Peptide synthesis has become more capable every year. Automated platforms have made sequences that once required weeks of bench time routine in hours. Yet as the modalities being pursued by drug developers grow more ambitious, so do the challenges they bring.
Ask a peptide chemist what makes a sequence difficult, and they'll talk about chemistry. Unnatural building blocks. Cyclizations. Hydrophobic stretches that aggregate mid-synthesis. Residue combinations that create downstream impurities: the N-terminal glutamine that forms pyroglutamate, the aspartate-glycine junction prone to aspartimide formation.
These are real problems. But Oliver describes a second axis of difficulty that operates independently: the operational demand of delivering a large number of sequences on a fixed timeline.
"It's the complexity of the peptides themselves on one hand," he explains, "and the complexity of the overall request — number of sequences, timelines, what can be automated and what has to be done manually."
In the most demanding projects, both arrive at once.
Nowhere is the tension between chemical complexity and timeline pressure more acute than in the manufacturing of personalized neoantigen cancer vaccines.
The concept is well-established: sequence-specific peptides derived from a patient's own tumour mutations are used to train the immune system to recognize and attack cancer cells. In practice, every patient has a unique set of peptides, and each set must be synthesized to GMP-compliant quality before treatment can begin.
"The combination of speed, because the patient is in late-stage treatment, and the quality demanded for peptides going into a human patient is the biggest challenge in this field," Oliver says.
Currently, the end-to-end timeline from receiving sequences to delivering the final product runs approximately six weeks per patient. That target is under pressure from two directions: the urgency of late-stage disease, and tumour biology. Neoantigens can mutate, meaning some targets identified at the start of the process may no longer be active six weeks later.
The practical response is a buffer strategy: requesting more sequences from customers than strictly needed, so that the most challenging to synthesize can be set aside if necessary. But this has limits, and those limits drive the case for reliable synthesis from the first run.
When Oliver's team receives a difficult sequence, the routing decision starts with the fundamentals: scale, purity requirements, and the number of peptides. But for genuinely challenging work, instrumentation capability matters in ways that go beyond throughput.
"It goes beyond looking at a synthesizer as just a pipetting machine," he says. "When you can track parameters within the synthesizer, heating, and real-time Fmoc deprotection monitoring, you get more certainty that the synthesis is working, especially for complex and difficult-to-make peptides."
The ability to apply heat at specific coupling steps, monitor deprotection in real time, and adapt protecting-group strategies for problematic sequences is what makes a difficult synthesis reliable rather than merely faster.
Perhaps the least obvious insight from the conversation is how closely reliability and efficiency are connected in a high-volume service setting.
At a service provider, every synthesis decision carries an economic consequence. Adding more reagent equivalents increases the probability of success, but overshooting has a direct cost. Under-investing and failing has a larger one: resynthesis means lost time, additional reagent spend, and, in a clinical project, a delay in the patient's timeline may not be absorbed.
"Finding the balance," Oliver explains, "is one of the major critical points in everything we do, whether it's automated or manual."
The goal is not maximum resource input. It is the minimum intervention that gives you justified confidence in the outcome.
This is where upstream sequence knowledge, instrumentation that monitors the synthesis as it runs, and accumulated experience across a high volume of diverse sequences all converge. "It is also," Oliver suggests, "where a synthesis partner earns its place in a program earlier than most researchers typically engage in one."
Oliver will share real examples from Intavis's work: sequences that required non-standard synthesis routes, how his team navigates the neoantigen manufacturing timeline, and what instrumentation decisions look like when the margin for error is narrow. He'll also address the regulatory dimension — why the frameworks designed for standard GMP peptides don't map cleanly onto the personalized medicine context.
If your work involves sequences that don't leave room for a second run, Oliver's examples are worth an hour of your time.