PBPK model development to study the effect of age, race
and organ dysfunction
PBPK modeling and simulations were performed using Simcyp V19 simulator
(Certara UK Ltd., Sheffield, UK) for clinical trial scenarios as shown
in Supplementary Figure 1 . Well established or fit-for-purpose
PBPK models were verified against available clinical data before being
utilized to simulate scenarios where no clinical data are available.
PBPK models of acalabrutinib, baricitinib, ruxolitinib, ritonavir,
darunavir and dapagliflozin were previously verified extensively and
they have been submitted previously as part of new drug application
submission to the FDA. The rest of the PBPK models (azithromycin,
atazanavir, lopinavir, chloroquine, hydroxychloroquine) were reported in
peer reviewed journals with reasonable level of verification. These
verified PBPK compound files were obtained from either Simcyp Simulator
compound library file or repository
(https://members.simcyp.com/account/libraryFiles/) or were built
using reported compound input parameters. The PBPK model input
parameters are shown in Supplementary Table 1 and key drug
parameters required for simulating lung concentration are shown inSupplementary Table 2 . Trial designs for all simulations were
set to 10 trials of 10 patients with the relevant populations
constrained by age range and the proportion of females which reflected
the reported study design of clinical studies where available. Dosing
amounts and schedules were chosen as per the respective compound dosing
recommendations and administered as an oral single-dose or multiple-dose
across all simulations. The predictive performance PBPK model of probe
compounds was measured by the ratio of the mean predicted AUC to the
mean observed AUC where possible (Supplementary Table 3 ). For
organ dysfunction simulations, AUCdiseased to
AUChealthy ratios for both observed and predicted
scenarios were calculated as predictive performance measures. The
predictability was considered acceptable if this metric fell between the
upper and lower limits (0.5 to 2-fold of observed data). Differences in
exposure from race in Caucasian, Japanese and Chinese populations were
predicted using Simcyp’s population library repository using the
“Healthy-Volunteer,” “Japanese,” and “Chinese Healthy Volunteer”
population models, respectively. Simulations for hepatic impairment were
performed using Simulator’s “Cirrhosis CP-A”, “Cirrhosis CP-B”, and
“Cirrhosis CP-C” populations, while for renal impairment, populations
“RenalGFR_30-60”, and “RenalGFR_less_30” were used. The effect of
age, race and organ impairment simulations were not simulated for
baloxavir, remdesivir and large molecules, as adequate PBPK modelling
was not possible due to limited data availability in the literature.
Based on recent findings from IQ consortium [16] the impact of renal
impairment can be well predicted within 2-fold of observed PK (AUC) and
for hepatic impairment >70% of the cases were predicted
within 2-fold, supporting the utility of PBPK modeling for the study of
organ dysfunction.