Rapamune^® (sirolimus) is approved to prevent organ rejection in patients aged 13 years or older receiving renal transplants by the Food and Drug Administration (Author Anonymous, 2022a). In addition, the European Medicines Agency (EMA) approved Rapamune^® for prophylaxis of organ rejection in adults at low to moderate immunological risk receiving a renal transplant and for treatment of patients with sporadic lymphangioleiomyomatosis with moderate lung disease or declining lung function (EMA, 2022). Over the past 20 years since these initial approvals, sirolimus has expanded to treat children undergoing heart (Rossano et al., 2017), hematopoietic cell (Monagel et al., 2021), intestine (Andres et al., 2021), liver (Hendrickson et al., 2019), or lung transplant (Hayes et al., 2014). Furthermore, children with vascular anomalies are treated with sirolimus (Mizuno et al., 2017c).

Sirolimus is a lipophilic macrocytic lactone that binds distinctly to FK binding protein 12 (FKBP12), forming a complex with the mammalian target of rapamycin (mTOR) (Cutler and Antin, 2004). This sirolimus–FKBP12–mTOR complex inhibits multiple cytokine–stimulated cell cycling pathways by reducing DNA transcription, DNA translation, protein synthesis, and cell signaling (Sehgal, 2003). It also inhibits interleukin–2–mediated proliferation signaling, leading to T–cell apoptosis (Sehgal, 2003).

The most common toxicities (>20%) in patients taking sirolimus include hypertriglyceridemia (45%–57%), stomatitis, diarrhea, abdominal pain, nausea, nasopharyngitis, acne, chest pain, peripheral edema, upper respiratory tract infection, headache, dizziness, and myalgia (Author Anonymous, 2022a). Sirolimus is available as tablets (0.5, 1, 2 mg) and, in some countries, as a liquid solution (1 mg/mL) formulation. The long half–life of sirolimus allows for convenient once–daily dosing, but a loading dose is required to rapidly achieve target drug concentrations in whole blood. Therefore, it is usually administered once daily at a fixed dose in adults (one 6–12 mg loading dose, followed by 2–4 mg daily) and as a body surface area–based dose in children (2.5 mg/m²/day) (Supplementary Table S1).

Sirolimus has a narrow therapeutic window. Its product labeling recommends monitoring sirolimus trough concentrations for all patients, especially those likely to have altered drug metabolism, in patients ≥13 years who weigh less than 40 kg, in patients with hepatic impairment, when a change in the sirolimus dosage form is made, and during concurrent administration of strong cytochrome P450 3A (CYP3A) inducers and inhibitors (Author Anonymous, 2022a). Therefore, its doses are personalized dosing using therapeutic drug monitoring (TDM) of whole blood obtained immediately before the subsequent dose (i.e., trough or predose samples). This provides a useful strategy to optimize transplant pharmacotherapy (Kahan et al., 2000). For the past 20 years, the majority of patients have undergone the following TDM process for precision dosing of sirolimus: 1. choose the target trough concentration in whole blood, typically between 3 and 14 ng/mL (Claxton et al., 2005; Alyea et al., 2008; Ho et al., 2009; Nakamura et al., 2012; Khaled et al., 2013); 2. administer a sirolimus loading dose based on weight or body surface area; 3. Obtain a trough pharmacokinetic sample; 4. quantitate the sirolimus concentrations in whole blood, typically using liquid chromatography–mass spectrometry (LC–MS); 5. use that trough concentration to personalize the dose to achieve the target trough concentration. Notably, sirolimus whole blood concentrations may be measured by either chromatographic or immunoassay methods (Mahalati and Kahan, 2001; Schmid et al., 2009). Due to cross–reactivity with sirolimus metabolites, immunoassay methods have a positive bias ranging from 14% to 39% compared to LC–MS methods (Schmid et al., 2009). Because sirolimus whole blood concentrations vary by the type of assay used, concentrations are not interchangeable between methods. Therefore, sirolimus TDM should be conducted using one consistent bioanalytical method within an institution.

Dosing sirolimus based on trough concentrations has been the current standard of care (Stenton et al., 2005) for over 20 years. However, trough concentrations only modestly correlate with AUC_0–24hr, with R ² values between trough concentrations and area under the plasma concentration-time curve for sirolimus ranging from 0.52 to 0.84 (Schachter et al., 2004; Schubert et al., 2004; Goyal et al., 2013). We have not found publications regarding the existence or results from a sirolimus proficiency program that evaluates the accuracy of quantitation, pharmacokinetic modeling, and dose recommendations. Hopefully, such a proficiency program will be developed for sirolimus TDM because such programs have discovered challenges with TDM of other drugs (Neef et al., 2006; McCune et al., 2021b).

Although TDM is accepted for sirolimus, trough concentrations are limited because they fail to provide a rich, mechanistic description of the pharmacokinetic/pharmacodynamic relationship (Dupuis et al., 2013) that could advance our understanding of why certain patients experience adverse outcomes. Model-informed precision dosing (MIPD) may improve clinical outcomes by optimizing the personalized dose for an individual patient (Darwich et al., 2017). The development of such mechanistic models can help improve individual patients’ clinical outcomes, which can be attributed to the pharmacokinetics and pharmacodynamics of sirolimus. Regarding the pharmacokinetics, low immunosuppressant concentrations or exposure is multifactorial; they can result from insufficient dosing or dose personalization, aberrant pharmacokinetics due to patient covariates, and/or non-adherence (McCune and Bemer, 2016; McCune et al., 2016; Vaisbourd et al., 2022). Non-adherence is associated with the development and severity of adverse outcomes such as graft loss in solid organ transplant (Foster et al., 2018) and graft-versus-host disease (GVHD) hematopoietic cell transplant patients (Gresch et al., 2017; Ice et al., 2020). Thus, improvements in sirolimus dosing to minimize the between-patient variability in sirolimus concentrations through novel -omics techniques or point-of-care monitoring at home may be beneficial. These will be the focus of this review.

However, the promising innovations in -omics techniques and mathematical modeling related to the immune system necessitate that we briefly describe factors possibly affecting the pharmacodynamics of sirolimus. Pharmacodynamic monitoring of the cellular targets of immunosuppressant drugs may reflect clinical outcomes better than TDM (Monchaud and Marquet, 2009a; Monchaud and Marquet, 2009b). For example, recipient pretransplant inosine monophosphate dehydrogenase activity is associated with clinical outcomes after renal transplant or allogeneic hematopoietic cell transplant recipients treated with mycophenolate mofetil (MMF) (Glander et al., 2004; Bemer et al., 2014). However, a drug-specific pharmacodynamic biomarker for sirolimus’s effectiveness or toxicity has yet to be identified. Turning to the use of sirolimus to alter the immune system, the ontogeny of innate and adaptive immune responses involve more than 1,600 genes (Simon et al., 2015). These genes are essential to sustain life in a hostile environment. Yet the immune system is relatively immature at birth. It has to evolve during a lifetime of exposure to many foreign challenges through childhood, young and mature adulthood (including pregnancy). It subsequently declines in old age (Simon et al., 2015). Beyond the effects of these genes, patients may have several transcriptomic (Furlan et al., 2020), proteomic (Cohen Freue et al., 2013; Levitsky et al., 2013), metabolomic [reviewed in Supplementary Table S1 of McCune et al. (2021a)], and lipidomic (Liggett et al., 2022) characteristics that could influence the effectiveness and toxicity of transplantation and/or sirolimus. For example, metabolomics can offer discoveries yielding new insights into how metabolites (here, endogenous, not sirolimus metabolites) influence gut physiology, organ function, and immune function (Wishart, 2019). Given their signaling properties in addition to a multitude of other functions (Cockcroft, 2021), lipidomics may enable a deeper mechanistic understanding of the T-cell migration (Cucchi et al., 2020), drug-target interactions, and systems physiology from the molecular (genomic, proteomic, metabolomic) to cellular to whole-body levels. Collectively, these works could lead to a system-wide perspective of pathophysiology wherein genes, proteins, metabolites, and lipids are understood to interact synergistically to modify the functions within a patient receiving sirolimus. These insights may provide the foundation for enhanced pharmacokinetic/dynamic modeling to comprehensive quantitative systems pharmacology (QSP) models (Ayyar and Jusko, 2020). In homogenous and sufficiently powered populations of patients treated with sirolimus, multi-omic tools should be explored for precision dosing and for building QSP models to improve clinical outcomes.

Mathematical modeling and simulation can characterize the complexity and multiscale nature of the mammalian immune response and provide a mechanistic understanding of the data generated from the novel–omics technologies (Palsson et al., 2013). As an example of such modeling and simulation, the Fully-integrated Immune Response Model (FIRM) represents a multi-organ structure comprised of the target organ, where the immune response occurs, and circulating blood, lymphoid T, and lymphoid B tissue (Palsson et al., 2013). FIRM can be expanded to include novel biological findings relevant to sirolimus, such as incorporating novel medications that target antigen-presenting cells (B-cells), T-cell subsets, T-cell signal transduction, costimulatory molecules, or cytokines into QSP models. Early steps are being taken to apply QSP models to precision dosing, specifically in the context of the well-characterized coagulation cascade (Hartmann et al., 2016). However, the inherent complexity of the immune system and the difficulty of measuring many aspects of a patient’s immune state in vivo makes it challenging to develop such QSP models of immune response (Laubenbacher et al., 2022). Because the immune system has an important role in such a wide range of diseases and health conditions, digital twins of the immune system are of keen interest. Advanced medical digital twins will make precision medicine a reality (Laubenbacher et al., 2022).

Therefore, it is crucial to explore newer methods for precision dosing of sirolimus (Table 1). Here, we summarize our findings from a series of literature reviews (Supplementary Methods and Supplementary Figures S1–S5) about various –omic or point–of–care tools focusing on the sirolimus pharmacokinetics that may improve the precision dosing of sirolimus in children.

After oral administration, sirolimus achieves its peak whole blood concentrations within 1–3.5 h (MacDonald et al., 2000; Stenton et al., 2005). The apparent oral bioavailability of sirolimus is poor (Stenton et al., 2005). Extensive intestinal and hepatic first-pass metabolism contributes to the low oral bioavailability.

Sirolimus is highly lipophilic (log P of 4.3, Supplementary Table S1). Sirolimus has a large apparent volume of distribution of (5.6–22.8 L/kg) (Brattstrom et al., 2000; Stenton et al., 2005). This large distribution volume implies extensive distribution to organs and tissues and contributes to the long half-life. The free fraction in plasma is 8% (MacDonald et al., 2000). Sirolimus is partitioned extensively into blood cells with a blood-to-plasma ratio of 35.6 (Tejani et al., 2004). In kidney transplant (KT) patients, this ratio is independent of sirolimus concentration and exhibits a large variability (Ferron et al., 1997). In humans, sirolimus is distributed among red blood cells (94.5%), whole blood (3.1%), lymphocytes (1.01%), and granulocytes (1.0%) (Stenton et al., 2005). The sequestration of sirolimus in red blood cells is believed to be partially due to their rich content of immunophilins (Stenton et al., 2005). In the whole blood compartment, sirolimus exhibits concentration–dependent binding to lipoproteins (40%) with a minor fraction (<4%) bound to plasma proteins. Therefore, whole blood is considered the most favorable matrix for TDM (Stenton et al., 2005).

The primary route of elimination occurs via fecal/biliary pathways, with an estimated terminal elimination half–life of approximately 62 ± 16 h (Stenton et al., 2005). After a single oral dose in healthy adults, sirolimus has a half-life of 81.5 h and a total body clearance is 278 mL/h/kg (Brattstrom et al., 2000).

Sirolimus is metabolized by CYP3A4 and CYP3A5 in both human liver and small intestinal microsomes to various demethylated and hydroxylated species (Paine et al., 2002). Degradation products, including an ester hydrolysis product and a ring–opened isomer, have also been described (Paine et al., 2002). In the liver, sirolimus is primarily metabolized by CYP3A4, with CYP3A5 and CYP2C8 having lesser roles (Jacobsen et al., 2001; Emoto et al., 2013). Sirolimus is transported by the multidrug resistance gene product pump p–glycoprotein (PgP) (Stenton et al., 2005), an apically directed ATP–dependent transmembrane secretory (efflux) pump expressed at high levels in enterocytes (Paine et al., 2002).

Examples of potential drug–drug interaction (DDI) with sirolimus result from concomitant calcium channel blockers, imatinib, antibiotics, or antifungals (Leather, 2004; Bernard et al., 2014; Bleyzac et al., 2014). Because of routine TDM of trough concentrations of the sirolimus, these results can be used to identify a DDI and appropriately personalize the sirolimus dose. For example, the DDI between azole antifungals and sirolimus has long been recognized (Yee and McGuire, 1990b; Yee and McGuire, 1990a) and can be managed through TDM (Leather, 2004). The azoles have variable CYP3A4 inhibition, potentially affecting CYP2C9, CYP2C19, and PgP (Leather, 2004). Although these azole–immunosuppression DDI are well known, their management can be variable and could benefit from improved pharmacokinetic/pharmacodynamic modeling. The EMA changed the drug label for sirolimus to include therapeutic monitoring during dose adjustments when inducers or inhibitors of CYP3A are concurrently administered and/or discontinued (Ehmann et al., 2014).

Furthermore, sirolimus is also susceptible to being the victim drug to natural products altering CYP3A or PgP activity, such as grapefruit juice and St. John’s wort (Edwards et al., 1999; Mai et al., 2003; Brantley et al., 2013). Sirolimus pharmacokinetics may have circadian variability, as the maximum plasma concentration and AUC of other CYP3A/PgP substrates (i.e., cyclosporine and tacrolimus) are higher in the morning than in the afternoon (Baraldo and Furlanut, 2006). Seasonal variation is also of concern, as it has recently been reported that duodenal CYP3A4 mRNA is significantly higher between April and September than between October and March (Thirumaran et al., 2012).

The maintenance dose of sirolimus should be adjusted in patients with hepatic impairment or at risk of interactions with sirolimus, either due to concomitant drugs (Author Anonymous, 2022a) or natural products (Paine et al., 2018) affecting CYP3A or PgP activity.

Pharmacometrics enables developing models that describe factors affecting the pharmacokinetics and/or pharmacodynamics in children (Mehrotra et al., 2016). Children are not small adults because of differences in biochemical, body composition, and physiology processes (Rodman et al., 1993; Murry et al., 1995; de Wildt et al., 1999; Blanco et al., 2000; Kearns et al., 2003; Ince et al., 2009; Mahmood, 2014). Specific to sirolimus, the data regarding the maturation of CYP3A4 is conflicting, with different age variations in enzyme activity. For example, Salem et al. (2014) suggested that hepatic CYP3A4 increases from an early age and reaches the adult level by 2.5 years. In contrast, Upreti and Wahlstrom (2016) suggested that CYP3A4 maturation exceeds the adult level between 0.1 and 11 years. Using such enzyme maturation data is critical in creating physiologically based pharmacokinetic models (PBPK) to predict pediatric pharmacokinetics and dosing (Mehrotra et al., 2016).

A PBPK model for children aged 1 month to 2 years demonstrated that the relationship between allometrically scaled in vivo sirolimus clearance and age was described by the Emax model (Emoto et al., 2015a). Consistent with this increased clearance in patients, in vitro intrinsic clearance of sirolimus using pediatric liver microsomes shows a similar age-dependent increase. In children older than 2 years, allometrically scaled apparent oral clearance of sirolimus did not show further maturation. Simulated clearance estimates with a sirolimus PBPK model that included CYP3A4/5/7 and CYP2C8 maturation profiles were in close agreement with observed in vivo clearance values (Emoto et al., 2015a). However, further exploration of the impact of different assumptions regarding CYP3A4 age–related changes on the PBPK model predictive performance may be beneficial (Lang et al., 2021). In addition, PBPK model-simulated sirolimus pharmacokinetic profiles predicted the actual observations well (Emoto et al., 2015a). The mean sirolimus clearance was 11 ± 3 L/h, 17 ± 4 L/h, 21 ± 3 L/h, and 18 ± 6 L/h for the age groups of 1–8 months (<1), 1 year (1 to <2), 2 years (2 to <3), and 3–18 years (≥3), respectively (Emoto et al., 2015a). Sex, ethnicity, or race did not show a statistically significant association with sirolimus clearance in a cohort of 44 children (Emoto et al., 2015a). These results demonstrate the utility of a PBPK modeling approach for predicting the developmental trajectory of sirolimus metabolic activity and its effects on total body clearance in neonates and infants (Emoto et al., 2015a).

Complementing PBPK modeling, population pharmacokinetic (PopPK) models (Beal and Sheiner, 1982) can address relevant hurdles by accounting for variability and mitigating the resource intensity obtaining more pharmacokinetic samples beyond trough samples. PopPK models mathematically describe typical drug kinetics while accounting for between subject variability and residual unknown variability (Holford et al., 2000) and the role of demographic covariates responsible for or related to variability, such as age or gender. PopPK models also facilitate the development of limited sampling schedules, which is essential since most immunosuppression is administered in the outpatient clinic (Li et al., 2012; Li et al., 2013). PopPK models could overcome the major challenge of the –omics tools, specifically the interference from confounding factors (Ioannidis et al., 2009; Gu et al., 2012). Pharmacokinetics can be used to address these confounding factors by identifying factors associated with aberrant metabolism. Theoretical allometrically scaled body weight accounted for differences in body size (Mizuno et al., 2017b). Using a popPK model and more comprehensive blood sampling of sirolimus, MIPD was conducted in children with vascular anomalies as part of a prospective phase II trial (Mizuno et al., 2017a). In 52 children, the target trough was attained in 94%, specifically 49 of 52 children, across the age range of 3 weeks–18 years after 2–3 months of therapy (Mizuno et al., 2017a). The mean sirolimus dose to achieve the target trough of ∼10 ng/mL for patients older than 2 years was 1.8 mg/m² twice daily (range 0.8–2.9), while it was 0.7–1.6 mg/m² twice daily for patients 3 weeks of age to 2 years. The final popPK model included a maturation function for sirolimus clearance and allometrically scaled body weight to account for size differences. The mean allometrically scaled sirolimus clearance estimates increased from 3.9 to 17.0 L/h/70 kg with age from shortly after birth to 2 years of age, while the mean estimate for patients older than 2 years was 18.5 L/h/70 kg. This MIPD can be extended to other pediatric populations and perhaps adults (Mizuno et al., 2017a).

Another approach to improving the precision dosing of sirolimus is simplifying the collection of the whole blood samples used for sirolimus TDM. There has been extensive interest in using dried blood spot (DBS) as a point-of-care method for obtaining blood samples to be used in sirolimus TDM. DBS sampling is a blood sampling method alternative to venipuncture and requires less blood volume, which makes it an attractive option for children. The DBS sampling process is not difficult to perform and does not require a trained phlebotomist for blood spot collection (Fokkema et al., 2009; Wagner et al., 2016). The patient provides a venipunctures capillary blood drop, places the blood onto a filter card, and subsequently allows the blood spot to dry. The DBS sample is then sent to a laboratory to quantify the sirolimus concentration with LC–MS (Wagner et al., 2016). DBS sampling may improve patient satisfaction by reducing commute times and possibly improving the precision dosing of sirolimus (Dickerson et al., 2015). For DBS to replace whole blood samples, it is important to evaluate if they provide similar sirolimus concentrations. Sirolimus concentration in DBS may be lower than in venous whole blood samples because drug concentrations tend to be lower in capillary blood (Klak et al., 2019). Veenhof et al. (2019) collected DBS and whole blood samples from patients and found that 76.9% of the samples were within acceptable limits.

The accurate quantitation of sirolimus concentration in DBS sampling depends on various factors, summarized in Supplementary Table S2. These factors range from 1. patient characteristics, 2. depositing the blood drop on the filter card; 3. the effect of how long it takes for the blood to dry on the filter card (i.e., drying time); 4. storing and transporting the DBS sample from the patient’s home to the laboratory; 5. punching the DBS section for quantitation; 6. extracting and quantitating sirolimus concentrations.

Table 3 summarizes the findings regarding the quantitation of sirolimus concentrations (abbreviated [SIR] in Table 3) from DBS samples. The patient’s characteristics, specifically their hematocrit and the sirolimus concentration at the time of the DBS collection, appreciably change the accuracy of sirolimus concentrations in DBS. Varying hematocrit influences the blood viscosity, the drying time needed for a DBS, and the potential interference with analyte recovery (De Kesel et al., 2013; Klak et al., 2019). Low hematocrit (less than 0.20 L/L) and high hematocrit (greater than 0.5 L/L) are associated with lower sirolimus recovery from DBS (den Burger et al., 2012; Koster et al., 2013). To obtain reliable sirolimus concentrations, it is optimal that the patient’s hematocrit range is between 0.23 and 0.50 L/L (den Burger et al., 2012; Koster et al., 2013; Koster et al., 2017). Therefore, DBS samples with high or low hematocrit concentrations must be corrected and interpreted cautiously (Klak et al., 2019). Also, better accuracy is achieved when sirolimus concentrations are at least 3.0 ng/mL; however, this may limit the use of DBS as trough concentrations may be lower than 3.0 ng/mL (Koster et al., 2015a; Koster et al., 2017).

The second factor influencing sirolimus concentrations from DBS is depositing the blood spot on the filter card. Hydrogen bridges are known to form between sirolimus and cellulose filters (Klak et al., 2019). Because the sirolimus concentration is influenced by the type of filter card used, it is recommended that a laboratory use only one type of filter card for calibration purposes (Koster et al., 2015a). The Whatman FTA DMPK–C, 31 ET CHR, and Whatman DMPK–C filter cards have consistent sirolimus extraction recovery (Koster et al., 2013; Koster et al., 2015a). However, the Whatman 903 and Ahlstrom 226 filter cards are preferred because of their untreated cellulose filter and because they comply with CLSI guidelines (Klak et al., 2019; CLSI document EP09-A3, 2013). In addition to the effects of the filter card, the blood spot volume and homogeneity of its placement within one spot affect the accuracy of sirolimus concentrations from DBS samples. Filter card oversaturation by blood spot volumes equal to or greater than 100 µL (den Burger et al., 2012; Sadilkova et al., 2013) and volumes less than 20 or 30 µL can lead to inaccurate results (den Burger et al., 2012; Koster et al., 2013; Koster et al., 2017). Patients are more likely to provide low blood spot volumes when self–sampling than the recommended 50 µL blood spot volumes (Klak et al., 2019). Dickerson and others evaluated point-of-care (i.e., at-home) collection by providing and educating families about how to collect and mail DBS samples back to the laboratory (Dickerson et al., 2015). A small negative, but not statistically significant, bias between DBS and whole blood samples was found. The sirolimus concentrations in the DBS samples were within clinically acceptable limits (Dickerson et al., 2015). Although this is encouraging for self-sampling, additional studies in children are needed to evaluate if they can provide the recommended blood spot volume studied to date (den Burger et al., 2012; Koster et al., 2013; Koster et al., 2017; Klak et al., 2019; Veenhof et al., 2019).

The third and fourth factors influencing sirolimus concentrations are drying the filter card and storing and transporting the DBS on the filter card, respectively. DBS samples are recommended to be dried at room temperature and away from light for at least 24 hr to allow for accurate hematocrit effects during sirolimus recovery (Koster et al., 2015b; Klak et al., 2019). DBS samples can be stored for 20–29 weeks in the lab at −20°C before losing the stability of sirolimus concentrations (Koster et al., 2015b; Veenhof et al., 2019). All DBS samples require spot-checking for appropriate volume size (Veenhof et al., 2019). Sirolimus DBS samples degrade rapidly within 24 hr at 60°C, but sirolimus is relatively stable at 25°C (Sadilkova et al., 2013; Klak et al., 2019). Therefore, patients using DBS sampling must be educated on properly collecting, storing, and transporting their DBS samples.

The fifth and sixth factors influencing DBS’ sirolimus concentrations are punching out the DBS section for quantitation and the subsequent extraction and quantitation of the sirolimus concentrations, respectively. Punch size and location of the DBS do not appear to influence sirolimus concentrations (den Burger et al., 2012; Sadilkova et al., 2013; Koster et al., 2017; Klak et al., 2019). The highest extraction and recovery rates of sirolimus were found at high hematocrit and low sirolimus concentrations (Table 3) (Koster et al., 2013). When utilizing flow-through desorption (FTD) with LC-MS-enhanced temperature desorption, sirolimus recovery improved significantly (Hempen et al., 2015). However, further trials are still needed to confirm whether FTD–LC–MS–MS can be established as an accurate DBS tool.

In summary, some factors (i.e., hematocrit, sirolimus concentration, filter card, drying time) influence sirolimus concentrations from DBS. However, patient education is necessary for parents to collect sufficient blood spot volumes at the correct time (Dickerson et al., 2015; Klak et al., 2019). In addition, potentially losing samples in the mail is an ongoing concern (Dickerson et al., 2015; Urquhart and Knauer, 2015). Therefore, precision dosing of sirolimus using DBS samples is not recommended for children.

Other matrices, such as saliva or sweat, can also be used in TDM. As a common alternative to a blood sample, the non-invasive and easily accessible nature of saliva samples makes it optimal for TDM in outpatient settings and potentially in children. Furthermore, with the help of PBPK modeling, the system drug exposure may be predicted from those non-plasma samples. Using PBPK models, we hypothesize that sirolimus concentrations in saliva or sweat can be related to whole blood concentrations to predict sirolimus exposure.

In combination with tacrolimus, sirolimus can be used to prevent GVHD in recipients of an allogeneic hematopoietic cell transplant (HCT) (Khaled et al., 2016; McCune and Bemer, 2016; McCune et al., 2016). Because GVHD persists as a significant source of non-relapse mortality (Bidgoli et al., 2022), we are describing preliminary data from two pilot studies seeking to improve the dosing of sirolimus and tacrolimus in HCT recipients.

Dosing sirolimus based on trough concentrations is the current standard of care, although MIPD will be beneficial (McCune and Bemer, 2016; McCune et al., 2016). Therefore, the use of MIPD should be implemented. To date, the data do not suggest the use of DBS for point-of-care monitoring of sirolimus concentrations for precision dosing of sirolimus. Future research should focus on the use of preemptive CYP3A5–guided sirolimus dosing or the use of wearables for point-of-care quantitation of sirolimus in blood, saliva, or sweat.