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Simcyp® Lactation

Advanced PBPK Modeling to Predict Drug Exposure in Lactating Individuals and Breastfed Infants

预约演示

Advancing inclusion of breastfeeding individuals in drug development with PBPK modeling

Simcyp® Lactation supports lactating individuals and their breastfed infants, one of the most underserved populations in drug development. Because lactating women are routinely excluded from clinical trials, clinicians are left to make dosing decisions without evidence, creating uncertainty for both the physician and patient. Total avoidance of medications while breastfeeding is also not feasible.

As part of Simcyp Simulator, the most widely adopted platform for physiologically-based pharmacokinetic (PBPK) modeling, Simcyp Lactation uses mechanistic, physiology-based modeling to predict drug exposure in the lactating individual and the quantity of drug transferred into breast milk, enabling informed assessment of infant exposure. For this historically overlooked population, simulation offers a rigorous, ethically sound path to evidence-based dosing.

Predict maternal and infant exposure
Combine drug-related data with physiological changes
Assess impact of inter-individual variability on drugs PK
Evaluate requirement of dose adjustment or proposing new dosing regimens during breastfeeding
Predict relative infant dose and milk to plasma ratio
Support ICH E21 guideline

ICH E21: A regulatory call to action

The ICH E21 guideline on inclusion of pregnant and breastfeeding individuals in clinical trials marks a significant shift in global regulatory expectations. For the first time, regulatory agencies, including the FDA and EMA, have issued coordinated guidance encouraging the proactive inclusion of lactating populations across clinical development.

ICH E21 explicitly supports the use of PBPK modeling as a tool for:

Predicting drug exposure in lactating women and breast-milk-exposed infants when direct clinical data are limited

  • Informing study design for lactation-specific pharmacokinetic studies
  • Characterizing variability in drug exposure across the postpartum period
  • Supporting labeling decisions with model-informed evidence

For drug developers, ICH E21 is both a mandate and an opportunity. Companies that build lactation PBPK modeling into their development strategy will be better positioned to meet evolving regulatory expectations, strengthen label language, and serve a population that has long been without answers.

Simcyp experts have worked alongside global regulators for over two decades to advance the acceptance of simulation-based approaches. The Lactation Module is purpose-built to generate the evidence regulators are now asking for.

Hear from mechanistic modeling experts Dr. Karen Rowland Yeo and Dr. Piet van der Graaf about using QSP and PBPK modeling approaches to streamline regulatory submissions.

Key features of Simcyp Lactation

Two mechanistic model types to match your data and question

The module offers three Perfusion-limited Lactation models (predicts M/P ratio from QSAR parameters, assuming only unbound/unionized drug crosses the blood-milk barrier) and a Permeability-limited Lactation model (calculates M/P ratio from full plasma/milk concentration-time profiles, and can incorporate transporter kinetics and permeability data). Both account for milk pH and milk protein/lipid binding, letting you choose the level of mechanistic detail appropriate to your compound.

Static and dynamic simulation options

Static: changing maternal physiology with post-partum age but static milk composition and infant weight and daily intake

Dynamic: changing maternal physiology with post-partum age plus changing milk composition and changing infant weight and daily intake

Transporter-mediated and permeability-limited drug transfer

Beyond passive diffusion, the Permeability-limited model can incorporate active transporter kinetics (e.g., BCRP, OATP, P-gp) at the mammary epithelium, supported by in vitro mammary epithelial cell (MEC) permeability data which is important for drugs like cimetidine and atenolol that are actively transported into milk.

Parent-metabolite modeling

Both lactation model types can simulate parent drug and metabolite simultaneously (e.g., fluoxetine/nor-fluoxetine), giving a fuller picture of infant exposure beyond the parent compound alone.

Built-in infant exposure and safety metrics

The module automatically calculates key safety outputs including Infant Daily Dose (IDD), Relative Infant Daily Dose (RIDD), and M/P ratio, using Cmax, Caverage, or cumulative dose methods, directly supporting dose-adjustment decisions and regulatory submissions.

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预约演示

Connect with our experts to see how Simcyp Lactation can support your drug development program.

Predict maternal and infant drug exposure with mechanistic PBPK modeling
Generate regulatory-ready evidence for lactating populations
Inform the design of targeted lactation pharmacokinetic studies

联系我们

Simcyp Lactation FAQs

What is Simcyp Lactation?

Simcyp Lactation is a module within Simcyp Simulator that uses PBPK modeling to predict drug exposure in the lactating individual, the quantity of drug transferred into breast milk, and potential exposure in the breastfed infant.

How does Simcyp Lactation support drug development?

Simcyp Lactation uses PBPK modeling to predict drug distribution into breast milk and potential infant exposure, providing quantitative evidence to inform dosing decisions for an understudied population. By addressing evidence gaps where lactation-specific clinical data are limited, Simcyp Lactation can help inform drug development strategies and support the evaluation of medicines for use during breastfeeding.

How does Simcyp Lactation align with the ICH E21 guideline?

Simcyp Lactation supports the objectives of ICH E21 by using PBPK modeling to predict drug exposure in lactating individuals and breastfed infants, particularly when clinical data are limited. It can help inform lactation-specific study design, characterize variability in drug exposure across the postpartum period, and provide model-informed evidence to support regulatory and labeling decisions.

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