ABSTRACT Sorfequiline is a novel diarylquinoline in development for treatment of tuberculosis. In vitro data suggest metabolism via CYP3A4, carrying the potential for presystemic metabolism and drug–drug interactions. Its major metabolite, M3, is pharmacologically active. We developed a semi‐mechanistic joint population pharmacokinetic model of sorfequiline and M3 in healthy volunteers. Data were pooled from two studies (CL‐001 and CL‐002). CL‐001 comprised a single‐ascending‐dose part (10–800 mg suspension fasted plus 100 mg fed), a multiple‐ascending‐dose part (25, 75, or 200 mg/day for 14 days, fed), and a relative bioavailability assessment including 100 mg in tablet form under fed and fasted conditions. In CL‐002, fed participants received 200 mg/day suspension for 8 days followed by 165 mg/day for 4 days. Plasma concentrations were quantified using HPLC–MS/MS, and pharmacokinetic modeling and simulations were conducted in NONMEM. A three‐compartment disposition model for both analytes, with transit‐compartment absorption, adequately described the data. A fraction of the dose was assumed to be absorbed as M3 due to gut first‐pass metabolism. This fraction was 46% (95% CI: 41%–50%) in fasted and 15% (13%–19%) in fed participants. Overall bioavailability decreased by 46% (32%–61%) at fasted doses ≥ 400 mg. Model‐based simulations showed that exposures for all investigated regimens remained below safety reference values. Food reduced the fraction of sorfequiline dose absorbed as M3, likely due to limited gut first‐pass metabolism. The developed model provides an initial platform for future pharmacokinetic/pharmacodynamic analyses and simulation of both analytes' exposures under drug–drug interaction scenarios.
Exposure-response (ER) analyses assessing the efficacy of large molecule therapeutics are often susceptible to confounding, where associations between drug exposure and patient disease severity can obscure true ER signals and complicate dose selection. To better understand the pharmaceutical industry perspectives on this challenge, the IQ consortium conducted a comprehensive survey targeting clinical pharmacologists, pharmacometricians, and statisticians. The survey, completed by 125 individuals from 23 pharmaceutical companies, aimed to assess awareness, perceived prevalence, mitigation strategies, and the overall role of ER analyses in the context of confounding. Results revealed strong industry awareness, with 88.0% of respondents acknowledging the relevance of ER confounding. However, uncertainty regarding its pervasiveness persists, particularly in non-oncology settings (31.2% unsure). A consensus emerged on the value of dose-ranging study designs as an effective mitigation strategy (81.6% agreement). In contrast, the use of advanced statistical methods for causal inference is inconsistent (45.6% usage), and confidence in their reliability is mixed, with 32.8% of respondents expressing uncertainty and most others rating them only moderate or somewhat reliable. While the majority agreed that confounded ER analyses should be interpreted with caution (79.2%), opinions diverged regarding their value in decision-making when dose-ranging data is insufficient. This uncertainty, coupled with a recognized need for additional alignment with health authorities, led to a call for best-practice guidance (92.8% view as valuable). Overall, the survey findings highlight a need for an industry-wide common approach and the development of clear frameworks to manage and interpret ER confounding for large molecule therapeutics.
ABSTRACT Semi‐mechanistic modeling approaches could be useful for optimal dose selection, and one of the approaches is pharmacokinetic (PK)/receptor occupancy (RO) model for monoclonal antibodies. This study aimed to develop a semi‐mechanistic PK/RO model of S‐531011, a humanized monoclonal antibody against human CCR8 and under development for the treatment of solid tumors, in order to predict human RO values in tumor tissues which are not available in the ongoing clinical study. The model consists of three compartments (central, peripheral, and tumor) and includes four compartments (S‐531011, CCR8, and complexes 1/2 within each compartment). The model parameters were set using available clinical data, non‐clinical data, and physiological information. The human PK/RO model was developed by refinement of a mouse PK/RO model. The time courses of RO values in the tumor compartment were simulated using the model, together with a conservative condition of slow transfer of S‐531011 from blood to the tumor. It was predicted that the RO value in the tumor compartment at 21 days (trough after the third dose after administration of S‐531011 80 mg) would be maintained at > 90% under typical model assumptions. Under the conservative condition, the RO value in the tumor after administration of S‐531011 800 mg would remain at > 90%. The developed semi‐mechanistic PK/RO model could predict human RO values of S‐531011 in tumor tissues. Under typical model assumptions, simulations suggested the dose range of 80–800 mg of S‐531011 every 3 weeks would achieve > 90% RO in tumor tissues.
ABSTRACT Tacrolimus dosing in liver transplantation is complicated by a narrow therapeutic index and high CYP3A5 genetic variability. While saturable Michaelis–Menten kinetics can explain nonlinearities, identifying saturable parameters from routine clinical data remains challenging. This study aimed to determine the optimal structural model and develop a precision dosing algorithm. A population pharmacokinetic analysis was conducted in 114 patients, yielding 1989 observations. CYP3A5 genotypes were determined for both recipients and donors. Using Phoenix NLME, we rigorously compared linear versus Michaelis–Menten elimination structures. Stepwise covariate modeling was conducted to quantify the impact of genetic, physiological, and pharmacological factors, followed by Monte Carlo simulations to optimize dosing. A conventional two‐compartment model adequately described the data without requiring a Michaelis–Menten structure. The combined CYP3A5 genotype exhibited a distinct stepwise reduction in apparent clearance from the homozygous expressor to the non‐expressor group. Fluconazole emerged as a major inhibitor, reducing clearance by 33%, whereas prednisolone showed modest induction. Hemoglobin displayed a significant inverse relationship with clearance. Crucially, incorporating the daily dose as a covariate on clearance effectively captured the apparent nonlinear disposition. Simulations confirmed that fluconazole‐treated patients require substantially lower doses (1.5–3.0 mg every 12 h) compared with fluconazole‐free patients (2.5–7.0 mg every 12 h). A dose‐dependent two‐compartment model offers a basis for model‐informed dose selection, addressing reported nonlinearities through physiological covariates. We provide a model‐informed dosing algorithm that accounts for combined recipient/donor genetics and drug interactions, which may improve target attainment in liver transplant populations.
Maximum likelihood estimation in population pharmacokinetic/pharmacodynamic (PK/PD) nonlinear mixed-effects (NLME) models targets fixed-effect, interindividual-variability, and residual-variability parameters through a marginal likelihood that integrates each subject's contribution over individual random effects. Because these subject-level integrals are usually unavailable analytically, Laplace estimation approximates each contribution using a second-order Taylor expansion around the empirical Bayes estimate (EBE; conditional posterior mode of the individual's random effects), making EBE estimation and curvature evaluation recurring tasks during objective function value (OFV) and derivative evaluation. Finite differences (FD) are commonly used for these derivative calculations but are costly and step-size sensitive. We developed automatic differentiation (AD)-based methods for Laplace NLME estimation and compared three ways to account for EBE sensitivities during updates: FULL-implicit uses the EBE mode equations, FULL-unroll differentiates through the Newton steps used to find EBEs, and STOP omits EBE sensitivity during outer differentiation. In 100 matched starts for a synthetic one-compartment PK example with absorption/elimination ambiguity, FULL-implicit, FULL-unroll, and FD matched the lowest OFV within 10 - 4 OFV units, whereas STOP had maximum ΔOFV = 1.9 relative to OFV min = - 1594.62 . Median wall times were 0.045, 0.108, 0.068, and 0.758 s for FULL-implicit, FULL-unroll, STOP, and FD, respectively, making FULL-implicit about 17-fold faster than FD. In 10 runs on public warfarin PK/PD data using the ODE representation, FULL-implicit achieved a lower best OFV than FD (1624.02 vs. 1628.60) and was 36-fold faster by median wall time (0.45 vs. 16.3 min). In both scenarios, FULL-implicit provided a faster AD-based alternative to FD while reaching comparable or lower OFVs.
