Target occupancy — how you define it can make or break your drug
Why 100% target occupancy can be misleading for soluble targets — total target accumulation reflects binding, not the free-target reduction that actually drives therapeutic effect, and how the two can diverge based on complex vs. target half-life.
A drug can hit 100% “target occupancy” and still fail. Here's why.
For soluble targets at low concentrations (think cytokines), the usual PD assay is total target. If two ascending doses show no further accumulation, it's tempting to call the target saturated.
But when an antibody targets a cytokine, the antibody's half-life is usually longer than the target's — so binding "stabilizes" the target, and total target accumulates above baseline. That accumulation proves binding is happening. However, it doesn't prove free target has dropped enough for a therapeutic effect.
Target occupancy = bound target / total target. The saturated accumulation level (relative to baseline) is roughly set by the ratio of the drug-target complex half-life to the target's own half-life. If your complex's half-life is 100x the target's, occupancy approaches 100%, while telling you almost nothing about how much free target actually dropped. And free target reduction is the effect you're actually chasing.
So what does this mean for clinical strategy? It depends on whether a free-target assay is even feasible — post-dose, free target may be too low to measure reliably. Total target is still useful: with the right PK/PD model, it can reveal real information about the target. But it isn't sufficient on its own to determine the right dose or the right outcome.
Free target reduction is the outcome that matters.
Some references in the first post below
De-risk your drug development strategy through mechanistic modeling. Learn more at pharmath.io.
#PKPD #RO #DrugDevelopment #Biologics #TMDD
A non-exhaustive list of references for a deeper dive:
Stein, A. and Ramakrishna, R. https://doi.org/10.1002/psp4.12169
Dua P, Hawkins E, van der Graaf PH. doi: 10.1002/psp4.41
Kapitanov et al., doi: 10.3389/fbinf.2021.731340 — "Right Dose" section.
Agoram BM. doi: 10.1111/j.1365-2125.2008.03297
Lowe, P.J., et al. https://doi.org/10.1111/j.1742-7843.2009.00513.x
Chimalakonda AP et al. doi: 10.1208/s12248-013-9477-3.
Davda JP, Hansen RJ. doi: 10.4161/mabs.2.5.12833.
Mager DE, Jusko WJ. doi: 10.1023/a:1014414520282.
Translating biologics PK from preclinical species to humans
A practical breakdown of the approaches for translating biologics PK from preclinical species to humans — from full PBPK to minimal and site-of-action models — and how to pick the right one for your program.
Clients often need to translate biologics PK from preclinical species to humans. The right method depends on the modeling needs of the program.
If full PBPK is required, for mAbs the standard in my practice remains the Shah & Betts PBPK [1] or the Jones et al. paper [2]. More broadly, anything from Shah's lab over the past 15+ years is gold for understanding biologics distribution. For translation, you can take the rates and volumes from the original papers, calibrate against available data, and apply allometric scaling to translate to human.
When a full PBPK model is overkill, several minimal PBPK models can be applied depending on the situation — [3], [4], [5] (and many others!). These models offer the advantage of physiological detail, but the disadvantage is that they don't easily convert to a typical two-compartment framework — which can limit intuition when discussing with a classically trained pharmacometrician.
Sometimes you only care about what happens in a specific organ. In that case, an ultra-minimal PBPK model focused on the site of action may be preferred. I was fortunate to be involved in publishing two such models — SoA [6] and piPK [7]. These still describe the compartment of interest physiologically, while using parameters from classical two-compartment PK models.
For the classical two-compartment model itself, standard allometric scaling applies — Betts et al. is a useful reference here: [8].
The right approach depends heavily on your program's specific needs.
What am I missing? Do you have a go-to method or publication for this?
References in the first comment below 👇
#QSP #Pharmacometrics #PBPK #DrugDevelopment #Biologics
De-risk your drug development strategy through mechanistic modeling. Learn more at pharmath.io.
References:
[1] Shah DK, Betts AM. J Pharmacokinet Pharmacodyn. 2012. doi: 10.1007/s10928-011-9232-2
[2] Jones HM et al. CPT Pharmacometrics Syst Pharmacol. 2019. doi: 10.1002/psp4.12461
[3] Cao et al. J Pharmacokinet Pharmacodyn. 2013. doi: 10.1007/s10928-013-9332-2
[4] Bloomingdale P et al. J Pharmacokinet Pharmacodyn. 2021. doi: 10.1007/s10928-021-09776-7
[5] Spinosa P et al. CPT Pharmacometrics Syst Pharmacol. 2026. doi: 10.1002/psp4.70167
[6] Kapitanov GI et al. Front Bioinform. 2021. doi: 10.3389/fbinf.2021.731340
[7] Kapitanov GI et al. CPT Pharmacometrics Syst Pharmacol. 2026. doi: 10.1002/psp4.70160
[8] Betts A et al. MAbs. 2018. doi: 10.1080/19420862.2018.1462429.