Model development

A combination of physicochemical parameters, parameters from in vitro absorption and metabolism studies, and concentration‐time data from clinical studies (Table  ​2)2) were used to build a physiologically based model for acalabrutinib and ACP‐5862 in Simcyp, version 14 (Certara, Sheffield, UK).18 A minimal PBPK model with a single adjusting compartment (SAC), which considers metabolism in both the liver and intestine and lumps other tissues together, was incorporated in Simcyp for acalabrutinib. The SAC is a nonphysiological compartment that permits the adjustment of the drug concentration profiles in the systemic compartment and represents a lump of all tissues, excluding the liver and the portal vein. The concentration‐time profiles of six individuals from ACE‐HV‐001 cohort 6 who received 100 mg acalabrutinib were used to estimate the rate of drug in and out of the SAC and the volume of the compartment using the parameter estimation module within Simcyp.

Input parameters used to simulate the kinetics of acalabrutinib and ACP‐5862

B/P, blood‐to‐plasma ratio; CLint, intrinsic clearance; f_u,gut, fraction unbound in enterocyte; f_u,plasma, unbound fraction in plasma; fa, fraction of absorption; fa, fraction of absorption; fm, fraction of metabolism; HLM, human liver microsome; ka, rate of absorption; Ki, inhibitory constant for reversible inhibition; KI, inhibitory constant for time‐dependent inhibition; kin, kout, first order rate constants inand out Vsac; Kinact, rate of enzyme inactivation; Km, Michaelis‐Menten constant; Peff,man, human intestinal effective permeability; pKa, acid dissociation constant (logarithmic scale); Qgut, nominal flow through the gut; Vmax, maximum rate of metabolism formation; Vsac, volume of single adjusting compartment; Vss, volume of distribution at steady state.

A range of human intestinal effective permeability (P_eff,man) values were investigated using the clinical results from ACE‐HV‐001 cohort 4 and cohort 6 ( Figure S1 ), and a P_eff,man value of 4.0 × 10⁻⁴ cm/second was most consistent with the observed acalabrutinib peak plasma concentration (C_max) values and was therefore applied in the model. The volume of distribution at steady state was predicted using logP, pKa, and blood‐to‐plasma partition ratio values by the Rodgers and Rowland19 method. Fraction unbound in enterocyte was assumed to be the same as unbound fraction in plasma (Table  ​22).

In vitro incubation of 20 μM ¹⁴C‐acalabrutinib (for qualitative metabolite profiling) with human hepatocytes (two male donors, CellzDirect‐Invitrogen, Austin, TX) indicated relative contributions of glutathione and CYP‐mediated metabolic pathways of 21% and 79%, respectively, and the CYP3A enzyme was the main enzyme (~ 90%) involved in the formation of CYP metabolites (data on file). Eventually, the percentage of acalabrutinib metabolized by CYP3A4 was assigned as 0.82 based on itraconazole interaction study ACE‐HV‐001, cohort 7. The renal clearance of 1.33 L/hour for acalabrutinib observed in clinical study ACE‐HV‐00915 was applied directly. The intrinsic metabolic clearance of acalabrutinib was estimated from the in vivo total clearance of 169 L/hour (ACE‐HV‐001) with the retrograde method. The total intrinsic metabolic clearance was then separated into CYP3A4 and undefined components based on the percentage of acalabrutinib metabolized by CYP3A4. The intrinsic clearance value of 9.63 μL/minutes/pmol was assigned for CYP3A4 with 289.5 μL/minutes/mg for additional human liver microsome clearance for the elimination of acalabrutinib (Table  ​22).

In vitro studies using MDCKII‐MDR1 cells (Netherlands Cancer Institute, Amsterdam, The Netherlands) indicated that acalabrutinib is a substrate of P‐gp with efflux ratio (42 at 1 μM; data on file). In the absence of detailed information on the specificity and affinity of acalabrutinib for transport, it was not possible to include the active transport process in the model. However, the sensitivity of the final parent model to the impact of intestinal efflux was investigated using the advanced dissolution, absorption and metabolism model within Simcyp in conjunction with a range of in vitro intestinal P‐gp maximum rate of transporter mediated efflux and Michaelis‐Menten constant (K_m) values.

The combined parent metabolite model was developed as an extension of the parent‐alone model (Table  ​2).2). The logP, pKa, and blood‐to‐plasma partition ratio of acalabrutinib in the parent‐alone model were updated with experimental values. The volume of distribution at steady state of 0.36 L/kg for ACP‐5862 was predicted using the Rodgers and Rowland19 method. A SAC was also applied for the metabolite, and the concentration‐time profile from the ACE‐HV‐113 period 2 oral dose (100 mg acalabrutinib) were used to optimize the volume of SAC and the rate of drug in and out of the SAC for ACP‐5862.

The metabolism of acalabrutinib to ACP‐5862 was characterized in recombinant CYP enzymes, and CYP3A4 was identified as the sole enzyme responsible for the conversion of acalabrutinib to ACP‐5862. The reported maximum rate of metabolism formation (V_max) of 4.13 pmol/minutes/pmol and K_m of 2.78 μM in CYP3A4 were incorporated in the model to describe the conversion of acalabrutinib to ACP‐5862. Therefore, the elimination of acalabrutinib via CYP3A4 (9.63 μL/minutes/pmol) was further divided into two clearance pathways: one to the active metabolite ACP‐5862 (V_max of 4.13 pmol/minutes/pmol and K_m of 2.78 μM) and the other to the rest of the metabolites (8.14 μL/minutes/pmol). The in vitro studies also demonstrated that the CYP3A enzyme was responsible for the further metabolism of ACP‐5862, and a clearance value of 23.6 μL/minutes/mg protein in human liver microsomes was assigned to CYP3A4 for the elimination of ACP‐5862. A renal clearance of 0.3 L/hour for ACP‐5862 reported in ACE‐HV‐00917 was applied in the model. Such assignment suggested that the ACP‐5862–related metabolites contributed about 12% of total acalabrutinib elimination, which is in close agreement with the 10% of total dose observed in human mass balance study.15

The competitive and time‐dependent inhibition parameters against the CYP2C8, CYP2C9, CYP2C19, and CYP3A enzymes obtained from in vitro studies were applied for both acalabrutinib and ACP‐5862 (Table  ​2).2). Acalabrutinib did not cause significant induction of the metabolism of the marker substrates of CYP1A2, CYP2B6, or CYP3A4 at any concentration evaluated.