Microscale thermophoresis and docking studies suggest lapachol and auraptene are ligands of IDO1.

Indoleamine 2,3-dioxygenase 1 (IDO1) is a key target for the development of small molecule immunotherapies in oncology. In this framework, the screening of chemotherapeutic agents to identify compounds binding to IDO1 represents a valuable strategy for the development of multitarget drug candidates that combine synergic immunoregulatory properties to cytotoxic activity. In this study, we report that two natural compounds endowed with anticancer activity, namely lapachol and auraptene, act as IDO1 ligands with dissociation constant (Kd) in the micromolar range of potency. Docking studies provide plausible binding modes of these compounds to the catalytic cleft of IDO1. Our results support the notion that lapachol and auraptene may be considered interesting lead compounds in the immuno-oncology setting

Binding properties of different categories of IDO1 inhibitors: a microscale thermophoresis study

Aim: Inhibition of IDO1 is a strategy pursued in the immune-oncology pipeline for the development of novel anticancer therapies. At odds with an ever-increasing number of inhibitors being disclosed in the literature and patent applications, only very few compounds have hitherto advanced in clinical settings. Materials & methods: We have used MicroScale Thermophoresis analysis and docking calculations to assess on a quantitative basis the binding properties of distinct categories of inhibitors to IDO1. Results: Results shed further light on hidden molecular aspects governing the recognition by the enzyme of compounds with different mechanism of inhibition. Conclusion: Results pinpoint specific binding features of distinct inhibitors to IDO1 that offer clues for the design of next-generation inhibitors of the enzyme

Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure

Funder: GlaxoSmithKline (GB)Abstract: Background: Drug-induced liver injury (DILI) is a major safety concern characterized by a complex and diverse pathogenesis. In order to identify DILI early in drug development, a better understanding of the injury and models with better predictivity are urgently needed. One approach in this regard are in silico models which aim at predicting the risk of DILI based on the compound structure. However, these models do not yet show sufficient predictive performance or interpretability to be useful for decision making by themselves, the former partially stemming from the underlying problem of labeling the in vivo DILI risk of compounds in a meaningful way for generating machine learning models. Results: As part of the Critical Assessment of Massive Data Analysis (CAMDA) “CMap Drug Safety Challenge” 2019 (http://camda2019.bioinf.jku.at), chemical structure-based models were generated using the binarized DILIrank annotations. Support Vector Machine (SVM) and Random Forest (RF) classifiers showed comparable performance to previously published models with a mean balanced accuracy over models generated using 5-fold LOCO-CV inside a 10-fold training scheme of 0.759 ± 0.027 when predicting an external test set. In the models which used predicted protein targets as compound descriptors, we identified the most information-rich proteins which agreed with the mechanisms of action and toxicity of nonsteroidal anti-inflammatory drugs (NSAIDs), one of the most important drug classes causing DILI, stress response via TP53 and biotransformation. In addition, we identified multiple proteins involved in xenobiotic metabolism which could be novel DILI-related off-targets, such as CLK1 and DYRK2. Moreover, we derived potential structural alerts for DILI with high precision, including furan and hydrazine derivatives; however, all derived alerts were present in approved drugs and were over specific indicating the need to consider quantitative variables such as dose. Conclusion: Using chemical structure-based descriptors such as structural fingerprints and predicted protein targets, DILI prediction models were built with a predictive performance comparable to previous literature. In addition, we derived insights on proteins and pathways statistically (and potentially causally) linked to DILI from these models and inferred new structural alerts related to this adverse endpoint

Reproductive biology of the Cisalpine pike (Esox cisalpinus Bianco and Delmastro, 2011) in Trasimeno Lake (Italy).

The Cisalpine pike Esox cisalpinus is an endemic species to Northern and Central Italy, recently separated from the transalpine populations of northern pike Esox lucius L. 1758, based on morphology and genetics (Bianco and Delmastro, 2011; Lucentini et al., 2011). The information about the biology of E. cisalpinus is very limited and the trends of population are still unknown. Hence, currently the species is “Not Evaluated” according to the IUCN Red List of Threatened Species and it is listed as “Data Deficient” in the Italian IUCN Red List (Rondinidi et al., 2013). In the Trasimeno Lake (43°9’11’’ N and 12°15’E), the largest laminar lake in Italy, E. cisalpinus is one of the few native species of the fish community, mainly dominated by non-native species (Pompei et al., 2012). A strong reduction of the abundances of E. cisalpinus in the Trasimeno Lake occurred in the recent years primarily due to different factors relating to non-native species introduction: competition for food with largemouth bass Micropterus salmoides (Lacépedè, 1802), negative interaction with Carassius sp. and hybridization with E. lucius (Lorenzoni et al., 2002; Lorenzoni and Ghetti, 2011). Even the degradation of breeding areas contributed to the E. cisalpinus decline and actually its reproduction is widely supported by controlled spawning. The aims of this study were to provide more information on reproductive biology of the population of E. cisalpinus inhabiting Trasimeno Lake by analyzing some reproductive traits such as Gonadosomatic Index, fecundity, egg size and their temporal trend over a period of 5 years. Females of E. cisalpinus were collected from 2010 to 2014 (except 2013) during the reproductive period (February-March) by the staff of Ichthyogenic Centre of Trasimeno Lake. All fish were measured (TL, cm) weighted (W, g) and a sample of scales was removed for age determination (Bagenal and Tesch 1985). The total weight of eggs squeezed from each female was measured (Wg). Gonadosomatic Index (GSI) was calculated as: GSI=100(Wg/W). A subsample of eggs was weighted (Wgn) and the eggs in the subsample were counted (Ns) to obtain fecundity (F=NsWgn-1). Relative Fecundity (RF) was expressed as the number of eggs produced (F) per unit of body weight: RF=1000(FW-1). The diameters of 10 randomly selected eggs from each ovary were measured by means of the image processing program software ImageJ and the mean value (δ, cm) for each female was used in the subsequent analysis. In order to analyse whether F, RF and δ varied with the increase size, the following regressions were calculated: log10F=a+b log10TL; RF=a+bTL; δ=a+bTL. The mean annual values of F, RF and δ were compared by Analysis of Covariance (ANCOVA) (using TL as covariate mean); moreover variations in these parameters over time were evaluated using linear regression analysis. Body condition was determined using Relative Weight Wr=100(W’/Ws) (Murphy et al., 1991), where W’ is the somatic weight ad Ws is the Standard Weight calculated as follows: log10Ws=– 5.37+3.06 log10TL (Willis, 1991). Variations in condition over time were evaluated using a linear regression analysis. Total sample consisted of 292 females ranging in size from 28 to 103 cm (2+ -14+ age classes). Descriptive statistic of GSI, F, FR, δ and Wr is reported in Table 1. Fecundity was positively correlated with TL (r2=0.67, r=0.82, p0.05; covariate mean: TL=61.03 cm). On the contrary, RF varied remarkably from year to year (ANCOVA: F=3.57, p<0.05; covariate mean: TL=61.03 cm), without a clear temporal trend in the regression analysis (r2<0.01; r=-0.02; p=0.74). The great variability of RF values should be justified by the high unpredictability of environmental condition (i.e. water level, water temperature) that characterize the Trasimeno Lake (Lorenzoni et al. 2002). The mean diameter of eggs showed a marked decrease from 2010 to 2012, with a slight increase in 2014 (ANCOVA: F=48.56, p<0.01; covariate mean: TL=61.03 cm); however the regression analysis highlights a general decrease over time (r2=0.03; r=-0.17, p<0.01) (Fig. 2A). A similar pattern was revealed from the regression between Wr and years (r2=0.02; r=-0.15, p<0.01) (Fig. 2B). In many fish species, egg size, which correlates with female size or condition, has been found to contribute to egg quality (Kamler, 1992). The reduction of the eggs diameter ultimately results in the quality of the offspring, since the amount and composition of the yolk have a great influence on the development of the larvae (Laine and Rajasilta, 1999). Condition of spawners is known to have significant effects on reproductive potential (Laine and Rajasilta, 1999), but it also influences the viability of eggs (Kjørsvik et al., 1990), thus the decrease in eggs size may be correlated to the worsening of condition during years that seems to affect E. cisalpinus in Trasimeno Lake. The worsening of both condition and quality of the eggs make necessary the continuation of the monitoring study to safeguard the conservation of the population. The preliminary results reported in this research increase the knowledge on the biological characteristic of E. cisalpinus and should assist decision-making in relation to the future management of this endemic species

Exploiting Vector Pattern Diversity of Molecular Scaffolds for Cheminformatics Tasks in Drug Discovery

Chemical diversity is challenging to describe objectively. Despite this, various notions of chemical diversity are used throughout the medicinal chemistry optimization process in drug discovery. In this work, we show the usefulness of considering exploited vectors during different phases of the drug design process to provide a quantitative and objective description of chemical diversity. We have developed a concise and fast approach to enumerate and analyze the exploited vector patterns (EVPs) of molecular compound series, which can then be used in archetypal compound selection tasks from hit matter identification to hit expansion and lead optimization. We firstly show that EVPs can be used to assess the progressibility of compounds in a fragment library design exercise. By considering EVPs, we then show how a set of compounds can be prioritized for hit expansion using EVP-based, customizable diversity sampling approaches, reducing the time taken and mitigating human biases. We also show that EVPs are a useful tool to analyze SAR data, offering the chance to uncover correlations between different vectors without pre-determining the molecular scaffold structures. The codes used to perform these tasks are presented as easy-to-use Jupyter notebooks, which can be readily adapted for further related tasks