The role of ligand efficiency metrics in drug discovery

The judicious application of ligand or binding efficiencies, which quantify the molecular properties required to gain binding affinity for a drug target, is gaining traction in the selection and optimisation of fragments, hits, and leads. Retrospective analysis of recently marketed oral drugs shows that they frequently have highly optimised ligand efficiency values for their target. Optimising ligand efficiencies based on both molecular size and lipophilicity, when set in the context of the specific target, has the potential to ameliorate the molecular inflation that pervades current practice in medicinal chemistry, and to increase the developability of drug candidates

Dissociable effects of 5-HT2C receptor antagonism and genetic inactivation on perseverance and learned non-reward in an egocentric spatial reversal task

Cognitive flexibility can be assessed in reversal learning tests, which are sensitive to modulation of 5-HT2C receptor (5-HT2CR) function. Successful performance in these tests depends on at least two dissociable cognitive mechanisms which may separately dissipate associations of previous positive and negative valence. The first is opposed by perseverance and the second by learned non-reward. The current experiments explored the effect of reducing function of the 5-HT2CR on the cognitive mechanisms underlying egocentric reversal learning in the mouse. Experiment 1 used the 5-HT2CR antagonist SB242084 (0.5 mg/kg) in a between-groups serial design and Experiment 2 used 5-HT2CR KO mice in a repeated measures design. Animals initially learned to discriminate between two egocentric turning directions, only one of which was food rewarded (denoted CS+, CS−), in a T- or Y-maze configuration. This was followed by three conditions; (1) Full reversal, where contingencies reversed; (2) Perseverance, where the previous CS+ became CS− and the previous CS− was replaced by a novel CS+; (3) Learned non-reward, where the previous CS− became CS+ and the previous CS+ was replaced by a novel CS-. SB242084 reduced perseverance, observed as a decrease in trials and incorrect responses to criterion, but increased learned non-reward, observed as an increase in trials to criterion. In contrast, 5-HT2CR KO mice showed increased perseverance. 5-HT2CR KO mice also showed retarded egocentric discrimination learning. Neither manipulation of 5-HT2CR function affected performance in the full reversal test. These results are unlikely to be accounted for by increased novelty attraction, as SB242084 failed to affect performance in an unrewarded novelty task. In conclusion, acute 5-HT2CR antagonism and constitutive loss of the 5-HT2CR have opposing effects on perseverance in egocentric reversal learning in mice. It is likely that this difference reflects the broader impact of 5HT2CR loss on the development and maintenance of cognitive function

Physiochemical property space distribution among human metabolites, drugs and toxins

<p>Abstract</p> <p>Background</p> <p>The current approach to screen for drug-like molecules is to sieve for molecules with biochemical properties suitable for desirable pharmacokinetics and reduced toxicity, using predominantly biophysical properties of chemical compounds, based on empirical rules such as Lipinski's "rule of five" (Ro5). For over a decade, Ro5 has been applied to combinatorial compounds, drugs and ligands, in the search for suitable lead compounds. Unfortunately, till date, a clear distinction between drugs and non-drugs has not been achieved. The current trend is to seek out drugs which show metabolite-likeness. In identifying similar physicochemical characteristics, compounds have usually been clustered based on some characteristic, to reduce the search space presented by large molecular datasets. This paper examines the similarity of current drug molecules with human metabolites and toxins, using a range of computed molecular descriptors as well as the effect of comparison to clustered data compared to searches against complete datasets.</p> <p>Results</p> <p>We have carried out statistical and substructure functional group analyses of three datasets, namely human metabolites, drugs and toxin molecules. The distributions of various molecular descriptors were investigated. Our analyses show that, although the three groups are distinct, present-day drugs are closer to toxin molecules than to metabolites. Furthermore, these distributions are quite similar for both clustered data as well as complete or unclustered datasets.</p> <p>Conclusion</p> <p>The property space occupied by metabolites is dissimilar to that of drugs or toxin molecules, with current drugs showing greater similarity to toxins than to metabolites. Additionally, empirical rules like Ro5 can be refined to identify drugs or drug-like molecules that are clearly distinct from toxic compounds and more metabolite-like. The inclusion of human metabolites in this study provides a deeper insight into metabolite/drug/toxin-like properties and will also prove to be valuable in the prediction or optimization of small molecules as ligands for therapeutic applications.</p

Analysis of in vitro bioactivity data extracted from drug discovery literature and patents: Ranking 1654 human protein targets by assayed compounds and molecular scaffolds

<p>Abstract</p> <p>Background</p> <p>Since the classic Hopkins and Groom druggable genome review in 2002, there have been a number of publications updating both the hypothetical and successful human drug target statistics. However, listings of research targets that define the area between these two extremes are sparse because of the challenges of collating published information at the necessary scale. We have addressed this by interrogating databases, populated by expert curation, of bioactivity data extracted from patents and journal papers over the last 30 years.</p> <p>Results</p> <p>From a subset of just over 27,000 documents we have extracted a set of compound-to-target relationships for biochemical <it>in vitro </it>binding-type assay data for 1,736 human proteins and 1,654 gene identifiers. These are linked to 1,671,951 compound records derived from 823,179 unique chemical structures. The distribution showed a compounds-per-target average of 964 with a maximum of 42,869 (Factor Xa). The list includes non-targets, failed targets and cross-screening targets. The top-278 most actively pursued targets cover 90% of the compounds. We further investigated target ranking by determining the number of molecular frameworks and scaffolds. These were compared to the compound counts as alternative measures of chemical diversity on a per-target basis.</p> <p>Conclusions</p> <p>The compounds-per-protein listing generated in this work (provided as a supplementary file) represents the major proportion of the human drug target landscape defined by published data. We supplemented the simple ranking by the number of compounds assayed with additional rankings by molecular topology. These showed significant differences and provide complementary assessments of chemical tractability.</p

Exhaustive Sampling of Docking Poses Reveals Binding Hypotheses for Propafenone Type Inhibitors of P-Glycoprotein

Overexpression of the xenotoxin transporter P-glycoprotein (P-gp) represents one major reason for the development of multidrug resistance (MDR), leading to the failure of antibiotic and cancer therapies. Inhibitors of P-gp have thus been advocated as promising candidates for overcoming the problem of MDR. However, due to lack of a high-resolution structure the concrete mode of interaction of both substrates and inhibitors is still not known. Therefore, structure-based design studies have to rely on protein homology models. In order to identify binding hypotheses for propafenone-type P-gp inhibitors, five different propafenone derivatives with known structure-activity relationship (SAR) pattern were docked into homology models of the apo and the nucleotide-bound conformation of the transporter. To circumvent the uncertainty of scoring functions, we exhaustively sampled the pose space and analyzed the poses by combining information retrieved from SAR studies with common scaffold clustering. The results suggest propafenone binding at the transmembrane helices 5, 6, 7 and 8 in both models, with the amino acid residue Y307 playing a crucial role. The identified binding site in the non-energized state is overlapping with, but not identical to, known binding areas of cyclic P-gp inhibitors and verapamil. These findings support the idea of several small binding sites forming one large binding cavity. Furthermore, the binding hypotheses for both catalytic states were analyzed and showed only small differences in their protein-ligand interaction fingerprints, which indicates only small movements of the ligand during the catalytic cycle

Low potency toxins reveal dense interaction networks in metabolism

Background The chemicals of metabolism are constructed of a small set of atoms and bonds. This may be because chemical structures outside the chemical space in which life operates are incompatible with biochemistry, or because mechanisms to make or utilize such excluded structures has not evolved. In this paper I address the extent to which biochemistry is restricted to a small fraction of the chemical space of possible chemicals, a restricted subset that I call Biochemical Space. I explore evidence that this restriction is at least in part due to selection again specific structures, and suggest a mechanism by which this occurs. Results Chemicals that contain structures that our outside Biochemical Space (UnBiological groups) are more likely to be toxic to a wide range of organisms, even though they have no specifically toxic groups and no obvious mechanism of toxicity. This correlation of UnBiological with toxicity is stronger for low potency (millimolar) toxins. I relate this to the observation that most chemicals interact with many biological structures at low millimolar toxicity. I hypothesise that life has to select its components not only to have a specific set of functions but also to avoid interactions with all the other components of life that might degrade their function. Conclusions The chemistry of life has to form a dense, self-consistent network of chemical structures, and cannot easily be arbitrarily extended. The toxicity of arbitrary chemicals is a reflection of the disruption to that network occasioned by trying to insert a chemical into it without also selecting all the other components to tolerate that chemical. This suggests new ways to test for the toxicity of chemicals, and that engineering organisms to make high concentrations of materials such as chemical precursors or fuels may require more substantial engineering than just of the synthetic pathways involved

Association between SNPs and gene expression in multiple regions of the human brain

Identifying the genetic cis associations between DNA variants (single-nucleotide polymorphisms (SNPs)) and gene expression in brain tissue may be a promising approach to find functionally relevant pathways that contribute to the etiology of psychiatric disorders. In this study, we examined the association between genetic variations and gene expression in prefrontal cortex, hippocampus, temporal cortex, thalamus and cerebellum in subjects with psychiatric disorders and in normal controls. We identified cis associations between 648 transcripts and 6725 SNPs in the various brain regions. Several SNPs showed brain regional-specific associations. The expression level of only one gene, PDE4DIP, was associated with a SNP, rs12124527, in all the brain regions tested here. From our data, we generated a list of brain cis expression quantitative trait loci (eQTL) genes that we compared with a list of schizophrenia candidate genes downloaded from the Schizophrenia Forum (SZgene) database (http://www.szgene.org/). Of the SZgene candidate genes, we found that the expression levels of four genes, HTR2A, PLXNA2, SRR and TCF4, were significantly associated with cis SNPs in at least one brain region tested. One gene, SRR, was also involved in a coexpression module that we found to be associated with disease status. In addition, a substantial number of cis eQTL genes were also involved in the module, suggesting eQTL analysis of brain tissue may identify more reliable susceptibility genes for schizophrenia than case–control genetic association analyses. In an attempt to facilitate the identification of genetic variations that may underlie the etiology of major psychiatric disorders, we have integrated the brain eQTL results into a public and online database, Stanley Neuropathology Consortium Integrative Database (SNCID; http://sncid.stanleyresearch.org)

Early influences on cardiovascular and renal development

The hypothesis that a developmental component plays a role in subsequent disease initially arose from epidemiological studies relating birth size to both risk factors for cardiovascular disease and actual cardiovascular disease prevalence in later life. The findings that small size at birth is associated with an increased risk of cardiovascular disease have led to concerns about the effect size and the causality of the associations. However, recent studies have overcome most methodological flaws and suggested small effect sizes for these associations for the individual, but an potential important effect size on a population level. Various mechanisms underlying these associations have been hypothesized, including fetal undernutrition, genetic susceptibility and postnatal accelerated growth. The specific adverse exposures in fetal and early postnatal life leading to cardiovascular disease in adult life are not yet fully understood. Current studies suggest that both environmental and genetic factors in various periods of life may underlie the complex associations of fetal growth retardation and low birth weight with cardiovascular disease in later life. To estimate the population effect size and to identify the underlying mechanisms, well-designed epidemiological studies are needed. This review is focused on specific adverse fetal exposures, cardiovascular adaptations and perspectives for new studies. Copyrigh

The NEWMEDS rodent touchscreen test battery for cognition relevant to schizophrenia.

RATIONALE: The NEWMEDS initiative (Novel Methods leading to New Medications in Depression and Schizophrenia, http://www.newmeds-europe.com ) is a large industrial-academic collaborative project aimed at developing new methods for drug discovery for schizophrenia. As part of this project, Work package 2 (WP02) has developed and validated a comprehensive battery of novel touchscreen tasks for rats and mice for assessing cognitive domains relevant to schizophrenia. OBJECTIVES: This article provides a review of the touchscreen battery of tasks for rats and mice for assessing cognitive domains relevant to schizophrenia and highlights validation data presented in several primary articles in this issue and elsewhere. METHODS: The battery consists of the five-choice serial reaction time task and a novel rodent continuous performance task for measuring attention, a three-stimulus visual reversal and the serial visual reversal task for measuring cognitive flexibility, novel non-matching to sample-based tasks for measuring spatial working memory and paired-associates learning for measuring long-term memory. RESULTS: The rodent (i.e. both rats and mice) touchscreen operant chamber and battery has high translational value across species due to its emphasis on construct as well as face validity. In addition, it offers cognitive profiling of models of diseases with cognitive symptoms (not limited to schizophrenia) through a battery approach, whereby multiple cognitive constructs can be measured using the same apparatus, enabling comparisons of performance across tasks. CONCLUSION: This battery of tests constitutes an extensive tool package for both model characterisation and pre-clinical drug discovery.This work was supported by the Innovative Medicine Initiative Joint Undertaking under grant agreement no. 115008 of which resources are composed of EFPIA in-kind contribution and financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013). The authors thank Charlotte Oomen for valuable comments on the manuscript.This is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/s00213-015-4007-