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Intravitreal aflibercept (Bihorel 2026)

Source: vignettes/articles/Bihorel_2026_aflibercept.Rmd
Bihorel_2026_aflibercept.Rmd

Model and source

  • Citation: Bihorel S, Chiu J, Chittenden J, Eissing T, Turner KC, Hochel J, Zimmermann T, He ZW, Davis JD, DiCioccio AT, Harnisch LO. Integrated Population Pharmacokinetic Analysis of Intravitreal Aflibercept Drug Products. CPT Pharmacometrics Syst Pharmacol. 2026. doi:10.1002/psp4.70287. Adapts the IV-only free/bound aflibercept model of Thai et al. (2011); see modellib(‘Thai_2011_aflibercept’).
  • Description: Integrated semi-mechanistic population PK model for free and VEGF-bound (adjusted bound) aflibercept in plasma after intravitreal (AFL-2 2 mg and AFL-HD 8 mg drug products), intravenous and subcutaneous administration (Bihorel 2026). Study and fellow eyes (fixed 4 mL vitreous volumes) exchange free aflibercept bidirectionally with the central compartment at the ocular clearance QE; free aflibercept distributes to two peripheral compartments, is eliminated first-order (K20), is taken up by a capacity-limited platelet pool, and binds VEGF in the central compartment by a Michaelis-Menten process that feeds the bound-aflibercept compartment (equal volume to central, first-order elimination K40). SC doses are absorbed first-order. Allometric body weight on all volumes and on K20/K40, albumin on K40, age and drug product (AFL-HD 39.4% slower) on QE, and healthy-volunteer / oncology indicators on Vmax. Pooled 2744 participants from 16 trials (healthy volunteers, nAMD, DME, solid tumors / lymphoma).
  • Article: https://doi.org/10.1002/psp4.70287 (open access)

Bihorel et al. pooled free and VEGF-bound (“adjusted bound”) aflibercept plasma concentrations from 16 trials in which aflibercept was given intravitreally (IVT), intravenously or subcutaneously. They adapted the IV-only model of Thai et al. (2011; modellib("Thai_2011_aflibercept")) by adding both eyes, a second peripheral compartment, a platelet-uptake pathway and SC absorption, and by moving VEGF binding from the peripheral to the central compartment. The main finding is that the 8 mg drug product (AFL-HD) has a 39.4% slower ocular clearance (QE) than the 2 mg drug product (AFL-2), which prolongs ocular exposure beyond what the 4-fold dose increase alone would give.

Population

The analysis included 2744 participants: healthy volunteers (n = 76), neovascular age-related macular degeneration (nAMD, n = 1662), diabetic macular edema (DME, n = 968), and solid tumors or lymphoma (n = 38). IVT doses were 0.05-4 mg (AFL-2) or 8 mg (AFL-HD), IV doses 0.3-4 mg/kg and SC doses 0.025-2 mg/kg. The baseline demographics are in Table S5 of the supplement. The reference patient for the covariate model (Figure 4 caption) is a 71-year-old, 76 kg patient with nAMD and a serum albumin of 4.3 g/dL. Of 31,326 plasma samples, 62.9% of the free and 19.6% of the bound aflibercept concentrations were below the limit of quantitation; these were handled with Beal’s M3 method in NONMEM 7.5.

Source trace

The structural equations are the Figure 1 ODE system (states A1-A8) and the typical-value equations of Section 3.1. Parameter values are from Table 1.

Model element Value Source
ODE system, 8 states (vitreous = A1, central = A2, peripheral1 = A3, complex = A4, vitreous_fellow = A5, depot = A6, plt_afl = A7, peripheral2 = A8) – Figure 1 caption
F_IVT on doses into either eye; F_SC on SC doses; IV into central – Figure 1 diagram
lf_vitreous (F_IVT) 0.701 Table 1
lq_vitreous (QE, AFL-2) 0.608 mL/day Table 1; Section 3.1 equation
e_form_afl_hd_q_vitreous (0.606^HD) 0.606 Table 1; Section 3.1 equation
e_age_q_vitreous ((AGE/71)^x) -1.36 Table 1; Section 3.1 equation
lv_vitreous (V1 = V5, fixed) 4 mL Table 1
lkel (K20), e_wt_kel 0.0807 1/day, -0.192 Table 1; Section 3.1 equation
lvc (V2 = V4), e_wt_vc 4.99 L, 0.872 Table 1; Section 3.1 equation
lvp (V3), e_wt_vp 1.08 L, 1.08 Table 1; Section 3.1 equation
lvp2 (V8), e_wt_vp2 1.18 L, 1.16 Table 1; Section 3.1 equation
lq (QF1), lq2 (QF2) 0.849, 0.0763 L/day Table 1
lvmax_plt (Vmax,p), lkm_plt (Km,p), lkel_plt (K70) 0.0310 1/day/L, 42.7 mg, 0.0265 1/day Table 1
logitfdepot (F_SC), lka (K62) 0.536, 0.368 1/day Table 1
lvmax (Vmax, nAMD/DME) 0.224 mg/day/L Table 1 (the Section 3.1 equation prints 0.0224; see below)
e_dis_healthy_vmax, e_dis_cancer_vmax 0.613, 1.24 Table 1; Section 3.1 equation
lkm (Km) 0.411 mg/L Table 1
lkint (K40), e_wt_kint, e_alb_kint 0.0350 1/day, -0.153, -0.767 Table 1; Section 3.1 equation
IIV variances: QE 0.597; K20 0.207, cov -0.0727, V2/V4 0.0618; Vmax 0.158; F_SC 0.629 (logit); K62 0.852; K40 0.0452 – Table 1
Residual SDs after IVT: free add 0.00644 mg/L, prop 0.415; bound add 0.0180 mg/L, prop 0.227 – Table 1

The Table 1 note says the IIV entries are variances on the log scale (logit for F_SC). Every reported CV% reproduces from sqrt(exp(omega^2) - 1), which confirms that reading.

mod <- rxode2::rxode2(readModelDb("Bihorel_2026_aflibercept"))
#> ℹ parameter labels from comments will be replaced by 'label()'
mod_typ <- rxode2::zeroRe(mod)

ref_cov <- data.frame(
  WT = 76, AGE = 71, ALB = 43, FORM_AFL_HD = 0,
  DIS_HEALTHY = 0, DIS_CANCER = 0
)

# Two error endpoints (Cc, Cc_complex): observation rows carry dvid; both
# observables come back as columns on every observation row.
ivt_events <- function(dose_times, dose, obs_times, id = 1L, cmt = "vitreous") {
  dplyr::bind_rows(
    data.frame(id = id, time = dose_times, amt = dose, evid = 1L, cmt = cmt, dvid = NA_integer_),
    data.frame(id = id, time = obs_times, amt = 0, evid = 0L, cmt = "central", dvid = 1L)
  ) |>
    dplyr::arrange(id, time, dplyr::desc(evid))
}

auc_trap <- function(t, y) sum(diff(t) * (head(y, -1) + tail(y, -1)) / 2)

Vmax: Table 1 versus the Section 3.1 equation

Table 1 gives Vmax = 0.224 mg/day/L (95% CI 0.213-0.227, RSE 1.63%). The typical-value equation in Section 3.1 prints Vmax = 0.0224 x 0.613^HV x 1.24^ONCO. The confidence interval excludes 0.0224 by a factor of ten. The bound analyte is the time-integral of the Vmax binding term, so it tells the two readings apart. The chunk below solves the typical patient after a single IVT dose of each product under both readings and compares the result with the post hoc medians in Table 2.

obs_28 <- sort(unique(c(seq(0, 7, by = 0.02), seq(7, 28, by = 0.1))))
typ_metrics <- function(dose, hd, vmax) {
  m <- rxode2::ini(mod_typ, lvmax = log(vmax))
  ev <- ivt_events(0, dose, obs_28)
  cov <- ref_cov
  cov$FORM_AFL_HD <- hd
  s <- as.data.frame(rxode2::rxSolve(m, events = cbind(ev, cov)))
  data.frame(
    free_auc = auc_trap(s$time, s$Cc), free_cmax = max(s$Cc),
    bound_auc = auc_trap(s$time, s$Cc_complex), bound_cmax = max(s$Cc_complex),
    bound_ctrough = s$Cc_complex[s$time == 28]
  )
}
vmax_tab <- dplyr::bind_rows(
  cbind(product = "AFL-2 2 mg", reading = "Table 1 (0.224)", typ_metrics(2, 0, 0.224)),
  cbind(product = "AFL-2 2 mg", reading = "Equation (0.0224)", typ_metrics(2, 0, 0.0224)),
  cbind(product = "AFL-HD 8 mg", reading = "Table 1 (0.224)", typ_metrics(8, 1, 0.224)),
  cbind(product = "AFL-HD 8 mg", reading = "Equation (0.0224)", typ_metrics(8, 1, 0.0224))
)
#> ℹ change initial estimate of `lvmax` to `-1.4961092271271`
#> ℹ omega/sigma items treated as zero: 'etalq_vitreous', 'etalkel', 'etalvc', 'etalvmax', 'etalogitfdepot', 'etalka', 'etalkint'
#> ℹ change initial estimate of `lvmax` to `-3.79869432012114`
#> ℹ omega/sigma items treated as zero: 'etalq_vitreous', 'etalkel', 'etalvc', 'etalvmax', 'etalogitfdepot', 'etalka', 'etalkint'
#> ℹ change initial estimate of `lvmax` to `-1.4961092271271`
#> ℹ omega/sigma items treated as zero: 'etalq_vitreous', 'etalkel', 'etalvc', 'etalvmax', 'etalogitfdepot', 'etalka', 'etalkint'
#> ℹ change initial estimate of `lvmax` to `-3.79869432012114`
#> ℹ omega/sigma items treated as zero: 'etalq_vitreous', 'etalkel', 'etalvc', 'etalvmax', 'etalogitfdepot', 'etalka', 'etalkint'
paper_bound <- data.frame(
  product = c("AFL-2 2 mg", "AFL-HD 8 mg"),
  paper_bound_auc = c(2.91, 9.58), paper_bound_cmax = c(0.138, 0.451),
  paper_bound_ctrough = c(0.0963, 0.344)
)
vmax_tab <- dplyr::left_join(vmax_tab, paper_bound, by = "product")
vmax_tab |>
  dplyr::transmute(
    product, reading,
    `Bound AUC0-4wk (sim)` = signif(bound_auc, 3), `Bound AUC0-4wk (Table 2)` = paper_bound_auc,
    `Bound Cmax (sim)` = signif(bound_cmax, 3), `Bound Cmax (Table 2)` = paper_bound_cmax,
    `Bound Ctrough wk4 (sim)` = signif(bound_ctrough, 3), `Bound Ctrough wk4 (Table 2)` = paper_bound_ctrough
  ) |>
  knitr::kable(caption = "Typical-patient bound aflibercept after one IVT dose under the two Vmax readings, against the Table 2 post hoc medians.")
Typical-patient bound aflibercept after one IVT dose under the two Vmax readings, against the Table 2 post hoc medians.
product reading Bound AUC0-4wk (sim) Bound AUC0-4wk (Table 2) Bound Cmax (sim) Bound Cmax (Table 2) Bound Ctrough wk4 (sim) Bound Ctrough wk4 (Table 2)
AFL-2 2 mg Table 1 (0.224) 2.670 2.91 0.1220 0.138 0.0950 0.0963
AFL-2 2 mg Equation (0.0224) 0.589 2.91 0.0284 0.138 0.0252 0.0963
AFL-HD 8 mg Table 1 (0.224) 8.650 9.58 0.4070 0.451 0.3740 0.3440
AFL-HD 8 mg Equation (0.0224) 1.640 9.58 0.0861 0.451 0.0850 0.3440 

tab1 <- dplyr::filter(vmax_tab, reading == "Table 1 (0.224)")
eqn <- dplyr::filter(vmax_tab, reading == "Equation (0.0224)")
stopifnot(
  # The Table 1 value reproduces the bound medians to within 20% at both doses.
  all(abs(tab1$bound_auc / tab1$paper_bound_auc - 1) < 0.20),
  all(abs(tab1$bound_ctrough / tab1$paper_bound_ctrough - 1) < 0.20),
  # The equation value is low by more than 3-fold on every bound metric.
  all(eqn$bound_auc / eqn$paper_bound_auc < 1 / 3),
  all(eqn$bound_cmax / eqn$paper_bound_cmax < 1 / 3)
)

The model therefore uses 0.224; the printed equation is off by a factor of ten.

Ocular concentrations and the drug-product effect (Figure 5)

The paper’s headline claim is quantitative. The median ocular concentration of free aflibercept at the end of the 8-week AFL-2 interval (2q8, week 56) is reached about 14 weeks after an AFL-HD injection (Abstract; “about 7 weeks later” than the 8-week interval in the Discussion). If both products had the AFL-2 ocular clearance, the time would be only 9-9.5 weeks.

The chunk below reproduces this for the typical 71-year-old patient. Figure 5 assumes that the whole dose enters the vitreous and applies the F_IVT loss on exit, whereas the fitted model applies F_IVT on entry. The difference scales all ocular concentrations by the same factor, so the time to reach a given 2q8 trough does not depend on it. The plot shows the paper’s convention (fitted vitreous concentration divided by F_IVT).

f_ivt <- 0.701
wk <- 7
regimens <- list(
  "2q8" = list(dose = 2, hd = 0, times = c(0, 4, 8, 16, 24, 32, 40, 48) * wk),
  "8q12" = list(dose = 8, hd = 1, times = c(0, 4, 8, 20, 32, 44) * wk),
  "8q16" = list(dose = 8, hd = 1, times = c(0, 4, 8, 24, 40) * wk),
  "8q12, AFL-2 QE" = list(dose = 8, hd = 0, times = c(0, 4, 8, 20, 32, 44) * wk)
)
obs_eye <- seq(0, 64 * wk, by = 0.25)
eye <- dplyr::bind_rows(lapply(names(regimens), function(nm) {
  r <- regimens[[nm]]
  cov <- ref_cov
  cov$FORM_AFL_HD <- r$hd
  s <- as.data.frame(rxode2::rxSolve(mod_typ, events = cbind(ivt_events(r$times, r$dose, obs_eye), cov)))
  data.frame(regimen = nm, time = s$time, c_eye = s$C_vitreous / f_ivt)
}))
#> ℹ omega/sigma items treated as zero: 'etalq_vitreous', 'etalkel', 'etalvc', 'etalvmax', 'etalogitfdepot', 'etalka', 'etalkint'
#> ℹ omega/sigma items treated as zero: 'etalq_vitreous', 'etalkel', 'etalvc', 'etalvmax', 'etalogitfdepot', 'etalka', 'etalkint'
#> ℹ omega/sigma items treated as zero: 'etalq_vitreous', 'etalkel', 'etalvc', 'etalvmax', 'etalogitfdepot', 'etalka', 'etalkint'
#> ℹ omega/sigma items treated as zero: 'etalq_vitreous', 'etalkel', 'etalvc', 'etalvmax', 'etalogitfdepot', 'etalka', 'etalkint'

ggplot(eye, aes(time / wk, c_eye * 1000, colour = regimen, linetype = regimen)) +
  geom_line() +
  scale_y_log10() +
  scale_linetype_manual(values = c("2q8" = "solid", "8q12" = "solid", "8q16" = "solid", "8q12, AFL-2 QE" = "dashed")) +
  coord_cartesian(ylim = c(1e-2, 1e4)) +
  labs(
    x = "Time (weeks)", y = "Free aflibercept in the study eye (mg/L)",
    colour = NULL, linetype = NULL,
    caption = "Replicates the median lines of Figure 5 of Bihorel 2026 for the typical patient."
  ) +
  theme_bw()

# 2q8 trough at week 56 (8 weeks after the week-48 dose)
trough_2q8 <- eye$c_eye[eye$regimen == "2q8" & eye$time == 56 * wk]
time_to_reach <- function(dose, hd) {
  cov <- ref_cov
  cov$FORM_AFL_HD <- hd
  tt <- seq(0, 30 * wk, by = 0.05)
  s <- as.data.frame(rxode2::rxSolve(mod_typ, events = cbind(ivt_events(0, dose, tt), cov)))
  c_eye <- s$C_vitreous / f_ivt
  s$time[which(c_eye <= trough_2q8)[1]] / wk
}
ttr <- data.frame(
  scenario = c("AFL-HD 8 mg (QE x 0.606)", "8 mg with the AFL-2 QE"),
  `Simulated (weeks)` = c(time_to_reach(8, 1), time_to_reach(8, 0)),
  `Paper (weeks)` = c("~14 (Abstract); ~15 (Discussion)", "9-9.5"),
  check.names = FALSE
)
#> ℹ omega/sigma items treated as zero: 'etalq_vitreous', 'etalkel', 'etalvc', 'etalvmax', 'etalogitfdepot', 'etalka', 'etalkint'
#> ℹ omega/sigma items treated as zero: 'etalq_vitreous', 'etalkel', 'etalvc', 'etalvmax', 'etalogitfdepot', 'etalka', 'etalkint'
knitr::kable(ttr, digits = 1, caption = "Time after a single 8 mg injection for the ocular concentration to fall to the 2q8 week-56 trough.")
Time after a single 8 mg injection for the ocular concentration to fall to the 2q8 week-56 trough.
scenario Simulated (weeks) Paper (weeks)
AFL-HD 8 mg (QE x 0.606) 15.3 ~14 (Abstract); ~15 (Discussion)
8 mg with the AFL-2 QE 9.3 9-9.5 
stopifnot(
  ttr$`Simulated (weeks)`[1] > 13, ttr$`Simulated (weeks)`[1] < 16,
  ttr$`Simulated (weeks)`[2] > 8.5, ttr$`Simulated (weeks)`[2] < 10.5
)

The typical patient reproduces both numbers. The paper’s figures are medians over a 10,000-patient virtual population with resampled ages, which explains the small offset from the AFL-HD value.

Virtual cohort

Table 2 summarises post hoc exposures in the dense-PK substudies of CANDELA, PULSAR (nAMD) and PHOTON (DME): 33 patients after AFL-2 2 mg and 51 after AFL-HD 8 mg. The cohort below has 200 patients per arm. Table S5 (demographics) is in the supplement, so the covariate distributions are assumptions centred on the Figure 4 reference patient: body weight log-normal (median 76 kg, 20% CV), age normal (mean 71, SD 10 years) and albumin normal (mean 4.3, SD 0.3 g/dL). Values outside plausible bands are redrawn rather than clipped.

set.seed(2026)
draw_band <- function(n, rfun, lo, hi) {
  x <- rfun(n)
  while (any(bad <- x < lo | x > hi)) x[bad] <- rfun(sum(bad))
  x
}
n_arm <- 200L
make_arm <- function(n, dose, hd, label, id_offset) {
  data.frame(
    id = id_offset + seq_len(n),
    WT = draw_band(n, function(k) 76 * exp(rnorm(k, 0, 0.2)), 45, 140),
    AGE = draw_band(n, function(k) rnorm(k, 71, 10), 45, 95),
    ALB = 10 * draw_band(n, function(k) rnorm(k, 4.3, 0.3), 3.2, 5.4),
    FORM_AFL_HD = hd, DIS_HEALTHY = 0, DIS_CANCER = 0,
    dose = dose, treatment = label
  )
}
subjects <- dplyr::bind_rows(
  make_arm(n_arm, 2, 0, "AFL-2 2 mg", 0L),
  make_arm(n_arm, 8, 1, "AFL-HD 8 mg", n_arm)
)
stopifnot(!anyDuplicated(subjects$id))

# Three monthly injections so the week 8-12 interval gives the accumulation ratio
dose_times <- c(0, 28, 56)
obs_84 <- sort(unique(c(seq(0, 7, by = 0.05), seq(7, 84, by = 0.25))))
events <- dplyr::bind_rows(lapply(seq_len(nrow(subjects)), function(i) {
  s <- subjects[i, ]
  cbind(ivt_events(dose_times, s$dose, obs_84, id = s$id), s[, setdiff(names(s), c("id", "dose"))])
}))
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#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
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Simulation 

rxode2::rxSetSeed(2026)
sim <- rxode2::rxSolve(mod, events = events, keep = "treatment", returnType = "data.frame")
sim |>
  dplyr::select(id, time, treatment, `Free aflibercept` = Cc, `Adjusted bound aflibercept` = Cc_complex) |>
  tidyr::pivot_longer(-c(id, time, treatment), names_to = "analyte", values_to = "conc") |>
  dplyr::group_by(treatment, analyte, time) |>
  dplyr::summarise(
    med = median(conc), lo = quantile(conc, 0.05), hi = quantile(conc, 0.95),
    .groups = "drop"
  ) |>
  ggplot(aes(time / 7, med, colour = treatment, fill = treatment)) +
  geom_ribbon(aes(ymin = lo, ymax = hi), alpha = 0.2, colour = NA) +
  geom_line() +
  facet_wrap(~analyte, scales = "free_y") +
  labs(
    x = "Time (weeks)", y = "Plasma concentration (mg/L)", colour = NULL, fill = NULL,
    caption = "Median and 90% prediction interval (no residual error), three monthly IVT injections; compare Figure 3."
  ) +
  theme_bw()

PKNCA validation

One PKNCA run per analyte. AUC, Cmax and Tmax are taken over weeks 0-4 after the first injection and the trough at day 28 (just before the second dose). The accumulation ratio is AUC over weeks 8-12 divided by AUC over weeks 0-4.

run_nca <- function(conc_col) {
  conc <- sim |>
    dplyr::transmute(id, time, treatment, Cc = .data[[conc_col]]) |>
    dplyr::filter(!is.na(Cc))
  doses <- events |>
    dplyr::filter(evid == 1) |>
    dplyr::select(id, time, amt, treatment)
  conc_obj <- PKNCA::PKNCAconc(conc, Cc ~ time | treatment + id)
  dose_obj <- PKNCA::PKNCAdose(doses, amt ~ time | treatment + id)
  intervals <- data.frame(
    start = c(0, 56), end = c(28, 84),
    auclast = TRUE, cmax = c(TRUE, FALSE), tmax = c(TRUE, FALSE), ctrough = c(TRUE, FALSE)
  )
  PKNCA::pk.nca(PKNCA::PKNCAdata(conc_obj, dose_obj, intervals = intervals))
}
nca_free <- run_nca("Cc")
nca_bound <- run_nca("Cc_complex")

summarise_nca <- function(nca) {
  res <- as.data.frame(nca)
  first <- dplyr::filter(res, start == 0, PPTESTCD %in% c("auclast", "cmax", "tmax", "ctrough"))
  ar <- res |>
    dplyr::filter(PPTESTCD == "auclast") |>
    dplyr::select(treatment, id, start, PPORRES) |>
    tidyr::pivot_wider(names_from = start, values_from = PPORRES, names_prefix = "auc") |>
    dplyr::transmute(treatment, id, PPTESTCD = "ar", PPORRES = auc56 / auc0)
  dplyr::bind_rows(dplyr::select(first, treatment, id, PPTESTCD, PPORRES), ar)
}
free_long <- summarise_nca(nca_free)
bound_long <- summarise_nca(nca_bound)

Comparison against published exposure metrics

Table 2 reports post hoc medians. The free trough at week 4 was below the limit of quantitation in both arms and is not compared.

units_tab <- c(auclast = "mg*day/L", cmax = "mg/L", tmax = "day", ctrough = "mg/L")
pub_free <- data.frame(
  treatment = c("AFL-2 2 mg", "AFL-HD 8 mg"),
  auclast = c(0.238, 1.90), cmax = c(0.0274, 0.230), tmax = c(2.14, 2.79)
)
pub_bound <- data.frame(
  treatment = c("AFL-2 2 mg", "AFL-HD 8 mg"),
  auclast = c(2.91, 9.58), cmax = c(0.138, 0.451), ctrough = c(0.0963, 0.344), tmax = c(12.5, 15.8)
)
cmp_free <- nlmixr2lib::ncaComparisonTable(
  dplyr::filter(free_long, PPTESTCD != "ar"), pub_free,
  by = "treatment", units = units_tab, tolerance_pct = 20
)
cmp_bound <- nlmixr2lib::ncaComparisonTable(
  dplyr::filter(bound_long, PPTESTCD != "ar"), pub_bound,
  by = "treatment", units = units_tab, tolerance_pct = 20
)
cmp <- dplyr::bind_rows(
  cbind(Analyte = "Free", cmp_free),
  cbind(Analyte = "Adjusted bound", cmp_bound)
)
knitr::kable(cmp, caption = "Simulated cohort medians vs. Table 2 post hoc medians (weeks 0-4 after the first IVT dose). * differs by >20%.")
Simulated cohort medians vs. Table 2 post hoc medians (weeks 0-4 after the first IVT dose). * differs by >20%.
Analyte NCA parameter treatment Reference Simulated % diff
Free Cmax (mg/L) AFL-2 2 mg 0.0274 0.0331 +20.8%*
Free Cmax (mg/L) AFL-HD 8 mg 0.23 0.0911 -60.4%*
Free Tmax (day) AFL-2 2 mg 2.14 2.8 +30.8%*
Free Tmax (day) AFL-HD 8 mg 2.79 3.85 +38.0%*
Free AUClast (mg*day/L) AFL-2 2 mg 0.238 0.34 +42.8%*
Free AUClast (mg*day/L) AFL-HD 8 mg 1.9 1.28 -32.6%*
Adjusted bound Cmax (mg/L) AFL-2 2 mg 0.138 0.111 -19.6%
Adjusted bound Cmax (mg/L) AFL-HD 8 mg 0.451 0.366 -18.7%
Adjusted bound Tmax (day) AFL-2 2 mg 12.5 15 +20.0%
Adjusted bound Tmax (day) AFL-HD 8 mg 15.8 20.9 +32.1%*
Adjusted bound AUClast (mg*day/L) AFL-2 2 mg 2.91 2.38 -18.2%
Adjusted bound AUClast (mg*day/L) AFL-HD 8 mg 9.58 7.55 -21.2%*
Adjusted bound Ctrough (mg/L) AFL-2 2 mg 0.0963 0.0864 -10.3%
Adjusted bound Ctrough (mg/L) AFL-HD 8 mg 0.344 0.325 -5.6% 
ar_tab <- dplyr::bind_rows(
  cbind(Analyte = "Free", free_long),
  cbind(Analyte = "Adjusted bound", bound_long)
) |>
  dplyr::filter(PPTESTCD == "ar") |>
  dplyr::group_by(Analyte, treatment) |>
  dplyr::summarise(`Simulated median AR` = median(PPORRES), .groups = "drop") |>
  dplyr::left_join(
    data.frame(
      Analyte = rep(c("Free", "Adjusted bound"), each = 2),
      treatment = rep(c("AFL-2 2 mg", "AFL-HD 8 mg"), 2),
      `Table 2 median AR` = c(1.01, 1.04, 1.94, 1.90),
      check.names = FALSE
    ),
    by = c("Analyte", "treatment")
  )
knitr::kable(ar_tab, digits = 2, caption = "Accumulation ratio AUC(week 8-12) / AUC(week 0-4) after three monthly injections.")
Accumulation ratio AUC(week 8-12) / AUC(week 0-4) after three monthly injections.
Analyte treatment Simulated median AR Table 2 median AR
Adjusted bound AFL-2 2 mg 1.96 1.94
Adjusted bound AFL-HD 8 mg 2.34 1.90
Free AFL-2 2 mg 1.03 1.01
Free AFL-HD 8 mg 1.18 1.04 

bound_med <- bound_long |>
  dplyr::group_by(treatment, PPTESTCD) |>
  dplyr::summarise(med = median(PPORRES), .groups = "drop")
bound_auc <- bound_med$med[bound_med$PPTESTCD == "auclast"]
stopifnot(
  # Cohort-level envelope. The tight structural check is the deterministic
  # typical-patient solve in the Vmax section; these medians sit 0-25% from
  # Table 2 and depend on the assumed demographics and the cohort draw, so
  # they get headroom. A transcription error in QE, Vmax, Km, K40 or V2 moves
  # them by far more (the 0.0224 Vmax reading is 5-fold low).
  all(abs(bound_auc / pub_bound$auclast - 1) < 0.35),
  all(abs(ar_tab$`Simulated median AR` / ar_tab$`Table 2 median AR` - 1) < 0.40)
)

For the adjusted bound analyte the simulated cohort medians are 0-25% from Table 2: trough within about 10%, AUC and Cmax about 20% lower, and Tmax later. The accumulation ratios match for AFL-2 (bound and free). For AFL-HD they are higher than Table 2 (bound 2.3 vs 1.9), and the bound Tmax is later (about 21 vs 16 days). Both are consistent with faster ocular release in the dense-PK AFL-HD patients than the typical AFL-HD ocular clearance implies. For comparison, the typical patient given 8 mg with the AFL-2 ocular clearance has a bound Tmax of 15.3 days. The free analyte does not reproduce well at either dose; see the next section.

Assumptions and deviations

  • Vmax. The model uses the Table 1 value 0.224 mg/day/L rather than the 0.0224 printed in the Section 3.1 equation. Table 1’s confidence interval and the bound-aflibercept exposures in Table 2 both rule out 0.0224 (see the Vmax section).
  • Free aflibercept. The printed parameters give a typical free AUC over weeks 0-4 of about 1.4 mg*day/L and a Cmax of about 0.10 mg/L after AFL-HD 8 mg, against Table 2 medians of 1.90 and 0.230. At 2 mg the simulated free AUC and Cmax are instead 20-45% above the Table 2 medians, and the free Tmax is later than Table 2 at both doses. The paper reports that free exposure rose about 10.5-fold (AUC) and 9-fold (Cmax) for the 4-fold dose increase, but no setting of the printed constants produces that. The Michaelis-Menten binding constant Km = 0.411 mg/L is about four times the typical free peak after AFL-HD (about 0.1 mg/L), and the platelet pool holds at most about 1.5 mg against Km,p = 42.7 mg, so neither saturable pathway saturates enough; the slower AFL-HD ocular clearance lowers the free peak further. Free concentrations after IVT dosing are mostly below the limit of quantitation (62.9% of free samples overall), so post hoc free exposures are weakly informed by data. The typical-patient bound exposures and the Figure 5 ocular time course reproduce the paper. No parameter has been adjusted to close the free-analyte gap.
  • Residual error. Table 1 gives separate residual-error magnitudes by route (IV/SC vs IVT) and assay LLOQ. The model carries the IVT values at the LLOQs of the pivotal CANDELA / PULSAR / PHOTON assays (free 0.0156 mg/L, bound 0.0224 mg/L; Figure 3 caption). The other sets are listed in comments in the model file.
  • Bioavailability convention. The fitted model applies F_IVT = 0.701 to the dose entering either eye, as in Figure 1, so C_vitreous is the fitted-model vitreous concentration. The paper’s Figure 5 ocular simulations instead put the whole dose into the vitreous and apply the loss on exit; divide C_vitreous by F_IVT for that convention, as above.
  • One ocular clearance per subject. The drug-product indicator FORM_AFL_HD describes the product injected in the study eye. Fellow-eye doses, which in the trials were always AFL-2, share that clearance, as in the source model.
  • Oncology indicator. The paper’s ONCO indicator covers participants with solid tumors or lymphoma (n = 38). It is mapped to DIS_CANCER.
  • Albumin units. The paper’s albumin effect uses g/dL (reference 4.3). The model takes the canonical g/L column and converts it inline.
  • Virtual cohort. Demographics (Table S5) are in the supplement; the covariate distributions above are centred on the Figure 4 reference patient and are assumptions. The Table 2 comparison is between a simulated cohort and the post hoc estimates of 33 and 51 patients, so differences of 10-20% are expected.
  • Platelet pool. The paper presents the platelet-uptake compartment as a hypothesis that improved the fit to high IV and SC concentrations. Nothing measures it, and after IVT dosing it acts as a linear elimination pathway.