SBIR-STTR Award

Three ideas motivate this project. First, bioactive small molecules often act on multiple targets- sometimes this "polypharmacology" is key to their efficacy, sometimes it is the source of unwanted side-effects, rarely is it entirely absent. Second, these off-targets may be predicted comprehensively across all of pharmacological space. The last motivating idea is that drugs are so rare that when one is found every effort should be made to discover areas where it might be useful. Here we seek new indications for established drugs, focusing in Aim 1 on two specific disease areas, and in Aim 2 on developing a comprehensive map of drug off-target interactions to guide future studies. To do so we will use a chemoinformatic method, the Similarity Ensemble Approach (SEA), which predicts associations among targets based on the ligands that bind to them. In proof-of-concept studies, it has predicted 36 previously unknown "off-targets" for 23 drugs, with confirmatory experimental affinities ranging from 1.2 to 14,000 nM. The specific aims are: Aim 1. To predict and test established drugs that act on targets in the areas of Multiple Sclerosis and cardiovascular disease. a. SEA predicts the molecular target for an investigational Multiple Sclerosis drug-until now, no good molecular target has been identified for this drug. This prediction will be tested experimentally in receptor-binding assays. b. If this drug is active at the receptor at relevant concentrations, we will subsequently screen for and design analogs that further optimize its activity for this target. Preliminary to second stage animal studies (to be conducted in a follow up project), we also will identify known receptor ligands that are dissimilar to the drug but that, because they bind to the same target, are expected to phenocopy its effect, establishing the relevance of this receptor for MS. c. In a pure drug repurposing effort, SEA suggests a novel receptor for an existing drug, and this receptor is a target for cardiovascular disease. This will be tested experimentally in ligand displacement assays. d. If confirmed, we will investigate optimization of affinity against this "off-target" receptor by testing drug analogs. Aim 2. To identify all addressable, high-likelihood off-targets for all FDA and worldwide drugs. The Similarity Ensemble Approach is model-free, and does not use particular structural or pharmacophore models, rather comparing all the chemical information in a drug or class of drugs against the same information in a set of ligands that have been established for a given target. SEA may thus be applied systematically and comprehensively, querying all target classes with all drugs and investigational drugs. We will therefore predict all likely off-targets for all 3665 FDA, worldwide and investigational drugs, across all targets for which ligands are known. a. To do so as comprehensively as possible, we will exploit the StARlite database of ligand-protein interactions; this database doubles the list of drug targets to 2,100 and doubles the number of ligands annotated for them to 455,000. This will provide a comprehensive view of drug off-target effects. b. We will correlate the new drug-target pairs in this map to known side effects for the drugs for which the new targets are relevant. In proof of concept studies, several of these will be tested in receptor binding assays. c. We also investigate those targets in the map that have been identified as current drug targets, testing several experimentally in proof-of-concept studies. This will be a first step in repurposing these drugs for new indications. Whereas these aims are ambitious, extensive preliminary results support their feasibility. Potent activity for these drugs would, in a longer-term project, be followed by animal efficacy studies for the disease. As these molecules are drugs already, animal efficacy would not long precede human trials.

Public Health Relevance:
Bioactive small molecules often act on multiple targets. Whether this is key to their efficacy or the origin of unwanted side effects, it can be predicted comprehensively. In this proposal we focus on drug repurposing in two specific disease areas, and more generally on developing a comprehensive map of drug off-target interactions to guide future studies.

Thesaurus Terms:
Active Follow-Up; Adverse Drug Effect; Adverse Effects; Affinity; Animals; Area; Assay; Binding; Binding (Molecular Function); Bioassay; Biologic Assays; Biological Assay; Cb2 Receptor; Cardiovascular Diseases; Chemicals; Data Banks; Data Bases; Databank, Electronic; Databanks; Database, Electronic; Databases; Disease; Disorder; Drug Delivery; Drug Delivery Systems; Drug Receptors; Drug Side Effects; Drug Targeting; Drug Targetings; Drugs; Drugs, Investigational; Evaluation; Future; Government; Human; Human, General; Immunosuppressants; Immunosuppressive Agents; Investigational Drugs; Investigational New Drugs; Ligand Binding; Ligands; Ms (Multiple Sclerosis); Man (Taxonomy); Man, Modern; Maps; Medication; Methods; Modeling; Molecular Interaction; Molecular Target; Multiple Sclerosis; Persons; Pharmaceutic Preparations; Pharmaceutical Preparations; Phenocopy; Proteins; Receptor Protein; Receptor, Cannabinoid, Cb2; Sclerosis, Disseminated; Source; Staging; Testing; Treatment Side Effects; Analog; Animal Efficacy; Base; Cardiovascular Disorder; Clinical Data Repository; Clinical Data Warehouse; Data Repository; Design; Designing; Disease/Disorder; Drug Detection; Drug Testing; Drug/Agent; Follow-Up; Gene Product; Immunosuppressive; Insular Sclerosis; Novel; Pharmacophore; Public Health Relevance; Receptor; Receptor Binding; Relational Database; Side Effect; Small Molecule; Therapy Adverse Effect; Treatment Adverse Effect

Bioactive small molecules can act on multiple targets, and these off-target activities underlie many of the adverse reactions from which drugs suffer. The motivating idea of this proposal is that these Adverse Drug Reaction (ADR) targets may be predicted comprehensively and systematically using chemoinformatic inference. The "Similarity Ensemble Approach" (SEA), developed by us, classifies targets based on their ligands rather than their sequence identity, structural similarity, pathway role or function, and predicts associations that are otherwise inaccessible and often surprising. In the first phase of this SBIR we constructed a global target map using ligand similarity, exploiting this to predict off-targets. This map suggested mechanism of action targets for several drugs, candidates for repositioning-both aims in the first phase-and predicted ADR associations. It is this latter goal that we focus on here. Extensive preliminary results, including a collaboration with pharma and a proof-of-concept federal collaboration, support the scientific and financial pragmatism of exploiting this platform for predicting adverse off-targets. In the second phase of this project we develop a direct drug-target-ADR map and develop new techniques to make the method more robust. The specific aims are: 1. To create a full drug-target-ADR map, and demonstrate proof-of-concept. We propose to create a direct drug-target-ADR map. This will be done comprehensively across all approved and investigational drugs, all accessible targets, and all adverse reactions for which targets may be associated. We anticipate that the most important commercial use of these methods and this map will be to prioritize ADR targets to test against for molecules that are clinical and preclinical candidates. To show proof of concept against such molecules, we will test investigational drugs for their ability to modulate ADR targets predicted for them. 2. To improve SEA with new methods and new ligand-physiology databases. To make the ligand- target-ADR association map more robust, we will improve the methods and databases underlying SEA. (a) We will develop descriptors of and filters for physical properties of molecules, rather than using ligand topology alone. (b) We will cluster target ligands, rather than assuming they always form a single, cohesive set. (c) We will incorporate ligand affinity weighting into SEA and test the resulting predictions. (d) Finally, we will derive drug-ADR associations from predicted target profiles, rather than relying on individual target predictions alone. Substantial preliminary results support the promise of our platform for predicting target-based drug adverse events. These are among the most common reasons for drug failures in clinical trials, and there has thus been great interest in this method. The studies proposed here have the potential to greatly improve the breadth and reliability of the method and, correspondingly, its commercial application.

Public Health Relevance Statement:


Public Health Relevance:
Drugs can act on more targets in the body than intended. These off-target interactions underlie many adverse drug reactions and are difficult to anticipate. In this proposal we improve on a computational method to predict undesirable off-targets proactively, to save time and money in drug discovery by improving drug safety.

Project Terms:
Adverse effects; Adverse event; Adverse reactions; Affinity; base; Binding (Molecular Function); Clinical; Clinical Trials; Collaborations; commercial application; Computing Methodologies; Databases; Descriptor; Development; drug candidate; drug discovery; Drug Targeting; Failure (biologic function); Fingerprint; Funding; Goals; improved; Individual; interest; Investigational Drugs; Letters; Ligands; Manuscripts; Maps; Measures; Methods; Molecular; Pathway interactions; Pharmaceutical Preparations; Phase; physical property; Physiological; Physiology; pre-clinical; public health relevance; Reaction; Relative (related person); Role; Safety; Site; Small Business Innovation Research Grant; small molecule; Techniques; Test Result; Testing; Time; Weight