Data from 60 Hu/Mu ICAM-1 transgenic mice (n = 30 male) were used to build the PBPK model (training group), which was externally validated using additional data (validation group) from 99 Hu/Mu ICAM-1 transgenic mice (n = 50 male).

The training group included 12–14-week-old mice. The animals were housed with 3–5 mice per cage (an HEPA-filtered Techniplast cage (1145 IVC) connected to a Techniplast Slim Line air handling system) in a PC2 laboratory with a 12/12 light/dark cycle. Bedding made from corn cob (Shepherd’s cob) was used to line the cages (Shepherd Specialty Papers Inc., TN, United States). The mice were fed ad libitum with rat and mouse cubes/pellets manufactured by Specialty Feeds, WA, Australia. This standard mouse feed is formulated to be low in fat content (approximately 5%) and is meat free. The airflow in the room was 12–15 air changes per hour, but the airflow in the Techniplast cages was at a rate of 70 changes per hour. The mice were identified by tail marking with a permanent pen.

For the rest of the experiments, the same animal care conditions were reproduced. The age of the mice in the case of the single-dose IV administration experiment was 6–8 weeks. The age of mice in the case of multiple-dose subcutaneous experiments was 8–10 weeks. All animals were euthanized by CO₂ asphyxiation following tissue and organ extraction.

The study was performed according to an Animal Use Protocol approved by the University of Newcastle Animal Care and Ethics Committee, project license number A2010-143.

The mice in the training group received a single rapid IV injection containing 2.5 × 10⁷ TCID₅₀ (half-maximal tissue culture infectious dose) of V937 through the tail vein. A blood sample was obtained 45 min after administration. Then, 12 animals per day were euthanized at days 1, 3, 7, 10, and 14, and viral levels (copies/µg RNA) were quantified in the following tissues: blood, lung, brain, heart, muscle, pancreas, spleen, liver, kidney, and lymph node.

Data used for model validation were obtained from three independent experiments where multiple doses of V937 were given either intravenously (one experiment) or subcutaneously (two experiments).

In the IV experiment, 1 × 10⁸ TCID₅₀ of V937 were injected in the tail vein at days 1, 3, 6, 8, 10, 13, 15, 17, 19, and 22. A blood sample was obtained 45 min after administration. Then, eight animals per day were euthanized at days 6, 24, and 36 after the start of the treatment. Viral levels (copies/µg RNA) were quantified in the following tissues: blood, heart, lung, brain, spleen, kidney, liver, muscle, and pancreas.

In the first subcutaneous experiment, 2.5 × 10⁷ TCID₅₀ of V937 were injected alternating between the intercapsular region (between the shoulder blades) and the flanks at days 1, 3, 5, 10, 12, 15, 17, 19, and 22. Blood samples were obtained 30 min after injection and at days 9 and 24. Tissue samples were obtained only at the end of the experiment on day 24 (n = 8 mice). Viral levels (copies/µg RNA) were quantified in the following tissues: the heart, lung, brain, spleen, kidney, liver, ileum, gastrocnemius muscle, stomach, lower intestines, and gonads. Similarly, in the second subcutaneous experiment, the same dose level of V937 was given at days 1, 3, 6, 8, 10, 13, 15, 17, 19, and 22. Tissue samples were obtained at days 6 (n = 7 mice) and 24 (n = 8 mice) from the following organs: the blood, heart, lung, brain, spleen, kidney, liver, muscle, pancreas, gonads, ileum, stomach, and lymph.

Figure 1 provides a schematic representation of the different experimental setups included in the current evaluation along with the corresponding longitudinal raw data profiles.

A transformation of units of the administered dose to copies was performed using the conversion factor estimated by Parra-Guillen et al. to maintain coherence between administration and observation units (TCID₅₀ and copies/µg RNA, respectively). This study had access to measurements of V937 in serum over time in both units: copies/mL and TCID₅₀/mL. A good correlation was observed between these two measurement types, leading to the conversion factor of 170 copies/TCID₅₀.

V937 tissue concentrations were determined using a quantitative reverse transcriptase–polymerase chain reaction (qRT-PCR) method. The limit of detection was 5.3 × 10² copies/µg RNA for all experiments except for the quantification of blood samples obtained in the last experiment, which was 1.5 × 10³ copies/mL.

The non-linear mixed-effects modeling approach using the software NONMEM 7.4 with the Laplacian estimation method and interaction was used for data analysis. Observations below the limit of detection were analyzed as censored information, maximizing the likelihood of an observation being below the limit of quantification (M3 method (Beal, 2001)). Inter-animal variability in the drug-specific parameters (see the following) was modeled exponentially. The residual error was modeled with an additive model in the logarithmic scale, as a logarithmic transformation of the data was performed during the analysis.

Figure 2 schematically represents the structure of the physiologically based pharmacokinetic model that was initially fitted to the data. In that model, perfusion-limited distribution is assumed for all organs. The following general Eq. 1 characterizes the rate of change of the viral load in each organ. Supplementary Appendix (Supplementary Data Sheet S1) lists the full set of model equations, while the main NM-TRAN code has been included in Supplementary Data Sheet S3. d A o r g d t = Q o r g ∙ A b l o o d V b l o o d − Q o r g − L o r g ∙ A o r g V o r g / K P o r g − L o r g ∙ A o r g V o r g . (1)

Here, A _org is the viral load (copies) in that specific organ, Q _org and L _org represent the blood and lymph flows, respectively, V _org corresponds to the volume, and KP _org is the partition coefficient of the lung, brain, heart, muscle, pancreas, spleen, liver, or kidney. Values of blood and organ volumes were calculated according to the weight of the mice using the algorithms implemented in PK-sim^® (Open Systems Pharmacology Suite 10). Lymph flows were assumed 500 times lower than the corresponding organ blood flow (Shah and Betts, 2012). An additional compartment (rest) merging the remaining organs from the body was included in the model structure. Values of the volume and flow rate corresponding to the rest compartment were calculated from the difference between total body volume and cardiac output and the corresponding values resulting from the sum of all organs from which measurements were available.

Concentrations (copies/mL) were calculated by dividing the amounts in the organ by the respective physiological volume of each organ. As the viral levels were measured in copies/µg RNA, a scaling factor was estimated during the model-building process to maintain consistency between units. These scaling factors represent the µg of RNA per mL of tissue ( R N A o r g , i . Supplementary Table S1 lists the values of blood and lymph flows and organ volumes used in the current investigation.

During model building, the partition coefficients, the scaling factors, and the total clearance (CL) assumed to occur in the systemic circulation (Russell and Peng, 2007; Tan et al., 2017; Parra-Guillen et al., 2021) were the parameters estimated.

The process of virus entry into organs is described by a flow rate. In the case of organs in which a saturation of this flow into the compartment is perceived, flow-limited models were initially used, showing a clear over-prediction of the levels reached. Accordingly, a saturation mechanism was assumed, attributing it to the exceeding of the binding capacity of the ICAM-1, as this receptor is accountable for the distribution of V937 into tissues. That non-linear mechanism was also considered during model development and was implemented as described in the following equation: d A o r g d t = V M A X o r g , i K m o r g , i + A b l o o d V b l o o d ∙ A b l o o d V b l o o d − Q o r g , i − L o r g , i ∙ A o r g , i V o r g , i / K P o r g , i − L o r g , i ∙ A o r g , i V o r g , i , (2) where V _MAX represents the maximum zero-order input rate constant and K _m , the concentration of copies/mL at which 50% of V _MAX is reached. K _m is derived from the ratio between VMAX and the organ-specific blood flow rate.

In the case of the subcutaneous administration experiments and according to experimental evidence (Davis and Bugelski, 1998), the lymph was considered as the compartment from which absorption takes place. Characterization of the absorption process took place during model validation.

Dataset organization, graphical exploration of raw data, and model evaluation were performed using R version 4.0.5 through RStudio interface version 1.4.1106. The software NONMEM 7.4 was used for the analysis. Model management was carried out using Pirana 3.0.0. The Xpose R package was used to display VPCs performed using PsN 5.3.0. Modeling software PK-Sim^® (Open Systems Pharmacology Suite 10) was used to obtain physiological parameters.

The raw longitudinal viral levels vs. time profiles after receiving a single IV dose of V937 were used to establish the model, as presented in Figure 3. After injection, viral levels in blood decreased from 10⁸ copies/µg RNA measured at day 1 to 10⁵ copies/µg RNA measured at day 2. Maximum viral copies were seen already at day 1 after injection in the brain, lymph node, kidney, liver, lung, and spleen. The heart, muscle, and pancreas showed the peak of viral copies at day 3 after administration. With the exception of blood, maximum viral levels ranged from 1.37 × 10⁶ (muscle) to 3.70 × 10³ (brain) copies/µg RNA. The last sample was taken 2 weeks after injection, with levels ranging from 2.31 × 10³ (heart) to 22.28 (spleen) copies/µg RNA. The data were explored graphically to evaluate potential differences between males and females without detecting major differences (Supplementary Figure S1).

Regarding the IV multiple-dose experiment, maximum values were observed at day 6 (after the second administration) and ranged from 1.12 × 10⁶ (heart) to 118.28 (liver) copies/µg RNA. Viral levels were only obtained at the last sampling time in the heart, lymph, and spleen, with values ranging from 8.59 × 10⁴ (spleen) to 5.27 × 10³ (lymph) copies/µg RNA after the administration of 10 doses (Supplementary Figure S2).

In the first subcutaneous administration experiment, viral levels ranged from 2.12 × 10⁵ (heart) to 54.10 (liver) copies/µg RNA at the first sampling time after two dose administrations. On day 24, after the 10^th dose, viral levels ranged from 3 × 10³ (lymph) to 4.3 (pancreas) copies/µg RNA (Supplementary Figure S3).

Given that sampling was sparse in the second SC multiple-dosing experiment, data were summarized as boxplots (Supplementary Figure S4). Maximum levels of viral copies/µg RNA were measured on day 24 in the spleen (1.44 × 10³ copies/µg RNA).

Supplementary Figure S5 represents both subcutaneous experiment concentration–time profiles.

For most of the organs, viral distribution was characterized using a perfusion-limited model assuming homogenous distribution within each of the organs; however, for the case of the muscle and heart, data were better described using a non-linear model for tissue uptake (p < 0.01). Models splitting organs in two or more compartments did not lead to a significant improvement in the fit (p > 0.05). The selected model resulted in a variant of the structure shown in Figure 2, as the parameters corresponding to the compartment lumping the rest of organs from which viral copies could not be estimated with sufficient precision and, therefore, were removed from the model.

Figure 4 depicts the outcomes obtained from a simulation model diagnostic evaluation, which generally indicates that the model accurately captures both the typical trend and the variability in the observations over time. Despite the fact that more than one blood sample was taken from each animal, data did not support the estimation of inter-animal variability in any of the parameters of the model. Therefore, the degree of uncertainty used to generate the virtual studies and the concentration results shown in Figure 4 correspond to the magnitude of the residual variability, described using an additive error model on the logarithmic scale with estimates varying between 3.33 (muscle) and 1.01 (lung) log (copies/µg RNA). The estimates of the structural model parameters are listed in Table 1. In no case was the lower value of the 95% confidence interval calculated based on the log-likelihood profiles lower than or equal to zero, and therefore, the point estimates were considered precise.

Total elimination clearance was estimated in 18.2 mL/h with a 95% confidence interval of 0.72–35.68 mL/h. Variants of the selected models considering the possibility that each organ could contribute to virus clearance did not improve the fit (p > 0.05). Results show a great degree of disparity in the estimates of the partition coefficients, ranging from the lowest value of 9.94 10⁻³ in the brain to the highest value of 548 in the lung. With respect to the scaling factors, estimates varied across the different tissues, although at a lesser degree compared to the partition coefficients. The derived values of the Michaelis–Menten constant for the muscle and heart were 0.568 and 0.038 copies/mL, respectively.

The estimates of parameters of the partition coefficients and those corresponding to the scaling factors were not correlated, as shown in Figure 5.

A prolonged infusion was simulated to achieve a steady-state condition, allowing the comprehensive examination of the effects of these processes on the predicted organ level. In Supplementary Figure S6, the simulated 0–24 h concentration vs. time profiles in different organs is shown, assuming a 5 h continuous IV injection of 2.04 × 10⁹ TCID₅₀. After a 5 h infusion, the decrease of virus concentration in the different organs of the body was observed. The uptake process is fast and similar for all organs except for the heart and muscle. The levels of copies in the heart and muscle are maintained after the end of the infusion time, the elimination of the virus occurring later than in the rest of the organs (Supplementary Figure S6A). Viral concentration in the heart begins to decline 10 h post-infusion initiation, while in the muscle tissue, the elimination of the virus commences 40 h after infusion initiation (Supplementary Figure S6B).

The results of the sensitivity analysis evaluation are shown in Supplementary Figure S8. Modulating the clearance parameter results in alterations of the AUC_0-tend across all organs, with the most significant impact observed in the lung AUC_0-tend. Among the KP values, the most notable impacts on AUC_0-tend are observed when altering the KP values related to the lung and lymphatic system, influencing the AUC_0-tend of the lung and spleen, respectively.

Results obtained during the initial steps of the validation procedure warranted additional model refinements/expansions. For example, when the selected model was used to simulate the blood and organ viral profiles during the IV multiple-dosing study, it was found that although the observed viral levels on day 6 after initiating the treatment were reasonably well captured, later measurements (day 36) were, in general, over-predicted by the model. The development of an immunogenicity response affecting viral elimination triggered by the continuous viral exposure consequence of the multiple-dosing schemas was considered and implemented empirically in the model as a time-varying clearance of the form CL = CL₀ + θ_CL,T × time, where CL₀ is the total clearance prior to the development of the immunogenic response and θ_CL,T represents the linear increase in CL with time (h). The time-variant clearance resulted in a significant reduction in the −2LL (−382). The estimate of θ_CL,T was 0.576 h⁻¹ with a 95% confidence interval of 0.18–0.971 h⁻¹, representing a 96.78 mL/h increase in CL per week of continuous viral exposure. Figure 6A shows that model-based predictions well match the observed viral copies for the multiple-dosing IV experiment. The figure also shows the predictions obtained from the time-invariant clearance.

After subcutaneous administration, a model considering the lymph node as the place receiving the viral dose and from which an instantaneous absorption (with an absolute value of bioavailability of 10%) occurs described the observed data of the multiple subcutaneous dosing experiment adequately (Figure 6B).