Physiologically‐based pharmacokinetic model for alectinib, ruxolitinib, and panobinostat in the presence of cancer, renal impairment, and hepatic impairment

Abstract

Renal (RIP) and hepatic (HIP) impairments are prevalent conditions in cancer patients. They can cause changes in gastric emptying time, albumin levels, hematocrit, glomerular filtration rate, hepatic functional volume, blood flow rates, and metabolic activity that can modify drug pharmacokinetics. Performing clinical studies in such populations has ethical and practical issues. Using predictive physiologically‐based pharmacokinetic (PBPK) models in the evaluation of the PK of alectinib, ruxolitinib, and panobinostat exposures in the presence of cancer, RIP, and HIP can help in using optimal doses with lower toxicity in these populations. Verified PBPK models were customized under scrutiny to account for the pathophysiological changes induced in these diseases. The PBPK model‐predicted plasma exposures in patients with different health conditions within average 2‐fold error. The PBPK model predicted an area under the curve ratio (AUCR) of 1, and 1.8, for ruxolitinib and panobinostat, respectively, in the presence of severe RIP. On the other hand, the severe HIP was associated with AUCR of 1.4, 2.9, and 1.8 for alectinib, ruxolitinib, and panobinostat, respectively, in agreement with the observed AUCR. Moreover, the PBPK model predicted that alectinib therapeutic cerebrospinal fluid levels are achieved in patients with non‐small cell lung cancer, moderate HIP, and severe HIP at 1‐, 1.5‐, and 1.8‐fold that of healthy subjects. The customized PBPK models showed promising ethical alternatives for simulating clinical studies in patients with cancer, RIP, and HIP. More work is needed to quantify other pathophysiological changes induced by simultaneous affliction by cancer and RIP or HIP.

In vitro–in vivo extrapolation of bexarotene metabolism in the presence of chronic kidney disease and acute kidney injury in rat using physiologically based pharmacokinetic modeling and extrapolation to human

“…The affinity of the BXT to blood cells (Kp BC ) was estimated to be equal to 274.84 based on the healthy rat blood‐to‐plasma concentration ratio (R = 1.87 as calculated by PK‐Sim® software) (Alsmadi, Al‐Daoud, et al., 2021): $$${\mathrm{K}\mathrm{p}}_{\mathrm{B}\mathrm{C}}=\frac{H-1+R}{{f}_{u}\ast H}$$$ …”

“…Where H is the hematocrit. R values in AKI and CKD rats were calculated as (Alsmadi, Al‐Daoud et al., 2021): $$$R=1+H\,\ast \left({f}_{u}\,\ast \,{\mathrm{K}\mathrm{p}}_{\mathrm{B}\mathrm{C}}-1\right)$$$ …”

“…Also, the difference in total plasma clearance was estimated in healthy, AKI, and CKD rats. The GFR for rats in different groups was calculated based on experimentally measured serum creatinine (Alsmadi, Al‐Daoud, et al., 2021). Renal blood flow was decreased to 92 mL/min/100 g organ for AKI rats while the healthy control value of (401.45 mL/min/100 g organ) was used for healthy and CKD rats (Ayer et al., 1971; Dreisbach & Lertora, 2008a).…”

“…BXT K pBC in humans was calculated based on healthy human R (0.76 as calculated by PK‐Sim® software) to be equal to 266 in a similar approach used in rats using Equation (8). The values of $${\mathrm{f}}_{\mathrm{u}}^{\prime }$$ in patients with cancer, CKD 3, CKD 4, and CKD 5 were estimated based on the albumin levels in these groups ($${[\mathrm{A}\mathrm{L}\mathrm{B}]}^{\prime }$$) and BXT fraction unbound in healthy subjects ($${\mathrm{f}}_{\mathrm{u}}$$) with normal albumin levels ($$[\mathrm{A}\mathrm{L}\mathrm{B}]$$) (Alsmadi, Al‐Daoud, et al., 2021): $$${f}_{u}^{\prime }=\frac{1}{1+\frac{\left(1-{f}_{u}\right)\ast {[\mathrm{A}\mathrm{L}\mathrm{B}]}^{\prime }}{[\mathrm{A}\mathrm{L}\mathrm{B}]\ast {f}_{u}}}$$$ …”

“…The affinity of the BXT to blood cells (Kp BC ) was estimated to be equal to 274.84 based on the healthy rat blood-to-plasma concentration ratio (R = 1.87 as calculated by PK-Sim® software) (Alsmadi, Al-Daoud, et al, 2021):…”