Current Pharmaceutical Design - Volume 16, Issue 15, 2010

Intelligent and rational drug discovery and design are of paramount importance in the field of drug development due to a constant need of innovative drugs in the battle of difficult to treat diseases and an increasing number of newly revealed targets. In the multidisciplinary processes of drug discovery and design we are currently facing two facts: (i) Statistics show that the myriad of structurally diverse natural compounds are the most favoured source of new drugs for clinical use [1]. In the last decades, natural products have received a substantial boost as sources of novel drug candidates. Most of the known natural compounds are secondary metabolites which provide living systems with their characteristic features mandatory for survival. They are inherently structurally very diverse. About 40% of the chemical scaffolds of published natural products are unique and have not been made by any chemist [2]. Their biosynthesis is controlled and selected by evolution potentially to interact with numerous macromolecular targets that may probably be relevant for the prosperity of the host organism. This so-called bioactivity of a natural compound is not only restricted to the evolutionary focused target, they are also an excellent source of validated substructures for the design of novel drug candidates. Thus it can be assumed that a large number of drug leads and hits are conserved in this inexhaustible natural pool of molecules pre-screened by evolution. Digging out and recognizing the respective drug leads are challenging tasks for industry and academia, for medicinal chemists, pharmacognosists and pharmacologists. (ii) Drug design and discovery have moved toward more rational concepts based on the increasing understanding of the molecular principles of protein-ligand interactions and the millions of published structural data on activities (hits and non-hits) - a highly valuable pool of information, which is all too often lying idle. Spurred on by economic interests, fundamental advances have been made in integrating various computational tools to accelerate the drug development process. The rationale and the quality of hit compounds can be increased by adequate virtual predictions of biological activities based on the understanding of the relationships between a molecule's structure and its biological activity using different data mining strategies, e.g. pharmacophore-based virtual screening, docking procedures, pattern recognition methods, artificial neural networks. The goal of applying such methods is to mine more or less large compound databases in silico and to select a limited number of candidates for experimental investigations. Additional drug-like filters and predicted ADME properties may help to reduce failures in later stages of drug development. Though the impact of natural products as drug candidates on the one hand, and the high potential of computer-assisted drug discovery and design on the other hand are known [3, 4], their combined benefit has barely been tasted. A sensible adaptation of computational strategies is to profit from the unique chemical and biological diversity associated with natural products [5-7]. In silico techniques, however, must not be used exclusively as activity-predicting tools, since the results provide merely an indication for a putative activity. It is only by the creation of interfaces between computational tools, experimental methods, and in depth know-how from different disciplines of natural product sciences and drug design technologies that a reasonable standard of success can be achieved. It seems to be a challenging, but worthwhile endeavour to skilfully exploit knowledge from all these fields, and to sift through the enormous wealth of wonderful molecules from Nature. The review articles included in this hot topic issue of Current Pharmaceutical Design summarize current computational technologies, approaches and applications to access the natural products' outstanding properties and bioactivities from different perspectives. In the first review [8], Hiss et al. provide a thorough insight into techniques and applicability of nature-inspired algorithms for drug design. In addition to application scenarios, the authors discuss the strengths and limitations of ‘natural computing’....

Evolutionary algorithms, particle swarm optimization, and ant colony optimization have emerged as robust optimization methods for molecular modeling and peptide design. Such algorithms mimic combinatorial molecule assembly by using molecular fragments as building-blocks for compound construction, and relying on adaptation and emergence of desired pharmacological properties in a population of virtual molecules. Nature-inspired algorithms might be particularly suited for bioisosteric replacement or scaffold-hopping from complex natural products to synthetically more easily accessible compounds that are amenable to optimization by medicinal chemistry. The theory and applications of selected nature-inspired algorithms for drug design are reviewed, together with practical applications and a discussion of their advantages and limitations.

Natural products have been exposed to a long selection process to interact with biological targets and are therefore a valuable source for ideas for novel chemical entities in drug development. However, the process to determine activities of natural products is mainly based on serendipity, and can thus become time- and cost-intensive. In this review we present strategies on how modern in-silico molecular modeling techniques can be used to make this process more efficient and discuss how to discover and optimize drug candidates inspired by nature. Focusing on 3D pharmacophore modeling techniques, we provide an overview of virtual screening and modeling methods, review available in silico databases as sources for chemical structures of natural products, discuss techniques for biological activity profiling, and summarize recent success stories for the combination of in-silico approaches and pharmacognosy.

A huge amount of data has been generated by decades of pharmacognosy supported by the rapid evolution of chemical, biological and computational techniques. How can we cope with this overwhelming mass of information? Reverse pharmacognosy was introduced with this aim in view. It proceeds from natural molecules to organisms that contain them via biological assays in order to identify an activity. In silico techniques and particularly inverse screening are key technologies to achieve this goal efficiently. Reverse pharmacognosy allows us to identify which molecule(s) from an organism is (are) responsible for the biological activity and the biological pathway(s) involved. An exciting outcome of this approach is that it not only provides evidence of the therapeutic properties of plants used in traditional medicine for instance, but may also position other plants containing the same active compounds for the same usage, thus increasing the curative arsenal e.g. development of new botanicals. This is particularly interesting in countries where western medicines are still not affordable. At the molecular level, in organisms, families of metabolites are synthesized and seldom have a single structure. Hence, when a natural compound has an interesting activity, it may be desirable to check whether there are more active and/or less toxic derivatives in organisms containing the hit-this corresponds to a kind of “natural combinatorial” chemistry. At a time when the pharmaceutical industry is lacking drug candidates in clinical trials, drug repositioning -i.e. exploiting existing knowledge for innovation-has never been so critical. Reverse pharmacognosy can contribute to addressing certain issues in current drug discovery- such as the lack of clinical candidates, toxicity - by exploiting existing data from pharmacognosy. This review will focus on recent advances in computer science applied to natural substance research that consolidate the new concept of reverse pharmacognosy.

The Drug Discovery Portal (DDP) is a research initiative based at the University of Strathclyde in Glasgow, Scotland. It was initiated in 2007 by a group of researchers with expertise in virtual screening. Academic research groups in the university working in drug discovery programmes estimated there was a historical collection of physical compounds going back 50 years that had never been adequately catalogued. This invaluable resource has been harnessed to form the basis of the DDP library, and has attracted a highpercentage uptake from universities and research groups internationally. Its unique attributes include the diversity of the academic database, sourced from synthetic, medicinal and phytochemists working in academic laboratories and the ability to link biologists with appropriate chemical expertise through a target-matching virtual screening approach, and it has resulted in seven emerging hit development programmes between international contributors.

Natural products found a wide use in folk medicine. Presently, when routine development of new drugs faced a considerable challenge, they become an inspiration and valuable source in drugs discovery. Rather complex and diverse chemical structures of natural compounds provide a basis for modulation of different biological targets. Natural compounds exhibit a multitargeted action that may lead to additive/synergistic or antagonistic effects. Rational design of more safe and potent pharmaceuticals requires an estimation of probable multiple actions of natural products. Our software PASS can perform such estimation. It predicts with reasonable accuracy over 3500 pharmacotherapeutic effects, mechanisms of action, interaction with the metabolic system, and specific toxicity for drug-like molecules on the basis of their structural formulae. We analyzed PASS predictions utilizing PharmaExpert, which performs selection of compounds with multiple mechanisms of action, analysis of activity-activity relationships and drug-drug interactions. The paper describes an application of PASS and PharmaExpert to the evaluation of biological activity of natural compounds including marine sponge alkaloids, triterpenoids of lupane group, and their derivatives. Proposed computer-aided methods can generate combinatorial libraries of macrolides. They help to select the most promising pharmaceutical leads with the required properties. Case study, based on the analysis of biological activity spectra predicted for St John's Wort constituents, clearly demonstrates capabilities of computational methods in the evaluation of multitargeted actions, additive/synergistic and/or antagonistic effects of natural products.

Since the late 1990's, novel insights into molecular biology and carcinogenesis enabled the rational design of mechanism-based anticancer therapeutics. The large number of natural product (NP)-derived drugs currently under clinical evaluation and the recent approval of temsirolimus (Torisel®) as a first mTOR protein kinase inhibitor indicate that NPs have to be considered not only as a seminal source of cytotoxic, but also as a source of molecularly targeted agents. Whereas molecular modeling is well established as an important and successful method to discover and rationalize bioactivities in medicinal chemistry research, its application has proven to be also a powerful tool in the field of NPs. This review highlights the impact of computer-assisted approaches on NPs as molecularly targeted anticancer drugs. Examples of applications are provided focusing on innovative targets such as protein kinases, tumour vasculature, epigenetic modulators, heat shock protein (Hsp) 90, and direct apoptosis enhancers.

ABC-transporters have been recognized as being responsible for multiple drug resistance in tumor therapy, for decreased brain uptake and low oral bioavailability of drug candidates, and for drug-drug interactions and drug induced cholestasis. P-glycoprotein (ABCB1), the paradigm protein in the field, is mainly effluxing natural product toxins and shows very broad substrate specificity. Within this article we will highlight SAR and QSAR approaches for designing natural product type inhibitors of ABCB1 and related proteins as well as in silico strategies to predict ABCB1 substrates and inhibitors in order to design out undesirable drug/protein interaction.

Thailand has a vast number of plant species. Up to 3000 of them are believed by traditional Thai medicine to possess some biological activity with which researchers have attempted for many years to identify and formulate new drugs. Many chemical compounds from Thai plant species are identified and tested for biological activity that may enable them to be declared lead compounds in drug discovery. Modern methods of drug discovery are rarely used to rationalize and speed-up the process. Within this decade, the first structural database of Thai medicinal plants, Chemiebase, has been built as a platform for virtual screening, using knowledge from Thai traditional medicine. Although this effort is a promising protocol which can be used to validate Thai traditional medicine, there exists another problem that should be resolved before proceeding: It is almost impossible to trace the knowledge to its primary source. Thai traditional knowledge has been passed on orally or - less frequently - in ancient texts. We have built another database, the Thai Herbal Repository Access Initiative (THRAI) database, in order to compile the traditional knowledge into electronic format suitable for the drug design process. Three examples using data from these databases and other computer-aided drug discovery methods to rationalize Thai traditional medicine are presented here, starting with virtual screening exercised on anti-HIV-1 reverse transcriptase, anti-HIV-1 protease, anti-influenza A neuraminidase, and anti-cyclooxygenase (COX), candidates. The second example consists of the use of molecular modeling to propose drug mechanism for anti-tumor compounds. The last one is the study on toxicity assessment of some compounds from Thai medicinal plants.

While many experimental and clinical studies of traditional Chinese medicine (TCM) have been reported over recent years, the applications of computational methods to drug discovery from Chinese herbs are still at an early stage. In the light of the spread of TCM to other parts of the world over the last few decades, and the growing number of publications in languages other than Chinese, this article focuses on work published in English and accessible to an international audience. Sources of information in appropriate format are particularly important for informatics, and the growing number of TCM-related databases is discussed. Applications of virtual screening both to the identification of single and multiple target ligands are covered, as are developments in ‘ target fishing’, a novel technique which seeks to identify multiple receptors to which a compound may bind. Finally, the role of informatics in bridging the gulf between the paradigms of TCM and biomedical science is explored, and a discussion presented as to its use in probing the molecular basis of TCM.