Prediction of Fetal Darunavir Exposure by Integrating Human Ex-Vivo Placental Transfer and Physiologically Based Pharmacokinetic Modeling
- 1.1k Downloads
Fetal antiretroviral exposure is usually derived from the cord-to-maternal concentration ratio. This static parameter does not provide information on the pharmacokinetics in utero, limiting the assessment of a fetal exposure–effect relationship.
The aim of this study was to incorporate placental transfer into a pregnancy physiologically based pharmacokinetic model to simulate and evaluate fetal darunavir exposure at term.
An existing and validated pregnancy physiologically based pharmacokinetic model of maternal darunavir/ritonavir exposure was extended with a feto-placental unit. To parameterize the model, we determined maternal-to-fetal and fetal-to-maternal darunavir/ritonavir placental clearance with an ex-vivo human cotyledon perfusion model. Simulated maternal and fetal pharmacokinetic profiles were compared with observed clinical data to qualify the model for simulation. Next, population fetal pharmacokinetic profiles were simulated for different maternal darunavir/ritonavir dosing regimens.
An average (±standard deviation) maternal-to-fetal cotyledon clearance of 0.91 ± 0.11 mL/min and fetal-to-maternal clearance of 1.6 ± 0.3 mL/min was determined (n = 6 perfusions). Scaled placental transfer was integrated into the pregnancy physiologically based pharmacokinetic model. For darunavir 600/100 mg twice a day, the predicted fetal maximum plasma concentration, trough concentration, time to maximum plasma concentration, and half-life were 1.1, 0.57 mg/L, 3, and 21 h, respectively. This indicates that the fetal population trough concentration is higher or around the half-maximal effective darunavir concentration for a resistant virus (0.55 mg/L).
The results indicate that the population fetal exposure after oral maternal darunavir dosing is therapeutic and this may provide benefits to the prevention of mother-to-child transmission of human immunodeficiency virus. Moreover, this integrated approach provides a tool to prevent fetal toxicity or enhance the development of more selectively targeted fetal drug treatments.
Fetal exposure to maternally administered medication is an important determinant of fetal drug toxicity or efficacy. Darunavir crosses the placenta and is frequently used in pregnancy.
We can predict fetal exposure to darunavir, co-administered with ritonavir, by integrating human ex-vivo placental transfer and physiologically based pharmacokinetic modeling.
The placental perfusion setup is a valuable experimental tool that can be integrated with physiologically based pharmacokinetic modeling to simulate fetal drug exposure to darunavir during pregnancy. Fetal darunavir trough concentration is just above the half-maximal effective concentration for wild-type human immunodeficiency virus and may benefit the prevention of mother-to-child transmission.
The approach described in this study can be used to optimize maternal pharmacotherapy to prevent fetal toxicity or enhance the development of more selectively targeted fetal drug treatments.
Many women use medications during the course of their pregnancy for different reasons [1, 2]. For ethical and practical considerations, however, pregnant women are generally excluded from clinical studies [3, 4, 5]. Consequently, for the large majority of medications used during pregnancy, there is inadequate or no information on fetal exposure and safety. This imposes severe limitations on drug therapy during pregnancy as placental transfer potentially poses a threat to fetal well-being. However, it may allow drugs to be administered to the mother for therapeutic benefit of the fetus in utero.
Experimentally, fetal drug exposure remains difficult to quantify, as the fetus and placenta are not readily accessible for sampling until delivery. Sampling of umbilical cord blood and maternal blood at the time of delivery is an ethically acceptable and easily accessible method, allowing the calculation of a cord blood-to-maternal blood concentration ratio. Though a cord blood-to-maternal blood concentration ratio is a clinically useful index of relative fetal drug exposure, there is often large variability between mother-infant pairs owing to variability in the timing of the sample collection relative to the last maternal dose and it does not provide any information on the fetal concentration–time profile [6, 7]. Ex-vivo dual-side perfusion of a single human placental cotyledon has proven to be a clinically relevant model for studying placental transport of various endogenous compounds and xenobiotics . Although this model gives a good estimate of placental transfer, it does not provide information on in-vivo fetal pharmacokinetics and exposure.
Physiologically based pharmacokinetic (PBPK) models may provide a solution. Such models incorporate pregnancy-induced changes in various physiological and anatomical parameters and represent a feasible approach for appropriate dose optimization in pregnant women . A number of pregnancy PBPK (p-PBPK) models have been developed to predict the disposition of various drugs in pregnant women; however, these models are mainly related to maternal pharmacokinetics [9, 10, 11, 12].
It remains a challenge to predict fetal drug exposure using p-PBPK models that include human placental transfer parameters. Various animal models have been used to study placental drug transfer, but data are of poor translational value because of interspecies variability in placental structure and hemodynamics [6, 13]. Very few p-PBPK models include actual human placental transfer parameters from ex-vivo placenta perfusion experiments [14, 15]. Such parameters are obtained by using intact placental tissue with corresponding hemodynamics and should include active as well as passive drug transfer processes.
Darunavir administered once daily (QD) or twice daily (BID), in combination with low-dose ritonavir (darunavir/ritonavir) is a preferred agent to be used in human immunodeficiency virus (HIV)-positive pregnant women . In plasma, darunavir is approximately 94% protein bound, mainly to α-1-acid glycoprotein (AAG). Biotransformation is almost exclusively mediated by cytochrome P450 (CYP) 3A4. Clinically, darunavir is co-administered with the potent CYP3A4 inhibitor ritonavir, to reduce darunavir clearance and maintain higher plasma concentrations throughout the dosing interval . Ritonavir is also known to have potent inhibitory effects on the efflux drug transporter, P-glycoprotein (P-gp), which in the intestine, may contribute to the increased darunavir bioavailability . Recently, we reported on a whole-body p-PBPK model to describe the maternal pharmacokinetics of ritonavir-boosted darunavir in pregnancy . Darunavir was considered a good candidate for further development of the p-PBPK model by including placental transfer and an in-utero compartmental structure because clinical data on its pharmacokinetics in pregnancy and cord blood concentrations are available .
In this study, the main focus was to develop a p-PBPK model to quantitatively predict fetal exposure to the antiretroviral compound, darunavir, co-administered with ritonavir. To achieve this, a previously developed p-PBPK model of darunavir  was extended by incorporating a feto-placental unit into the maternal model. Bi-directional placental transfer parameters for darunavir were determined using the ex-vivo, dual-sided, perfused isolated cotyledon model.
Maternal-to-fetal (MTF) transfer of darunavir has been previously studied with the ex-vivo cotyledon perfusion model . In contrast to this study, we included ritonavir and evaluated MTF and fetal-to-maternal (FTM) placental darunavir transfer to account for the potential interaction with placental drug transporters . In brief, placental transfer parameters for darunavir were determined using the ex-vivo, dually perfused, isolated cotyledon model. Next, the p-PBPK model of darunavir developed previously  was extended by a feto-placental unit including placental transfer based on ex-vivo data using Berkeley Madonna software (Berkeley Madonna Inc, California). After model parameterization, simulated maternal and fetal pharmacokinetic profiles were compared with observed clinical data.
2.1 Placenta Perfusion
The study was approved by the local Ethics Committee of Radboud University Medical Center, Nijmegen, the Netherlands (file number 2014-1397). The experimental setup and methodology were detailed previously . The Electronic Supplementary Material provides more details and specifications on the ex-vivo placenta perfusions and bioanalyses.
A full-body PBPK model comprising 13 tissue/organ compartments was built and coded in Berkeley Madonna syntax, Version 184.108.40.206 (http://www.berkeleymadonna.com/). The model was largely based on the darunavir/ritonavir p-PBPK model developed in Simcyp® Version 13 release 2 (Simcyp Limited, a Certara company, Sheffield, UK), and described previously for maternal darunavir pharmacokinetics . All Berkeley Madonna codes are available upon request. No model fitting and/or parameter estimation was performed in the current study.
2.3 Physiologically Based Pharmacokinetic Model Development
Human physiological parameters were obtained from literature as well as from virtual populations of healthy volunteers and pregnant women implemented in Simcyp®. Physicochemical and in-vitro pharmacokinetic parameters of darunavir and ritonavir were obtained from literature .
Additionally, we explored the influence of different darunavir/ritonavir dosing regimens on fetal exposure. This was mainly done to illustrate its usefulness for assessing exposure–response relations. For this purpose, we assumed a minimum effective fetal plasma C trough of 0.55 mg/L. This is based on the half-maximal effective darunavir concentration for resistant HIV, a target frequently used in therapeutic drug monitoring .
3.1 Ex-vivo Perfusion of Human Placental Cotyledon
A total of 15 placentas were collected from elective Caesarean sections (n = 8) and uncomplicated vaginal deliveries (n = 7). Six perfusions fulfilled the quality criteria for successful perfusion. On average, perfusion was started within 45 min of delivery; which is consistent with previous studies . We conducted a perfusion experiment without placental tissue and found no indications for system adherence of darunavir and ritonavir (data not shown).
The MTF and FTM cotyledon clearances were calculated as follows:
3.2 Development and Verification of a Darunavir/Ritonavir Physiologically Based Pharmacokinetic Model Model in Non-Pregnant Subjects
Time-based differential equations used to describe changes in drug concentrations in various organ compartments were as follows:
Absorption was defined by a one-compartment model with first-order absorption rate, ka. Drug administered in the dosing compartment (gut lumen) is absorbed into the gut wall, from which the drug is released into the portal circulation.
Perfusion-limited distribution kinetics was used for all non-eliminating tissues; hence, passive diffusion from plasma into tissue and homogenous distribution across the tissue mass was assumed. Tissue-to-plasma partition coefficients (Kp) used in this model were previously predicted by us in Simcyp®, using the Rodgers and Rowland method [12, 25]. Because a well-stirred liver model (perfusion-limited) resulted in substantial overestimation of darunavir exposure (in the absence of ritonavir) , a permeability-limited liver model including active uptake of the drug into the liver was used for the previous p-PBPK model of darunavir in pregnancy. In addition, empirical clinical data exist suggesting permeability-limited distribution into the eliminating tissues . However, full parameterization of such a model requires data on activity and abundance of the drug transporters involved, which are currently not available. Because the scope of this work was mainly related to fetal pharmacokinetics, we used a simplified and refined well-stirred liver model against the background of their permeability-limited liver model, which includes active uptake of the drug into the liver. In refining the well-stirred liver model, we assumed that drug exchange in the liver is primarily driven by passive diffusion between plasma and the interstitial space; and that there is active transporter-mediated uptake of drug into the cell, in addition to passive diffusion. This leads to increased intracellular darunavir availability and thus, biotransformation.
Systemic clearance of darunavir was considered to mainly occur via the liver; therefore, renal clearance was not included in the model . Because enterohepatic recirculation of darunavir was a major determinant in modeling the interaction of darunavir with ritonavir in the existing p-PBPK model, and was suggested in a previous clinical study , an enterohepatic recirculation parameter was added to the current model as an empirical solution by visually fitting the simulated PK profile to the observed clinical data (non-pregnant). The enterohepatic recirculation was defined as a constant fraction of the modeled darunavir dose available for re-absorption from the gut lumen. This is based on the assumption that a small fraction of darunavir amount is excreted through biliary clearance to the gut lumen.
3.3 Development and Verification of a Darunavir/Ritonavir Physiologically Based Pharmacokinetic Model Model in Pregnant Subjects
After successful simulation of PK profiles in non-pregnant subjects, physiological parameters were modified to reflect changes in pregnancy, while keeping all drug-specific parameters constant. Physiological and metabolic changes (e.g., body weight and CYP enzyme activity) with gestational age were implemented using data and algorithms described previously . Variations in protein binding during pregnancy as well as in the fetus at term were implemented in the model based on algorithms from Simcyp®, assuming the absence of changes in protein binding kinetics. Further details on model parameterization were described previously .
3.4 Incorporation of a Feto-Placental Unit
3.4.1 Scaling Ex-Vivo Cotyledon Darunavir Clearance to In-Vivo Placental Clearance
In-vitro and physiological parameters for the feto-placental unit
Parameter; gestational age: 38 weeks
Fetal cardiac output, Q fco (L/h)
Fetal weight (kg)
Amniotic fluid volume (L)
Fetal blood volume, V bloodFetal (L)
DRV CLaf (L/h)
DRV CLfa (L/h)
DRV CLcot, mf (mL/min)
0.91 ± 0.11
DRV CLcot, mf (mL/min)
1.6 ± 0.3
Variations in protein binding in maternal and fetal blood were taken into account in this model. Fraction of unbound drug in maternal and fetal blood was predicted from algorithms available in Simcyp®, assuming the absence of changes in binding kinetics for AAG, the predominant binding protein for darunavir [12, 27]. However, fetal plasma AAG levels are appreciably lower than in the maternal circulation (fetal:maternal AAG ratio = 0.37) . Predicting the darunavir fraction unbound in fetal plasma based on AAG as the primary binding protein and assuming an average fetal AAG level at term of 0.17 g/L , resulted in a predicted fupF of 0.22. This value is higher than the unbound fraction (fuperf) of 0.16 determined in the experimental placenta perfusion medium in the absence of AAG, but in the presence of albumin (30 g/L). This indicates that in vivo, albumin (33.5 g/L) may be more important in binding darunavir in the fetal circulation  relative to the low AAG levels. Therefore, predictions of the darunavir fraction unbound in the fetal circulation were based on albumin levels. The predicted fraction unbound in maternal and fetal blood were 0.12 and 0.27, respectively.
3.4.2 Fetal Physiologically Based Pharmacokinetics of Darunavir/Ritonavir
The placenta was considered as a barrier between maternal and fetal blood. The fetal compartment was split into fetal blood and rest of fetal body (Fig. 1). During late pregnancy, 20% of fetal cardiac output is distributed to the placenta, with the remaining 80% distributed to the rest of the fetal body (Q RoB; Fig. 1) . Fetal tissues were lumped into one compartment. Tissue partitioning was based on reported adult V ss, assuming linearity between volume of distribution and body volume . Although there may be differences in fetal V ss and adult V ss, no data are currently available to address such differences. Moreover, within-species V ss is usually well predicted by allometry .
Darunavir is also reported to distribute into amniotic fluid up to ~25% of maternal concentrations . Although these data are limited, we assumed slow mass transfer (Table 1) from and into amniotic fluid to reach a steady-state amniotic concentration of about 25%. Mass transfer was assumed to be relatively slow compared with placental clearance because the processes of excretion and absorption into and from amniotic fluid are likely to be much slower than placental clearance.
Because of low abundance and activity of CYP3A enzymes in the placenta , it was assumed in this study that there was negligible placental metabolism of darunavir. Furthermore, in the presence of ritonavir, we would expect any remaining placental CYP metabolism to be completely inhibited. In addition, the relevance of fetal hepatic metabolism was assumed to be negligible and therefore not taken into account. Although large variability is reported, relative CYP3A4 gene expression was found to be 40,000 times higher in adults than in fetuses, whereas CYP3A7 expression was 5500 higher in fetuses than in adults . It cannot be excluded that in utero other CYP450 enzymes (e.g., CYP3A7) may be involved in the biotransformation of darunavir. More research is needed to generate adequate data to be able to incorporate fetal hepatic metabolism into p-PBPK models in a mechanistic manner.
Darunavir disposition in the feto-placental unit was described as follows:
The parameters used to model the feto-placental unit are listed in Table 1. Simulated fetal plasma darunavir concentrations were comparable to observed cord blood concentrations (Fig. 4). From the observed clinical data, the median (range) ratio for darunavir cord plasma/maternal plasma was 0.2 (0.0–0.8) . The simulated population fetal plasma-to-maternal plasma concentration ratio over a dosing interval is 0.30 (0.16–0.37).
3.5 Sensitivity Analysis
3.6 Exploratory Simulations of Fetal Exposure to Darunavir at Different Doses
We developed a p-PBPK model for fetal drug exposure during pregnancy that allowed us to predict the fetal pharmacokinetics of ritonavir-boosted darunavir, at term. In this study we introduced a feto-placental unit in the model using bidirectional placental clearance parameters determined separately from an ex-vivo human cotyledon perfusion set-up.
To date, few p-PBPK models have simulated human fetal PK profiles by integrating ex-vivo human placental transfer parameters within a p-PBPK model. Two studies report placental transfer of two other antiretroviral treatments based on placental diffusion and placental elimination, using closed system, ex-vivo placental perfusion experiments and non-linear compartmental modeling [14, 15]. Although the derived physiological parameters can be used to simulate fetal pharmacokinetics, it is challenging to precisely identify all the required parameters using a one-way, closed-system perfusion setup .
The approach used in this study provides an alternative to integrating placental transfer into p-PBPK models. Bidirectional transfer parameters were determined separately, by performing MTF and FTM perfusion experiments. The placental clearances estimated from the current ex-vivo experiments can be considered as whole organ clearances and allowed inclusion of placental transfer as a single barrier, rather than a compartmental structure with corresponding flows, partitioning coefficients, and tissue volumes. In terms of future development of more mechanistic compartmental placental distribution models, for instance for in-vitro-to-in-vivo extrapolation of placental transfer data from passive permeability and placental active transport studies, this approach in PBPK modeling may provide a valuable starting point for verification steps.
With regard to the verification of simulated fetal exposure to drugs in general, hardly any information is available. Preclinical studies indicate substantial fetal darunavir exposure in rats, but extrapolation of these data to human pregnancy is challenging . Umbilical cord-to-plasma concentration ratios in pregnant women taking darunavir/ritonavir were reported . Such data allow a rough verification of the model by comparing simulated profiles with actual observed cord blood concentrations, as this is the closest proxy for fetal exposure clinically and ethically available . The simulated fetal concentration–time profile corresponds with the range of observed cord blood concentrations, indicating that the developed darunavir PBPK model provides a good approximation of the fetal PK profile. The observed fetal plasma concentrations following darunavir 600 mg BID (Fig. 4c) show high variability. This can be misinterpreted as accumulation of the drug, which is unlikely under steady-state conditions. Caution is needed when interpreting concentration–time profiles based on one observation per subject, with a limited amount of subjects, which is also the reason why data-driven population pharmacokinetics is not an option in this case. Consequently, we looked at the range rather than the shape of these observed concentrations.
The observed FTM placental clearance in this study was higher than MTF placental clearance. One explanation could be that darunavir is a substrate for P-gp/ABCB1, an efflux transporter located at the apical surface of placental syncytiotrophoblast membrane. Darunavir efflux from trophoblasts into the maternal plasma could therefore result in relatively low MTF clearance. However, the experiment was conducted in the presence of ritonavir, which is known to inhibit P-gp, while also, P-gp functional expression is expected to be low in the term placentas used in this study because of reported gestational changes  Overall, the role of transporters in regulating passage across the placenta remains unclear [40, 41]. Because the system was operated under sink conditions, the higher FTM clearance can also be related by the higher flow rate in the maternal compartment, which maintains a steeper concentration gradient. Nevertheless, it is of note that the placental clearance parameters determined from this experimental set-up are based on physiological flows.
Because term placentas were used, the findings of this study are limited mainly to drug administration at term. Additionally, inferences on the impact of ritonavir concentrations on the darunavir placental transfer cannot be made. In general, human ex-vivo cotyledon perfusion experiments are limited to small numbers of term placentas. Studies over a large concentration range or with placentas from earlier stages of pregnancy are time consuming and generally less feasible . More data on placental CYP450 expression, changes in hemodynamics, tissue composition, and active uptake and efflux transport may provide a better mechanistic basis for the description and prediction of the placental transfer processes. Placental transfer models could be developed that allow for the simulation of fetal PK profiles in early pregnancy and fetal exposure-response relationships, including drug–drug interactions.
Another interesting finding in this study was the observation that cotyledon clearance did not correlate in a linear manner with cotyledon weight. This is consistent with data from a previous study, possibly indicating that tissue volume is a poor indicator of membrane surface area . Based on this observation, the cotyledon clearance was scaled per cotyledon assuming that with multiple perfusion experiments, the population clearance per cotyledon is approximated. Moreover, cutting the perfused part from the remainder of the placenta is not very precise; therefore, cotyledon weight was inappropriate to use for further calculations.
With regard to the prevention of mother-to-child transmission, vertical transmission of HIV from mothers with a low or undetectable viral load can occur . Successful quantitative simulations of fetal exposure to antiretroviral treatments provide a good basis for administering antiretroviral agents for pre-exposure prophylaxis of the fetus, but the actual clinical relevance is still under debate . Nevertheless, as shown by this study, p-PBPK models can be employed as tools to optimize maternal pharmacotherapy to prevent fetal toxicity or enhance the development of more selectively targeted, fetal drug treatments.
A p-PBPK model was developed to quantitatively predict fetal exposure at term to the protease inhibitor darunavir co-administered with ritonavir. By extending the existing maternal PBPK model with a feto-placental unit, in-vitro-to-in-vivo extrapolation was performed. If applied appropriately, the placental perfusion set-up is a valuable experimental tool that can be integrated with PBPK modeling to simulate fetal drug exposure during pregnancy.
We thank all the women who donated placentas and the midwives who collected them. We thank Gerard Zijderveld for inclusion of participants, Nielka van Erp for supervision of the bioanalyses, and Noor van Ewijk-Beneken Kolmer for her help with the bioanalyses. We also are grateful for Khaled Abduljalil’s comments on an earlier version of the manuscript and Certara for the Simcyp Grant and Partnership Scheme grant allowing further research on this topic.
Compliance with Ethical Standards
This study was supported by Health ~ Holland, top sector Life Sciences & Health.
Conflict of interest
Stein Schalkwijk, Aaron O. Buaben, Jolien J.M. Freriksen, Angela P. Colbers, David M. Burger, Rick Greupink, and Frans G.M. Russel have no conflicts of interest directly related to this study.
- 1.Pisa FE, Casetta A, Clagnan E, Michelesio E, Vecchi Brumatti L, Barbone F. Medication use during pregnancy, gestational age and date of delivery: agreement between maternal self-reports and health database information in a cohort. BMC Pregnancy Childbirth. 2015;15:310.CrossRefPubMedPubMedCentralGoogle Scholar
- 5.Mitchell AA, Gilboa SM, Werler MM, Kelley KE, Louik C, Hernandez-Diaz S, et al. Medication use during pregnancy, with particular focus on prescription drugs: 1976–2008. Am J Obstet Gynecol. 2011;205(1):51 e1–8.Google Scholar
- 13.Carter AM. Animal models of human placentation: a review. Placenta. 2007;28(Suppl. A):S41–7.Google Scholar
- 16.DHHS. Recommendations for use of antiretroviral drugs in pregnant HIV-1-infected women for maternal health and interventions to reduce perinatal HIV transmission in the United States [updated August 6, 2015]. Available from: http://aidsinfo.nih.gov/contentfiles/lvguidelines/perinatalgl.pdf. Accessed 12 July 2017.
- 17.Holmstock N, Annaert P, Augustijns P. Boosting of HIV protease inhibitors by ritonavir in the intestine: the relative role of cytochrome P450 and P-glycoprotein inhibition based on Caco-2 monolayers versus in situ intestinal perfusion in mice. Drug Metab Dispos. 2012;40(8):1473–7.CrossRefPubMedGoogle Scholar
- 20.US Food and Drug Administration. Center for Drug Evaluation and Research: pharmacology review. Maryland: Silver spring; 2009.Google Scholar
- 23.US Food and Drug Administration. Prezista label information. Maryland: Silver spring; 2014.Google Scholar
- 26.Yoshikado T, Maeda K, Furihata S, Terashima H, Nakayama T, Ishigame K, et al. A clinical cassette dosing study for evaluating the contribution of hepatic OATPs and CYP3A to drug-drug interactions. Pharm Res. 2017;34(8):1570–83.Google Scholar
- 28.EMA. Prezista; summary of product characteristics 2014. Available from: http://www.ema.europa.eu/docs/en_GB/document_library/EPAR_-_Product_Information/human/000707/WC500041756.pdf. Accessed 12 July 2017.
- 29.Ter Heine R, Mulder JW, van Gorp EC, Wagenaar JF, Beijnen JH, Huitema AD. Intracellular and plasma steady-state pharmacokinetics of raltegravir, darunavir, etravirine and ritonavir in heavily pre-treated HIV-infected patients. Br J Clin Pharmacol. 2010;69(5):475–83.CrossRefPubMedPubMedCentralGoogle Scholar
- 30.Abduljalil K, Furness P, Johnson TN, Rostami-Hodjegan A, Soltani H. Anatomical, physiological and metabolic changes with gestational age during normal pregnancy: a database for parameters required in physiologically based pharmacokinetic modelling. Clin Pharmacokinet. 2012;51(6):365–96.CrossRefPubMedGoogle Scholar
- 38.US Food and Drug Administration. Center for Drug Evaluation and Research: pharmacology review: Prezista. Maryland: Silver spring; 2009.Google Scholar
Open AccessThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.