The value of MBMA for determining best-in-class
Why Model-Based Meta-Analysis is one of the most useful tools for determining whether a drug pathway has hit its ceiling — and whether a best-in-class molecule is still achievable before you invest in optimization.
Before you optimize a molecule, ask: has this pathway hit its ceiling?
Model-Based Meta-Analysis (MBMA) is a modeling technique for running a head-to-head comparison among therapeutics with similar mechanisms of action in the same indication. It lets you assess the relationship between target engagement and clinical outcome — and determine what properties a molecule needs to hit your Target Product Profile.
I love doing MBMA. There's an efficiency and universality to it: multiple molecules' data get calibrated to a single modeling framework, so you're comparing apples to apples rather than squinting at separate publications. It's also genuinely challenging — sometimes the relationships aren't clean, and the modeling becomes part science, part art. Done well, it's one of the best tools for determining whether a pathway is saturated, and whether there's still room for a best-in-class molecule or optimized dosing — or whether that door is already closed.
Every company going after a clinically validated target needs to benchmark against the clinical competition. But a single head-to-head comparison only tells part of the story — an MBMA across several (or all) competitors gives you the full picture.
De-risk your drug development strategy through mechanistic modeling. Learn more at pharmath.io.
A couple of general references:
Chan et al. PMID: 35174432
Upreti & Venkatakrishnan. PMID: 30993679
Some of the MBMA work I've been involved in:
Johnson et al. https://doi.org/10.1002/psp4.70195.
Panday & Lang et al. https://doi.org/10.1002/cpt.3696
Kapitanov. doi: https://doi.org/10.1101/2021.03.07.21253086
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.