estradiol pharmacokinetics playground

The line is a lie! The cloud a little less so. All current models tend to underestimate uncertainty, particularly patch models, which may underestimate it by up to a factor of 10.
custom doses
c u dose days model
drag-n-drop csv anywhere to import
repeated doses
c u dose every model

The cloud does not represent data points, it is a visual representation of the uncertainty in the model itself and it underestimates the total uncertainty. It only captures the uncertainty of something akin to a population mean, or more precisely the uncertainty over the parameters of the deterministic estradiol curve. It does not capture the full distribution of outcomes in the population at large. This is bound to change in the future once a better representation of this uncertainty gets implemented (which would include the additional population variability around the deterministic curve).

The emerging consensus is that the pharmacokinetics of estradiol ester depots is highly variable and depends on many factors that are not well understood and rarely taken into account. Those factors can lead to vastly different outcomes and are not well represented, if at all, in the current models. Those include, among others, the ester concentration, the type of oil used, the proportion and type of excipients such as benzyl benzoate that are present in the formulation, the injection site, whether the depot is injected intramuscularly or subcutaneously, the injection depth, individual differences in metabolism, and the presence of other drugs. Some of those confounding factors are sometimes captured to an extent in the uncertainty of the models, but only when the data is abundant and spans multiple studies done under different conditions. This is, of course, generally not the case. Several of those factors also apply to transdermal, oral, and sublingual estradiol. I am actively working to improve and generalize the current models in ways that will better capture this variability, but ultimately their accuracy and scope will always be limited by the data available.

Inferences for the estradiol benzoate, valerate, and cypionate intramuscular depot models (eb im, ev im, ec im) use part of the data collected and made available by the amazing people behind the tfs meta-analysis but will be reprocessed in the future to allow for a better quantification of uncertainty.

The data behind the estradiol undecylate model for intramuscular depots using castor oil (eun im) was entirely reprocessed from scratch using the per-patient data found in Geppert 1975 together with the data and uncertainty reported in Vermeulen 1975. This data is extremely sparse and incomplete and thus the model is highly uncertain.

The data behind the estradiol undecylate model for subcutaneous depots using castor oil (eun casubq) was inferred on top of the eun im model by augmenting it with very sparse self-reported community data. Its predictions are also highly uncertain.

The data behind the estradiol enanthate model for intramuscular depots using sunflower oil (een im) was reprocessed from scratch using studies with Perlutan from the 80s and 90s. This data is also extremely sparse and incomplete and the model highly uncertain.

The once-weekly patch model (patch ow) was inferred using data found in drug labels of once-weekly Climara and Menostar patches. The twice-weekly patch model (patch tw) was inferred using data taken from two 2003 studies by Houssain et al. looking at twice-weekly Estradot and Menorest patches, and in the drug label of twice-weekly Mylan patches. In light of knowledge gathered from self-reported community data, please be advised that both models excessively underestimate the uncertainty which should be close to 10 times larger. The models will be improved to better reflect this in the future. Currently there is no way to change the wearing period of the patch in the interface (i.e. they are fixed at 3½ and 7 days) but it is planned for the future.

The data for the menstrual cycle comes from Stricker et al. 2006. Download the E2/P/LH/FSH data.

Data for target ranges is based on guidance from WPATH Standards of Care, Version 8 in addition to the Endocrine Society's Clinical Practice Guideline.


Note regarding "inapproprite WPATH regimens" presets

In the WPATH Standards of Care (Appendix C, page S254), several hormone replacement therapy (HRT) regimens for transfeminine people are listed. However, some of these recommendations might not align with WPATH's own guidelines and could even be harmful. Here's a breakdown of the issues.

  1. Problem with recommended 5-30 mg intramuscular injections every 2 weeks:
    • The half-life of estradiol valerate and cypionate are approximately 4 and 8 days, respectively. Injecting every 2 weeks requires higher doses and can cause large fluctuations in hormone levels, leading to very high levels shortly after the injection and very low levels before the next injection. This can cause mood swings and potentially restart testosterone production in the last few days before the next injection.
    • Using the lower dose limit (5 mg every 2 weeks), hormone levels may drop too low for monotherapy after about 7 days, potentially allowing testosterone production to restart, and further down below 100 pg/mL at odds with WPATH's own recommended levels.
    • Using the upper dose limit (30 mg every 2 weeks), hormone levels could spike dangerously high (upwards of 2,000 pg/mL with EV and 1,000 pg/mL with EC), increasing the risk of blood clots for those predisposed to thrombotic events.
  2. Problem with recommended 2 to 10 mg intramuscular injections every week:
    • Using the lower dose limit (2 mg every week), hormone levels may consistently stay below 150 pg/mL throughout the week with EC and during the second half of the week with EV, and below 100 pg/mL in the last 2 days of the week for both, which might necessitate using an anti-androgen to suppress testosterone.
    • Using the upper dose limit (10 mg every week), hormone levels may consistently stay above 300-400 pg/mL, which is much higher than WPATH’s own recommended levels.
For a more in-depth analysis we refer the reader to Rothman et al. 2024.


Acknowledgements

Initial idea and development by alix. Code improvement and refactoring by Meringue. Additional help from GearKite, Jess, Xea, and idk2848.

Encouragements, entertainment, and memetic pumping from
Torble Lea photino Annie emily Izzy Ashley Cassandra Sapphira


DISCLAIMER

This page, designed as a playground to explore estradiol pharmacokinetics, provides a simulation for informational and entertainment purposes only. The developer(s) cannot guarantee the accuracy of the predictions generated by the simulator. Users acknowledge that the software is offered "as is," without any warranties. The developer(s) assume no liability for direct or indirect damages, either physical, psychological, or otherwise, resulting from the use of the simulator. Users are strongly advised to exercise caution and seek professional medical advice for health-related queries.

estrannaise.js is entirely client-side. Your data, whether entered, imported, or stashed, remains exclusively within your browser and will never be transmitted to the developer(s) or any third-party by our application.


estrannaise.js (v0.3.6)
JavaScript license information
mit license (c) 2024 alix

If this kind of thing interests you, you can contact me on signal a1ix2.91 or by email _a1ix2_at_proton_dot_me_ (no underscores). For feedback, bugs, and feature requests submit an issue on github.


tipjars (click address to copy)
xmr
85Tz3LXEU65h6Lf6tjXLk744FMYW5f4yeTx1pVjsqURQ3YQxtawQJoMAcXDAtEbyFhBLZeEdsXGmpTy5RzzDCWQo6QvcvzE
btc
1EK9wp876WhA8W3m7a1mR1Aka6LDsoWpCR
ltc
ltc1qz4auzjh0dtfqlu5yjk85tvz83aed60ajmy8src
eth
0xccC52F9E0CFA1D365A69b5D624dA357192bb3c83