By Sarah Constantin (blog – 1, 2)

The More Dakka story is common in medicine. You do an intervention; the disease doesn’t get better, or gets only marginally better; the research literature concludes it doesn’t work; nobody tries doing MORE of that intervention, but when somebody just raises the dose high enough, it does work.

Examples:

a.) Chemotherapy didn’t work on cancer until doctors made cocktails of drugs, raised the dose so high it would kill you, and then mitigated the side effects with prednisone and intermittent dosing schedules. If they just used a safe daily dose of a single chemotherapeutic agent, they’d have concluded chemo didn’t work.

b.) Light therapy barely works for SAD; two internet-famous people have independently found that REALLY BRIGHT light therapy completely fixes SAD.

c.) The example in the post is about allopurinol. Allopurinol prevents gout attacks by lowering uric acid. “In studies, [allopurinol] improved [uric acid] linearly with dosage. Studies observed that sick patients whose [uric acid] reached healthy levels experienced full remission. The treatment was fully safe. No one tried increasing the dose enough to reduce [uric acid] to healthy levels.“

d.) The standard treatment for hypothyroidism is thyroid hormone. People with “subclinical hypothyroidism”– people whose thyroid hormone levels are lower than average, but still above the cutoff for hypothyroid, and still suffer from exactly the same symptoms as hypothyroid–, ALSO benefit from thyroid hormone therapy. It’s not standard of care yet, though.

e.) I believe some vitamin deficiencies, don’t remember which exactly, are the same way; there’s an official cutoff for “deficient” but people slightly above that cutoff still have symptoms and still experience symptom relief from supplementation.

f.) Same deal with HIV. Virus has a replication rate & a clearance rate; its replication rate is also its mutation rate; an antiviral drug can raise the clearance rate above the replication rate, which will make the population drop exponentially, but if there’s only one drug the virus will have a chance to evolve to be resistant before the population drops low enough to be undetectable. And this is a simple differential equation that you can calculate years before you know what the drugs even are. One drug: death. Two drugs: death. Three or more drugs: survival.

Luckily David Ho was a physicist and thought about it this way, so when the antiviral drugs came out he was ready to test them in cocktails.

So “single antibiotics don’t work for chronic Lyme but cocktails do and this wasn’t realized for decades” isn’t an unprecedented story. It could turn out that way.

I bet this is something that has a more formal and accurate phrasing, but: if there’s an exponential-growth dynamic (like in a malignant cancer or an infection) where you’re trying to kill the exponentially-growing population, and if there’s a dose-response relationship where higher dose = more killing, then you have a bifurcation point in the outcome as t -> infinity, where a dose below that point means the enemy takes over and the patient dies and a dose above that point means “the enemy is killed faster than it can reproduce and so dies out in the long run.” And in principle you can calculate this cutoff if you know the dose-response relationship, as Ho did.

And separately, there’s a safety threshold; is the minimum effective dose safe or unsafe? With chemotherapy, the minimum effective dose is UNSAFE, which is why they have to get clever with ways to give you doses high enough to kill you while keeping you alive anyway. (Or “find a better drug”, but nobody has found a cytotoxic drug with strictly better tolerability/effectiveness tradeoffs since the 1960’s.)

This is kinda how you get a continuous/analog system to give you discrete outcomes: bifurcation points! Works in gene regulation too. “This regulatory gene turns on that gene’s transcription” – well, what’s actually happening is a continuous scalar, a rate of transcription and a rate of clearance, but because exponential functions are involved you get bifurcations in “steady-state” outcomes over the several-hour timescales needed to get to “this cell has tons of mRNAs for that gene or it’s literally empty of them”.

Systems biology is cool, it explains the math that gets you from a statistical-chemistry model of the cell (as a bag of molecules that bump into each other and have a probability of interaction) to a tinkertoy model that you can treat like a graph. (Gene regulatory networks, protein-protein interaction networks, neuron networks, etc.)