Jeremy L. Jenkins

Large Scale Prediction and Testing of Drug Activity on Side-Effect Targets

Discovering the unintended ‘off-targets’ that predict adverse drug reactions is daunting by empirical methods alone. Drugs can act on several protein targets, some of which can be unrelated by conventional molecular metrics, and hundreds of proteins have been implicated in side effects. Here we use a computational strategy to predict the activity of 656 marketed drugs on 73 unintended ‘side-effect’ targets. Approximately half of the predictions were confirmed, either from proprietary databases unknown to the method or by new experimental assays. Affinities for these new off-targets ranged from 1u2009nM to 30u2009μM. To explore relevance, we developed an association metric to prioritize those new off-targets that explained side effects better than any known target of a given drug, creating a drug–target–adverse drug reaction network. Among these new associations was the prediction that the abdominal pain side effect of the synthetic oestrogen chlorotrianisene was mediated through its newly discovered inhibition of the enzyme cyclooxygenase-1. The clinical relevance of this inhibition was borne out in whole human blood platelet aggregation assays. This approach may have wide application to de-risking toxicological liabilities in drug discovery.

Analysis of Pharmacology Data and the Prediction of Adverse Drug Reactions and Off-Target Effects from Chemical Structure

Preclinical Safety Pharmacology (PSP) attempts to anticipate adverse drug reactions (ADRs) during early phases of drug discovery by testing compounds in simple, inu2005vitro binding assays (that is, preclinical profiling). The selection of PSP targets is based largely on circumstantial evidence of their contribution to known clinical ADRs, inferred from findings in clinical trials, animal experiments, and molecular studies going back more than forty years. In this work we explore PSP chemical space and its relevance for the prediction of adverse drug reactions. Firstly, inu2005silico (computational) Bayesian models for 70 PSP‐related targets were built, which are able to detect 93u2009% of the ligands binding at IC50≤10u2005μM at an overall correct classification rate of about 94u2009%. Secondly, employing the World Drug Index (WDI), a model for adverse drug reactions was built directly based on normalized side‐effect annotations in the WDI, which does not require any underlying functional knowledge. This is, to our knowledge, the first attempt to predict adverse drug reactions across hundreds of categories from chemical structure alone. On average 90u2009% of the adverse drug reactions observed with known, clinically used compounds were detected, an overall correct classification rate of 92u2009%. Drugs withdrawn from the market (Rapacuronium, Suprofen) were tested in the model and their predicted ADRs align well with known ADRs. The analysis was repeated for acetylsalicylic acid and Benperidol which are still on the market. Importantly, features of the models are interpretable and back‐projectable to chemical structure, raising the possibility of rationally engineering out adverse effects. By combining PSP and ADR models new hypotheses linking targets and adverse effects can be proposed and examples for the opioid μ and the muscarinic M2 receptors, as well as for cyclooxygenase‐1 are presented. It is hoped that the generation of predictive models for adverse drug reactions is able to help support early SAR to accelerate drug discovery and decrease late stage attrition in drug discovery projects. In addition, models such as the ones presented here can be used for compound profiling in all development stages.

Prediction of biological targets for compounds using multiple-category bayesian models trained on chemogenomics databases

Target identification is a critical step following the discovery of small molecules that elicit a biological phenotype. The present work seeks to provide an in silico correlate of experimental target fishing technologies in order to rapidly fish out potential targets for compounds on the basis of chemical structure alone. A multiple-category Laplacian-modified naïve Bayesian model was trained on extended-connectivity fingerprints of compounds from 964 target classes in the WOMBAT (World Of Molecular BioAcTivity) chemogenomics database. The model was employed to predict the top three most likely protein targets for all MDDR (MDL Drug Database Report) database compounds. On average, the correct target was found 77% of the time for compounds from 10 MDDR activity classes with known targets. For MDDR compounds annotated with only therapeutic or generic activities such as antineoplastic, kinase inhibitor, or anti-inflammatory, the model was able to systematically deconvolute the generic activities to specific targets associated with the therapeutic effect. Examples of successful deconvolution are given, demonstrating the usefulness of the tool for improving knowledge in chemogenomics databases and for predicting new targets for orphan compounds.

Structure of the DDB1-CRBN E3 ubiquitin ligase in complex with thalidomide

In the 1950s, the drug thalidomide, administered as a sedative to pregnant women, led to the birth of thousands of children with multiple defects. Despite the teratogenicity of thalidomide and its derivatives lenalidomide and pomalidomide, these immunomodulatory drugs (IMiDs) recently emerged as effective treatments for multiple myeloma and 5q-deletion-associated dysplasia. IMiDs target the E3 ubiquitin ligase CUL4–RBX1–DDB1–CRBN (known as CRL4CRBN) and promote the ubiquitination of the IKAROS family transcription factors IKZF1 and IKZF3 by CRL4CRBN. Here we present crystal structures of the DDB1–CRBN complex bound to thalidomide, lenalidomide and pomalidomide. The structure establishes that CRBN is a substrate receptor within CRL4CRBN and enantioselectively binds IMiDs. Using an unbiased screen, we identified the homeobox transcription factor MEIS2 as an endogenous substrate of CRL4CRBN. Our studies suggest that IMiDs block endogenous substrates (MEIS2) from binding to CRL4CRBN while the ligase complex is recruiting IKZF1 or IKZF3 for degradation. This dual activity implies that small molecules can modulate an E3 ubiquitin ligase and thereby upregulate or downregulate the ubiquitination of proteins.

How similar are similarity searching methods? A principal component analysis of molecular descriptor space.

Different molecular descriptors capture different aspects of molecular structures, but this effect has not yet been quantified systematically on a large scale. In this work, we calculate the similarity of 37 descriptors by repeatedly selecting query compounds and ranking the rest of the database. Euclidean distances between the rank-ordering of different descriptors are calculated to determine descriptor (as opposed to compound) similarity, followed by PCA for visualization. Four broad descriptor classes are identified, which are circular fingerprints; circular fingerprints considering counts; path-based and keyed fingerprints; and pharmacophoric descriptors. Descriptor behavior is much more defined by those four classes than the particular parametrization. Using counts instead of the presence/absence of fingerprints significantly changes descriptor behavior, which is crucial for performance of topological autocorrelation vectors, but not circular fingerprints. Four-point pharmacophores (piDAPH4) surprisingly lead to much higher retrieval rates than three-point pharmacophores (28.21% vs 19.15%) but still similar rank-ordering of compounds (retrieval of similar actives). Looking into individual rankings, circular fingerprints seem more appropriate than path-based fingerprints if complex ring systems or branching patterns are present; count-based fingerprints could be more suitable in databases with a large number of repeated subunits (amide bonds, sugar rings, terpenes). Information-based selection of diverse fingerprints for consensus scoring (ECFP4/TGD fingerprints) led only to marginal improvement over single fingerprint results. While it seems to be nontrivial to exploit orthogonal descriptor behavior to improve retrieval rates in consensus virtual screening, those descriptors still each retrieve different actives which corroborates the strategy of employing diverse descriptors individually in prospective virtual screening settings.

From in silico target prediction to multi-target drug design: Current databases, methods and applications

Given the tremendous growth of bioactivity databases, the use of computational tools to predict protein targets of small molecules has been gaining importance in recent years. Applications span a wide range, from the designed polypharmacology of compounds to mode-of-action analysis. In this review, we firstly survey databases that can be used for ligand-based target prediction and which have grown tremendously in size in the past. We furthermore outline methods for target prediction that exist, both based on the knowledge of bioactivities from the ligand side and methods that can be applied in situations when a protein structure is known. Applications of successful in silico target identification attempts are discussed in detail, which were based partly or in whole on computational target predictions in the first instance. This includes the authors own experience using target prediction tools, in this case considering phenotypic antibacterial screens and the analysis of high-throughput screening data. Finally, we will conclude with the prospective application of databases to not only predict, retrospectively, the protein targets of a small molecule, but also how to design ligands with desired polypharmacology in a prospective manner.

Modeling Promiscuity Based on in vitro Safety Pharmacology Profiling Data

This study describes a method for mining and modeling binding data obtained from a large panel of targets (inu2005vitro safety pharmacology) to distinguish differences between promiscuous and selective compounds. Two naïve Bayes models for promiscuity and selectivity were generated and validated on a test set as well as publicly available drug databases. The model shows a higher score (lower promiscuity) for marketed drugs than for compounds in early development or compounds that failed during clinical development. Such models can be used in triaging high‐throughput screening data or for lead optimization.

Gaining Insight into Off-Target Mediated Effects of Drug Candidates with a Comprehensive Systems Chemical Biology Analysis

We present a workflow that leverages data from chemogenomics based target predictions with Systems Biology databases to better understand off-target related toxicities. By analyzing a set of compounds that share a common toxic phenotype and by comparing the pathways they affect with pathways modulated by nontoxic compounds we are able to establish links between pathways and particular adverse effects. We further link these predictive results with literature data in order to explain why a certain pathway is predicted. Specifically, relevant pathways are elucidated for the side effects rhabdomyolysis and hypotension. Prospectively, our approach is valuable not only to better understand toxicities of novel compounds early on but also for drug repurposing exercises to find novel uses for known drugs.

Mapping adverse drug reactions in chemical space.

We present a novel method to better investigate adverse drug reactions in chemical space. By integrating data sources about adverse drug reactions of drugs with an established cheminformatics modeling method, we generate a data set that is then visualized with a systems biology tool. Thereby new insights into undesired drug effects are gained. In this work, we present a global analysis linking chemical features to adverse drug reactions.

Bayes affinity fingerprints improve retrieval rates in virtual screening and define orthogonal bioactivity space : When are multitarget drugs a feasible concept?

Conventional similarity searching of molecules compares single (or multiple) active query structures to each other in a relative framework, by means of a structural descriptor and a similarity measure. While this often works well, depending on the target, we show here that retrieval rates can be improved considerably by incorporating an external framework describing ligand bioactivity space for comparisons (Bayes affinity fingerprints). Structures are described by Bayes scores for a ligand panel comprising about 1000 activity classes extracted from the WOMBAT database. The comparison of structures is performed via the Pearson correlation coefficient of activity classes, that is, the order in which two structures are similar to the panel activity classes. Compound retrieval on a recently published data set could be improved by as much as 24% relative (9% absolute). Knowledge about the shape of the bioactive chemical universe is thus beneficial to identifying similar bioactivities. Principal component analysis was employed to further analyze activity space with the objective to define orthogonal ligand bioactive chemical space, leading to nine major (roughly orthogonal) activity axes. Employing only those nine activity classes, retrieval rates are still comparable to original Bayes affinity fingerprints; thus, the concept of orthogonal bioactive ligand chemical space was validated as being an information-rich but low-dimensional representation of bioactivity space. Correlations between activity classes are a major determinant to gauge whether the desired multitarget activity of drugs is (on the basis of current knowledge) a feasible concept because it measures the extent to which activities can be optimized independently, or only by strongly influencing one another.