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.

Dosing regimen optimisation for oseltamivir in immunocompromised paediatric patients with influenza: Extrapolation of efficacy.

AIMS: To optimise the dosing regimen of oseltamivir for immunocompromised (IC) paediatric patients (<18 years) with influenza, we used an extrapolation approach alongside clinical data. METHODS: Efficacy was extrapolated from adult IC patients to paediatric IC patients by leveraging existing efficacy, safety, pharmacokinetic (PK)/pharmacodynamic (PD), and disease-progression models of oseltamivir and oseltamivir carboxylate (OC). Data of IC paediatric patients from two studies (NV25719 and NV20234) were included in the population PK (n = 30), PK/PD analysis (n = 22) and disease modelling approach (n = 36). Simulations were performed to identify the optimal dosing regimen. RESULTS: Clearance of oseltamivir (CL) and OC (CLM ) were similar in IC and otherwise-healthy (OwH) patients <10 years, but decreased by 44.4% (95% CI: 26.8-62.0) and 49.1% (95% CI: 34.5-63.8), respectively, in IC patients aged 10-17 years versus OwH patients. There were no notable exposure-response relationships for any of the virologic PD analyses. Thus, no additional benefit was seen with oseltamivir carboxylate exposures higher than achieved with the conventional dose (75 mg twice daily, age- and weight-adjusted for children <13 years). The disease model illustrated that doses above the conventional oseltamivir dose had limited impact on viral kinetics in IC paediatric patients and a prolonged treatment duration of 10 days was favoured to limit potential viral rebound. CONCLUSION: An oseltamivir dosage recommendation (conventional dose, twice daily for 10 days) was established in IC paediatric patients with influenza, based on extrapolation of efficacy from IC adults, leveraging population PK, PK/PD, and disease modelling, whilst taking resistance and safety data into account.

Integrated multiple analytes and semi-mechanistic population pharmacokinetic model of tusamitamab ravtansine, a DM4 anti-CEACAM5 antibody-drug conjugate.

Tusamitamab ravtansine (SAR408701) is an antibody-drug conjugate (ADC), combining a humanized monoclonal antibody (IgG1) targeting carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) and a potent cytotoxic maytansinoid derivative, DM4, inhibiting microtubule assembly. SAR408701 is currently in clinical development for the treatment of advanced solid tumors expressing CEACAM5. It is administered intravenously as a conjugated antibody with an average Drug Antibody Ratio (DAR) of 3.8. During SAR408701 clinical development, four entities were measured in plasma: conjugated antibody (SAR408701), naked antibody (NAB), DM4 and its methylated metabolite (MeDM4), both being active. Average DAR and proportions of individual DAR species were also assessed in a subset of patients. An integrated and semi-mechanistic population pharmacokinetic model describing the time-course of all entities in plasma and DAR measurements has been developed. All DAR moieties were assumed to share the same drug disposition parameters, excepted for clearance which differed for DAR0 (i.e. NAB entity). The conversion of higher DAR to lower DAR resulted in a DAR-dependent ADC deconjugation and was represented as an irreversible first-order process. Each conjugated antibody was assumed to contribute to DM4 formation. All data were fitted simultaneously and the model developed was successful in describing the pharmacokinetic profile of each entity. Such a structural model could be translated to other ADCs and gives insight of mechanistic processes governing ADC disposition. This framework will further be expanded to evaluate covariates impact on SAR408701 pharmacokinetics and its derivatives, and thus can help identifying sources of pharmacokinetic variability and potential efficacy and safety pharmacokinetic drivers.

Pharmacologic effects of oseltamivir in immunocompromised adult patients as assessed by population PK/PD analysis and drug-disease modelling for dosing regimen optimization.

AIM: Pharmacologic effects were analysed to determine a dose recommendation for oseltamivir in immunocompromised (IC) adults with influenza. METHODS: Quantitative clinical pharmacology methods were applied to data from 160 adult IC patients (aged 18-78 years) from two studies (NV20234, 150 patients; NV25118, 10 patients) who received oseltamivir 75-200 mg twice daily for up to 10 days. An established population-pharmacokinetic (PK) model with additional effects on oseltamivir and oseltamivir carboxylate (OC) clearance described the PK characteristics of oseltamivir in IC patients versus otherwise healthy (OwH) patients from previous clinical trials. Estimated PK parameters were used to evaluate exposure-response relationships for virologic endpoints (time to cessation of viral shedding, viral load measures and treatment-emergent resistance). A drug-disease model characterized the viral kinetics of influenza accounting for the effect of OC on viral production. RESULTS: Oseltamivir clearance was 32.5% lower (95% confidence interval [CI], 26.1-38.8) and OC clearance was 33.7% lower (95% CI, 23.2-44.1) in IC versus OwH patients. No notable exposure-response relationships were identified for exposures higher than those achieved after conventional dose oseltamivir 75 mg, which appeared to be close to the maximum effect of oseltamivir. Simulations of the drug-disease model predicted that initiating treatment within 48 hours of symptom onset had maximum impact, and a treatment duration of 10 days was favourable over 3-5 days to limit viral rebound. CONCLUSIONS: Our findings support the use of conventional-dose oseltamivir 75 mg twice daily for 10 days in the treatment of IC adult patients with influenza.

Application of a Two-Analyte Integrated Population Pharmacokinetic Model to Evaluate the Impact of Intrinsic and Extrinsic Factors on the Pharmacokinetics of Polatuzumab Vedotin in Patients with Non-Hodgkin Lymphoma.

PURPOSE: The established two-analyte integrated population pharmacokinetic model was applied to assess the impact of intrinsic/extrinsic factors on the pharmacokinetics (PK) of polatuzumab vedotin (pola) in patients with non-Hodgkin lymphoma (NHL) following bodyweight-based dosing. METHODS: Model simulations based on individual empirical Bayes estimates were used to evaluate the impact of intrinsic/extrinsic factors as patient subgroups on Cycle 6 exposures. Intrinsic factors included bodyweight, age, sex, hepatic and renal functions. Extrinsic factors included rituximab/obinutuzumab or bendamustine combination with pola and manufacturing process. The predicted impact on exposures along with the established exposure-response relationships were used to assess clinical relevance. RESULTS: No clinically meaningful differences in Cycle 6 pola exposures were found for the following subgroups: bodyweight 100-146 kg versus 38-<100 kg, age ≥ 65 years versus <65 years, female versus male, mild hepatic impairment versus normal, mild-to-moderate renal impairment versus normal. Co-administration of rituximab/obinutuzumab or bendamustine, and change in the pola manufacturing process, also had no meaningful impact on PK. CONCLUSIONS: In patients with NHL, bodyweight-based dosing is adequate, and no further dose adjustment is recommended for the heavier subgroup (100-146 kg). In addition, no dose adjustments are recommended for other subgroups based on intrinsic/extrinsic factors evaluated.

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.

Platform model describing pharmacokinetic properties of vc-MMAE antibody-drug conjugates.

Antibody-drug conjugates (ADCs) developed using the valine-citrulline-MMAE (vc-MMAE) platform, consist of a monoclonal antibody (mAb) covalently bound with a potent anti-mitotic toxin (MMAE) through a protease-labile vc linker. Recently, clinical data for a variety of vc-MMAE ADCs has become available. The goal of this analysis was to develop a platform model that simultaneously described antibody-conjugated MMAE (acMMAE) pharmacokinetic (PK) data from eight vc-MMAE ADCs, against different targets and tumor indications; and to assess differences and similarities of model parameters and model predictions, between different compounds. Clinical PK data of eight vc-MMAE ADCs from eight Phase I studies were pooled. A population PK platform model for the eight ADCs was developed, where the inter-compound variability (ICV) was described explicitly, using the third random effect level (ICV), and implemented using LEVEL option of NONMEM 7.3. The PK was described by a two-compartment model with time dependent clearance. Clearance and volume of distribution increased with body weight; volume was higher for males, and clearance mildly decreased with the nominal dose. Michaelis-Menten elimination had only minor effect on PK and was not included in the model. Time-dependence of clearance had no effect beyond the first dosing cycle. Clearance and central volume were similar among ADCs, with ICV of 15 and 5%, respectively. Thus, PK of acMMAE was largely comparable across different vc-MMAE ADCs. The model may be applied to predict PK-profiles of vc-MMAE ADCs under development, estimate individual exposure for the subsequent PK-pharmacodynamics (PD) analysis, and project optimal dose regimens and PK sampling times.

Model-based pharmacokinetic analysis of elotuzumab in patients with relapsed/refractory multiple myeloma.

Elotuzumab is a humanized immunoglobulin G1 monoclonal antibody in development for the treatment of patients with multiple myeloma who have received one or more prior therapies. In this work, 6958 elotuzumab serum concentrations from 375 patients enrolled in four Phase 1 to 3 clinical trials were used to analyze the pharmacokinetics (PK) of elotuzumab. A population PK model with parallel linear and Michaelis-Menten elimination from the central compartment and limited-capacity target-mediated elimination from the peripheral compartment described the elotuzumab concentration-time course. Clearance of elotuzumab increased with increasing body weight and weight-based dosing generated uniform exposures across a range of body weights. Coadministration of lenalidomide/dexamethasone background therapy decreased elotuzumab nonspecific clearance by 35 %. Target-mediated elimination of elotuzumab increased with increasing baseline serum M-protein, resulting in lower exposure in patients with high baseline serum M-protein concentration. Age, race, sex, renal and hepatic function, Eastern Cooperative Oncology Group performance status, lactate dehydrogenase, albumin and ß2-microglobulin had less than 20 % effect on model parameters and are unlikely to have clinically meaningful effects. Impact of anti-drug antibodies (ADAs) on the PK of elotuzumab was assessed as an ad hoc analysis. In the majority of ADA-positive patients, immunogenicity started early, was transient and resolved by 2-4 months. Since the majority of patients had ADAs detected early, this resulted in a corresponding transient increase in nonspecific clearance at these time points. Nonspecific clearance appeared to return to baseline at later time points when ADAs were no longer detected.

Investigating clinically adequate concentrations of oseltamivir carboxylate in end-stage renal disease patients undergoing hemodialysis using a population pharmacokinetic approach.

End-stage renal disease (ESRD) patients receiving hemodialysis (HD) are at heightened risk for influenza, but the optimal oseltamivir dosage regimen for treating or preventing influenza in this high-risk population is still uncertain. Pharmacokinetic data for 24 adults with ESRD were pooled from a single-dose and a multiple-dose study to develop a population pharmacokinetic model using nonlinear mixed-effects modeling. The final model comprised five compartments, two each to describe the systemic pharmacokinetics of oseltamivir phosphate and its metabolite, oseltamivir carboxylate (OC), and a delay compartment to describe oseltamivir metabolism. Estimated OC clearance in the model was markedly faster during HD sessions (7.43 liters/min) than at other times (0.19 liter/min). Model simulations showed that 30 mg oseltamivir given after every HD session is the most suitable regimen for influenza treatment, producing trough OC concentrations above the median value achieved with the 75-mg twice-daily regimen in patients with normal renal function and peak concentrations below the highest oseltamivir exposures known to be well tolerated (median exposures after twice-daily dosing of 450 mg). Administration of the first dose following diagnosis of influenza need not wait until after the next HD session: addition of a single 30-mg dose during the 12 h before the next HD session raises OC exposures quickly without posing any safety risk. Further simulation showed that 30 mg oseltamivir given after every other HD session is the most suitable regimen for influenza prophylaxis.

A mechanistic pharmacokinetic/pharmacodynamic model of factor D inhibition in cynomolgus monkeys by lampalizumab for the treatment of geographic atrophy.

Lampalizumab is an antigen-binding fragment of a humanized monoclonal antibody against complement factor D (CFD), a rate-limiting enzyme in the activation and amplification of the alternative complement pathway (ACP), which is in phase III clinical trials for the treatment of geographic atrophy. Understanding of the pharmacokinetics, pharmacodynamics, and biodistribution of lampalizumab following intravitreal administration in the ocular compartments and systemic circulation is limited but crucial for selecting doses that provide optimal efficacy and safety. Here, we sought to construct a semimechanistic and integrated ocular-systemic pharmacokinetic-pharmacodynamic model of lampalizumab in the cynomolgus monkey to provide a quantitative understanding of the ocular and systemic disposition of lampalizumab and CFD inhibition. The model takes into account target-mediated drug disposition, target turnover, and drug distribution across ocular tissues and systemic circulation. Following intravitreal administration, lampalizumab achieves rapid equilibration across ocular tissues. Lampalizumab ocular elimination is relatively slow, with a τ1/2 of approximately 3 days, whereas systemic elimination is rapid, with a τ1/2 of 0.8 hours. Target-independent linear clearance is predominant in the eye, whereas target-mediated clearance is predominant in the systemic circulation. Systemic CFD synthesis was estimated to be high (7.8 mg/day); however, the amount of CFD entering the eye due to influx from the systemic circulation was small (<10%) compared with the lampalizumab dose and is thus expected to have an insignificant impact on the clinical dose-regimen decision. Our findings support the clinical use of intravitreal lampalizumab to achieve significant ocular ACP inhibition while maintaining low systemic exposure and minimal systemic ACP inhibition.

Population pharmacokinetic analysis of oseltamivir and oseltamivir carboxylate following intravenous and oral administration to patients with and without renal impairment.

This work characterizes the pharmacokinetics (PK) of oseltamivir phosphate (OP) and its active metabolite, oseltamivir carboxylate (OC), and investigates oseltamivir i.v. dosing regimens for treatment of influenza in patients with normal renal function and with various degrees of renal impairment. Initially, data collected from 149 subjects with normal renal function and mild to severe renal impairment who were administered 40-200 mg oseltamivir i.v. were described by a four-compartment model. Two compartments described OP, one compartment described OC and one compartment described OP to OC metabolism. Then, data of 128 subjects administered 20-1,000 mg oseltamivir orally were added. The absorption model included three first-order processes with direct (via first-pass) input in the OC compartment and two (direct and delayed) inputs in the OP compartment. Simulations and PK bridging were used to recommend i.v. dosing regimens. The analysis demonstrated that renal function had a major effect on OC clearance (CL M ) and exposure. CL M for subjects with mild, moderate and severe renal impairment was 18, 50, and 84 % lower than for subjects with normal renal function. Simulations were used to select i.v. dosing regimens that provide OC Cmin coverage and exposures comparable to those achieved in subjects with normal renal function administered 75 mg b.i.d. orally. The oseltamivir dose depended on the degree of renal impairment and was independent of route of administration. Specifically, 75 mg b.i.d. is recommended for subjects with normal renal function or mild renal impairment, 30 mg b.i.d. for subjects with moderate renal impairment, and 30 mg q.d. for subjects with severe renal impairment. Recommended i.v. doses were the same as those recommended for oral administration in corresponding renal impairment groups.

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.

Population pharmacokinetics and exposure-response analyses of polatuzumab vedotin in patients with previously untreated DLBCL from the POLARIX study.

Polatuzumab vedotin is a CD79b-directed antibody-drug conjugate that targets B cells and delivers the cytotoxic payload monomethyl auristatin E (MMAE). The phase III POLARIX study (NCT03274492) evaluated polatuzumab vedotin in combination with rituximab, cyclophosphamide, doxorubicin, and prednisone (R-CHP) as first-line treatment of diffuse large B-cell lymphoma (DLBCL). To examine dosing decisions for this regimen, population pharmacokinetic (popPK) analysis, using a previously developed popPK model, and exposure-response (ER) analysis, were performed. The popPK analysis showed no clinically meaningful relationship between cycle 6 (C6) antibody-conjugated (acMMAE)/unconjugated MMAE area under the concentration-time curve (AUC) or maximum concentration, and weight, sex, ethnicity, region, mild or moderate renal impairment, mild hepatic impairment, or other patient and disease characteristics. In the ER analysis, C6 acMMAE AUC was significantly associated with longer progression-free and event-free survival (both p = 0.01). An increase of <50% in acMMAE/unconjugated MMAE exposure did not lead to a clinically meaningful increase in adverse events of special interest. ER data and the benefit-risk profile support the use of polatuzumab vedotin 1.8 mg/kg once every 3 weeks with R-CHP for six cycles in patients with previously untreated DLBCL.

Clinical pharmacology strategies to accelerate the development of polatuzumab vedotin and summary of key findings.

The favorable benefit-risk profile of polatuzumab vedotin, as demonstrated in a pivotal Phase Ib/II randomized study (GO29365; NCT02257567), coupled with the need for effective therapies in relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL), prompted the need to accelerate polatuzumab vedotin development. An integrated, fit-for-purpose clinical pharmacology package was designed to support regulatory approval. To address key clinical pharmacology questions without dedicated clinical pharmacology studies, we leveraged non-clinical and clinical data for polatuzumab vedotin, published clinical data for brentuximab vedotin, a similar antibody-drug conjugate, and physiologically based pharmacokinetic and population pharmacokinetic modeling approaches. We review strategies and model-informed outcomes that contributed to regulatory approval of polatuzumab vedotin plus bendamustine and rituximab in R/R DLBCL. These strategies made polatuzumab vedotin available to patients earlier than previously possible; depending on the strength of available data and the regulatory/competitive environment, they may also prove useful in accelerating the development of other agents.

Population PK/PD modeling of low-density lipoprotein cholesterol response in hypercholesterolemic participants following administration of bococizumab, a potent anti-PCSK9 monoclonal antibody.

We sought to characterize the population pharmacokinetic/pharmacodynamic (PK/PD) relationship of bococizumab (RN316/PF-04950615), a humanized IgG2Δa monoclonal antibody that binds to secreted human proprotein convertase subtilisin kexin type 9 (PCSK9), using data derived from 16 phase I, II, and III clinical studies (36,066 bococizumab observations, 46,790 low-density lipoprotein cholesterol [LDL-C] measurements, 3499 participants). A two-compartment disposition model with parallel linear and Michaelis-Menten elimination and an indirect response model was used to characterize the population PK and LDL-C response of bococizumab. Potential model parameters and covariate relationships were explored, and visual predictive checks were used for model assessment and validation. Key covariates included the effect of anti-drug antibodies (ADAs) on exposure through impact on clearance and bioavailability; impact of statins on bococizumab elimination (maximal rate of metabolism); and impact of statins, Asian race, and male sex on LDL-C efficacy (maximum effect). ADAs and neutralizing ADAs did not have additional effects on LDL-C beyond the influence on bococizumab exposure. In conclusion, the population PK/PD model adequately describes bococizumab concentration and LDL-C efficacy. The covariate effects are consistent with the presumed mechanism of action of PCSK9 inhibitors. With increasing availability of antibody-based therapeutics, improved understanding of the effect of ADAs and statins on bococizumab PK/PD adds to the literature and enhances our pharmacological understanding of how immunogenicity and concomitant medications may impact the PK/PD of biotherapeutics.