Note on importance of correct stoichiometric assumptions for modeling of monoclonal antibodies.

Pharmacokinetic modeling of monoclonal antibodies (mAbs) with non-linear binding is based on equations of the target-mediated drug disposition (Mager and Jusko, J Pharmacokinet Pharmacodyn 28:507-532, 2001). These equations demonstrated their utility in countless examples and drug development programs. The model assumes that the mAb drug and the target have only one binding site each while, in reality, most antibodies have two binding sites. Thus, the currently used model does not correspond to the biological process that it aims to describe. The correct mechanistic model should take into account both binding sites. We investigated, using simulations, whether this discrepancy is important and when it is advisable to use a model with correct stoichiometric 2-to-1 ratio. We show that for soluble targets when elimination rate of the drug-target complex is comparable with the elimination rate of the drug or lower, and when measurements of both total drug and total target concentrations are available, the model with 1-to-1 (monovalent) binding cannot describe data simulated from the model with 2-to-1 (bivalent) binding. In these cases, models with correct stoichiometric assumptions may be necessary for an adequate description of the observed data. Also, a model with allosteric binding that encompasses both 2-to-1 and 1-to-1 binding models as particular cases was proposed and applied. It was shown to be identifiable given the detailed concentration data of total drug and total target.

Target-mediated drug disposition model for drugs with N > 2 binding sites that bind to a target with one binding site.

The paper extended the TMDD model to drugs with more than two (N > 2) identical binding sites (N-to-one TMDD). The quasi-steady-state (N-to-one QSS), quasi-equilibrium (N-to-one QE), irreversible binding (N-to-one IB), and Michaelis-Menten (N-to-one MM) approximations of the model were derived. To illustrate properties of new equations and approximations, N = 4 case was investigated numerically. Using simulations, the N-to-one QSS approximation was compared with the full N-to-one TMDD model. As expected, and similarly to the standard TMDD for monoclonal antibodies (mAb), N-to-one QSS predictions were nearly identical to N-to-one TMDD predictions, except for times of fast changes following initiation of dosing, when equilibrium has not yet been reached. Predictions for mAbs with soluble targets (slow elimination of the complex) were simulated from the full 4-to-one TMDD model and were fitted to the 4-to-one TMDD model and to its QSS approximation. It was demonstrated that the 4-to-one QSS model provided nearly identical description of not only the observed (simulated) total drug and total target concentrations, but also unobserved concentrations of the free drug, free target, and drug-target complexes. For mAb with a membrane-bound target, the 4-to-one MM approximation adequately described the data. The 4-to-one QSS approximation converged 8 times faster than the full 4-to-one TMDD.

Ocrelizumab in relapsing and primary progressive multiple sclerosis: Pharmacokinetic and pharmacodynamic analyses of OPERA I, OPERA II and ORATORIO.

AIMS: Ocrelizumab is a humanized monoclonal antibody that selectively targets CD20-positive B cells and is indicated for treatment of patients with relapsing forms of multiple sclerosis (RMS) or primary progressive multiple sclerosis (PPMS). The pharmacokinetics and pharmacodynamics of ocrelizumab in patients with RMS or PPMS were assessed. METHODS: A population pharmacokinetic model was developed based on data from the Phase II study and the Phase III studies OPERA I and OPERA II in patients with RMS. Data from the ORATORIO Phase III study in patients with PPMS became available after model finalization and was used for external model evaluation. RESULTS: The ocrelizumab serum concentration vs time course was accurately described by a 2-compartment model with time-dependent clearance. Body weight was found to be the main covariate. The area under the concentration-time curve over the dosing interval was estimated to be 26% higher for patients with RMS weighing <60 kg and 21% lower for patients weighing >90 kg when compared with the 60-90 kg group. The terminal half-life of ocrelizumab was estimated as 26 days. The extent of B-cell depletion in blood, as the pharmacodynamic marker, was greater with increasing ocrelizumab exposure. CONCLUSION: The pharmacokinetics of ocrelizumab was described with pharmacokinetic parameters typical for an immunoglobulin G1 monoclonal antibody, with body weight as the main covariate. The pharmacokinetics and B-cell depletion in blood were comparable across the RMS and PPMS trials, and the extent of blood B-cell depletion was greater with higher exposure.

Population Pharmacokinetic Analysis of Delamanid in Patients with Pulmonary Multidrug-Resistant Tuberculosis.

A population pharmacokinetic (PopPK) model of delamanid in patients with pulmonary multidrug-resistant tuberculosis (MDR-TB) was developed using data from four delamanid clinical trials. The final PopPK data set contained 20,483 plasma samples from 744 patients with MDR-TB receiving an optimized background regimen (OBR). Delamanid PK was adequately described for all observed dosing regimens and subpopulations by a two-compartment model with first-order elimination and absorption, an absorption lag time, and decreased relative bioavailability with increasing dose. Relative bioavailabilities of 200-mg and higher doses (250 and 300 mg) were 76% and 58% of a 100-mg dose, respectively. Relative bioavailability was 26% higher after evening doses than morning doses and 9% higher in outpatient settings than inpatient settings. The rate of absorption was higher, and lag time was shorter, following a morning dose than an evening dose. Relative bioavailabilities in patients in Northeast Asian and Southeast Asian regions were 53% and 40% higher, respectively, than in patients in non-Asian regions. Apparent clearance was higher (to the power of -0.892) in patients with hypoalbuminemia (albumin levels of <3.4 g/dl). Coadministration of efavirenz in patients with HIV increased delamanid clearance by 35%. Delamanid exposure was not affected by age (18 to 64 years), mild or moderate renal impairment, anti-TB antibiotic resistance status, HIV status, or markers of hepatic dysfunction or by concomitant administration of OBR, lamivudine, tenofovir, pyridoxine, CYP3A4 inhibitors and inducers, or antacids. Model evaluation suggested reasonable model fit and predictive power, indicating that the model should prove reliable to derive PK metrics for subsequent PK/PD analyses.

Pharmacokinetics, exposure, efficacy and safety of obinutuzumab in rituximab-refractory follicular lymphoma patients in the GADOLIN phase III study.

AIMS: Rituximab is standard care in a number of lymphoma subtypes, including follicular lymphoma (FL), although many patients are resistant to rituximab, or develop resistance with repeated treatment, and a high proportion relapse. Obinutuzumab is a novel anti-CD20 monoclonal antibody with improved efficacy over rituximab. It is approved for previously untreated chronic lymphocytic leukaemia (CLL), and for use with bendamustine in patients with rituximab-relapsed/refractory FL. METHODS: Using a previously described population pharmacokinetic (PK) model of obinutuzumab in patients with non-Hodgkin lymphoma and CLL, we conducted an exposure-response analysis using data from 6 clinical trials in patients with CD20+ B-cell malignancies (CLL11, GADOLIN, GATHER, GAUDI, GAUGUIN and GAUSS) to describe the PK properties of obinutuzumab, identify covariates influencing exposure, and explore how exposure affects safety, efficacy and pharmacodynamics. RESULTS: A 2-compartment model with linear and time-dependent clearance described obinutuzumab PK. Disease type and subtype, body weight, baseline tumour size, and sex had the largest effects on PK. Obinutuzumab exposure was not associated with occurrence or severity of adverse events, but higher exposure appeared to be associated with greater efficacy, particularly longer progression-free survival. However, in multivariate Cox regression analysis, progression-free survival benefit in the obinutuzumab plus bendamustine arm was independent of exposure. CONCLUSION: The updated population PK model reported here accurately describes the PK of obinutuzumab patients with non-Hodgkin lymphoma and CLL. The selected obinutuzumab dosing regimen offers clinical benefit in a majority of rituximab-refractory FL patients treated with bendamustine, irrespective of variability in exposure, whilst minimising adverse events.

Role of obinutuzumab exposure on clinical outcome of follicular lymphoma treated with first-line immunochemotherapy.

AIMS: Obinutuzumab (G) is a humanized type II, Fc-glycoengineered anti-CD20 monoclonal antibody used in various indications, including patients with previously untreated front-line follicular lymphoma. We investigated sources of variability in G exposure and association of progression-free survival (PFS) with average concentration over induction (CmeanIND ) in front-line follicular lymphoma patients treated with G plus chemotherapy (bendamustine, CHOP, or CVP) in the GALLIUM trial. METHODS: Individual exposures (CmeanIND ) were obtained from a previously established population pharmacokinetic model updated with GALLIUM data. Multivariate Cox proportional hazard models and univariate Kaplan-Meier plots investigated relationships of PFS with exposure and other potential prognostic factors. RESULTS: Overall, G exposure was lower in high body-weight patients and in males, and slightly lower in patients with high baseline tumour burden. Analysis of clinical outcomes showed that variability in G exposure did not impact PFS in G-bendamustine-treated patients; PFS was inferior in males and patients with FCGR2a/2b T232 T low-affinity receptor variant, and superior in patients with FCGR2a/2b I232T variant. In G-CHOP/CVP arms, PFS improved with increasing CmeanIND (hazard ratio = 1.74 and 0.394 at 5th and 95th percentile compared to median CmeanIND ) and was inferior in patients with high baseline tumour size and B symptoms. CONCLUSIONS: It remains unclear whether for G-CHOP/CVP patients lower G exposure is a consequence of adverse disease biology and/or resistance to chemotherapy backbone (higher clearance in nonresponder patients, as demonstrated for rituximab) rather than being the cause of poorer clinical outcome. A study with >1 dose level of G could help resolve this uncertainty.

Mathematical description of drug-target interactions: application to biologics that bind to targets with two binding sites.

The emerging discipline of mathematical pharmacology occupies the space between advanced pharmacometrics and systems biology. A characteristic feature of the approach is application of advance mathematical methods to study the behavior of biological systems as described by mathematical (most often differential) equations. One of the early application of mathematical pharmacology (that was not called this name at the time) was formulation and investigation of the target-mediated drug disposition (TMDD) model and its approximations. The model was shown to be remarkably successful, not only in describing the observed data for drug-target interactions, but also in advancing the qualitative and quantitative understanding of those interactions and their role in pharmacokinetic and pharmacodynamic properties of biologics. The TMDD model in its original formulation describes the interaction of the drug that has one binding site with the target that also has only one binding site. Following the framework developed earlier for drugs with one-to-one binding, this work aims to describe a rigorous approach for working with similar systems and to apply it to drugs that bind to targets with two binding sites. The quasi-steady-state, quasi-equilibrium, irreversible binding, and Michaelis-Menten approximations of the model are also derived. These equations can be used, in particular, to predict concentrations of the partially bound target (RC). This could be clinically important if RC remains active and has slow internalization rate. In this case, introduction of the drug aimed to suppress target activity may lead to the opposite effect due to RC accumulation.

Target-mediated drug disposition model for drugs with two binding sites that bind to a target with one binding site.

The paper extended the TMDD model to drugs with two identical binding sites (2-1 TMDD). The quasi-steady-state (2-1 QSS), quasi-equilibrium (2-1 QE), irreversible binding (2-1 IB), and Michaelis-Menten (2-1 MM) approximations of the model were derived. Using simulations, the 2-1 QSS approximation was compared with the full 2-1 TMDD model. As expected and similarly to the standard TMDD for monoclonal antibodies (mAb), 2-1 QSS predictions were nearly identical to 2-1 TMDD predictions, except for times of fast changes following initiation of dosing, when equilibrium has not yet been reached. To illustrate properties of new equations and approximations, several variations of population PK data for mAbs with soluble (slow elimination of the complex) or membrane-bound (fast elimination of the complex) targets were simulated from a full 2-1 TMDD model and fitted to 2-1 TMDD models, to its approximations, and to the standard (1-1) QSS model. For a mAb with a soluble target, it was demonstrated that the 2-1 QSS model provided nearly identical description of the observed (simulated) free drug and total target concentrations, although there was some minor bias in predictions of unobserved free target concentrations. The standard QSS approximation also provided a good description of the observed data, but was not able to distinguish between free drug concentrations (with no target attached and both binding site free) and partially bound drug concentrations (with one of the binding sites occupied by the target). For a mAb with a membrane-bound target, the 2-1 MM approximation adequately described the data. The 2-1 QSS approximation converged 10 times faster than the full 2-1 TMDD, and its run time was comparable with the standard QSS model.

Target-mediated drug disposition model and its approximations for antibody-drug conjugates.

Antibody-drug conjugate (ADC) is a complex structure composed of an antibody linked to several molecules of a biologically active cytotoxic drug. The number of ADC compounds in clinical development now exceeds 30, with two of them already on the market. However, there is no rigorous mechanistic model that describes pharmacokinetic (PK) properties of these compounds. PK modeling of ADCs is even more complicated than that of other biologics as the model should describe distribution, binding, and elimination of antibodies with different toxin load, and also the deconjugation process and PK of the released toxin. This work extends the target-mediated drug disposition (TMDD) model to describe ADCs, derives the rapid binding (quasi-equilibrium), quasi-steady-state, and Michaelis-Menten approximations of the TMDD model as applied to ADCs, derives the TMDD model and its approximations for ADCs with load-independent properties, and discusses further simplifications of the system under various assumptions. The developed models are shown to describe data simulated from the available clinical population PK models of trastuzumab emtansine (T-DM1), one of the two currently approved ADCs. Identifiability of model parameters is also discussed and illustrated on the simulated T-DM1 examples.

Methods to detect non-compliance and reduce its impact on population PK parameter estimates.

This work proposes and evaluates two methods (CM1 and CM2) for detecting non-compliance using concentration-time data and for obtaining estimates of population pharmacokinetic model parameters in a population with prevalent non-compliance. CM1 estimates individual residual variability (RV) and identifies subjects with higher than average RV as non-compliant. Exclusion of subjects with high RV from the analysis dataset reduces the bias in the estimates of the model parameters. Various methods of identification and exclusion of non-compliant subjects were tested, compared, and shown to reduce or eliminate bias in parameter estimates associated with non-compliance. The tested methods were (i) a pre-defined cutoff value of the random effect on RV, (ii) sequential exclusion of subjects with the highest RV percentiles, and (iii) use of a mixture model for RV. CM2 is applicable for the data with a specific sampling pattern that includes a potentially non-compliant outpatient part with several trough samples followed by a dense profile after the inpatient (compliant) dose. It relies only on the doses known to be administered (e.g., inpatient doses). In this method, all concentration measurements during the outpatient part of the study (except the trough value immediately preceding the inpatient dose) are removed from the dataset and an additional parameter (individual relative bioavailability of the outpatient doses) is introduced. For a number of simulated datasets with various sampling schemes and non-compliance patterns the proposed methods allowed to identify subjects with compliance problems and to reduce or eliminate bias in the estimates of the model parameters.

Comparison of Nonmem 7.2 estimation methods and parallel processing efficiency on a target-mediated drug disposition model.

The paper compares performance of Nonmem estimation methods--first order conditional estimation with interaction (FOCEI), iterative two stage (ITS), Monte Carlo importance sampling (IMP), importance sampling assisted by mode a posteriori (IMPMAP), stochastic approximation expectation-maximization (SAEM), and Markov chain Monte Carlo Bayesian (BAYES), on the simulated examples of a monoclonal antibody with target-mediated drug disposition (TMDD), demonstrates how optimization of the estimation options improves performance, and compares standard errors of Nonmem parameter estimates with those predicted by PFIM 3.2 optimal design software. In the examples of the one- and two-target quasi-steady-state TMDD models with rich sampling, the parameter estimates and standard errors of the new Nonmem 7.2.0 ITS, IMP, IMPMAP, SAEM and BAYES estimation methods were similar to the FOCEI method, although larger deviation from the true parameter values (those used to simulate the data) was observed using the BAYES method for poorly identifiable parameters. Standard errors of the parameter estimates were in general agreement with the PFIM 3.2 predictions. The ITS, IMP, and IMPMAP methods with the convergence tester were the fastest methods, reducing the computation time by about ten times relative to the FOCEI method. Use of lower computational precision requirements for the FOCEI method reduced the estimation time by 3-5 times without compromising the quality of the parameter estimates, and equaled or exceeded the speed of the SAEM and BAYES methods. Use of parallel computations with 4-12 processors running on the same computer improved the speed proportionally to the number of processors with the efficiency (for 12 processor run) in the range of 85-95% for all methods except BAYES, which had parallelization efficiency of about 70%.

Population pharmacodynamic modeling of various extended-release formulations of methylphenidate in children with attention deficit hyperactivity disorder via meta-analysis.

Placebo and pharmacodynamic (PD) models were developed which link temporal measures of efficacy in children with attention deficit hyperactivity disorder (ADHD) and methylphenidate (MPH) plasma concentrations from adults. These models can be used to predict daily pediatric clinical measure profiles following administration of different MPH formulations in children without conducting pediatric pharmacokinetic (PK) or PD studies by using more easily obtained adult PK data. Mean PK data from various extended-release MPH formulations studied in adults and mean PD data from nine pediatric efficacy studies were obtained from the literature. The individual time-course of the clinical measures from three pediatric trials were also analyzed after being combined with the meta-analysis data. The clinical measure profiles following placebo administration were described by indirect response models with time-varying elimination rates. MPH pharmacodynamic effect was described by E(max) models, which included time-dependent tolerance. Internal and external evaluations using a visual predictive check technique confirmed the prediction capability of the models. This modeling exercise demonstrated that time courses of MPH concentrations in adults with different drug release patterns can be used to predict time courses of clinical efficacy parameters in pediatrics by employing the models developed by meta-analysis.

Population pharmacokinetic and exposure-response analyses of intravenous and subcutaneous rituximab in patients with chronic lymphocytic leukemia.

A subcutaneous formulation of the anti-CD20 antibody rituximab has been developed. Fixed-dose subcutaneous rituximab delivers noninferior serum trough concentrations (Ctrough ), ensuring similar target saturation and comparable efficacy/safety, to intravenous rituximab, but with simplified and shortened preparation and administration. We aimed to characterize the pharmacokinetic (PK) and exposure-response properties of subcutaneous rituximab. Data from two clinical trials were analyzed to describe PKs and pharmacodynamics in patients with chronic lymphocytic leukemia following intravenous and subcutaneous rituximab administration. Intravenous and subcutaneous rituximab were described by a linear two-compartment population PK model with time-dependent and time-independent clearances, and first-order subcutaneous absorption. Main covariates influencing exposure were body size and baseline white blood cell count. Occurrence of adverse events was not correlated with rituximab exposure. Although greater and more sustainable B-cell depletion was observed with higher exposure, inherent limitations to the data (use of one dose level, and time-dependent and target-impacted PKs) prevented reliable assessment of exposure-response relationships.

Quantitative Clinical Pharmacology Supports the Bridging From i.v. Dosing and Approval of s.c. Rituximab in B-Cell Hematological Malignancies.

A fixed-dose subcutaneous (s.c.) formulation of the anti-CD20 antibody, rituximab, has been developed to address safety, infusion time, and patient comfort concerns relating to intravenous (i.v.) dosing, and has been approved based upon a pharmacokinetic (PK)-clinical bridging strategy, which demonstrated noninferiority of s.c. vs. i.v. dosing in malignancies, including follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL). A clinical development plan was undertaken to identify rituximab s.c. doses achieving noninferior exposure to rituximab i.v., and to confirm PK-clinical bridging, with the same efficacy and similar safety. This drew upon data from 1,579 patients with FL, CLL, or diffuse large B-cell lymphoma in 5 clinical studies, and showed minimum steady-state serum concentration (Ctrough ) as the most appropriate exposure bridging measure. Population PK models were developed, simulations were run using covariates and PK parameters from clinical studies, and exposure-efficacy and -safety analyses performed. Population PKs showed a two-compartment model with time-dependent and -independent clearances. Clearance and volume were predominantly influenced by body surface area; disposition and elimination were similar for the s.c. and i.v. formulations. After s.c. administration, patients with FL and CLL achieved noninferior exposures to i.v. dosing. Overall, rituximab exposure and route of administration did not influence clinical responses in patients with FL or CLL, and there was no association between exposure and safety events. Ctrough was shown to be an effective pharmacologic-clinical bridging parameter for rituximab in patients with FL or CLL. Clinically effective exposures are achieved with either s.c. or i.v. dosing.

Target-mediated drug disposition model for drugs that bind to more than one target.

Until recently, most therapeutic monoclonal antibodies (mAb) were designed to bind only one target. However, several existing mAbs bind to soluble and membrane forms of the same receptor. Moreover, design of bi-specific and multi-specific proteins that bind to more than one target is a promising direction of drug design. The pharmacokinetics and pharmacodynamics of these drugs may be described by the target-mediated drug disposition (TMDD). This work extended the TMDD model to drugs that bind more than one target. The quasi-steady-state (QSS) and Michaelis-Menten (MM) approximations of the model were also derived. Identifiability of model parameters was studied by simulations. The drug and target parameters used in simulations were chosen to imitate a monoclonal antibody that binds to the soluble (S) and membrane-bound (M) targets. The data were simulated for 224 subjects using the full TMDD model and dosing that mimicked typical Phase I and Phase II designs with rich sampling. Four population pharmacokinetic models were fitted to the free (unbound) drug and total (unbound and bound to the drug) S-target data: a one-target QSS model that simultaneously described the free drug and the total S-target (M1), a model with parallel linear and MM elimination that described the free drug combined with a separate S-target model that utilized the free drug concentrations but did not influence them (M2), a two-target QSS model where the S-target was described by the QSS approximation while the contribution of the M-target was described by the MM elimination term (M3), and a two-target full TMDD model (M4). The influence of relative contributions of the S and M-targets to target-mediated elimination on identifiability of the model parameters was investigated. The influence of assay sensitivity and availability of the total rather than free drug concentration measurements were also investigated. The results indicated that for the dosing regimens and system parameters investigated in this work the pharmacokinetic data alone did not allow to distinguish influences of the two targets. When the drug and S-target data were available, the model M1 described the data with the deficiencies of the fit visible only at the lowest dose level. However, the parameter estimates were strongly biased. The model M2 improved the fit and provided the precise estimates of the S-target parameters. However, no information concerning the M-target could be obtained from this model. The model M3 provided an excellent description of the data and the unbiased estimates of all the parameters. It also provided the unbiased estimates of change from baseline of the unobservable M-target concentrations. The models M1-M3 were robust while M4 was unstable despite the prohibitively long run time. The results were similar when the total rather than free drug was measured. The M-target parameters were estimated only when M-target elimination was at least comparable to S-target elimination. Improvement of the assay sensitivity has not resulted in marked improvement of the parameter estimates. In summary, for the cases investigated in this work the QSS approximation of the two-target TMDD model provided the unbiased and robust estimates of all the relevant TMDD parameters.

Target-mediated drug disposition model: relationships with indirect response models and application to population PK-PD analysis.

The paper focuses on approximations of the target-mediated drug disposition (TMDD) model as applied to pharmacodynamic (target kinetics) modeling. The TMDD equation for the total target concentration is shown to coincide with the indirect response model with stimulation or inhibition of elimination. This correspondence allows estimation of pharmacodynamic TMDD parameters and unobservable free target concentrations using indirect-response models. The ability of the TMDD model and its approximations to estimate the unobservable free target concentration is investigated by simulation. Pharmacokinetic parameters used for simulations were parameters typical for monoclonal antibodies. TMDD binding and target turnover parameters were similar to those estimated for omalizumab. Free drug and total target concentrations were measured. The simulated population PK-PD study demonstrated that for drugs with TMDD, indirect-response models are in fact mechanistic models that can be used to estimate TMDD model parameters and unobservable free target concentrations that are important for pharmacodynamic modeling.

A Phase I Pharmacokinetic Study of Trastuzumab Emtansine (T-DM1) in Patients with Human Epidermal Growth Factor Receptor 2-Positive Metastatic Breast Cancer and Normal or Reduced Hepatic Function.

OBJECTIVE: The aim of this study was to evaluate the pharmacokinetics (PK) of trastuzumab emtansine (T-DM1) and relevant analytes in patients with human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer and hepatic impairment. METHODS: Patients were enrolled in three independent parallel cohorts based on hepatic function per Child-Pugh criteria: normal hepatic function, mild hepatic impairment, and moderate hepatic impairment. Patients received T-DM1 3.6 mg/kg intravenously every 3 weeks. PK samples were collected during cycles 1 and 3, and the PK of T-DM1 and relevant analytes were characterized and compared across cohorts. RESULTS: Compared with patients with normal hepatic function (n = 10), T-DM1 clearance at cycle 1 was 1.8- and 4.0-fold faster in the mild (n = 10) and moderate (n = 8) cohorts, respectively. The trend of faster clearance was less apparent in cycle 3, with similar T-DM1 clearance across cohorts (mean ± standard deviation 8.16 ± 3.27 [n = 9], 9.74 ± 3.62 [n = 7], and 8.99 and 10.2 [individual values, n = 2] mL/day/kg for the normal, mild, and moderate cohorts, respectively). T-DM1 clearance at cycle 1 correlated significantly with baseline albumin, aspartate aminotransferase, and HER2 extracellular domain concentrations (p < 0.05). Plasma concentrations of DM1 and DM1-containing catabolites were low and were comparable across cohorts. CONCLUSIONS: No increase in systemic DM1 concentration was observed in patients with mild or moderate hepatic impairment versus those with normal hepatic function. The faster T-DM1 clearance observed at cycle 1 in patients with hepatic impairment appeared to be transient. After repeated dosing (three cycles), T-DM1 exposure in patients with mild and moderate hepatic impairment was within the range seen in those with normal hepatic function.

Population pharmacokinetics of dabrafenib, a BRAF inhibitor: effect of dose, time, covariates, and relationship with its metabolites.

Dabrafenib is a BRAF kinase inhibitor indicated for the treatment of BRAF V600E mutation-positive melanoma. The population pharmacokinetics of dabrafenib, including changes over time and relevant covariates, were characterized based on results from four clinical studies using a nonlinear mixed effects model with a full covariate approach. Steady-state exposures of dabrafenib metabolites (hydroxy-, carboxy-, and desmethyl-dabrafenib) were characterized separately. The pharmacokinetics of dabrafenib were adequately described by non-inducible and inducible apparent clearance that increased with dose and time. Total steady-state clearance (CL/F) at 150 mg BID dose was 34.3 L/h. Based on the induction half-life (67 hours), steady state should be achieved within 14 days of dosing. Capsule shell was the most significant covariate (55%) while sex and weight had only a small impact on exposure (<20%). The AUC ratio (hypromellose:gelatin capsule) is predicted to be 1.80 and 1.42 following single and repeat dosing, respectively. Age, renal (mild and moderate), and hepatic (mild) impairment were not significant covariates. Steady-state pre-dose concentration (%CV) of dabrafenib and of hydroxy-, carboxy-, and desmethyl-dabrafenib at 150 mg BID were 46.6 ng/mL (83.5%), 69.3 ng/mL (64.1%), 3608 ng/mL (14.7%), and 291 ng/mL (17.2%), respectively. Capsule shell, concomitant medications, older age, and weight were predictors of metabolite exposure.