Drug interactions (ICH M12) and QT assessment (ICH E14/S7B)

ICH M12 status

The first globally harmonised guidance on pharmacokinetic drug interactions mediated by metabolic enzymes and transporters. Step 4 in 2024, then:

It replaces the previous FDA in vitro and clinical DDI guidances and EMA's DDI guideline as the operative framework.

The stepwise, risk-based approach

1. In vitro characterisation — is the drug a substrate, inhibitor or inducer of the major enzymes and transporters?
2. Basic models with conservative cut-offs — do the in vitro data rule the interaction out?
3. Mechanistic static or PBPK modelling — refine a positive basic-model signal.
4. Clinical study — where modelling cannot rule it out or the interaction is decision-relevant.
5. Labelling — dose adjustment, contraindication, or monitoring.

The basic models are deliberately conservative: they are built to over-predict, so a negative result is meaningful and a positive one is a trigger for further work, never an estimate of clinical magnitude.

Basic model cut-offs

The hepatic inlet concentration for uptake transporters is

Iu,inlet,max = fu * (Imax + Fa*Fg*ka*Dose / (Qh * RB))

which is higher than systemic Imax and is what the liver actually sees during absorption.

An alternative induction assessment is the correlation / relative induction score approach, calibrated against known inducers, which is less conservative than the basic R3 model.

Mechanistic static model

AUCR = 1 / (Ag·Bg·Cg·(1 - Fg) + Fg)  ×  1 / (Ah·Bh·Ch·fm + (1 - fm))

with, at each site,

A = 1 / (1 + I/Ki)                                    reversible inhibition
B = kdeg / (kdeg + kinact·I/(KI + I))                 time-dependent inhibition
C = 1 + d·Emax·I/(EC50 + I)                           induction

fm and Fg dominate the result. The ceiling on any inhibition of a single pathway is 1/(1 - fm): with fm = 0.9 no inhibitor can raise AUC more than 10-fold, and with fm = 0.7, no more than 3.3-fold. These two fractions are usually the least well established inputs, and a sensitivity analysis across their plausible range is more informative than the point prediction. ddi_static.py --msm prints the ceiling alongside the prediction.

Perpetrator classification

Clinical study design points

• Use index perpetrators (itraconazole or clarithromycin for strong CYP3A4 inhibition, rifampicin for strong induction, and the corresponding index substrates) so the result is interpretable against the classification bands.
• Worst-case first: a study with a strong index perpetrator that shows no interaction removes the need for weaker ones.
• Induction requires multiple-dose administration of the perpetrator; a single dose can even show inhibition from the same compound.
• Timing matters for time-dependent inhibition and for induction, both of which take days to develop and days to reverse.
• A cocktail study can assess several pathways at once, provided the probes are validated as non-interacting.

QT assessment: ICH E14 and S7B

The framework

• The threshold of regulatory concern is a QTc effect above 10 ms, assessed as the upper bound of the two-sided 90% confidence interval for placebo-corrected change-from-baseline QTc (ΔΔQTc) at the clinically relevant high exposure.
• A prospective concentration-QTc analysis on Phase I data can substitute for a dedicated thorough QT study, and this is now the routine path.
• The 2022 E14/S7B Q&As introduced the "double negative" integrated nonclinical risk assessment — a negative hERG assay plus a negative in vivo QTc study — as supplementary evidence. This allows a submission to cover high clinical exposure without attaining a high multiple of clinically relevant exposure, and its uptake in FDA reviews rose sharply after 2022.

Getting a C-QTc analysis right

• Correction method: QTcF (Fridericia) is the standard. QTcB (Bazett) over-corrects at high heart rates and should not be primary. Where heart rate changes materially with treatment, a study-specific or individual correction is preferable.
• Model: linear mixed effects on time-matched ΔQTc against plasma concentration, with a random intercept and slope per subject and a treatment-specific intercept. Assess the intercept — a non-zero one suggests the baseline or the placebo correction is wrong.
• Linearity: the extrapolation to supratherapeutic exposure depends on it. Check for curvature, and check that the highest observed concentrations actually cover the exposure of interest.
• Hysteresis: if the QTc effect lags concentration, a direct model is misspecified and an effect compartment is needed. Plot ΔQTc against concentration coloured by time to see it.
• Sample size is driven by the number of subjects and the spread of concentrations achieved; a study where everyone has similar exposure estimates the slope poorly regardless of N.

exposure_response.py --cqtc implements the ordinary linear version for screening and flags extrapolation beyond the observed concentration range. It is not a substitute for the mixed model in a submission.