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.
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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