We often see news stories about the "biggest" drugs based on sales, however this way of looking at drug sales is somewhat skewed by the high cost of some therapeutics, particularly biologics. It is also noteworthy that the majority of the drugs are indicated for cancer.
|Drug||Indication||Worldwide Sales 2018|
|Humira||Rheumatoid Arthritis||$19.936 billion|
|Enbrel||Rheumatoid Arthritis||$7.126 billion|
|Remicade||Crohn's Disease||$5.908 billion|
|Prevnar 13||Pneumonia||$5.802 billion|
I can't help but think that a better metric might be the number of patients treated. Whilst I don't have access to worldwide prescriptions the NHS in the UK does provide some information as part of the Prescription cost analysis for 2018. Whilst the number of prescriptions does correspond exactly with the number of patients treated I suspect it gives a very good indication.
Looking at the categories of drugs it is interesting to note that cancer does not figure in the top 20 categories. As you might expect lipid-lowering drugs, gastric ulcer treatment, treatments for cardiovascular disease, anti-depressants and analgesics are the most prescribed.
|Drug Category||Number of items|
|Proton Pump Inhibitors||60,024,837|
|Angiotensin-Converting Enzyme Inhibitors||44,159,042|
|Beta-Adrenoceptor Blocking Drugs||38,617,728|
|Selective Serotonin Re-Uptake Inhibitors||38,216,924|
|Non-Opioid Analgesics And Compound Prep||35,998,332|
|Control Of Epilepsy||27,989,893|
|Angiotensin-II Receptor Antagonists||20,499,156|
|Tricyclic & Related Antidepressant Drugs||16,704,980|
|Other Antidepressant Drugs||15,911,182|
|Thiazides And Related Diuretics||14,628,130|
Looking at the top most prescribed drugs, small molecule drugs dominate for all indications.
|Levothyroxine Sodium||Thyroid Hormones||32,187,950|
|Omeprazole||Proton Pump Inhibitors||31,038,076|
|Ramipril||Angiotensin-Converting Enzyme Inhibitors||28,605,025|
|Lansoprazole||Proton Pump Inhibitors||25,461,167|
|Bisoprolol Fumarate||Beta-Adrenoceptor Blocking Drugs||23,625,562|
|Aspirin||Non-Opioid Analgesics And Compound Prep||23,397,042|
|Paracetamol||Non-Opioid Analgesics And Compound Prep||18,516,491|
|Co-Codamol (Codeine Phos/Paracetamol)||Opioid Analgesics||15,179,951|
|Sertraline Hydrochloride||Selective Serotonin Re-Uptake Inhibitors||14,815,719|
|Citalopram Hydrobromide||Selective Serotonin Re-Uptake Inhibitors||14,136,645|
|Amitriptyline Hydrochloride||Tricyclic & Related Antidepressant Drugs||13,532,567|
|Furosemide||Thiazides And Related Diuretics||11,945,445|
|Beclometasone Dipropionate||Corticosteroids (Respiratory)||10,671,698|
Also of note is the number of prescriptions for drugs that are readily available over the counter.
No Support for Historical Candidate Gene or Candidate Gene-by-Interaction Hypotheses for Major Depression Across Multiple Large Samples
For depression, SLC6A4 seemed like a great candidate and was supported by very early gene studies
Am J Psychiatry. 2019 May 1;176(5):376-387. DOI
The study results do not support previous depression candidate gene findings, in which large genetic effects are frequently reported in samples orders of magnitude smaller than those examined here. Instead, the results suggest that early hypotheses about depression candidate genes were incorrect and that the large number of associations reported in the depression candidate gene literature are likely to be false positives.
How many other early gene disease association studies need to be checked?
RSC-BMCS and RSC-CICAG are delighted to announce registration is now open for Twenty Years of the Rule of Five, Wednesday, 20th November 2019, Sygnature Discovery, BioCity, Nottingham, UK. #RuleofFive2019
It has been over twenty years since Lipinski published his work determining the properties of drug molecules associated with good solubility and permeability. Since then, there have been a number of additions and expansions to these “rules”. There has also been keen interest in the application of these guidelines in the drug discovery process and how these apply to new emerging chemical structures such as macrocycles.
This symposium will bring together researchers from a number of different areas of drug discovery and will provide a historical overview of the use of Lipinski’s rules, as well as looking to the future and how we use these rules in the changing drug compound landscape.
Full details and registration are here https://www.maggichurchouseevents.co.uk/bmcs/twenty-years-Ro5.htm.
As part of the Boehringer Ingelheim's efforts to foster innovation, they are share selected molecules with the scientific community all for free. The opnme portal gives access to a range of novel ligands. The latest addition is BI-9740
BI-9740 is a very potent and highly selective inhibitor of the enzymatic activity of Cathepsin C. It blocks human CatC in vitro with an IC50 of 1.8 nM and shows > 1500x selectivity versus the related proteases Cathepsin B, F, H, K, L and S. BI-9740 displays no activity against 34 unrelated proteases from different classes up to a concentration of 10 µM.
Chemical probes are absolutely essential for target validation and it is great to see so many high quality tools being made available.
The European Lead Factory (ELF) secured a total project budget of EUR 36.5 million under the second framework of the Innovative Medicines Initiative (IMI). 20 partners in 7 countries will push forward the transformation of potential drug targets to new medicines in the new project ESCulab (European Screening Centre: unique library for attractive biology) under the European Lead Factory brand.
Over the next five years, the European Lead Factory will initiate 185 new drug discovery projects by screening medically relevant drug targets from European researchers, small and medium-sized enterprises and pharmaceutical industry against the ELF library of 550,000 unique chemical compounds.
Clinical trial data is some of the most important information in Drug Discovery, after all it is humans we are looking to treat! However analysis of 30 leading institutions found that just 17% of study results had been posted online. The 30 universities surveyed are those that sponsor the most clinical trials in the EU. Fourteen of these institutions had failed to publish a single results summary.
The Universities that have failed to publish a single trial result are highlighted in red in the table below.
The contrast between the UK universities and the rest of Europe could not be starker,
UK universities in the survey performed significantly better than those in the rest of Europe. The University of Oxford and King’s College London had both published 93% of the trial results due on the register, and University College London had posted 81%.
According to the report every single medical university in the UK is currently working hard to upload missing clinical trial results onto the EU registry, and in many cases onto other registries such as ISRCTN and the US registry Clinicaltrials.gov as well. This demonstrates that where there is a will, there is a way.
If we remove the UK Universities from the analysis the level of reporting falls to a pitiful 7%.
Lack of transparency in clinical trials harms patients. The timely posting of summary results is an ethical and scientific obligation.
The lineup for the 2nd RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry Monday-Tuesday, 2nd to 3rd September 2019 Fitzwilliam College, Cambridge, UK has been updated.
Artificial Intelligence is presently experiencing a renaissance in development of new methods and practical applications to ongoing challenges in Chemistry. Following the success of the inaugural “Artificial Intelligence in Chemistry” meeting in 2018, we are pleased to announce that the Biological & Medicinal Chemistry Sector (BMCS) and Chemical Information & Computer Applications Group (CICAG) of the Royal Society of Chemistry are once again organising a conference to present the current efforts in applying these new methods. The meeting will be held over two days and will combine aspects of artificial intelligence and deep machine learning methods to applications in chemistry.
Deep learning applied to ligand-based de novo design: a real-life lead optimization case study, Quentin Perron, IKTOS, USA
A Turing test for molecular generators, Jacob Bush, GlaxoSmithKline, UK
Presentation title to be confirmed, Keynote: Regina Barzilay, Massachusetts Institute of Technology, USA
Artificial intelligence for predicting molecular Electrostatic Potentials (ESPs): a step towards developing ESP-guided knowledge-based scoring functions, Prakash Rathi, Astex Pharmaceuticals, UK
Molecular transformer for chemical reaction prediction and uncertainty estimation, Alpha Lee, University of Cambridge, UK
Drug discovery disrupted - quantum physics meets machine learning, Noor Shaker, GTN, UK
Presentation title to be confirmed, Christian Tyrchan, AstraZeneca,
Presentation title to be confirmed, Anthony Nicholls, OpenEye Scientific Software, USA
Deep generative models for 3D compound design from fragment screens, Fergus Imrie, University of Oxford, UK
DeeplyTough: learning to structurally compare protein binding sites, Joshua Meyers, BenevolentAI, UK
Presentation title to be confirmed, Maciej Haranczyk, IMDEA, Spain
Deep learning for drug discovery, Keynote: David Koes, University of Pittsburgh, USA
Presentation title to be confirmed, Olexandr Isayev, University of North Carolina at Chapel Hill, USA
Dreaming functional molecules with generative ML models, Christoph Kreisbeck, Kebotix, USA
Presentation title to be confirmed, Keynote: Adrian Roitberg, University of Florida, USA
Applications for poster presentations are welcomed, the closing date for submission is 5th July. A number of RSC-BMCS and RSC-CICAG student bursaries are available up to a value of £250, to support registration, travel and accommodation costs for PhD and post-doctoral applicants studying at European academic institutions. The closing date for bursary applications is 15th July.
Full details are on the conference website
I suspect many will have noticed the recent announcement of the Early Results in Drug Discovery Partnership with AI Biotech Company. These are the first results of the Atomwise AIMS awards:
The researchers have been using Atomwise’s AI-powered in silico screening technology to develop therapeutic treatments for, among others, certain types of strokes, hand-foot-and-mouth disease, and an infection that causes reproductive failure in pigs.
The AIMS award program is a great opportunity for university research scientists to easily access AI-assisted structure-based virtual screening technology:
- Customized small molecule virtual screen using AtomNet™ technology
- 72 small molecules predicted to bind to a specific target protein – QC verified by mass spectrophotometry, resuspended and diluted to a convenient concentration, aliquoted into microtiter plates, and delivered at no cost to the researcher
- Support from Atomwise’s medicinal chemists and structural biologists
- Opportunity to receive up to $30K USD to subsidize assay work
If you have a target protein with an X-ray crystal, Cryo-EM, or NMR structure, or with close sequence homology to a protein with available structures, and an assay in place to evaluate 72 potential hits, then you should consider applying.
Full details are on the AIMs awards page and the closing date is 29 April 2019.
I just thought I’d highlight a new project I’m involved with.
Open Source Antibiotics (https://github.com/opensourceantibiotics) is intended to be a platform for a collaborative effort towards antibiotic discovery.
The first projects have been initiated
Mur Ligase (https://github.com/opensourceantibiotics/murligase) and the background to these exciting targets can be found on the wiki page.
This also provides details of the first two fragment screens.
What we want now is for people to join in and suggest the next round of fragments that should be screened. Ideally these should be commercially available but if people want to design, make and submit their own fragments we would be happy to screen them.
If you feel appropriate, we would appreciate any publicity on this exciting new project