A Quantitative Systems Pharmacology Model of the Pathophysiology and Treatment of COVID-19 Predicts Optimal Timing of Pharmacological Interventions

Abstract

A quantitative systems pharmacology (QSP) model of the pathogenesis and treatment of SARS-CoV-2 infection can streamline and accelerate the development of novel medicines to treat COVID-19. Simulation of clinical trials allows in silico exploration of the uncertainties of clinical trial design and can rapidly inform their protocols. We previously published a preliminary model of the immune response to SARS-CoV-2 infection. To further our understanding of COVID-19 and treatment we significantly updated the model by matching a curated dataset spanning viral load and immune responses in plasma and lung. We identified a population of parameter sets to generate heterogeneity in pathophysiology and treatment and tested this model against published reports from interventional SARS-CoV-2 targeting Ab and anti-viral trials. Upon generation and selection of a virtual population, we match both the placebo and treated responses in viral load in these trials. We extended the model to predict the rate of hospitalization or death within a population. Via comparison of the in silico predictions with clinical data, we hypothesize that the immune response to virus is log-linear over a wide range of viral load. To validate this approach, we show the model matches a published subgroup analysis, sorted by baseline viral load, of patients treated with neutralizing Abs. By simulating intervention at different timepoints post infection, the model predicts efficacy is not sensitive to interventions within five days of symptom onset, but efficacy is dramatically reduced if more than five days pass post-symptom onset prior to treatment.

Innovative Randomized Phase I Study and Dosing Regimen Selection to Accelerate and Inform Pivotal COVID‐19 Trial of Nirmatrelvir

“…To select the treatment duration, a quantitative systems pharmacology (QSP) model capable of describing viral dynamics over time was used to predict potential viral load reduction as a surrogate of efficacy. 22 , 23 The model was updated to include expected nirmatrelvir pharmacokinetic data at the proposed phase II/III dose, preclinical data from a mouse model of SARS‐CoV‐2, and publicly available viral load data from randomized controlled trials. 24 , 25 , 26 Specifically, to predict the antiviral effect and optimal dosing regimen of nirmatrelvir, the QSP model was updated to incorporate: (i) the mean simulated pharmacokinetic profile of nirmatrelvir/ritonavir 300 mg/100 mg b.i.d.…”

“…To select the treatment duration, a quantitative systems pharmacology (QSP) model capable of describing viral dynamics over time was used to predict potential viral load reduction as a surrogate of efficacy 22,23 . The model was updated to include expected nirmatrelvir pharmacokinetic data at the proposed phase II/III dose, preclinical data from a mouse model of SARS‐CoV‐2, and publicly available viral load data from randomized controlled trials 24‐26 .…”

Innovative Randomized Phase 1 Study and Dosing Regimen Selection to Accelerate and Inform Pivotal COVID-19 Trial of Nirmatrelvir

“…To select the treatment duration, a quantitative systems pharmacology (QSP) model capable of describing viral dynamics over time was used to predict potential viral load reduction as a measure of efficacy. 18,19 The model was updated to include expected nirmatrelvir pharmacokinetic data at the proposed phase 2/3 dose, preclinical data from a mouse model of SARS-CoV-2, and publicly available viral load data from randomized controlled trials. [20][21][22] Specifically, to predict the efficacy and optimal dosing regimen of nirmatrelvir, the QSP model was updated to incorporate: (1) the mean simulated pharmacokinetic profile of nirmatrelvir/ritonavir 300 mg/100 mg BID 5 day and 10 day regimens from the population pharmacokinetic model described in the preceding section; (2) preclinical data on nirmatrelvir pharmacology in a mouse model of SARS-CoV-2 that was used to estimate the in vivo potency of nirmatrelvir with the QSP model; and (3) a virtual population (N=502) that matched the placebo and treatment response of viral load and severity as reported in publicly available data.…”

“…It also allowed flexibility to implement ritonavir and evaluate relative bioavailability and food effect, as feasible. The SAD part also included an exploratory objective to implement metabolite identification by a novel 19 F-NMR method. The MAD part was a randomized, double-blind, placebo-controlled, sponsor-open design and included 5 cohorts including 1 Japanese and 2 non-Japanese cohorts being optional.…”

“…An exploratory objective of evaluating the feasibility of 19 F-NMR for metabolite identification provided us enough confidence in our ability to fulfil regulatory requirements using this novel method. Therefore, to expedite the drug development timeline, the protocol was amended on June 2, 2021, to include a metabolism and excretion cohort.…”