Bayesian estimation in NONMEM.

Bayesian estimation is a powerful but underutilized tool for answering drug development questions. In this tutorial, the principles of Bayesian model development, assessment, and prior selection will be outlined. An example pharmacokinetic (PK) model will be used to demonstrate the implementation of Bayesian modeling using the nonlinear mixed-effects modeling software NONMEM.

Model-based meta-analysis using latent variable modeling to set benchmarks for new treatments of systemic lupus erythematosus.

Several investigational agents are under evaluation in systemic lupus erythematosus (SLE) clinical trials but quantitative frameworks to enable comparison of their efficacy to reference benchmark treatments are lacking. To benchmark SLE treatment effects and identify clinically important covariates, we developed a model-based meta-analysis (MBMA) within a latent variable model framework for efficacy end points and SLE composite end point scores (BILAG-based Composite Lupus Assessment and Systemic Lupus Erythematosus Responder Index) using aggregate-level data on approved and investigational therapeutics. SLE trials were searched using PubMed and www.clinicaltrials.gov for treatment name, SLE and clinical trial as search criteria that resulted in four data structures: (1) study and investigational agent, (2) dose and regimen, (3) baseline descriptors, and (4) outcomes. The final dataset consisted of 25 studies and 81 treatment arms evaluating 16 different agents. A previously developed (K Goteti et al. 2022) SLE latent variable model of data from placebo arms (placebo + standard of care treatments) was used to describe aggregate SLE end points over time for the various SLE placebo and treatment arms in a Bayesian MBMA framework. Continuous dose-effect relationships using a maximum effect model were included for anifrolumab, belimumab, CC-220 (iberdomide), epratuzumab, lulizumab pegol, and sifalimumab, whereas the remaining treatments were modeled as discrete dose effects. The final MBMA model was then used to benchmark these compounds with respect to the maximal efficacy on the latent variable compared to the placebo. This MBMA illustrates the application of latent variable models in understanding the trajectories of composite end points in chronic diseases and should enable model-informed development of new investigational agents in SLE.

Population model-based analysis of the memory B-cell response following belimumab therapy in the treatment of systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by B-cell hyperactivity and breach of tolerance. Autoreactive memory B cells, which have a decreased activation threshold and the ability to survive in absence of antigen, are believed to contribute to chronicity in autoimmune diseases like SLE. Belimumab, the first approved biological treatment of active SLE and lupus nephritis, reduces B cells dependent on B-lymphocyte stimulator protein (BLyS) for survival, whereas memory B cells are spared; several studies reported circulating memory B-cell concentrations increase following BLyS neutralization. This analysis investigated the effect of dose, demographics, and disease status on memory B-cell response after starting belimumab treatment. Population pharmacodynamic models were fitted to a pooled dataset from seven belimumab SLE trials. The optimal model was selected using maximum likelihood methods and was then refit to the data using Bayesian analysis and used to simulate memory B-cell response by belimumab dose and covariate subgroups. At the belimumab approved doses (10 mg/kg intravenously every 4 weeks, 200 mg subcutaneously every week), circulatory memory B cells increase in the first 4-8 weeks after belimumab initiation, typically returning to baseline levels over 76 weeks. The model analysis suggested belimumab stimulates memory B-cell transition from lymphoid and/or inflamed tissues into the circulation, rather than inhibiting trafficking in the reverse direction. Baseline BLyS and anti-double-stranded deoxyribonucleic acid antibody concentrations were statistically identifiable covariates of memory B-cell response, although their impact on predicting size and response duration was small.

Bayesian PBPK modeling using R/Stan/Torsten and Julia/SciML/Turing.Jl.

Physiologically-based pharmacokinetic (PBPK) models are mechanistic models that are built based on an investigator's prior knowledge of the in vivo system of interest. Bayesian inference incorporates an investigator's prior knowledge of parameters while using the data to update this knowledge. As such, Bayesian tools are well-suited to infer PBPK model parameters using the strong prior knowledge available while quantifying the uncertainty on these parameters. This tutorial demonstrates a full population Bayesian PBPK analysis framework using R/Stan/Torsten and Julia/SciML/Turing.jl.

Flexible and efficient Bayesian pharmacometrics modeling using Stan and Torsten, Part I.

Stan is an open-source probabilistic programing language, primarily designed to do Bayesian data analysis. Its main inference algorithm is an adaptive Hamiltonian Monte Carlo sampler, supported by state-of-the-art gradient computation. Stan's strengths include efficient computation, an expressive language that offers a great deal of flexibility, and numerous diagnostics that allow modelers to check whether the inference is reliable. Torsten extends Stan with a suite of functions that facilitate the specification of pharmacokinetic and pharmacodynamic models and makes it straightforward to specify a clinical event schedule. Part I of this tutorial demonstrates how to build, fit, and criticize standard pharmacokinetic and pharmacodynamic models using Stan and Torsten.

Time-to-Event Modeling of Peripheral Neuropathy: Platform Analysis of Eight Valine-Citrulline-Monomethylauristatin E Antibody-Drug Conjugates.

Peripheral neuropathy (PN) is a common long-term debilitating toxicity of antimicrotubule agents. PN was the most frequent adverse event resulting in dose modifications and/or discontinuation of treatment for valine-citrulline-monomethylauristatin E antibody-drug conjugates (ADCs) developed at Genentech. A pooled time-to-event analysis across eight ADCs (~700 patients) was performed to evaluate the relationship between the ADC exposure and the risk for developing a clinically significant (grade ≥ 2) PN. In addition, the impact of demographic and pathophysiological risk factors on the risk for PN was explored. The time-to-event analysis suggested that the development of PN risk increased with ADC exposure, treatment duration, body weight, and previously reported PN. This model can be used to inform clinical strategies such as adaptations to dosing regimen and/or treatment duration as well as inform clinical eligibility to reduce the incidence of grade ≥ 2 PN.

Facilitating pharmacometric workflow with the metrumrg package for R.

metrumrg is an R package that facilitates workflow for the discipline of pharmacometrics. Support is provided for data preparation, modeling, simulation, diagnostics, and reporting. Existing tools and techniques are emphasized where available; original solutions are provided for otherwise unmet needs. In particular, metrumrg implements an R interface for the NONMEM(®) modeling software, optionally run in a distributed computing environment. The paradigm allows start-to-finish analyses in a single scripting language. Emphasis on text-based formats promotes traceability of results.

Combining patient-level and summary-level data for Alzheimer's disease modeling and simulation: a ß regression meta-analysis.

Our objective was to develop a beta regression (BR) model to describe the longitudinal progression of the 11 item Alzheimer's disease (AD) assessment scale cognitive subscale (ADAS-cog) in AD patients in both natural history and randomized clinical trial settings, utilizing both individual patient and summary level literature data. Patient data from the coalition against major diseases database (3,223 patients), the Alzheimer's disease neruroimaging initiative study database (186 patients), and summary data from 73 literature references (representing 17,235 patients) were fit to a BR drug-disease-trial model. Treatment effects for currently available acetyl cholinesterase inhibitors, longitudinal changes in disease severity, dropout rate, placebo effect, and factors influencing these parameters were estimated in the model. Based on predictive checks and external validation, an adequate BR meta-analysis model for ADAS-cog using both summary-level and patient-level data was developed. Baseline ADAS-cog was estimated from baseline MMSE score. Disease progression was dependent on time, ApoE4 status, age, and gender. Study drop out was a function of time, baseline age, and baseline MMSE. The use of the BR constrained simulations to the 0-70 range of the ADAS-cog, even when residuals were incorporated. The model allows for simultaneous fitting of summary and patient level data, allowing for integration of all information available. A further advantage of the BR model is that it constrains values to the range of the original instrument for simulation purposes, in contrast to methodologies that provide appropriate constraints only for conditional expectations.