Early Stages Of Drug Discovery Research Articles

A Network-Based Computational and Experimental Framework for Repurposing Compounds Towards the Treatment of Non-Alcoholic Fatty Liver Disease

Non-Alcoholic Fatty Liver Disease (NAFLD) is among the most common liver pathologies, however, none approved condition-specific therapy yet exists. The present study introduces a drug repositioning (DR) approach that combines in vitro steatosis models with a network-based computational platform, constructed upon genomic data from diseased liver biopsies and compound-treated cell lines, to propose effectively repositioned therapeutic compounds. The introduced in silico approach screened 20'000 compounds, whilst complementary in vitro and proteomic assays were developed to test the efficacy of the 46 in silico predictions. This approach successfully identified 6 compounds, including the known anti-steatogenic drugs Resveratrol and Sirolimus. In short, Gallamine triethiotide, Diflorasone, Fenoterol and Pralidoxime ameliorate steatosis similarly to Resveratrol/Sirolimus. The implementation holds great potential in reducing screening time in the early drug discovery stages and in delivering promising compounds for in vivo testing.

A Review on Zebra Fish as a Preclinical Model for Natural Drug Discovery

Natural products represent a significant reservoir of unexplored chemical diversity for early-stage drug discovery. Zebra fish has been used as a preclinical model for identifications of various natural compounds presents on natural products, early life stage tests with zebra fish used to detect toxicity of compounds from natural bloom extract on their embryolarval development. The use of Medicinal plants for treatment of neurological & neuro-degenerative disorders, cardiovascular and anti-melongenesis are very common. Bitter gourd shows anti-diabetic effects by using zebra fish model in a designed experiment. Zebra fish has been used as a tool for discovering of phyto-chemical compounds in natural products and their activities such as, fruit of <i>Terminalia chebula</i> possessing the activity of anti- epileptic, <i>Oxygonum sinuatum</i> and <i>Plectranthus barbatus</i> showed anti-angogensis with the active compounds Emodin & Coleon A lactone. Cardiac toxicity by the fruit extract of <i>M. charantia</i> reported. Zebra fish is gaining more interest as a preclinical tool for drug discovery because it has been demonstrate that pharmacokinetic analysis can be under-taken in this model. The study concludes that and gives a desktop reference to the new researchers in finding out the earlier attempts which were made on zebra fish (Danio rerio) as a preclinical tool in natural product discovery.

Deep Graph Learning with Property Augmentation for Predicting Drug-Induced Liver Injury.

Drug-induced liver injury (DILI) is a crucial factor in determining the qualification of potential drugs. However, the DILI property is excessively difficult to obtain due to the complex testing process. Consequently, an in silico screening in the early stage of drug discovery would help to reduce the total development cost by filtering those drug candidates with a high risk to cause DILI. To serve the screening goal, we apply several computational techniques to predict the DILI property, including traditional machine learning methods and graph-based deep learning techniques. While deep learning models require large training data to tune huge model parameters, the DILI data set only contains a few hundred annotated molecules. To alleviate the data scarcity problem, we propose a property augmentation strategy to include massive training data with other property information. Extensive experiments demonstrate that our proposed method significantly outperforms all existing baselines on the DILI data set by obtaining a 81.4% accuracy using cross-validation with random splitting, 78.7% using leave-one-out cross-validation, and 76.5% using cross-validation with scaffold splitting.

“Pulling the Plug:” Time Allocation between Drug Discovery and Development Projects

Biotechnology and pharmaceutical firms invest billions of dollars in R&amp;D, primarily in new drug discovery. Nonetheless, the industry is facing declining returns on R&amp;D investments. Failure to discontinue less‐promising new drug discovery projects is a key driver of this decreased productivity. Hence, firms are scrambling to restructure the new drug discovery process to improve decision‐making and to limit the attrition rate to the early stages of drug discovery. Drawing on insights from the exploration–exploitation literature, our study addresses this timing problem by using a formal model and empirically testing its implications on how the time to discontinuation of new drug discovery projects are impacted by project‐ and firm‐specific characteristics, so that a firm's resources can be promptly redeployed to more fruitful endeavors. Our findings, based on an analysis of 1274 early‐stage drug discovery projects worldwide, suggest that the time to discontinuation of early‐stage drug discovery projects requires careful consideration of these project‐ and firm‐specific characteristics. These findings hold important implications for the industry, which is undergoing tremendous stress and transformation. The results also contribute to the exploration–exploitation literature by modeling and testing the time‐allocation decision between exploratory and exploitative activities.

Analisis &lt;i&gt;In Silico &lt;/i&gt;pada VCO untuk Terapi Dermatitis Atopik

Introduction: In silico analysis is used in the early stages of drug discovery to speed the rate of discovery that is considered cost and time-efficient. Virgin Coconut Oil (VCO) is a treatment option for atopic dermatitis because it has function to repair skin barrier and as anti-inflammation. Objective: To evaluate the VCO content using computational in silico analysis for the treatment of atopic dermatitis. Methods: The active compounds of Cocos nucifera extracted from KNApSAcK database were predicted experimentally and analyzed computationally using Simple Molecular-Input Line-Entry System (SMILES), Prediction of Activity Spectra for biologically active Substances (PASS) server, and Search Tool for Interactions of Chemicals (STITCH). Results: There are 19 active compounds found in VCO. The analysis shows that VCO has free fatty acid (FFA) protein which acts as a receptor for long and medium chain saturated and unsaturated fatty acids. The highest potential bioactivity of active VCO compounds was as antieczema, with the most important component was linoleic acid, with an average value of probable to be active (Pa) = 0.872, predicted to be highly potential in computational and laboratory tests. Conclusion: Based on this study, we recommend the use of VCO as treatment for atopic dermatitis because VCO has anti-inflammatory potential bioactivity, histamine inhibitor, improves skin barrier function and antieczema which could improve the outcome of atopic dermatitis.

3D‐chemocentric target deconvolution of unprecedented anti‐inflammatory drug scaffolds

AbstractBackgroundA typical hit‐to‐lead flow focuses on a well‐established molecular target. However, imperfect pathological backgrounds of the diseases can bring out low confidence on the chosen target. Therefore, despite unclear targets, the regulation of neuroinflammation is an important approach of early stage drug discovery in AD and neurological disorders. The potential targets of anti‐inflammatory agents are enormous but don't be clear investigated with their atomic level mechanism.MethodChOS (Chemistry‐oriented synthesis), a kind of inverse drug design, suggests target deconvolution of unprecedented anti‐inflammatory agents (in‐house compounds) through 3D‐chemocentric approach.ResultThe inverse approach identified an in‐house anti‐inflammatory agent, as the first‐in‐class small molecule antagonist for non‐histaminergic neurons, investigated a new therapeutic indication of a known target, and provided pharmacological profile of in‐house anti‐inflammatory agents.ConclusionThe inverse drug discovery approach of in‐house anti‐inflammatory agents, ChOS, can be a compensatory alternative for neurological disorders and the results wait for AD &amp; dementia collaboration.

Accelerating the identification of subtype selective inhibitors via Three-Dimensional Biologically Relevant Spectrum (BRS-3D): The monoamine oxidase subtypes as a case study

Subtype-selective drugs are of great therapeutic importance as they are expected to be more effective and with less side-effects. However, discovery of subtype selective inhibitors was hampered by the high similarity of the binding sites within subfamilies. In this study, we further evaluated the applicability of “Three-Dimensional Biologically Relevant Spectrum (BRS-3D)” for the identification of subtype-selective inhibitors. A case study was performed on monoamine oxidase, which has two subtypes related to distinct diseases. The inhibitory activity against MAO-A/B of 347 compounds experimentally tested in this research was reported. Compound M124 (5H-thiazolo[3,2-a]pyrimidin-5-one) with IC50 less than 100 nM (SI = 23) was selected as a probe to investigate the structure selectivity relationship. Similarity search led to the identification of compound M229 and M249 with IC50 values of 7.4 nM, 4 nM and acceptable selectivity index over MAO-A (M229 SI > 1351, M249 SI > 2500). The molecular basis for subtype selectivity was explored through docking study and attention based DNN model. Additionally, in silico ADME properties were characterized. Accordingly, it is found that BRS-3D is a robust method for subtype selectivity in the early stage of drug discovery and the compounds reported here can be promising leads for further experimental analysis.

Toward an automated workflow for the study of plasma protein-drug interactions based on capillary electrophoresis-frontal analysis combined with in-capillary mixing of interacting partners

Capillary electrophoresis-frontal analysis (CE-FA) together with mobility shift affinity CE is the most frequently used mode of affinity CE for a study of plasma protein-drug interactions, which is a substantial part of the early stage of drug discovery. Whereas in the classic CE-FA setup the sample is prepared by off-line mixing of the interaction partners in the sample vial outside the CE instrument and after a short incubation period loaded into the capillary and analysed, in this work a new methodological approach has been developed that combines CE-FA with the mixing of interacting partners directly inside the capillary. This combination gives rise to a fully automated and versatile methodology for the characterization of these binding interactions besides a substantial reduction in the amounts of sample compounds used. The minimization of possible experimental errors due to the full involving of sophisticated CE instrument in the injection procedure, mixing and separation instead of manual manipulation is another fundamental benefit.The in-capillary mixing is based on the transverse diffusion of laminar flow profile methodology introduced by Krylov et al. using its multi-zone injection modification presented by Řemínek at al.. Actually, after the method optimization, the alternate introduction of six plugs of drug and six plugs of bovine serum protein in BGE, each injected for 3 s at a pressure of -10 mbar (-1 kPa) into the capillary filled by BGE, was found to be the best injection procedure. The method repeatability calculated as RSDs of plateau highs of bovine serum albumin and propranolol as model sample compounds were better than 3.44 %. Its applicability was finally demonstrated on the determination of apparent binding parameters of bovine serum albumin for basic drugs propranolol and lidocaine and acid drug phenylbutazone. The values obtained by a new on-line CE-FA methodology are in agreement with values estimated by classic off-line CE-FA, as well as with literature data obtained using different techniques.

Diamond Light Source: contributions to SARS-CoV-2 biology and therapeutics

The impact of COVID-19 on public health and the global economy has led to an unprecedented research response, with a major emphasis on the development of safe vaccines and drugs. However, effective, safe treatments typically take over a decade to develop and there are still no clinically approved therapies to treat highly pathogenic coronaviruses. Repurposing of known drugs can speed up development and this strategy, along with the use of biologicals (notably monoclonal antibody therapy) and vaccine development programmes remain the principal routes to dealing with the immediate impact of COVID-19. Nevertheless, the development of broadly-effective highly potent antivirals should be a major longer term goal. Structural biology has been applied with enormous effect, with key proteins structurally characterised only weeks after the SARS-CoV-2 sequence was released. Open-access to advanced infrastructure for structural biology techniques at synchrotrons and high-end cryo-EM and NMR centres has brought these technologies centre-stage in drug discovery. We summarise the role of Diamond Light Source in responses to the pandemic and note the impact of the immediate release of results in fuelling an open-science approach to early-stage drug discovery.

Extension of the Mechanistic Tissue Distribution Model of Rodgers and Rowland by Systematic Incorporation of Lysosomal Trapping: Impact on Unbound Partition Coefficient and Volume of Distribution Predictions in the Rat.

Physiologically based pharmacokinetic modeling has become a standard tool to predict drug distribution in early stages of drug discovery; however, this does not currently encompass lysosomal trapping. For basic lipophilic compounds, lysosomal sequestration is known to potentially influence intracellular as well as tissue distribution. The aim of our research was to reliably predict the lysosomal drug content and ultimately integrate this mechanism into pharmacokinetic prediction models. First, we further validated our previously presented method to predict the lysosomal drug content (Schmitt et al., 2019) for a larger set of compounds (n = 41) showing a very good predictivity. Using the lysosomal marker lipid bis(monoacylglycero)phosphate, we estimated the lysosomal volume fraction for all major tissues in the rat, ranging from 0.03% for adipose up to 5.3% for spleen. The pH-driven lysosomal trapping was then estimated and fully integrated into the mechanistic distribution model published by Rodgers et al. (2005) Predictions of Kpu improved for all lysosome-rich tissues. For instance, Kpu increased for nicotine 4-fold (spleen) and 2-fold (lung and kidney) and for quinidine 1.8-fold (brain), although for most other drugs the effects were much less (≤7%). Overall, the effect was strongest for basic compounds with a lower lipophilicity, such as nicotine, for which the unbound volume of distribution at steady-state prediction changed from 1.34 to 1.58 l/kg. For more lipophilic (basic) compounds or those that already show strong interactions with acidic phospholipids, the additional contribution of lysosomal trapping was less pronounced. Nevertheless, lysosomal trapping will also affect intracellular distribution of such compounds. SIGNIFICANCE STATEMENT: The estimation of the lysosomal content in all body tissues facilitated the incorporation of lysosomal sequestration into a general physiologically based pharmacokinetic model, leading to improved predictions as well as elucidating its influence on tissue and subcellular distribution in the rat.

LightBBB: computational prediction model of blood-brain-barrier penetration based on LightGBM.

Identification of blood-brain barrier (BBB) permeability of a compound is a major challenge in neurotherapeutic drug discovery. Conventional approaches for BBB permeability measurement are expensive, time-consuming and labor-intensive. BBB permeability is associated with diverse chemical properties of compounds. However, BBB permeability prediction models have been developed using small datasets and limited features, which are usually not practical due to their low coverage of chemical diversity of compounds. Aim of this study is to develop a BBB permeability prediction model using a large dataset for practical applications. This model can be used for facilitated compound screening in the early stage of brain drug discovery. A dataset of 7162 compounds with BBB permeability (5453 BBB+ and 1709 BBB-) was compiled from the literature, where BBB+ and BBB- denote BBB-permeable and non-permeable compounds, respectively. We trained a machine learning model based on Light Gradient Boosting Machine (LightGBM) algorithm and achieved an overall accuracy of 89%, an area under the curve (AUC) of 0.93, specificity of 0.77 and sensitivity of 0.93, when 10-fold cross-validation was performed. The model was further evaluated using 74 central nerve system compounds (39 BBB+ and 35 BBB-) obtained from the literature and showed an accuracy of 90%, sensitivity of 0.85 and specificity of 0.94. Our model outperforms over existing BBB permeability prediction models. The prediction server is available at http://ssbio.cau.ac.kr/software/bbb.

Machine learning-based QSAR models to predict sodium ion channel (Nav 1.5) blockers.

Aim: Conventional experimental approaches used for the evaluation of the proarrhythmic potential of compounds in the drug discovery process are expensive and time consuming but an integral element in the safety profile required for a new drug to be approved. The voltage-gated sodium ionchannel 1.5 (Nav 1.5), a target known for arrhythmic drugs, causes adverse cardiac complications when the channel is blocked. Results: Machine learningclassification and regression models were built to predict the possibility of blocking these channels by small molecules. The finalized models tested with balanced accuracies of 0.88, 0.93 and 0.94 at three thresholds (1, 10 and 30 µmol, respectively). The regression model built to predict the pIC50 of compounds had q2 of 0.84 (root-mean-square error=0.46). Conclusion: The machine learningmodels that have been built can act as effective filters to screen out the potentially toxic compounds in the early stages of drug discovery.

Optimization of a Colorimetric Assay to Determine Lactate Dehydrogenase B Activity Using Design of Experiments.

Lactate dehydrogenase B (LDH-B) is overexpressed in lung and breast cancer, and it has been considered as a potential target to treat these types of cancer. Herein, we propose a straightforward incomplete factorial (IF) design composed of 12 combinations of two reaction buffers, three pH values, three salt (NaCl) concentrations, and three incubation times, which we called IF-BPST (Buffer/pH/Salt/Time), for the optimization of a colorimetric LDH-B assay in a final volume of 100 µL using 96-well plates. The assay is based on the absorbance change at ~570 nm and the color change of the reaction mixture due to the release of NADH that reacts with nitroblue tetrazolium (NBT) and phenazine methosulfate (PMS), resulting in the formation of a blue-purple formazan. The results obtained using the IF-BPST were comparable with those obtained by response surface methodology. Our work revealed that the NBT/PMS assay with some modifications can be used to measure the activity of LDH-B and other dehydrogenases in a high-throughput screening format at the early stages of drug discovery. LDH-B containing lysates cannot be assayed directly, however, due to the sensitivity of the method toward detergents. Thus, we suggest precipitating the proteins in the lysates to remove the interfering detergents, and then to dissolve the protein pellet in a suitable buffer and carry out the assay.

Scopy: an integrated negative design python library for desirable HTS/VS database design

High-throughput screening (HTS) and virtual screening (VS) have been widely used to identify potential hits from large chemical libraries. However, the frequent occurrence of 'noisy compounds' in the screened libraries, such as compounds with poor drug-likeness, poor selectivity or potential toxicity, has greatly weakened the enrichment capability of HTS and VS campaigns. Therefore, the development of comprehensive and credible tools to detect noisy compounds from chemical libraries is urgently needed in early stages of drug discovery. In this study, we developed a freely available integrated python library for negative design, called Scopy, which supports the functions of data preparation, calculation of descriptors, scaffolds and screening filters, and data visualization. The current version of Scopy can calculate 39 basic molecular properties, 3 comprehensive molecular evaluation scores, 2 types of molecular scaffolds, 6 types of substructure descriptors and 2 types of fingerprints. A number of important screening rules are also provided by Scopy, including 15 drug-likeness rules (13 drug-likeness rules and 2 building block rules), 8 frequent hitter rules (four assay interference substructure filters and four promiscuous compound substructure filters), and 11 toxicophore filters (five human-related toxicity substructure filters, three environment-related toxicity substructure filters and three comprehensive toxicity substructure filters). Moreover, this library supports four different visualization functions to help users to gain a better understanding of the screened data, including basic feature radar chart, feature-feature-related scatter diagram, functional group marker gram and cloud gram. Scopy provides a comprehensive Python package to filter out compounds with undesirable properties or substructures, which will benefit the design of high-quality chemical libraries for drug design and discovery. It is freely available at https://github.com/kotori-y/Scopy.

Understanding virtual lead optimization through a simple online task.

Understanding virtual lead optimization through a simple online task.

Programmable droplet-based microfluidic serial dilutor

A programmable droplet-based microfluidic serial dilutor platform is presented, which is capable of generating a series of droplets with the scalable stepwise concentration gradient of a sample. Sequential dilution of a target molecule was automatically performed in sub-nanoliter scale droplets by synchronizing a microfluidic peristaltic mixer and a valve-assisted droplet generator. The volume of droplets dispensed from the mixer was controlled by microvalve operation, which enabled to tune the dilution with various dilution factors. After evaluation of the mixer efficiency and calibration of the droplet size at different valve operating conditions, serial dilutions of rhodamine B isothiocyanate-dextran was demonstrated, in an automated manner, at three different dilution factors. Specifically, the effect of the rhodamine B isothiocyanate-dextran concentration and temperature on variations of the fluorescent intensity was quantified. This programmable microfluidic droplet serial dilutor will open new avenues, an analytical tool, to evaluate complex chemical and biochemical reactions, especially when limited sample volume is available, for example, at the early stage of drug discovery and biochemical process developing.

Drug target deconvolution by chemical proteomics

Drug target identification can help establish the association between drug activity and phenotypes, elucidate the mechanism of action, discover off-target effects and drug resistance mechanisms, discover new targets for therapeutic drugs, and predict possible side effects and toxicity at an early stage of drug discovery, thereby reducing the risk of failure in drug development. Although rapid progress has been made in scientific discoveries and technological advancements, the identification of drug targets is still a daunting task. Herein, the research progress in drug target identification during the past decade, especially novel technologies without chemical labeling, have been reviewed.

IDrug: Integration of drug repositioning and drug-target prediction via cross-network embedding.

Computational drug repositioning and drug-target prediction have become essential tasks in the early stage of drug discovery. In previous studies, these two tasks have often been considered separately. However, the entities studied in these two tasks (i.e., drugs, targets, and diseases) are inherently related. On one hand, drugs interact with targets in cells to modulate target activities, which in turn alter biological pathways to promote healthy functions and to treat diseases. On the other hand, both drug repositioning and drug-target prediction involve the same drug feature space, which naturally connects these two problems and the two domains (diseases and targets). By using the wisdom of the crowds, it is possible to transfer knowledge from one of the domains to the other. The existence of relationships among drug-target-disease motivates us to jointly consider drug repositioning and drug-target prediction in drug discovery. In this paper, we present a novel approach called iDrug, which seamlessly integrates drug repositioning and drug-target prediction into one coherent model via cross-network embedding. In particular, we provide a principled way to transfer knowledge from these two domains and to enhance prediction performance for both tasks. Using real-world datasets, we demonstrate that iDrug achieves superior performance on both learning tasks compared to several state-of-the-art approaches. Our code and datasets are available at: https://github.com/Case-esaC/iDrug.

CcpNmr AnalysisScreen, a new software programme with dedicated automated analysis tools for fragment-based drug discovery by NMR

Fragment-based drug discovery or FBDD is one of the main methods used by industry and academia for identifying drug-like candidates in early stages of drug discovery. NMR has a significant impact at any stage of the drug discovery process, from primary identification of small molecules to the elucidation of binding modes for guiding optimisations. The essence of NMR as an analytical tool, however, requires the processing and analysis of relatively large amounts of single data items, e.g. spectra, which can be daunting when managed manually. One bottleneck in FBDD by NMR is a lack of adequate and well-integrated resources for NMR data analysis that are freely available to the community. Thus, scientists typically resort to manually inspecting large datasets and relying predominantly on subjective interpretations. In this manuscript, we present CcpNmr AnalysisScreen, a software package that provides computational tools for automated analysis of FBDD data by NMR. We outline how the quality of collected spectra can be evaluated quickly, and how robust workflows can be optimised for reliable and rapid hit identification. With an intuitive graphical user interface and powerful algorithms, AnalysisScreen enables easy analysis of the large datasets needed in the early process of drug discovery by NMR.

Multi-Omics Technologies Applied to Tuberculosis Drug Discovery

Multi-omics strategies are indispensable tools in the search for new anti-tuberculosis drugs. Omics methodologies, where the ensemble of a class of biological molecules are measured and evaluated together, enable drug discovery programs to answer two fundamental questions. Firstly, in a discovery biology approach, to find new targets in druggable pathways for target-based investigation, advancing from target to lead compound. Secondly, in a discovery chemistry approach, to identify the mode of action of lead compounds derived from high-throughput screens, progressing from compound to target. The advantage of multi-omics methodologies in both of these settings is that omics approaches are unsupervised and unbiased to a priori hypotheses, making omics useful tools to confirm drug action, reveal new insights into compound activity, and discover new avenues for inquiry. This review summarizes the application of Mycobacterium tuberculosis omics technologies to the early stages of tuberculosis antimicrobial drug discovery.