Case Study
Reducing First-in-Human Uncertainty Through
Predictive PK Modelling
A development-stage molecule with promising efficacy faced uncertainty in human pharmacokinetics and dose selection. We applied mechanistic modelling to enable confident first-in-human planning.
Mechanistic modelling pathway
The Challenge
Translational uncertainty threatening
clinical progression
Despite encouraging preclinical efficacy, the molecule presented significant uncertainty in translation to human pharmacokinetics.
- Inconsistent scaling from animal models to predicted human exposure
- Limited mechanistic understanding of absorption and clearance pathways
- High variability across preclinical datasets from different species
- Unclear and poorly justified dose range for first-in-human studies
Clinical implications
Why This Posed a Critical Development Risk
Empirical extrapolation was
insufficient
Traditional allometric scaling approaches provided divergent predictions, making dose selection unreliable and scientifically unjustifiable for regulatory submission.
What was needed
A more predictive and integrated approach was required to bridge preclinical data to human outcomes — one that was mechanistically grounded, quantitatively rigorous, and aligned to clinical exposure targets from the outset.
Our Approach
A mechanistic, model-informed
development strategy
We implemented an integrated approach combining physiologically based pharmacokinetic modelling with preclinical data to simulate human pharmacokinetics before clinical entry.
Data Integration
Collation of in vitro ADME data and in vivo PK across species. Identification of key drivers of variability including solubility, permeability, and clearance mechanisms.
PBPK Model Development
Construction of a physiologically based pharmacokinetic model incorporating absorption kinetics, first-pass metabolism, and tissue distribution parameters.
Scenario Simulation
Simulation of multiple clinical dosing scenarios. Sensitivity analysis to evaluate the impact of formulation variables and physiological variability on predicted exposure.
Dose Selection Framework
Identification of the therapeutic exposure-response window. Translation into a rational starting dose and evidence-based escalation strategy for regulatory submission.
Process at a glance
Key Intervention
Anchoring dose selection to
predicted human exposure
Rather than relying solely on empirical scaling, we anchored dose selection to predicted human exposure targets, informed by mechanistic modelling and quantified uncertainty analysis.
Alignment of formulation strategy with predicted PK behaviour in vivo
Quantification of uncertainty before clinical entry — not after
Data-driven decision-making shared across clinical, regulatory, and development teams
The strategic pivot
“Not empirical extrapolation — mechanistic prediction anchored to clinical relevance.”
This shift from allometric scaling to PBPK-informed dose selection is precisely where development risk is reduced and clinical confidence is built.
Outcomes
Confident clinical progression
from a predictive foundation
What Made the Difference
Mechanistic rigour over
empirical assumption
Four distinguishing factors that separated this programme from conventional development approaches and from the standard CRO model.
Mechanistic PBPK modelling applied instead of unreliable empirical allometric extrapolation
Integration of formulation considerations into PK predictions — not treated as a separate workstream
Early alignment between preclinical data and clinical objectives established before study design was finalised
Iterative refinement using scenario-based simulations to quantify uncertainty and optimise the dose strategy
Strategic Impact
Programme-level value
beyond the model
The mechanistic modelling approach delivered strategic value well beyond the individual dose decision.
Accelerated transition from preclinical to clinical development
Reduced development risk associated with dose selection uncertainty
Improved likelihood of meaningful first-in-human study outcomes
Strengthened regulatory readiness through data-backed dose justification
Where This Approach Applies
Built for programmes where
PK uncertainty is the constraint
This methodology is directly applicable wherever human pharmacokinetic prediction is uncertain and clinical dose selection is at risk.
Process Snapshot
Five stages to
clinical readiness
Data aggregation
Mechanistic modelling
Scenario simulation
Dose optimisation
Clinical readiness
Details have been generalised to preserve client confidentiality whilst accurately representing the scientific methodology and development approach applied throughout this programme.
Work With Us
Planning a first-in-human
study?
We help translate preclinical data into confident clinical decisions through predictive modelling and integrated development strategy. We work with complex, high-risk assets to design structured, de-risked pathways to clinical success.
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