iiinova — where your digital self keeps evolving

Alexander Robertson

@alexrob.bsky.social

359 followers 785 following 22 posts

PhD candidate at UW Seattle. I study how interactions between viruses shapes their evolution. Also 📷, 🎹, 📽️, 🐦

Posts Replies Media

Alexander Robertson @alexrob.bsky.social · 08/12/2025

Big picture: the feedback loop between viral density leading to social interaction, which leads to realized phenotype, which alters fitness, leading to new viral densities, should be considered when designing optimal treatments.

A cyclic plot depicting how viral density determines the degree of intracellular interactions, which in turn shapes realized phenotypes, influencing absolute fitness, which then feeds back into viral density in the next generation.

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Alexander Robertson @alexrob.bsky.social · 08/12/2025

When we simulated clinical trials using weaker drugs, they cleared later on average (mostly due to fewer early clearers). But the overall viral load was lower in the x100 group, and resistance frequency was likewise reduced.

We simulated clinical trials of individuals treated with drugs 1×, 10×, 100× and 1,000× weaker than pocapavir. Reducing drug potency delayed mean clearance time measured by days post infection but had a non-monotonic effect on the sum viral load over the course of the infection (n = 100 for each group). In both plots, dots represent individual simulations and are colored based on the frequency of resistance in the population over the course of the infection (scale in right panel)

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Alexander Robertson @alexrob.bsky.social · 08/12/2025

Counterintuitively, our model suggests that a weaker treatment may maintain enough coinfection to keep resistant viruses wrapped in susceptible capsids, while still maintaining low viral load.

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Alexander Robertson @alexrob.bsky.social · 08/12/2025

The reason for this was due to the effectiveness of the treatment. As the treatment works, viral density collapses, coinfection plummets, and genotypes become linked to their phenotype. Resistant genomes are wrapped in their own proteins, and the drug now selects for them.

At low MOI, viruses singly infect cells, and rare resistant genomes are encapsidated by phenotypically resistant capsids, enabling selection for resistance.

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Alexander Robertson @alexrob.bsky.social · 08/12/2025

Adding a simple immune clearance step to our model and running out over multiple generations lets the model reproduce the pattern observed in the clinical trial, where late clearers predominantly had resistant infections.

Clearance dates from the observed clinical trial and one simulated clinical trial of n = 93 viral populations treated with pocapavir. The dashed line indicates the date of the earliest placebo clearance. As in the clinical trial, we observed a population of early clearers (who cleared before the earliest placebo clearance date) and late clearers, who cleared on par with placebo.

Late and early clearers both experienced drops in resistant and susceptible viral population size with diverging outcomes following the population bottleneck. Throughout the figure, resistant variants are illustrated in red and susceptible variants are illustrated in blue.

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Alexander Robertson @alexrob.bsky.social · 08/12/2025

At high density, viruses have a much higher rate of coinfection. Rare resistant genomes get packaged into capsids with lots of susceptible proteins. This means that the drug works well and resistance is suppressed.

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Alexander Robertson @alexrob.bsky.social · 08/12/2025

Our model captures the dynamics observed in cell culture, where coinfection with larger susceptible virus populations reduces resistant viral yield. We realized, however, that the MOI was quite large, and this was only a single round of passaging.

Resistant viral yield under different intensities of susceptible virus coinfection shows a density-dependent effect in vitro (again, compared to Tanner et al.'s 2014 paper mentioned above) and in silico under our model.

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Alexander Robertson @alexrob.bsky.social · 08/12/2025

So what gives? To investigate this discrepancy, we built an eco-evolutionary model for poliovirus treated by pocapavir and found that 𝐚 𝐬𝐢𝐧𝐠𝐥𝐞 𝐦𝐨𝐝𝐞𝐥 𝐜𝐨𝐮𝐥𝐝 𝐞𝐱𝐩𝐥𝐚𝐢𝐧 𝐛𝐨𝐭𝐡 𝐨𝐮𝐭𝐜𝐨𝐦𝐞𝐬.

A cartoon schematic of the eco-evolutionary model that we developed.
We simulate intracellular poliovirus dynamics in four stages: (1) viral entry into host cells, (2) genome replication with mutation, (3) production of capsid subunits and (4) assembly and packaging of progeny virions. We then apply pocapavir.

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Alexander Robertson @alexrob.bsky.social · 08/12/2025

However, clinical trials with pocapavir had mixed results. In 3/4 placebo-matched groups, pocapavir did not significantly reduce clearance time in people infected with OPV. Additionally, resistance evolved in nearly half of the experimental group, highly enriched compared to placebo.

In the clinical trial reported by Collett et al., pocapavir failed to significantly reduce time to infection clearance compared with a placebo in three of four matched groups of participants administered the live attenuated poliovirus vaccine, and resistance was enriched in the pocapavir group. Points represent the clearance dates of individual trial participants and are colored by resistance status (resistance in red, susceptible in blue), and grey boxes indicate dates that were not sampled during the trial (DPI, days post infection).

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Alexander Robertson @alexrob.bsky.social · 08/12/2025

A previous study by Tanner et al. showed the power of this phenomenon. They studied pocapavir, a capsid inhibitor that targets poliovirus. In coinfected cells, increasing amounts of susceptible viruses decreased resistant virus output, suggesting that resistance should be hard to evolve in PV.

In vitro experiments by Tanner et al. demonstrated that coinfection of drug-resistant (res.) and susceptible (sus.) poliovirus strains suppresses the yield of resistant virus under pocapavir treatment.

Link to their study: https://elifesciences.org/articles/03830

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Alexander Robertson @alexrob.bsky.social · 08/12/2025

First, some background. Most organisms have a direct link between their genotype and their phenotype. However, since viruses replicate inside of cells, different genotypes can co-occupy a cell and “share” their proteins with each other.

A two-part comparison of how resistance evolves in bacteria versus viruses.
The top panels show bacteria: rare resistant cells (red) become common after treatment because each cell’s genes match its traits.
The bottom panels show viruses: resistant and susceptible genomes (red and blue) can mix inside the same infected cell, creating viral progeny with "chimeric" capsids. Mixed particles carrying resistant genomes may still get blocked by the drug (yellow triangles), even if their capsid has some number of resistant subunits.

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