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.

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.

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.

Computational methods for early predictive safety assessment from biological and chemical data.

INTRODUCTION: The goal of early predictive safety assessment (PSA) is to keep compounds with detectable liabilities from progressing further in the pipeline. Such compounds jeopardize the core of pharmaceutical research and development and limit the timely delivery of innovative therapeutics to the patient. Computational methods are increasingly used to help understand observed data, generate new testable hypotheses of relevance to safety pharmacology, and supplement and replace costly and time-consuming experimental procedures. AREAS COVERED: The authors survey methods operating on different scales of both physical extension and complexity. After discussing methods used to predict liabilities associated with structures of individual compounds, the article reviews the use of adverse event data and safety profiling panels. Finally, the authors examine the complexities of toxicology data from animal experiments and how these data can be mined. EXPERT OPINION: A significant obstacle for data-driven safety assessment is the absence of integrated data sets due to a lack of sharing of data and of using standard ontologies for data relevant to safety assessment. Informed decisions to derive focused sets of compounds can help to avoid compound liabilities in screening campaigns, and improved hit assessment of such campaigns can benefit the early termination of undesirable compounds.

IonFlux: a microfluidic patch clamp system evaluated with human Ether-à-go-go related gene channel physiology and pharmacology.

Ion channel assays are essential in drug discovery, not only for identifying promising new clinical compounds, but also for minimizing the likelihood of potential side effects. Both applications demand optimized throughput, cost, and predictive accuracy of measured membrane current changes evoked or modulated by drug candidates. Several competing electrophysiological technologies are available to address this demand, but important gaps remain. We describe the industrial application of a novel microfluidic-based technology that combines compounds, cells, and buffers on a single, standard well plate. Cell trapping, whole cell, and compound perfusion are accomplished in interconnecting microfluidic channels that are coupled to pneumatic valves, which emancipate the system from robotics, fluidic tubing, and associated maintenance. IonFlux™ is a state-of-the-art, compact system with temperature control and continuous voltage clamp for potential application in screening for voltage- and ligand-gated ion channel modulators. Here, ensemble recordings of the IonFlux system were validated with the human Ether-à-go-go related gene (hERG) channel (stably expressed in a Chinese hamster ovary cell line), which has established biophysical and pharmacological characteristics in other automated planar patch systems. We characterized the temperature dependence of channel activation and its reversal potential. Concentration response characteristics of known hERG blockers and control compounds obtained with the IonFlux system correlated with literature and internal data obtained on this cell line with the QPatch HT system. Based on the biophysical and pharmacological data, we conclude that the IonFlux system offers a novel, versatile, automated profiling, and screening system for ion channel targets with the benefit of temperature control.

High-content micronucleus assay in genotoxicity profiling: initial-stage development and some applications in the investigative/lead-finding studies in drug discovery.

This article describes the first step toward full (that includes conditions for both absence and presence of metabolic activation) validation and drug discovery application of a 96-well, automated, high-content micronucleus (HCMN) assay. The current validation tests were performed using Chinese hamster ovary cells, in the absence of metabolic activation, against three distinct sets of drug-like compounds that represent all stages of a drug discovery pipeline. A compound categorization scheme was created based on quantitative relationships between micronucleus (MN) signals, cytotoxicity, and compound solubility. Results from initial validation compounds (n = 38) set the stage for differentiating overall positive and negative MN inducers. To delve deeper into the compound categorization process, a more extensive validation set, consisting of a larger set (n = 370) of "drug-like but less optimized" early-stage compounds, was used for further refinement of positive and negative compound categories. The predictivity and applicability of the assay for clinical stage compounds was ascertained using (n = 168) clinically developed marketed drugs or well-studied compounds. Upon full validation, a detailed analysis of results established five compound categories--NEG (negative), NEG/xx µM (negative up to the solubility limit of xx µM), WPOS (weak positive), POS (positive), and INCON (inconclusive). Furthermore, examples of lead-finding applications and ongoing investigative HCMN activities are described. A proposal is offered on how the HCMN assay can be positioned in parallel to the overall stage gates (e.g., scaffold selection, lead optimization, late-stage preclinical development) of drug discovery programs. Because of its greater throughput, 1-week turnaround time, and a substantially reduced (1-2 mg) requirement for compound consumption, the HCMN assay is appropriate for developing structure-genotoxicity relationships and for mechanistic genotoxicity studies. The assay does not replace the Organization for Economic Cooperation and Development-compliant, non-good laboratory practice in vitro MN test (e.g., slide-based MN test in TK6 lymphoblastoid cells) that is used for full characterization of lead candidates.

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.

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.

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.

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.

Preclinical pharmacology of lumiracoxib: a novel selective inhibitor of cyclooxygenase-2.

1. This manuscript presents the preclinical profile of lumiracoxib, a novel cyclooxygenase-2 (COX-2) selective inhibitor. 2. Lumiracoxib inhibited purified COX-1 and COX-2 with K(i) values of 3 and 0.06 microM, respectively. In cellular assays, lumiracoxib had an IC(50) of 0.14 microM in COX-2-expressing dermal fibroblasts, but caused no inhibition of COX-1 at concentrations up to 30 microM (HEK 293 cells transfected with human COX-1). 3. In a human whole blood assay, IC(50) values for lumiracoxib were 0.13 microM for COX-2 and 67 microM for COX-1 (COX-1/COX-2 selectivity ratio 515). 4. Lumiracoxib was rapidly absorbed following oral administration in rats with peak plasma levels being reached between 0.5 and 1 h. 5. Ex vivo, lumiracoxib inhibited COX-1-derived thromboxane B(2) (TxB(2)) generation with an ID(50) of 33 mg kg(-1), whereas COX-2-derived production of prostaglandin E(2) (PGE(2)) in the lipopolysaccharide-stimulated rat air pouch was inhibited with an ID(50) value of 0.24 mg kg(-1). 6. Efficacy of lumiracoxib in rat models of hyperalgesia, oedema, pyresis and arthritis was dose-dependent and similar to diclofenac. However, consistent with its low COX-1 inhibitory activity, lumiracoxib at a dose of 100 mg kg(-1) orally caused no ulcers and was significantly less ulcerogenic than diclofenac (P<0.05). 7. Lumiracoxib is a highly selective COX-2 inhibitor with anti-inflammatory, analgesic and antipyretic activities comparable with diclofenac, the reference NSAID, but with much improved gastrointestinal safety.

Anti-hyperalgesic activity of the cox-2 inhibitor lumiracoxib in a model of bone cancer pain in the rat.

Chronic pain resulting from metastatic bone cancer remains poorly understood and resistant to treatment. Here we have examined the effect of the novel COX-2 enzyme inhibitor lumiracoxib in a model of bone cancer pain in the rat. Lumiracoxib was administered orally twice daily from day 10 to day 20 after injection of MRMT-1 tumour cells into one tibia. Mechanical hyperalgesia, measured as the reduction in weight-bearing of the ipsilateral limb, and the development of static and dynamic allodynia were significantly inhibited by repeated lumaricoxib administration. A similar reduction in hyperalgesia and allodynia was noted after twice daily administration of another COX-2 inhibitor, valdecoxib, whilst a single acute administration of either drug on day 20, produced no anti-nociceptive activity. Bone mineral density measurements, radiological scores and histological analysis showed that chronic lumaricoxib treatment also significantly attenuated bone destruction induced by tumour cell injection. These data indicate that lumiracoxib and other COX-2 inhibitors have potential therapeutic benefit in the treatment of bone cancer pain.

The effects of GABA(B) agonists and gabapentin on mechanical hyperalgesia in models of neuropathic and inflammatory pain in the rat.

We have examined the effects of a novel GABA(B) agonist, CGP35024, in models of chronic neuropathic (partial sciatic ligation) and inflammatory (Freund's complete adjuvant) pain in the rat, and its inhibitory action on spinal transmission in vitro. The effects of CGP35024 were compared with L-baclofen and gabapentin. CGP35024 and L-baclofen reversed neuropathic mechanical hyperalgesia following single subcutaneous or intrathecal administration, but did not affect inflammatory mechanical hyperalgesia. Gabapentin only moderately affected neuropathic hyperalgesia following a single administration by either route, but produced significant reversal following daily administration for 5 days. It was only weakly active against inflammatory hyperalgesia following single or repeated administration. The antihyperalgesic effects of L-baclofen and CGP35024, but not gabapentin, were blocked by the selective GABA(B) receptor antagonist CGP56433A. CGP35024 was seven times more potent against neuropathic hyperalgesia than in the rotarod test for motor co-ordination, whilst L-baclofen was approximately equipotent in the two tests. In the isolated hemisected spinal cord from the rat, CGP35024, L-baclofen and gabapentin all inhibited capsaicin-evoked ventral root potentials (VRPs). CGP35024 and L-baclofen, but not gabapentin, also inhibited the polysynaptic and monosynaptic phases of electrically-evoked VRPs, as well as the 'wind-up' response to repetitive stimulation. These data indicate that CGP35024 and L-baclofen modulate nociceptive transmission in the spinal cord to inhibit neuropathic hyperalgesia, and that CGP35024 has a therapeutic window for antihyperalgesia over spasmolysis.