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Cilgavimab component serum exposure

Explore conditional IM absorption and IV two-compartment antibody disposition with fixed baseline covariates. Component assignment follows the supplement caption provisionally; population behavior, combined-fit equivalence and clinical protection are unqualified.

Accelerating therapeutics development during a pandemic: population pharmacokinetics of the long-acting antibody combination AZD7442 (tixagevimab/cilgavimab) in the prophylaxis and treatment of COVID-19 ↗
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Reference gluteal IM, 300 mg

How does 300 mg IM administration produce exposure under the reference covariates? Deterministic conditional exploration.

0–1,000 day

Reference gluteal IM, 300 mg

How does 300 mg IM administration produce exposure under the reference covariates? Deterministic conditional exploration.

Readouts

Central serum concentration· ug/mL
Central amount divided by apparent central volume; mg/L equals ug/mL.
Exposure (AUC)· ug*day/mL
Concentration integral from simulation start; interval exposure requires differencing integral values.
Covariate adjusted clearance· L/day
Fixed clearance coefficient under the selected baseline covariates.

Study purpose

  • How does 300 mg IM administration produce exposure under the reference covariates?

Interpretation limits

  • The total AZD7442 fit is independent of the two component fits; do not add component trajectories or synthesize an unreported cross-analyte population covariance.
  • Component parameter identities provisionally follow the supplemental captions. Twenty-six coefficient rows conflict with article Table 1; the opposite component column matches 23 exactly, with three remaining differences. Author adjudication is absent.
  • IM dose enters a first-order depot; the fitted IM scaling multiplier can exceed one in the total thigh configuration (approximately 1.017). It is empirical and must not be interpreted as a conserved physical fraction.
  • IV scenarios assume one-hour administration. Original infusion duration and component timing are unavailable; no original peak or sequential-infusion claim is made.
  • Categorical covariates and route fractions are fixed setup/context, not continuous axes. Body weight changes disposition under the fixed mg schedule.
  • The diagnostic disposition half-time uses the slow two-compartment eigenvalue and omits a possible absorption-controlled terminal phase; it is not a population median.
  • Original individual data, censored likelihood, fitted effects, bootstrap, VPC, adolescent cohort and clinical protection are not reconstructed.
  • Supplementary-file reuse permission remains unresolved separately from the article license. Source documents and artwork are not distributed; this executable implementation and its figures are independently authored.
  • The component identity provisionally follows Supplement Table S5; article Table 1 assigns the component coefficients differently. No author adjudication is available.
  • Weight bounds span the 55.6 and 122 kg Figure 4 anchors as an exploratory interval, not a validated joint patient range. The other control bounds are authored 0.5–1.5× default ranges, not confidence intervals.
  • The higher-weight context changes weight alone; it does not reproduce every factor of the Figure 4 low-exposure population scenario.
  • Changing the start time resets supplied initial states at that start; earlier bolus history is not reconstructed. An overlapping IV infusion contributes only the remaining segment.
  • The IV context does not depend on IM absorption or empirical IM scaling. Use an IM context to explore these controls.
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Edit inputs

Model setup

Figure 4 typical comparator

kg
80.6 → —
IM absorption

Final cilgavimab estimate; distinct from interim Table S7

1/day
0.122 → —

Empirical gluteal FIM; multiplicative thigh effect can exceed one. Not a mechanistic mass fraction.

1
0.615 → —
PK clearance

Final cilgavimab estimate; distinct from interim Table S7

L/day
0.0456 → —
PK distribution

Final cilgavimab estimate; distinct from interim Table S7

L
3.17 → —
Simulation time
day
day
1000 → 168

Time between reported samples.

day

1 change · Variation 1

Continue in the background and keep the result.

Biology

Explore conditional IM absorption and IV two-compartment antibody disposition with fixed baseline covariates. Component assignment follows the supplement caption provisionally; population behavior, combined-fit equivalence and clinical protection are unqualified.

Explore conditional IM absorption and IV two-compartment antibody disposition with fixed baseline covariates. Component assignment follows the supplement caption provisionally; population behavior, combined-fit equivalence and clinical protection are unqualified.
States & equation dependencies
What are the modeled states and equation-linked processes?Representative technical topology with 5 selected states: Central-compartment drug amount, Administration-site drug amount, Peripheral-compartment drug amount, Exposure (AUC), Elapsed time. Displayed equation-linked dependencies: from Administration-site drug amount to Central-compartment drug amount; between Peripheral-compartment drug amount and Central-compartment drug amount; from Central-compartment drug amount to Exposure (AUC). Displayed model or scenario entry points: Administered input enters Central-compartment drug amount; Administered input enters Administration-site drug amount. There are no separately labeled equation controls.TECHNICAL TOPOLOGYWhat are the modeled states and equation-linked processes?Selected equation-linked states · model time unit: dayAdministered inputAdministered inputCentral-compartment drugamountmgAdministration-site drugamountmgPeripheral-compartment drugamountmgExposure (AUC)ug*day/mLElapsed timeday
Teal arrows show relationships between modeled states and do not always represent material transfer. Peach arrows mark administered or scenario-defined entry points.

Assumptions & evidence

Assumptions & limitations

  • The total AZD7442 fit is independent of the two component fits; do not add component trajectories or synthesize an unreported cross-analyte population covariance.
  • Component parameter identities provisionally follow the supplemental captions. Twenty-six coefficient rows conflict with article Table 1; the opposite component column matches 23 exactly, with three remaining differences. Author adjudication is absent.
  • IM dose enters a first-order depot; the fitted IM scaling multiplier can exceed one in the total thigh configuration (approximately 1.017). It is empirical and must not be interpreted as a conserved physical fraction.
  • IV scenarios assume one-hour administration. Original infusion duration and component timing are unavailable; no original peak or sequential-infusion claim is made.
  • Categorical covariates and route fractions are fixed setup/context, not continuous axes. Body weight changes disposition under the fixed mg schedule.
  • The diagnostic disposition half-time uses the slow two-compartment eigenvalue and omits a possible absorption-controlled terminal phase; it is not a population median.
  • Original individual data, censored likelihood, fitted effects, bootstrap, VPC, adolescent cohort and clinical protection are not reconstructed.
  • Supplementary-file reuse permission remains unresolved separately from the article license. Source documents and artwork are not distributed; this executable implementation and its figures are independently authored.
  • The component identity provisionally follows Supplement Table S5; article Table 1 assigns the component coefficients differently. No author adjudication is available.
  • Weight bounds span the 55.6 and 122 kg Figure 4 anchors as an exploratory interval, not a validated joint patient range. The other control bounds are authored 0.5–1.5× default ranges, not confidence intervals.
  • The higher-weight context changes weight alone; it does not reproduce every factor of the Figure 4 low-exposure population scenario.
  • Changing the start time resets supplied initial states at that start; earlier bolus history is not reconstructed. An overlapping IV infusion contributes only the remaining segment.
  • The IV context does not depend on IM absorption or empirical IM scaling. Use an IM context to explore these controls.

Parameter origins

  • Clegg 2024 Supplement Table S5
  • Figure 4 typical comparator
  • Released executable model definition; exact publication locator not reported
  • Runtime route switch
  • Final covariate model
  • SI Tables S3-S5 covariate comments

Validation & references

One or more implementation checks are available; this is not a claim of full model reproduction.

Accelerating therapeutics development during a pandemic: population pharmacokinetics of the long-acting antibody combination AZD7442 (tixagevimab/cilgavimab) in the prophylaxis and treatment of COVID-19 ↗

Available reference curves can be added to the plot when their scenario, readout, time and units match.

Model details

28 parameters · 5 states · 5 equations · 12 derived outputs

Classification & API access
Therapeutic
cilgavimab
Modality
Monoclonal antibody
Target
SARS-CoV-2 Spike RBD
Disease / scope
COVID-19 exposure study
ParameterDefaultUnitDescription
Diabetes effect on clearancebeta_cl_diabetes0.1671Final cilgavimab estimate; distinct from interim Table S7
Thigh injection effect on fimbeta_fim_thigh0.3781Final cilgavimab estimate; distinct from interim Table S7
Age 65 effect on absorption ratebeta_ka_age_over65-0.4431Final cilgavimab estimate; distinct from interim Table S7
Bmi 30 effect on absorption ratebeta_ka_bmi_ge30-0.2171Final cilgavimab estimate; distinct from interim Table S7
Diabetes effect on absorption ratebeta_ka_diabetes-0.2621Final cilgavimab estimate; distinct from interim Table S7
Male effect on absorption ratebeta_ka_male0.6321Final cilgavimab estimate; distinct from interim Table S7
Black race effect on central distribution volumebeta_vc_black-0.2421Final cilgavimab estimate; distinct from interim Table S7
Body weightbody_weight_kg80.6kgFigure 4 typical comparator
Systemic clearancecl_l_day0.0456L/dayFinal cilgavimab estimate; distinct from interim Table S7
Discriminant floor DAY2discriminant_floor_day21e-181/day**2Fixed unit-bearing name for the existing numerical floor; not a source coefficient or independently variable analysis control.
Dose fraction to central compartmentdose_to_central_fraction01Runtime route switch
Dose fraction to im depotdose_to_depot_fraction11Runtime route switch
Intramuscular bioavailability fractionfim_fraction0.6151Empirical gluteal FIM; multiplicative thigh effect can exceed one. Not a mechanistic mass fraction.
Diabetes flaghas_diabetes01Final covariate model
Age 65 flagis_age_over6501Final covariate model
Black race flagis_black_race01Final covariate model
Bmi 30 flagis_bmi_ge3001Final covariate model
Male sex flagis_male11Figure 4 typical comparator
Activ 2 thigh im flagis_thigh_injection01Final covariate model
Absorption rate constantka_1_day0.1221/dayFinal cilgavimab estimate; distinct from interim Table S7
Intercompartmental clearanceq_l_day0.432L/dayFinal cilgavimab estimate; distinct from interim Table S7
Rate floorrate_floor_per_day1e-121/dayFixed unit-bearing name for the existing numerical floor; not a source coefficient or independently variable analysis control.
Allometric reference body weightreference_weight_kg70kgSI Tables S3-S5 covariate comments
Time floortime_floor_day1e-12dayFixed unit-bearing name for the existing numerical floor; not a source coefficient or independently variable analysis control.
Central distribution volumevc_l3.17LFinal cilgavimab estimate; distinct from interim Table S7
Volume floorvolume_floor_L1e-12LFixed unit-bearing name for the existing numerical floor; not a source coefficient or independently variable analysis control.
Peripheral distribution volumevp_l1.77LFinal cilgavimab estimate; distinct from interim Table S7
Weight floorweight_floor_kg1kgFixed unit-bearing name for the existing numerical floor; not a source coefficient or independently variable analysis control.

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