Boosting UK life sciences R&D and international competitiveness

Today, Medicines Discovery Catapult’s Virtual R&D Discovery Services platform have announced new partnerships to boost UK life sciences R&D and international competitiveness.

Cresset Discovery Services is pleased to be partnering with Medicines Discovery Catapult to deliver solutions to biological problems; working alongside your scientists we will help you solve problems, provide fresh ideas, remove roadblocks, add direction and insight, and reach your next milestone faster and more cost effectively.

In silico advantages for antibiotic development

Cresset Discovery Services has extensive experience working on established and novel anti-microbial targets. We understand the unique challenges of antibiotic development and bacterial resistance and can partner with your development team to reduce your development costs and decrease time to market for new antibiotics.

Bacteria are among the most prolific and successful organisms on the face of the earth. They are essential for life and for the most part we live in harmony. However, the wrong bacteria in the wrong place can lead to serious problems, which is when we turn to antibiotics for help.

The media is predicting an antibiotic resistance apocalypse, talking up superbugs, MRSA and the failure to develop new antibiotics. Behind these headlines there is a growing need for new and novel antibiotic drugs.

Developing new antibiotic drugs has always been difficult. Many of the easier targets have been successfully used, but resistant mechanisms are constantly evolving, limiting the efficacy of current antibiotics. Most of the larger pharmaceutical companies have moved out of the area, so the development of new antibiotics has fallen to small/medium biotechs, new start-up companies, academic researchers and public private partnerships, who are all trying to fill this crucial development void. Any help in identifying and prioritizing where chemical and biological effort should be focused will only to reduce development costs but also minimize the time required to develop these much need antibiotics.

The challenges for antibiotic drug design are comparable to those for traditional drug discovery, plus the additional obstacle of the established resistance mechanisms of bacteria:

  • The first and physically obstructive problem is getting antibiotics through the bacterial cell wall. This is especially true for gram negative bacteria which have an additional lipid membrane.
  • Once inside the cells there is an array of efflux pumps waiting to transport the compound back out of the cell before it can have an effect.
  • Bacteria have also developed enzymes specifically designed to stop antibiotics working. The best example of this is the b-lactamase proteins that decarboxylate b-lactam rings rendering penicillin and related antibiotics ineffective (figure 1).
  • The final trick up the bacterial sleeve is mutagenesis. Since bacteria grow rapidly there are multiple strains of the same bacteria in a single infection event. If just one of these strains is viable and infers resistance to the prescribed antibiotic then an antibiotic resistant infection is promoted through the whole infection.


Figure 1: The electrostatic protein surface of staphylococcus aureus beta-lactamase with a decarboxylated beta-lactamase from the 1GHP Crystal Structure1 highlighting a resistance mechanism for this class of antibiotics.

How can in silico methods help to address this growing need?

Through our extensive experience working on anti-microbial targets, both novel and currently established, we understand the challenges that this class of targets offer. For example, how simply designing Lipinski type molecules that fit into an active site without the consideration of the additional bacterial resistant mechanisms is unlikely to yield favorable results.

Computational techniques such as 3D electrostatic field comparisons (figure 2) and multi-parameter optimization are key in this field where cell penetration, efflux rates, frequency of mutation are factors. Potentially, other project-specific properties must also be considered in addition to normal drug properties.


Figure 2: Field comparison of a set of fluoroquinolones antibiotic compounds displaying the conserved electrostatic nature of this class of compounds which is key for DNA intercalation and complex stability.


These advanced techniques in addition to traditional methods provide customers with the rationalization underpinning the predictions. This means that Cresset Discovery Services adds value to your project long after our direct engagement has finished. In addition, Cresset’s exceptional visual analysis tools help to understand and share ideas.

We collaborate with your development team to leverage experience to identify the key proteins to investigate, identify the best hypothesis to test, and ultimately to choose the most promising compounds for synthesis, accelerating your antibacterial project towards successful completion.

Advance your antibiotic project

Contact us for a free confidential discussion to see how we can advance your antibiotic project.

Reference

1. Structures of the Acyl−Enzyme Complexes of the Staphylococcus aureus β-Lactamase Mutant Glu166Asp:Asn170Gln with Benzylpenicillin and Cephaloridine Celia C. H. Chen and Osnat Herzberg Biochemistry, 2001, 40 (8), pp 2351–2358

The virtuous circle: Cultivating closer ties between academia and industry

Academia and industry always have a lot to gain from cultivating close ties with each other. As with any relationship, getting the balance right between give and take starts with a generous attitude from both parties.

Cresset develops software and services for small molecule discovery and design, primarily for the pharmaceutical R&D. We work with the global academic community in a number of ways, including collaborative projects, Knowledge Transfer Partnerships and commercialization of academic methods. We also invest in academic programs with the hope of shared benefits on several different timescales.

“Cresset’s software gives new insight to projects I’ve been working on for the past three to four years. I specifically use it to see how proteins, DNA and molecules interact and bind to each other. If we can design drug molecules that will bind to DNA the same way proteins do, we can open up whole new lines of therapy.

“Cresset is supportive of academic research and they’ve been wonderful to work with to get everything up and running.”

Dr Daniel Barr, Assistant Professor of Biochemistry, Department of Chemistry and Biochemistry, Utica College, USA

A long-term investment of lecturing and training

We wholeheartedly believe that our methods shorten the path to new compounds. That by using Cresset computational software, more and better compounds will be discovered more efficiently. Therefore, we want to spread the word to the next generation of computational chemists, who are already far more computationally literate than any previous generation. We are actively involved in teaching and training the next generation of computational chemists.Here’s what we’re doing to support that goal:

  • Applied for a network grant with European Training Network ETN to help with training computational chemists at the Universities of Oxford, Southampton, Bristol, Portsmouth and Sussex
  • Mark Mackey, Cresset’s CSO, is a visiting lecturer at the University of London
  • A range of summer projects to A-level and undergraduate students
  • Successful Knowledge Transfer Partnerships with the University of Edinburgh and the University of Bristol.

By being closely associated with the up and coming generation of computational chemists, we’re also staying in touch with how our user base is growing and changing.

The traditional gap between medicinal chemists and computational chemists is becoming blurred. It is unthinkable that today’s medicinal chemist graduates will not use computers in their every-day work. They may not want to work with command line code, but they feel very at home with our chemistry software.

In response, we’re changing how we develop new tools in order to make them more accessible to a widening group of users. We continue to invest heavily in usability and good design, and we also continually review and add new tools that support workflow.

Collaboration for new scientific methods

Everyone who works in computational chemistry knows that the open source community develops many and varied methods, most of which are useful and some of which are outstanding.

Rather than viewing this as competition for commercial software, we see it as an opportunity for partnership. Rather than developing new methods, we can work with academics to build their excellent code into our computational chemistry environments.

But why would anyone pay for an open source method?

The answer is convenience, usability, validation and support. Some professionals working in industry find it difficult to adopt academic methods for their work since they’re not supported by their in-house IT policies. We put academic methods into a professional environment with a highly usable GUI and make sure it integrates with the rest of our software and complies with other protocols. We also offer a professional level of support.

In return, the academic authors may receive royalties, additional kudos from broader usage of their code, valuable links with industrial scientists, plus a broader user community contributes to the ongoing development of their methods.
For example, WaterSwap is a widely used, open source method implemented in SIRE. It was developed by Dr Chris Woods at the University of Bristol. Cresset collaborated with Dr Woods to license WaterSwap and include it in Flare, our structure-based design application.

WaterSwap in Flare
Figure 1: WaterSwap is a thermodynamic integration method for investigating ligand-protein energetics. Green residues show where the ligand is gaining the most energy from interaction. Red residues show where water binding is preferred and represents opportunities to improve ligand design.

“Visualizing the inhibitor/substrate binding site of protein crystal structure in Cresset using field points calculated by XED force field is very informative. The protocols for all the modules in Cresset are very quick and easy to use. Forge and Spark are excellent programs for LBDD. The radial plots obtained from alignment methods implemented in Forge provide a visual inspection of results and could be effectively used for simultaneously comparing any number of physical properties for the compounds in the dataset. I strongly believe that Cresset software is an important inclusion in the spectrum of software programs used for Computer Aided Drug Discovery paradigm.”

Dr. Prija Ponnan, Department of Chemistry, University of Delhi

Low cost software for academic research

One of the hurdles faced by many software vendors in validating their methods is the understandably high level of confidentiality associated with pharmaceutical R&D. To take one (anonymized) example. Two years ago Blaze, our virtual screening platform, was used to successfully identify a potential hit from the large compound libraries of a major organization. That compound is now going into clinical trials. However, the time scales are such that it could be ten years before anything is ever published about the methods used to discover the compound.

By contrast, academic groups using our software are extremely likely to report their results in papers, thus creating a healthy supply of citations. Our customers need proof that our software delivers results, and academia is an ideal source.

We offer significant reductions on software used for academic research, and we offer flexible licensing options for software used for teaching or PhD research.

Listening to and delivering what researchers want

Finally, we want to hear from opinion makers, and this can be the most mutually valuable exchange of all.

Our software needs to provide outstanding science and support changing workflows. As we develop and improve applications we need to keep listening to researchers about how they are working and what will help them stay on leading edge of research. Close relationships, sometimes over many years, encourages an open dialog that helps us to get our products right, and helps leading academics to get the software that will be of most use to them most in their research.

We want academics to be able to work as efficiently as possible to find compounds that will help in therapeutic areas of need. And we also want to provide the best possible tools to the broader market.

Access our software and work with us

See the licensing options for our ligand-based and structure-based applications. Or contact us for a free confidential discussion to find out how Cresset Discovery Services experts can add value to your project.

How do I turn my biology insight into a novel therapeutic?

The question that many of the people talking to Cresset Discovery Services ask is “I have discovered some interesting biology – how do I turn it into a drug?”. If this is you, then read on.

Let’s look at some of the ways that we can use in silico technology to translate your ideas into chemical tools in the first instance. Optimizing the drug-like properties of these early hit compounds will put you on the road to defining a lead series and onwards to nominating a drug candidate for clinical studies.

Finding a chemical starting point

The first step in the drug discovery journey is to find a chemical starting point, a molecule that binds to your target and either blocks or enhances its activity. Your overall aim may be to block a key point in a signalling pathway, or divert it to an alternative route.

Alternatively, you may want to prevent levels of a particular cytokine from building up or block the activity of an enzyme, a receptor or an ion channel. Don’t overlook the fact that you may already have found chemical tools that modulate your biological target – what are its physiological activators, are there any literature compounds or known drugs we can use to get you started?  What is known about the protein target? Do you have structures of your system, or are there examples of close relatives on the Protein Data Bank? Can we learn from off-target effects of other compounds or scaffold-hop from one chemical series to another to create new intellectual property.

Your next milestone faster and more cost effectively

Using computational chemistry effectively can both considerably speed up hit finding and reduce the costs of running large physical screens. The best approach to take will depend on the type of bioassay that you intend to use. For example, you can test many more compounds in a plate-based enzyme assay than you can screen in a complex phenotypic system so you need to decide whether you want to test thousands of compounds or would prefer to look at a smaller bespoke set of compounds.

In both cases, initial steps will focus on defining the bioactive conformation of one or more compounds that bind to your protein. We use these as pharmacophores to search for similar compounds that will have the same biological effect. By applying the XED force field we take a unique view of molecules which means that we can compare different chemical classes directly. This is a better reflection of how proteins ‘see’ other molecules and explains how drugs bind to sites that evolved to interact with peptides, DNA or other natural products. In summary, we are looking at the wider electrostatic and shape properties of molecules that extend beyond their atomic skeletons and are responsible for their biological properties.

Efficient virtual screening for diverse new structures

One popular option for kick starting the search for a hit compound is to use Cresset Discovery Services to run virtual screens. By using Blaze (an effective ligand-based virtual screening platform optimized to return diverse new structures) or Flare (which provides fresh insights into structure-based design) we can search a library of 20 million commercially available compounds. The output will be a list of compounds ranked by the similarity of their field point patterns to your pharmacophore. We can filter the list to prioritize compounds that have the appropriate physicochemical properties based on the nature of your target. Most importantly, this will give you a shopping list of compounds that you can purchase and test before you build significant synthetic chemistry resources in your team.

Scaffold-hopping

The same approach allows you to move from one chemical series to another – particularly useful when you are looking for a backup compound to fill a hole in your portfolio or overcome a deficit in your existing series.

Building bespoke libraries

You can purchase millions of compounds from chemical vendors, however, these still only represent a fraction of all possible compounds – even if we only consider molecules that are small enough to be used as drugs. Substances produced by organisms have evolved to form interactions which are not always available to off-the-shelf chemicals. Sometimes it is better to design your own library so that you can build in features that explore different regions of chemical space or mimic the properties of natural products. We can help you design libraries that move you into new areas of chemical space or focus on specific features of a molecule.


3D similarity-based clustering workflow.

Working with fragments

One approach to getting better coverage of chemical space is to work with low molecular weight compounds (<300 Da), termed fragments. Linking these together can generate larger drug like molecules, however, accomplishing this is recognized as a difficult task. Spark, a scaffold hopping and R-group exploration application, enables us to offer you a tailored solution to this problem, using fragment libraries constructed from those that occur in biologically active molecules. New suggestions for compounds to make can be built to fit the binding cavity in a protein structure. See how Spark was used to grow and link fragments.

Free confidential discussion

We have worked on hundreds of projects on different biological targets and would be happy to discuss the best approach for accelerating your assets through the pipeline. Contact us for a free confidential discussion.

 

Modeling the intricacies of molecular recognition: Make ‘smart antibodies’ into biologics

Antibodies are fantastically versatile molecular recognition engines, capable of creating artificial enzymes in response to recognized interactions. Similarly, enzymes depend on molecular recognition for catalysis. Cresset Discovery Services has a depth of experience in modeling molecular recognition scenarios, stretching back to the early nineties.

Nature’s molecular recognition engines

In 1992 I was about to start a PhD on ‘catalytic antibodies’, investigating the amazingly versatile ability of the immune system to prepare artificial enzymes. It all sounded fascinating, and still does. Alas, rather than building antibodies, the path of my PhD altered, resulting in me developing my organo-phosphorus chemistry skills and producing ‘transition-state inhibitors’ of beta lactamase enzymes instead.

However, I retained a keen interest in molecular recognition and a familiarity with antibodies as a protein class and this has proved useful for our work at Cresset Discovery Services today. Indeed, it wasn’t until much, much, later that immunoglobulins would reveal their impact as important future medicines and of course this was way before the multi-billion-dollar blockbuster TNFa targeting biologic Adalibumab (Humira) (Figure 1, right).

Immunoglobulins are biologically evolved to recognize an enormously variable patch of molecular surface using a single protein architecture; an arrangement of two polypeptide chains (heavy and light) each bearing three hyper variable loops.

Figure 1 illustrates the diversity of recognition possible in antibodies, as modeled in Flare™, ranging from small molecules (sulfathiazole) to big molecules (Fullerene C60)1 and peptides (TNFa)2.

Figure 1: Left: an example of a fullerene recognizing mouse antibody. Middle:another mouse antibody recognizing a sulphonamide drug, Sulfathiazole. Right: the human engineered antibody Adalibumab (green) with TNFa. Heavy chain (crimson) variable loops (magenta), Light chain (grey) variable loops (black). All modeled in Flare using PDB codes: 5CP3, 1EMT and 3WD5.

Similarly, enzymes also use molecular recognition to great advantage via a diverse array of protein architectures. In contrast to antibodies, they specifically recognize molecular surfaces that represent a transition state of a chemical transformation. Lowering the energy of the transition state through binding allows catalysis to happen in enzymes.Figure 1: Left: an example of a fullerene recognizing mouse antibody. Middle:another mouse antibody recognizing a sulphonamide drug, Sulfathiazole. Right: the human engineered antibody Adalibumab (green) with TNFa. Heavy chain (crimson) variable loops (magenta), Light chain (grey) variable loops (black). All modeled in Flare using PDB codes: 5CP3, 1EMT and 3WD5.

The main premise of catalytic antibodies is to elicit their production in response to a transition state that can be mimicked by a ligand structure, which in theory then reproduces enzymic behaviour in the resultant protein. This pursuit simultaneously probes our fundamental knowledge of molecular recognition and of enzyme catalysis.

A catalytic antibody that cleaves cocaine

In 2006 workers at Scripps published work on a catalytic antibody3 that cleaves cocaine at the benzoyl ester. The antibody was elicited using an aryl phosphonate ester – a mimic of the carboxyl ester hydrolysis transition state.

The insights gained through the X-ray structures that were solved for this system were remarkable. Multiple components of the reaction coordinate are shown to exploit common recognition patterns, whilst the different geometries are accommodated by both residue and backbone movements in a dynamic process (Arg and Tyr movements in Figure 2).

This is an excellent model system. It shows how some interactions are very favourable e.g., the cation-pi interaction of the tropane is very solid across the structures, whilst others e.g., the ester, are variable. It also demonstrates that proteins, and in particular enzymes, ‘breathe’ – they are not statues (Figure 2).

Figure 2: The Scripps catalytic antibody in the different protein conformations that recognize the substrate and the transition state for cocaine ester hydrolysis. This shows highly mobile Arg (magenta) and Tyr (cyan) residues and the ligand (cocaine hydrolysis reaction coordinate – light to dark green). All modeled in Flare using PDB codes: 2AJU, 2AJV, 2AJZ, 2AJX, 2AJY and 2AJS.

All molecular recognition is not equal

One very interesting facet of molecular recognition demonstrated by antibody antigen interactions is that these interactions are not all equal. In fact, there are lesser and greater interactions – the latter of which can predominate in driving antibody production. Preferred interactions (or ‘hot spots’) can be visualised using various techniques (e.g., as demonstrated using the previous cocaine system modeling in Flare shown in Figure 3).

Figure 3: The Scripps catalytic antibody conformer ligand interaction surfaces. All modeled in Flare using PDB codes: 2AJV and 2AJX. This shows highly mobile surface changes required to squash the substrate into the transition state geometry (this time with the phosphono mimetic) and the hydrophobic hot spot surface (yellow) calculated by Flare.

Antibodies are produced via natural selection processes as a cellular response to a presented antigen. Some parts of the antigen are better than others at eliciting antibody responses, since they may involve preferred interactions, and so the resultant antibody may not bind in a therapeutically useful way.

Modeling and design of these interactions (i.e., via antigen engineering) becomes a useful task, since leaving it to nature can divert us from our intended goals.

Cresset Discovery Services work on biologics

While we are actively working and delivering on projects involving biologics, client confidentiality means that it remains a challenge to describe details of work done for commercial clients. We can, however, speak more generally and we have recently delivered on projects to:

  • Predict / design mutants that prevent binding
  • Model strategic glycan positioning at unhelpful yet potently antigenic surfaces
  • Model / characterize the observed binding order of an antigen – receptor series.

Read more about Cresset’s capabilities in biologics: Modeling ‘big’: Applying the XED force field to biologics.

Biologics remain a very important class of protein targets for disease therapeutics which we intend to continue to support through our innovative modeling services.

Free confidential discussion

Find out how we can accelerate your project by requesting a free confidential discussion.

References

  1. X-ray crystal structure of an anti-Buckminsterfullerene antibody Fab fragment: Biomolecular recognition of C60, Braden*, B. C., Goldbaum F. A., Chen B., Kirschner A. N., Wilson S. R. and Erlanger B. F. PNAS 97, no. 22, 12193–12197, 2000.
  2. Comparison of the Inhibition Mechanisms of Adalimumab and Infliximab in Treating Tumor Necrosis Factor a-Associated Diseases from a Molecular View, Shi Hu, Shuaiyi Liang, Huaizu Guo, Dapeng Zhang, Hui Li, Xiaoze Wang, Weili Yang, Weizhu Qian, Sheng Hou, Hao Wang§, Yajun Guo and Zhiyong Lou, Journal of Biological Chemistry, 288, 38, pp. 27059 –27067, 2013
  3. Complete Reaction Cycle of a Cocaine Catalytic Antibody at Atomic Resolution, Zhu X., Dickerson T. J., Rogers C. J., Kaufmann G. F., Mee J. M., McKenzie K. M., Janda K. D. and Wilson I. A. Structure 14, 205–216, 2006.

Enhancements in Flare V2 allow our scientists to spend more time on your design and analysis project

The release of Flare V2 brings quicker analysis of new design ideas and more automation of workflow processes. Andy Smith explains the advantages that Flare V2 brings to Cresset Discovery Services customers, including more time spent on high-value design and analysis work.

Why wait for the full release of Cresset software?

Before a new version of the software is released, we have already applied the new functionality to our customers’ projects once it has been fully validated by our development team. This means that in additional to the existing available packages within our software Cresset Discovery Services customers get to the added benefit of the upcoming functionality before it is generally released. We always send files which are compatible with customer’s software: if pre-release functionality is used to illuminate our analysis, we save the data in a format so that customers can still review the analysis. Flare V2 opens up exciting new functionality to Cresset’s software customers that invariably has already been extensively tried out by the Cresset Discovery Services team.

Electrostatic Complementarity™ – intuitive SAR and great pictures

The Flare development which has us most excited and is likely to benefit our customers the most is the ability to calculate the Electrostatic Complementarity (EC) of protein-ligand complexes, and project the values onto surfaces for both the proteins and ligands.

EC is a new method developed inhouse by Cresset scientists that distils complex protein ligand interactions into an obvious and informative pair of surfaces which are accessible and understandable to a broad-spectrum audience.

I recently showed the EC feature in Flare V2 to a group of senior medicinal chemists. They were very impressed with the intuitive representation. They commented that the visual surfaces were consistent with their understanding of the SAR of the system, and also added, ‘This will make a great series of pictures for the CEO to use.’ I took this as a huge endorsement of the EC calculations and visualization!

 


Figure 1: Electrostatic Complementarity surfaces for the Aurora A kinase 6hjk crystal structure.

Get a live analysis of good and bad design ideas

The data available from an EC calculation feels similar to that obtained from a Fragment Molecular Orbital (FMO) approach but at a small fraction of the computational expense and in a more visually intuitive form. Within Flare it is possible to process hundreds or thousands of ligands in a reasonable time frame, allowing for live EC analysis of ideas during meetings.

We worked with a team in a brain storming session and used the EC surfaces to quickly highlight the good and bad ideas and emphasize regions of the ligand that required additional attention.

At Cresset Discovery Services we are now using EC as an additional scoring and filtration method for docking and virtual screening ranking, and to further evaluate protein ligand interactions.

Automate day to day workflows with the Python API and RDKit integration

Python has become an increasingly popular and useful scripting language amongst the chemistry and cheminformatics communities, particularly when combined with the RDKit. Flare V2 features a Python API that provides access to all functionality available within the Flare graphical user interface from a Python script. Furthermore, the Flare V2 Python environment allows tight integration with the RDKit: native Cresset methods and RDKit methods can be used interchangeably on molecules loaded into a Flare project. The Python API and the RDKit integration also have an important automation focus feature rather than a purely scientific advancement, and the ability to use Python and the RDKit from within Flare allows fully customizable workflows to be designed and executed.

More time for interesting design and analysis

Including Python and the RDKit directly into Flare makes the process of using Python and the RDKit far smoother. Coupled with the support expertise available from Cresset and the vibrant Python RDKit community it makes this an exciting addition.

I’ve been using the Python RDKit integrated tools and workflow to automate standard protocols, which frees more of my time for the interesting design and analysis work for which Cresset Discovery Services are known. Processing and managing molecules in Flare V2 just got a whole lot more efficient, which means we can pass on this time saving to customers as extra time for analysis or design.

Free confidential discussion

If you’d like to discuss how Cresset Discovery Services can deploy these new tools to advance your project, please contact us for a free confidential discussion.

A measure of success: Being named on patents and references

The most common question asked of every contract research organization is, ‘What have you produced?’ or, in other words, ‘What evidence is there that you will provide value for our money?’

In industries that produce products to order it’s easy to calculate metrics for total output and time and cost per item. But in the scientific service industry the outcomes are less tangible: knowledge, ideas and computational models that usually form part of a wider discovery pathway.

One important endorsement is the number of returning customers and on that front Cresset Discovery Services has a very strong track record. Indeed, the majority of our customers come back to us for repeat business. An alternative metric is the number of patents and references that Cresset Discovery Services and its employees are named on jointly with customers.

The rightly rare nature of CRO patents

Most CROs, including Cresset Discovery Services, are employed to be anonymous. All of the scientific work and all associated IP is rightly the property of the customer, and there is no obligation whatsoever for a customer to name Cresset on any subsequent patents or scientific papers. It is, therefore, highly unusual for any contract research organization to be included on any patent.

Despite this, we’re proud to say that several customers over the years have chosen to name Cresset employees on their patents. This speaks volumes to the quality of the research and the relationship between Cresset Discovery Services and the customer. The diversity of the targets in these patents also demonstrates the broad spectrum of projects we tackle at Cresset Discovery Services.

Table: Patents naming Cresset or Cresset employees.

Company

Patent  number

Target area

Patent date

Monsanto Technology LLC

US20160130229A1

US20180070589A1

Acetyl-coa carboxylase modulators

2016

2018

Drexel University WO2015051230A1 Novel compositions useful for inhibiting hiv-1 infection and methods using same 2015
Redx WO2012063085A2 Drug derivatives 2012

 

A steady track record of references

Cresset Discovery Services has always had strong relationships with the academic community and there is a growing number of scientific papers in which our work is directly referenced. For example, the following references are based on HIV-1 work from Cresset Discovery Services.

  • Bioorganic Medicinal Chemistry Letters 2016, 26(3) 824 – 828
  • Bioorganic Medicinal Chemistry Letters 2016, 26(1) 228-234
  • Bioorganic Medicinal Chemistry Letters 2014, 24(23) 5439 – 5445

A description of work carried out as part of a project with Manchester Fungal Infection Group, Institute of Inflammation and Repair, University of Manchester, published in PNAS 2016 113 (45) 12809-12814 can be found on Wikipedia.

These references show not only the quality of the work from Cresset Discovery services but also the duration of collaborations and the lasting impact that insightful computational chemistry can have on a project.

We do not exclusively use Cresset software. Citations for these applications cover a very wide range of targets and industries, and demonstrate the varied projects that our expertise can help to progress.

At Cresset Discovery Services we are committed to propelling your project forward towards patents. We really don’t mind not been included on them, but we greatly appreciate the repeated business our customers provide.

Find out how our experts can help advance your project

Contact us for a free confidential discussion.

Introducing Andy Smith, Discovery Services Scientist

The last time I worked for Cresset I ended up being outsourced to a customer – but it was all part of the service! I’m delighted to be back at Cresset Discovery Services to work on a new set of customer projects. Here’s a brief overview of my computational chemistry journey so far.

An early sniff at GPCRs

I was introduced to computational chemistry when I stayed on at Warwick for a research MSc. The project was an SAR analysis of bell pepper odorants predicting the structural and electronic features that are important for olfactory recognition. My sponsor, Ernest Pollack, was getting very excited about proteins called GPCRs, which had just been identified as the receptors responsible for smell, and could possibly be the largest collection of proteins yet found. Meanwhile, I moved on to a PhD using quantum mechanics to investigate the Heck reaction.

Working closely with synthetic chemists

In 2001 I joined Proteus (later Protherics, then Tularik), who had a history with Factor Xa and already had a licensing agreement with Lilly for LY517717 established. After the takeover by Tularik the focus moved onto kinases which were difficult for our in-house software to model accurately. It was here that, with help from Mick Knaggs, I moved back into ligand-based design. I started homology modeling to augment the range of targets available for the in-house structure-based design methods and to expand the range of target we could work on using ligand-based design tools.

A narrow focus on structure-based design

I joined Sterix/Ipsen in 2004. Sterix had three areas of research: tubulin, hydroxysteroid dehydrogenase and dual aromatase-sulfatase inhibitors. Frustratingly, our work on these projects was limited to structure-based design, due to the fact that the group had made a large investment in the GOLD docking package from CCDC.

Back to the full range of computational chemistry methods

Upon moving to Peakdale Molecular in 2006 I worked on a range of diverse and interesting projects using a wide range of computational chemistry methods, including structure-based, ligand-based, ADMET predictions, homology modeling, molecular dynamics, quantum mechanics, cheminformatics, bioinformatics, library design, diversity and similarity analysis. The diversity of the projects meant I quickly learned to adapt to customer priorities, selecting the most appropriate method for the best possible modeling solution, whilst maintaining value for money.

It was here that I first used Cresset software. Torch (then FieldAlign) and Forge (then FieldTemplater) provided intuitive and visually informative methods for analyzing molecules which were understandable to our customers. These applications offered very useful methods of treating molecules which were different from other approaches I’d used before; both tools gave a more insightful understanding of ligand similarities and a deeper understanding of the features driving activity across a wide range of chemotypes and targets.

My first move to Cresset

Redx were working closely with Cresset, but were unsure how much support they would need, so in 2013 I took on the joint role of computational chemist at Redx and application scientist at Cresset.

Redx had fully functional medicinal chemistry projects in anti-infectives, oncology and crop protection, with large repositories of data. The established computational methods from Cresset Discovery Services showed the potential of computational chemistry and was the reason for Redx wanting to bring computational chemistry in-house. I provided the computational chemistry component and established a close working relationship with the medicinal chemistry teams.

Spark, Cresset’s scaffold hopping and R-group exploration application was integral to the development of a patent busting portfolio. As the company matured, Spark was used for backup series generation and to prevent the same patent busting approach to be applied to our own patents.

As an application scientist at Cresset, I carried out a very preliminary investigation of protein fields to determine whether we could transfer the Cresset methodology from ligands to proteins. It’s very gratifying to see the evolution of this early work brought to market in Flare, which provides fresh insights into structure-based design by integrating cutting edge approaches in an accessible and flexible user interface.

Outsourced to a customer

I joined Redx full time when it became clear that they needed more computational support. Working closely with Cresset software support, synthetic chemists were trained on Forge, a powerful ligand-focused workbench for SAR and design, and were encouraged to undertake general computational chemistry project related tasks. This freed up my time to provide more in-depth investigations to several key problems on projects.

A significant success was a compound that was sold to LOXO Oncology for $40 million. The computational work for this project was originally outsourced to Cresset Discovery Services. The compound was developed in 5 years and is a testament to the quality science undertaken at Redx.

In fact, the Redx CEO at the time was so impressed with the contribution of Cresset Discovery Services that he wrote:

“Cresset is a valuable partner in our drug discovery programs. Their deep knowledge of computational chemistry and its application to drug discovery is enabling us to progress multiple projects across a wide range of target classes very quickly and cost-effectively.

“Cresset has consistently delivered insightful support to our drug discovery efforts. In our experience, their knowledge, responsiveness and collaborative approach have set them apart from their competitors.”

 Dr Neil Murray, CEO, Redx Pharma, UK

Back to Cresset Discovery Services

I was delighted when I heard that Cresset Discovery Services was looking for a computational chemist to join the team. I’m very pleased to be back to working on a wide range of challenges on varied customer projects and putting my skills to use in the real world of modeling.

Contact us for a free confidential discussion to see how Cresset Discovery Services can work alongside your chemists to solve problems, provide fresh ideas, remove roadblocks and add direction and insight to you project. If you’re based near Alderley Park and would like to meet for a confidential chat, get in touch as I’m based at the BioHub part-time.

Artificial Intelligence and machine learning: Where’s the intelligence?

“Of course we have Artificial Intelligence (AI), who doesn’t?” This was a throw-away line, during the closing remarks at a recent conference about future directions in medicinal chemistry. Currently it seems that every company inside drug discovery has an AI project or collaboration. But what is AI in drug discovery, what is it being used for, what are the challenges and what are the opportunities?

The distinction between AI and machine learning

Whilst the phrases ‘Artificial Intelligence’ (AI) and ‘Machine Learning’ (ML) are often used interchangeably, they are subtly different. A true AI method would have the ability to make decisions and propose new ideas from outside its knowledge base, whereas machine learning methods can only use the information from within their knowledge base. In fact, many AI systems are built on a foundation of one or more ML techniques – particularly neural networks. If you were to give the two methods human characteristics, you could say that AI is imaginative and might suggest a completely new series, whereas machine learning is intuitive – it could identify improvements within a compound series, but it would be limited to the transformations present within the training set.

Given that presently, most applications referred to as using ‘AI methods’ in drug discovery utilize modern machine learning methods, we will also utilize the shorthand of talking about “AI” to mean machine learning methods such as ‘deep learning’ which is a method of training very complex neural networks to find patterns in large volumes of complex data (so called ‘big data’). Most of these applications in drug discovery could equally utilize other machine learning methods, such as regular neural networks, support vector machines or random forest regression models as data sets are generally not that complex and not that large. However, there are areas where AI methods have real traction, for example for those working on genomics data, the separation in performance between traditional machine learning and modern AI methods starts to become tangible due to the large size and complex inter-relationships of the data sets involved.

Mining big data in drug discovery

The biggest promise of AI and machine learning for drug discovery is the capability of mining big data. AI methods such as deep learning have been deployed effectively in other industries, most notably in social media interactions.

Most established pharmaceutical companies have large repositories of data stored in large corporate databases built up over many years. This data tends to be noisy simply because of the volume, number of sources and the long time frame during which the data was collected. AI methods are tolerant and accepting of this type of data, providing the data sets are large enough, which makes them eminently suitable for extracting and analysing data from corporate databases. The ability to leverage this corporate knowledge to current targets has the potential to provide tremendous benefit, both in direction and speed of development, which translates to lower development costs.

Over recent years, a number of leading companies have formed strategic partnerships to investigate whether these methods can be transferred to drug discovery (Table 1). AI and machine learning methods are being deployed in a diverse range of areas, ranging from identifying compounds from existing libraries, target identification, target validation to new target discovery. There are also genomics companies who are looking to provide custom drug therapies based on an individual’s bio markers.

However, as with any emerging technology, there are very few case studies where these methods have successfully been applied, so at present these methods remain exciting, but generally unproven.

Table 1: Non-exhaustive list of companies who have formed strategic partnerships involving use of AI.

Company Partner Date Started Target Area
Roche1 GNS Healthcare 2018 Drug candidates
Pfizer2 IBM 2016 Drug candidates
Sanofi3 Berg 2017 Vaccines efficiency
Merck4 Numerate 2012 Drug candidates
Amgen5 GNS Healthcare 2018 Drug candidates
AstraZeneca6 Berg 2017 Drug candidates
Evotec6 Exscientia 2016 Drug candidates
Genentech8 GNS Healthcare 2017 Target validation
Takeda9 Numerate 2017 Target validation
GSK

Exscientia10

Insilico11

Cloud Pharmaceuticals12

2017

2017

2018

10 disease targets

Biological targets

Multiple targets

 

Prediction and understanding for smaller data sets

Big data applications are not the sole domain of AI and machine learning; smaller data sets can also be used to generate focused models which are suitable only for a single chemotype. These models can be used to progress a series toward the desired goal, usually removal of some unwanted toxicology, and there are numerous examples where this has been applied – both successfully and unsuccessfully.

Most computational chemists have, at some point, applied machine learning methods to a particularly intractable problem. However, it tends to be a second-tier resource, not because these methods are particularly difficult to employ, but because extracting understanding from these methods is often difficult. Whilst the predictions these models generate tend to be highly reliable, understanding the factors governing the predictability can be difficult or impossible depending on the properties used. If the properties used are chemical drug relevant properties (e.g., logP, PSA, pKa, etc.) then the underlying factors controlling the property under investigation maybe illuminated, but the predictability of the model tends to be lower. If, as often happens, all possible calculated molecular properties are used, then the model predictability tends to increase but the underlying factors controlling the models predictive mechanism can remain a mystery and the user gains no practical insight into the problem, just a method of prediction.

Therefore, there could be a lot of potential in analysing the AI systems currently in operation to confirm the validity of their predictions. It may be possible to use these methods in a way that makes it possible to glean more information about the interplay of factors that govern the predictability of the models, making them more useful to computational chemists.

AI and machine learning at Cresset Discovery Services

Cresset Discovery Services have successfully used machine learning methods when the data is amenable. Of course, producing a ‘black box’ predictive model is not generally enough for us or our customers. Instead, we look for a deeper understanding so a customer can make informed decisions in the context of the rationale behind the predictions made.

AI and machine learning methods are exciting new tools in a growing tool box of approaches which we can apply to bring value to customer projects. The real intelligence lies with the operator in being able to select the appropriate tool for the job. Another comment from the conference mentioned at the start of this blog post: “will AI techniques replace chemists in drug discovery? Probably not for the foreseeable future, but it is clear that chemists who use AI will replace chemists who don’t in the very near future.” Contact us for a free confidential discussion and to discover how we can apply intelligence to your drug discovery challenges.

1) https://www.roche.com/partnering/partnering-in-a-digital-era.htm
2) https://www.pfizer.com/news/press-release/press-release-detail/ibm_and_pfizer_to_accelerate_immuno_oncology_research_with_watson_for_drug_discovery
3) https://www.sanofi.com/en/science-and-innovation/artificial-intelligence-in-healthcare-created-by-humans-to-do-no-harm/
4) http://www.numerate.com/numerate-forms-drug-discovery-collaboration-merck-utilize-numerates-silico-drug-design-technology/
5) https://www.gnshealthcare.com/news/ai-collaboration-gns-amgen-alliance-colorectal-cancer/
6) https://www.astrazeneca.com/content/dam/az/PDF/2018/Pages%2046-49.pdf
7) https://www.evotec.com/en/invest/news–announcements/press-releases/p/evotec-invests-in-exscientia-to-advance-ai-driven-drug-discovery-5502
8) https://www.gene.com/stories/personalizing-the-future-of-healthcare
9) http://www.numerate.com/numerate-takeda-enter-agreement-generate-novel-clinical-candidates-using-ai-driven-drug-discovery/
10) https://www.exscientia.co.uk/news/2017/7/2/exscientia-collaboration-gsk
11) https://www.eurekalert.org/pub_releases/2017-08/imi-iec081417.php
12) https://www.businesswire.com/news/home/20180530006184/en/Cloud-Pharmaceuticals-forms-Drug-Design-Collaboration-GSK

Review of the XXV EFMC International Symposium on Medicinal Chemistry 2018

The September meeting of EFMC-IMCS was in the picturesque city of Ljubljana, nestled amongst the surrounding mountains, with its many bridges, hilltop castle and numerous riverside eateries. The good food, wines and beers alone would have made the visit worthwhile, but there was more than enough excellent science to keep us in the conference centre.

With three separate scientific tracks: Therapeutic Areas, Technologies and Chemical Biology, it was impossible for the 800 plus delegates to see everything (much like an ACS meeting), but the busy schedule meant that there was something for everybody.

A cracking start

Fittingly, the opening on Sunday included a fascinating talk by the Slovenian academic and Zika virus expert Prof. Tatjana Avsic-Zupanc (University of Ljubljana) who was the first to characterize the link between Zika infection and foetal abnormalities and to identify a causative molecular target: PrM protein mutant S139N.

Bayard Huck (Merck Biopharm) presented his interpretation of their strategy towards the evolution of the medicinal chemist, a technology and multi-discipline embracing ‘Versatilist’, the scientist equivalent of a ‘Swiss army knife’. Bayard went on to describe virtual screening (one of Cresset Discovery Services’ areas of expertize) as vital for increasing cost-effectiveness. He also described the current mania with all things AI as being at the peak of inflated expectations in the Gartner hype cycle, although he noted that useful applications are emerging from the hype. Bayard elaborated further on the nature of workspace and how the removal of ‘silos’ is essential for effective innovation to occur between scientists, which sounded remarkably familiar to me; I realised later that it was the open structure of the Cresset office I was thinking of.

Prof. Christa Müller’s (University of Bonn) talk about purines: P2X, P2Y, P1 and P0 related receptors and enzymes, and Prof. Peter Seeberger’s (Max Planck Institute of Colloids and Interfaces) talk about the development and use of a remarkable, state-of-the-art automated glycan synthesiser for synthetic vaccine discovery got the conference off to a cracking start.

First disclosures, GPCRs and other highlights

There were several themes running through the conference including: immuno-oncology, a real opportunity to tackle cancer, targeted degradation as a completely new therapeutic paradigm and the emergence of various AI strategies. Marwin Segler (Benevolent AI) showed how deep learning could be used for evaluation of millions of possible synthetic routes to a novel compound. First time disclosures are shown in table 1.

Table 1: 2D structures of first disclosures.​

Entry Description Structure
1 Risdiplam (RG7916); developed as a splicing modifier for the treatment of spinal muscular atrophy (SMA), from Hoffmann-La Roche and PTC therapeutics
2 Highly selective covalent BTK inhibitor LOU064 from Novartis  
3 Potent Novel Oral sCG Activtor from Bayer: Bay1101042
4 PDE9 selective inhibitor BI409306 by Boehringer Ingelheim for CNS disorders  

 

Risdiplam has the electrostatic hallmarks of an RNA/DNA nucleotide base interactor (a relatively electron deficient heterocycle which is then more suited to face on face interaction with nucleotide bases) in a similar manner to the fluoroquinolone antibiotics e.g., grepafloxacin (Figure 1).

Figure 1. Risdiplam structure (left) with electrostatic surface (red = positive and Blue = negative) and that of Grepafloxin (right).

GPCR talks that caught my eye are captured in table 2 and include: A MgluR2 ligand talk by Jose Cid (Jannsen), a GabbaB ligand talk by Sean Turner (AbbVie). Also, a very nice talk by Anne Valade (UCB) describing deuterium exchange as a method for identifying the allosteric ligand binding site for the dopamine D1 receptor. Finally, a first disclosure from Stefan Bäurle (Bayer) on their collaboration with Evotec.

Table 2: GPCR ligand structures.

Entry Description Hit Optimized lead
5 Metabotropic glutamate receptor 2 from Janssen
6 GabbaB positive allosteric modulators from AbbVie
7 Bradykinin B1 antagonist first disclosure Bay-840 from Bayer-Evotec

Other ligands captured from talks that were interesting are shown in table 3. Of particular interest was the superb talk by Allan Jordan, Cancer Research UK, which possibly wins the prize for the ugliest looking validated hit.

Table 3: Ligands from other diverse talks of note.

Entry Description Hit Optimized lead
8 David Tully from Novartis described their FXR agonist for NASH: Tropifexor
9 Anna Miggalautsch Graz University  Adipose trigliceride lipase – mouse active Atglistatin
10 Paul Fish from UCL Notum inhibitor for AD
11 Allan Jordan NMT1 inhibitor CRUK
12 Rainer Machauer disclosure on CNP-520 a new Bace2 inhibitor from Novartis
13 Juraj Velcicky Gamma Secretase-like membrane bound peptidase SPPL2A developed from Gamma Secretase SAR, for Endometriosis

 

Overall it was a successful, enjoyable and informative EFMC-ISMC symposium.