I have updated the Drug Discovery section on Screening Collection design.
During 2016 Global Health are running a series of webinars on the subject of compound design. The programme for future meetings is available below (the agenda will develop through the year).
Date Agenda (& timing of each item in the recording when available)
21st Jan 2016
Introduction to meetings, Mark Gardner Application of PK Tools in the optimisation of a series for the treatment of leishmaniasis, Gavin Whitlock, Sandexis, working with DNDi Hints and tips to working with DataWarrior, Isabelle Giraud, Actelion, slides Isabelle Giraud, DataWarrior demonstration" Recording
25th Feb 2016
Visceral leishmaniasis TCP & screen sequence, Charlie Mowbrary, DNDi Malaria Target Candidate Profiles, stage gates and implications for successful malaria drug discovery, Paul Willis, MMV Registration
17th Mar 2016 "DataWarrior advanced data analysis, Isabelle Giraud, Actelion Using the RSC Medicinal Chemistry Toolkit in Drug Discovery Projects, Andy Davis, AZ The RSC Medicinal Chemistry Toolkit is a free suite of resources to support the day-to-day work of drug discovery scientists. It was developed to provide difficult-to-access, but industry-validated tools in a portable format. The presentation will show with worked examples, how the RSC Medicinal Chemistry Toolkit (Apple only) can be used to support design strategy thinking and structure-activity optimization. https://itunes.apple.com/gb/app/medicinal-chemistry-toolkit/id910073742?mt=8" Registration
21st Apr 2016
Free data pipelining tool KNIME in compound design & analysis Introduction to KNIME & use cases in drug discovery – further details tbd Registration
There are more details here.
As the oldest chemical society in the world, we're proud to be celebrating our 175th anniversary during 2016. We want to mark this milestone by recognising the rich heritage and community of which we're all a part. We'd also like to acknowledge the important role we all play in contributing to the future of the chemical sciences
I thought I'd contribute to the activities by highlighting "Molecule 175" from various databases.
First up ChemSpider a free chemical structure database supported by the Royal Society of Chemistry providing fast text and structure search access to over 40 million structures from hundreds of data sources. ChemSpider ID 175 refers to acetone, a very important solvent with millions of tonnes produced annually.
Next ChEMBL a database of over 1,7 million small molecules and associated biological activity data. ChEMBL175 is Dexibuprofen, this is the active enantiomer of ibuprofen, a well known non-steroidal anti-inflammatory drug.
Drugbank is a richly annotated database of drug and drug target information. DB00175 is Pravastatin a HMG-CoA reductase inhibitor used as a cholesterol-lowering agent.
Pubchem released in 2004, provides information on the biological activities of small molecules. Pubchem CID175 belongs to acetate, the ionised form of acetic acid, Acetate is the most common building block for biosynthesis.
BindingDB is a public, web-accessible database of measured binding affinities, focusing chiefly on the interactions of protein considered to be drug-targets with small, drug-like molecules. BDBM175 refers to an inhibitor of HIV protease designed to take advantage of the C2 axis of symmetry found for this dimeric protease.
Zinc is a free database of commercially available compounds ideal for virtual screening, entry 175 appears to be a hydrated form of the benzodiazepine Clorazepate.
In the IUPHAR/BPS Guide to PHARMACOLOGY entry 175 is Spiramide, a 5-HT2 antagonist.
Webinar to discuss compound design. This meeting:
* Brief introduction - Mark Gardner (AMG Consultants)
* Application of PK Tools in the optimisation of a series for the treatment of leishmaniasis, Gavin Whitlock, Sandexis, working with DNDi.
* Hints and tips to working with DataWarrior, Isabelle Giraud, Actelion
The increase in antibiotic resistant bacteria has highlighted the need to target infections with the correct drug. A recent paper ‘Rapid antibiotic resistance predictions from genome sequence data for S. aureus and M. tuberculosis’, by P Bradley, et al. Nature Communications, 21 December 2015 DOI describes a program to identify species and resistance profiles of clinical isolates.
The Mykrobe predictor is designed for use by microbiologists and doctors, providing information needed in order to choose the best treatment. It analyses the whole genome of a bacterial sample, all within a couple of minutes, and predicts which drugs the infection is resistant to. No expertise is needed to run or interpret it, and it works on a standard desktop or laptop.
Supports Illumina sequencing data as standard. Antibiotics supported: Beta-lactams (methicillin, penicillin), quinolones (ciprofloxacin), macrolides/lincosamides (erythromycin, clindamycin), tetracycline, aminoglycosides (gentamicin), glycopeptides (vancomycin), rifampicin, mupirocin, fusidic acid, trimethoprim.
The software is available for download from the Mykrobe website.
As 2015 ends I'd like to take the chance to wish you all a Happy New Year and hope for great success in your drug discovery endeavours.
The website increases in popularity with 93,000 page views in 2015 an increase of 24% over last year. Nearly 25% of the visitors come back on multiple occasions which I hope means people are finding the content useful.
Nine of the top ten most popular pages were from the Drug Discovery Resources Pages which I am delighted to see, since it suggests that the work entailed in putting the resources together is worthwhile.
The most viewed pages were
- Distribution and Plasma Protein Binding
- Calculating Physicochemical Properties
- Molecular Interactions
- Fragment based screening
- Aspartic Acid Protease Inhibitors
- Serine Protease Inhibitors
As might be expected the Books page only seems popular coming up to Christmas ;-)
The visitors come from over 100 different countries with US and UK topping the list. Whilst desktop systems predominate nearly 20% now access the site from a mobile device.
The company N2MO offers the use of insects as model organisms. They can be used for ADME screening in particular brain penetration studies.
The Grasshopper: A Novel Model for Assessing Vertebrate Brain UptakeOlga Andersson, Steen Honoré Hansen, Karin Hellman, Line Rørbæk Olsen, Gunnar Andersson, Lassina Badolo, Niels Svenstrup, and Peter Aadal Nielsen EntomoPharm R&D, Medicon Village, Lund, Sweden (O.A., K.H., G.A., P.A.N.); Department of Pharmacy, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark (S.H.H., L.R.O.); and Division of Discovery Chemistry and Drug Metabolism and Pharmacokinetics, H. Lundbeck A/S, Copenhagen, Denmark (L.B., N.S.) Received April 10, 2013; accepted May 10, 2013
ABSTRACT The aim of the present study was to develop a blood-brain barrier (BBB) permeability model that is applicable in the drug discovery phase. The BBB ensures proper neural function, but it restricts many drugs from entering the brain, and this complicates the development of new drugs against central nervous system diseases. Many in vitro models have been developed to predict BBB permeability, but the permeability characteristics of the human BBB are notoriously complex and hard to predict.
Consequently, one single suitable BBB permeability screening model, which is generally applicable in the early drug discovery phase, does not yet exist. A new refined ex vivo insect-based BBB screening model that uses an intact, viable whole brain under controlled in vitro-like exposure conditions is presented.
This model uses intact brains from desert locusts, which are placed in a well containing the compound solubilized in an insect buffer. After a limited time, the brain is removed and the compound concentration in the brain is measured by conventional liquid chromatography-mass spectrometry. The data presented here include 25 known drugs, and the data show that the ex vivo insect model can be used to measure the brain uptake over the hemolymph-brain barrier of drugs and that the brain uptake shows linear correlation with in situ perfusion data obtainedinvertebrates.Moreover,this study shows that the insect ex vivo model is able to identify P-glycoprotein (Pgp) substrates, and the model allows differentiation between low-permeability compounds and compounds that are Pgp substrates.
There has been much discussion about the attrition of drugs in development due to lack of efficacy in man and this in part can be due to poor target validation. That is proof that modulation of the identified target in a model system has the desired impact on biological activity and can be linked to therapeutic utility.
This is an absolutely critical step, almost everything else can be fixed.
For this reason two new resources seem particularly valuable.
The Centre for Therapeutic Target Validation platform (https://www.targetvalidation.org) brings together information on the relationships between potential drug targets and diseases. The core concept is to identify evidence of an association between a target and disease from various data types.
A target can be a protein, protein complex or RNA molecule, but we integrate evidence through the gene that codes for the target. In the same way, we describe diseases through a structure of relationships called the Experimental Factor Ontology (EFO) that allows us to bring together evidence across different but related diseases.The platform supports workflows starting from either a target or disease and presents the evidence for target – disease associations in a number of ways through association and evidence pages.
The current version contains (DisGeNET v3.0) contains 429111 associations, between 17181 genes and 14619 diseases, disorders and clinical or abnormal human phenotypes.
I've updated the hit identification section of the Drug Discovery Resources. In particular I've added to the high-throughput screening analysis including more information on PAINS (Pan Assay Interference Compounds) first described by Baell et al DOI and subsequently summarised in an excellent Nature comment.
Academic researchers, drawn into drug discovery without appropriate guidance, are doing muddled science. When biologists identify a protein that contributes to disease, they hunt for chemical compounds that bind to the protein and affect its activity. A typical assay screens many thousands of chemicals. ‘Hits’ become tools for studying the disease, as well as starting points in the hunt for treatments.
These molecules — pan-assay interference compounds, or PAINS — have defined structures, covering several classes of compound. But biologists and inexperienced chemists rarely recognize them. Instead, such compounds are reported as having promising activity against a wide variety of proteins. Time and research money are consequently wasted in attempts to optimize the activity of these compounds. Chemists make multiple analogues of apparent hits hoping to improve the ‘fit’ between protein and compound. Meanwhile, true hits with real potential are neglected.
Also added a page on Aggregators. Promiscuous inhibition caused by small molecule aggregation is a major source of false positive results in high-throughput screening. To mitigate this, use of a nonionic detergent such as Triton X-100 or Tween-80 has been studied, which can disrupt aggregates, and is now common in screening campaigns DOI.