Drug Discovery Procedures Research Articles

Relevance of In Vitro Metabolism Models to PET Radiotracer Development: Prediction of In Vivo Clearance in Rats from Microsomal Stability Data.

The prediction of in vivo clearance from in vitro metabolism models such as liver microsomes is an established procedure in drug discovery. The potentials and limitations of this approach have been extensively evaluated in the pharmaceutical sector; however, this is not the case for the field of positron emission tomography (PET) radiotracer development. The application of PET radiotracers and classical drugs differs greatly with regard to the amount of substance administered. In typical PET imaging sessions, subnanomolar quantities of the radiotracer are injected, resulting in body concentrations that cannot be readily simulated in analytical assays. This raises concerns regarding the predictability of radiotracer clearance from in vitro data. We assessed the accuracy of clearance prediction for three prototypical PET radiotracers developed for imaging the A1 adenosine receptor (A1AR). Using the half-life (t1/2) approach and physiologically based scaling, in vivo clearance in the rat model was predicted from microsomal stability data. Actual clearance could be accurately predicted with an average fold error (AFE) of 0.78 and a root mean square error (RMSE) of 1.6. The observed slight underprediction (1.3-fold) is in accordance with the prediction accuracy reported for classical drugs. This result indicates that the prediction of radiotracer clearance is possible despite concentration differences of more than three orders of magnitude between in vitro and in vivo conditions. Consequently, in vitro metabolism models represent a valuable tool for PET radiotracer development.

Evaluating the pharmacological response in fluorescence microscopy images: The Δm algorithm.

Current drug discovery procedures require fast and effective quantification of the pharmacological response evoked in living cells by agonist compounds. In the case of G-protein coupled receptors (GPCRs), the efficacy of a particular drug to initiate the endocytosis process is related to the formation of endocytic vesicles or endosomes and their subsequent internalisation within intracellular compartments that can be observed with high spatial and temporal resolution by fluorescence microscopy techniques. Recently, an algorithm has been proposed to evaluate the pharmacological response by estimating the number of endosomes per cell on time series of images. However, the algorithm was limited by the dependence on some manually set parameters and in some cases the quality of the image does not allow a reliable detection of the endosomes. Here we propose a simple, fast and automated image analysis method—the Δm algorithm- to quantify a pharmacological response with data obtained from fluorescence microscopy experiments. This algorithm does not require individual object detection and computes the relative increment of the third order moment in fluorescence microscopy images after filtering with the Laplacian of Gaussian function. It was tested on simulations demonstrating its ability to discriminate different experimental situations according to the number and the fluorescence signal intensity of the simulated endosomes. Finally and in order to validate this methodology with real data, the algorithm was applied to several time-course experiments based on the endocytosis of the mu opioid receptor (MOP) initiated by different agonist compounds. Each drug displayed a different Δm sigmoid time-response curve and statistically significant differences were observed among drugs in terms of efficacy and kinetic parameters.

Life cycle maintenance and drug-sensitivity assays for early drug discovery in Schistosoma mansoni

Drug discovery for schistosomiasis is still limited to a handful of academic laboratories worldwide, with only a few novel antischistosomal lead compounds being actively researched. Despite recent international mobilization against the disease to stimulate and promote antischistosomal drug discovery, setting up a drug-screening flow with schistosome parasites remains challenging. Whereas numerous different protocols to obtain and cultivate schistosomes have been published, those describing the drug-screening process are scarce, and none gather together parasite cultivation and early drug discovery procedures. To help overcome this hurdle, we provide here a set of integrated methods either adapted from already-published protocols or based on our long-term experience in schistosomiasis research. Specifically, we detail the establishment and maintenance of the complex and several-week-long Schistosoma mansoni life cycle in a laboratory setting, as well as the means of retrieving and culturing the parasites at their relevant life stages. The in vitro and in vivo assays that are performed along the drug-screening cascade are also described. In these assays, which can be performed within 5 d, the effect of a drug is determined by phenotypic assessment of the parasites' viability and morphology, for which stage-specific scoring scales are proposed. Finally, the modalities for testing and evaluating a compound in vivo, constituting a procedure lasting up to 10 weeks, are presented in order to go from in vitro hit identification to the selection of early lead candidates.

Computational evaluation of 2-amino-5-sulphonamido-1,3,4-thiadiazoles as human carbonic anhydrase-IX inhibitors: an insight into the structural requirement for the anticancer activity against HEK 293

Carbonic anhydrase inhibitors are very interesting target for designing anticancer agents. A computational procedure was performed on some thiadiazoles derived from carbonic anhydrase inhibitor acetazolamide. Two important procedures in computational drug discovery, namely docking for modeling ligand–receptor interactions and quantitative structure–activity relationships were employed. The relationship between cytotoxic activity and various descriptors was established by stepwise multiple regression analysis. The analyses have produced well predictive and statistically significant quantitative structure–activity relationships models, which were further cross validated. Among several models, one model has good statistical significance (r = 0.89, F test = 6.88, S = 0.33, chance correlation < 0.01), indicates that steric descriptors like EleE are contributing positively to the biological activity, electronic descriptors like connolly molecular surface area and Chi descriptors like chi0 and information theory index like IdAvg are contributing negatively to the biological activity and play a significant role in receptor binding which helps to design some expectedly potent compounds. In order to confirm the obtained results through this ligand-based method, docking was performed on the selected compounds by the use of Schrodinger GLIDE program. Incorporating available biochemical and computational data to the model by correcting the conformation of a single residue lining the binding pocket resulted in significantly improved docking poses. The molecular modeling study allowed confirming the preferential binding mode of reported compounds inside the active site.

PL-PatchSurfer2: Improved Local Surface Matching-Based Virtual Screening Method That Is Tolerant to Target and Ligand Structure Variation.

Virtual screening has become an indispensable procedure in drug discovery. Virtual screening methods can be classified into two categories: ligand-based and structure-based. While the former have advantages, including being quick to compute, in general they are relatively weak at discovering novel active compounds because they use known actives as references. On the other hand, structure-based methods have higher potential to find novel compounds because they directly predict the binding affinity of a ligand in a target binding pocket, albeit with substantially lower speed than ligand-based methods. Here we report a novel structure-based virtual screening method, PL-PatchSurfer2. In PL-PatchSurfer2, protein and ligand surfaces are represented by a set of overlapping local patches, each of which is represented by three-dimensional Zernike descriptors (3DZDs). By means of 3DZDs, the shapes and physicochemical complementarities of local surface regions of a pocket surface and a ligand molecule can be concisely and effectively computed. Compared with the previous version of the program, the performance of PL-PatchSurfer2 is substantially improved by the addition of two more features, atom-based hydrophobicity and hydrogen-bond acceptors and donors. Benchmark studies showed that PL-PatchSurfer2 performed better than or comparable to popular existing methods. Particularly, PL-PatchSurfer2 significantly outperformed existing methods when apo-form or template-based protein models were used for queries. The computational time of PL-PatchSurfer2 is about 20 times shorter than those of conventional structure-based methods. The PL-PatchSurfer2 program is available at http://www.kiharalab.org/plps2/ .

Milp-hyperbox classification for structure-based drug design in the discovery of small molecule inhibitors of SIRTUIN6

Virtual screening of chemical libraries following experimental assays of drug candidates is a common procedure in structure-based drug discovery. However, virtual screening of chemical libraries with millions of compounds requires a lot of time for computing and data analysis. A priori classification of compounds in the libraries as low- and high-binding free energy sets decreases the number of compounds for virtual screening experiments. This classification also reduces the required computational time and resources. Data analysis is demanding since a compound can be described by more than one thousand attributes that make any data analysis very challenging. In this paper, we use the hyperbox classification method in combination with partial least squares regression to determine the most relevant molecular descriptors of the drug molecules for an efficient classification. The effectiveness of the approach is illustrated on a target protein, SIRT6. The results indicate that the proposed approach outperforms other approaches reported in the literature with 83.55% accuracy using six common molecular descriptors (SC-5, SP-6, SHBd, minHaaCH, maxwHBa, FMF). Additionally, the top 10 hit compounds are determined and reported as the candidate inhibitors of SIRT6 for which no inhibitors have so far been reported in the literature.

Optimization of 3D Digital Microfluidic Biochips for the Multiplexed Polymerase Chain Reaction

A digital microfluidic biochip (DMFB) is an attractive technology platform for revolutionizing immunoassays, clinical diagnostics, drug discovery, DNA sequencing, and other laboratory procedures in biochemistry. In most of these applications, real-time polymerase chain reaction (PCR) is an indispensable step for amplifying specific DNA segments. To reduce the reaction time to meet the requirement of “real-time” applications, multiplexed PCR is widely utilized. In recent years, three-dimensional (3D) DMFBs that integrate photodetectors (i.e., cyberphysical DMFBs) have been developed, which offer the benefits of smaller size, higher sensitivity, and faster result generations. However, current DMFB design methods target optimization in only two dimensions, thus ignoring the 3D two-layer structure of a DMFB. Furthermore, these techniques ignore practical constraints related to the interference between on-chip device pairs, the performance-critical PCR thermal loop, and the physical size of devices. Moreover, some practical issues in real scenarios are not stressed (e.g., the avoidance of the cross-contamination for multiplexed PCR). In this article, we describe an optimization solution for a 3D DMFB and present a three-stage algorithm to realize a compact 3D PCR chip layout, which includes: (i) PCR thermal-loop optimization, (ii) 3D global placement based on Strong-Push-Weak-Pull (SPWP) model, and (iii) constraint-aware legalization. To avoid cross-contamination between different DNA samples, we also propose a Minimum-Cost-Maximum-Flow-based (MCMF-based) method for reservoir assignment. Simulation results for four laboratory protocols demonstrate that the proposed approach is effective for the design and optimization of a 3D chip for multiplexed real-time PCR.

Abstract 304: Formation of uniform and reproducible 3D cancer spheroids in a high throughput plate format

Abstract Cells grown on flat two dimensional (2D) tissue culture substrates can differ considerably from the three dimensional (3D) physiological environment. There is growing evidence that tumor cell aggregates or cancer spheroids are more representative of tumors in vivo and they exhibit several physiological traits including similar morphology, the formation of cell-cell contacts, decreased proliferation, increased survival rates and a hypoxic core. However, variability in cancer spheroid culture has been a persistent problem for researchers. This variability is linked to medium composition and volume, cell density, duration in culture, and most importantly, the cellular interactions with the culture dish itself. Here, we demonstrate that a hydrophilic polymer coated surface, Thermo Scientific Nunclon Sphera, inhibits cell attachment to the culture dish by blocking binding of the extracellular matrix (ECM). The U bottom wells in the Nunclon Sphera plate provide the ideal size and shape for single spheroid formation in each well, offering consistency and reproducibility to the 3D tumor model system in vitro. The growth of cancer spheroids in the Nunclon Sphera 96U-well plate is superior to that of the non-treated plate with less “satellite colonies” and faster cell aggregation at low seeding density. Cancer cell viability and spheroid health are also evaluated by fluorescence- and colorimetric- based assays in the Nunclon Sphera plates. The Nunclon Sphera 96U-well plate provides an excellent solution for reproducibly generating uniform 3D cancer spheroids that are suitable for many cell-based drug discovery procedures, co-culture studies, and high-throughput drug screening. Citation Format: Cindy Neeley, Chetana M. Revankar. Formation of uniform and reproducible 3D cancer spheroids in a high throughput plate format. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 304. doi:10.1158/1538-7445.AM2015-304

Explicit Drug Re-positioning: Predicting Novel Drug-Target Interactions of the Shelved Molecules with QM/MM Based Approaches.

With the demand to enhance the speed of the drug discovery process there has been an increased usage of computational approaches in drug discovery studies. However because of their probabilistic outcomes, the challenge is to exactly mimic the natural environment which can provide the exact charge polarization effect while estimating the binding energy between protein and ligand. There has been a large number of scoring functions from simple one to the complex one available for estimating binding energy. The quantum mechanics/molecular mechanics (QM/MM) hybrid approach has been the preferred choice of interest since last decade for modeling reactions in biomolecular systems. The application of QM/MM approach has been expanded right from rescoring the already known complexes and depicting the correct position of some novel molecule to ranking a large number of molecules. It is expected that the application of QM/MM-based scoring will grow in all areas of drug discovery. However, the most promising area will be its application in repositioning, that is, assigning novel functions or targets to the already existing drugs, as this would stop the rising attrition rates as well as reduce the overall time and cost of drug discovery procedure.

Discovery of GPCR ligands for probing signal transduction pathways.

G protein-coupled receptors (GPCRs) are seven integral transmembrane proteins that are the primary targets of almost 30% of approved drugs and continue to represent a major focus of pharmaceutical research. All of GPCR targeted medicines were discovered by classical medicinal chemistry approaches. After the first GPCR crystal structures were determined, the docking screens using these structures lead to discovery of more novel and potent ligands. There are over 360 pharmaceutically relevant GPCRs in the human genome and to date about only 30 of structures have been determined. For these reasons, computational techniques such as homology modeling and molecular dynamics simulations have proven their usefulness to explore the structure and function of GPCRs. Furthermore, structure-based drug design and in silico screening (High Throughput Docking) are still the most common computational procedures in GPCRs drug discovery. Moreover, ligand-based methods such as three-dimensional quantitative structure–selectivity relationships, are the ideal molecular modeling approaches to rationalize the activity of tested GPCR ligands and identify novel GPCR ligands. In this review, we discuss the most recent advances for the computational approaches to effectively guide selectivity and affinity of ligands. We also describe novel approaches in medicinal chemistry, such as the development of biased agonists, allosteric modulators, and bivalent ligands for class A GPCRs. Furthermore, we highlight some knockout mice models in discovering biased signaling selectivity.

In silico molecular docking, preclinical evaluation of spiroindimicins A-D, lynamicin A and D isolated from deep marine sea derived Streptomyces sp. SCSIO 03032.

The criteria used for successful drug discovery involves high throughput screening for preclinical evaluation and its interaction with target enzymes. In silico approach resulting in the creation of drug like library and identification of essential reactions and pathways spreads across several parts of metabolism. The aim of the present study was to evaluate the preclinical property and interaction to various drug target enzymes for spiroindimicins A-D and lynamicin A and D isolated from deep marine sea derived Streptomyces sp. SCSIO 03032 with 7 selected drug target enzymes. The preclinical and molecular docking simulation was performed using In silico pharmacology and docking tool. Drug likeliness, ADME and toxicity testing findings suggested the compounds with oral drug candidate's probability. Interaction of isolated compounds against drug target enzymes was satisfactory with Spiroindimicins C, D and Lynamicin D emerging as most potent Topoisomerase II, Cathepsin K, Cytochrome P4503A4, Aromatase P450, protein kinase and histone deacetylase inhibitors. Our results suggest that In silico approach in drug discovery procedure in later stage of development can ease up making lead molecules library.

Identification of small-molecule binding pockets in the soluble monomeric form of the Aβ42 peptide

The aggregation of intrinsically disordered peptides and proteins is associated with a wide range of highly debilitating neurological and systemic disorders. In this work we explored the potential of a structure-based drug discovery procedure to target one such system, the soluble monomeric form of the Aβ42 peptide. We utilised for this purpose a set of structures of the Aβ42 peptide selected from clusters of conformations within an ensemble generated by molecular dynamics simulations. Using these structures we carried out fragment mapping calculations to identify binding "hot spots" on the monomeric form of the Aβ42 peptide. This procedure provided a set of hot spots with ligand efficiencies comparable to those observed for structured proteins, and clustered into binding pockets. Such binding pockets exhibited a propensity to bind small molecules known to interact with the Aβ42 peptide. Taken together these results provide an initial indication that fragment-based drug discovery may represent a potential therapeutic strategy for diseases associated with the aggregation of intrinsically disordered proteins.

ChemBioServer: a web-based pipeline for filtering, clustering and visualization of chemical compounds used in drug discovery

ChemBioServer is a publicly available web application for effectively mining and filtering chemical compounds used in drug discovery. It provides researchers with the ability to (i) browse and visualize compounds along with their properties, (ii) filter chemical compounds for a variety of properties such as steric clashes and toxicity, (iii) apply perfect match substructure search, (iv) cluster compounds according to their physicochemical properties providing representative compounds for each cluster, (v) build custom compound mining pipelines and (vi) quantify through property graphs the top ranking compounds in drug discovery procedures. ChemBioServer allows for pre-processing of compounds prior to an in silico screen, as well as for post-processing of top-ranked molecules resulting from a docking exercise with the aim to increase the efficiency and the quality of compound selection that will pass to the experimental test phase. The ChemBioServer web application is available at: http://bioserver-3.bioacademy.gr/Bioserver/ChemBioServer/. gspyrou@bioacademy.gr

Computational evaluation of some indenopyrazole derivatives as anticancer compounds; application of QSAR and docking methodologies

A computational procedure was performed on some indenopyrazole derivatives. Two important procedures in computational drug discovery, namely docking for modeling ligand-receptor interactions and quantitative structure activity relationships were employed. MIA-QSAR analysis of the studied derivatives produced a model with high predictability. The developed model was then used to evaluate the bioactivity of 54 proposed indenopyrazole derivatives. In order to confirm the obtained results through this ligand-based method, docking was performed on the selected compounds. An ADME–Tox evaluation was also carried out to search for more suitable compounds. Satisfactory bioactivities and ADME–Tox profiles for two of the compounds, namely 62 and S13, propose that further studies should be performed on such devoted chemical structures.

Rapid Flexible Docking Using a Stochastic Rotamer Library of Ligands

Existing flexible docking approaches model the ligand and receptor flexibility either separately or in a loosely coupled manner, which captures the conformational changes inefficiently. Here, we propose a flexible docking approach, MedusaDock, which models both ligand and receptor flexibility simultaneously with sets of discrete rotamers. We developed an algorithm to build the ligand rotamer library "on-the-fly" during docking simulations. MedusaDock benchmarks demonstrate a rapid sampling efficiency and high prediction accuracy in both self- (to the cocrystallized state) and cross-docking (to a state cocrystallized with a different ligand), the latter of which mimics the virtual screening procedure in computational drug discovery. We also perform a virtual screening test of four flexible kinase targets, including cyclin-dependent kinase 2, vascular endothelial growth factor receptor 2, HIV reverse transcriptase, and HIV protease. We find significant improvements of virtual screening enrichments when compared to rigid-receptor methods. The predictive power of MedusaDock in cross-docking and preliminary virtual-screening benchmarks highlights the importance to model both ligand and receptor flexibility simultaneously in computational docking.

Assessing the structural conservation of protein pockets to study functional and allosteric sites: implications for drug discovery

BackgroundWith the classical, active-site oriented drug-development approach reaching its limits, protein ligand-binding sites in general and allosteric sites in particular are increasingly attracting the interest of medicinal chemists in the search for new types of targets and strategies to drug development. Given that allostery represents one of the most common and powerful means to regulate protein function, the traditional drug discovery approach of targeting active sites can be extended by targeting allosteric or regulatory protein pockets that may allow the discovery of not only novel drug-like inhibitors, but activators as well. The wealth of available protein structural data can be exploited to further increase our understanding of allosterism, which in turn may have therapeutic applications. A first step in this direction is to identify and characterize putative effector sites that may be present in already available structural data.ResultsWe performed a large-scale study of protein cavities as potential allosteric and functional sites, by integrating publicly available information on protein sequences, structures and active sites for more than a thousand protein families. By identifying common pockets across different structures of the same protein family we developed a method to measure the pocket's structural conservation. The method was first parameterized using known active sites. We characterized the predicted pockets in terms of sequence and structural conservation, backbone flexibility and electrostatic potential. Although these different measures do not tend to correlate, their combination is useful in selecting functional and regulatory sites, as a detailed analysis of a handful of protein families shows. We finally estimated the numbers of potential allosteric or regulatory pockets that may be present in the data set, finding that pockets with putative functional and effector characteristics are widespread across protein families.ConclusionsOur results show that structurally conserved pockets are a common feature of protein families. The structural conservation of protein pockets, combined with other characteristics, can be exploited in drug discovery procedures, in particular for the selection of the most appropriate target protein and pocket for the design of drugs against entire protein families or subfamilies (e.g. for the development of broad-spectrum antimicrobials) or against a specific protein (e.g. in attempting to reduce side effects).

In Silico Prediction of SARS Protease Inhibitors by Virtual High Throughput Screening

A structure‐based in silico virtual drug discovery procedure was assessed with severe acute respiratory syndrome coronavirus main protease serving as a case study. First, potential compounds were extracted from protein–ligand complexes selected from Protein Data Bank database based on structural similarity to the target protein. Later, the set of compounds was ranked by docking scores using a Electronic High‐Throughput Screening flexible docking procedure to select the most promising molecules. The set of best performing compounds was then used for similarity search over the 1 million entries in the Ligand.Info Meta‐Database. Selected molecules having close structural relationship to a 2‐methyl‐2,4‐pentanediol may provide candidate lead compounds toward the development of novel allosteric severe acute respiratory syndrome protease inhibitors.

Screening for transient biological interactions as applied to albumin ligands: A new concept for drug discovery

The notion that many biological interactions are based on transient binding (dissociation constants ( K d) in the range of 10–0.01 mM) is familiar, yet the implications for biological sciences have been realized only recently. An important area of biological sciences is drug design, where the traditional “lock and key” view of binding has prevailed and drug candidates are usually selected on their merits as being tight binders. However, the rationale that transient interactions are of importance for drug discovery is slowly gaining acceptance. These interactions may relate not only to the desired target interaction but also to unwanted interactions creating, for example, toxicity problems. Here we demonstrate, in a high-throughput screening format, affinity selection of weak binders to a model target of albumin by zonal retardation chromatography. It is perceived that this approach can define the “transient drug” as a complement to current drug discovery procedures.

Ligand Docking and Design in a Flexible Receptor Site

Intrinsic protein flexibility is often ignored in the drug discovery process, however, there is increasing evidence suggesting that many therapeutic targets are flexible. In this work, we describe a method for incorporating receptor flexibility, based on protein side-chain rearrangements, in ligand docking and design. The approach is applied to the docking of a highly potent inhibitor in the acetylcholinesterase binding site and to the generation of ligands in the Sl' cavity of human collagenase. Simulations are conducted for both static and flexible binding sites and from the results we assess the impact of receptor flexibility in drug discovery procedures.

The Application of Microwaves in Combinatorial and High‐Throughput Synthesis as New Synthetic Procedure in Drug Discovery

AbstractHeterocyclic compounds hold a special place among pharmaceutically important natural and synthetic materials. The remarkable ability of heterocyclic nuclei to serve both as biomimetics and reactive pharmacophores has largely contributed to their unique value as traditional key elements of numerous drugs. In both lead identification and optimization processes there is an acute need for new organic small molecules. Traditional methods of organic synthesis are orders of magnitude too slow to satisfy the demand for these compounds; so, the fields of combinatorial and automated medicinal chemistry have been developed to meet the increasing requirement of new compounds for drug discovery; within these fields, speed is of the essence. The efficiency of microwave chemistry in dramatically reducing reaction times (reduced from days and hours to minutes and seconds) has recently been proven in several different fields of organic chemistry. We believe that the time saved by using focused microwaves is potentially important in traditional organic synthesis but could be of even greater importance in high‐speed combinatorial and medicinal chemistry.