Ciprofol is primarily glucuronidated by UGT1A9 and predicted not to cause drug-drug interactions with typical substrates of CYP1A2, CYP2B6, and CYP2C19

Ciprofol is a novel intravenous anesthetic agent. Its major glucuronide metabolite, M4, is found in plasma and urine. However, the specific isoforms of UDP-glucuronosyltransferases (UGTs) that metabolize ciprofol to M4 remain unknown. This study systematically characterized UGTs that contribute to the formation of M4 using human liver microsomes (HLM), human intestinal microsomes (HIM), and human recombinant UGTs. The inhibitory potential of ciprofol and M4 against major human UGTs and cytochrome P450 enzymes (P450s) was also explored. In vitro-in vivo extrapolation (IVIVE) and physiologically-based pharmacokinetic (PBPK) simulations were performed to predict potential in vivo drug-drug interactions (DDIs) caused by ciprofol. Glucuronidation of ciprofol followed Michaelis-Menten kinetics in both HLM and HIM with apparent Km values of 345 and 412 μM, Vmax values of 2214 and 444 nmol min-1·mg protein-1, respectively. The in vitro intrinsic clearances (CLint = Vmax/Km) for ciprofol glucuronidation by HLM and HIM were 6.4 and 1.1 μL min-1·mg protein-1, respectively. Human recombinant UGT studies revealed that UGT1A9 is the predominant isoform mediating M4 formation, followed by UGT1A7, with UGT1A8 playing a minor role. Ciprofol competitively inhibited CYP1A2 (Ki = 12 μM) and CYP2B6 (Ki = 4.7 μM), and noncompetitively inhibited CYP2C19 (Ki = 29 μM). No time-dependent inhibition by ciprofol was noted for CYP1A2, CYP2B6, or CYP2C19. In contrast, M4 showed limited or no inhibitory effects against selected P450s. Neither ciprofol nor M4 inhibited UGTs significantly. Initial IVIVE suggested potential ciprofol-mediated inhibition of CYP1A2, CYP2B6, and CYP2C19 inhibition in vivo. However, PBPK simulations showed no significant effect on phenacetin, bupropion, and S-mephenytoin exposure or peak plasma concentration. Our findings are pertinent for future DDI studies of ciprofol as either a perpetrator or victim drug.

Application of a PBPK Model of Rivaroxaban to Prospective Simulations of Drug-Drug-Disease Interactions with Protein Kinase Inhibitors in CA-VTE

BACKGROUND AND PURPOSE

Rivaroxaban is a viable anticoagulant for the management of cancer associated venous thromboembolism (CA-VTE). A previously verified physiologically-based pharmacokinetic (PBPK) model of rivaroxaban established how its multiple pathways of elimination via both CYP3A4/2J2-mediated hepatic metabolism and organic anion transporter 3 (OAT3)/P-glycoprotein-mediated renal secretion predisposes rivaroxaban to drug-drug-disease interactions (DDDIs) with clinically relevant protein kinase inhibitors (PKIs). We proposed the application of PBPK modelling to prospectively interrogate clinically significant DDIs between rivaroxaban and PKIs (erlotinib and nilotinib) for dose adjustments in CA-VTE.

EXPERIMENTAL APPROACH

The inhibitory potencies of the PKIs on CYP3A4/2J2-mediated metabolism of rivaroxaban were characterized. Using prototypical OAT3 inhibitor ketoconazole, in vitro OAT3 inhibition assays were optimized to ascertain the in vivo relevance of derived transport inhibitory constants (K_i ). Untested DDDIs between rivaroxaban and erlotinib or nilotinib were simulated.

KEY RESULTS

Mechanism-based inactivation (MBI) of CYP3A4-mediated rivaroxaban metabolism by both PKIs and MBI of CYP2J2 by erlotinib were established. The importance of substrate specificity and nonspecific binding to derive OAT3-inhibitory K_i values of ketoconazole and nilotinib for the accurate prediction of interactions was illustrated. When simulated rivaroxaban exposure variations with concomitant erlotinib and nilotinib therapy were evaluated using published dose-exposure equivalence metrics and bleeding risk analyses, dose reductions from 20 mg to 15 mg and 10 mg in normal and mild renal dysfunction, respectively, were warranted.

CONCLUSION AND IMPLICATIONS

We established a PBPK-DDDI model to prospectively evaluate clinically relevant interactions between rivaroxaban and PKIs for the safe and efficacious management of CA-VTE.

Pitfalls of using numerical predictive checks for population physiologically-based pharmacokinetic model evaluation

Comparisons between observed data and model simulations represent a critical component for establishing confidence in population physiologically-based pharmacokinetic (Pop-PBPK) models. Numerical predictive checks (NPC) that assess the proportion of observed data that correspond to Pop-PBPK model prediction intervals (PIs) are frequently used to qualify such models. We evaluated the effects of three components on the performance of NPC for qualifying Pop-PBPK model concentration-time predictions: (1) correlations (multiple samples per subject), (2) residual error, and (3) discrepancies in the distribution of demographics between observed and virtual subjects. Using a simulation-based study design, we artificially created observed pharmacokinetic (PK) datasets and compared them to model simulations generated under the same Pop-PBPK model. Observed datasets containing uncorrelated and correlated observations (± residual error) were formulated using different random-sampling techniques. In addition, we created observed datasets where the distribution of subject body weights differed from that of the virtual population used to generate model simulations. NPC for each observed dataset were computed based on the Pop-PBPK model's 90% PI. NPC were associated with inflated type-I-error rates (> 0.10) for observed datasets that contained correlated observations, residual error, or both. Additionally, the performance of NPC were sensitive to the demographic distribution of observed subjects. Acceptable use of NPC was only demonstrated for the idealistic case where observed data were uncorrelated, free of residual error, and the demographic distribution of virtual subjects matched that of observed subjects. Considering the restricted applicability of NPC for Pop-PBPK model evaluation, their use in this context should be interpreted with caution.

A whole-body physiologically based pharmacokinetic (WB-PBPK) model of ciprofloxacin: a step towards predicting bacterial killing at sites of infection

The purpose of this study was to develop a whole-body physiologically based pharmacokinetic (WB-PBPK) model for ciprofloxacin for ICU patients, based on only plasma concentration data. In a next step, tissue and organ concentration time profiles in patients were predicted using the developed model. The WB-PBPK model was built using a non-linear mixed effects approach based on data from 102 adult intensive care unit patients. Tissue to plasma distribution coefficients (Kp) were available from the literature and used as informative priors. The developed WB-PBPK model successfully characterized both the typical trends and variability of the available ciprofloxacin plasma concentration data. The WB-PBPK model was thereafter combined with a pharmacokinetic-pharmacodynamic (PKPD) model, developed based on in vitro time-kill data of ciprofloxacin and Escherichia coli to illustrate the potential of this type of approach to predict the time-course of bacterial killing at different sites of infection. The predicted unbound concentration-time profile in extracellular tissue was driving the bacterial killing in the PKPD model and the rate and extent of take-over of mutant bacteria in different tissues were explored. The bacterial killing was predicted to be most efficient in lung and kidney, which correspond well to ciprofloxacin's indications pneumonia and urinary tract infections. Furthermore, a function based on available information on bacterial killing by the immune system in vivo was incorporated. This work demonstrates the development and application of a WB-PBPK-PD model to compare killing of bacteria with different antibiotic susceptibility, of value for drug development and the optimal use of antibiotics .

The simcyp population based simulator: architecture, implementation, and quality assurance

Developing a user-friendly platform that can handle a vast number of complex physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) models both for conventional small molecules and larger biologic drugs is a substantial challenge. Over the last decade the Simcyp Population Based Simulator has gained popularity in major pharmaceutical companies (70% of top 40 - in term of R&D spending). Under the Simcyp Consortium guidance, it has evolved from a simple drug-drug interaction tool to a sophisticated and comprehensive Model Based Drug Development (MBDD) platform that covers a broad range of applications spanning from early drug discovery to late drug development. This article provides an update on the latest architectural and implementation developments within the Simulator. Interconnection between peripheral modules, the dynamic model building process and compound and population data handling are all described. The Simcyp Data Management (SDM) system, which contains the system and drug databases, can help with implementing quality standards by seamless integration and tracking of any changes. This also helps with internal approval procedures, validation and auto-testing of the new implemented models and algorithms, an area of high interest to regulatory bodies.