Quantifying biogenic bias in screening libraries.

In lead discovery, libraries of 10(6) molecules are screened for biological activity. Given the over 10(60) drug-like molecules thought possible, such screens might never succeed. The fact that they do, even occasionally, implies a biased selection of library molecules. We have developed a method to quantify the bias in screening libraries toward biogenic molecules. With this approach, we consider what is missing from screening libraries and how they can be optimized

Ligand-based virtual screening using binary kernel discrimination

This paper discusses the use of a machine-learning technique called binary kernel discrimination (BKD) for virtual screening in drug- and pesticide-discovery programmes. BKD is compared with several other ligand-based tools for virtual screening in databases of 2D structures represented by fragment bit-strings, and is shown to provide an effective, and reasonably efficient, way of prioritising compounds for biological screening

Quantifying biogenic bias in screening libraries.

In lead discovery, libraries of 10(6) molecules are screened for biological activity. Given the over 10(60) drug-like molecules thought possible, such screens might never succeed. The fact that they do, even occasionally, implies a biased selection of library molecules. We have developed a method to quantify the bias in screening libraries toward biogenic molecules. With this approach, we consider what is missing from screening libraries and how they can be optimized

Identifying Targets for Drugs and Probes with Unknown Mechanisms of Action

Notwithstanding their key roles in therapy and as probes for biology, as many as 7% of drugs have no known primary target, while up to 21% have been reported to lack a well-defined mechanism of action. Using a chemoinformatics approach, we sought to “deorphanize” drugs that lack primary targets in DrugBank, MDDR and NPC databases. Surprisingly, for many of these drugs targets could be easily predicted. Too easily: whereas these targets were not previously known to us nor to the common databases, almost all could be readily confirmed by literature search, leaving only 13 FDA drugs with unknown targets; this suggests that the number of drugs without molecular targets is far fewer than reported. For worldwide drugs in MDDR, the number of molecules without sensible targets similarly dropped; from 352 to 44 (4%), and to 3% in the NPC. Nevertheless, there remained at least seven drugs for which targets appeared to be genuinely unknown and for which sensible mechanism of action targets could be predicted. These included the anti-cough drugs clemastine, cloperastine and nepinalone, the anti-emetic benzquinamide, the muscle relaxant cyclobenzaprine, the analgesic nefopam and the immunomodulator lobenzarit. For each of these, the predicted targets were confirmed experimentally with affinities ranging from 20 nM to mid-micromolar, all within the physiological concentration ranges achieved by the drugs. This approach to target association may be turned toward identifying established molecules as chemical probes for biology. A combination of chemoinformatic and literature-based specificity found over 100 drugs that can be used as chemical probes by standard definitions, and over 50 by stringent criteria

Quantifying biogenic bias in screening libraries

In lead discovery, libraries of 106 molecules are screened for biological activity. Given the over 1060 drug-like molecules thought possible, such screens might never succeed. That they do, even occasionally, implies a biased selection of library molecules. Here a method is developed to quantify the bias in screening libraries towards biogenic molecules. With this approach, we consider what is missing from screening libraries and how they can be optimized. High-throughput screening (HTS) is the dominant method of lead discovery in pharmaceutical research and chemical biology. A plurality of the new chemical entities in clinical trials may have their origins in this technique, as do at least two drug.1 Whereas these screens have been productive against traditional drug targets, such as GPCRs, ligand-gated ion channels, and kinases, screening libraries of synthetic molecules has been problematic for others, such as antimicrobial targets and those identified from genomic studies. The reasons for these successes and failures have been widely debated.2-5 From a theoretical perspective, however, one might wonder not that screens of 106 molecules sometimes fail, but rather that they ever succeed

Synthesis, characterization, and in vivo\textit {in vivo} evaluation of a novel potent autotaxin-inhibitor

The autotaxin-lysophosphatidic acid (ATX-LPA) signaling pathway plays a role in a variety of autoimmune diseases, such as rheumatoid arthritis or neurodegeneration. A link to the pathogenesis of glaucoma is suggested by an overactive ATX-LPA axis in aqueous humor samples of glaucoma patients. Analysis of such samples suggests that the ATX-LPA axis contributes to the fibrogenic activity and resistance to aqueous humor outflow through the trabecular meshwork. In order to inhibit or modulate this pathway, we developed a new series of ATX-inhibitors containing novel bicyclic and spirocyclic structural motifs. A potent lead compound ($IC_{50}$ against ATX: 6 nM) with good $\textit {in vivo}$ PK, favorable $\textit {in vitro}$ property, and safety profile was generated. This compound leads to lowered LPA levels $\textit {in vivo}$ after oral administration. Hence, it was suitable for chronic oral treatment in two rodent models of glaucoma, the experimental autoimmune glaucoma (EAG) and the ischemia/reperfusion models. In the EAG model, rats were immunized with an optic nerve antigen homogenate, while controls received sodium chloride. Retinal ischemia/reperfusion (I/R) was induced by elevating the intraocular pressure (IOP) in one eye to 140 mmHg for 60 min, followed by reperfusion, while the other untreated eye served as control. Retinae and optic nerves were evaluated 28 days after EAG or 7 and 14 days after I/R induction. Oral treatment with the optimized ATX-inhibitor lead to reduced retinal ganglion cell (RGC) loss in both glaucoma models. In the optic nerve, the protective effect of ATX inhibition was less effective compared to the retina and only a trend to a weakened neurofilament distortion was detectable. Taken together, these results provide evidence that the dysregulation of the ATX-LPA axis in the aqueous humor of glaucoma patients, in addition to the postulated outflow impairment, might also contribute to RGC loss. The observation that ATX-inhibitor treatment in both glaucoma models did not result in significant IOP increases or decreases after oral treatment indicates that protection from RGC loss due to inhibition of the ATX-LPA axis is independent of an IOP lowering effect

Predicting new molecular targets for known drugs

Although drugs are intended to be selective, at least some bind to several physiological targets, explaining side effects and efficacy. Because many drug–target combinations exist, it would be useful to explore possible interactions computationally. Here we compared 3,665 US Food and Drug Administration (FDA)-approved and investigational drugs against hundreds of targets, defining each target by its ligands. Chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations. Thirty were tested experimentally, including the antagonism of the β1 receptor by the transporter inhibitor Prozac, the inhibition of the 5-hydroxytryptamine (5-HT) transporter by the ion channel drug Vadilex, and antagonism of the histamine H4 receptor by the enzyme inhibitor Rescriptor. Overall, 23 new drug–target associations were confirmed, five of which were potent (less than 100 nM). The physiological relevance of one, the drug N,N-dimethyltryptamine (DMT) on serotonergic receptors, was confirmed in a knockout mouse. The chemical similarity approach is systematic and comprehensive, and may suggest side-effects and new indications for many drugs