary, the STPP-UP methodology represents a useful tool to advance drug discovery and drug repurposing efforts.

A massively parallel approach for assessing CRISPR off-targets in vitro

In a recent issue of Med, Tian etal.1 present AID-seq, an approach that enables massively parallel identification of off-targets for different CRISPR nucleases invitro. By using a pooled strategy to simultaneously identify the on-/off-targets of multiple gRNAs, the authors could screen the most efficient and safe gRNA candidates.

Molecular insights on bioactive compounds againstCovid-19: A Network pharmacological and computational study.

Network pharmacology based identification of phytochemicals in the form of cocktails against off-targets can play a significant role in inhibition of SARS_CoV2 viral entry and its propagation. This study includes network pharmacology, virtual screening, docking and molecular dynamics to investigate the distinct antiviral mechanisms of effective phytochemicals against SARS_CoV2. SARS_CoV2 human-protein interaction network was explored from the BioGRID database and analysed using Cytoscape. Further analysis was performed to explore biological function, protein-phytochemical/drugs network and up-down regulation of pathological host target proteins. This lead to understand the antiviral mechanism of phytochemicals against SARS_CoV2. The network was explored through g:Profiler, EnrichR, CTD, SwissTarget, STITCH, DrugBank, BindingDB, STRING and SuperPred. Virtual screening of phytochemicals against potential antiviral targets such as M-Pro, NSP1, Receptor binding domain, RNA binding domain, and ACE2 discloses the effective interaction between them. Further, the binding energy calculations through simulation of the docked complex explains the efficiency and stability of the interactions. The network analysis identified quercetin, genistein, luteolin, eugenol, berberine, isorhamnetin and cinnamaldehyde to be interacting with host proteins ACE2, DPP4, COMT, TUBGCP3, CENPF, BRD2 and HMOX1 which are involved in antiviral mechanisms such as viral entry, viral replication, host immune response, and antioxidant activity. Thus indicating that herbal cocktails can effectively tackle the viral hijacking of the crucial biological functions of human host. Further exploration through Virtual screening, docking and molecular dynamics recognizes the effective interaction of phytochemicals such as punicalagin, scutellarin, and solamargine with their respective potential targets. This work illustrates probable strategy for identification of phytochemical based cocktails and off-targets which are effective against SARS_CoV 2.

CaSilico: A versatile CRISPR package for in silico CRISPR RNA designing for Cas12, Cas13, and Cas14.

The efficiency of the CRISPR-Cas system is highly dependent on well-designed CRISPR RNA (crRNA). To facilitate the use of various types of CRISPR-Cas systems, there is a need for the development of computational tools to design crRNAs which cover different CRISPR-Cas systems with off-target analysis capability. Numerous crRNA design tools have been developed, but nearly all of them are dedicated to design crRNA for genome editing. Hence, we developed a tool matching the needs of both beginners and experts, named CaSilico, which was inspired by the limitations of the current crRNA design tools for designing crRNAs for Cas12, Cas13, and Cas14 CRISPR-Cas systems. This tool considers a comprehensive list of the principal rules that are not yet well described to design crRNA for these types. Using a list of important features such as mismatch tolerance rules, self-complementarity, GC content, frequency of cleaving base around the target site, target accessibility, and PFS (protospacer flanking site) or PAM (protospacer adjacent motif) requirement, CaSilico searches all potential crRNAs in a user-input sequence. Considering these features help users to rank all crRNAs for a sequence and make an informed decision about whether a crRNA is suited for an experiment or not. Our tool is sufficiently flexible to tune some key parameters governing the design of crRNA and identification of off-targets, which can lead to an increase in the chances of successful CRISPR-Cas experiments. CaSilico outperforms previous crRNA design tools in the following aspects: 1) supporting any reference genome/gene/transcriptome for which an FASTA file is available; 2) designing crRNAs that simultaneously target multiple sequences through conserved region detection among a set of sequences; 3) considering new CRISPR-Cas subtypes; and 4) reporting a list of different features for each candidate crRNA, which can help the user to select the best one. Given these capabilities, CaSilico addresses end-user concerns arising from the use of sophisticated bioinformatics algorithms and has a wide range of potential research applications in different areas, especially in the design of crRNA for pathogen diagnosis. CaSilico was successfully applied to design crRNAs for different genes in the SARS-CoV-2 genome, as some of the crRNAs have been experimentally tested in the previous studies.

Identification of novel off targets of baricitinib and tofacitinib by machine learning with a focus on thrombosis and viral infection

As there are no clear on-target mechanisms that explain the increased risk for thrombosis and viral infection or reactivation associated with JAK inhibitors, the observed elevated risk may be a result of an off-target effect. Computational approaches combined with in vitro studies can be used to predict and validate the potential for an approved drug to interact with additional (often unwanted) targets and identify potential safety-related concerns. Potential off-targets of the JAK inhibitors baricitinib and tofacitinib were identified using two established machine learning approaches based on ligand similarity. The identified targets related to thrombosis or viral infection/reactivation were subsequently validated using in vitro assays. Inhibitory activity was identified for four drug-target pairs (PDE10A [baricitinib], TRPM6 [tofacitinib], PKN2 [baricitinib, tofacitinib]). Previously unknown off-target interactions of the two JAK inhibitors were identified. As the proposed pharmacological effects of these interactions include attenuation of pulmonary vascular remodeling, modulation of HCV response, and hypomagnesemia, the newly identified off-target interactions cannot explain an increased risk of thrombosis or viral infection/reactivation. While further evidence is required to explain both the elevated thrombosis and viral infection/reactivation risk, our results add to the evidence that these JAK inhibitors are promiscuous binders and highlight the potential for repurposing.

Proteome-wide Identification of Off-Targets of a Potent EGFRL858R/T790M Mutant Inhibitor.

Target identification is an essential step in drug discovery. It facilitates an understanding of drug action and potential toxicities and offers opportunities to repurpose drug candidates. HP-1, a potent EGFRL858R/T790M (epidermal growth factor receptor) mutant inhibitor, was developed by the group in an effort to treat acquired resistance in nonsmall cell lung cancer (NSCLC), but its cellular off-targets were not identified. An activity-based probe, HJ-1, was created followed by chemical proteomics and bioimaging studies. A total of 13 protein hits, including EGFR and NT5DC1, were identified by pull-down/LC-MS. Subsequent validation experiments indicated the involvement of a major off-target, NT5DC1, in the biological function of HP-1.

Differential Gene Expression Profiles between N-Terminal Domain and Ligand-Binding Domain Inhibitors of Androgen Receptor Reveal Ralaniten Induction of Metallothionein by a Mechanism Dependent on MTF1.

Simple SummaryInhibition of the androgen receptor (AR) remains the mainstay treatment for prostate cancer. All current therapies involving AR inhibition either directly or indirectly target its ligand-binding domain (LBD). We have developed the first novel compounds which target the N-terminal domain, (NTD) a region which is essential for AR transcriptional activity. First-generation ralaniten (NCT02606123), and second-generation EPI-7386 (NCT04421222) remain the only AR-NTD inhibitors to progress to clinical trials. Here we aim to characterize differences between different classes of AR antagonists targeting the AR-LBD and the AR-NTD, as well as next generation AR-NTD inhibitors. An incidental finding was that ralaniten was uniquely associated with increased metallothionein expression which was independent of AR activity. Instead, expression of metallothionein genes was driven by MTF-1 indicating a potential off-target effect. Neither AR-LBD inhibitor enzalutamide nor second-generation AR-NTD inhibitor EPI-7170 had this effect. This work has important implications for the development of novel AR-NTD inhibitors.Hormonal therapies for prostate cancer target the androgen receptor (AR) ligand-binding domain (LBD). Clinical development for inhibitors that bind to the N-terminal domain (NTD) of AR has yielded ralaniten and its analogues. Ralaniten acetate is well tolerated in patients at 3600 mgs/day. Clinical trials are ongoing with a second-generation analogue of ralaniten. Binding sites on different AR domains could result in differential effects on AR-regulated gene expression. Here, we provide the first comparison between AR-NTD inhibitors and AR-LBD inhibitors on androgen-regulated gene expression in prostate cancer cells using cDNA arrays, GSEA, and RT-PCR. LBD inhibitors and NTD inhibitors largely overlapped in the profile of androgen-induced genes that they each inhibited. However, androgen also represses gene expression by various mechanisms, many of which involve protein–protein interactions. De-repression of the transcriptome of androgen-repressed genes showed profound variance between these two classes of inhibitors. In addition, these studies revealed a unique and strong induction of expression of the metallothionein family of genes by ralaniten by a mechanism independent of AR and dependent on MTF1, thereby suggesting this may be an off-target. Due to the relatively high doses that may be encountered clinically with AR-NTD inhibitors, identification of off-targets may provide insight into potential adverse events, contraindications, or poor efficacy.

Botanical drugs: a new strategy for structure-based target prediction.

Target identification of small molecules is an important and still changeling work in the area of drug discovery, especially for botanical drug development. Indistinct understanding of the relationships of ligand-protein interactions is one of the main obstacles for drug repurposing and identification of off-targets. In this study, we collected 9063 crystal structures of ligand-binding proteins released from January, 1995 to April, 2021 in PDB bank, and split the complexes into 5133 interaction pairs of ligand atoms and protein fragments (covalently linked three heavy atoms) with interatomic distance ≤5Å. The interaction pairs were grouped into ligand atoms with the same SYBYL atom type surrounding each type of protein fragment, which were further clustered via Bayesian Gaussian Mixture Model (BGMM). Gaussian distributions with ligand atoms ≥20 were identified as significant interaction patterns. Reliability of the significant interaction patterns was validated by comparing the difference of number of significant interaction patterns between the docked poses with higher and lower similarity to the native crystal structures. Fifty-one candidate targets of brucine, strychnine and icajine involved in Semen Strychni (Mǎ Qián Zǐ) and eight candidate targets of astragaloside-IV, formononetin and calycosin-7-glucoside involved in Astragalus (Huáng Qí) were predicted by the significant interaction patterns, in combination with docking, which were consistent with the therapeutic effects of Semen Strychni and Astragalus for cancer and chronic pain. The new strategy in this study improves the accuracy of target identification for small molecules, which will facilitate discovery of botanical drugs.

Recent Advances in In Silico Target Fishing.

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

How We Think about Targeting RNA with Small Molecules.

RNA offers nearly unlimited potential as a target for small molecule chemical probes and lead medicines. Many RNAs fold into structures that can be selectively targeted with small molecules. This Perspective discusses molecular recognition of RNA by small molecules and highlights key enabling technologies and properties of bioactive interactions. Sequence-based design of ligands targeting RNA has established rules for affecting RNA targets and provided a potentially general platform for the discovery of bioactive small molecules. The RNA targets that contain preferred small molecule binding sites can be identified from sequence, allowing identification of off-targets and prediction of bioactive interactions by nature of ligand recognition of functional sites. Small molecule targeted degradation of RNA targets (ribonuclease-targeted chimeras, RIBOTACs) and direct cleavage by small molecules have also been developed. These growing technologies suggest that the time is right to provide small molecule chemical probes to target functionally relevant RNAs throughout the human transcriptome.

Plant-type pentatricopeptide repeat proteins with a DYW domain drive C-to-U RNA editing in Escherichia coli

RNA editing converting cytidines into uridines is a hallmark of gene expression in land plant chloroplasts and mitochondria. Pentatricopeptide repeat (PPR) proteins have a key role in target recognition, but the functional editosome in the plant organelles has remained elusive. Here we show that individual Physcomitrella patens DYW-type PPR proteins alone can perform efficient C-to-U editing in Escherichia coli reproducing the moss mitochondrial editing. Single amino acid exchanges in the DYW domain abolish RNA editing, confirming it as the functional cytidine deaminase. The modification of RNA targets and the identification of numerous off-targets in the E. coli transcriptome reveal nucleotide identities critical for RNA recognition and cytidine conversion. The straightforward amenability of the new E. coli setup will accelerate future studies on RNA target recognition through PPRs, on the C-to-U editing deamination machinery and towards future establishment of transcript editing in other genetic systems.

The Development of Benzimidazole-Based Clickable Probes for the Efficient Labeling of Cellular Protein Arginine Deiminases (PADs).

Citrullination is the post-translational hydrolysis of peptidyl-arginines to form peptidyl-citrulline, a reaction that is catalyzed by the protein arginine deiminases (PADs), a family of calcium-regulated enzymes. Aberrantly increased protein citrullination is associated with a slew of autoimmune diseases (e.g., rheumatoid arthritis (RA), multiple sclerosis, lupus, and ulcerative colitis) and certain cancers. Given the clear link between increased PAD activity and human disease, the PADs are therapeutically relevant targets. Herein, we report the development of next generation cell permeable and "clickable" probes (BB-Cl-Yne and BB-F-Yne) for covalent labeling of the PADs both in vitro and in cell-based systems. Using advanced chemoproteomic technologies, we also report the off targets of both BB-Cl-Yne and BB-F-Yne. The probes are highly specific for the PADs, with relatively few off targets, especially BB-F-Yne, suggesting the preferential use of the fluoroacetamidine warhead in next generation irreversible PAD inhibitors. Notably, these compounds can be used in a variety of modalities, including the identification of off targets of the parent compounds and as activity-based protein profiling probes in target engagement assays to demonstrate the efficacy of PAD inhibitors.

PatchSearch: A Fast Computational Method for Off-Target Detection.

Many therapeutic molecules are known to bind several proteins, which can be different from the initially targeted one. Such unexpected interactions with proteins called off-targets can lead to adverse effects. Potential off-target identification is important to predict to avoid drug side effects or to discover new targets for existing drugs. We propose a new program named PatchSearch that implements local nonsequential searching for similar binding sites on protein surfaces with a controlled amount of flexibility. It is based on detection of quasi-cliques in product graphs representing all the possible matchings between two compared structures. This method has been benchmarked on a large diversity of ligands and on five data sets ranging from 12 to more than 7000 protein structures. The experiments conducted in this study show that the PatchSearch method could be useful in the early identification of off-targets. The program and the benchmarks presented in this paper are available as an R package at https://github.com/MTiPatchSearch .

Identification of gefitinib off-targets using a structure-based systems biology approach; their validation with reverse docking and retrospective data mining.

Gefitinib, an EGFR tyrosine kinase inhibitor, is used as FDA approved drug in breast cancer and non-small cell lung cancer treatment. However, this drug has certain side effects and complications for which the underlying molecular mechanisms are not well understood. By systems biology based in silico analysis, we identified off-targets of gefitinib that might explain side effects of this drugs. The crystal structure of EGFR-gefitinib complex was used for binding pocket similarity searches on a druggable proteome database (Sc-PDB) by using IsoMIF Finder. The top 128 hits of putative off-targets were validated by reverse docking approach. The results showed that identified off-targets have efficient binding with gefitinib. The identified human specific off-targets were confirmed and further analyzed for their links with biological process and clinical disease pathways using retrospective studies and literature mining, respectively. Noticeably, many of the identified off-targets in this study were reported in previous high-throughput screenings. Interestingly, the present study reveals that gefitinib may have positive effects in reducing brain and bone metastasis, and may be useful in defining novel gefitinib based treatment regime. We propose that a system wide approach could be useful during new drug development and to minimize side effect of the prospective drug.

A high-throughput, multiplexed assay for superfamily-wide profiling of enzyme activity.

The selectivity of an enzyme inhibitor is a key determinant of its usefulness as a tool compound or its safety as a drug. Yet selectivity is never assessed comprehensively in the early stages of the drug discovery process, and only rarely even in the later stages, because technical limitations prohibit doing otherwise. Here, we report EnPlex, an efficient, high-throughput method for simultaneously assessing inhibitor potency and specificity, and pilot its application to 96 serine hydrolases. EnPlex analysis of widely used serine hydrolase inhibitors revealed numerous previously unrecognized off-target interactions, some of which may help to explain previously confounding adverse effects. In addition, EnPlex screening of a hydrolase-directed library of boronic acid- and nitrile-containing compounds provided dual potency/selectivity structure-activity relationships from which lead candidates could be more effectively prioritized. Follow-up of a series of dipeptidyl peptidase 4 (DPP4) inhibitors showed that EnPlex indeed predicted efficacy and safety in animal models. These results demonstrate the feasibility and value of high-throughput, superfamily-wide selectivity profiling, and suggest such profiling can be incorporated into the earliest stages of drug discovery.

"Minimalist" cyclopropene-containing photo-cross-linkers suitable for live-cell imaging and affinity-based protein labeling.

Target identification of bioactive compounds within the native cellular environment is important in biomedical research and drug discovery, but it has traditionally been carried out in vitro. Information about how such molecules interact with their endogenous targets (on and off) is currently highly limited. An ideal strategy would be one that recapitulates protein-small molecule interactions in situ (e.g., in living cells) and at the same time enables enrichment of these complexes for subsequent proteome-wide target identification. Similarly, small molecule-based imaging approaches are becoming increasingly available for in situ monitoring of a variety of proteins including enzymes. Chemical proteomic strategies for simultaneous bioimaging and target identification of noncovalent bioactive compounds in live mammalian cells, however, are currently not available. This is due to a lack of photoaffinity labels that are minimally modified from their parental compounds, yet chemically tractable using copper-free bioorthogonal chemistry. We have herein developed novel minimalist linkers containing both an alkyl diazirine and a cyclopropene. We have shown chemical probes (e.g., BD-2) made from such linkers could be used for simultaneous in situ imaging and covalent labeling of endogenous BRD-4 (an important epigenetic protein) via a rapid, copper-free, tetrazine-cyclopropene ligation reaction (k2 > 5 M(-1) s(-1)). The key features of our cyclopropenes, with their unique C-1 linkage to BRD-4-targeting moiety, are their tunable reactivity and solubility, relative stability, and synthetic accessibility. BD-2, which is a linker-modified analogue of (+)-JQ1 (a recently discovered nanomolar protein-protein-interaction inhibitor of BRD-4), was subsequently used in a cell-based proteome profiling experiment for large-scale identification of potential off-targets of (+)-JQ1. Several newly identified targets were subsequently confirmed by preliminary validation experiments.

Quantitative Chemical Proteomics Identifies Novel Targets of the Anti-cancer Multi-kinase Inhibitor E-3810

Novel drugs are designed against specific molecular targets, but almost unavoidably they bind non-targets, which can cause additional biological effects that may result in increased activity or, more frequently, undesired toxicity. Chemical proteomics is an ideal approach for the systematic identification of drug targets and off-targets, allowing unbiased screening of candidate interactors in their natural context (tissue or cell extracts).E-3810 is a novel multi-kinase inhibitor currently in clinical trials for its anti-angiogenic and anti-tumor activity. In biochemical assays, E-3810 targets primarily vascular endothelial growth factor and fibroblast growth factor receptors. Interestingly, E-3810 appears to inhibit the growth of tumor cells with low to undetectable levels of these proteins in vitro, suggesting that additional relevant targets exist. We applied chemical proteomics to screen for E-3810 targets by immobilizing the drug on a resin and exploiting stable isotope labeling by amino acids in cell culture to design experiments that allowed the detection of novel interactors and the quantification of their dissociation constant (Kd imm) for the immobilized drug. In addition to the known target FGFR2 and PDGFRα, which has been described as a secondary E-3810 target based on in vitro assays, we identified six novel candidate kinase targets (DDR2, YES, LYN, CARDIAK, EPHA2, and CSBP). These kinases were validated in a biochemical assay and—in the case of the cell-surface receptor DDR2, for which activating mutations have been recently discovered in lung cancer—cellular assays.Taken together, the success of our strategy—which integrates large-scale target identification and quality-controlled target affinity measurements using quantitative mass spectrometry—in identifying novel E-3810 targets further supports the use of chemical proteomics to dissect the mechanism of action of novel drugs.

Identification of distant drug off-targets by direct superposition of binding pocket surfaces.

Correctly predicting off-targets for a given molecular structure, which would have the ability to bind a large range of ligands, is both particularly difficult and important if they share no significant sequence or fold similarity with the respective molecular target (“distant off-targets”). A novel approach for identification of off-targets by direct superposition of protein binding pocket surfaces is presented and applied to a set of well-studied and highly relevant drug targets, including representative kinases and nuclear hormone receptors. The entire Protein Data Bank is searched for similar binding pockets and convincing distant off-target candidates were identified that share no significant sequence or fold similarity with the respective target structure. These putative target off-target pairs are further supported by the existence of compounds that bind strongly to both with high topological similarity, and in some cases, literature examples of individual compounds that bind to both. Also, our results clearly show that it is possible for binding pockets to exhibit a striking surface similarity, while the respective off-target shares neither significant sequence nor significant fold similarity with the respective molecular target (“distant off-target”).

High-throughput identification of off-targets for the mechanistic study of severe adverse drug reactions induced by analgesics

Drugs may induce adverse drug reactions (ADRs) when they unexpectedly bind to proteins other than their therapeutic targets. Identification of these undesired protein binding partners, called off-targets, can facilitate toxicity assessment in the early stages of drug development. In this study, a computational framework was introduced for the exploration of idiosyncratic mechanisms underlying analgesic-induced severe adverse drug reactions (SADRs). The putative analgesic-target interactions were predicted by performing reverse docking of analgesics or their active metabolites against human/mammal protein structures in a high-throughput manner. Subsequently, bioinformatics analyses were undertaken to identify ADR-associated proteins (ADRAPs) and pathways. Using the pathways and ADRAPs that this analysis identified, the mechanisms of SADRs such as cardiac disorders were explored. For instance, 53 putative ADRAPs and 24 pathways were linked with cardiac disorders, of which 10 ADRAPs were confirmed by previous experiments. Moreover, it was inferred that pathways such as base excision repair, glycolysis/glyconeogenesis, ErbB signaling, calcium signaling, and phosphatidyl inositol signaling likely play pivotal roles in drug-induced cardiac disorders. In conclusion, our framework offers an opportunity to globally understand SADRs at the molecular level, which has been difficult to realize through experiments. It also provides some valuable clues for drug repurposing.