This week, it was my turn to give the short talk at our group meeting. I chose to present a recently published paper on thermostability prediction for nanobodies. The motivation for this work, at least in part, is the need for thermostability in the diverse applications of nanobodies. At OPIG, our research primarily revolves around the therapeutic uses of nanobodies, but their potential extends beyond this. I thought it would be interesting to highlight some of these broader applications here:
Continue readingAuthor Archives: Gemma Gordon
How can FemTech help close the gender health gap?
An excellent previous blog post from Sarah [1] describes the gender data gap and touches on the fact that women experience poorer healthcare outcomes. This arises from, amongst other things, the historical exclusion of women from clinical trials and this idea of the ‘male default’, where, for example, drug dosages and diagnostic thresholds are benchmarked against men, or even surgical instruments are designed to fit male hands [2]. I thought I would follow up on Sarah’s blog post and discuss how FemTech can help to close this gender health gap.
Continue readingVHH -vs- VNAR
As one of the group’s resident nanobody enthusiasts, on the OPIG retreat this year I presented a talk on shark VNARs, their therapeutic potential and how they differ from VHHs. Here are some of the main points covered:
Continue readingOPIG Retreat 2023
With the new academic year approaching, OPIG flew off to the rural paradise of Wilderhope Manor in sunny Shropshire for their annual group getaway. The goal of this retreat was assumed to be a mixture of team building, ‘conference-esque’ academic immersion, a reconnection with nature in the British countryside, and of course, a bit of fun. It is fair to say OPIG Retreat ‘23 delivered on all accounts, leaving the OPIGlets refreshed and ready for what the next year may bring.
Continue readingAI-pril Fools
As my turn to write a blog post has fallen a few days after April 1st, I decided I would write an April Fools’ Day-inspired post and ask everyone’s favourite chatbot to tell me some jokes.
I asked ChatGPT to tell me a knock-knock joke, prompting it with various topics relevant to OPIG (including AI, antibodies, drug discovery and proteins) to see what it could come up with. I’d argue that we’re playing fast and loose with the definition of a joke (several of these just made me cringe), but here are some of the results…
SUMO wrestling with developability
When engineering antibodies into effective biotherapeutics, ideally, factors such as affinity, specificity, chemical stability and solubility should all be optimised. In practice, we know that it’s often not feasible to co-optimise all of these, and so compromises are made, but identifying these developability issues early on in the antibody drug discovery process could save costs and reduce attrition rates. For example, we could avoid choosing a candidate that expresses poorly, which would make it expensive to manufacture as a drug, or one with a high risk of aggregation that would drive unwanted immunogenicity.
On this theme, I was interested to read recently a paper by the Computational Chemistry & Biologics group at Merck (Evers et al., 2022 https://www.biorxiv.org/content/10.1101/2022.11.19.517175v1). They have developed a pipeline called SUMO (In Silico Sequence Assessment Using Multiple Optimization Parameters), that brings together publicly-available software for in silico developability assessment and creates an overall developability profile as a starting point for antibody or VHH optimisation.
Read more: SUMO wrestling with developabilityFor each sequence assessed, they report factors such as sequence liabilities (residues liable to chemical modifications that can alter properties such as binding affinity or aggregation propensity), surface hydrophobicity, sequence identity compared to most similar human germline and predicted immunogenicity (based on MHC-II binding). Also provided are an annotated sequence viewer and 3D visualisation of calculated properties. Profiles are annotated with a red-yellow-green colour-coding system to indicate which sequences have favourable properties.
Overall, this approach is a useful way to discriminate between candidates and steer away from those with major developability issues prior to the optimisation stage. Given that the thresholds for their colour-coding system are based on data from marketed therapeutic antibodies, and that the software used has primarily been designed for use on antibody datasets, I would be interested to see whether the particular descriptors chosen for SUMO translate well to VHHs, or whether there are other properties that are stronger indicators of nanobody developability.
Llamas and nanobodies
Nanobodies are an exciting area of increasing interest in the biotherapeutics domain. They consist only of a heavy chain variable domain so are much smaller than conventional antibodies (about 1/10th of their mass) but despite this, manage to achieve comparable affinity for their targets, in addition to being more soluble and stable – good things come in small packages! Nanobodies are not naturally produced in humans but can be derived from camelids (VHHs) or sharks (vNARs) and then engineered to humanise them. For the rest of this blog post we will skip over the science entirely and learn how to draw a llama, a great example of a camelid species.
