Search tips
Search criteria

Results 1-25 (826121)

Clipboard (0)

Related Articles

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.  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
4.  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
flavonoid biosynthesis; unnatural flavonoids; metabolic engineering; dynamic regulation; metabolic control; secondary metabolites; combinatorial biosynthesis
5.  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
6.  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
7.  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
8.  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
9.  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
10.  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
11.  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
12.  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
13.  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
14.  Medical Students' Exposure to and Attitudes about the Pharmaceutical Industry: A Systematic Review 
PLoS Medicine  2011;8(5):e1001037.
A systematic review of published studies reveals that undergraduate medical students may experience substantial exposure to pharmaceutical marketing, and that this contact may be associated with positive attitudes about marketing.
The relationship between health professionals and the pharmaceutical industry has become a source of controversy. Physicians' attitudes towards the industry can form early in their careers, but little is known about this key stage of development.
Methods and Findings
We performed a systematic review reported according to PRISMA guidelines to determine the frequency and nature of medical students' exposure to the drug industry, as well as students' attitudes concerning pharmaceutical policy issues. We searched MEDLINE, EMBASE, Web of Science, and ERIC from the earliest available dates through May 2010, as well as bibliographies of selected studies. We sought original studies that reported quantitative or qualitative data about medical students' exposure to pharmaceutical marketing, their attitudes about marketing practices, relationships with industry, and related pharmaceutical policy issues. Studies were separated, where possible, into those that addressed preclinical versus clinical training, and were quality rated using a standard methodology. Thirty-two studies met inclusion criteria. We found that 40%–100% of medical students reported interacting with the pharmaceutical industry. A substantial proportion of students (13%–69%) were reported as believing that gifts from industry influence prescribing. Eight studies reported a correlation between frequency of contact and favorable attitudes toward industry interactions. Students were more approving of gifts to physicians or medical students than to government officials. Certain attitudes appeared to change during medical school, though a time trend was not performed; for example, clinical students (53%–71%) were more likely than preclinical students (29%–62%) to report that promotional information helps educate about new drugs.
Undergraduate medical education provides substantial contact with pharmaceutical marketing, and the extent of such contact is associated with positive attitudes about marketing and skepticism about negative implications of these interactions. These results support future research into the association between exposure and attitudes, as well as any modifiable factors that contribute to attitudinal changes during medical education.
Please see later in the article for the Editors' Summary
Editors' Summary
The complex relationship between health professionals and the pharmaceutical industry has long been a subject of discussion among physicians and policymakers. There is a growing body of evidence that suggests that physicians' interactions with pharmaceutical sales representatives may influence clinical decision making in a way that is not always in the best interests of individual patients, for example, encouraging the use of expensive treatments that have no therapeutic advantage over less costly alternatives. The pharmaceutical industry often uses physician education as a marketing tool, as in the case of Continuing Medical Education courses that are designed to drive prescribing practices.
One reason that physicians may be particularly susceptible to pharmaceutical industry marketing messages is that doctors' attitudes towards the pharmaceutical industry may form early in their careers. The socialization effect of professional schooling is strong, and plays a lasting role in shaping views and behaviors.
Why Was This Study Done?
Recently, particularly in the US, some medical schools have limited students' and faculties' contact with industry, but some have argued that these restrictions are detrimental to students' education. Given the controversy over the pharmaceutical industry's role in undergraduate medical training, consolidating current knowledge in this area may be useful for setting priorities for changes to educational practices. In this study, the researchers systematically examined studies of pharmaceutical industry interactions with medical students and whether such interactions influenced students' views on related topics.
What Did the Researchers Do and Find?
The researchers did a comprehensive literature search using appropriate search terms for all relevant quantitative and qualitative studies published before June 2010. Using strict inclusion criteria, the researchers then selected 48 articles (from 1,603 abstracts) for full review and identified 32 eligible for analysis—giving a total of approximately 9,850 medical students studying at 76 medical schools or hospitals.
Most students had some form of interaction with the pharmaceutical industry but contact increased in the clinical years, with up to 90% of all clinical students receiving some form of educational material. The highest level of exposure occurred in the US. In most studies, the majority of students in their clinical training years found it ethically permissible for medical students to accept gifts from drug manufacturers, while a smaller percentage of preclinical students reported such attitudes. Students justified their entitlement to gifts by citing financial hardship or by asserting that most other students accepted gifts. In addition, although most students believed that education from industry sources is biased, students variably reported that information obtained from industry sources was useful and a valuable part of their education.
Almost two-thirds of students reported that they were immune to bias induced by promotion, gifts, or interactions with sales representatives but also reported that fellow medical students or doctors are influenced by such encounters. Eight studies reported a relationship between exposure to the pharmaceutical industry and positive attitudes about industry interactions and marketing strategies (although not all included supportive statistical data). Finally, student opinions were split on whether physician–industry interactions should be regulated by medical schools or the government.
What Do These Findings Mean?
This analysis shows that students are frequently exposed to pharmaceutical marketing, even in the preclinical years, and that the extent of students' contact with industry is generally associated with positive attitudes about marketing and skepticism towards any negative implications of interactions with industry. Therefore, strategies to educate students about interactions with the pharmaceutical industry should directly address widely held misconceptions about the effects of marketing and other biases that can emerge from industry interactions. But education alone may be insufficient. Institutional policies, such as rules regulating industry interactions, can play an important role in shaping students' attitudes, and interventions that decrease students' contact with industry and eliminate gifts may have a positive effect on building the skills that evidence-based medical practice requires. These changes can help cultivate strong professional values and instill in students a respect for scientific principles and critical evidence review that will later inform clinical decision-making and prescribing practices.
Additional Information
Please access these Web sites via the online version of this summary at
Further information about the influence of the pharmaceutical industry on doctors and medical students can be found at the American Medical Students Association PharmFree campaign and PharmFree Scorecard, Medsin-UKs PharmAware campaign, the nonprofit organization Healthy Skepticism, and the Web site of No Free Lunch.
PMCID: PMC3101205  PMID: 21629685
15.  Prediction of potential drug targets based on simple sequence properties 
BMC Bioinformatics  2007;8:353.
During the past decades, research and development in drug discovery have attracted much attention and efforts. However, only 324 drug targets are known for clinical drugs up to now. Identifying potential drug targets is the first step in the process of modern drug discovery for developing novel therapeutic agents. Therefore, the identification and validation of new and effective drug targets are of great value for drug discovery in both academia and pharmaceutical industry. If a protein can be predicted in advance for its potential application as a drug target, the drug discovery process targeting this protein will be greatly speeded up. In the current study, based on the properties of known drug targets, we have developed a sequence-based drug target prediction method for fast identification of novel drug targets.
Based on simple physicochemical properties extracted from protein sequences of known drug targets, several support vector machine models have been constructed in this study. The best model can distinguish currently known drug targets from non drug targets at an accuracy of 84%. Using this model, potential protein drug targets of human origin from Swiss-Prot were predicted, some of which have already attracted much attention as potential drug targets in pharmaceutical research.
We have developed a drug target prediction method based solely on protein sequence information without the knowledge of family/domain annotation, or the protein 3D structure. This method can be applied in novel drug target identification and validation, as well as genome scale drug target predictions.
PMCID: PMC2082046  PMID: 17883836
16.  Chemistry and Biology of the Caged Garcinia Xanthones 
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.
PMCID: PMC3144150  PMID: 20648491
cyclic compounds; cycloaddition; domino reactions; natural products; synthesis design
17.  Discovery of novel biomarkers and phenotypes by semantic technologies 
BMC Bioinformatics  2013;14:51.
Biomarkers and target-specific phenotypes are important to targeted drug design and individualized medicine, thus constituting an important aspect of modern pharmaceutical research and development. More and more, the discovery of relevant biomarkers is aided by in silico techniques based on applying data mining and computational chemistry on large molecular databases. However, there is an even larger source of valuable information available that can potentially be tapped for such discoveries: repositories constituted by research documents.
This paper reports on a pilot experiment to discover potential novel biomarkers and phenotypes for diabetes and obesity by self-organized text mining of about 120,000 PubMed abstracts, public clinical trial summaries, and internal Merck research documents. These documents were directly analyzed by the InfoCodex semantic engine, without prior human manipulations such as parsing. Recall and precision against established, but different benchmarks lie in ranges up to 30% and 50% respectively. Retrieval of known entities missed by other traditional approaches could be demonstrated. Finally, the InfoCodex semantic engine was shown to discover new diabetes and obesity biomarkers and phenotypes. Amongst these were many interesting candidates with a high potential, although noticeable noise (uninteresting or obvious terms) was generated.
The reported approach of employing autonomous self-organising semantic engines to aid biomarker discovery, supplemented by appropriate manual curation processes, shows promise and has potential to impact, conservatively, a faster alternative to vocabulary processes dependent on humans having to read and analyze all the texts. More optimistically, it could impact pharmaceutical research, for example to shorten time-to-market of novel drugs, or speed up early recognition of dead ends and adverse reactions.
PMCID: PMC3605201  PMID: 23402646
In silico drug research; Semantic technologies; Text mining; Biomedical ontologies; Discovery of novel relationships
18.  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
19.  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
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
21.  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
22.  Protein Reporter Bioassay Systems for the Phenotypic Screening of Candidate Drugs: A Mouse Platform for Anti-Aging Drug Screening 
Sensors (Basel, Switzerland)  2012;12(2):1648-1656.
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.
PMCID: PMC3304132  PMID: 22438730
drug development; high throughput screening; reporter mice; age-related disorders
23.  Drug Discovery Using Chemical Systems Biology: Repositioning the Safe Medicine Comtan to Treat Multi-Drug and Extensively Drug Resistant Tuberculosis 
PLoS Computational Biology  2009;5(7):e1000423.
The rise of multi-drug resistant (MDR) and extensively drug resistant (XDR) tuberculosis around the world, including in industrialized nations, poses a great threat to human health and defines a need to develop new, effective and inexpensive anti-tubercular agents. Previously we developed a chemical systems biology approach to identify off-targets of major pharmaceuticals on a proteome-wide scale. In this paper we further demonstrate the value of this approach through the discovery that existing commercially available drugs, prescribed for the treatment of Parkinson's disease, have the potential to treat MDR and XDR tuberculosis. These drugs, entacapone and tolcapone, are predicted to bind to the enzyme InhA and directly inhibit substrate binding. The prediction is validated by in vitro and InhA kinetic assays using tablets of Comtan, whose active component is entacapone. The minimal inhibition concentration (MIC99) of entacapone for Mycobacterium tuberculosis (M.tuberculosis) is approximately 260.0 µM, well below the toxicity concentration determined by an in vitro cytotoxicity model using a human neuroblastoma cell line. Moreover, kinetic assays indicate that Comtan inhibits InhA activity by 47.0% at an entacapone concentration of approximately 80 µM. Thus the active component in Comtan represents a promising lead compound for developing a new class of anti-tubercular therapeutics with excellent safety profiles. More generally, the protocol described in this paper can be included in a drug discovery pipeline in an effort to discover novel drug leads with desired safety profiles, and therefore accelerate the development of new drugs.
Author Summary
The rise of multi-drug resistant (MDR) and extensively drug resistant (XDR) tuberculosis around the world, including in industrialized nations, poses a great threat to human health. This resistance highlights the need to develop new, effective and inexpensive anti-tubercular agents. Unfortunately, conventional approaches have yielded very few successes in the field of anti-infective drug discovery. It is a challenge to design drugs with both efficacy and safety. These challenges are reflected in the high costs involved in bringing new drugs to market. It has been estimated that the cost to launch a successful new drug is in excess of US$800 million. We have developed a novel computational strategy to systematically identify cross-reactivity between different drug target families. In this paper we demonstrate the strength of this approach through the discovery that existing commercially available drugs prescribed for the treatment of Parkinson's disease have the potential to treat MDR and XDR tuberculosis. The protocol described herein can be included in a drug discovery pipeline in an effort to accelerate the development of new drugs with reduced side effects.
PMCID: PMC2699117  PMID: 19578428
24.  United States Private-Sector Physicians and Pharmaceutical Contract Research: A Qualitative Study 
PLoS Medicine  2012;9(7):e1001271.
Jill Fisher and Corey Kalbaugh describe their findings from a qualitative research study evaluating the motivations of private-sector physicians conducting contract research for the pharmaceutical industry.
There have been dramatic increases over the past 20 years in the number of nonacademic, private-sector physicians who serve as principal investigators on US clinical trials sponsored by the pharmaceutical industry. However, there has been little research on the implications of these investigators' role in clinical investigation. Our objective was to study private-sector clinics involved in US pharmaceutical clinical trials to understand the contract research arrangements supporting drug development, and specifically how private-sector physicians engaged in contract research describe their professional identities.
Methods and Findings
We conducted a qualitative study in 2003–2004 combining observation at 25 private-sector research organizations in the southwestern United States and 63 semi-structured interviews with physicians, research staff, and research participants at those clinics. We used grounded theory to analyze and interpret our data. The 11 private-sector physicians who participated in our study reported becoming principal investigators on industry clinical trials primarily because contract research provides an additional revenue stream. The physicians reported that they saw themselves as trial practitioners and as businesspeople rather than as scientists or researchers.
Our findings suggest that in addition to having financial motivation to participate in contract research, these US private-sector physicians have a professional identity aligned with an industry-based approach to research ethics. The generalizability of these findings and whether they have changed in the intervening years should be addressed in future studies.
Please see later in the article for the Editors' Summary.
Editors' Summary
Before a new drug can be used routinely by physicians, it must be investigated in clinical trials—studies that test the drug's safety and effectiveness in people. In the past, clinical trials were usually undertaken in academic medical centers (institutes where physicians provide clinical care, do research, and teach), but increasingly, clinical trials are being conducted in the private sector as part of a growing contract research system. In the US, for example, most clinical trials completed in the 1980s took place in academic medical centers, but nowadays, more than 70% of trials are conducted by nonacademic (community) physicians working under contract to pharmaceutical companies. The number of private-sector nonacademic physicians serving as principal investigators (PIs) for US clinical trials (the PI takes direct responsibility for completion of the trial) increased from 4,000 in 1990 to 20,250 in 2010, and research contracts for clinical trials are now worth more than USṩ11 billion annually.
Why Was This Study Done?
To date, there has been little research on the implications of this change in the conduct of clinical trials. Academic PIs are often involved in both laboratory and clinical research and are therefore likely to identify closely with the science of trials. By contrast, nonacademic PIs may see clinical trials more as a business opportunity—pharmaceutical contract research is profitable to US physicians because they get paid for every step of the trial process. As a result, pharmaceutical companies may now have more control over clinical trial data and more opportunities to suppress negative data through selective publication of study results than previously. In this qualitative study, the researchers explore the outsourcing of clinical trials to private-sector research clinics through observations of, and in-depth interviews with, physicians and other research staff involved in the US clinical trials industry. A qualitative study collects non-quantitative data such as how physicians feel about doing contract research and about their responsibilities to their patients.
What Did the Researchers Do and Find?
Between October 2003 and September 2004, the researchers observed the interactions between PIs, trial coordinators (individuals who undertake many of the trial activities such as blood collection), and trial participants at 25 US research organizations in the southwestern US and interviewed 63 informants (including 12 PIs) about the trials they were involved in and their reasons for becoming involved. The researchers found that private-sector physicians became PIs on industry-sponsored clinical trials primarily because contract research was financially lucrative. The physicians perceived their roles in terms of business rather than science and claimed that they offered something to the pharmaceutical industry that academics do not—the ability to carry out a diverse range of trials quickly and effectively, regardless of their medical specialty. Finally, the physicians saw their primary ethical responsibility as providing accurate data to the companies that hired them and did not explicitly refer to their ethical responsibility to trial participants. One possible reason for this shift in ethical concerns is the belief among private-sector physicians that pharmaceutical companies must be making scientifically and ethically sound decisions when designing trials because of the amount of money they invest in them.
What Do These Findings Mean?
These findings suggest that private-sector physicians participate as PIs in pharmaceutical clinical trials primarily for financial reasons and see themselves as trial practitioners and businesspeople rather than as scientists. The accuracy of these findings is likely to be limited by the small number of PIs interviewed and by the time that has elapsed since the researchers collected their qualitative data. Moreover, these findings may not be generalizable to other regions of the US or to other countries. Nevertheless, they have potentially troubling implications for drug development. By hiring private-sector physicians who see themselves as involved more with the business than the science of contract research, pharmaceutical companies may be able to exert more control over the conduct of clinical trials and the publication of trial results than previously. Compared to the traditional investigatorinitiated system of clinical research, this new system of contract research means that clinical trials now lack the independence that is at the heart of best science practices, a development that casts doubt on the robustness of the knowledge being produced about the safety and effectiveness of new drugs.
Additional Information
Please access these websites via the online version of this summary at
The website is a searchable register of federally and privately supported clinical trials in the US; it provides information about all aspects of clinical trials
The US National Institutes of Health provides information about clinical trials, including personal stories about clinical trials from patients and researchers
The UK National Health Service Choices website has information for patients about clinical trials and medical research, including personal stories about participating in clinical trials
The UK Medical Research Council Clinical Trials Unit also provides information for patients about clinical trials and links to information on clinical trials provided by other organizations
MedlinePlus has links to further resources on clinical trials (in English and Spanish)
PMCID: PMC3404112  PMID: 22911055
25.  An Orthology-Based Analysis of Pathogenic Protozoa Impacting Global Health: An Improved Comparative Genomics Approach with Prokaryotes and Model Eukaryote Orthologs 
A key focus in 21st century integrative biology and drug discovery for neglected tropical and other diseases has been the use of BLAST-based computational methods for identification of orthologous groups in pathogenic organisms to discern orthologs, with a view to evaluate similarities and differences among species, and thus allow the transfer of annotation from known/curated proteins to new/non-annotated ones. We used here a profile-based sensitive methodology to identify distant homologs, coupled to the NCBI's COG (Unicellular orthologs) and KOG (Eukaryote orthologs), permitting us to perform comparative genomics analyses on five protozoan genomes. OrthoSearch was used in five protozoan proteomes showing that 3901 and 7473 orthologs can be identified by comparison with COG and KOG proteomes, respectively. The core protozoa proteome inferred was 418 Protozoa-COG orthologous groups and 704 Protozoa-KOG orthologous groups: (i) 31.58% (132/418) belongs to the category J (translation, ribosomal structure, and biogenesis), and 9.81% (41/418) to the category O (post-translational modification, protein turnover, chaperones) using COG; (ii) 21.45% (151/704) belongs to the categories J, and 13.92% (98/704) to the O using KOG. The phylogenomic analysis showed four well-supported clades for Eukarya, discriminating Multicellular [(i) human, fly, plant and worm] and Unicellular [(ii) yeast, (iii) fungi, and (iv) protozoa] species. These encouraging results attest to the usefulness of the profile-based methodology for comparative genomics to accelerate semi-automatic re-annotation, especially of the protozoan proteomes. This approach may also lend itself for applications in global health, for example, in the case of novel drug target discovery against pathogenic organisms previously considered difficult to research with traditional drug discovery tools.
PMCID: PMC4108940  PMID: 24960463

Results 1-25 (826121)