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1.  Analysis of multiple compound–protein interactions reveals novel bioactive molecules 
The authors use machine learning of compound-protein interactions to explore drug polypharmacology and to efficiently identify bioactive ligands, including novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein coupled receptors and protein kinases.
We have demonstrated that machine learning of multiple compound–protein interactions is useful for efficient ligand screening and for assessing drug polypharmacology.This approach successfully identified novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein-coupled receptors and protein kinases.These bioactive compounds were not detected by existing computational ligand-screening methods in comparative studies.The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.
The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. Perturbations of biological systems by chemical probes provide broader applications not only for analysis of complex systems but also for intentional manipulations of these systems. Nevertheless, the lack of well-characterized chemical modulators has limited their use. Recently, chemical genomics has emerged as a promising area of research applicable to the exploration of novel bioactive molecules, and researchers are currently striving toward the identification of all possible ligands for all target protein families (Wang et al, 2009). Chemical genomics studies have shown that patterns of compound–protein interactions (CPIs) are too diverse to be understood as simple one-to-one events. There is an urgent need to develop appropriate data mining methods for characterizing and visualizing the full complexity of interactions between chemical space and biological systems. However, no existing screening approach has so far succeeded in identifying novel bioactive compounds using multiple interactions among compounds and target proteins.
High-throughput screening (HTS) and computational screening have greatly aided in the identification of early lead compounds for drug discovery. However, the large number of assays required for HTS to identify drugs that target multiple proteins render this process very costly and time-consuming. Therefore, interest in using in silico strategies for screening has increased. The most common computational approaches, ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS; Oprea and Matter, 2004; Muegge and Oloff, 2006; McInnes, 2007; Figure 1A), have been used for practical drug development. LBVS aims to identify molecules that are very similar to known active molecules and generally has difficulty identifying compounds with novel structural scaffolds that differ from reference molecules. The other popular strategy, SBVS, is constrained by the number of three-dimensional crystallographic structures available. To circumvent these limitations, we have shown that a new computational screening strategy, chemical genomics-based virtual screening (CGBVS), has the potential to identify novel, scaffold-hopping compounds and assess their polypharmacology by using a machine-learning method to recognize conserved molecular patterns in comprehensive CPI data sets.
The CGBVS strategy used in this study was made up of five steps: CPI data collection, descriptor calculation, representation of interaction vectors, predictive model construction using training data sets, and predictions from test data (Figure 1A). Importantly, step 1, the construction of a data set of chemical structures and protein sequences for known CPIs, did not require the three-dimensional protein structures needed for SBVS. In step 2, compound structures and protein sequences were converted into numerical descriptors. These descriptors were used to construct chemical or biological spaces in which decreasing distance between vectors corresponded to increasing similarity of compound structures or protein sequences. In step 3, we represented multiple CPI patterns by concatenating these chemical and protein descriptors. Using these interaction vectors, we could quantify the similarity of molecular interactions for compound–protein pairs, despite the fact that the ligand and protein similarity maps differed substantially. In step 4, concatenated vectors for CPI pairs (positive samples) and non-interacting pairs (negative samples) were input into an established machine-learning method. In the final step, the classifier constructed using training sets was applied to test data.
To evaluate the predictive value of CGBVS, we first compared its performance with that of LBVS by fivefold cross-validation. CGBVS performed with considerably higher accuracy (91.9%) than did LBVS (84.4%; Figure 1B). We next compared CGBVS and SBVS in a retrospective virtual screening based on the human β2-adrenergic receptor (ADRB2). Figure 1C shows that CGBVS provided higher hit rates than did SBVS. These results suggest that CGBVS is more successful than conventional approaches for prediction of CPIs.
We then evaluated the ability of the CGBVS method to predict the polypharmacology of ADRB2 by attempting to identify novel ADRB2 ligands from a group of G-protein-coupled receptor (GPCR) ligands. We ranked the prediction scores for the interactions of 826 reported GPCR ligands with ADRB2 and then analyzed the 50 highest-ranked compounds in greater detail. Of 21 commercially available compounds, 11 showed ADRB2-binding activity and were not previously reported to be ADRB2 ligands. These compounds included ligands not only for aminergic receptors but also for neuropeptide Y-type 1 receptors (NPY1R), which have low protein homology to ADRB2. Most ligands we identified were not detected by LBVS and SBVS, which suggests that only CGBVS could identify this unexpected cross-reaction for a ligand developed as a target to a peptidergic receptor.
The true value of CGBVS in drug discovery must be tested by assessing whether this method can identify scaffold-hopping lead compounds from a set of compounds that is structurally more diverse. To assess this ability, we analyzed 11 500 commercially available compounds to predict compounds likely to bind to two GPCRs and two protein kinases. Functional assays revealed that nine ADRB2 ligands, three NPY1R ligands, five epidermal growth factor receptor (EGFR) inhibitors, and two cyclin-dependent kinase 2 (CDK2) inhibitors were concentrated in the top-ranked compounds (hit rate=30, 15, 25, and 10%, respectively). We also evaluated the extent of scaffold hopping achieved in the identification of these novel ligands. One ADRB2 ligand, two NPY1R ligands, and one CDK2 inhibitor exhibited scaffold hopping (Figure 4), indicating that CGBVS can use this characteristic to rationally predict novel lead compounds, a crucial and very difficult step in drug discovery. This feature of CGBVS is critically different from existing predictive methods, such as LBVS, which depend on similarities between test and reference ligands, and focus on a single protein or highly homologous proteins. In particular, CGBVS is useful for targets with undefined ligands because this method can use CPIs with target proteins that exhibit lower levels of homology.
In summary, we have demonstrated that data mining of multiple CPIs is of great practical value for exploration of chemical space. As a predictive model, CGBVS could provide an important step in the discovery of such multi-target drugs by identifying the group of proteins targeted by a particular ligand, leading to innovation in pharmaceutical research.
The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold-hopping compounds. Through a machine-learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G-protein-coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand-screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.
PMCID: PMC3094066  PMID: 21364574
chemical genomics; data mining; drug discovery; ligand screening; systems chemical biology
2.  Natural products in modern life science 
Phytochemistry Reviews  2010;9(2):279-301.
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.
PMCID: PMC2912726  PMID: 20700376
Pharmacognosy; Geodia; COX-2; ChemGPS-NP; Chemical space; Phylogeny; Cyclotide; Viola; Truffle; Tuber; Lateral gene transfer; Trypanosoma
3.  Peptide Inhibition of Topoisomerase IB from Plasmodium falciparum 
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.
PMCID: PMC3200115  PMID: 22091414
4.  Drug Discovery for Schistosomiasis: Hit and Lead Compounds Identified in a Library of Known Drugs by Medium-Throughput Phenotypic Screening 
Praziquantel (PZQ) is the only widely available drug to treat schistosomiasis. Given the potential for drug resistance, it is prudent to search for novel therapeutics. Identification of anti-schistosomal chemicals has traditionally relied on phenotypic (whole organism) screening with adult worms in vitro and/or animal models of disease—tools that limit automation and throughput with modern microtiter plate-formatted compound libraries.
A partially automated, three-component phenotypic screen workflow is presented that utilizes at its apex the schistosomular stage of the parasite adapted to a 96-well plate format with a throughput of 640 compounds per month. Hits that arise are subsequently screened in vitro against adult parasites and finally for efficacy in a murine model of disease. Two GO/NO GO criteria filters in the workflow prioritize hit compounds for tests in the animal disease model in accordance with a target drug profile that demands short-course oral therapy. The screen workflow was inaugurated with 2,160 chemically diverse natural and synthetic compounds, of which 821 are drugs already approved for human use. This affords a unique starting point to ‘reposition’ (re-profile) drugs as anti-schistosomals with potential savings in development timelines and costs.
Multiple and dynamic phenotypes could be categorized for schistosomula and adults in vitro, and a diverse set of ‘hit’ drugs and chemistries were identified, including anti-schistosomals, anthelmintics, antibiotics, and neuromodulators. Of those hits prioritized for tests in the animal disease model, a number of leads were identified, one of which compares reasonably well with PZQ in significantly decreasing worm and egg burdens, and disease-associated pathology. Data arising from the three components of the screen are posted online as a community resource.
To accelerate the identification of novel anti-schistosomals, we have developed a partially automated screen workflow that interfaces schistosomula with microtiter plate-formatted compound libraries. The workflow has identified various compounds and drugs as hits in vitro and leads, with the prescribed oral efficacy, in vivo. Efforts to improve throughput, automation, and rigor of the screening workflow are ongoing.
Author Summary
The flatworm disease schistosomiasis infects over 200 million people with just one drug (praziquantel) available—a concern should drug resistance develop. Present drug discovery approaches for schistosomiasis are slow and not conducive to automation in a high-throughput format. Therefore, we designed a three-component screen workflow that positions the larval (schistosomulum) stage of S. mansoni at its apex followed by screens of adults in culture and, finally, efficacy tests in infected mice. Schistosomula are small enough and available in sufficient numbers to interface with automated liquid handling systems and prosecute thousands of compounds in short time frames. We inaugurated the workflow with a 2,160 compound library that includes known drugs in order to cost effectively ‘re-position’ drugs as new therapies for schistosomiasis and/or identify compounds that could be modified to that end. We identify a variety of ‘hit’ compounds (antibiotics, psychoactives, antiparasitics, etc.) that produce behavioral responses (phenotypes) in schistosomula and adults. Tests in infected mice of the most promising hits identified a number of ‘leads,’ one of which compares reasonably well with praziquantel in killing worms, decreasing egg production by the parasite, and ameliorating disease pathology. Efforts continue to more fully automate the workflow. All screen data are posted online as a drug discovery resource.
PMCID: PMC2702839  PMID: 19597541
5.  Diverse Inhibitor Chemotypes Targeting Trypanosoma cruzi CYP51 
Chagas Disease, a WHO- and NIH-designated neglected tropical disease, is endemic in Latin America and an emerging infection in North America and Europe as a result of population moves. Although a major cause of morbidity and mortality due to heart failure, as well as inflicting a heavy economic burden in affected regions, Chagas Disease elicits scant notice from the pharmaceutical industry because of adverse economic incentives. The discovery and development of new routes to chemotherapy for Chagas Disease is a clear priority.
Methodology/Principal Findings
The similarity between the membrane sterol requirements of pathogenic fungi and those of the parasitic protozoon Trypanosoma cruzi, the causative agent of Chagas human cardiopathy, has led to repurposing anti-fungal azole inhibitors of sterol 14α-demethylase (CYP51) for the treatment of Chagas Disease. To diversify the therapeutic pipeline of anti-Chagasic drug candidates we exploited an approach that included directly probing the T. cruzi CYP51 active site with a library of synthetic small molecules. Target-based high-throughput screening reduced the library of ∼104,000 small molecules to 185 hits with estimated nanomolar KD values, while cross-validation against T. cruzi-infected skeletal myoblast cells yielded 57 active hits with EC50 <10 µM. Two pools of hits partially overlapped. The top hit inhibited T. cruzi with EC50 of 17 nM and was trypanocidal at 40 nM.
The hits are structurally diverse, demonstrating that CYP51 is a rather permissive enzyme target for small molecules. Cheminformatic analysis of the hits suggests that CYP51 pharmacology is similar to that of other cytochromes P450 therapeutic targets, including thromboxane synthase (CYP5), fatty acid ω-hydroxylases (CYP4), 17α-hydroxylase/17,20-lyase (CYP17) and aromatase (CYP19). Surprisingly, strong similarity is suggested to glutaminyl-peptide cyclotransferase, which is unrelated to CYP51 by sequence or structure. Lead compounds developed by pharmaceutical companies against these targets could also be explored for efficacy against T. cruzi.
Author Summary
Chagas Disease is the leading cause of heart disease in Latin America and an emerging infection in Europe and North America. The clinical presentation of Chagas Disease arises from infection by the protozoan parasite Trypanosoma cruzi, which leads to progressive cardiomyopathy. No vaccine is available and chemotherapeutic options are limited to the drugs benznidazole and nifurtimox, which are used during the acute phase but may cause severe gastrointestinal and neurological side effects and are not commonly used in the chronic phase. Neither drug is approved by the FDA for use in the United States. The need for effective new therapy is urgent. A validated therapeutic target in T. cruzi is CYP51, an essential enzyme in the sterol biosynthesis pathway. We report results of high-throughput screening of small molecules directly against CYP51, confirmed by in vitro medium-throughput screening of the hits against T. cruzi-infected mammalian cells. We have identified a potent T. cruzi inhibitor as well as a diverse collection of low molecular weight hits with high affinity to CYP51. We have applied computational chemistry to relate CYP51 to other pharmacologic targets. This analysis allowed us to identify molecules already produced by pharmaceutical companies for future experimental testing against T. cruzi.
PMCID: PMC3409115  PMID: 22860142
6.  Drug discovery for neglected tropical diseases at the Sandler Center 
Future medicinal chemistry  2011;3(10):1279-1288.
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.
PMCID: PMC3199145  PMID: 21859302
7.  When plants produce not enough or at all: metabolic engineering of flavonoids in microbial hosts 
As a result of the discovery that flavonoids are directly or indirectly connected to health, flavonoid metabolism and its fascinating molecules that are natural products in plants, have attracted the attention of both the industry and researchers involved in plant science, nutrition, bio/chemistry, chemical bioengineering, pharmacy, medicine, etc. Subsequently, in the past few years, flavonoids became a top story in the pharmaceutical industry, which is continually seeking novel ways to produce safe and efficient drugs. Microbial cell cultures can act as workhorse bio-factories by offering their metabolic machinery for the purpose of optimizing the conditions and increasing the productivity of a selective flavonoid. Furthermore, metabolic engineering methodology is used to reinforce what nature does best by correcting the inadequacies and dead-ends of a metabolic pathway. Combinatorial biosynthesis techniques led to the discovery of novel ways of producing natural and even unnatural plant flavonoids, while, in addition, metabolic engineering provided the industry with the opportunity to invest in synthetic biology in order to overcome the currently existing restricted diversification and productivity issues in synthetic chemistry protocols. In this review, is presented an update on the rationalized approaches to the production of natural or unnatural flavonoids through biotechnology, analyzing the significance of combinatorial biosynthesis of agricultural/pharmaceutical compounds produced in heterologous organisms. Also mentioned are strategies and achievements that have so far thrived in the area of synthetic biology, with an emphasis on metabolic engineering targeting the cellular optimization of microorganisms and plants that produce flavonoids, while stressing the advances in flux dynamic control and optimization. Finally, the involvement of the rapidly increasing numbers of assembled genomes that contribute to the gene- or pathway-mining in order to identify the gene(s) responsible for producing species-specific secondary metabolites is also considered herein.
PMCID: PMC4310283  PMID: 25688249
flavonoid biosynthesis; unnatural flavonoids; metabolic engineering; dynamic regulation; metabolic control; secondary metabolites; combinatorial biosynthesis
8.  Enhancing the discovery and development of immunotherapies for cancer using quantitative and systems pharmacology: Interleukin-12 as a case study 
Recent clinical successes of immune checkpoint modulators have unleashed a wave of enthusiasm associated with cancer immunotherapy. However, this enthusiasm is dampened by persistent translational hurdles associated with cancer immunotherapy that mirror the broader pharmaceutical industry. Specifically, the challenges associated with drug discovery and development stem from an incomplete understanding of the biological mechanisms in humans that are targeted by a potential drug and the financial implications of clinical failures. Sustaining progress in expanding the clinical benefit provided by cancer immunotherapy requires reliably identifying new mechanisms of action. Along these lines, quantitative and systems pharmacology (QSP) has been proposed as a means to invigorate the drug discovery and development process. In this review, I discuss two central themes of QSP as applied in the context of cancer immunotherapy. The first theme focuses on a network-centric view of biology as a contrast to a “one-gene, one-receptor, one-mechanism” paradigm prevalent in contemporary drug discovery and development. This theme has been enabled by the advances in wet-lab capabilities to assay biological systems at increasing breadth and resolution. The second theme focuses on integrating mechanistic modeling and simulation with quantitative wet-lab studies. Drawing from recent QSP examples, large-scale mechanistic models that integrate phenotypic signaling-, cellular-, and tissue-level behaviors have the potential to lower many of the translational hurdles associated with cancer immunotherapy. These include prioritizing immunotherapies, developing mechanistic biomarkers that stratify patient populations and that reflect the underlying strength and dynamics of a protective host immune response, and facilitate explicit sharing of our understanding of the underlying biology using mechanistic models as vehicles for dialogue. However, creating such models require a modular approach that assumes that the biological networks remain similar in health and disease. As oncogenesis is associated with re-wiring of these biological networks, I also describe an approach that combines mechanistic modeling with quantitative wet-lab experiments to identify ways in which malignant cells alter these networks, using Interleukin-12 as an example. Collectively, QSP represents a new holistic approach that may have profound implications for how translational science is performed.
PMCID: PMC4468964  PMID: 26082838
9.  A new approach to the rationale discovery of polymeric biomaterials 
Biomaterials  2007;28(29):4171-4177.
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.
PMCID: PMC2200635  PMID: 17644176
Biomaterials design; computational modeling; combinatorial synthesis; high throughput experimentation
10.  Sterol 14alpha-Demethylase (CYP51) as a Therapeutic Target for Human Trypanosomiasis and Leishmaniasis 
Current Topics in Medicinal Chemistry  2011;11(16):2060-2071.
Pathogenic protozoa threaten lives of several hundred million people throughout the world and are responsible for large numbers of deaths globally. The parasites are transmitted to humans by insect vectors, more than a hundred of infected mammalian species forming reservoir. With human migrations, HIV-coinfections, and blood bank contamination the diseases are now spreading beyond the endemic tropical countries, being found in all parts of the world including the USA, Canada and Europe. In spite of the widely appreciated magnitude of this health problem, current treatment for sleeping sickness (Trypanosoma brucei), Chagas disease (Trypanosoma cruzi) and leishmaniasis (Leishmania spp.) remains unsatisfactory. The drugs are decades old, their efficacy and safety profiles are unacceptable. This review describes sterol 14α-demethylase, an essential enzyme in sterol biosynthesis in eukaryotes and clinical target for antifungal azoles, as a promising target for antiprotozoan chemotherapy. While several antifungal azoles have been proven active against Trypanosomatidae and are under consideration as antiprotozoan agents, crystal structures of sterol 14α-demethylases from three protozoan pathogens, Trypanosoma brucei, Trypanosoma cruzi and Leishmania infantum provide the basis for the development of new, highly potent and pathogen-specific drugs with rationally optimized pharmacological properties.
PMCID: PMC3391166  PMID: 21619513
Antiprotozoan chemotherapy; crystal structure; enzyme inhibitors; leishmaniasis; sterol 14alpha-demethylase (CYP51); sterol biosynthesis; trypanosomiasis
11.  Current Developments in the Therapy of Protozoan Infections 
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.
PMCID: PMC3103884  PMID: 21629507
Protozoa; protease; topoisomerase; RNAi; nanovectors.
12.  Discovery and Development of Natural Product-derived Chemotherapeutic Agents Based on a Medicinal Chemistry Approach⊥† 
Journal of natural products  2010;73(3):500-516.
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.
PMCID: PMC2893734  PMID: 20187635
13.  Selective Inhibitors of Protozoan Protein N-myristoyltransferases as Starting Points for Tropical Disease Medicinal Chemistry Programs 
Inhibition of N-myristoyltransferase has been validated pre-clinically as a target for the treatment of fungal and trypanosome infections, using species-specific inhibitors. In order to identify inhibitors of protozoan NMTs, we chose to screen a diverse subset of the Pfizer corporate collection against Plasmodium falciparum and Leishmania donovani NMTs. Primary screening hits against either enzyme were tested for selectivity over both human NMT isoforms (Hs1 and Hs2) and for broad-spectrum anti-protozoan activity against the NMT from Trypanosoma brucei. Analysis of the screening results has shown that structure-activity relationships (SAR) for Leishmania NMT are divergent from all other NMTs tested, a finding not predicted by sequence similarity calculations, resulting in the identification of four novel series of Leishmania-selective NMT inhibitors. We found a strong overlap between the SARs for Plasmodium NMT and both human NMTs, suggesting that achieving an appropriate selectivity profile will be more challenging. However, we did discover two novel series with selectivity for Plasmodium NMT over the other NMT orthologues in this study, and an additional two structurally distinct series with selectivity over Leishmania NMT. We believe that release of results from this study into the public domain will accelerate the discovery of NMT inhibitors to treat malaria and leishmaniasis. Our screening initiative is another example of how a tripartite partnership involving pharmaceutical industries, academic institutions and governmental/non-governmental organisations such as Medical Research Council and Wellcome Trust can stimulate research for neglected diseases.
Author Summary
Inhibition of N-myristoyltransferase has been validated pre-clinically as a target for the treatment of fungal and trypanosome infections, using species-specific inhibitors. In order to identify inhibitors of protozoan NMTs, we chose to screen a diverse subset of the Pfizer corporate collection against Plasmodium falciparum and Leishmania donovani NMTs. Primary screening hits against either enzyme were tested for selectivity over both human NMT isoforms (HsNMT1 and HsNMT2) and for broad-spectrum anti-protozoan activity against the NMT from Trypanosoma brucei. We have identified eight series of protozoan NMT inhibitors, six having good selectivity for either Plasmodium or Leishmania NMTs over the other orthologues in this study. We believe that all of these series could form the basis of medicinal chemistry programs to deliver drug candidates against either malaria or leishmaniasis. Our screening initiative is another example of how a tripartite partnership involving pharmaceutical industries, academic institutions and governmental/non-governmental organisations such as the UK Medical Research Council and Wellcome Trust can stimulate research for neglected diseases.
PMCID: PMC3335879  PMID: 22545171
14.  CYP51 structures and structure-based development of novel, pathogen-specific inhibitory scaffolds 
Graphical abstract
► CYP51s (sterol 14alpha-demethylases) are efficient drug target enzymes. ► CYP51s have a highly rigid substrate binding cavity. ► CYP51 structure-based development of a new inhibitory scaffold is described.
CYP51 (sterol 14α-demethylase) is a cytochrome P450 enzyme essential for sterol biosynthesis and the primary target for clinical and agricultural antifungal azoles. The azoles that are currently in clinical use for systemic fungal infections represent modifications of two basic scaffolds, ketoconazole and fluconazole, all of them being selected based on their antiparasitic activity in cellular experiments. By studying direct inhibition of CYP51 activity across phylogeny including human pathogens Trypanosoma brucei, Trypanosoma cruzi and Leishmania infantum, we identified three novel protozoa-specific inhibitory scaffolds, their inhibitory potency correlating well with antiprotozoan activity. VNI scaffold (carboxamide containing β-phenyl-imidazoles) is the most promising among them: killing T. cruzi amastigotes at low nanomolar concentration, it is also easy to synthesize and nontoxic. Oral administration of VNI (up to 400 mg/kg) neither leads to mortality nor reveals significant side effects up to 48 h post treatment using an experimental mouse model of acute toxicity. Trypanosomatidae CYP51 crystal structures determined in the ligand-free state and complexed with several azole inhibitors as well as a substrate analog revealed high rigidity of the CYP51 substrate binding cavity, which must be essential for the enzyme strict substrate specificity and functional conservation. Explaining profound potency of the VNI inhibitory scaffold, the structures also outline guidelines for its further development. First steps of the VNI scaffold optimization have been undertaken; the results presented here support the notion that CYP51 structure-based rational design of more efficient, pathogen-specific inhibitors represents a highly promising direction.
PMCID: PMC3596085  PMID: 23504044
Sterol 14α-demethylase; CYP51; Inhibition; Crystal structure
15.  Advances in Nuclear Magnetic Resonance for Drug Discovery 
Expert opinion on drug discovery  2009;4(10):1077-1098.
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.
PMCID: PMC2843924  PMID: 20333269
NMR; Drug Discovery; Structural Biology; Fragment-Based Screening; Metabolomics
16.  DNA Display II. Genetic Manipulation of Combinatorial Chemistry Libraries for Small-Molecule Evolution 
PLoS Biology  2004;2(7):e174.
Biological in vitro selection techniques, such as RNA aptamer methods and mRNA display, have proven to be powerful approaches for engineering molecules with novel functions. These techniques are based on iterative amplification of biopolymer libraries, interposed by selection for a desired functional property. Rare, promising compounds are enriched over multiple generations of a constantly replicating molecular population, and subsequently identified. The restriction of such methods to DNA, RNA, and polypeptides precludes their use for small-molecule discovery. To overcome this limitation, we have directed the synthesis of combinatorial chemistry libraries with DNA “genes,” making possible iterative amplification of a nonbiological molecular species. By differential hybridization during the course of a traditional split-and-pool combinatorial synthesis, the DNA sequence of each gene is read out and translated into a unique small-molecule structure. This “chemical translation” provides practical access to synthetic compound populations 1 million-fold more complex than state-of-the-art combinatorial libraries. We carried out an in vitro selection experiment (iterated chemical translation, selection, and amplification) on a library of 106 nonnatural peptides. The library converged over three generations to a high-affinity protein ligand. The ability to genetically encode diverse classes of synthetic transformations enables the in vitro selection and potential evolution of an essentially limitless collection of compound families, opening new avenues to drug discovery, catalyst design, and the development of a materials science “biology.”
The authors use DNA "genes" to direct the synthesis of combinatorial chemistry libraries, and show in an in vitro selection experiment that specific drugs can be developed
PMCID: PMC434149  PMID: 15221028
17.  Diversity-Oriented Syntheses Using the Build/Couple/Pair Strategy** 
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.
PMCID: PMC2645036  PMID: 18080276
diversity; oriented synthesis; build/couple/pair; functional group pairing; molecular diversity; synthesis design
18.  Towards the Optimal Screening Collection: A Synthesis Strategy 
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.
PMCID: PMC2645036  PMID: 18080276
diversity-oriented synthesis; build/couple/pair; functional group pairing; molecular diversity; synthesis design
19.  Insights into an Original Pocket-Ligand Pair Classification: A Promising Tool for Ligand Profile Prediction 
PLoS ONE  2013;8(6):e63730.
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.
PMCID: PMC3688729  PMID: 23840299
20.  Machine Learning Assisted Design of Highly Active Peptides for Drug Discovery 
PLoS Computational Biology  2015;11(4):e1004074.
The discovery of peptides possessing high biological activity is very challenging due to the enormous diversity for which only a minority have the desired properties. To lower cost and reduce the time to obtain promising peptides, machine learning approaches can greatly assist in the process and even partly replace expensive laboratory experiments by learning a predictor with existing data or with a smaller amount of data generation. Unfortunately, once the model is learned, selecting peptides having the greatest predicted bioactivity often requires a prohibitive amount of computational time. For this combinatorial problem, heuristics and stochastic optimization methods are not guaranteed to find adequate solutions. We focused on recent advances in kernel methods and machine learning to learn a predictive model with proven success. For this type of model, we propose an efficient algorithm based on graph theory, that is guaranteed to find the peptides for which the model predicts maximal bioactivity. We also present a second algorithm capable of sorting the peptides of maximal bioactivity. Extensive analyses demonstrate how these algorithms can be part of an iterative combinatorial chemistry procedure to speed up the discovery and the validation of peptide leads. Moreover, the proposed approach does not require the use of known ligands for the target protein since it can leverage recent multi-target machine learning predictors where ligands for similar targets can serve as initial training data. Finally, we validated the proposed approach in vitro with the discovery of new cationic antimicrobial peptides. Source code freely available at
Author Summary
Part of the complexity of drug discovery is the sheer chemical diversity to explore combined to all requirements a compound must meet to become a commercial drug. Hence, it makes sense to automate this chemical exploration endeavor in a wise, informed, and efficient fashion. Here, we focused on peptides as they have properties that make them excellent drug starting points. Machine learning techniques may replace expensive in-vitro laboratory experiments by learning an accurate model of it. However, computational models also suffer from the combinatorial explosion due to the enormous chemical diversity. Indeed, applying the model to every peptides would take an astronomical amount of computer time. Therefore, given a model, is it possible to determine, using reasonable computational time, the peptide that has the best properties and chance for success? This exact question is what motivated our work. We focused on recent advances in kernel methods and machine learning to learn a model that already had excellent results. We demonstrate that this class of model has mathematical properties that makes it possible to rapidly identify and sort the best peptides. Finally, in-vitro and in-silico results are provided to support and validate this theoretical discovery.
PMCID: PMC4388847  PMID: 25849257
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.
PMCID: PMC2917822  PMID: 20687180
Small molecule; drug discovery; chemical screening; medicinal chemistry
22.  Statistical Design for Biospecimen Cohort Size in Proteomics-based Biomarker Discovery and Verification Studies 
Journal of proteome research  2013;12(12):5383-5394.
Protein biomarkers are needed to deepen our understanding of cancer biology and to improve our ability to diagnose, monitor and treat cancers. Important analytical and clinical hurdles must be overcome to allow the most promising protein biomarker candidates to advance into clinical validation studies. Although contemporary proteomics technologies support the measurement of large numbers of proteins in individual clinical specimens, sample throughput remains comparatively low. This problem is amplified in typical clinical proteomics research studies, which routinely suffer from a lack of proper experimental design, resulting in analysis of too few biospecimens to achieve adequate statistical power at each stage of a biomarker pipeline. To address this critical shortcoming, a joint workshop was held by the National Cancer Institute (NCI), National Heart, Lung and Blood Institute (NHLBI), and American Association for Clinical Chemistry (AACC), with participation from the U.S. Food and Drug Administration (FDA). An important output from the workshop was a statistical framework for the design of biomarker discovery and verification studies. Herein, we describe the use of quantitative clinical judgments to set statistical criteria for clinical relevance, and the development of an approach to calculate biospecimen sample size for proteomic studies in discovery and verification stages prior to clinical validation stage. This represents a first step towards building a consensus on quantitative criteria for statistical design of proteomics biomarker discovery and verification research.
PMCID: PMC4039197  PMID: 24063748
Statistical Experiment Design; Biomarker; Proteomics; Unbiasedness; Power Calculation
23.  In Vitro and In Vivo Anti-Schistosomal Activity of the Alkylphospholipid Analog Edelfosine 
PLoS ONE  2014;9(10):e109431.
Schistosomiasis is a parasitic disease caused by trematodes of the genus Schistosoma. Five species of Schistosoma are known to infect humans, out of which S. haematobium is the most prevalent, causing the chronic parasitic disease schistosomiasis that still represents a major problem of public health in many regions of the world and especially in tropical areas, leading to serious manifestations and mortality in developing countries. Since the 1970s, praziquantel (PZQ) is the drug of choice for the treatment of schistosomiasis, but concerns about relying on a single drug to treat millions of people, and the potential appearance of drug resistance, make identification of alternative schistosomiasis chemotherapies a high priority. Alkylphospholipid analogs (APLs), together with their prototypic molecule edelfosine (EDLF), are a family of synthetic antineoplastic compounds that show additional pharmacological actions, including antiparasitic activities against several protozoan parasites.
Methodology/Principal Findings
We found APLs ranked edelfosine> perifosine> erucylphosphocholine> miltefosine for their in vitro schistosomicidal activity against adult S. mansoni worms. Edelfosine accumulated mainly in the worm tegument, and led to tegumental alterations, membrane permeabilization, motility impairment, blockade of male-female pairing as well as induction of apoptosis-like processes in cells in the close vicinity to the tegument. Edelfosine oral treatment also showed in vivo schistosomicidal activity and decreased significantly the egg burden in the liver, a key event in schistosomiasis.
Our data show that edelfosine is the most potent APL in killing S. mansoni adult worms in vitro. Edelfosine schistosomicidal activity seems to depend on its action on the tegumental structure, leading to tegumental damage, membrane permeabilization and apoptosis-like cell death. Oral administration of edelfosine diminished worm and egg burdens in S. mansoni-infected CD1 mice. Here we report that edelfosine showed promising antischistosomal properties in vitro and in vivo.
PMCID: PMC4193788  PMID: 25302497
24.  Drug repurposing and human parasitic protozoan diseases 
Graphical abstract
•Anti-protozoan drug discovery is challenging, time consuming and expensive.•Drug repurposing has historically played a role in anti-protozoan drug discovery.•Drug repurposing can result in significant time and cost savings.•Here we review drug repurposing for major human protozoan diseases.
Parasitic diseases have an enormous health, social and economic impact and are a particular problem in tropical regions of the world. Diseases caused by protozoa and helminths, such as malaria and schistosomiasis, are the cause of most parasite related morbidity and mortality, with an estimated 1.1 million combined deaths annually. The global burden of these diseases is exacerbated by the lack of licensed vaccines, making safe and effective drugs vital to their prevention and treatment. Unfortunately, where drugs are available, their usefulness is being increasingly threatened by parasite drug resistance. The need for new drugs drives antiparasitic drug discovery research globally and requires a range of innovative strategies to ensure a sustainable pipeline of lead compounds. In this review we discuss one of these approaches, drug repurposing or repositioning, with a focus on major human parasitic protozoan diseases such as malaria, trypanosomiasis, toxoplasmosis, cryptosporidiosis and leishmaniasis.
PMCID: PMC4095053  PMID: 25057459
Drug repurposing; Antiparasitic; Malaria; Leishmaniasis; Cryptosporidium; Trypanosomiasis; Toxoplasmosis
25.  Antileishmanial High-Throughput Drug Screening Reveals Drug Candidates with New Scaffolds 
Drugs currently available for leishmaniasis treatment often show parasite resistance, highly toxic side effects and prohibitive costs commonly incompatible with patients from the tropical endemic countries. In this sense, there is an urgent need for new drugs as a treatment solution for this neglected disease. Here we show the development and implementation of an automated high-throughput viability screening assay for the discovery of new drugs against Leishmania. Assay validation was done with Leishmania promastigote forms, including the screening of 4,000 compounds with known pharmacological properties. In an attempt to find new compounds with leishmanicidal properties, 26,500 structurally diverse chemical compounds were screened. A cut-off of 70% growth inhibition in the primary screening led to the identification of 567 active compounds. Cellular toxicity and selectivity were responsible for the exclusion of 78% of the pre-selected compounds. The activity of the remaining 124 compounds was confirmed against the intramacrophagic amastigote form of the parasite. In vitro microsomal stability and cytochrome P450 (CYP) inhibition of the two most active compounds from this screening effort were assessed to obtain preliminary information on their metabolism in the host. The HTS approach employed here resulted in the discovery of two new antileishmanial compounds, bringing promising candidates to the leishmaniasis drug discovery pipeline.
Author Summary
Every year, more than 2 million people worldwide suffer from leishmaniasis, a neglected tropical disease present in 88 countries. The disease is caused by the single-celled protozoan parasite species of the genus Leishmania, which is transmitted to humans by the bite of the sandfly. The disease manifests itself in a broad range of symptoms, and its most virulent form, named visceral leishmaniasis, is lethal if not treated. Most of the few available treatments for leishmaniasis were developed decades ago and are often toxic, sometimes even leading to the patient's death. Furthermore, the parasite is developing resistance to available drugs, making the discovery and development of new antileishmanials an urgent need. To tackle this problem, the authors of this study employed the use of high-throughput technologies to screen a large library of small, synthetic molecules for their ability to interfere with the viability of Leishmania parasites. This study resulted in the discovery of two novel compounds with leishmanicidal properties and promising drug-like properties, bringing new candidates to the leishmaniasis drug discovery pipeline.
PMCID: PMC2864270  PMID: 20454559

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