Medicinal plants have long been an excellent source of pharmaceutical agents. Accordingly, the long term objectives of the author's research program are to discover and design new chemotherapeutic agents based on plant-derived compound leads by using a medicinal chemistry approach, which is a combination of chemistry and biology. Different examples of promising bioactive natural products and their synthetic analogs, including sesquiterpene lactones, quassinoids, naphthoquinones, phenylquinolones, dithiophenediones, neo-tanshinlactone, tylophorine, suksdorfin, DCK, and DCP, will be presented with respect to their discovery and preclinical development as potential clinical trial candidates. Research approaches include bioactivity- or mechanism of action-directed isolation and characterization of active compounds, rational drug design-based modification and analog synthesis, as well as structure-activity relationship and mechanism of action studies. Current clinical trials agents discovered by the Natural Products Research Laboratories, University of North Carolina, include bevirimat (dimethyl succinyl betulinic acid), which is now in Phase IIb trials for treating AIDS. Bevirimat is also the first in a new class of HIV drug candidates called “maturation inhibitors”. In addition, an etoposide analog, GL-331, progressed to anticancer Phase II clinical trials, and the curcumin analog JC-9 is in Phase II clinical trials for treating acne and in development for trials against prostate cancer. The discovery and development of these clinical trials candidates will also be discussed.
The Sandler Center’s approach to target-based drug discovery for neglected tropical diseases is to focus on parasite targets that are homologous to human targets being actively investigated in the pharmaceutical industry. In this way we attempt to use both the know-how and actual chemical matter from other drug-development efforts to jump start the discovery process for neglected tropical diseases. Our approach is akin to drug repurposing, except that we seek to repurpose leads rather than drugs. Medicinal chemistry can then be applied to optimize the leads specifically for the desired antiparasitic indication.
Drug discovery is a complex and unpredictable endeavor with a high failure rate. Current trends in the pharmaceutical industry have exasperated these challenges and are contributing to the dramatic decline in productivity observed over the last decade. The industrialization of science by forcing the drug discovery process to adhere to assembly-line protocols is imposing unnecessary restrictions, such as short project time-lines. Recent advances in nuclear magnetic resonance are responding to these self-imposed limitations and are providing opportunities to increase the success rate of drug discovery.
A review of recent advancements in NMR technology that have the potential of significantly impacting and benefiting the drug discovery process will be presented. These include fast NMR data collection protocols and high-throughput protein structure determination, rapid protein-ligand co-structure determination, lead discovery using fragment-based NMR affinity screens, NMR metabolomics to monitor in vivo efficacy and toxicity for lead compounds, and the identification of new therapeutic targets through the functional annotation of proteins by FAST-NMR.
NMR is a critical component of the drug discovery process, where the versatility of the technique enables it to continually expand and evolve its role. NMR is expected to maintain this growth over the next decade with advancements in automation, speed of structure calculation, in-cell imaging techniques, and the expansion of NMR amenable targets.
NMR; Drug Discovery; Structural Biology; Fragment-Based Screening; Metabolomics
The development of effective small-molecule probes and drugs entails a discovery phase, often requiring the synthesis and screening of candidate compounds, an optimization phase requiring the synthesis and analysis of structural variants, and a manufacturing phase requiring the efficient, large-scale synthesis of the optimized probe or drug. In the pharmaceutical industry, the original chemistry team-based approach is evolving to a bucket brigade-based approach where, increasingly, contracted (outsourced) chemists perform the first activity while in-house medicinal and process chemists, respectively, perform the second and third activities. The up-front coordination of these activities tends not to be optimized – each has a life of its own and each can result in a bottleneck. Therefore, a challenge for the field of synthetic chemistry is to develop a new kind of chemistry that yields small molecules that increase the probability of success in all subsequent facets of the probe- and drug-discovery pipelines, including discovery, optimization and manufacturing. Whereas this transformative chemistry remains elusive, progress is being made. Here, we review a newly emerging strategy in diversity-oriented small-molecule synthesis that may have the potential to achieve these challenging goals in the future.
diversity; oriented synthesis; build/couple/pair; functional group pairing; molecular diversity; synthesis design
The development of effective small-molecule probes and drugs entails a discovery phase, often requiring the synthesis and screening of candidate compounds, an optimization phase requiring the synthesis and analysis of structural variants, and a manufacturing phase requiring the efficient, large-scale synthesis of the optimized probe or drug. In the pharmaceutical industry, the original chemistry team-based approach is evolving to a bucket brigade-based approach where, increasingly, contracted (outsourced) chemists perform the first activity while in-house medicinal and process chemists, respectively, perform the second and third activities. The up-front coordination of these activities tends not to be optimized - each has a life of its own and each can result in a bottleneck. Therefore, a challenge for the field of synthetic chemistry is to develop a new kind of chemistry that yields small molecules that increase the probability of success in all subsequent facets of the probe- and drug-discovery pipelines, including discovery, optimization and manufacturing. Whereas this transformative chemistry remains elusive, progress is being made. Here, we review a newly emerging strategy in diversity-oriented small-molecule synthesis that may have the potential to achieve these challenging goals in the future.
diversity-oriented synthesis; build/couple/pair; functional group pairing; molecular diversity; synthesis design
Natural products have been a great source of many small molecule drugs for various diseases. In spite of recent advances in biochemical engineering and fermentation technologies that allow us to explore microorganisms and the marine environment as alternative sources of drugs, more than 70% of the current small molecule therapeutics derive their structures from plants used in traditional medicine. Natural-product-based drug discovery relies heavily on advances made in the sciences of biology and chemistry. Whereas biology aims to investigate the mode of action of a natural product, chemistry aims to overcome challenges related to its supply, bioactivity, and target selectivity. This review summarizes the explorations of the caged Garcinia xanthones, a family of plant metabolites that possess a unique chemical structure, potent bioactivities, and a promising pharmacology for drug design and development.
cyclic compounds; cycloaddition; domino reactions; natural products; synthesis design
This paper attempts to illustrate both the need for new approaches to biomaterials discovery as well as the significant promise inherent in the use of combinatorial and computational design strategies. The key observation of this Leading Opinion Paper is that the biomaterials community has been slow to embrace advanced biomaterials discovery tools such as combinatorial methods, high throughput experimentation, and computational modeling in spite of the significant promise shown by these discovery tools in materials science, medicinal chemistry and the pharmaceutical industry. It seems that the complexity of living cells and their interactions with biomaterials has been a conceptual as well as a practical barrier to the use of advanced discovery tools in biomaterials science. However, with the continued increase in computer power, the goal of predicting the biological response of cells in contact with biomaterials surfaces is within reach. Once combinatorial synthesis, high throughput experimentation, and computational modeling are integrated into the biomaterials discovery process, a significant acceleration is possible in the pace of development of improved medical implants, tissue regeneration scaffolds, and gene/drug delivery systems.
Biomaterials design; computational modeling; combinatorial synthesis; high throughput experimentation
Since the beginning of the human genome project there has been considerable speculation about how this resource and the knowledge creation it enabled would change therapeutic discovery, development, and delivery. As the project neared completion, considerable claims and predictions were made about the changes that soon would be forthcoming. Many of these early predictions failed to materialize, however, leading to further speculation about the reasons, including the role of the pharmaceutical industry in realizing the promise of “genomic medicine”. During this same period, considerable strides were made in other areas of molecular biology and medicine, and in response scientific thinking naturally evolved. Researchers and regulators moved from a genotype-centric view to a view that all biomarkers are potential tools to improve drug development and therapeutic decision making. Molecular biology is now seen as encouraging more “personalized medicine”—the closer alignment of biological information (derived from molecular diagnostics) and therapy selection. Meanwhile, there are growing concerns that increasing expenditures in pharmaceutical research and development are not sustainable and not reaping sufficient gains for shareholders or society at large. Thus, there is new speculation about how biomarkers, personalized medicine, and the industry will interact and create value for patients. This overview seeks to explore the issues driving pharmaceutical productivity and the likely contribution of biomarkers in the future.
Pharmaceutical productivity; biomarkers; molecular diagnostic; drug discovery; innovation
Recent drug discovery efforts have utilized high throughput screening (HTS) of large chemical libraries to identify compounds that modify the activity of discrete molecular targets. The molecular target approach to drug screening is widely used in the pharmaceutical and biotechnology industries, because of the amount of knowledge now available regarding protein structure that has been obtained by computer simulation. The molecular target approach requires that the structure of target molecules, and an understanding of their physiological functions, is known. This approach to drug discovery may, however, limit the identification of novel drugs. As an alternative, the phenotypic- or pathway-screening approach to drug discovery is gaining popularity, particularly in the academic sector. This approach not only provides the opportunity to identify promising drug candidates, but also enables novel information regarding biological pathways to be unveiled. Reporter assays are a powerful tool for the phenotypic screening of compound libraries. Of the various reporter genes that can be used in such assays, those encoding secreted proteins enable the screening of hit molecules in both living cells and animals. Cell- and animal-based screens enable simultaneous evaluation of drug metabolism or toxicity with biological activity. Therefore, drug candidates identified in these screens may have increased biological efficacy and a lower risk of side effects in humans. In this article, we review the reporter bioassay systems available for phenotypic drug discovery.
drug development; high throughput screening; reporter mice; age-related disorders
The historic distinction between academic- and industry-driven drug discovery, whereby investigators at universities worked to uncover the elusive principles of basic science and drug companies advanced the identification of drug targets and probe discovery, has been blurred by an academic high throughput chemical genomic revolution. It is now common for academic labs to use biochemical or cell-based high throughput screening (HTS) to investigate the effects of thousands or even hundreds of thousands of chemical probes on one or more targets over a period of days or weeks. To support the efforts of individual investigators, many universities have established core facilities where screening can be performed collaboratively with large chemical libraries managed by highly trained HTS personnel and guided by the experience of computational, medicinal and synthetic organic chemists. The identification of large numbers of promising hits from such screens has driven the need for independent labs to scale-down secondary in vitro assays in the hit to lead identification process. In this chapter we will describe the use of luminescent and quantitative reverse transcription real-time PCR (qRT-PCR) technologies that permit evaluation of the expression patterns of multiple Unfolded Protein Response (UPR) and apoptosis-related genes and simultaneously evaluate proliferation and cell death in 96 or 384 well format.
Pockets are today at the cornerstones of modern drug discovery projects and at the crossroad of several research fields, from structural biology to mathematical modeling. Being able to predict if a small molecule could bind to one or more protein targets or if a protein could bind to some given ligands is very useful for drug discovery endeavors, anticipation of binding to off- and anti-targets. To date, several studies explore such questions from chemogenomic approach to reverse docking methods. Most of these studies have been performed either from the viewpoint of ligands or targets. However it seems valuable to use information from both ligands and target binding pockets. Hence, we present a multivariate approach relating ligand properties with protein pocket properties from the analysis of known ligand-protein interactions. We explored and optimized the pocket-ligand pair space by combining pocket and ligand descriptors using Principal Component Analysis and developed a classification engine on this paired space, revealing five main clusters of pocket-ligand pairs sharing specific and similar structural or physico-chemical properties. These pocket-ligand pair clusters highlight correspondences between pocket and ligand topological and physico-chemical properties and capture relevant information with respect to protein-ligand interactions. Based on these pocket-ligand correspondences, a protocol of prediction of clusters sharing similarity in terms of recognition characteristics is developed for a given pocket-ligand complex and gives high performances. It is then extended to cluster prediction for a given pocket in order to acquire knowledge about its expected ligand profile or to cluster prediction for a given ligand in order to acquire knowledge about its expected pocket profile. This prediction approach shows promising results and could contribute to predict some ligand properties critical for binding to a given pocket, and conversely, some key pocket properties for ligand binding.
Infectious diseases are an enormous burden to global health and since drug discovery is costly, those infectious diseases that affect the developing world are often not pursued by commercial drug-discovery efforts. Therefore, pragmatic means by which new therapeutics can be discovered are needed. One such approach is target repurposing, where pathogen targets are matched with homologous human targets that have been pursued for drug discovery for other indications. In many cases, the medicinal chemistry, structural biology and biochemistry knowledge around these human targets can be directly repurposed to launch and accelerate new drug-discovery efforts against the pathogen targets. This article describes the overarching strategy of target repurposing as a tool for initiating and prosecuting neglected disease drug-discovery programs, highlighting this approach with three case studies.
Chemogenomics is an emerging inter-disciplinary approach to drug discovery that combines traditional ligand-based approaches with biological information on drug targets and lies at the interface of chemistry, biology and informatics. The ultimate goal in chemogenomics is to understand molecular recognition between all possible ligands and all possible drug targets. Protein and ligand space have previously been studied as separate entities, but chemogenomics studies deal with large datasets that cover parts of the joint protein-ligand space. Since drug discovery has traditionally focused on ligand optimization, the chemical space has been studied extensively. The protein space has been studied to some extent, typically for the purpose of classification of proteins into functional and structural classes. Since chemogenomics deals not only with ligands but also with the macromolecules the ligands interact with, it is of interest to find means to explore, compare and visualize protein-ligand subspaces.
Two chemogenomics protein-ligand interaction datasets were prepared for this study. The first dataset covers the known structural protein-ligand space, and includes all non-redundant protein-ligand interactions found in the worldwide Protein Data Bank (PDB). The second dataset contains all approved drugs and drug targets stored in the DrugBank database, and represents the approved drug-drug target space. To capture biological and physicochemical features of the chemogenomics datasets, sequence-based descriptors were computed for the proteins, and 0, 1 and 2 dimensional descriptors for the ligands. Principal component analysis (PCA) was used to analyze the multidimensional data and to create global models of protein-ligand space. The nearest neighbour method, computed using the principal components, was used to obtain a measure of overlap between the datasets.
In this study, we present an approach to visualize protein-ligand spaces from a chemogenomics perspective, where both ligand and protein features are taken into account. The method can be applied to any protein-ligand interaction dataset. Here, the approach is applied to analyze the structural protein-ligand space and the protein-ligand space of all approved drugs and their targets. We show that this approach can be used to visualize and compare chemogenomics datasets, and possibly to identify cross-interaction complexes in protein-ligand space.
Protozoan infections remain a major unsolved medical problem in many parts of our world. A major obstacle to their treatment is the blatant lack of medication that is affordable, effective, safe and easy to administer. For some of these diseases, including human sleeping sickness, very few compounds are available, many of them old and all of them fraught with toxic side effects. We explore a new concept for developing new-generation antiprotozoan drugs that are based on phosphodiesterase (PDE) inhibitors. Such inhibitors are already used extensively in human pharmacology. Given the high degree of structural similarity between the human and the protozoan PDEs, the vast expertise available in the human field can now be applied to developing disease-specific PDE inhibitors as new antiprotozoan drugs.
New models of drug discovery have been developed to overcome the lack of modern and effective drugs for neglected diseases such as human African trypanosomiasis (HAT; sleeping sickness), leishmaniasis, and Chagas disease, which have no financial viability for the pharmaceutical industry. With the purpose of combining the skills and research capacity in academia, pharmaceutical industry, and contract researchers, public–private partnerships or product development partnerships aim to create focused research consortia that address all aspects of drug discovery and development. These consortia not only emulate the projects within pharmaceutical and biotechnology industries, eg, identification and screening of libraries, medicinal chemistry, pharmacology and pharmacodynamics, formulation development, and manufacturing, but also use and strengthen existing capacity in disease-endemic countries, particularly for the conduct of clinical trials. The Drugs for Neglected Diseases initiative (DNDi) has adopted a model closely related to that of a virtual biotechnology company for the identification and optimization of drug leads. The application of this model to the development of drug candidates for the kinetoplastid infections of HAT, Chagas disease, and leishmaniasis has already led to the identification of new candidates issued from DNDi’s own discovery pipeline. This demonstrates that the model DNDi has been implementing is working but its DNDi, neglected diseases sustainability remains to be proven.
R&D; screening; lead optimization; human African trypanosomiasis; leishmaniasis; Chagas disease; product development partnerships
It is generally recognized that drug discovery and development are very time and resources consuming processes. There is an ever growing effort to apply computational power to the combined chemical and biological space in order to streamline drug discovery, design, development and optimization. In biomedical arena, computer-aided or in silico design is being utilized to expedite and facilitate hit identification, hit-to-lead selection, optimize the absorption, distribution, metabolism, excretion and toxicity profile and avoid safety issues. Commonly used computational approaches include ligand-based drug design (pharmacophore, a 3-D spatial arrangement of chemical features essential for biological activity), structure-based drug design (drug-target docking), and quantitative structure-activity and quantitative structure-property relationships. Regulatory agencies as well as pharmaceutical industry are actively involved in development of computational tools that will improve effectiveness and efficiency of drug discovery and development process, decrease use of animals, and increase predictability. It is expected that the power of CADDD will grow as the technology continues to evolve.
Drug discovery; Drug development; Molecular modeling; Virtual screening; Computational modeling; In silico drug design; QSAR/QSPR; Predictive toxicology
The approach to new drugs through natural products has proved to be the single most successful strategy for the discovery of new drugs, but in recent years its use has been deemphasized by many pharmaceutical companies in favor of approaches based on combinatorial chemistry and genomics, among others.
Drug discovery from natural sources requires continued access to plant, marine, and microbial biomass, and so the preservation of tropical rainforests is an important part of our drug discovery program. Sadly, many of the tropical forests of the world are under severe environmental pressure, and deforestation is a serious problem in most tropical countries. One way to combat this loss is to demonstrate their value as potential sources of new pharmaceutical or agrochemical products.
As part of an effort to integrate biodiversity conservation and drug discovery with economic development, we initiated an International Cooperative biodiversity Group (ICBG) to discover potential pharmaceuticals from the plant biodiversity of Suriname and Madagascar. The Group, established with funding from agencies of the United States government, involved participants from the USA, Suriname, and Madagascar. The basic approach was to search for bioactive plants in the Suriname and Malagasy flora, and to isolate their bioactive constituents by the best available methods, but the work included capacity building as well as research. Progress on this project will be reported, drawing on results obtained from the isolation of bioactive natural products from Suriname and Madagascar. The benefits of this general approach to biodiversity and drug discovery will also be discussed.
Suriname; Madagascar; biodiversity conservation; bioactive compounds; alkaloids; cardenolides; terpenoids; marine metabolites
Rapid advances in biomedical sciences in recent years have drastically accelerated the discovery of the molecular basis of human diseases. The great challenge is how to translate the newly acquired knowledge into new medicine for disease prevention and treatment. Drug discovery is a long and expensive process and the pharmaceutical industry has not been very successful at it despite its enormous resources and spending on the process. It is increasingly realized that academic biomedical research institutions ought to be engaged in early stage drug discovery, especially when it can be coupled to their basic research. To leverage the productivity of new drug development a substantial acceleration in validation of new therapeutic targets is required, which would require small molecules that can precisely control target functions in complex biological systems in a temporal and dose-dependent manner. In this review, we describe a process of integration of small molecule discovery and chemistry in academic biomedical research, which will ideally bring together the elements of innovative approaches to new molecular targets; existing basic and clinical research; screening infrastructure; and synthetic and medicinal chemistry to follow-up on small molecule hits. Such integration of multi-disciplinary resources and expertise will enable academic investigators to discover novel small molecules that are expected to facilitate their efforts in both mechanistic research and new drug target validation. More broadly academic drug discovery should contribute new entities to therapy for intractable human diseases especially for orphan diseases, and hopefully stimulate and synergize with the commercial sector.
Small molecule; drug discovery; chemical screening; medicinal chemistry
With a realistic threat against biodiversity in rain forests and in the sea, a sustainable use of natural products is becoming more and more important. Basic research directed against different organisms in Nature could reveal unexpected insights into fundamental biological mechanisms but also new pharmaceutical or biotechnological possibilities of more immediate use. Many different strategies have been used prospecting the biodiversity of Earth in the search for novel structure–activity relationships, which has resulted in important discoveries in drug development. However, we believe that the development of multidisciplinary incentives will be necessary for a future successful exploration of Nature. With this aim, one way would be a modernization and renewal of a venerable proven interdisciplinary science, Pharmacognosy, which represents an integrated way of studying biological systems. This has been demonstrated based on an explanatory model where the different parts of the model are explained by our ongoing research. Anti-inflammatory natural products have been discovered based on ethnopharmacological observations, marine sponges in cold water have resulted in substances with ecological impact, combinatory strategy of ecology and chemistry has revealed new insights into the biodiversity of fungi, in depth studies of cyclic peptides (cyclotides) has created new possibilities for engineering of bioactive peptides, development of new strategies using phylogeny and chemography has resulted in new possibilities for navigating chemical and biological space, and using bioinformatic tools for understanding of lateral gene transfer could provide potential drug targets. A multidisciplinary subject like Pharmacognosy, one of several scientific disciplines bridging biology and chemistry with medicine, has a strategic position for studies of complex scientific questions based on observations in Nature. Furthermore, natural product research based on intriguing scientific questions in Nature can be of value to increase the attraction for young students in modern life science.
Pharmacognosy; Geodia; COX-2; ChemGPS-NP; Chemical space; Phylogeny; Cyclotide; Viola; Truffle; Tuber; Lateral gene transfer; Trypanosoma
By generating structural complexity in a single step from three or more reactants, multicomponent reactions (MCRs) make it possible to synthesize target compounds with greater efficiency and atom economy. The history of such reactions can be traced to the mid-nineteenth century when Strecker first produced α-aminonitriles from the condensation of aldehydes with ammonia and hydrogen cyanide.
Recently, academic chemists have renewed their interest in MCRs. In part, the pharmaceutical industry has fueled this resurgence because of the growing need to assemble libraries of structurally complex substances for evaluation as lead compounds in drug discovery and development programs. The application of MCRs to that increasingly important objective remains limited by the relatively small number of such reactions that can be broadly applied to prepare biologically relevant or natural-product-like molecular frameworks.
We were interested in applying logic-based approaches, such as our single reactant replacement (SRR) approach, as a way both to improve known MCRs and design new multiple-component routes to bioactive structures. This Account provides several examples that illustrate the use of SRR with known MCRs as starting points for synthetic innovation in this area.
As part of our working hypothesis, we initially explored strategies for engineering improvements into known MCRs, either by increasing the dimensionality—i.e. changing an n-component to an (n+1)-component reaction—or broadening the scope of useful input structures, or both. By exhaustively applying retrosynthetic analysis to the cognate MCR to identify and exploit alternative entry points into the overall reaction manifold, we have devised several such re-engineered MCRs. Serendipitous findings have also augmented the yield of useful developments from our logic-inspired approach. In some cases, we have identified surprising links between different compound families that provide useful new entry points for chemical library synthesis. In other cases, the same re-engineering logic made it possible (sometimes in unexpected ways) to transform certain non-elementary two-component reactions into higher order MCRs.
While logic may also inspire the search for new MCRs, the design process requires added chemical creativity, which cannot be reduced to a simple formula. The long-term goal of our research is to expand the useful repertoire of such reactions, which are important as complexity-generating tools in both combinatorial and diversity-oriented synthesis.
Multicomponent Reactions; Diversity-Oriented Synthesis; Synthetic Methodology
Discovery of efficient catalysts is one of the most compelling objectives of modern chemistry. Chiral catalysts are in particularly high demand, as they facilitate synthesis of enantiomerically enriched small molecules that are critical to developments in medicine, biology and materials science1. Especially noteworthy are catalysts that promote—with otherwise inaccessible efficiency and selectivity levels—reactions demonstrated to be of great utility in chemical synthesis. Here we report a class of chiral catalysts that initiate alkene metathesis1 with very high efficiency and enantioselectivity. Such attributes arise from structural fluxionality of the chiral catalysts and the central role that enhanced electronic factors have in the catalytic cycle. The new catalysts have a stereogenic metal centre and carry only monodentate ligands; the molybdenum-based complexes are prepared stereoselectively by a ligand exchange process involving an enantiomerically pure aryloxide, a class of ligands scarcely used in enantioselective catalysis2,3. We demonstrate the application of the new catalysts in an enantioselective synthesis of the Aspidosperma alkaloid, quebrachamine, through an alkene metathesis reaction that cannot be promoted by any of the previously reported chiral catalysts.
Control of diseases inflicted by protozoan parasites such as Leishmania, Trypanosoma, and Plasmodium, which pose a serious threat to human health worldwide, depends on a rather small number of antiparasite drugs, of which many are toxic and/or inefficient. Moreover, the increasing occurrence of drug-resistant parasites emphasizes the need for new and effective antiprotozoan drugs. In the current study, we describe a synthetic peptide, WRWYCRCK, with inhibitory effect on the essential enzyme topoisomerase I from the malaria-causing parasite Plasmodium falciparum. The peptide inhibits specifically the transition from noncovalent to covalent DNA binding of P. falciparum topoisomerase I, while it does not affect the ligation step of catalysis. A mechanistic explanation for this inhibition is provided by molecular docking analyses. Taken together the presented results suggest that synthetic peptides may represent a new class of potential antiprotozoan drugs.
Protozoan parasites cause serious human and zoonotic infections, including life-threatening diseases such as malaria, African and American trypanosomiasis, and leishmaniasis. These diseases are no more common in the developed world, but together they still threaten about 40% of the world population (WHO estimates). Mortality and morbidity are high in developing countries, and the lack of vaccines makes chemotherapy the only suitable option. However, available antiparasitic drugs are hampered by more or less marked toxic side effects and by the emergence of drug resistance. As the main prevalence of parasitic diseases occurs in the poorest areas of the world, the interest of the pharmaceutical companies in the development of new drugs has been traditionally scarce. The establishment of public-private partnerships focused on tropical diseases is changing this situation, allowing the exploitation of the technological advances that took place during the past decade related to genomics, proteomics, and in silico drug discovery approaches. These techniques allowed the identification of new molecular targets that in some cases are shared by different parasites. In this review we outline the recent developments in the fields of protease and topoisomerase inhibitors, antimicrobial and cell-penetrating peptides, and RNA interference. We also report on the rapidly developing field of new vectors (micro and nano particles, mesoporous materials) that in some cases can cross host or parasite natural barriers and, by selectively delivering new or already in use drugs to the target site, minimize dosage and side effects.
Protozoa; protease; topoisomerase; RNAi; nanovectors.
Current drug discovery is impossible without sophisticated modeling and computation. In this review we outline previous advances in computational biology and, by tracing the steps involved in pharmaceutical development, explore a range of novel, high-value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy–industry links for scientific and human benefit. Attention to these opportunities could promise punctuated advance and will complement the well-established computational work on which drug discovery currently relies.
Drug discovery today is impossible without sophisticated modeling and computation. In this review we touch on previous advances in computational biology and by tracing the steps involved in pharmaceutical development, we explore a range of novel, high value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy-industry ties for scientific and human benefit. Attention to these opportunities could promise punctuated advance, and will complement the well-established computational work on which drug discovery currently relies.