Navigating the landscape of parameter identifiability methods: A workflow recommendation for model development.

In pharmacometric modeling, it is often important to know whether the data is sufficiently rich to identify the parameters of a proposed model. While it may be possible to assess this based on the results of a model fit, it is often difficult to disentangle identifiability issues from other model fitting and numerical problems. Furthermore, it can be of value to ascertain identifiability beforehand. This paper compares four methods for parameter identifiability, namely Differential Algebra for Identifiability of SYstems (DAISY), the sensitivity matrix method (SMM), Aliasing, and the Fisher information matrix method (FIMM). We discuss the characteristics of the methods and apply them to a set of applications, consisting of frequently used PK model structures, with suitable dosing regimens and sampling times. These applications were selected to validate the methods and demonstrate their usefulness. While traditional identifiability analysis provides a categorical result [PLoS One, 6, 2011, e27755; CPT Pharmacometrics Syst Pharmacol, 8, 2019, 259; Bioinformatics, 30, 2014, 1440], we argue that in practice a continuous scale better reflects the limitations on the data and is more informative. The methods were generally consistent in their evaluation of the applications. The Fisher information matrix method seemed to provide the most consistent answers. All methods provided information on the parameters that were unidentifiable. Some of the results were unexpected, indicating identifiability issues where none were foreseen, but could be explained upon further analysis. This illustrated the usefulness of identifiability assessment.

Two new user-friendly methods to assess pharmacometric parameter identifiability on categorical and continuous scales.

Parameter identifiability methods assess whether the parameters of a model are uniquely determined by the observations. While the success of a model fit can provide some information on this, it can be valuable to determine identifiability before any fit has been attempted, or to separate identifiability from other issues. Two concepts that lean themselves well for identifiability analysis and have been underutilized are the sensitivity matrix (SM) and the Fisher information matrix (FIM). This paper presents two newly developed methods, one based on the SM and one based on the FIM. Both methods can assess local identifiability for a wide set of models, can be used with limited effort, and are freely available. The methods require the proposed model in the form of a set of differential equations, the parameter values, and the study design as input. They can be used a priori, as they do not need observed values or a successful model fit. Traditional methods provide a single categorical (yes/no) answer to the question of identifiability. In many cases, this is not very informative, and identifiability depends on study design (e.g., dose levels or observation times) and parameter values. Indicators on a continuous scale characterizing the level of identifiability would provide more detailed and relevant information, for example, to guide model development. Our two methods provide both categorical and continuous indicators. Both methods indicate which parameter combinations are difficult to identify by calculating the directions in parameter space that are least identifiable. The methods were validated with an example problem.

Pharmacokinetics and pharmacodynamics of finerenone in patients with chronic kidney disease and type 2 diabetes: Insights based on FIGARO-DKD and FIDELIO-DKD.

AIMS: To perform dose-exposure-response analyses to determine the effects of finerenone doses. MATERIALS AND METHODS: Two randomized, double-blind, placebo-controlled phase 3 trials enrolling 13 026 randomized participants with type 2 diabetes (T2D) from global sites, each with an estimated glomerular filtration rate (eGFR) of 25 to 90 mL/min/1.73 m2 , a urine albumin-creatinine ratio (UACR) of 30 to 5000 mg/g, and serum potassium ≤ 4.8 mmol/L were included. Interventions were titrated doses of finerenone 10 or 20 mg versus placebo on top of standard of care. The outcomes were trajectories of plasma finerenone and serum potassium concentrations, UACR, eGFR and kidney composite outcomes, assessed using nonlinear mixed-effects population pharmacokinetic (PK)/pharmacodynamic (PD) and parametric time-to-event models. RESULTS: For potassium, lower serum levels and lower rates of hyperkalaemia were associated with higher doses of finerenone 20 mg compared to 10 mg (p < 0.001). The PK/PD model analysis linked this observed inverse association to potassium-guided dose titration. Simulations of a hypothetical trial with constant finerenone doses revealed a shallow but increasing exposure-potassium response relationship. Similarly, increasing finerenone exposures led to less than dose-proportional increasing reductions in modelled UACR. Modelled UACR explained 95% of finerenone's treatment effect in slowing chronic eGFR decline. No UACR-independent finerenone effects were identified. Neither sodium-glucose cotransporter-2 (SGLT2) inhibitor nor glucagon-like peptide-1 receptor agonist (GLP-1RA) treatment significantly modified the effects of finerenone in reducing UACR and eGFR decline. Modelled eGFR explained 87% of finerenone's treatment effect on kidney outcomes. No eGFR-independent effects were identified. CONCLUSIONS: The analyses provide strong evidence for the effectiveness of finerenone dose titration in controlling serum potassium elevations. UACR and eGFR are predictive of kidney outcomes during finerenone treatment. Finerenone's kidney efficacy is independent of concomitant use of SGLT2 inhibitors and GLP-1RAs.

Longitudinal efficacy and safety modeling and simulation framework to aid dose selection of belantamab mafodotin for patients with multiple myeloma.

Belantamab mafodotin, a monomethyl auristatin F (MMAF)-containing monoclonal antibody-drug conjugate (ADC), demonstrated deep and durable responses in the DRiving Excellence in Approaches to Multiple Myeloma (DREAMM)-1 and pivotal DREAMM-2 studies in patients with relapsed/refractory multiple myeloma. As with other MMAF-containing ADCs, ocular adverse events were observed. To predict the effects of belantamab mafodotin dosing regimens and dose-modification strategies on efficacy and ocular safety end points, DREAMM-1 and DREAMM-2 data across a range of doses were used to develop an integrated simulation framework incorporating two separate longitudinal models and the published population pharmacokinetic model. A concentration-driven tumor growth inhibition model described the time course of serum M-protein concentration, a measure of treatment response, whereas a discrete time Markov model described the time course of ocular events graded with the GSK Keratopathy and Visual Acuity scale. Significant covariates included baseline ß2 -microglobulin on growth rate, baseline M-protein on kill rate, extramedullary disease on the effect compartment rate constant, and baseline soluble B cell maturation antigen on maximal effect. Efficacy and safety end points were simulated for various doses with dosing intervals of 1, 3, 6, and 9 weeks and various event-driven dose-modification strategies. Simulations predicted that lower doses and longer dosing intervals were associated with lower probability and lower overall time with Grade 3+ and Grade 2+ ocular events compared with the reference regimen (2.5 mg/kg every 3 weeks), with a less-than-proportional reduction in efficacy. The predicted improved benefit-risk profiles of certain dosing schedules and dose modifications from this integrated framework has informed trial designs for belantamab mafodotin, supporting dose-optimization strategies.

Dose-Exposure-Response Analysis of the Nonsteroidal Mineralocorticoid Receptor Antagonist Finerenone on UACR and eGFR: An Analysis from FIDELIO-DKD.

BACKGROUND AND OBJECTIVE: Finerenone reduces the risk of kidney failure in patients with chronic kidney disease and type 2 diabetes. Changes in the urine albumin-to-creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) are surrogates for kidney failure. We performed dose-exposure-response analyses to determine the effects of finerenone on these surrogates in the presence and absence of sodium glucose co-transporter-2 inhibitors (SGLT2is) using individual patient data from the FIDELIO-DKD study. METHODS: Non-linear mixed-effects population pharmacokinetic/pharmacodynamic models were used to quantify disease progression in terms of UACR and eGFR during standard of care and pharmacodynamic effects of finerenone in the presence and absence of SGLT2i use. RESULTS: The population pharmacokinetic/pharmacodynamic models adequately described effects of finerenone exposure in reducing UACR and slowing eGFR decline over time. The reduction in UACR achieved with finerenone during the first year predicted its subsequent effect in slowing progressive eGFR decline. SGLT2i use did not modify the effects of finerenone. The population pharmacokinetic/pharmacodynamic model demonstrated with 97.5% confidence that finerenone was at least 94.1% as efficacious in reducing UACR in patients using an SGLT2i compared with patients not using an SGLT2i based on the 95% confidence interval of the SGLT2i-finerenone interaction from 94.1 to 122%. The 95% confidence interval of the SGLT2i-finerenone interaction for the UACR-mediated effect on chronic eGFR decline was 9.5-144%. CONCLUSIONS: We developed a model that accurately describes the finerenone dose-exposure-response relationship for UACR and eGFR. The model demonstrated that the early UACR effect of finerenone predicted its long-term effect on eGFR decline. These effects were independent of concomitant SGLT2i use.

Mechanism-based modeling of the effect of a novel inhibitor of vascular adhesion protein-1 on albuminuria and renal function markers in patients with diabetic kidney disease.

The vascular adhesion protein-1 (VAP-1) inhibitor ASP8232 reduces albuminuria in patients with type 2 diabetes and chronic kidney disease. A mechanism-based model was developed to quantify the effects of ASP8232 on renal markers from a placebo-controlled Phase 2 study in diabetic kidney disease with 12 weeks of ASP8232 treatment. The model incorporated the available pharmacokinetic, pharmacodynamic (plasma VAP-1 concentration and activity), serum and urine creatinine, serum cystatin C, albumin excretion rate, urinary albumin-to-creatinine ratio, and urine volume information in an integrated manner. Drug-independent time-varying changes and different drug effects could be quantified for these markers using the model. Through simulations, this model provided the opportunity to dissect the relationship and longitudinal association between the estimated glomerular filtration rate and albuminuria and to quantify the pharmacological effects of ASP8232. The developed drug-independent model may be useful as a starting point for other compounds affecting the same biomarkers in a similar time scale.

Can cyclist safety be improved with intelligent transport systems?

In recent years, Intelligent Transport Systems (ITS) have assisted in the decrease of road traffic fatalities, particularly amongst passenger car occupants. Vulnerable Road Users (VRUs) such as pedestrians, cyclists, moped riders and motorcyclists, however, have not been that much in focus when developing ITS. Therefore, there is a clear need for ITS which specifically address VRUs as an integrated element of the traffic system. This paper presents the results of a quantitative safety impact assessment of five systems that were estimated to have high potential to improve the safety of cyclists, namely: Blind Spot Detection (BSD), Bicycle to Vehicle communication (B2V), Intersection safety (INS), Pedestrian and Cyclist Detection System+Emergency Braking (PCDS+EBR) and VRU Beacon System (VBS). An ex-ante assessment method proposed by Kulmala (2010) targeted to assess the effects of ITS for cars was applied and further developed in this study to assess the safety impacts of ITS specifically designed for VRUs. The main results of the assessment showed that all investigated systems affect cyclist safety in a positive way by preventing fatalities and injuries. The estimates considering 2012 accident data and full penetration showed that the highest effects could be obtained by the implementation of PCDS+EBR and B2V, whereas VBS had the lowest effect. The estimated yearly reduction in cyclist fatalities in the EU-28 varied between 77 and 286 per system. A forecast for 2030, taking into accounts the estimated accident trends and penetration rates, showed the highest effects for PCDS+EBR and BSD.