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.

Characterisation of cotadutide's dual GLP-1/glucagon receptor agonistic effects on glycaemic control using an in vivo human glucose regulation quantitative systems pharmacology model.

BACKGROUND AND PURPOSE: Cotadutide is a dual GLP-1 and glucagon receptor agonist with balanced agonistic activity at each receptor designed to harness the advantages on promoting liver health, weight loss and glycaemic control. We characterised the effects of cotadutide on glucose, insulin, GLP-1, GIP, and glucagon over time in a quantitative manner using our glucose dynamics systems model (4GI systems model), in combination with clinical data from a multiple ascending dose/Phase 2a (MAD/Ph2a) study in overweight and obese subjects with a history of Type 2 diabetes mellitus (NCT02548585). EXPERIMENTAL APPROACH: The cotadutide PK-4GI systems model was calibrated to clinical data by re-estimating only food related parameters. In vivo cotadutide efficacy was scaled based on in vitro potency. The model was used to explore the effect of weight loss on insulin sensitivity and predict the relative contribution of the GLP-1 and glucagon receptor agonistic effects on glucose. KEY RESULTS: Cotadutide MAD/Ph2a clinical endpoints were successfully predicted. The 4GI model captured a positive effect of weight loss on insulin sensitivity and showed that the stimulating effect of glucagon on glucose production counteracts the GLP-1 receptor-mediated decrease in glucose, resulting in a plateau for glucose decrease around a 200-µg cotadutide dose. CONCLUSION AND IMPLICATIONS: The 4GI quantitative systems pharmacology model was able to predict the clinical effects of cotadutide on glucose, insulin, GLP-1, glucagon and GIP given known in vitro potency. The analyses demonstrated that the quantitative systems pharmacology model, and its successive refinements, will be a valuable tool to support the clinical development of cotadutide and related compounds.

A novel integrated QSP model of in vivo human glucose regulation to support the development of a glucagon/GLP-1 dual agonist.

Glucagon-like peptide-1 (GLP-1) receptor agonists (GLP-1RAs) and dual GLP-1/glucagon receptor agonists improve glycaemic control and cause significant weight loss in patients with type 2 diabetes.1 These effects are driven in part by augmenting glucose-stimulated insulin release (incretin effect), reducing caloric intake and delayed gastric emptying. We developed and externally validated a novel integrated quantitative systems pharmacology (QSP) model to gain quantitative insight into the relative contributions and mechanisms of drugs modulating glucose regulatory pathways. This model (4GI model) incorporates known feedback mechanisms among glucose, GLP-1, glucagon, glucose-dependent insulinotropic peptide (GIP), and insulin after glucose provocation (i.e., food intake) and drug intervention utilizing published nonpharmacological and pharmacological (liraglutide, a GLP-1RA) data. The resulting model accurately describes the aforementioned mechanisms and independently predicts the effects of the GLP-1RAs (dulaglutide and semaglutide) on system dynamics. Therefore, the validated 4GI model represents a quantitative decision-making tool to support the advancement of novel therapeutics and combination strategies modulating these pathways.

Making Better Dose Decisions: Using Exposure-Response Modeling to Integrate Efficacy Outcome of Two Phase IIb Clinical Trials of Ubrogepant for Migraine Treatment.

Ubrogepant (MK-1602) is a novel, oral, calcitonin gene-related peptide receptor antagonist in clinical development with positive phase III outcomes for acute treatment of migraine. This paper describes the population exposure-response (E-R) modeling and simulations, which were used to inform the phase III dose-selection rationale, based on ~ 800 participants pooled across two phase IIb randomized dose-finding clinical trials. The E-R model describes the placebo and ubrogepant treatment effects based on migraine pain end points (2-hour pain relief and 2-hour pain freedom) at various dose levels. Sensitivity analyses were conducted to evaluate various assumptions of placebo response in light of the high placebo response observed in one phase II trial. A population pharmacokinetic model describing the effect of formulations was included in the E-R simulation framework to assess potential dose implications of a formulation switch from phase II to phase III. Model-based simulations predict that a dose of 25 mg or higher is likely to achieve significantly better efficacy than placebo with desirable efficacy levels. The understanding of E-R helped support the dose selection for the phase III clinical trials.

Population PK Analyses of Ubrogepant (MK-1602), a CGRP Receptor Antagonist: Enriching In-Clinic Plasma PK Sampling With Outpatient Dried Blood Spot Sampling.

Merck & Co., Inc. (Kenilworth, New Jersey) has recently published an integrated strategy for implementation of dried blood spots (DBS) in late-stage trials for population pharmacokinetic (PK) modeling. We applied this strategy for another late-stage clinical program: ubrogepant (MK-1602), a novel oral calcitonin gene-related peptide receptor antagonist for acute treatment of migraine. At the time of implementation, ubrogepant was entering phase 2 development. DBS was implemented to acquire PK information proximal to an acute migraine event to enable exposure-response modeling. The clinical endpoint was a spontaneous event, which generally occurs outside a clinic visit. Thus, an innovative feature of this trial was facilitating DBS in an outpatient setting. In vitro and bioanalytical tests established initial method feasibility and suitability for further evaluations in the clinic. A quantitative relationship was developed between blood and plasma concentrations from concurrently collected samples in a phase 1 (healthy subjects) and phase 2 (target patient population) study using graphical and population PK approaches. This integrated information was presented to the Food and Drug Administration for regulatory input. Following regulatory concurrence, DBS was poised for use in further clinical studies. Population PK modeling was used to dissect sources of variability contributing to DBS collection in the outpatient setting. What has been learned from this program has informed the broader integrated strategy of Merck & Co., Inc. (Kenilworth, NJ) for DBS implementation in clinical trials and research to improve the precision of PK data collected in an outpatient setting.

A PK-PD model-based assessment of sugammadex effects on coagulation parameters.

Exposure-response analyses of sugammadex on activated partial thromboplastin time (APTT) and prothrombin time international normalized ratio (PT(INR)) were performed using data from two clinical trials in which subjects were co-treated with anti-coagulants, providing a framework to predict these responses in surgical patients on thromboprophylactic doses of low molecular weight or unfractionated heparin. Sugammadex-mediated increases in APTT and PT(INR) were described with a direct effect model, and this relationship was similar in the presence or absence of anti-coagulant therapy in either healthy volunteers or surgical patients. In surgical patients on thromboprophylactic therapy, model-based predictions showed 13.1% and 22.3% increases in respectively APTT and PT(INR) within 30min after administration of 16mg/kg sugammadex. These increases remain below thresholds seen following treatment with standard anti-coagulant therapy and were predicted to be short-lived paralleling the rapid decline in sugammadex plasma concentrations.

X-ray structures of progesterone receptor ligand binding domain in its agonist state reveal differing mechanisms for mixed profiles of 11ß-substituted steroids.

We present here the x-ray structures of the progesterone receptor (PR) in complex with two mixed profile PR modulators whose functional activity results from two differing molecular mechanisms. The structure of Asoprisnil bound to the agonist state of PR demonstrates the contribution of the ligand to increasing stability of the agonist conformation of helix-12 via a specific hydrogen-bond network including Glu(723). This interaction is absent when the full antagonist, RU486, binds to PR. Combined with a previously reported structure of Asoprisnil bound to the antagonist state of the receptor, this structure extends our understanding of the complex molecular interactions underlying the mixed agonist/antagonist profile of the compound. In addition, we present the structure of PR in its agonist conformation bound to the mixed profile compound Org3H whose reduced antagonistic activity and increased agonistic activity compared with reference antagonists is due to an induced fit around Trp(755), resulting in a decreased steric clash with Met(909) but inducing a new internal clash with Val(912) in helix-12. This structure also explains the previously published observation that 16α attachments to RU486 analogs induce mixed profiles by altering the binding of 11ß substituents. Together these structures further our understanding of the steric and electrostatic factors that contribute to the function of steroid receptor modulators, providing valuable insight for future compound design.

Structural basis for agonism and antagonism for a set of chemically related progesterone receptor modulators.

The progesterone receptor is able to bind to a large number and variety of ligands that elicit a broad range of transcriptional responses ranging from full agonism to full antagonism and numerous mixed profiles inbetween. We describe here two new progesterone receptor ligand binding domain x-ray structures bound to compounds from a structurally related but functionally divergent series, which show different binding modes corresponding to their agonistic or antagonistic nature. In addition, we present a third progesterone receptor ligand binding domain dimer bound to an agonist in monomer A and an antagonist in monomer B, which display binding modes in agreement with the earlier observation that agonists and antagonists from this series adopt different binding modes.

Enantioselective substrate binding in a monooxygenase protein model by molecular dynamics and docking.

The two-component flavoenzyme styrene monooxygenase (SMO) is an efficient alternative to several chemical epoxidation catalysts on a preparative scale. A first homology model of the catalytic domain (StyA) of SMO was constructed (Protein Data Bank ID 2HD8) based on the structure of para-hydroxybenzoate hydroxylase. The StyA protein structure was optimized by restrained molecular dynamics to reproduce specific pre-S binding orientations of styrene. Effects of all 10 point mutations examined were explained by the distance of the site to the styrene and FAD binding sites. Thirteen of 20 ligands could be accommodated in a catalytically active binding orientation, and predicted affinities correlated well with experimental turnover and inhibition. The binding cavity is almost completely hydrophobic except for a hydrogen-bonded network formed by three water molecules, the backbone of residues 300-302, and the flavin ribityl, similar to P293, and three crystal waters in para-hydroxybenzoate hydroxylase suggest that P302, T47, and the waters in StyA are a vital component of the catalytic mechanism. The current optimized and validated StyA model provides a good starting point for elucidation of the structural basis of StyA ligand binding and catalysis. Novel insights in the binding of ligands to SMO/StyA, provided by the current protein model, will aid the rational design of mutants with specific, altered enantioselective properties.