A two-step deconvolution-analysis-informed population pharmacodynamic modeling approach for drugs targeting pulsatile endogenous compounds

The power of modelling pulsatile profiles

The quantitative description of individual observations in non-linear mixed effects models over time is complicated when the studied biomarker has a pulsatile release (e.g. insulin, growth hormone, luteinizing hormone). Unfortunately, standard non-linear mixed effects population pharmacodynamic models such as turnover and precursor response models (with or without a cosinor component) are unable to quantify these complex secretion profiles over time. In this study, the statistical power of standard statistical methodology such as 6 post-dose measurements or the area under the curve from 0 to 12 h post-dose on simulated dense concentration–time profiles of growth hormone was compared to a deconvolution-analysis-informed modelling approach in different simulated scenarios. The statistical power of the deconvolution-analysis-informed approach was determined with a Monte-Carlo Mapped Power analysis. Due to the high level of intra- and inter-individual variability in growth hormone concentrations over time, regardless of the simulated effect size, only the deconvolution-analysis informed approach reached a statistical power of more than 80% with a sample size of less than 200 subjects per cohort. Furthermore, the use of this deconvolution-analysis-informed modelling approach improved the description of the observations on an individual level and enabled the quantification of a drug effect to be used for subsequent clinical trial simulations.

Quantification of the endogenous growth hormone and prolactin lowering effects of a somatostatin-dopamine chimera using population PK/PD modeling

A phase 1 clinical trial in healthy male volunteers was conducted with a somatostatin-dopamine chimera (BIM23B065), from which information could be obtained on the concentration-effect relationship of the inhibition of pulsatile endogenous growth hormone and prolactin secretion. Endogenous growth hormone profiles were analyzed using a two-step deconvolution-analysis-informed population pharmacodynamic modeling approach, which was developed for the analyses of pulsatile profiles. Prolactin concentrations were modelled using a population pool model with a circadian component on the prolactin release. During treatment with BIM23B065, growth hormone secretion was significantly reduced (maximal effect [E_MAX] = − 64.8%) with significant reductions in the pulse frequency in two out of three multiple ascending dose cohorts. A circadian component in prolactin secretion was identified, modelled using a combination of two cosine functions with 24 h and 12 h periods. Dosing of BIM23B065 strongly inhibited (E_MAX = − 91%) the prolactin release and demonstrated further reduction of prolactin secretion after multiple days of dosing. This study quantified the concentration-effect relationship of BIM23B065 on the release of two pituitary hormones, providing proof of pharmacology of the chimeric actions of BIM23B065.

A Novel Somatostatin-Dopamine Chimera (BIM23B065) Reduced GH Secretion in a First-in-Human Clinical Trial

CONTEXT

A somatostatin-dopamine chimera (BIM23B065) was under investigation to reduce GH secretion for the treatment of pituitary adenomas.

OBJECTIVE

To determine pharmacokinetics, safety, and tolerability and to monitor hormonal changes after single and multiple subcutaneous BIM23B065 administrations.

DESIGN

Randomized, double-blind, placebo-controlled, parallel-group design with five single and three 13-day multiple ascending-dose cohorts.

PATIENTS

A total of 63 healthy male white volunteers were enrolled (47 active, 16 placebo).

MAIN OUTCOME MEASURES

Pharmacokinetics, GH, prolactin (PRL), IGF-1, GH after GHRH administration, and general clinical safety criteria.

RESULTS

The maximum dosage of BIM23B065 administered in this study was 1.5 mg. BIM23B065 reduced the mean GH concentrations after 8 and 13 days of treatment. A decrease in GH release after GHRH administration indicated inhibition of the hypothalamic-pituitary-somatotropic axis. IGF-1 was not altered after single doses but showed a significant change from baseline after multiple dosing. PRL secretion was reduced in all subjects who were treated. Orthostatic hypotension and injection site reactions were commonly observed at high dosages. A 6-day uptitration period was included to successfully lower the cardiovascular effects in the multiple ascending dose part of the study.

CONCLUSIONS

Proof of pharmacology of BIM23B065 was shown by a reduction in GH, IGF-1, and PRL concentrations in healthy male volunteers, supporting activity of the somatostatin analog and dopamine agonist moieties. The safety and tolerability of the higher dosing regions was limited mainly by orthostatic hypotension.

Modeling associations between latent event processes governing time series of pulsing hormones

This work is motivated by a desire to quantify relationships between two time series of pulsing hormone concentrations. The locations of pulses are not directly observed and may be considered latent event processes. The latent event processes of pulsing hormones are often associated. It is this joint relationship we model. Current approaches to jointly modeling pulsing hormone data generally assume that a pulse in one hormone is coupled with a pulse in another hormone (one-to-one association). However, pulse coupling is often imperfect. Existing joint models are not flexible enough for imperfect systems. In this article, we develop a more flexible class of pulse association models that incorporate parameters quantifying imperfect pulse associations. We propose a novel use of the Cox process model as a model of how pulse events co-occur in time. We embed the Cox process model into a hormone concentration model. Hormone concentration is the observed data. Spatial birth and death Markov chain Monte Carlo is used for estimation. Simulations show the joint model works well for quantifying both perfect and imperfect associations and offers estimation improvements over single hormone analyses. We apply this model to luteinizing hormone (LH) and follicle stimulating hormone (FSH), two reproductive hormones. Use of our joint model results in an ability to investigate novel hypotheses regarding associations between LH and FSH secretion in obese and non-obese women.