In silico pharmacology for drug discovery: applications to targets and beyond

Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediting the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets.     

In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling

Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.     

NMR-based approaches for lead discovery

NMR methods have long been used for studying molecular interactions. In the last few years, various NMR approaches have been developed to aid lead discovery. These involve different NMR screening methods to identify initial compounds, which often bind only weakly (in the micro- to millimolar range) to the drug target. Intelligent and focused follow-up strategies enable the development of these compounds into potent, submicromolar drug-like inhibitors for use as leads in drug discovery projects. NMR can be used as both a remarkably reliable screening tool and a structural tool; thus, this technique has unique opportunities for lead discovery.     

The evolving role of information technology in the drug discovery process

Information technologies for chemical structure prediction, heterogeneous database access, pattern discovery, and systems and molecular modeling have evolved to become core components of the modern drug discovery process. As this evolution continues, the balance between in silico modeling and 'wet' chemistry will continue to shift and it might eventually be possible to step through the discovery pipeline without the aid of traditional laboratory techniques. Rapid advances in the industrialization of gene sequencing combined with databases of protein sequence and structure have created a target-rich but lead-poor environment. During the next decade, newer information technologies that facilitate the molecular modeling of drug-target interactions are likely to shift this balance towards molecular-based personalized medicine -- the ultimate goal of the drug discovery process.     

Macromolecular crystallography as a tool for investigating drug, enzyme and receptor interactions

1. Protein crystallography is an essential tool for the discovery and investigation of pharmacological interactions at the molecular level. It allows investigators to directly visualize the three-dimensional structures of proteins, including enzymes, receptors and hormones. 2. Increasingly, knowledge of these interactions is being used in the drug-discovery process. This is popularly called structure-based drug design. The desired drug could be an enzyme inhibitor or an agonist that mimics endogenous transmitters or hormones. 3. Once the 3-D structure of a pharmacologically relevant target is known, computational processes can be used to search databases of compounds to identify ones that may interact strongly with the target. Lead compounds can be improved using the 3-D structure of the complex of the lead compound and its biological target. 4. The present review describes the processes involved in the determination of a structure by means of protein crystallography and the use of structures in the drug-discovery process. A number of successful examples of structure-based drug design are described. The limitations of the techniques are discussed.     

Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness

Identification and validation of viable targets is an important first step in drug discovery and new methods, and integrated approaches are continuously explored to improve the discovery rate and exploration of new drug targets. An in silico machine learning method, support vector machines, has been explored as a new method for predicting druggable proteins from amino acid sequence independent of sequence similarity, thereby facilitating the prediction of druggable proteins that exhibit no or low homology to known targets.     

Natural products in drug discovery

Natural products have been the single most productive source of leads for the development of drugs. Over a 100 new products are in clinical development, particularly as anti-cancer agents and anti-infectives. Application of molecular biological techniques is increasing the availability of novel compounds that can be conveniently produced in bacteria or yeasts, and combinatorial chemistry approaches are being based on natural product scaffolds to create screening libraries that closely resemble drug-like compounds. Various screening approaches are being developed to improve the ease with which natural products can be used in drug discovery campaigns, and data mining and virtual screening techniques are also being applied to databases of natural products. It is hoped that the more efficient and effective application of natural products will improve the drug discovery process.     

Pharmacophore-based discovery of ligands for drug transporters

The ability to identify ligands for drug transporters is an important step in drug discovery and development. It can both improve accurate profiling of lead pharmacokinetic properties and assist in the discovery of new chemical entities targeting transporters. In silico approaches, especially pharmacophore-based database screening methods have great potential in improving the throughput of current transporter ligand identification assays, leading to a higher hit rate by focusing in vitro testing to the most promising hits. In this review, the potential of different in silico methods in transporter ligand identification studies are compared and summarized with an emphasis on pharmacophore modeling. Various implementations of pharmacophore model generation, database compilation and flexible screening algorithms are also introduced. Recent successful utilization of database searching with pharmacophores to identify novel ligands for the pharmaceutically significant transporters hPepT1, P-gp, BCRP, MRP1 and DAT are reviewed and the challenges encountered with current approaches are discussed.     

Chemogenomic approaches to rational drug design

Paradigms in drug design and discovery are changing at a significant pace. Concomitant to the sequencing of over 180 several genomes, the high-throughput miniaturization of chemical synthesis and biological evaluation of a multiple compounds on gene/protein expression and function opens the way to global drug-discovery approaches, no more focused on a single target but on an entire family of related proteins or on a full metabolic pathway. Chemogenomics is this emerging research field aimed at systematically studying the biological effect of a wide array of small molecular-weight ligands on a wide array of macromolecular targets. Since the quantity of existing data (compounds, targets and assays) and of produced information (gene/protein expression levels and binding constants) are too large for manual manipulation, information technologies play a crucial role in planning, analysing and predicting chemogenomic data. The present review will focus on predictive in silico chemogenomic approaches to foster rational drug design and derive information from the simultaneous biological evaluation of multiple compounds on multiple targets. State-of-the-art methods for navigating in either ligand or target space will be presented and concrete drug design applications will be mentioned.     

Receptorome screening for CNS drug discovery

An estimated 50% of currently marketed drugs target G protein-coupled receptors (GPCRs) for a wide variety of indications, including central nervous system (CNS) disorders. Although drug discovery efforts have focused on GPCRs, less than 10% of GPCRs are currently used as drug targets. Thus, GPCRs continue to represent a significant opportunity for future CNS drug development. Identifying the molecular targets of psychoactive compounds may result in the elucidation of novel targets for CNS drug discovery. This commentary will describe discovery-based approaches and provide several recent examples of novel ligand-receptor interactions discovered through systematic screening of the 'receptorome'.     

Identifying multiple-target ligands via computational chemogenomics approaches

Despite the rapidly growing knowledge of functional and structural information regarding pharmaceutically relevant targets during the past decade, target-based drug discovery has remained a high-cost and low-yield process. Particularly, single-target drugs often turn out to be less effective in treating complicated diseases such as cancers, metabolic disorders and CNS diseases. However, discovering compounds that are effective against multiple desired targets raises an enormous challenge to the current mode of drug innovation. Computational chemogenomics approaches aim at predicting all potential interactions between small molecular ligands and biomolecular targets, thus the derived information can be directly applied to "design in" (i.e. engineer desirable binding spectrum) and "design out" (i.e. eliminate the unwanted interactions) specific biological activities. The present review will focus on introducing the recent methodological development and successful applications of structure-based and ligand-based approaches on predicting the ligand binding profiles, which is the very first and essential step toward rationally designing the multiple-target ligands. Structure-based methods (e.g. binding site mapping and inverse molecular docking) generally require the structures of known targets to navigate the receptor-ligand binding space, while ligand-based approaches (e.g. chemical similarity analysis and pharmacophore search) can only rely on the series of active compounds to derive the structural characteristics for describing certain biological activities.     

In silico identification of potential drug targets in Clostridium difficile R20291: modeling and virtual screening analysis of a candidate enzyme MurG

Clostridium difficile is an etiologic agent of a variety of gastrointestinal diseases in human including mild sporadic diarrhea and severe life-threatening pseudomembranous colitis. The continuous rise of C. difficile infection worldwide accompanied by rapid emergence of multidrug-resistant and hypervirulent strains has necessitated the search for novel drug targets. The present study is aimed at identifying putative therapeutic targets in this pathogen by in silico approach which encompassed four steps, viz, similarity search between pathogen and host, essentiality study using the database of essential genes, metabolic functional association study using Kyoto Encyclopedia of Genes and Genomes database, and choke point analysis. The study identified 19 promising drug targets which are non-homologous to host proteins, potentially essential for the pathogen, choke point enzymes, and participate in four pathogen-specific pathways, namely peptidoglycan biosynthesis, phosphotransferase system, two component system, and d-alanine metabolism pathways. The peptidoglycan biosynthesis pathway is the highest donor to the list of candidate target proteins. Furthermore, a three-dimensional model of one of the identified potential targets, MurG from peptidoglycan biosynthesis pathway, was constructed by homology modeling. Subsequently, by means of a virtual screening approach, the study identified eight potential inhibitors from small molecules databases, which have better docking scores, varying from ?7.9 to ?10.3 kcal/mol, and stronger binding affinity with target compared to known inhibitors and natural substrate of MurG. The docking analysis revealed that the active site residue Gln298 plays a critical role in ligand–target interactions which was validated through in silico mutational study. Other active site residues like Arg168, Ser198, Arg202, Ser269, and His297 were also found to play a role in binding interactions. The identified compounds may facilitate the development of new drugs to combat C. difficile-associated diseases.     

Progress in predicting human ADME parameters in silico

Understanding the development of a scientific approach is a valuable exercise in gauging the potential directions the process could take in the future. The relatively short history of applying computational methods to absorption, distribution, metabolism and excretion (ADME) can be split into defined periods. The first began in the 1960s and continued through the 1970s with the work of Corwin Hansch et al. Their models utilized small sets of in vivo ADME data. The second era from the 1980s through 1990s witnessed the widespread incorporation of in vitro approaches as surrogates of in vivo ADME studies. These approaches fostered the initiation and increase in interpretable computational ADME models available in the literature. The third era is the present were there are many literature data sets derived from in vitro data for absorption, drug-drug interactions (DDI), drug transporters and efflux pumps [P-glycoprotein (P-gp), MRP], intrinsic clearance and brain penetration, which can theoretically be used to predict the situation in vivo in humans. Combinatorial synthesis, high throughput screening and computational approaches have emerged as a result of continual pressure on pharmaceutical companies to accelerate drug discovery while decreasing drug development costs. The goal has become to reduce the drop-out rate of drug candidates in the latter, most expensive stages of drug development. This is accomplished by increasing the failure rate of candidate compounds in the preclinical stages and increasing the speed of nomination of likely clinical candidates. The industry now understands the reasons for clinical failure other than efficacy are mainly related to pharmacokinetics and toxicity. The late 1990s saw significant company investment in ADME and drug safety departments to assess properties such as metabolic stability, cytochrome P-450 inhibition, absorption and genotoxicity earlier in the drug discovery paradigm. The next logical step in this process is the evaluation of higher throughput data to determine if computational (in silico) models can be constructed and validated from it. Such models would allow an exponential increase in the number of compounds screened virtually for ADME parameters. A number of researchers have started to utilize in silico, in vitro and in vivo approaches in parallel to address intestinal permeability and cytochrome P-450-mediated DDI. This review will assess how computational approaches for ADME parameters have evolved and how they are likely to progress.     

Targeting cancer using fragment based drug discovery

Over the past decade, fragment-based drug discovery has developed significantly and has gained increasing popularity in the pharmaceutical industry as a powerful alternative and complement to traditional high-throughput screening approaches for hit identification. Fragment-based methods are capable of rapidly identifying starting points for structure-based drug design from relatively small libraries of low molecular weight compounds. The main constraints are the need for sensitive methods that can reliably detect the typically weak interactions between fragments and the target protein, and strategies for transforming fragments into higher molecular weight drug candidates. This approach has recently been validated as series of compounds from various programs have entered clinical trials.     

A Discovery Funnel for Nucleic Acid Binding Drug Candidates

Computational approaches are becoming increasingly popular for the discovery of drug candidates against a target of interest. Proteins have historically been the primary targets of many virtual screening efforts. While in silico screens targeting proteins has proven successful, other classes of targets, in particular DNA, remain largely unexplored using virtual screening methods. With the realization of the functional importance of many non-cannonical DNA structures such as G-quadruplexes, increased efforts are underway to discover new small molecules that can bind selectively to DNA structures. Here, we describe efforts to build an integrated in silico and in vitro platform for discovering compounds that may bind to a chosen DNA target. Millions of compounds are initially screened in silico for selective binding to a particular structure and ranked to identify several hundred best hits. An important element of our strategy is the inclusion of an array of possible competing structures in the in silico screen. The best hundred or so hits are validated experimentally for binding to the actual target structure by a high-throughput 96-well thermal denaturation assay to yield the top ten candidates. Finally, these most promising candidates are thoroughly characterized for binding to their DNA target by rigorous biophysical methods, including isothermal titration calorimetry, differential scanning calorimetry, spectroscopy and competition dialysis.This platform was validated using quadruplex DNA as a target and a newly discovered quadruplex binding compound with possible anti-cancer activity was discovered. Some considerations when embarking on virtual screening and in silico experiments are also discussed.     

QSAR and QM/MM approaches applied to drug metabolism prediction

In modern drug discovery process, ADME/Tox properties should be determined as early as possible in the test cascade to allow a timely assessment of their property profiles. To help medicinal chemists in designing new compounds with improved pharmacokinetics, the knowledge of the soft spot position or the site of metabolism (SOM) is needed. In recent years, large number of in silico approaches for metabolism prediction have been developed and reported. Among these methods, QSAR models and combined quantum mechanics/molecular mechanics (QM/MM) methods for predicting drug metabolism have undergone significant advances. This review provides a perspective of the utility of QSAR and QM/MM approaches on drug metabolism prediction, highlighting the present challenges, limitations, and future perspectives in medicinal chemistry.