Ramon I. Garcia, Ph.D.

Senior Scientist I

Ramon joined Metrum in January 2017. He obtained his Ph.D. in Biostatistics from the University of North Carolina where his dissertation topic was “variable selection in models with missing data using a Bayesian framework”. Ramon did research at the EPA where he investigated identifiability and estimation of physiologically based pharmacokinetic models. Ramon’s interests include model selection and estimation of nonlinear mixed effects models and Bayesian analysis of pharmacokinetic models.

Recent publications by this scientist

Bayesian sparse regression for exposure–response analyses of efficacy and safety endpoints to justify the clinical dose of valemetostat for adult T-cell leukemia/lymphoma

October 2, 2024

The developed models characterized E–R relationships and covariate effects for efficacy and safety endpoints. The efficacious and safe exposure range was established and supported the clinical dose of 200 mg. The utility of logistic regressions in a Bayesian framework with spike and slab priors, in which all the covariate effects were included and simultaneously estimated, was demonstrated.

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Population exposure–response analysis supports efficacy outcomes of garadacimab in patients with hereditary angioedema

June 26, 2024

Presented at ASCPT Annual Meeting 2024. Garadacimab (anti-activated factor XII monoclonal antibody) demonstrated efficacy for the prevention of hereditary angioedema (HAE) attacks with a favorable safety profile in Phase 2, pivotal Phase 3 (VANGUARD), and Phase 3 open-label extension studies. In this exposure-response (ER) analysis, the efficacy of garadacimab and effect of covariates on attack rate (number of attacks/month) were assessed to support garadacimab dosing in patients with HAE.

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

December 11, 2023

Leveraging a sophisticated Bayesian framework and latent variable models, this research compared treatment effects against placebos, setting a new benchmark for assessing efficacy in SLE treatments. This novel MBMA not only benchmarks the efficacy of these compounds but also showcases the application of latent variable models in understanding chronic disease trajectories.

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