To link drug concentrations in the QSP simulations with free plasma drug concentrations that are likely to be observed in patients, we used PK models to simulate drug disposition of AZ, 11 CQ, 12 or LP + RT. 10 These models, implemented as published, allowed us to simulate temporal changes in either total or free plasma concentrations with different dosing regimens.

The LP + RT model, 10 built with data from 35 treatment‐naïve HIV‐infected patients, showed that plasma concentrations of these drugs were well‐characterized by a one‐compartment model with first‐order absorption and a lag time. An exponential term was used to incorporate the effect of RT concentration on the clearance of LP, and we assumed an unbound fraction of 0.01 to relate total drug concentrations to free plasma concentrations that are likely to be present in patients. 18 The AZ PK model 11 was a three‐compartment model with first‐order absorption, lag time, and first‐order elimination, and this model calculated free drug concentrations directly. Finally, a PK model for CQ plasma concentrations was recently published using data from 24 healthy subjects. 12 This model consists of two‐compartments, with first‐order absorption and elimination. Because CQ has been shown to accumulate in target tissues, including the heart, we also used results from a physiologically‐based PK model 9 developed in Simcyp (version 18; Certara, UK). This model was validated using data from different clinical studies, including PK data of 8 patients with COVID‐19 after an oral administration of 500 mg CQ phosphate b.i.d. for 7 days. This model predicted drug concentrations not only in plasma but also in the heart, which allowed us to extract relevant unbound cardiac drug concentrations by assuming that the unbound fraction in the heart (0.39) was identical to the fraction in plasma. 19

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