PBPK for Pregnancy and Lactation

1. What Is Pregnancy and Lactation PBPK?

Physiologically based pharmacokinetic (PBPK) modeling describes drug disposition by combining information about a drug with information about the physiology of a population. Unlike a purely empirical compartmental model, a PBPK model explicitly represents physiological organs, tissues, blood flows, and biochemical processes that influence drug absorption, distribution, metabolism, and excretion.

Pregnancy and lactation are particularly important applications because physiology changes substantially over time. During pregnancy, maternal plasma volume, cardiac output, renal function, body composition, gastrointestinal physiology, and several metabolic pathways can change. These changes can alter systemic exposure to medicines. PBPK models can represent these changes mechanistically and can also be extended to maternal–fetal and postpartum systems. :contentReference[oaicite:1]{index=1}

During lactation, the modeling problem changes. The key questions include how much drug reaches human milk, how milk concentrations change over time, and how much drug a breastfed infant could ingest and subsequently absorb.

2. Why Are Pregnancy and Lactation Important PBPK Applications?

Pregnancy creates a challenging pharmacology problem because the drug is administered to the mother while both maternal and fetal systems may be relevant to exposure and effect. Clinical PK data during pregnancy can be limited, and exposure can change over gestation as physiology evolves.

FDA materials recognize pregnancy as a special population for which PK information can be important for understanding dosing, while FDA's PBPK framework provides a mechanism for integrating drug and system information into quantitative simulations. :contentReference[oaicite:2]{index=2}

Lactation introduces a different pathway: drug administered to the mother can enter breast milk and subsequently be ingested by the infant. Recent PBPK research has developed mechanistic approaches for predicting milk concentrations, milk-to-plasma ratios, and infant dose. :contentReference[oaicite:3]{index=3}

3. What Changes During Pregnancy?

Pregnancy is associated with coordinated physiological changes that evolve throughout gestation and then change again after delivery. For PBPK modeling, these changes are represented as time-dependent system parameters rather than as a single categorical “pregnant” state.

The direction and magnitude of the change are drug- and pathway-dependent. A useful PBPK model therefore does not assume that every drug experiences the same proportional change in clearance or volume of distribution.

4. Building the Maternal Pregnancy PBPK Model

The starting point is usually a validated adult PBPK model. Pregnancy-specific physiology is then introduced to represent the maternal system during the relevant gestational period.

A simplified representation is:

The model may incorporate changes in:

• organ and tissue volumes;
• cardiac output and tissue blood flows;
• glomerular filtration and renal elimination;
• plasma protein concentrations and drug binding;
• drug-metabolizing enzyme abundance or activity;
• transporters where sufficient mechanistic information is available;
• hematocrit and blood-to-plasma relationships; and
• other physiological variables relevant to the drug's disposition.

Published pregnancy PBPK reviews show that these models have been used to explore changes in drug exposure and dosing across pregnancy, while also identifying important gaps in knowledge for several enzymes, transporters, and postpartum physiological processes. :contentReference[oaicite:4]{index=4}

5. Why Clearance Can Change During Pregnancy

In a conventional PK model, systemic clearance is often summarized as a single parameter. In PBPK, clearance can be decomposed into physiological and biochemical processes.

For example, hepatic clearance may involve hepatic blood flow, intrinsic metabolic capacity, protein binding, and transporter processes. Renal clearance can include filtration, secretion, and reabsorption.

Pregnancy can alter several of these components simultaneously. Consequently, a PBPK model can provide a mechanistic explanation for why a drug's clearance changes rather than simply applying an empirical clearance multiplier.

6. Why Trimester and Gestational Age Matter

Pregnancy PBPK models often treat physiology as a function of gestational age. This allows simulations to represent progressive changes rather than treating pregnancy as a single homogeneous state.

This matters because the same maternal dose can produce different exposure profiles at different points in pregnancy. A pregnancy PBPK simulation can therefore compare exposure at multiple gestational ages while keeping the drug model and dosing regimen constant.

7. Modeling Drug Transfer Across the Placenta

When fetal exposure is of interest, the model must extend beyond maternal systemic circulation. The placenta represents the interface between maternal and fetal circulations and can influence drug transfer.

Depending on the drug and model purpose, placental transfer may involve passive diffusion, membrane transport, binding, tissue partitioning, and other processes.

A simplified fetal exposure model might include:

• maternal plasma concentration as the driving input;
• placental permeability or transfer parameters;
• placental blood flow;
• fetal blood flow;
• fetal tissue distribution;
• fetal metabolism and elimination where relevant; and
• fetal growth and changing physiological capacity with gestational age.

Published pregnancy PBPK work has used maternal–fetal models to explore fetal exposure and placental diffusion, while reviews emphasize that fetal and placental data remain important sources of uncertainty. :contentReference[oaicite:5]{index=5}

8. What Can a Fetal PBPK Model Estimate?

A maternal–fetal PBPK model can be used to simulate fetal concentrations or exposure metrics under specified assumptions about placental transfer and fetal disposition.

These outputs are model predictions, not direct observations. Their credibility depends on the quality of the maternal model, placental transfer parameters, fetal physiology, and available validation data.

9. How Lactation Changes the PBPK Problem

Lactation introduces a new physiological interface: the mammary gland and human milk. The relevant question becomes not only how much drug is present in maternal plasma, but how the drug partitions between maternal plasma and milk.

Recent lactation PBPK approaches have incorporated milk composition, drug physicochemical properties, protein binding, partitioning, and estimates of milk intake to predict milk concentrations and infant dose. :contentReference[oaicite:6]{index=6}

10. The Milk-to-Plasma Ratio

A commonly used summary is the milk-to-plasma ratio:

The ratio can vary with time, drug properties, milk composition, maternal plasma concentrations, and the mechanisms represented by the model. Consequently, treating M/P as a universal constant can be inappropriate when concentration-time behavior is strongly dynamic.

Drug properties that can influence milk transfer include:

• lipophilicity;
• ionization and pK_a;
• plasma protein binding;
• milk protein binding;
• partitioning into milk fat;
• milk pH;
• molecular size; and
• active transport processes where relevant.

PBPK models can use mechanistic algorithms or observed clinical M/P information, depending on the drug and intended use. Recent studies have demonstrated both the potential and the limitations of mechanistic milk-transfer predictions. :contentReference[oaicite:7]{index=7}

11. From Milk Concentration to Infant Dose

Once milk concentrations have been predicted, infant exposure can be estimated by combining the concentration in milk with the volume of milk consumed.

A simplified daily infant dose is:

where \(\overline{C}_{\mathrm{milk}}\) represents an appropriate milk concentration summary and \(V_{\mathrm{milk}}\) is the volume consumed over the relevant period.

A weight-normalized dose can then be expressed as:

A commonly reported exposure metric is the relative infant dose (RID), which compares the infant dose received through milk with the maternal dose normalized by body weight:

12. Why Milk Exposure Is Not the Same as Infant Systemic Exposure

The infant does not simply receive the maternal milk concentration directly into systemic circulation. The drug must first be ingested and then undergo infant-specific absorption, distribution, metabolism, and elimination.

An infant PBPK model can account for age-dependent physiology such as body size, organ volumes, blood flows, metabolic capacity, renal function, and other developmental processes.

This is particularly important for neonates and young infants because drug disposition can differ substantially from that of older children and adults. A milk concentration that appears modest may still result in different systemic exposure depending on infant physiology and the drug's oral bioavailability.

13. Pregnancy, Delivery, and Lactation as a Continuum

Pregnancy and lactation are often discussed separately, but from a modeling perspective they can be viewed as consecutive physiological states.

Postpartum physiology does not instantly return to the pre-pregnancy state. For modeling purposes, this means that a lactation model may need to represent postpartum time, changing maternal physiology, milk production, and infant characteristics.

Reviews of pregnancy PBPK have specifically identified postpartum physiology as an area where additional mechanistic information is needed. :contentReference[oaicite:8]{index=8}

14. What Data Are Needed?

Pregnancy and lactation PBPK models require both drug-specific information and physiological information. The data requirements increase as the model becomes more mechanistic.

Data gaps are one of the major limitations of pregnancy and lactation PBPK. Recent systematic work on lactation PBPK found substantial variation in reporting and highlighted uncertainty around mammary physiology, milk intake, model structure, and other parameters. :contentReference[oaicite:9]{index=9}

15. How Should a Pregnancy or Lactation PBPK Model Be Evaluated?

Model development should not stop when a simulation looks plausible. Verification and validation should be considered at multiple levels.

1. Verify the adult drug model. Confirm that the model can describe observed PK in the reference adult population.
2. Verify the physiological model. Check pregnancy or lactation physiological assumptions against appropriate literature data.
3. Compare predicted and observed maternal PK. Where pregnancy data exist, evaluate concentration-time profiles and PK parameters.
4. Evaluate transfer predictions. For fetal or lactation models, compare predicted transfer or milk concentrations with available observations.
5. Evaluate infant exposure. Where data are available, compare predicted infant concentrations or exposure metrics with observations.
6. Perform sensitivity analysis. Identify parameters that drive maternal, fetal, milk, or infant exposure.
7. Quantify uncertainty. Evaluate how uncertain physiological or drug-specific parameters affect conclusions.

FDA's PBPK guidance emphasizes that PBPK analyses submitted for regulatory purposes should clearly describe the model, methods, results, discussion, and supporting information. FDA also notes that acceptance of PBPK analyses in place of clinical PK data is determined case by case based on intended use and the quality, relevance, and reliability of the analysis. :contentReference[oaicite:10]{index=10}

16. Why Sensitivity Analysis Is Especially Important

Pregnancy and lactation models can contain parameters that are difficult to measure directly. Sensitivity analysis helps determine which assumptions have the greatest influence on model outputs.

For a model output \(Y\) and parameter \(\theta\), a local sensitivity measure can be represented conceptually as:

In practice, relative or normalized sensitivity measures are often more useful because parameters may have very different units and magnitudes.

Examples of potentially influential parameters include:

• intrinsic clearance;
• fraction unbound;
• renal filtration or secretion;
• enzyme abundance;
• placental permeability;
• milk partitioning;
• milk intake volume;
• infant oral bioavailability; and
• infant clearance.

Sensitivity analysis can therefore help distinguish conclusions that are relatively robust from conclusions that depend strongly on uncertain physiological inputs.

17. Worked Example: Estimating Infant Exposure Through Breast Milk

Consider a hypothetical lactating individual receiving a drug for which a PBPK model predicts an average breast-milk concentration of 2 mg/L over a 24-hour dosing interval. Suppose the model assumes an infant consumes 0.75 L/day of milk and weighs 5 kg.

Step 1: Daily infant dose

Step 2: Weight-normalized infant dose

Step 3: Relative infant dose

Suppose the maternal dose is 100 mg/day and the mother's body weight is 70 kg.

This calculation illustrates how a PBPK model can connect maternal exposure to an infant exposure metric. It does not, by itself, establish whether breastfeeding is safe or unsafe. Clinical interpretation requires consideration of the drug, infant developmental stage, pharmacology, systemic infant exposure, available clinical evidence, and the uncertainty of the model.

18. What Can Pregnancy and Lactation PBPK Be Used For?

Potential applications include:

• understanding how pregnancy changes maternal systemic exposure;
• supporting dose selection or dose-adjustment evaluations;
• exploring exposure across different gestational ages;
• evaluating pregnancy-related drug-drug interactions;
• estimating maternal–fetal exposure when clinical fetal PK data are limited;
• predicting drug concentrations in human milk;
• estimating infant dose from breastfeeding;
• supporting the design of lactation studies;
• prioritizing drugs for additional pregnancy or lactation investigation; and
• integrating available clinical and mechanistic evidence during drug development.

FDA has also described a PBPK model-informed framework for prioritizing drugs to be studied in pregnant populations, reflecting the potential role of quantitative modeling when clinical evidence is sparse. :contentReference[oaicite:11]{index=11}

19. Pregnancy PBPK and Drug-Drug Interactions

Pregnancy can alter the baseline activity of metabolic and transport pathways. Consequently, pregnancy may interact mechanistically with a perpetrator or victim drug involved in a drug-drug interaction.

For example, if pregnancy increases the activity of an enzyme responsible for eliminating a victim drug, exposure may decrease. If a perpetrator inhibits that pathway, the net result depends on the magnitude of both effects.

Pregnancy PBPK models have been used to investigate such interactions, including examples involving UGT-mediated metabolism and interacting drugs. Published work illustrates how mechanistic models can separate and combine pregnancy-related and drug-related effects. :contentReference[oaicite:12]{index=12}

This is one reason mechanistic PBPK can be more informative than simply comparing observed clearance values between pregnant and nonpregnant populations.

20. Regulatory and Clinical Development Context

Pregnancy and lactation modeling sits within a broader evidence framework rather than replacing clinical, nonclinical, or observational evidence.

FDA's Pregnancy and Lactation Labeling Rule established the modern U.S. labeling framework for pregnancy, lactation, and reproductive potential. FDA states that labeling should summarize relevant risks, discuss supporting data, and provide information useful for clinical decision-making and counseling. :contentReference[oaicite:13]{index=13}

FDA also has a draft guidance specifically addressing PK studies in pregnancy and describes considerations for assessing the effect of pregnancy on PK and, where appropriate, PD. :contentReference[oaicite:14]{index=14}

Internationally, ICH E21 addresses inclusion of pregnant and breastfeeding individuals in clinical trials and emphasizes generating evidence that can support evidence-based decisions about medicine use in these populations. :contentReference[oaicite:15]{index=15}

21. What Pregnancy and Lactation PBPK Does Not Automatically Solve

PBPK can integrate mechanistic information, but it cannot eliminate uncertainty when the underlying biology is poorly characterized.

• Physiological data can be sparse. Some pregnancy and postpartum parameters are not well characterized across all gestational ages.
• Enzyme changes may be pathway-specific. It is inappropriate to assume that all metabolic pathways change in the same direction or magnitude.
• Placental transfer is complex. Maternal exposure does not uniquely determine fetal exposure without assumptions about transfer and fetal disposition.
• Milk transfer may be incompletely characterized. Mechanistic predictions can be sensitive to milk composition, drug properties, and transporter assumptions.
• Infant exposure is uncertain. Milk intake and infant physiology vary with age and individual characteristics.
• Validation data can be limited. Sparse observations make it difficult to evaluate every model component independently.
• Model uncertainty propagates. Uncertainty in one component can affect downstream maternal, fetal, milk, and infant predictions.
• Predictions are conditional. A simulated exposure is a model-based estimate under specified assumptions, not a direct measurement.

A recent systematic review of lactation PBPK models found substantial heterogeneity in reporting practices and identified important knowledge gaps involving mammary physiology, infant exposure, milk intake, and model assumptions. :contentReference[oaicite:16]{index=16}

22. A Practical Pregnancy and Lactation PBPK Workflow

1. Define the scientific question. Is the goal maternal dosing, fetal exposure, milk exposure, infant dose, infant systemic exposure, or another question?
2. Develop or verify the adult drug model. Establish that the drug model can describe relevant adult PK before extrapolating to special populations.
3. Define the population. Specify gestational age, postpartum time, maternal characteristics, infant age, or other relevant factors.
4. Apply physiological changes. Introduce pregnancy or lactation physiology appropriate to the population.
5. Add specialized interfaces. Add placental transfer for fetal modeling or mammary transfer for lactation modeling.
6. Add infant physiology when necessary. Convert milk exposure into predicted infant systemic exposure where the scientific question requires it.
7. Verify the model. Compare predictions against available maternal, fetal, milk, or infant data.
8. Perform sensitivity and uncertainty analyses. Identify assumptions that drive the conclusions.
9. Simulate the intended scenario. Explore doses, gestational ages, postpartum periods, or other relevant conditions.
10. Interpret within the evidence framework. Distinguish measured data, model-derived quantities, assumptions, and uncertainty.

For regulatory submissions, the PBPK analysis should also be documented in a transparent format. FDA's PBPK guidance recommends sections covering the executive summary, introduction, materials and methods, results, discussion, and appendices. :contentReference[oaicite:17]{index=17}

24. References

1. U.S. Food and Drug Administration. Physiologically Based Pharmacokinetic Analyses — Format and Content Guidance for Industry. 2018. FDA guidance. :contentReference[oaicite:18]{index=18}
2. U.S. Food and Drug Administration. Pharmacokinetics in Pregnancy — Study Design, Data Analysis, and Impact on Dosing and Labeling. Draft Guidance for Industry, 2004. FDA guidance. :contentReference[oaicite:19]{index=19}
3. U.S. Food and Drug Administration. Pregnancy, Lactation, and Reproductive Potential: Labeling for Human Prescription Drug and Biological Products — Content and Format. FDA guidance. :contentReference[oaicite:20]{index=20}
4. U.S. Food and Drug Administration. Pregnancy and Lactation Labeling Rule. 2014. FDA. :contentReference[oaicite:21]{index=21}
5. European Medicines Agency. ICH E21 Guideline on inclusion of pregnant and breastfeeding individuals in clinical trials. EMA. :contentReference[oaicite:22]{index=22}
6. Abduljalil K, et al. Physiologically Based Pharmacokinetic Modeling in Pregnancy: A Systematic Review of Published Models. Clinical Pharmacology & Therapeutics. 2018. PubMed. :contentReference[oaicite:23]{index=23}
7. Beery E, et al. Physiologically Based Pharmacokinetic modelling of drugs in pregnancy: A mini-review on availability and limitations. Fundamental & Clinical Pharmacology. 2024. PubMed. :contentReference[oaicite:24]{index=24}
8. Allegaert K, Quinney SK, Dallmann A. Physiologically Based Pharmacokinetic Modeling in Pregnancy, during Lactation and in Neonates: Achievements, Challenges and Future Directions. Pharmaceutics. 2024;16(4):500. PubMed. :contentReference[oaicite:25]{index=25}
9. Nauwelaerts N, et al. Generic Workflow to Predict Medicine Concentrations in Human Milk Using Physiologically-Based Pharmacokinetic (PBPK) Modelling—A Contribution from the ConcePTION Project. Pharmaceutics. 2023. PubMed. :contentReference[oaicite:26]{index=26}
10. Jamei M, et al. Prediction of drug concentrations in milk during breastfeeding, integrating predictive algorithms within a physiologically-based pharmacokinetic model. PubMed. :contentReference[oaicite:27]{index=27}
11. Yeung CHT, Ito S, Autmizguine J, Edginton AN. Incorporating Breastfeeding-Related Variability with Physiologically Based Pharmacokinetic Modeling to Predict Infant Exposure to Maternal Medication Through Breast Milk. AAPS Journal. 2021. PubMed. :contentReference[oaicite:28]{index=28}
12. Kayesh R, et al. Application of Lactation Physiologically Based Pharmacokinetic Modeling to Predict Milk Exposure of Passively Diffused Drugs. Journal of Clinical Pharmacology. 2025. PubMed. :contentReference[oaicite:29]{index=29}
13. Nauwelaerts N, et al. A tutorial on physiologically based pharmacokinetic approaches in lactation research. PubMed. :contentReference[oaicite:30]{index=30}
14. A systematic review of published PBPK models for drug excretion into human breastmilk. Knowledge Gaps and Opportunities to Optimize Reporting and Modeling Practices. 2026. PubMed. :contentReference[oaicite:31]{index=31}

Where to Go Next

A natural progression is to study PBPK for Neonates and Pediatrics, followed by placental transport in greater detail, fetal PBPK, lactation PBPK, milk-to-plasma partitioning, infant exposure modeling, and PBPK approaches for maternal drug-drug interactions.

For lactation specifically, the next tutorial can derive a mechanistic milk-transfer model and show how maternal plasma concentrations are translated into milk concentrations, relative infant dose, and infant systemic exposure.