The activities of drug inactive ingredients on biological targets.

Excipients, considered "inactive ingredients," are a major component of formulated drugs and play key roles in their pharmacokinetics. Despite their pervasiveness, whether they are active on any targets has not been systematically explored. We computed the likelihood that approved excipients would bind to molecular targets. Testing in vitro revealed 25 excipient activities, ranging from low-nanomolar to high-micromolar concentration. Another 109 activities were identified by testing against clinical safety targets. In cellular models, five excipients had fingerprints predictive of system-level toxicity. Exposures of seven excipients were investigated, and in certain populations, two of these may reach levels of in vitro target potency, including brain and gut exposure of thimerosal and its major metabolite, which had dopamine D3 receptor dissociation constant K d values of 320 and 210 nM, respectively. Although most excipients deserve their status as inert, many approved excipients may directly modulate physiologically relevant targets.

Optimization of novel monobactams with activity against carbapenem-resistant Enterobacteriaceae - Identification of LYS228.

Metallo-ß-lactamases (MBLs), such as New Delhi metallo-ß-lactamase (NDM-1) have spread world-wide and present a serious threat. Expression of MBLs confers resistance in Gram-negative bacteria to all classes of ß-lactam antibiotics, with the exception of monobactams, which are intrinsically stable to MBLs. However, existing first generation monobactam drugs like aztreonam have limited clinical utility against MBL-expressing strains because they are impacted by serine ß-lactamases (SBLs), which are often co-expressed in clinical isolates. Here, we optimized novel monobactams for stability against SBLs, which led to the identification of LYS228 (compound 31). LYS228 is potent in the presence of all classes of ß-lactamases and shows potent activity against carbapenem-resistant isolates of Enterobacteriaceae (CRE).

Reverse translation of adverse event reports paves the way for de-risking preclinical off-targets.

The Food and Drug Administration Adverse Event Reporting System (FAERS) remains the primary source for post-marketing pharmacovigilance. The system is largely un-curated, unstandardized, and lacks a method for linking drugs to the chemical structures of their active ingredients, increasing noise and artefactual trends. To address these problems, we mapped drugs to their ingredients and used natural language processing to classify and correlate drug events. Our analysis exposed key idiosyncrasies in FAERS, for example reports of thalidomide causing a deadly ADR when used against myeloma, a likely result of the disease itself; multiplications of the same report, unjustifiably increasing its importance; correlation of reported ADRs with public events, regulatory announcements, and with publications. Comparing the pharmacological, pharmacokinetic, and clinical ADR profiles of methylphenidate, aripiprazole, and risperidone, and of kinase drugs targeting the VEGF receptor, demonstrates how underlying molecular mechanisms can emerge from ADR co-analysis. The precautions and methods we describe may enable investigators to avoid confounding chemistry-based associations and reporting biases in FAERS, and illustrate how comparative analysis of ADRs can reveal underlying mechanisms.

Secondary pharmacology: screening and interpretation of off-target activities - focus on translation.

Secondary pharmacology is an essential component of drug discovery and is used extensively in the pharmaceutical industry for achieving optimal specificity of new drugs via early hazard identification and off-target mitigation. The importance of this discipline has been achieved by increasing its translational value, based on the recognition of biological target-drug molecule-adverse drug reaction (ADR) associations and integration of secondary pharmacology data with pharmacokinetic parameters. Information obtained from clinical ADRs, from recognition of specific phenotypes of animal models and from hereditary diseases provides increasing regulatory confidence in the target-based approach to ADR prediction and mitigation. Here, we review the progress of secondary pharmacology during the past decade and highlight and demonstrate its applications and impact in drug discovery.

Implications of Dynamic Occupancy, Binding Kinetics, and Channel Gating Kinetics for hERG Blocker Safety Assessment and Mitigation.

Blockade of the hERG potassium channel prolongs the ventricular action potential (AP) and QT interval, and triggers early after depolarizations (EADs) and torsade de pointes (TdP) arrhythmia. Opinions differ as to the causal relationship between hERG blockade and TdP, the relative weighting of other contributing factors, definitive metrics of preclinical proarrhythmicity, and the true safety margin in humans. Here, we have used in silico techniques to characterize the effects of channel gating and binding kinetics on hERG occupancy, and of blockade on the human ventricular AP. Gating effects differ for compounds that are sterically compatible with closed channels (becoming trapped in deactivated channels) versus those that are incompatible with the closed/closing state, and expelled during deactivation. Occupancies of trappable blockers build to equilibrium levels, whereas those of non-trappable blockers build and decay during each AP cycle. Occupancies of ~83% (non-trappable) versus ~63% (trappable) of open/inactive channels caused EADs in our AP simulations. Overall, we conclude that hERG occupancy at therapeutic exposure levels may be tolerated for nontrappable, but not trappable blockers capable of building to the proarrhythmic occupancy level. Furthermore, the widely used Redfern safety index may be biased toward trappable blockers, overestimating the exposure-IC50 separation in nontrappable cases.

A screening pattern recognition method finds new and divergent targets for drugs and natural products.

Computational target prediction methods using chemical descriptors have been applied exhaustively in drug discovery to elucidate the mechanisms-of-action (MOAs) of small molecules. To predict truly novel and unexpected small molecule-target interactions, compounds must be compared by means other than their chemical structure alone. Here we investigated predictions made by a method, HTS fingerprints (HTSFPs), that matches patterns of activities in experimental screens. Over 1,400 drugs and 1,300 natural products (NPs) were screened in more than 200 diverse assays, creating encodable activity patterns. The comparison of these activity patterns to an MOA-annotated reference panel led to the prediction of 5,281 and 2,798 previously unknown targets for the NP and drug sets, respectively. Intriguingly, there was limited overlap among the targets predicted; the drugs were more biased toward membrane receptors and the NPs toward soluble enzymes, consistent with the idea that they represent unexplored pharmacologies. Importantly, HTSFPs inferred targets that were beyond the prediction capabilities of standard chemical descriptors, especially for NPs but also for the more explored drug set. Of 65 drug-target predictions that we tested in vitro, 48 (73.8%) were confirmed with AC50 values ranging from 38 nM to 29 µM. Among these interactions was the inhibition of cyclooxygenases 1 and 2 by the HIV protease inhibitor Tipranavir. These newly discovered targets that are phylogenetically and phylochemically distant to the primary target provide an explanation for spontaneous bleeding events observed for patients treated with this drug, a physiological effect that was previously difficult to reconcile with the drug's known MOA.

Matched molecular pair analysis: significance and the impact of experimental uncertainty.

Matched molecular pair analysis (MMPA) has become a major tool for analyzing large chemistry data sets for promising chemical transformations. However, the dependence of MMPA predictions on data constraints such as the number of pairs involved, experimental uncertainty, source of the experiments, and variability of the true physical effect has not yet been described. In this contribution the statistical basics for judging MMPA are analyzed. We illustrate the connection between overall MMPA statistics and individual pairs with a detailed comparison of average CHEMBL hERG MMPA results versus pairs with extreme transformation effects. Comparing the CHEMBL results to Novartis data, we find that significant transformation effects agree very well if the experimental uncertainty is considered. This indicates that caution must be exercised for predictions from insignificant MMPAs, yet highlights the robustness of statistically validated MMPA and shows that MMPA on public databases can yield results that are very useful for medicinal chemistry.

Reducing safety-related drug attrition: the use of in vitro pharmacological profiling.

In vitro pharmacological profiling is increasingly being used earlier in the drug discovery process to identify undesirable off-target activity profiles that could hinder or halt the development of candidate drugs or even lead to market withdrawal if discovered after a drug is approved. Here, for the first time, the rationale, strategies and methodologies for in vitro pharmacological profiling at four major pharmaceutical companies (AstraZeneca, GlaxoSmithKline, Novartis and Pfizer) are presented and illustrated with examples of their impact on the drug discovery process. We hope that this will enable other companies and academic institutions to benefit from this knowledge and consider joining us in our collaborative knowledge sharing.

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 1 nM to 30 µM. 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.

Optimization of the in vitro cardiac safety of hydroxamate-based histone deacetylase inhibitors.

Histone deacetylase (HDAC) inhibitors have shown promise in treating various forms of cancer. However, many HDAC inhibitors from diverse structural classes have been associated with QT prolongation in humans. Inhibition of the human ether a-go-go related gene (hERG) channel has been associated with QT prolongation and fatal arrhythmias. To determine if the observed cardiac effects of HDAC inhibitors in humans is due to hERG blockade, a highly potent HDAC inhibitor devoid of hERG activity was required. Starting with dacinostat (LAQ824), a highly potent HDAC inhibitor, we explored the SAR to determine the pharmacophores required for HDAC and hERG inhibition. We disclose here the results of these efforts where a high degree of pharmacophore homology between these two targets was discovered. This similarity prevented traditional strategies for mitigating hERG binding/modulation from being successful and novel approaches for reducing hERG inhibition were required. Using a hERG homology model, two compounds, 11r and 25i, were discovered to be highly efficacious with weak affinity for the hERG and other ion channels.

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.

In vitro safety pharmacology profiling: what else beyond hERG?

One of the main reasons for drug failures in clinical development, or postmarket launch, is lacking or compromised safety margins at therapeutic doses. Organ toxicity with poorly defined mechanisms and adverse drug reactions associated with on- and off-target effects are the major contributors to safety-related shortfalls of many clinical drug candidates. Therefore, to avoid high attrition rates in clinical trials, it is imperative to test compounds for potential adverse reactions during early drug discovery. Beyond a small number of targets associated with clinically acknowledged adverse drug reactions, there is little consensus on other targets that are important to consider at an early stage for in vitro safety pharmacology assessment. We consider here a limited number of safety-related targets, from different target families, which were selected as part of in vitro safety pharmacology profiling panels integrated in the drug-development process at Novartis. The best way to assess these targets, using a biochemical or a functional readout, is discussed. In particular, the importance of using cell-based profiling assays for the characterization of an agonist action at some GPCRs is highlighted. A careful design of in vitro safety pharmacology profiling panels allows better prediction of potential adverse effects of new chemical entities early in the drug-discovery process. This contributes to the selection of the best candidate for clinical development and, ultimately, should contribute to a decreased attrition rate.

2-Cycloalkyl phenoxyacetic acid CRTh2 receptor antagonists.

High throughput screening identified a phenoxyacetic acid scaffold as a novel CRTh2 receptor antagonist chemotype, which could be optimised to furnish a compound with functional potency for inhibition of human eosinophil shape change and oral bioavailability in the rat.

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, in vitro 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, in silico (computational) Bayesian models for 70 PSP-related targets were built, which are able to detect 93% of the ligands binding at IC(50) < or = 10 microM at an overall correct classification rate of about 94%. 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 90% of the adverse drug reactions observed with known, clinically used compounds were detected, an overall correct classification rate of 92%. 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 mu 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.

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 (in vitro 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.

ABP688, a novel selective and high affinity ligand for the labeling of mGlu5 receptors: identification, in vitro pharmacology, pharmacokinetic and biodistribution studies.

[(11)C]ABP688 (2) has recently been demonstrated to be a useful PET tracer for in vivo imaging of the metabotropic glutamate receptors type 5 (mGluR5) in rodents. We describe here the identification and preclinical profiling of ABP688 and its tritiated version [(3)H]ABP688, and show that its high affinity (K(d)=2nM), selectivity, and pharmacokinetic properties fulfill all requirements for development as a PET tracer for clinical imaging of the mGlu5 receptor.

High-throughput in vitro profiling assays: lessons learnt from experiences at Novartis.

This article reviews the use of a selection of in vitro assays implemented at Novartis and intends to address exposure and safety in early drug discovery. The authors' own experience, based on a large number of 'real' drug discovery compounds, is described to reflect on what has worked, where improvement is needed and how to best use the data for decision making. Possible strategies are discussed, and guidelines are provided on how to organise assays, extract value and contribute knowledge from the data.

Keynote review: in vitro safety pharmacology profiling: an essential tool for successful drug development.

Broad-scale in vitro pharmacology profiling of new chemical entities during early phases of drug discovery has recently become an essential tool to predict clinical adverse effects. Modern, relatively inexpensive assay technologies and rapidly expanding knowledge about G-protein coupled receptors, nuclear receptors, ion channels and enzymes have made it possible to implement a large number of assays addressing possible clinical liabilities. Together with other in vitro assays focusing on toxicology and bioavailability, they provide a powerful tool to aid drug development. In this article, we review the development of this tool for drug discovery, its appropriate use and predictive value.

Discovery and SAR of potent, orally available and brain-penetrable 5,6-dihydro-4H-3-thia-1-aza-benzo[e]azulen- and 4,5-dihydro-6-oxa-3-thia-1-aza-benzo[e]azulen derivatives as neuropeptide Y Y5 receptor antagonists.

Combination of structural elements from a potent Y5 antagonist (2) with thiazole fragments that exhibit weak Y5 affinities followed by lead optimisation led to the discovery of (5,6-dihydro-4H-3-thia-1-aza-benzo[e]azulen-2-yl)-piperidin-4-ylmethyl-amino and (4,5-dihydro-6-oxa-3-thia-1-aza-benzo[e]azulen-2-yl)-piperidin-4-ylmethyl-amino derivatives. Both classes of compounds are capable of delivering potent and selective orally and centrally bioavailable NPY Y5 receptor antagonists.