To say many life changes occur during pregnancy would be an understatement. While many women change their behavior for the health of their baby, such as giving up caffeine and alcohol, not all risks to the fetus may be avoided. For example, not all medications a woman takes may be given up when she becomes pregnancy, particularly if not taking the medicine would create greater risks for the baby, such as in the case of HIV. In addition, pregnancy can induce health conditions, requiring medication. In fact, 64% of women continue to take prescribed medications during pregnancy.
As a woman progresses through pregnancy, the changes in her body can have an effect on the medications she takes. In addition to weight gain, the output of the heart increases, as well as blood volume and kidney filtration rates, important factors in drug metabolism. For example, Indinavir is a treatment for HIV whose concentration in the body changes with pregnancy. A dose that was sufficient for a woman before she became pregnant becomes too low during pregnancy to prevent a breakthrough of resistant virus, which could put her and the baby at risk. For this reason, FDA recommends that Indinavir by itself should not be prescribed to pregnant women.
So how do you determine the correct drug dose for pregnant women? You certainly can’t test out the drug at different doses on a population of pregnant women, as such testing may last longer than pregnancy and may cause damage to the fetus. In addition, you cannot collect multiple blood samples from the fetus during pregnancy. Instead, pharmacologists use mathematical models to predict the levels of drugs in a patient’s system.
Zufei Zhang and Dr. Jashvant Unadkat at the University of Washington believe that PBPK modeling may be the answer. PBPK, or physiologically based pharmacokinetic modeling, is the use of mathematical models to predict the levels of drugs in a patient’s system. Using data from in vitro, pre-clinical and clinical studies, it can estimate drug absorption, distribution, metabolism and excretion. This model breaks down the drug’s path through the body into discrete compartments, often organs and tissues, through which the drug travels at different rates or is chemically modified. This is how a pharmacologist sees a person:
Each compartment has its own equation, factoring in blood flow and volume, surface area, drug permeability, and information about drug transformation before elimination. For example, one compartment may have transporters that can specifically transport the drug. A drug may travel very fast through one compartment, but slowly through another. In addition, the drug’s chemical composition may be modified as it moves from one compartment to the next, on its way to a form that can be excreted from the body.
Previous models of the mother and fetus looked like this:
However, these models do not account for the growth of the fetus over time. For example, the ability of the fetus to metabolize drugs will change. In addition, the placenta contains transporters which actually efflux, or transport back out, drugs away from the fetus. Throughout the pregnancy, the numbers of these transporters change, affecting the ability of the placenta to protect the fetus from drug exposure. The amniotic compartment is also considered in their model. Drugs that are metabolized by the fetus are excreted into the amniotic fluid, which can then be redistributed in the mother or fetus.
Zhang modified the model to include these parameters:
Solid arrows: Tissue blood flow; dashed arrows: Clearance. CLPDMP and CLPDFP: bidirectional passive diffusion clearance between mother and placenta and that between fetus and placenta, respectively; CLMP and CLPM: unidirectional transporter-mediated clearances between mother and placenta. CLFP and CLPF: unidirectional transporter-mediated clearances between fetus and placenta. CLMA and CLAM: directional transporter-mediated clearances between mother and amniotic fluid. CLrenal: fetal renal clearance. CLmet, fetal hepatic metabolic clearance. CLreabsorp: fetal swallowing.
Zhang used Midazolam, a sedative, as a probe drug in which to test her refined PBPK model. When compared with known data, her model accurately predicted the concentration of the drug in the mother and her fetus at the time of birth. This suggests that this PBPK model could be used to estimate maternal and fetal exposure to Midazolam and drugs that have similar properties.
One advantage of PBPK modeling is its ability to incorporate variables such as different drug formulations, drug compounds, extrapolation across species, and biological changes over time. As previously mentioned, the ability of the placenta to transport drugs changes during pregnancy. Zhang then used the PBPK model to simulate maternal and fetal drug levels of Didanosine, an antiretroviral for HIV, in two different situations. In one, the drug passed through the placenta to the fetus. In the other, placenta included transporters which efflux the drug. This demonstrated the ability of the model to test multiple variables to create custom predictions of drug exposure.
The use of PBPK modeling in pharmacology is on the rise, due to increased computing capabilities and the recent support of the FDA. Recent regulatory attention and successful FDA applications will continue to promote PBPK modeling to inform trial design and dose regimens, improving the chance for success of novel drugs and safer exposures for pregnant mothers.
Ke, A.B. et al. (2014) Pharmacometrics in pregnancy: An unmet need. Annu Rev Pharmacol Toxicol. 54: 53-69.