Support to early clinical decisions in drug development and personalised medicine with checkpoint inhibitors using dynamic biomarker-overall survival models.

Longitudinal models of biomarkers such as tumour size dynamics capture treatment efficacy and predict treatment outcome (overall survival) of a variety of anticancer therapies, including chemotherapies, targeted therapies, immunotherapies and their combinations. These pharmacological endpoints like tumour dynamic (tumour growth inhibition) metrics have been proposed as alternative endpoints to complement the classical RECIST endpoints (objective response rate, progression-free survival) to support early decisions both at the study level in drug development as well as at the patients level in personalised therapy with checkpoint inhibitors. This perspective paper presents recent developments and future directions to enable wider and robust use of model-based decision frameworks based on pharmacological endpoints.

A longitudinal model for the Mayo Clinical Score and its sub-components in patients with ulcerative colitis.

Clinical trials in patients with ulcerative colitis (UC) face the challenge of high and variable placebo response rates. The Mayo Clinical Score (MCS) is used widely as the primary endpoint in clinical trials to describe the clinical status of patients with UC. The MCS is comprised of four subscores, each scored 0, 1, 2 and 3: rectal bleeding (RB), stool frequency (SF), physician's global assessment (PGA), and endoscopy (ENDO) subscore. Excluding the PGA subscore gives the modified MCS. Quantitative insight on the placebo response, and its impact on the components of the MCS over time, can better inform clinical trial design and interpretation. Longitudinal modeling of the MCS, and the modified MCS, can be challenging due to complex clinical trial design, population heterogeneity, and limited assessments for the ENDO subscore. The current study pooled patient-level placebo/standard of care (SoC) arm data from five clinical trials in the TransCelerate database to develop a longitudinal placebo response model that describes the MCS over time in patients with UC. MCS subscores were modeled using proportional odds models, and the removal of patients from the placebo/SoC arm, or "dropout", was modeled using logistic regression models. The subscore and dropout models were linked to allow for the prediction of the MCS and the modified MCS. Stepwise covariate modeling identified prior exposure to TNF-α antagonists as a statistically significant predictor on the RB + SF subscore. Patients with prior exposure to TNF-α antagonists had higher post-baseline RB + SF subscores than naive patients.

Assessment of pharmacokinetic linearity after repeated drug administration.

The prediction of drug concentration time courses after different dosing scenarios is greatly facilitated if the pharmacokinetics (PK) can be assumed linear. The assumption of linear PK thus needs careful evaluation for any new drug in development. Under linear PK, exposure is proportional to dose (linear PK across doses) and exposure at steady state can be predicted from a single dose based on the superposition principle (linear PK over time). While investigation of dose-proportionality is common practice, evaluation of time dependent PK has received less attention in the literature. In particular, the superposition principle can be used to assess whether the observed extent of accumulation after repeated administration is expected under the premise of linear PK. This work emphasizes the importance of the time related aspect of linear PK by introducing the predictability ratio (PR). Linear PK over time can be concluded if PR = 1. Accumulation is higher than expected if PR >1, and lower if PR <1. If PK data from multiple dose cohorts are available, the PR is assessed for each dose cohort and a supportive hypothesis test can be applied to test for potential differences between doses in PR.

Confounding factors in exposure-response analyses and mitigation strategies for monoclonal antibodies in oncology.

Dose selection and optimization is an important topic in drug development to maximize treatment benefits for all patients. While exposure-response (E-R) analysis is a useful method to inform dose-selection strategy, in oncology, special considerations for prognostic factors are needed due to their potential to confound the E-R analysis for monoclonal antibodies. The current review focuses on 3 different approaches to mitigate the confounding effects for monoclonal antibodies in oncology: (i) Cox-proportional hazards modelling and case-matching; (ii) tumour growth inhibition-overall survival modelling; and (iii) multiple dose level study design. In the presence of confounding effects, studying multiple dose levels may be required to reveal the true E-R relationship. However, it is impractical for pivotal trials in oncology drug development programmes. Therefore, the strengths and weaknesses of the other 2 approaches are considered, and the favourable utility of tumour growth inhibition-overall survival modelling to address confounding in E-R analyses is described. In the broader scope of oncology drug development, this review discusses the downfall of the current emphasis on E-R analyses using data from single dose level trials and proposes that development programmes be designed to study more dose levels in earlier trials.

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.

Estimation of drug receptor occupancy when non-displaceable binding differs between brain regions ­ extending the simplified reference tissue model.

AIM: The simplified reference tissue model (SRTM) is used for estimation of receptor occupancy assuming that the non-displaceable binding in the reference region is identical to the brain regions of interest. The aim of this work was to extend the SRTM to also account for inter-regional differences in non-displaceable concentrations, and to investigate if this model allowed estimation of receptor occupancy using white matter as reference. It was also investigated if an apparent higher affinity in caudate compared with other brain regions, could be better explained by a difference in the extent of non-displaceable binding. METHODS: The analysis was based on a PET study in six healthy volunteers using the 5-HT1B receptor radioligand [(11)C]-AZ10419369. The radioligand was given intravenously as a tracer dose alone and following different oral doses of the 5-HT1B receptor antagonist AZD3783. Non-linear mixed effects models were developed where differences between regions in non-specific concentrations were accounted for. The properties of the models were also evaluated by means of simulation studies. RESULTS: The estimate (95% CI) of Ki(PL) was 10.2 ng ml(-1) (5.4, 15) and 10.4 ng ml(-1) (8.1, 13.6) based on the extended SRTM with white matter as reference and based on the SRTM using cerebellum as reference, respectively. The estimate (95% CI) of Ki(PL) for caudate relative to other brain regions was 55% (48, 62%). CONCLUSIONS: The extended SRTM allows consideration of white matter as reference region when no suitable grey matter region exists. AZD3783 affinity appears to be higher in the caudate compared with other brain regions.

Improved precision of exposure-response relationships by optimal dose-selection. Examples from studies of receptor occupancy using PET and dose finding for neuropathic pain treatment.

An understanding of the relationship between drug exposure and response is a fundamental basis for any dosing recommendation. We investigate optimal dose-selection for two different types of studies, a receptor occupancy study assessed by positron emission tomography (PET) and a dose-finding study in neuropathic pain treatment. For the PET-study, an inhibitory E-max model describes the relationship between drug exposure and displacement of a radioligand from specific receptors in the brain. The model has a mechanistic basis in the law of mass action and the affinity parameter (Ki PL ) is of primary interest. For optimization of the neuropathic pain study, the model is empirical and the exposure response curve itself is of primary interest. An alternative parameterization of the sigmoid Emax model was therefore used where the plasma concentration corresponding to the minimum relevant efficacy was estimated as a parameter. Optimal design methodology was applied using the D-optimal criterion as well as the Ds-optimal criterion where parameters of interest were defined. For the PET-study it was shown that the precision of Ki PL can be improved by inclusion of brain regions with both high and low receptor density and that the need for high doses is reduced when a brain region with low receptor density is included in the analysis. In the case of the neuropathic pain study it was shown that a Ds-optimal study design using the reparameterized Emax model can improve the precision in the minimum effective dose compared to a D-optimal design.

Simulation-based evaluation of personalized dosing approaches for anti-FGFR/KLB bispecific antibody fazpilodemab.

Personalized dosing approaches play important roles in clinical practices to improve benefit: risk profiles. Whereas this is also important for drug development, especially in the context of drugs with narrow therapeutic windows, such approaches have not been fully evaluated during clinical development. Fazpilodemab (BFKB8488A) is an agonistic bispecific antibody which was being developed for the treatment of nonalcoholic steatohepatitis. The objective of this study was to characterize the exposure-response relationships of fazpilodemab with the purpose of guiding dose selection for a phase II study, as well as to evaluate various personalized dosing strategies to optimize the treatment benefit. Fazpilodemab exhibited clear exposure-response relationships for a pharmacodynamic (PD) biomarker and gastrointestinal adverse events (GIAEs), such as nausea and vomiting. Static exposure-response analysis, as well as longitudinal adverse event (AE) analysis using discrete-time Markov model, were performed to characterize the observations. Clinical trial simulations were performed based on the developed exposure-response models to evaluate probability of achieving target PD response and the frequency of GIAEs to inform phase II dose selection. Dynamic simulation of personalized dosing strategies demonstrated that the AE-based personalized dosing is the most effective approach for optimizing the benefit-risk profiles. The approach presented here can be a useful framework for quantifying the benefit of personalized dosing for drugs with narrow therapeutic windows.

gPKPDviz: A flexible R shiny tool for pharmacokinetic/pharmacodynamic simulations using mrgsolve.

GPKPDviz is a Shiny application (app) dedicated to real-time simulation, visualization, and assessment of the pharmacokinetic/pharmacodynamic (PK/PD) models. Within the app, gPKPDviz is capable of generating virtual populations and complex dosing and sampling scenarios, which, together with the streamlined workflow, is designed to efficiently assess the impact of covariates and dosing regimens on PK/PD end points. The actual population data from clinical trials can be loaded into the app for simulation if desired. The app-generated dosing regimens include single or multiple dosing, and more complex regimens, such as loading doses or intermittent dosing. When necessary, the dosing regimens can be defined externally and loaded to the app for simulation. Using mrgsolve as the simulation engine, gPKPDviz is typically used for population simulation, however, with a slight modification of the mrgsolve model, gPKPDviz is capable of performing individual simulations with individual post hoc parameters, individual dosing logs, and individual sampling timepoints through an external dataset. A built-in text editor has a debugging feature for the mrgsolve model, providing the same error messages as model compilation in R. GPKPDviz has had stringent validation by comparing simulation results between the app and using mrgsolve in R. GPKPDviz is a member of the suite of Modeling and Simulation Shiny apps developed at Genentech to facilitate the typical modeling work in Clinical Pharmacology. For broader access to the Pharmacometric community, gPKPDviz has been published as an open-source application in GitHub under the terms of GNU General Public License.

A positron emission tomography study in healthy volunteers to estimate mGluR5 receptor occupancy of AZD2066 - estimating occupancy in the absence of a reference region.

AZD2066 is a new chemical entity pharmacologically characterized as a selective, negative allosteric modulator of the metabotropic glutamate receptor subtype 5 (mGluR5). Antagonism of mGluR5 has been implicated in relation to various diseases such as anxiety, depression, and pain disorders. To support translation from preclinical results and previous experiences with this target in man, a positron emission tomography study was performed to estimate the relationship between AZD2066 plasma concentrations and receptor occupancy in the human brain, using the mGluR5 radioligand [(11)C]-ABP688. The study involved PET scans on 4 occasions in 6 healthy volunteers. The radioligand was given as a tracer dose alone and following oral treatment with different doses of AZD2066. The analysis was based on the total volume of distribution derived from each PET-assessment. A non-linear mixed effects model was developed where ten delineated brain regions of interest from all PET scans were included in one simultaneous fit. For comparison the analysis was also performed according to a method described previously by Lassen et al. (1995). The results of the analysis showed that the total volume of distribution decreased with increasing drug concentrations in all regions with an estimated Kipl of 1170 nM. Variability between individuals and occasions in non-displaceable volume of distribution could explain most of the variability in the total volume of distribution. The Lassen approach provided a similar estimate for Kipl, but the variability was exaggerated and difficult to interpret.

Norepinephrine transporter occupancy in the human brain after oral administration of quetiapine XR.

Quetiapine, originally developed as an antipsychotic, demonstrates efficacy in clinical studies of schizophrenia, bipolar mania and depression, major depressive disorder and generalized anxiety disorder. This broad spectrum of efficacy was not predicted from the preclinical pharmacology of quetiapine. Binding studies in vitro show that quetiapine and its major active human metabolite, norquetiapine, have moderate to high affinity for dopamine D2 and serotonin 5-HT2A receptors, while norquetiapine alone has high affinity for the norepinephrine transporter (NET). This positron emission tomography (PET) study measured NET occupancy in human subjects treated with extended-release quetiapine (quetiapine XR) at doses relevant in the treatment of depression. PET measurements using the specific NET radioligand (S,S)-[(18)F]FMeNER-D2 were performed before and after quetiapine XR treatment at 150 and 300 mg/d for 6-8 d in nine healthy males (aged 21-33 yr). Regions of interest were defined for the thalamus, using the caudate as reference region. NET occupancy was calculated using a target:reference region ratio method. Plasma concentrations of quetiapine and norquetiapine were monitored during PET measurements. Following quetiapine XR treatment, the mean NET occupancy in the thalamus was 19 and 35%, respectively, at quetiapine XR doses of 150 and 300 mg/d. The estimated plasma concentration of norquetiapine corresponding to 50% NET occupancy was 161 ng/ml. This is the first demonstration of NET occupancy by an antipsychotic in the human brain. NET inhibition is accepted as a mechanism of antidepressant activity. NET occupancy may therefore contribute to the broad spectrum of efficacy of quetiapine.

A disease model predicting placebo response and remission status of patients with ulcerative colitis using modified Mayo score.

The Mayo Clinical Score is used in clinical trials to describe the clinical status of patients with ulcerative colitis (UC). It comprises four subscores: rectal bleeding (RB), stool frequency (SF), physician's global assessment, and endoscopy (ENDO). According to recent US Food and Drug Administration guidelines (Ulcerative colitis: developing drugs for treatment, Guidance Document, https://www.fda.gov/regulatory-information/s. 2022), clinical response and remission should be based on modified Mayo Score (mMS) relying on RB, SF, and ENDO. Typically, ENDO is performed at the beginning and end of each phase, whereas RB and SF are more frequently available. Item response theory (IRT) models allow the shared information to be used for prediction of all subscores at each observation time; therefore, it leverages information from RB and SF to predict ENDO. A UC disease IRT model was developed based on four etrolizumab phase III studies to describe the longitudinal mMS subscores, placebo response, and remission at the end of induction and maintenance. For each subscore, a bounded integer model was developed. The placebo response was characterized by a mono-exponential function acting on all mMS subscores similarly. The final model reliably predicted longitudinal mMS data. In addition, remission was well-predicted by the model, with only 5% overprediction at the end of induction and 3% underprediction at the end of maintenance. External evaluation of the final model using placebo arms from five different studies indicated adequate performance for both longitudinal mMS subscores and remission status. These results suggest utility of the current disease model for informed decision making in UC clinical development, such as assisting future clinical trial designs and evaluations.

Population Pharmacokinetics of Ranibizumab Delivered via the Port Delivery System Implanted in the Eye in Patients with Neovascular Age-Related Macular Degeneration.

The port delivery system with ranibizumab (PDS) is designed to continuously deliver ranibizumab to maintain therapeutic drug concentrations in the vitreous of the eye for an extended duration. The PDS has been evaluated for the treatment of neovascular age-related macular degeneration in the Ladder (PDS 10, 40, and 100 mg/mL, with refill exchanges as needed, versus monthly intravitreal ranibizumab 0.5 mg), Archway (PDS 100 mg/mL with 24-week refill exchanges, versus monthly intravitreal ranibizumab 0.5 mg), and ongoing Portal (PDS 100 mg/mL with 24-week refill exchanges) clinical trials. Data from Ladder, Archway, and Portal were used to develop a population pharmacokinetics (PK) model to estimate the ranibizumab release rate from the PDS implant, describe ranibizumab PK in serum and aqueous humor, and predict the concentration in vitreous humor. A model was developed to adequately describe the serum and aqueous humor PK data, as suggested by goodness-of-fit plots as well as visual predictive checks. In the final model, the first-order implant release rate was estimated to be 0.00654 (1/day), corresponding to a half-life of 106 days, consistent with the implant release rate determined in vitro. The model-predicted vitreous concentrations achieved with PDS 100 mg/mL given every 24 weeks were below the intravitreal peak concentration and above the intravitreal trough concentration of ranibizumab over the entire 24-week refill interval. The results demonstrate a durable release of ranibizumab from the PDS with a half-life of 106 days, providing vitreous exposure to ranibizumab for at least 24 weeks that is within the range of exposure for monthly intravitreal treatment.

Non-linear mixed effects modelling of positron emission tomography data for simultaneous estimation of radioligand kinetics and occupancy in healthy volunteers.

The aim of this work was to develop a model simultaneously estimating (11)C-AZD9272 radioligand kinetics and the relationship between plasma concentration of AZD9272 and receptor occupancy in the human brain. AZD9272 is a new chemical entity pharmacologically characterised as a noncompetitive antagonist at the metabotropic glutamate receptor subtype 5 (mGluR5). Positron emission tomography (PET) was used to measure the time course of ((11)C-AZD9272) in the brain. The study included PET measurements in six healthy volunteers where the radioligand was given as a tracer dose alone as well as post oral treatment with different doses of unlabelled AZD9272. Estimation of radioligand kinetics, including saturation of receptor binding was performed by use of non-linear mixed effects modelling. Data from the regions with the highest (ventral striatum) and lowest (cerebellum) radioligand concentrations were included in the analysis. It was assumed that the extent of non-displaceable brain uptake was the same in both regions while the rate of CNS uptake and the receptor density differed. The results of the analysis showed that AZD9272 binding at the receptor is saturable with an estimated plasma concentration corresponding to 50% occupancy of approximately 200 nM. The density of the receptor binding sites was estimated to 800 nM and 200 nM in ventral striatum and cerebellum respectively. By simultaneously analysing data from several PET measurements and different brain regions in a non-linear mixed effects framework it was possible to estimate parameters of interest that would otherwise be difficult to quantify.

Pharmacokinetics of the Port Delivery System with Ranibizumab in the Ladder Phase 2 Trial for Neovascular Age-Related Macular Degeneration.

INTRODUCTION: Ladder was a phase 2 trial that evaluated the Port Delivery System with ranibizumab (PDS) for neovascular age-related macular degeneration. Serum and aqueous humor samples were collected to characterize the pharmacokinetics (PK) of ranibizumab delivered through the PDS. METHODS: Ladder was a multicenter, randomized, active treatment-controlled, phase 2 clinical trial. Patients with neovascular age-related macular degeneration (n = 220) were randomized (3:3:3:2) to PDS 10 mg/ml, PDS 40 mg/ml, PDS 100 mg/ml, or monthly intravitreal ranibizumab 0.5 mg. Serum PK samples were collected in all arms and analyzed for ranibizumab concentration using an enzyme-linked immunosorbent assay. The main PK analyses were conducted in the PK-evaluable population (n = 68), which excluded patients who received fellow eye intravitreal treatment, supplemental ranibizumab treatment, or had previous treatment with bevacizumab in either eye within 9 months of randomization. RESULTS: In the PDS 10 mg/ml arm, median serum ranibizumab concentrations were below the serum trough concentration (Ctrough; 130 pg/ml) expected with monthly intravitreal ranibizumab 0.5 mg at all time points. In the PDS 40 mg/ml and 100 mg/ml arms, median serum ranibizumab concentrations were above the Ctrough expected with monthly intravitreal ranibizumab 0.5 mg (130 pg/ml) through month 3 and month 12 after implantation, respectively, and remained above the lower limit of quantification through month 15 and month 16 after implantation, respectively. CONCLUSIONS: These PK data indicate that the implant in the PDS 100 mg/ml arm maintained ranibizumab concentrations within the range of monthly intravitreal ranibizumab 0.5 mg injections (130-2220 pg/ml) through month 12 after implantation. TRIAL REGISTRATION: ClinicalTrials.gov identifier, NCT02510794.

Model-Informed Therapeutic Dose Optimization Strategies for Antibody-Drug Conjugates in Oncology: What Can We Learn From US Food and Drug Administration-Approved Antibody-Drug Conjugates?

Antibody-drug conjugates (ADCs) combine the specificity of an antibody with the cytotoxicity of a chemical agent. They represent a rapidly evolving area of oncology drug development and hold significant promise. There are currently nine ADCs on the market, more than half of which gained US Food and Drug Administration approval more recently, since 2019. Despite their enormous promise, the therapeutic window for these ADCs remains relatively narrow, especially when compared with other oncology drugs, such as targeted therapies or checkpoint inhibitors. In this review, we provide a detailed overview of the five dosing regimen optimization strategies that have been leveraged to broaden the therapeutic window by mitigating the safety risks while maintaining efficacy. These include body weight cap dosing; treatment duration capping; dose schedule (e.g., dosing frequency and dose fractionation); response-guided dosing recommendations; and randomized dose-finding. We then discuss how the lessons learned from these studies can inform ADC development going forward. Informed application of these dosing strategies should allow researchers to maximize the safety and efficacy for next-generation ADCs.

Population pharmacokinetic and exploratory exposure-response analysis of the fixed-dose combination of pertuzumab and trastuzumab for subcutaneous injection in patients with HER2-positive early breast cancer in the FeDeriCa study.

PURPOSE: To characterize pertuzumab pharmacokinetics (PK) in FeDeriCa (NCT03493854: fixed-dose combination of pertuzumab and trastuzumab for subcutaneous injection [PH FDC SC] versus intravenous pertuzumab plus trastuzumab); derive individual pertuzumab exposures in the PH FDC SC arm for subsequent pertuzumab exposure-response (ER) analyses; compare observed trastuzumab PK with predicted exposures from a previous SC trastuzumab model; assess whether pertuzumab affects trastuzumab PK; evaluate pertuzumab exposure-efficacy and -safety relationships and support the approved SC dosing regimen. METHODS: Population pharmacokinetic modeling and simulations were used to describe the data. Standard goodness-of-fit diagnostics and prediction-corrected visual predictive checks were used for model performance assessment. Covariates were included from previously reported models. ER analysis was conducted using logistic regression. RESULTS: SC pertuzumab PK was described adequately by a two-compartment model with first-order absorption; significant covariates included in the final model were albumin, lean body weight, and Asian region; however, these appeared not to be clinically relevant. Trastuzumab concentrations were described adequately by the previous model; there was no evidence of a pertuzumab effect on trastuzumab PK as part of PH FDC SC and higher model-predicted pertuzumab exposure was not associated with differences in pathologic complete response rate or an increased probability of selected grade ≥ 3 adverse events of interest. CONCLUSION: The approved PH FDC SC dose [loading: 1200/600 mg pertuzumab/trastuzumab (15 mL); maintenance: 600 mg pertuzumab/trastuzumab (10 mL) and 2000 U/mL recombinant human hyaluronidase every 3 weeks] provides a positive benefit-risk profile with comparable efficacy and safety to intravenous pertuzumab plus trastuzumab.

A New Method to Model and Predict Progression Free Survival Based on Tumor Growth Dynamics.

Progression-free survival (PFS) has been increasingly used as a primary endpoint for early clinical development. The aim of the present work was to develop a model where target lesion dynamics and risk for nontarget progression are jointly modeled for predicting PFS. The model was developed based on a pooled platinum-resistant ovarian cancer dataset comprising four different treatments and a wide range of dose levels. The target lesion progression was derived from tumor growth dynamics based on the Response Evaluation Criteria in Solid Tumors (RECIST) criteria. The nontarget progression hazard was correlated to the first derivative of target lesion tumor size with respect to time. The PFS time was determined by the first occurring event, target lesion progression, or nontarget progression. The final joint model not only captured target lesion tumor growth dynamics but also predicted PFS well. A similar approach can potentially be used to predict PFS in future oncology studies.

Analysis of Longitudinal-Ordered Categorical Data for Muscle Spasm Adverse Event of Vismodegib: Comparison Between Different Pharmacometric Models.

Longitudinal-ordered categorical data, common in clinical trials, can be effectively analyzed with nonlinear mixed effect models. In this article, we systematically evaluated the performance of three different models in longitudinal muscle spasm adverse event (AE) data obtained from a clinical trial for vismodegib: a proportional odds (PO) model, a discrete-time Markov model, and a continuous-time Markov model. All models developed based on weekly spaced data can reasonably capture the proportion of AE grade over time; however, the PO model overpredicted the transition frequency between grades and the cumulative probability of AEs. The influence of data frequency (daily, weekly, or unevenly spaced) was also investigated. The PO model performance reduced with increased data frequency, and the discrete-time Markov model failed to describe unevenly spaced data, but the continuous-time Markov model performed consistently well. Clinical trial simulations were conducted to illustrate the muscle spasm resolution time profile during the 8-week dose interruption period after 12 weeks of continuous treatment.

Intrinsic and Extrinsic Pharmacokinetic Variability of Small Molecule Targeted Cancer Therapy.

Pharmacokinetic (PK) variability in cancer clinical trials may be due to heterogeneous populations and identifying sources of variability is important. Use of healthy subjects in clinical pharmacology studies together with detailed knowledge of the characteristics of patients with cancer can allow for quick identification and quantification of factors affecting PK variability. PK data and sources of variability of 40 marketed molecularly targeted oncology therapeutics were compiled from regulatory approval documents covering an 18-year period (1999-2017). Variability in PK parameters was compared and contributors to variability were identified. The results show that PK variability was ~ 16% higher for peak plasma concentration (Cmax ) and area under the concentration time curve (AUC) in patients with cancer compared with healthy subjects. Several factors were identified as major contributors to variability including hepatic/renal impairment and cytochrome P450 inhibition/induction. Lower PK variability in healthy subjects may represent an opportunity to perform rapid and robust pharmacological and PK assessments to inform subsequent studies in the development of new cancer therapies.