PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-25 (52)
 

Clipboard (0)
None

Select a Filter Below

Year of Publication
1.  A common feature pharmacophore for FDA-approved drugs inhibiting the Ebola virus 
F1000Research  2014;3:277.
We are currently faced with a global infectious disease crisis which has been anticipated for decades. While many promising biotherapeutics are being tested, the search for a small molecule has yet to deliver an approved drug or therapeutic for the Ebola or similar filoviruses that cause haemorrhagic fever. Two recent high throughput screens published in 2013 did however identify several hits that progressed to animal studies that are FDA approved drugs used for other indications. The current computational analysis uses these molecules from two different structural classes to construct a common features pharmacophore. This ligand-based pharmacophore implicates a possible common target or mechanism that could be further explored. A recent structure based design project yielded nine co-crystal structures of pyrrolidinone inhibitors bound to the viral protein 35 (VP35). When receptor-ligand pharmacophores based on the analogs of these molecules and the protein structures were constructed, the molecular features partially overlapped with the common features of solely ligand-based pharmacophore models based on FDA approved drugs. These previously identified FDA approved drugs with activity against Ebola were therefore docked into this protein. The antimalarials chloroquine and amodiaquine docked favorably in VP35. We propose that these drugs identified to date as inhibitors of the Ebola virus may be targeting VP35. These computational models may provide preliminary insights into the molecular features that are responsible for their activity against Ebola virus in vitro and in vivo and we propose that this hypothesis could be readily tested.
doi:10.12688/f1000research.5741.2
PMCID: PMC4304229  PMID: 25653841
ebola virus; computational models; machine learning
2.  Fusing Dual-Event Datasets for Mycobacterium Tuberculosis Machine Learning Models and their Evaluation 
The search for new tuberculosis treatments continues as we need to find molecules that can act more quickly, be accommodated in multi-drug regimens, and overcome ever increasing levels of drug resistance. Multiple large scale phenotypic high-throughput screens against Mycobacterium tuberculosis (Mtb) have generated dose response data, enabling the generation of machine learning models. These models also incorporated cytotoxicity data and were recently validated with a large external dataset.
A cheminformatics data-fusion approach followed by Bayesian machine learning, Support Vector Machine or Recursive Partitioning model development (based on publicly available Mtb screening data) was used to compare individual datasets and subsequent combined models. A set of 1924 commercially available molecules with promising antitubercular activity (and lack of relative cytotoxicity to Vero cells) were used to evaluate the predictive nature of the models. We demonstrate that combining three datasets incorporating antitubercular and cytotoxicity data in Vero cells from our previous screens results in external validation receiver operator curve (ROC) of 0.83 (Bayesian or RP Forest). Models that do not have the highest five-fold cross validation ROC scores can outperform other models in a test set dependent manner.
We demonstrate with predictions for a recently published set of Mtb leads from GlaxoSmithKline that no single machine learning model may be enough to identify compounds of interest. Dataset fusion represents a further useful strategy for machine learning construction as illustrated with Mtb. Coverage of chemistry and Mtb target spaces may also be limiting factors for the whole-cell screening data generated to date.
doi:10.1021/ci400480s
PMCID: PMC3910492  PMID: 24144044
Bayesian models; Collaborative Drug Discovery Tuberculosis database; Dual-event models; Function class fingerprints; Lead optimization; Mycobacterium tuberculosis; Recursive partitioning; Support vector machine; Tuberculosis
3.  A common feature pharmacophore for FDA-approved drugs inhibiting the Ebola virus 
F1000Research  2014;3:277.
We are currently faced with a global infectious disease crisis which has been anticipated for decades. While many promising biotherapeutics are being tested, the search for a small molecule has yet to deliver an approved drug or therapeutic for the Ebola or similar filoviruses that cause haemorrhagic fever. Two recent high throughput screens published in 2013 did however identify several hits that progressed to animal studies that are FDA approved drugs used for other indications. The current computational analysis uses these molecules from two different structural classes to construct a common features pharmacophore. This ligand-based pharmacophore implicates a possible common target or mechanism that could be further explored. A recent structure based design project yielded nine co-crystal structures of pyrrolidinone inhibitors bound to the viral protein 35 (VP35). When receptor-ligand pharmacophores based on the analogs of these molecules and the protein structures were constructed, the molecular features partially overlapped with the common features of solely ligand-based pharmacophore models based on FDA approved drugs. These previously identified FDA approved drugs with activity against Ebola were therefore docked into this protein. The antimalarials chloroquine and amodiaquine docked favorably in VP35. We propose that these drugs identified to date as inhibitors of the Ebola virus may be targeting VP35. These computational models may provide preliminary insights into the molecular features that are responsible for their activity against Ebola virus in vitro and in vivo and we propose that this hypothesis could be readily tested.
doi:10.12688/f1000research.5741.1
PMCID: PMC4304229  PMID: 25653841
ebola virus; computational models; machine learning
5.  Collecting rare diseases 
F1000Research  2014;3:260.
This editorial introduces the F1000Research rare disease collection. It is common knowledge that for new treatments to be successful there has to be a partnership between the many interested parties such as the patient, advocate, disease foundations, the academic scientists, venture funding organizations, biotech companies, pharmaceutical companies, NIH, and the FDA. Our intention is to provide a forum for discussion and dissemination of any rare disease related topics that will advance scientific understanding and progress to treatments.
doi:10.12688/f1000research.5577.1
PMCID: PMC4288410  PMID: 25580231
6.  Collaboration for rare disease drug discovery research 
F1000Research  2014;3:261.
Rare disease research has reached a tipping point, with the confluence of scientific and technologic developments that if appropriately harnessed, could lead to key breakthroughs and treatments for this set of devastating disorders. Industry-wide trends have revealed that the traditional drug discovery research and development (R&D) model is no longer viable, and drug companies are evolving their approach. Rather than only pursue blockbuster therapeutics for heterogeneous, common diseases, drug companies have increasingly begun to shift their focus to rare diseases. In academia, advances in genetics analyses and disease mechanisms have allowed scientific understanding to mature, but the lack of funding and translational capability severely limits the rare disease research that leads to clinical trials. Simultaneously, there is a movement towards increased research collaboration, more data sharing, and heightened engagement and active involvement by patients, advocates, and foundations. The growth in networks and social networking tools presents an opportunity to help reach other patients but also find researchers and build collaborations. The growth of collaborative software that can enable researchers to share their data could also enable rare disease patients and foundations to manage their portfolio of funded projects for developing new therapeutics and suggest drug repurposing opportunities. Still there are many thousands of diseases without treatments and with only fragmented research efforts. We will describe some recent progress in several rare diseases used as examples and propose how collaborations could be facilitated. We propose that the development of a center of excellence that integrates and shares informatics resources for rare diseases sponsored by all of the stakeholders would help foster these initiatives.
doi:10.12688/f1000research.5564.1
PMCID: PMC4314660
rare disease; patient advocacy; drug discovery; Twitter
7.  Ten Simple Rules of Live Tweeting at Scientific Conferences 
PLoS Computational Biology  2014;10(8):e1003789.
doi:10.1371/journal.pcbi.1003789
PMCID: PMC4140634  PMID: 25144683
8.  New target prediction and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0 
Background
We recently developed a freely available mobile app (TB Mobile) for both iOS and Android platforms that displays Mycobacterium tuberculosis (Mtb) active molecule structures and their targets with links to associated data. The app was developed to make target information available to as large an audience as possible.
Results
We now report a major update of the iOS version of the app. This includes enhancements that use an implementation of ECFP_6 fingerprints that we have made open source. Using these fingerprints, the user can propose compounds with possible anti-TB activity, and view the compounds within a cluster landscape. Proposed compounds can also be compared to existing target data, using a näive Bayesian scoring system to rank probable targets. We have curated an additional 60 new compounds and their targets for Mtb and added these to the original set of 745 compounds. We have also curated 20 further compounds (many without targets in TB Mobile) to evaluate this version of the app with 805 compounds and associated targets.
Conclusions
TB Mobile can now manage a small collection of compounds that can be imported from external sources, or exported by various means such as email or app-to-app inter-process communication. This means that TB Mobile can be used as a node within a growing ecosystem of mobile apps for cheminformatics. It can also cluster compounds and use internal algorithms to help identify potential targets based on molecular similarity. TB Mobile represents a valuable dataset, data-visualization aid and target prediction tool.
doi:10.1186/s13321-014-0038-2
PMCID: PMC4190048  PMID: 25302078
Mobile app; Mycobacterium tuberculosis; TB mobile; Tuberculosis; Target prediction
9.  Cross-reactivity of steroid hormone immunoassays: clinical significance and two-dimensional molecular similarity prediction 
Background
Immunoassays are widely used in clinical laboratories for measurement of plasma/serum concentrations of steroid hormones such as cortisol and testosterone. Immunoassays can be performed on a variety of standard clinical chemistry analyzers, thus allowing even small clinical laboratories to do analysis on-site. One limitation of steroid hormone immunoassays is interference caused by compounds with structural similarity to the target steroid of the assay. Interfering molecules include structurally related endogenous compounds and their metabolites as well as drugs such as anabolic steroids and synthetic glucocorticoids.
Methods
Cross-reactivity of a structurally diverse set of compounds were determined for the Roche Diagnostics Elecsys assays for cortisol, dehydroepiandrosterone (DHEA) sulfate, estradiol, progesterone, and testosterone. These data were compared and contrasted to package insert data and published cross-reactivity studies for other marketed steroid hormone immunoassays. Cross-reactivity was computationally predicted using the technique of two-dimensional molecular similarity.
Results
The Roche Elecsys Cortisol and Testosterone II assays showed a wider range of cross-reactivity than the DHEA sulfate, Estradiol II, and Progesterone II assays. 6-Methylprednisolone and prednisolone showed high cross-reactivity for the cortisol assay, with high likelihood of clinically significant effect for patients administered these drugs. In addition, 21-deoxycortisol likely produces clinically relevant cross-reactivity for cortisol in patients with 21-hydroxylase deficiency, while 11-deoxycortisol may produce clinically relevant cross-reactivity in 11β-hydroxylase deficiency or following metyrapone challenge. Several anabolic steroids may produce clinically significant false positives on the testosterone assay, although interpretation is limited by sparse pharmacokinetic data for some of these drugs. Norethindrone therapy may impact immunoassay measurement of testosterone in women. Using two-dimensional similarity calculations, all compounds with high cross-reactivity also showed a high degree of similarity to the target molecule of the immunoassay.
Conclusions
Compounds producing cross-reactivity in steroid hormone immunoassays generally have a high degree of structural similarity to the target hormone. Clinically significant interactions can occur with structurally similar drugs (e.g., prednisolone and cortisol immunoassays; methyltestosterone and testosterone immunoassays) or with endogenous compounds such as 21-deoxycortisol that can accumulate to very high concentrations in certain disease conditions. Simple similarity calculations can help triage compounds for future testing of assay cross-reactivity.
doi:10.1186/1472-6890-14-33
PMCID: PMC4112981  PMID: 25071417
Anabolic agents; Estradiol; Glucocorticoids; Immunoassays; Progesterone; Similarity; Testosterone
11.  Using cheminformatics to predict cross reactivity of “designer drugs” to their currently available immunoassays 
Background
A challenge for drug of abuse testing is presented by ‘designer drugs’, compounds typically discovered by modifications of existing clinical drug classes such as amphetamines and cannabinoids. Drug of abuse screening immunoassays directed at amphetamine or methamphetamine only detect a small subset of designer amphetamine-like drugs, and those immunoassays designed for tetrahydrocannabinol metabolites generally do not cross-react with synthetic cannabinoids lacking the classic cannabinoid chemical backbone. This suggests complexity in understanding how to detect and identify whether a patient has taken a molecule of one class or another, impacting clinical care.
Methods
Cross-reactivity data from immunoassays specifically targeting designer amphetamine-like and synthetic cannabinoid drugs was collected from multiple published sources, and virtual chemical libraries for molecular similarity analysis were built. The virtual library for synthetic cannabinoid analysis contained a total of 169 structures, while the virtual library for amphetamine-type stimulants contained 288 compounds. Two-dimensional (2D) similarity for each test compound was compared to the target molecule of the immunoassay undergoing analysis.
Results
2D similarity differentiated between cross-reactive and non-cross-reactive compounds for immunoassays targeting mephedrone/methcathinone, 3,4-methylenedioxypyrovalerone, benzylpiperazine, mephentermine, and synthetic cannabinoids.
Conclusions
In this study, we applied 2D molecular similarity analysis to the designer amphetamine-type stimulants and synthetic cannabinoids. Similarity calculations can be used to more efficiently decide which drugs and metabolites should be tested in cross-reactivity studies, as well as to design experiments and potentially predict antigens that would lead to immunoassays with cross reactivity for a broader array of designer drugs.
doi:10.1186/1758-2946-6-22
PMCID: PMC4029917  PMID: 24851137
Amphetamines; Cannabinoids; Molecular models; Similarity; Toxicology
12.  Using cheminformatics to predict cross reactivity of “designer drugs” to their currently available immunoassays 
Background
A challenge for drug of abuse testing is presented by ‘designer drugs’, compounds typically discovered by modifications of existing clinical drug classes such as amphetamines and cannabinoids. Drug of abuse screening immunoassays directed at amphetamine or methamphetamine only detect a small subset of designer amphetamine-like drugs, and those immunoassays designed for tetrahydrocannabinol metabolites generally do not cross-react with synthetic cannabinoids lacking the classic cannabinoid chemical backbone. This suggests complexity in understanding how to detect and identify whether a patient has taken a molecule of one class or another, impacting clinical care.
Methods
Cross-reactivity data from immunoassays specifically targeting designer amphetamine-like and synthetic cannabinoid drugs was collected from multiple published sources, and virtual chemical libraries for molecular similarity analysis were built. The virtual library for synthetic cannabinoid analysis contained a total of 169 structures, while the virtual library for amphetamine-type stimulants contained 288 compounds. Two-dimensional (2D) similarity for each test compound was compared to the target molecule of the immunoassay undergoing analysis.
Results
2D similarity differentiated between cross-reactive and non-cross-reactive compounds for immunoassays targeting mephedrone/methcathinone, 3,4-methylenedioxypyrovalerone, benzylpiperazine, mephentermine, and synthetic cannabinoids.
Conclusions
In this study, we applied 2D molecular similarity analysis to the designer amphetamine-type stimulants and synthetic cannabinoids. Similarity calculations can be used to more efficiently decide which drugs and metabolites should be tested in cross-reactivity studies, as well as to design experiments and potentially predict antigens that would lead to immunoassays with cross reactivity for a broader array of designer drugs.
Electronic supplementary material
The online version of this article (doi:10.1186/1758-2946-6-22) contains supplementary material, which is available to authorized users.
doi:10.1186/1758-2946-6-22
PMCID: PMC4029917  PMID: 24851137
Amphetamines; Cannabinoids; Molecular models; Similarity; Toxicology
13.  Recommendations to enable drug development for inherited neuropathies: Charcot-Marie-Tooth and Giant Axonal Neuropathy 
F1000Research  2014;3:83.
Approximately 1 in 2500 Americans suffer from Charcot-Marie-Tooth (CMT) disease. The underlying disease mechanisms are unique in most forms of CMT, with many point mutations on various genes causing a toxic accumulation of misfolded proteins. Symptoms of the disease often present within the first two decades of life, with CMT1A patients having reduced compound muscle and sensory action potentials, slow nerve conduction velocities, sensory loss, progressive distal weakness, foot and hand deformities, decreased reflexes, bilateral foot drop and about 5% become wheelchair bound. In contrast, the ultra-rare disease Giant Axonal Neuropathy (GAN) is frequently described as a recessively inherited condition that results in progressive nerve death. GAN usually appears in early childhood and progresses slowly as neuronal injury becomes more severe and leads to death in the second or third decade. There are currently no treatments for any of the forms of CMTs or GAN. We suggest that further clinical studies should analyse electrical impedance myography as an outcome measure for CMT. Further, additional quality of life (QoL) assessments for these CMTs are required, and we need to identify GAN biomarkers as well as develop new genetic testing panels for both diseases. We propose that using the Global Registry of Inherited Neuropathy (GRIN) could be useful for many of these studies. Patient advocacy groups and professional organizations (such as the Hereditary Neuropathy Foundation (HNF), Hannah's Hope Fund (HHF), The Neuropathy Association (TNA) and the American Association of Neuromuscular and Electrodiagnostic Medicine (AANEM) can play a central role in educating clinicians and patients. Undertaking these studies will assist in the correct diagnosis of disease recruiting patients for clinical studies, and will ultimately improve the endpoints for clinical trials. By addressing obstacles that prevent industry investment in various forms of inherited neuropathies, we can envision treatment options for these rare diseases in the near future.
doi:10.12688/f1000research.3751.2
PMCID: PMC4023663  PMID: 24860645
14.  Recommendations to enable drug development for inherited neuropathies: Charcot-Marie-Tooth and Giant Axonal Neuropathy 
F1000Research  2014;3:83.
Approximately 1 in 2500 Americans suffer from Charcot-Marie-Tooth (CMT) disease. The underlying disease mechanisms are unique in most forms of CMT, with many point mutations on various genes causing a toxic accumulation of misfolded proteins. Symptoms of the disease often present within the first two decades of life, with CMT1A patients having reduced compound muscle and sensory action potentials, slow nerve conduction velocities, sensory loss, progressive distal weakness, foot and hand deformities, decreased reflexes, bilateral foot drop and about 5% become wheelchair bound. In contrast, the ultra-rare disease Giant Axonal Neuropathy (GAN) is frequently described as a recessively inherited condition that results in progressive nerve death. GAN usually appears in early childhood and progresses slowly as neuronal injury becomes more severe and leads to death in the second or third decade. There are currently no treatments for any of the forms of CMTs or GAN. We suggest that further clinical studies should analyse electrical impedance myography as an outcome measure for CMT. Further, additional quality of life (QoL) assessments for these CMTs are required, and we need to identify GAN biomarkers as well as develop new genetic testing panels for both diseases. We propose that using the Global Registry of Inherited Neuropathy (GRIN) could be useful for many of these studies. Patient advocacy groups and professional organizations (such as the Hereditary Neuropathy Foundation (HNF), Hannah's Hope Fund (HHF), The Neuropathy Association (TNA) and the American Association of Neuromuscular and Electrodiagnostic Medicine (AANEM) can play a central role in educating clinicians and patients. Undertaking these studies will assist in the correct diagnosis of disease recruiting patients for clinical studies, and will ultimately improve the endpoints for clinical trials. By addressing obstacles that prevent industry investment in various forms of inherited neuropathies, we can envision treatment options for these rare diseases in the near future.
doi:10.12688/f1000research.3751.1
PMCID: PMC4023663  PMID: 24860645
15.  Bayesian Models Leveraging Bioactivity and Cytotoxicity Information for Drug Discovery 
Chemistry & biology  2013;20(3):370-378.
SUMMARY
Identification of unique leads represents a significant challenge in drug discovery. This hurdle is magnified in neglected diseases such as tuberculosis. We have leveraged public high-throughput screening (HTS) data, to experimentally validate virtual screening approach employing Bayesian models built with bioactivity information (single-event model) as well as bioactivity and cytotoxicity information (dual-event model). We virtually screen a commercial library and experimentally confirm actives with hit rates exceeding typical HTS results by 1-2 orders of magnitude. The first dual-event Bayesian model identified compounds with antitubercular whole-cell activity and low mammalian cell cytotoxicity from a published set of antimalarials. The most potent hit exhibits the in vitro activity and in vitro/in vivo safety profile of a drug lead. These Bayesian models offer significant economies in time and cost to drug discovery.
doi:10.1016/j.chembiol.2013.01.011
PMCID: PMC3607962  PMID: 23521795
16.  Structure Activity Relationship for FDA Approved Drugs as Inhibitors of the Human Sodium Taurocholate Co-transporting Polypeptide (NTCP) 
Molecular pharmaceutics  2013;10(3):1008-1019.
The hepatic bile acid uptake transporter Sodium Taurocholate Cotransporting Polypeptide (NTCP) is less well characterized than its ileal paralog, the Apical Sodium Dependent Bile Acid Transporter (ASBT), in terms of drug inhibition requirements. The objectives of this study were a) to identify FDA approved drugs that inhibit human NTCP, b) to develop pharmacophore and Bayesian computational models for NTCP inhibition, and c) to compare NTCP and ASBT transport inhibition requirements. A series of NTCP inhibition studies were performed using FDA approved drugs, in concert with iterative computational model development. Screening studies identified 27 drugs as novel NTCP inhibitors, including irbesartan (Ki =11.9 μM) and ezetimibe (Ki = 25.0 μM). The common feature pharmacophore indicated that two hydrophobes and one hydrogen bond acceptor were important for inhibition of NTCP. From 72 drugs screened in vitro, a total of 31 drugs inhibited NTCP, while 51 drugs (i.e. more than half) inhibited ASBT. Hence, while there was inhibitor overlap, ASBT unexpectedly was more permissive to drug inhibition than was NTCP, and this may be related to NTCP’s possessing fewer pharmacophore features. Findings reflected that a combination of computational and in vitro approaches enriched the understanding of these poorly characterized transporters and yielded additional chemical probes for possible drug-transporter interaction determinations.
doi:10.1021/mp300453k
PMCID: PMC3617406  PMID: 23339484
Sodium Taurocholate Cotransporting Polypeptide (NTCP); Apical Sodium Dependent Bile Acid Transporter (ASBT); pharmacophore; Bayesian; transporter
17.  The Discovery of Novel Antimalarial Compounds Enabled by QSAR-based Virtual Screening 
Quantitative structure–activity relationship (QSAR) models have been developed for a dataset of 3133 compounds defined as either active or inactive against P. falciparum. Since the dataset was strongly biased towards inactive compounds, different sampling approaches were employed to balance the ratio of actives vs. inactives, and models were rigorously validated using both internal and external validation approaches. The balanced accuracy for assessing the antimalarial activities of 70 external compounds was between 87% and 100% depending on the approach used to balance the dataset. Virtual screening of the ChemBridge database using QSAR models identified 176 putative antimalarial compounds that were submitted for experimental validation, along with 42 putative inactives as negative controls. Twenty five (14.2%) computational hits were found to have antimalarial activities with minimal cytotoxicity to mammalian cells, while all 42 putative inactives were confirmed experimentally. Structural inspection of confirmed active hits revealed novel chemical scaffolds, which could be employed as starting points to discover novel antimalarial agents.
doi:10.1021/ci300421n
PMCID: PMC3644566  PMID: 23252936
Antimalarial activity; quantitative structure–activity relationships; virtual screening; experimental confirmation
19.  Enhancing Hit Identification in Mycobacterium tuberculosis Drug Discovery Using Validated Dual-Event Bayesian Models 
PLoS ONE  2013;8(5):e63240.
High-throughput screening (HTS) in whole cells is widely pursued to find compounds active against Mycobacterium tuberculosis (Mtb) for further development towards new tuberculosis (TB) drugs. Hit rates from these screens, usually conducted at 10 to 25 µM concentrations, typically range from less than 1% to the low single digits. New approaches to increase the efficiency of hit identification are urgently needed to learn from past screening data. The pharmaceutical industry has for many years taken advantage of computational approaches to optimize compound libraries for in vitro testing, a practice not fully embraced by academic laboratories in the search for new TB drugs. Adapting these proven approaches, we have recently built and validated Bayesian machine learning models for predicting compounds with activity against Mtb based on publicly available large-scale HTS data from the Tuberculosis Antimicrobial Acquisition Coordinating Facility. We now demonstrate the largest prospective validation to date in which we computationally screened 82,403 molecules with these Bayesian models, assayed a total of 550 molecules in vitro, and identified 124 actives against Mtb. Individual hit rates for the different datasets varied from 15–28%. We have identified several FDA approved and late stage clinical candidate kinase inhibitors with activity against Mtb which may represent starting points for further optimization. The computational models developed herein and the commercially available molecules derived from them are now available to any group pursuing Mtb drug discovery.
doi:10.1371/journal.pone.0063240
PMCID: PMC3647004  PMID: 23667592
20.  A Substrate Pharmacophore for the Human Organic Cation/Carnitine Transporter Identifies Compounds Associated with Rhabdomyolysis 
Molecular Pharmaceutics  2012;9(4):905-913.
The human Organic Cation/Carnitine Transporter (hOCTN2), is a high affinity cation/carnitine transporter expressed widely in human tissues and is physiologically important for the homeostasis of L-carnitine. The objective of this study was to elucidate the substrate requirements of this transporter via computational modelling based on published in vitro data. Nine published substrates of hOCTN2 were used to create a common features pharmacophore that was validated by mapping other known OCTN2 substrates. The pharmacophore was used to search a drug database and retrieved molecules that were then used as search queries in PubMed for instances of a side effect (rhabdomyolysis) associated with interference with L-carnitine transport. The substrate pharmacophore was comprised of two hydrogen bond acceptors, a positive ionizable feature and ten excluded volumes. The substrate pharmacophore also mapped 6 out of 7 known substrate molecules used as a test set. After searching a database of ~800 known drugs, thirty drugs were predicted to map to the substrate pharmacophore with L-carnitine shape restriction. At least 16 of these molecules had case reports documenting an association with rhabdomyolysis and represent a set for prioritizing for future testing as OCTN2 substrates or inhibitors. This computational OCTN2 substrate pharmacophore derived from published data partially overlaps a previous OCTN2 inhibitor pharmacophore and is also able to select compounds that demonstrate rhabdomyolysis, further confirming the possible linkage between this side effect and hOCTN2.
doi:10.1021/mp200438v
PMCID: PMC3319199  PMID: 22339151
human Organic Cation/Carnitine Transporter (hOCTN2); carnitine; pharmacophore; transporters
21.  Combining Cheminformatics Methods and Pathway Analysis To Identify Molecules With Whole-Cell Activity Against Mycobacterium tuberculosis 
Pharmaceutical research  2012;29(8):2115-2127.
Purpose
New strategies for developing inhibitors of Mycobacterium tuberculosis (Mtb) are required in order to identify the next generation of tuberculosis (TB) drugs. Our approach leverages the integration of intensive data mining and curation and computational approaches, including cheminformatics combined with bioinformatics, to suggest biological targets and their small molecule modulators. Knowledge of which biological targets are essential for Mtb viability, under a given set of in vitro or in vivo assay conditions, and absent in the human host is a crucial input. We draw on the mimicry of the associated “essential metabolites” to suggest small molecule inhibitors of the essential protein target. Empirical studies are then utilized to delineate the effect of the small molecule putative mimic on cultured Mtb growth.
Methods
We now describe a combined cheminformatics and bioinformatics approach that uses the TBCyc pathway and genome database, the Collaborative Drug Discovery database of molecules with activity against Mtb and their associated targets, a 3D pharmacophore approach and Bayesian models of TB activity in order to select pathways and metabolites and ultimately prioritize molecules that may be acting as metabolite mimics and exhibit activity against TB.
Results
In this study we combined the TB cheminformatics and pathways databases that enabled us to computationally search >80,000 vendor available molecules and ultimately test 23 compounds in vitro that resulted in two compounds (N-(2-furylmethyl)-N′-[(5-nitro-3-thienyl)carbonyl]thioureaand N-[(5-nitro-3-thienyl)carbonyl]-N′-(2-thienylmethyl)thiourea) proposed as mimics of D-fructose 1,6 bisphosphate, (MIC of 20 and 40μg/ml, respectively).
Conclusion
This is a simple yet novel approach that has the potential to identify inhibitors of bacterial growth as illustrated by compounds identified in this study that have activity against Mtb.
doi:10.1007/s11095-012-0741-5
PMCID: PMC3601406  PMID: 22477069
Bayesian models; bioinformatics; cheminformatics; Collaborative Drug Discovery; D-fructose 1,6-bisphosphate; essential metabolites; metabolites; Mimics; Mycobacterium tuberculosis; pathways; pharmacophore
22.  TB Mobile: a mobile app for anti-tuberculosis molecules with known targets 
Background
An increasing number of researchers are focused on strategies for developing inhibitors of Mycobacterium tuberculosis (Mtb) as tuberculosis (TB) drugs.
Results
In order to learn from prior work we have collated information on molecules screened versus Mtb and their targets which has been made available in the Collaborative Drug Discovery (CDD) database. This dataset contains published data on target, essentiality, links to PubMed, TBDB, TBCyc (which provides a pathway-based visualization of the entire cellular biochemical network) and human homolog information. The development of mobile cheminformatics apps could lower the barrier to drug discovery and promote collaboration. Therefore we have used this set of over 700 molecules screened versus Mtb and their targets to create a free mobile app (TB Mobile) that displays molecule structures and links to the bioinformatics data. By input of a molecular structures and performing a similarity search within the app we can infer potential targets or search by targets to retrieve compounds known to be active.
Conclusions
TB Mobile may assist researchers as part of their workflow in identifying potential targets for hits generated from phenotypic screening and in prioritizing them for further follow-up. The app is designed to lower the barriers to accessing this information, so that all researchers with an interest in combatting this deadly disease can use it freely to the benefit of their own efforts.
doi:10.1186/1758-2946-5-13
PMCID: PMC3616884  PMID: 23497706
Collaborative drug discovery tuberculosis database; Drug discovery; Mobile applications; Mycobacterium tuberculosis; Tuberculosis; TB Mobile
23.  Why Open Drug Discovery Needs Four Simple Rules for Licensing Data and Models 
PLoS Computational Biology  2012;8(9):e1002706.
When we look at the rapid growth of scientific databases on the Internet in the past decade, we tend to take the accessibility and provenance of the data for granted. As we see a future of increased database integration, the licensing of the data may be a hurdle that hampers progress and usability. We have formulated four rules for licensing data for open drug discovery, which we propose as a starting point for consideration by databases and for their ultimate adoption. This work could also be extended to the computational models derived from such data. We suggest that scientists in the future will need to consider data licensing before they embark upon re-using such content in databases they construct themselves.
doi:10.1371/journal.pcbi.1002706
PMCID: PMC3459841  PMID: 23028298
24.  A generalizable pre-clinical research approach for orphan disease therapy 
With the advent of next-generation DNA sequencing, the pace of inherited orphan disease gene identification has increased dramatically, a situation that will continue for at least the next several years. At present, the numbers of such identified disease genes significantly outstrips the number of laboratories available to investigate a given disorder, an asymmetry that will only increase over time. The hope for any genetic disorder is, where possible and in addition to accurate diagnostic test formulation, the development of therapeutic approaches. To this end, we propose here the development of a strategic toolbox and preclinical research pathway for inherited orphan disease. Taking much of what has been learned from rare genetic disease research over the past two decades, we propose generalizable methods utilizing transcriptomic, system-wide chemical biology datasets combined with chemical informatics and, where possible, repurposing of FDA approved drugs for pre-clinical orphan disease therapies. It is hoped that this approach may be of utility for the broader orphan disease research community and provide funding organizations and patient advocacy groups with suggestions for the optimal path forward. In addition to enabling academic pre-clinical research, strategies such as this may also aid in seeding startup companies, as well as further engaging the pharmaceutical industry in the treatment of rare genetic disease.
doi:10.1186/1750-1172-7-39
PMCID: PMC3458970  PMID: 22704758
Orphan disease therapy; Preclinical drug development; Generalizable screening methods; Translational toolbox
25.  Evolution of promiscuous nuclear hormone receptors: LXR, FXR, VDR, PXR, and CAR 
Nuclear hormone receptors (NHRs) are transcription factors that work in concert with co-activators and co-repressors to regulate gene expression. Some examples of ligands for NHRs include endogenous compounds such as bile acids, retinoids, steroid hormones, thyroid hormone, and vitamin D. This review describes the evolution of liver X receptors α and β (NR1H3 and 1H2, respectively), farnesoid X receptor (NR1H4), vitamin D receptor (NR1I1), pregnane X receptor (NR1I2), and constitutive androstane receptor (NR1I3). These NHRs participate in complex, overlapping transcriptional regulation networks involving cholesterol homeostasis and energy metabolism. Some of these receptors, particularly PXR and CAR, are promiscuous with respect to the structurally wide range of ligands that act as agonists. A combination of functional and computational analyses has shed light on the evolutionary changes of NR1H and NR1I receptors across vertebrates, and how these receptors may have diverged from ancestral receptors that first appeared in invertebrates.
doi:10.1016/j.mce.2010.06.016
PMCID: PMC3033471  PMID: 20615451
Bile acids and salts; Ciona intestinalis; cholesterol; drug modeling; molecular evolution; oxysterols; phylogeny

Results 1-25 (52)